Podcasts about Excel

In this episode, host Josh interviews Steven Yates, CEO of Prime Guidance, about strategies for scaling e-commerce brands. Steve emphasizes optimizing Amazon listings and leveraging all available tools before expanding to other marketplaces like Walmart or eBay. He discusses the importance of having a direct-to-consumer website, maximizing Amazon advertising, and using analytics tools to track performance. Steve provides actionable advice on when and how to diversify sales channels, ensuring brands grow efficiently and profitably while building a strong foundation on Amazon first.Chapters:Introduction to Steven Yates and Prime Guidance (00:00:00)Josh introduces Steven Yates, his background, and expertise in retail management and e-commerce.When to Expand Beyond Amazon (00:00:48)Discussion on timing and considerations for expanding to other marketplaces like Walmart, eBay, Wayfair, and international markets.Sales Lift Estimates from Other Marketplaces (00:01:28)Steve provides rough estimates of sales lift from Walmart, eBay, and other channels compared to Amazon.Importance of Optimizing Amazon Before Expanding (00:01:39)Emphasis on being 80-90% optimized on Amazon before moving to other marketplaces.Choosing the Right Next Marketplace (00:03:32)Advice on analyzing where your customers are and not following a cookie-cutter approach to expansion.Launching a DTC E-commerce Website (00:04:04)Discussion on when and why to launch a direct-to-consumer website alongside Amazon.Benefits of Having a DTC Website (00:04:38)Steve explains the strategic advantages of having your own e-commerce site for brand building and customer retention.Capturing and Nurturing Website Visitors (00:05:46)Tactics for capturing emails and engaging visitors who land on your DTC website.Key Levers to Pull on Amazon (00:06:21)Josh asks for a list of actionable levers to increase sales and grow a brand on Amazon.Detailed Breakdown of Amazon Optimization Levers (00:06:33)Steve details optimization tactics: product pages, infographics, A+ content, pricing, assortment, advertising, and Amazon programs.Amazon Advertising and External Traffic Strategies (00:08:05)Discussion on types of Amazon ads, external traffic, and leveraging Amazon's Brand Referral Bonus.Utilizing Amazon Programs and Betas (00:09:11)Overview of Amazon programs like FBA Small and Lite, brand store, Amazon posts, and customer engagement emails.Order of Operations for Optimization and Traffic (00:10:31)Advice on optimizing for Amazon's algorithm and conversion before scaling advertising and traffic.Three Actionable Takeaways for Brands (00:11:21)Josh summarizes three key takeaways: maximize Amazon levers, focus on Amazon traffic, then expand to other channels.Tools for Tracking Amazon Metrics (00:13:40)Discussion on aggregating and analyzing Amazon data using third-party tools and Excel.Brand Analytics and Bonus Tool Recommendation (00:14:59)Steve recommends using Amazon Brand Analytics and nozzle.ai for tracking repeat purchases and customer lifetime value.Where to Learn More About Prime Guidance (00:16:21)Steve shares how listeners can contact or follow Prime Guidance for further help.Links and Mentions:Tools and Websites  Prime Guidance  Shopify WooCommerceAmazon Attribution Program  Amazon Posts  Helium 10Nozzle AI  Transcript:Josh 00:00:00  Today, I'm excited to introduce you to Steve Yates. He is the CEO and founder of Prime Guidance. Steve developed well-rounded expertise working for multi-billion dollar fortune 500 retailers such as Amazon, Dick's Sporting Goods and eBay enterprise prior to founding Prime guidance in all industry consulting. With 30 years experience in retail management and 23 years experience in e-commerce. Steve and his team provide companies with strategic advice and innovative solutions that are based on real life experience working for industry leading retailers. He helps companies grow faster, smarter and more profitably by providing advice, mentoring and coaching for today's busy executives. So welcome to the podcast, Steve.Steven 00:00:46  Thank you. Josh. Thanks for having me.Josh 00:00:48  One of the first questions I want to ask, just kind of selfishly for myself, because we're looking to expand onto different channels right now with our business. We've grown to eight figures just on Amazon alone. But we're we are looking to, you know, is it time to explore or double down more on Walmart eBay, Wayfair? Do we try to get into target? Do we go international right and start shipping stuff into Canada, Mexico, the UK, etc.? So my question to you here, Steve, is what kind of sales lift do you see from those different marketplaces? Right.Josh 00:01:28  Like what do you estimate as hey you go to Walmart it best case scenario, you're probably looking at a 10% lift eBay. Maybe it's a 2%, you know, so on and so forth.Steven 00:01:39  Yeah. So it's a very tricky question because I've seen it wildly different. So interesting. I had to if I had to, to put a rough assumption across a lot of different categories and product lines, I would say Walmart is the very next marketplace you're going to want to focus on outside of Amazon. And by the way, don't do it until you're what I like to say 80 to 90% optimized on Amazon. Don't spend your time on these smaller marketplaces, because that's oftentimes the shiny object that gets you in trouble when you're doing a whole bunch of different things, you're not doing any of them well. You've got to you've got to be really well positioned on Amazon. And when I say 80 to 90%, I don't mean of your total opportunity for growth. But if you've identified all these levers you need to pull on Amazon, you need to have a good storefront.Steven 00:02:26  You need to have A+ content. I need to have all of these different components pulled together. Do you feel good about how well optimized they are, and are they in place 80 to 90% of where they should be before you, you know, start migrating to another marketplace? Because if you don't, you're essentially lifting and shifting a catalog that's not optimized to another marketplace. And now all of your optimization efforts are going to be that much harder because you're doing full optimizations across a whole bunch of marketplaces. That's a that's always a risk. I would say Walmart is probably, in the number of 10 to 20% of the Amazon business, and eBay is probably the neighborhood of 10%, maybe 5 to 10% of the, of the Amazon business. but it really does differ quite a bit. I've seen some I've seen some people that actually sell more on Etsy than they do on Amazon because their product is sold out after on that website. I've seen people that do phenomenal on eBay, even though eBay is, you know, not not growing.Steven 00:03:32  It's. Yeah, it's it just so happens that their customers there and that's why I go goes back to, analyzing where your customers spend their time and money and make sure you're present there, do it in the right order. But ultimately make sure you're you're present there. And where you go next is not a cookie cutter answer just because everybody else goes to this next Walmart, you know, Walmart next or eBay after that or whatever, doesn't mean that's...

Ep. 304, Recorded 12/24/2025. Shopping for terrible gifts. Close the lid. Lights out at Waymo. Raise the roof. Apple products are good. Tech corner comes in hot. Buick is still a thing. Big Scoop out of Cincy. Geeks finally Excel at something. Teapot Despot. Apple won't take my money. The snack bar is open!

Eu sou Ludimeri Picelli, coordenadora da Casa Espírita Luiz Picelli, em Maringá-Paraná-Brasil e estou fazendo a leitura do livro FILOSOFIA ESPÍRITA, VOLUME 3,  ditado pelo espírito Miramez, ao médium João Nunes Maia, cujo livro traz reflexões elaboradas sobre a obra O livro dos Espíritos, de Allan Kardec.Este livro é de estudos e reflexões e por isto iremos lendo pergunta a pergunta de O Livro dos Espíritos, da Doutrina Espírita, codificada por Allan Kardec, para que possamos assimilar bem o seu conteúdo e estabelecermos a tal reforma de pensamento e comportamento, que Jesus espera de nós.Este é o segundo de uma série de vinte livros, em que Miramez comenta as perguntas de O Livro dos Espíritos, objetivando orientar-nos no estudo dessa obra ímpar, sem qualquer pretensão, a não ser fornecer subsídios para melhor entendê-la e trilhar com segurança o caminho da Luz.O que Miramez pretende não é decodificar O Livro dos Espíritos para nós leitores, mas apresentar-nos alguns pontos para nossa meditação, a respeito de cada pergunta, para que possamos entrar em sintonia com a bondade e a paz que são emitidas pelo Amor Maior.Nossos podcasts são leituras de livros e mensagens da doutrina espírita, que foram ditados pelos nobres espíritos de eskol e codificadas por Allan Kardec, bem como outras obras psicografadas por médiuns de renome, trazendo-nos uma doutrina reveladora e libertadora, para, assim, entender o Cristianismo Redivivo.- para nos ajudar, doe-nos um café, faça um pix caridadeamoreluz@gmail.com, ou pelo cnpj pix 37.965.614/0001-18, pois suas doações nos manterão no ar, e ajudarão nossas ações sociais, cursos, podcasts, com bastante elevação aos sentimentos daqueles que se aproximam de nós.- visite nosso site celp.org.br  - visite nossas redes sociais facebook e instagram, com o nome celppicelli e youtube celp-picelli- venha estudar em nossa escola: MAGNA - Centro de Excelência em Magnetismo e Apometria- venha somar conosco? Venha ser um caminheiro CELP-PICELLI

Double Tap Episode 441 This episode of Double Tap is brought to you by: Blue Alpha, Midwest Industries, Gideon Optics, Primary Arms, Die Free Co., and Mitchell Defense   Welcome to Double Tap, episode 441! Your hosts tonight are Jeremy Pozderac, Aaron Krieger, Nick Lynch, and me Shawn Herrin, welcome to the show! Text Dear WLS or Reviews +1 743 500 2171 - Dear WLS ThreeRaccoons InnaTrenchCoat - What programs do Shawn, Nick and Savage use for 3D modeling? Are they subscription based or free? I would like to dive into 3D modeling but don't really want to pay a subscription. A one-time fee would be okay i suppose. Tuul Steele - "I was thinking Hard the other day about that long, cylindrical reciprocating part of an Assault Railer - 15 and it got me curious (bi-curious) about steel selection for a BCG. YES, I know all steel is not created equally Jeremy, but in the case of the BCG which does the cast recommend and which is just more expensive for no reason. Not talking coatings, although you can add that to the pot, I am mainly referring to the materials; Carpenter 158 aka "The Thing", 9310 aka "The Beverly Hills BCG", or S7 Tool Steel aka "The Audi Tool". Love the show and the cast. You guys are keeping me topside on long days. Keep up the good work. Also just buy Aaron a huffy with a baseball card in the spokes and call it a day. Toss in a bottle of Malort for the basket on the handle bars. #WLSisLife #shootstraight" Plow Guy Dave - What do you think will be the cool, new trend at SHOT Show this year? Do you think that there will be an influx of NFA stuff like SBR's, SBS's and suppressors since the One Big Beautiful Bill will be active? I know you guys don't go to SHOT anymore, but what would you like to see come out? John B - So I was listening to the AR-15 podcast and Dauly keeps saying you need to buy mags in “generational wealth” volume. What is it that you guys think is the appropriate number of mags to have for your rifle and pistol? Do I really need to have 10 mags minimum? Gus Gus - Hello people and Aaron, What one thing do you each think we do or use or teach in the firearms space that we will one day look back on and say “wow, I can't believe we let that happen” (besides letting Aaron speak). Could be anything from gear to training to lawfare practices. For myself, I think open emitter red dots on concealed carry guns will be looked at as weird and genX level fuddery and anyone who uses one will always feel like explaining it away like Jeremy does with iron sights. Thanks guys and Aaron Unfit for Human Consumption - What software would you recommend for tracking a gun collection? I have (only) a few dozen guns, and have been using "NMCollector" since about 2005, and it meets my needs. It is now $15/year which is very reasonable, and a "lifetime" activation is less than 85-bucks. A quick search on the interwebs for alternatives reveals "ArmoryBook" which is $200/yr for up to 50 records, and "GunTrack" which is $100/yr for up to 100 records; both are way over budget for my needs. Are there other softwares I'm not aware of that you'd recommend? I don't keep pictures generally, and all I really need to track is make, model, caliber, description, year & country of manufacture, source, cost, appraised value, etc for insurance records and for my wife to cash in on this "401(G)uns" fund when I'm gone (hopefully not for several decades). Or should I just create an Excel spreadsheet? Sunshine Shooter - Forced Reset triggers seem to be a commercially available thing now, but I'm having trouble keeping track of what is out there. What forced reset triggers/super safeties do you recommend I look into getting for a poor man's MP5 I'm planning on putting together? Matthew P - Just bought a 5 inch VFM-9 from Foxtrot Mike. What accessories would you add to this if you are going to keep it as a backpack gun? Suppressor? Flip up sights and/or red dot/prism? sling? anything else?   The winner of this week's swag pack is Gus Gus! To win your own, go to welikeshooting.com/dashboard and submit a question!   Gun Industry News Cops Pick New Echelon Guns Henderson PD picked Springfield Armory's Echelon 9mm pistols (full-size 4.5F and compact 4.0C) as new duty guns after tough tests. Special modular grips fit all hand sizes, ambidextrous controls, optics-ready, 20-round mags. Every officer passed quals first try—first time ever. Boosts gun community's cred for civilian carry. Available now. New S&W .360 Buckhammer Rifle Out Now Smith & Wesson Model 1854 lever-action rifle now in .360 Buckhammer caliber for straight-wall hunting states. Mixes classic lever feel with modern Picatinny rail, M-LOK slots, 20-inch barrel. Hits medium/large game with low recoil. MSRP $1399. Available now. POF-USA Wins Big Rifle Deal for Asia POF-USA wins contract to supply Renegade rifles in .300 Blackout with 8-inch barrels to Asian client. Special: E² dual-extraction, roller cam pin, heat sink nut for reliability and heat control in suppressed ops. Boosts POF's global sales. Not for civilian sale. Germany Picks CZ P-10 C as New Gun Germany's army picked the CZ P-10C OR FDE pistol as its new P13 service gun, replacing the old HK USP from 1994. It's a 9mm striker-fired model with 15-round mag, 4-inch barrel, 26 oz weight, optic-ready slide in flat dark earth finish—beats Glock and Arex in competition. Huge win boosts CZ's rep; big order expected from Czech plant. Not yet in production for delivery. Colt Wins M4A1 and Suppressor Deal Colt got a $12.93M contract for M4A1 carbines, suppressors, and flash hiders for Israel via US Foreign Military Sales. Special: Includes suppressors, now common for stealth. Boosts Colt's gun community rep as key military supplier. Product not available now. Ruger Wins Patent for Double Stack .22LR Magazines Ruger patented a double-stack .22LR magazine for 22/45 pistols. It uses single-feed that splits to two columns, rotating rims sideways to center bullets—special for rimfire without bulky design. Fits current frames as aftermarket, no background check needed. Boosts gun community with higher capacity. Broader for .22 WMR, .30-30, 7.62x54R. Not available yet. New HK VP9A1 X: Perfect Size and Power Heckler & Koch launched VP9A1 X pistol, blending compact 4-inch slide from K model with full-size F frame for 17-round capacity. Fills gap between compact and full-size with A1 upgrades like better grip and trigger. MSRP $1,049 or $1,399 optics-ready. Available now. Gun fans get factory crossover size matching original VP9 but improved.   Before we let you go - Join Gun Owners of America   Tell your friends about the show and get backstage access by joining the Gun Cult at theguncult.com.   No matter how tough your battle is today, we want you here fight with us tomorrow. Don't struggle in silence, you can contact the suicide prevention line by dialing 988 from your phone. Remember - Always prefer Dangerous Freedom over peaceful slavery. We'll see you next time!   Nick - @busbuiltsystems | Bus Built Systems Jeremy - @ret_actual | Rivers Edge Tactical Aaron - @machinegun_moses Savage - @savage1r Shawn - @dangerousfreedomyt | @camorado.cam | Camorado

Get ready for a lively and insightful conversation on this episode of Count Me In! Host Adam Larson welcomes author, strategist, and founder of FractionalCMO.com and Redfern Media, Draye Redfern, as he shares his bold approach to staying resilient and thriving in uncertain times. Drawing on advice from his "Recession Survival Guide" and giving a sneak peek into his upcoming "Anchor Marketing" framework, Draye Redfern delivers practical strategies for teams and individuals who want to future-proof their businesses and careers. Whether you're running your own company or leading a department, you'll come away with ready-to-implement tips for attracting new leads, nurturing relationships, and building a solid foundation for growth—even during a downturn. Draye Redfern's real talk on marketing, team dynamics, and building lasting customer relationships will keep you hooked from start to finish. Tune in for high-energy ideas, personal stories, and a toolkit to help you turn challenges into your next big opportunity!

Hurt and hope can live in the same room, and sometimes that room is a borrowed auditorium in the heart of Seattle's tech campus. We sit down with pastor and church planter Tyler Gorseland to trace a winding path from AA basements and a late-teen encounter with Jesus to launching A Seattle Church surrounded by Amazon, Apple, Meta, and Google Cloud. Tyler opens up about layoffs, the slow work of healing after the Mars Hill collapse, and why his community refused to sprint past grief to chase growth. The result is a church that learned to name pain, practice forgiveness, and become a harbor that sends.We get practical about “church hurt”—what it is, why it's inevitable in real community, and how forgiveness works not as a feeling but as obedience that sets us free. Tyler shares how planting in South Lake Union forced a rethink of ministry: embrace the churn, disciple deeply and quickly, and send people well. From a co-working space that functions like “church without Jesus” to a discipleship training approach built for a transient neighborhood, he walks through the choices that turned constant turnover into a mission advantage.Send us a textWe want to help you find your next steps in ministry.Connect here with EXCEL. Ministry Partner: Christian Community Credit Union

Should I pay off the mortgage faster, or use that extra cash to invest? If this isn't the most common investor question, then it's definitely in the top five. The textbook, Excel spreadsheet, and your purist accountant friend are all likely to suggest paying down the mortgage. There's good reason for that, but when might it make sense to ignore the rules and do some investing on the side anyway?

Go shop the Shortcut Suites now.This is time sensitive and will only be live to get in until Jan-15-2026.AND all the shortcuts will be available until Feb-15th, so BOOKMARK the suite when you get inside.You can grab my Pitch Templates, which normal you have to pay for but inside the suite it's FREE.

When you're filling in a Microsoft Excel spreadsheet, the idea of an international competition testing those skills is probably far from your mind. Yet Excel has become an esport, with competitors from around the world solving high‑pressure challenges on a major stage in Las Vegas. This year, University of Pretoria student Pieter Pienaar became the world champion in the 2025 Microsoft Excel Collegiate Challenge. Pienaar, who is currently completing his chartered accountant articles with PwC, told BizNews that for him Excel is a powerful problem‑solving tool, and that is exactly what the championship tested. When it comes to future developments in Excel, he strongly supports the role of AI because it democratises problem‑solving, provided companies act responsibly and ensure proper auditing. But, he adds, it should definitely not be used by the National Treasury to do your taxes.

In this episode of FP&A Unlocked, host Paul Barnhurst welcomes back Jordan Goldmeier, Excel expert, author, and longtime friend of the show, for a wide-ranging and honest conversation about careers, technology, and growth. Jordan reflects on his unconventional career path, from auditing and operations research to becoming a Microsoft MVP, author, and entrepreneur. The discussion covers Excel's evolution, why many finance professionals underuse powerful tools, and Jordan's latest projects aimed at modernizing how power users work with spreadsheets.Jordan is an entrepreneur, event producer, author, and Microsoft Excel MVP based in the Lisbon Metropolitan Area. He is widely known for his work helping professionals master Excel, data analysis, and modern spreadsheet practices. Jordan has authored several well-known books, including Advanced Excel Essentials, Dashboards for Excel, and Becoming a Data Head. In addition to his work in Excel education, he produces global events that bring together leaders across finance, technology, and entrepreneurship.Expect to Learn:How Jordan's career twists shaped his approach to Excel, data, and problem-solvingWhy most professionals only scratch the surface of Excel's capabilitiesJordan's perspective on why VBA is outdated and what could replace itWhy vertical learning beats beginner–intermediate–advanced training pathsHere are a few quotes from the episode:“Excel isn't dead, but the way we develop in it needs to change.” – Jordan Goldmeier“You don't become great by learning everything. You become great by going deep where it matters.” – Jordan GoldmeierJordan also shares the story behind his latest project: a developer-style environment designed to help Excel power users work faster, cleaner, and more confidently, without relying on outdated tools like VBA. He explains why Excel should be treated as part of a broader finance tech stack and how modern coding concepts could dramatically improve spreadsheet workflows. Campfire: AI-First ERP:Campfire is the AI-first ERP that powers next-gen finance and accounting teams. With integrated solutions for the general ledger, revenue automation, close management, and more, all in one unified platform.Explore Campfire today: https://campfire.ai/?utm_source=fpaguy_podcast&utm_medium=podcast&utm_campaign=100225_fpaguyFollow JordanLinkedIn - https://www.linkedin.com/in/jordangoldmeier/Earn Your CPE Credit For CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FP&A Certificate, take the quiz on Earmark and contact Paul Barnhurst for further details.In Today's Episode:[02:15] – Jordan's Career Journey[08:30] – Setbacks, Resets, and Growth[15:00] – Writing Books on Excel[25:59] – How Excel Is Really Used[29:12] – Why VBA Is Outdated[33:54] – Building Better Tools for Excel[42:25] – Advice for FP&A Professionals[47:16] – Creating Your Own Network[52:12] – Rapid-Fire & Final Thoughts

Există domenii în care schimbarea se vede treptat, aproape imperceptibil. Și există domenii în care realitatea se transformă în timp real, sub ochii noștri. Mobilitatea face parte din a doua categorie. Firmele care folosesc zeci sau sute de vehicule nu mai pot funcționa doar pe intuiție sau pe obiceiurile trecutului. Aici intervine munca unor oameni care privesc funcționarea unei companii nu doar ca pe o operațiune logistică, ci ca pe un organism viu, cu nevoi, fluxuri și responsabilități.Valentin Vasiliță este unul dintre acești oameni. Conduce digitalizarea activităților DKV Mobility în România și lucrează cu companii care vor să își administreze flotele într-un mod mai așezat. Vorbim despre un sector care, deși pare tehnic la exterior, atinge fiecare bucată din viața unei companii: de la costuri, la modul în care lucrează echipele, până la felul în care sunt planificate resursele. Valentin vede aceste piese și încearcă să le pună într-o formă care să aibă sens pentru manageri și pentru șoferi.Principalele lucruri discutate:00:00 Introducere: de ce mobilitatea se schimbă rapid02:53 Parcursul lui Valentin04:54 De ce digitalizarea devine o prioritate pentru companii07:40 Ce înseamnă, concret, digitalizarea unei flote08:50 Vizibilitate, control și date centralizate11:12 Provocările reale în adoptarea soluțiilor digitale14:24 De ce renunțarea la Excel este un pas greu15:23 Sisteme esențiale: FMS, telematică și aplicații mobile18:44 Cum ajută digitalizarea la reducerea costurilor21:24 Datele din sistem și deciziile de business24:08 Cum se transformă datele în optimizare reală26:39 Cel mai important sfat pentru companii la început27:54 Concluzii și gânduri finale

You open Excel daily, but are you using AI to make it work for you? While 58% of professionals have tried AI, only 17% use it regularly—a missed opportunity. Join CPA Kyle Ashcraft in this hands-on webinar to learn vibe coding—a no-programming approach using Cursor AI to automate repetitive Excel tasks. Watch Kyle transform messy spreadsheets, organize GL data, and reconcile transactions with simple AI prompts while keeping data secure. You'll get three ready-to-use scripts plus a framework to automate countless tasks and reclaim hours weekly.(Originally recorded on October 20, 2025, on Earmark Webinars+)Chapters(02:18) - Meet Kyle Ashcraft and His AI Journey (02:31) - The Importance of AI in Accounting (03:30) - Kyle's Background and CPA Review (04:38) - Live Webinar and Audience Interaction (05:12) - Kyle's AI Projects and Cursor Introduction (07:39) - Data Privacy Concerns with AI (17:18) - Practical AI in Excel: Examples and Demonstration (20:03) - Getting Started with Cursor (23:55) - First Cursor Project: Cleaning Up Excel Data (31:38) - Jumping into Financial Document Verification (32:50) - Exploring Cursor's Privacy Settings (33:48) - Understanding Data Retention Policies (36:23) - Comparing Excel Files with Cursor (36:48) - Analyzing Complex GL Details (42:22) - Using Cursor for Recurring Accounting Tasks (49:20) - Leveraging AI for Audit and Analysis (50:53) - Practical Tips for Implementing Cursor (53:56) - Q&A: Advanced Cursor Features (59:04) - Conclusion and Next Steps Earn CPE for this episode: https://www.earmark.app/c/2854Sign up to get free CPE for listening to this podcasthttps://earmarkcpe.comhttps://earmark.app/Download the Earmark CPE App Apple: https://apps.apple.com/us/app/earmark-cpe/id1562599728Android: https://play.google.com/store/apps/details?id=com.earmarkcpe.appResourcesIntro to Cursor PDF Guide - https://mcusercontent.com/02dbcae4a3e3f15021db25a0c/files/deff5647-e0a3-51d7-4225-cf8b3a48532d/Cursor_AI_Quick_Guide.pdfWebinar presentation - https://ai.maxwellstudy.com/Connect with Our Guest, Kyle Ashcraft, CPALinkedIn: https://www.linkedin.com/in/kyle-ashcraft-cpa-7638a42aLearn more about Maxwell CPA Reviewhttps://maxwellcpareview.com/Connect with Blake Oliver, CPALinkedIn: https://www.linkedin.com/in/blaketoliverTwitter: https://twitter.com/blaketoliver/

Fluent Fiction - Norwegian: From Job Interview Fiasco to Heartwarming First Date Find the full episode transcript, vocabulary words, and more:fluentfiction.com/no/episode/2025-12-17-08-38-20-no Story Transcript:No: Det var en kald desemberdag, da snøfnugg begynte å danse utenfor kafévinduene i Bergen.En: It was a cold December day when snowflakes began to dance outside the café windows in Bergen.No: Den lille kaféen i Torget var pyntet med julelys og granbar, og duften av nystekte kanelboller fylte rommet.En: The little café in Torget was decorated with Christmas lights and pine branches, and the scent of freshly baked cinnamon rolls filled the room.No: Sindre satt nervøst ved et bord ved vinduet, iført en nypresset dress og slips.En: Sindre sat nervously at a table by the window, dressed in a freshly pressed suit and tie.No: Han kastet et blikk på klokken og justerte på et slips som allerede var rett.En: He glanced at the clock and adjusted a tie that was already straight.No: Kari hadde akkurat kommet inn i kaféen, iført en tykk, rød genser med en julekatt på.En: Kari had just entered the café, wearing a thick, red sweater with a Christmas cat on it.No: Hun så med et varmt smil på Sindre, som hadde reist seg litt for å hilse henne med et fast håndtrykk.En: She looked at Sindre with a warm smile, who had stood up slightly to greet her with a firm handshake.No: Kari lo lavt og ga ham en klem i stedet.En: Kari laughed softly and gave him a hug instead.No: "Hei! Så hyggelig å møte deg, Sindre," sa hun muntert mens hun satte seg ned.En: "Hi! So nice to meet you, Sindre," she said cheerfully as she sat down.No: Sindre smilte stivt. "Ja, det er hyggelig å møte deg også, Kari. Jeg er veldig spent."En: Sindre smiled stiffly. "Yes, it's nice to meet you too, Kari. I'm very excited."No: Kari la merke til at han virket mer anspent enn hun hadde forventet, men hun la det til side mens de bestilte kakao og kaffe.En: Kari noticed that he seemed more tense than she had expected, but she pushed it aside as they ordered cocoa and coffee.No: Snøen falt stille utenfor vinduet, og selv om innetemperaturen var behagelig, følte Sindre seg veldig varm.En: The snow fell quietly outside the window, and although the indoor temperature was comfortable, Sindre felt very warm.No: "Så, Sindre, hva liker du å gjøre i fritiden?" spurte Kari avslappet og kikket over koppen sin.En: "So, Sindre, what do you like to do in your free time?" asked Kari relaxedly, peering over her cup.No: Sindre rettet seg opp i stolen. "Jeg er veldig dedikert til å utvikle mine ferdigheter. Jeg jobber godt i team og har stor kapasitet til å takle utfordringer under press."En: Sindre straightened up in his chair. "I am very dedicated to developing my skills. I work well in a team and have a great capacity to handle challenges under pressure."No: Kari lo igjen, men stoppet da hun så det alvorlige uttrykket i ansiktet hans. "Eh... det er flott at du er så fokusert. Men hva med hobbyer? Noe du liker å gjøre for moro skyld?"En: Kari laughed again but stopped when she saw the serious expression on his face. "Eh... it's great that you're so focused. But what about hobbies? Something you like to do for fun?"No: Sindre blunket raskt, litt forvirret. Skulle han ikke diskutere sine kvalifikasjoner? "Jo, jeg liker... å lage presentasjoner og jobbe med excel," svarte han og hørtes litt stolt ut.En: Sindre blinked quickly, a bit confused. Was he not supposed to discuss his qualifications? "Well, I like... making presentations and working with Excel," he replied, sounding a bit proud.No: Det ble stille et øyeblikk, og Kari trengte et par sekunder på å forstå hva som skjedde.En: There was a moment of silence, and Kari needed a couple of seconds to understand what was happening.No: Hun måtte være rettferdig - det virket som en stor misforståelse. "Unnskyld, men tror du dette er noe annet enn det er?"En: She had to be fair—it seemed like a big misunderstanding. "Sorry, but do you think this is something other than what it is?"No: Sindre rødmet dypt. "Er dette ikke et jobbintervju?" spurte han nølende.En: Sindre blushed deeply. "Is this not a job interview?" he asked hesitantly.No: Kari klappet ham vennlig på hånden og smilte beroligende.En: Kari patted his hand kindly and smiled reassuringly.No: "Nei, dette er vår første date! Men jeg setter pris på all informasjon om dine kvaliteter, faktisk."En: "No, this is our first date! But I do appreciate all the information about your qualities, actually."No: Den plutselige realiseringen fikk Sindre til å le av lettelse. "Oi, jeg forstår nå. Jeg er virkelig lei for det. Jeg var så nervøs." Han strakte seg over bordet og sa oppriktig, "La oss begynne på nytt?"En: The sudden realization made Sindre laugh with relief. "Oh, I get it now. I'm really sorry about that. I was so nervous." He reached across the table and said sincerely, "Shall we start over?"No: Stemningen lettet betraktelig da de begge lo av det lille dramaet.En: The atmosphere lightened considerably as they both laughed at the little drama.No: Samtalen fløt naturlig, og begge oppdaget felles interesser; teater, fjellturer og en felles forkjærlighet for pepperkaker.En: The conversation flowed naturally, and they both discovered shared interests: theater, mountain hikes, and a mutual fondness for gingerbread cookies.No: Da de sa farvel ved døra, med vinden som bøyde trærne forsiktig, bestemte de seg for å møtes igjen.En: As they said goodbye at the door, with the wind gently bending the trees, they decided to meet again.No: Kanskje på Bryggen, eller ta en tur på Fløyen.En: Maybe at Bryggen, or take a trip to Fløyen.No: Varmen fra kaféen hadde fulgt dem ut i kulden.En: The warmth from the café had followed them out into the cold.No: Sindre lærte å finne humor i kaoset, mens Kari innså kraften i å møte små pinlige øyeblikk med et smil.En: Sindre learned to find humor in chaos, while Kari realized the power of facing small embarrassing moments with a smile.No: Med en ny dato på kalenderen, gikk de hver sin vei, men begge med et lett hjerte.En: With a new date on the calendar, they went their separate ways but both with a light heart.No: Det var, tross alt, juletid - en tid for å skape varme forbindelser.En: After all, it was Christmas time—a time to create warm connections. Vocabulary Words:snowflakes: snøfnuggcinnamon: kanelnervously: nervøstfreshly pressed: nypressetglanced: kastet et blikkadjusted: justertefirm handshake: fast håndtrykkhug: klemwarm smile: varmt smilserious expression: alvorlige uttrykketconfused: forvirretblushed: rødmethesitantly: nølendereassuringly: beroligendenew date: ny datoflowed naturally: fløt naturligfondness: forkjærlighetbending: bøyderealization: forståelserelief: lettelsemutual: fellesmisunderstanding: misforståelsededicated: dedikertcapacity: kapasitetchallenges: utfordringerhikes: fjellturerqualities: kvaliteterembarrassing: pinligeappreciate: sette pris påconnections: forbindelser

Czy Twój zespół naprawdę dowozi to, co zaplanuje? A może się przyzwyczailiście do tego, że zrealizowany jest tylko ułamek planu na iterację? Rozkładamy na czynniki pierwsze przewidywalność zespołu. Miara ta może być potężnym wsparciem dla zespołu, ale i źródłem frustracji czy złych decyzji. Pokażemy Ci jak mierzyć ją w Jira, Excelu czy innych narzędziach. Podpowiemy też, jak interpretować wyniki, by realnie ustabilizować w zespole proces dowożenia zaplanowanego zakresu prac. Cała rozmowa odnosi się do case study z naszej pracy z jednym z zespołów. Jeśli masz już dość niewykonanych planów oraz ciągłych tłumaczeń, ten odcinek jest dla Ciebie. Porządny Agile · Przewidywalność zespołu Zapraszamy Cię do obejrzenia nagrania podcastu Transkrypcja podcastu „Przewidywalność zespołu” Poniżej znajdziesz pełny zapis rozmowy z tego odcinka podcastu Porządny Agile. Jacek: Ostatnio na naszej stronie pojawiło się nowe case study. Dotyczy ono tego, jak w jednym z zespołów poprawiliśmy przewidywalność. Uznaliśmy z Kubą, że jest to dobra okazja, żeby powiedzieć trochę więcej o przewidywalności w ramach tego odcinka. Kuba: Adres case jest nie do przedyktowania w nagraniu audio, więc po prostu zachęcam Cię do tego, żeby znaleźć wspomniany case study w notatkach do odcinka i przeczytanie tego, co Jacek tam pisze. Jacek: Jaki spis treści na dzisiaj? Przede wszystkim zdefiniujemy, czym dla nas jest przewidywalność. Opowiemy, jak mierzyć przewidywalność. Podzielimy się wskazówkami na temat stosowania przewidywalności i na koniec damy kilka wskazówek, jak faktycznie, jakimi praktykami poprawić przewidywalność zespołu. Kuba: To przechodząc do rzeczy, pierwsza część to definicja przewidywalności. Przewidywalność rozumiemy to, jak zespół dowozi czy dostarcza to, co zaplanował. W jakim stopniu realizuje ten plan, który sobie przyjął? Czy, jak mówią, że coś będzie zrealizowane, czy to faktycznie będzie? Z jakim prawdopodobieństwem zespół realizuje swoje zamiary. Jacek: Więc jest to dla nas z jednej strony miara, o której powiemy za chwilę trochę więcej, bo można ją bardzo konkretnie wyrazić, a z drugiej strony, jak mówimy o tym, że zespół jest przewidywalny, to myślimy też w kontekście takim, że jest to pewna pożądana cecha zespołu. To jest taki zespół, na którym w kontekście tych prognoz, z którymi się dzielą z organizacją, można na nim polegać. Kuba: Dla równowagi powiemy też, czym nie jest przewidywalność według nas, choć niektórzy to też tak rozumieją. Niektórzy rozumieją przewidywalność jako pewną taką cechę generyczną rozumianą jako prawdopodobieństwo dostarczania, ale również prawdopodobieństwo o bardzo niskim stopniu albo o bardzo dużej zmienności tej wartości. W sensie, takim matematycznym, to też jest przewidywalność, tak samo jak smród jest zapachem albo jakiś brunatny też jest kolorem, ale jednak jako przewidywalność rozumiemy coś pozytywnego, zjawisko korzystne. W tym sensie nie cieszy nas fakt, że jakiś zespół ma przewidywalność, tylko ta przewidywalność to jest jedno zadanie na cztery zaplanowane albo 20% planu. W sensie matematycznym to jest przewidywalność, ale my się od takiej przewidywalności i takiego rozumienia tego słowa odcinamy, uważamy, że przewidywalność jest cechą czy charakterystyką pozytywną. Miarą, która powinna dążyć też do pewnych wartości. Zespół przewidywalny to taki, który dostarcza to, co planuje, a nie dostarcza tyle, ile zazwyczaj dostarcza. Nawet jeśli zazwyczaj dostarcza bardzo mało. Zespół, który przewidywalnie dostarcza mało, to jest dla nas zespół nieprzewidywalny, a nie przewidywalny w jakimś dziwnym znaczeniu. Jacek: To prowadzi nas do pytania, w jaki sposób możemy przeżyć przewidywalność. Ogólny wzór jest bardzo prosty. W dużym uproszczeniu jest to stosunek tego, co zostało faktycznie zrealizowane w konkretnym Sprincie czy w konkretnej iteracji w stosunku do tego, co było zaplanowane. Najczęściej jest to wyrażone po prostu w procentach. Kuba: Natomiast w szczegółach już może być trochę bogato. Różne zespoły uwzględniają do tego wzoru różne składowe elementy. Najprościej, gdy po prostu bierze się wszystko to, co zespół realizuje, niezależnie od tego, jakie typy pracy, jakie typy elementów planów wchodzą w skład danego Sprintu właśnie czy iteracji. Ale wiemy też i obserwujemy, i czasem ma to sens, że są zespoły, które liczą na przykład tylko historyjki użytkownika, storki, czy jakkolwiek to się w danym zespole nazywa. Czasem ficzery, czasem jakieś wyłącznie prace rozwojowe. Inne zespoły uwzględniają zadania czy jakiś rodzaj subtasków, jakaś praca techniczna do wykonania tego, co jest potrzebne do zrobienia w danym Sprincie. Kontrowersje mogą się zaczynać gdzieś w sferze tego, gdy się zaczyna liczyć do przewidywalności zaplanowane rozwiązania błędów, które wiemy, że istnieją, gdy zaczyna się Sprint, ale jest plan w zespole, żeby je rozwiązać. No i kontrowersją mogą być też zadania utrzymaniowe, czyli jakieś zadania powtarzalne, które z góry wiadomo, że trzeba zrealizować, no i choćby nie wiadomo, co się działo, to one po prostu faktycznie są częścią pracy w Sprincie. W ewentualnej kontrowersji głębiej się nie chcemy zagłębiać. Tutaj tylko jakby sygnalizuje, że jest temat, jakie typy pracy uwzględniać w mierze przewidywalności. No moim zdaniem jest tu temat do przemyślenia i bardzo świadomego zaplanowania czy do doprecyzowania, co jest uwzględnione we wzorze dla Twojego zespołu. Jacek: Może to jest dobry czas na taki prosty, namacalny przykład. Jeżeli zespół planował dostarczyć 10 elementów, nazwijmy to bardzo ogólnie, i dostarczył tylko dwa elementy, no to dla nas, patrząc na ten wzór przewidywalność, to jest 20%. Jeżeli planował dostarczyć 10, a dostarczył 5, no to przewidywalność jest 50%, natomiast jeśli planował dostarczyć 10, a dostarczył 12, to przewidywalność wynosi 120%. Tak więc przewidywalność jest miarą, w której ta wartość oczekiwana raczej jest pewnym zakresem. Takim dla nas powiedzmy akceptowalnym punktem do rozpoczęcia rozmowy, to jest przewidywalność między 80 a 120%. I bardziej chodzi nam o przebywanie w tym zakresie, niż osiąganie jakiegoś konkretnego, precyzyjnego wyniku. W szczególności powtarzalne osiąganie 100% może oczywiście wskazywać na to, że no ta miara być może za bardzo jest traktowana jako jakiś taki punkt do osiągnięcia. Z kolei o tym zakresie, który można nazwać, że jest powiedzmy zdrowy, można myśleć tak jak na przykład o wskaźnikach, kiedy idziemy na badanie krwi. Dostajemy wylistowaną listę, dostajemy poukładaną listę wyników i najczęściej jesteśmy w stanie znaleźć informacje, czy ta konkretna wartość zbadania jest w normie, czy mieści się w jakimś tam spodziewanym zakresie. I bardzo podobnie, właściwie można powiedzieć, wręcz identycznie działa to w przypadku przewidywalności. Kuba: Jeśli chodzi o przewidywalność, warto też wspomnieć to, jak narzędziowo można to mierzyć, jak można to liczyć, czyli jak konkretnie w narzędziu, jakim sposobem to zrealizować. Jest kilka opcji, wymienimy cztery. Jacek: Tak, pierwsze narzędzie takie, no, najczęściej nadal spotykane przez nas w organizacjach, to jest JIRA. Należałoby się skierować do sekcji raportów i znaleźć tam w wersji anglojęzycznej Velocity Chart i na tym wykresie oprócz tej informacji, ile faktycznie zespół zrealizował, czyli jaka jest prędkość zespołu, no, można również znaleźć tę informację o tym, ile na dany Sprint zostało zaplanowane. Te dane, te wykresy powinny się właściwie same wyświetlić, jeśli tylko przestrzegasz jakiejś takiej podstawowej higieny pracy w JIRA. To znaczy faktycznie uruchamiane są Sprinty. We właściwych momentach takich prawdziwych, kiedy zaczyna się Sprint, to ten Sprint jest startowany, powinien też być zamykany faktycznie wtedy, kiedy Sprint się kończy. Sprint powinien zawierać w sobie tę faktycznie wykonywaną pracę. Jak również pewna taka otoczka dotycząca tego boardu, na którym się znajdujemy, czy projektu, który realizujemy, te rzeczy też powinny być poprawnie skonfigurowane, no i wtedy można powiedzieć, że ten wykres dostajemy z pudełka. Właściwie nic nie musimy dodatkowego zrobić, żeby móc zobaczyć sobie historycznie, jak ta przewidywalność się w naszym zespole układała. Kuba: Drugą opcją narzędziową jest po prostu Excel. W porównaniu do JIRA, Excel stanie się, czy jest o wiele bardziej elastyczny, co prawda nie budują się dane same, jak w JIRA. Jeśli dobrze zachować tą dyscyplinę, o której mówi Jacek, no to JIRA liczy to sama, no w Excelu siłą rzeczy, ktoś odpowiedzialny za proces, albo członek zespołu, albo jakiś jego rodzaj lidera, po prostu musi te dane do tego Excela wprowadzić. Pamiętać o tym, żeby je przepisać, żeby złapać te dane historyczne bazowe i też pewnie w odpowiednie formuły wprowadzić te dane, żeby pokazały pewien wynik. Jest to oczywiście praca trochę ręczna, ale za to po drugiej stronie, zwłaszcza gdyby zespół miał jakąś bardziej skomplikowaną sytuację, albo bardziej wysublimowane warunki, co uwzględniać, czego nie uwzględniać, no to może się okazać, że ten Excel jest bardziej wiarygodny i pod większą kontrolą, niż narzędzia, które biorą po prostu wynik jakiegoś filtru lub nie są tak dobrze prowadzone. Jacek: Innymi narzędziami mogą być wszelkiego rodzaju narzędzia, które pomagają nam wizualizować pracę i pewne koncepcje z nią związaną. Czyli z jednej strony w warunkach online’owych to może być jakiś Mural czy Miro. W warunkach stacjonarnych to może być tablica, flipchart czy nawet wręcz kartka papieru. Tak naprawdę istotne jest, żeby te dane się znalazły w tych miejscach, wokół których będziemy się skupiać jako zespół. Na bazie moich doświadczeń bardzo często zespoły pracujące online dokonują refleksji na przykład na Muralu. No i w takim przypadku śledzenie tych informacji procesowych w kontekście tego odcinka, mówię tutaj w szczególności o przewidywalności, może być takim naturalnym miejscem, na które i tak spojrzymy w momencie, kiedy będziemy realizować cotygodniową czy co dwutygodniową refleksję. Tak więc posiadając komplet informacji w miejscu, do którego i tak rutynowo zaglądamy, drastycznie zwiększa szanse, że na te dane spojrzymy i zastanowimy się co z tych informacji, które posiadamy płynie, jakie wnioski do zespołu. Kuba: Ostatnią opcją, którą wymienimy, jeśli chodzi o narzędziowe pokazanie, mierzenie i uwidacznianie przewidywalności to są narzędzia BI-owe. W kilku organizacjach niezależnie od siebie widziałem taki efekt podłączenia bazy danych. Najczęściej pod spodem była jakaś JIRA, może Azure DevOps, albo tego typu narzędzia do mierzenia zadań, pokazywania tych zadań, kończenia ich. Dane surowe z takich narzędzi można przerzucić do narzędzi BI-owych. Czy to jest Power BI, czy to jest Tablo, czy to jest jeszcze coś innego. Kilka narzędzi różnie popularnych w różnych organizacjach. Oczywiście wymaga to już pewnych konkretnych kompetencji, żeby to wszystko podłączyć, żeby też być może odpowiednio skonfigurować raporty. No potencjalnie po stronie nagrody jest dosyć atrakcyjny sposób wizualizacji, być może sposób też jakiejś konfiguracji dodatkowego filtrowania dodatkowego, może dokładania kolejnych danych. W kontekście dużej organizacji wartością w sobie samo może być też pokazanie na jednym dash-boardzie wyników wielu zespołów, czy może pewien rodzaj standaryzacji pomiędzy zespołami, jakie aspekty są tam odpowiednio uwzględniane. Potencjalnie nagroda wielka, no ale tak jak wspomniałem też potencjalnie pewien koszt. Jeśli ma się te kompetencje w zespole, to może ten koszt jest siłą rzeczy pomijalny, a czasami warto to zainwestować, żeby dostać wartościowe widoki, czy wartościowe mierniki. Kuba: Ostatnią opcją, którą wymienimy, jeśli chodzi o narzędziowe pokazanie, mierzenie i uwidacznianie przewidywalności to są narzędzia BI-owe. W kilku organizacjach niezależnie od siebie widziałem taki efekt podłączenia bazy danych. Najczęściej pod spodem była jakaś JIRA, może Azure DevOps, albo tego typu narzędzia do mierzenia zadań, pokazywania tych zadań, kończenia ich. Dane surowe z takich narzędzi można przerzucić do narzędzi BI-owych. Czy to jest Power BI, czy to jest Tablo, czy to jest jeszcze coś innego? Kilka narzędzi różnie popularnych w różnych organizacjach. Oczywiście wymaga to już pewnych konkretnych kompetencji, żeby to wszystko podłączyć, żeby też być może odpowiednio skonfigurować raporty. No potencjalnie po stronie nagrody jest dosyć atrakcyjny sposób wizualizacji, być może sposób też jakiejś konfiguracji dodatkowego filtrowania dodatkowego, może dokładania kolejnych danych. W kontekście dużej organizacji wartością w sobie samo może być też pokazanie na jednym dash-boardzie wyników wielu zespołów, czy może pewien rodzaj standaryzacji pomiędzy zespołami, jakie aspekty są tam odpowiednio uwzględniane. Potencjalnie nagroda wielka, no ale tak jak wspomniałem też potencjalnie pewien koszt. Jeśli ma się te kompetencje w zespole, to może ten koszt jest siłą rzeczy pomijalny, a czasami warto to zainwestować, żeby dostać wartościowe widoki, czy wartościowe mierniki. Kuba: I zanim przejdziemy do następnego rozdziału, przypominamy, że jeżeli chcesz pogłębić wiedzę, jeszcze bardziej niż robimy to w podcaście, to znajdziesz nasze płatne produkty na stronie porzadnyagile.pl/sklep. Jacek: Przechodzimy do kolejnej sekcji dzisiejszego odcinka, czyli kilka wskazówek na temat tego, jak stosować miary przewidywalności w praktyce. Kuba: Pierwsza rzecz, od której chcę zacząć, to uwzględnij stopień innowacyjności zespołu. Przewidywalność jako miara w typowym zespole wytwórczym powinna być mierzona. To jest też cecha, którą taki zespół powinien posiadać. Natomiast mamy w swoim doświadczeniu kilka przykładów takich zespołów, które są naprawdę mocno innowacyjne, robią zadania takie mocno polegające na jakimś rodzaju research and development, jakimś badaniu, jakimś odkrywaniu, w takim stopniu innowacyjności naprawdę dużym. Te zespoły siłą rzeczy z racji na taką dużą chaotyczność czy dużą złożoność swojej pracy badawczej, po prostu tej przewidywalności osiągnąć nie za bardzo mogą, w takim znaczeniu, o jakim mówimy w tym odcinku. Dlatego tutaj bierzemy taką poprawkę, może taką dokładamy gwiazdkę do przewidywalności. W wybranej organizacji to niektóre zespoły będą siłą rzeczy nieprzewidywalne, w których firmach może w ogóle wszystkie, bo taka jest natura produktu czy branży, w której się działa, więc może wziąć warto poprawkę na to, że nie we wszystkich zespołach, nie we wszystkich firmach ta przewidywalność, o której dzisiaj powiedzieliśmy i jeszcze będziemy mówić, jest adekwatna, czy jest miarą, na którą warto spoglądać. Jacek: Jednocześnie przy tej okazji warto zwrócić uwagę na taki pewien ewenement, który obserwujemy z Kubą, że wiele zespołów wpada w poczucie, że są właśnie takim bardzo wyjątkowym i innowacyjnym zespołem, który ze względu na naturę swojej pracy nie jest w stanie pracować w przewidywalny sposób i nasze doświadczenie jest takie, że raczej nie do końca jest tak na takiej zasadzie, że faktycznie takie zespoły spotykamy, ale tych zespołów jest zdecydowania mniejszość. Nawet jeśli to faktycznie jest ten research, o którym wspominał Kuba, takie działania też można planować, można dzielić je na mniejsze kroki, bardzo precyzyjnie sobie określać kryteria akceptacji. I też w miarę w uporządkowany sposób decydować, czy to, co zaplanowaliśmy sobie zrobić, niekoniecznie te uzyskane rezultaty, ale tę pracę wykonaną, którą planowaliśmy, jesteśmy w stanie zaplanować. Raczej większość zespołów tę pracę, którą wykonuje ona, ma najczęściej jednak charakter taki, że jesteśmy w stanie przewidzieć, co będziemy realizować. Więc tutaj chcemy z Kubą wyraźnie zaznaczyć taką potencjalną pułapkę, żeby dokonać faktycznej refleksji, czy rzeczywiście ta nasza praca nosi znamiona takiej absolutnie niezarządzalnej, nieprzewidywalnej, czy tylko wpadliśmy w tę pułapkę, że tak o tej pracy myślimy. Jacek: Druga wskazówka, świadomie wybierz zmienne do wzoru. Wspomnieliśmy, jak taki wzór mógłby wyglądać, wspomnieliśmy, w jakiej jednostce wyrażony jest wynik. Taką główną wątpliwością osób, które podchodzą do tematu przewidywalności, jest wybór tego, czy powinniśmy patrzeć na konkretne elementy, które posiadamy jako zakres w danym konkretnym Sprincie, czy iteracji, czy raczej powinniśmy patrzeć na sumę story pointów I o ile historycznie pierwsze próby mierzenia się z przewidywalnością kierowały nas z Kubą w stronę story pointów, no to dzisiaj zdecydowanie jest nam bliżej do tego, żeby raczej patrzeć na tę liczbę elementów, które bierzemy do Sprintu. Konkretnie w Jirze można sobie przestawić wykres, ustawić go na to, żeby pokazywał issue count, czyli żeby po prostu policzył nam tę liczbę elementów, którą mamy w Sprincie. No i generalnie zbliża nas to do myślenia bardziej o patrzeniu i mierzeniu przepustowości i przewidywalności na tej bazie, niż na takie klasyczne Velocity, które najczęściej wyrażane jest jako suma story pointów zaplanowanych na konkretny Sprint. Kuba: Dlaczego poświęcamy na to czas w tym nagraniu? Bo wiele zespołów poświęca niepotrzebnie czas na przykład szczegółowy wycenianie, bo inaczej nie będzie pewien element uwzględniony we wzorze, a po wszystkim zwłaszcza też niezależne próby to potwierdzają w wielu zespołach, w wielu firmach korelacja między ilością skończonych elementów a story pointami zakończonymi jest na tyle silna, że w zasadzie nie ma potrzeby wkładać dodatkowej energii w to, żeby nawyceniać wszystkie prace. Zwłaszcza jeśli ma to prowadzić do, no naszym zdaniem, absurdów takich jak wycenianie błędów czy wycenianie jakichś zadań technicznych, tylko po to, żeby one się później ładnie w słupki sumowały. Może się okazać, że prosta suma ilości elementów jakichkolwiek, które uwzględniamy w takim predictability po prostu są do wzięcia i tyle, to jest dosyć łatwe, łatwo mechanicznie wyliczyć taki wzór i po prostu niepotrzebnie nie wkładać dodatkowej energii w coś, co nie wniesie dodatkowej wartości. I zaakcentuję, czy może tak trochę refrenem powtórzę to, co powiedział Jacek, niestety domyślnie Jira pokazuje, a Jira jest też najbardziej popularnym narzędziem z tego, co widzimy, pokazuje właśnie po story pointach, co może oznaczać, że nie uwzględnia rzeczy niewycenionych do tego typu wzorów na przewidywalność, no i z drugiej strony właśnie trochę miesza w przewidywalności, jeśli zespół cierpi na zadania przechodzące między Sprintami. Jeśli zespół właśnie uwzględnia w swoich działaniach również elementy, które są niewyceniane, więc tutaj domyślny sposób pokazania przewidywalności mierzonej w story pointach może być pewną pułapką, stąd wskazówka świadomie wybierz zmienne do wzoru. Kuba: Trzecia wskazówka to traktuj przewidywalność jako wewnętrzny kompas zespołu. Dużo nieszczęścia dzieje się w organizacjach, w których zostaje się celem. Jacek już to lekko zaznaczył, ja to wzmocnię. Są organizacje, które wręcz żądają, domagają się, zostawiają w celach rocznych, uzależniają premię od tego, czy zespół będzie przewidywalny, ustawiając też konkretne oczekiwane wartości. Najczęściej spotykam, że wartością oczekiwaną jest dokładnie 100%, czyli róbcie dokładnie tyle, ile planujecie, to poprowadzi do pewnych pułapek, ale znam też organizację, w której oczekiwana wartość przewidywalności to jest nie powinna przekraczać powiedzmy 80%. Czyli przewidywalny zespół to taki, który w przewidywalny sposób zawsze trochę nie dowozi. Też nie najszczęśliwszy pomysł. Więc tutaj mocno opieramy się na pomyśle, że przewidywalność to jest raczej miara wewnętrzna do mierzenia procesu przez zespół, do traktowania go jako punkt odniesienia przy usprawnianiu się, do myślenia o nim w czasie planowania, myślenia o nim w czasie Retrospektyw, myślenia o nim w jakimś tam dłuższym horyzoncie, ale na pewno nie jako sposób czy podstawa do tego, żeby dostać nagrodę albo karę, bo siłą rzeczy, zresztą jak każda inna miara tego typu, może się to łatwo przeinaczyć czy wręcz wypaczyć, stać się celem samym w sobie zamiast wiarygodną podstawą do usprawniania. Jacek: I czwarta porada, nie polegaj wyłącznie na przewidywalności. Tutaj zdecydowanie rekomendujemy, żeby przewidywalność nie była jedyną miarą procesu, którą zespół monitoruje. Dobrze jest od czegoś zacząć, ale zdecydowanie nie spoczywałbym tutaj na laurach. Przykładowo jednocześnie warto spojrzeć na throughput, czyli na przepustowość. Można do tego dołożyć sobie jakąś miarę jakości, można dołożyć jakąś miarę wartości biznesowej. To, co jest dla nas w danym momencie istotne i to, na co chcemy zwracać uwagę i wtedy patrzeć na pewien zestaw miar. Patrzeć jak one się wzajemnie zachowują. Może być tak, że poprawa jednej konkretnej miary może pogarszać wyniki w drugiej. Warto na to zwrócić uwagę i tak sobie skonfigurować te miary, żebyśmy mieli taki dosyć pełny obraz tego, jaka jest kondycja naszego zespołu i jego otoczenia. Kuba: I ostatni rozdział. Jak poprawiać przewidywalność zespołu? Ten rozdział będzie krótki, bo tak naprawdę to, co poprawia przewidywalność było tematem masy z poprzednich odcinków. My w zasadzie sami się z Jackiem zaśmialiśmy, że tak późno z naszej strony odcinek o przewidywalności w czasie, gdy mnóstwo praktyk poprawy przewidywalności już było przez nas poruszonych. Więc tutaj nie będziemy pogłębiać tematu, co dokładnie oznacza dana praktyka. Raczej potraktuj tę zawartość tego jako pewnego rodzaju spis treści czy nasze rekomendowane tak dokładnie osiem praktyk poprawy przewidywalności. Jeśli które z nich brzmi dla Ciebie intrygująco albo coś, czego jeszcze nie stosujesz, to po prostu odsyłamy Cię do materiałów, które też zamieszczamy w opisie odcinka. Jacek: Ok, czyli jakie praktyki zastosować, żeby poprawić przewidywalność zespołu? Kuba: Przede wszystkim zacznij kończyć, skończ zaczynać. Stosuj krótkie Sprinty. Wzmacniaj odpowiedzialność zespołu za produkt i dziel pracę na mniejsze kawałki. Jacek: Dodatkowo planuj zespołowo, zarządzaj zależnościami zewnętrznymi, traktuj codzienny stand-up jako bezpiecznik i usprawniaj się w oparciu o miary dostarczania produktu. Kuba: Wszystkie wymienione koncepcje, tak jak powiedziałem, znajdziesz w naszych starszych odcinkach, które linkujemy w opisie odcinka i na stronie tego odcinka porzadnyagile.pl/140 Jacek: Przewidywalność to miara i jednocześnie pożądana cecha zespołu, który realizuje zakres pracy, jaki sobie zaplanował na Sprint. Najczęściej przewidywalność podaje się w procentach jako stosunek liczby elementów faktycznie zrealizowanych do liczby elementów pierwotnie zaplanowanych. Kuba: Przewidywalność jest miarą, której wartość oczekiwana jest zakresem. Naszym zdaniem powinna mieścić się zazwyczaj między 80 a 120 procent. Istnieje szereg praktyk wspierających przewidywalność zespołu i zachęcamy do ich zastosowania w Twoim zespole. Jacek: Przyczyny braku przewidywalności w danym zespole mogą oczywiście być różne. Jako doświadczenie eksperci dołączamy do zespołu lub wskazanej części firmy i jasno je wskazujemy wraz z rekomendacjami sposobów, aby zmienić proces wytwórczy tak, by przewidywalność faktycznie rosła. Sprawdź naszą propozycję na stronie 202procent.pl/diagnoza. Kuba: A notatki do tego odcinka, artykuł, transkrypcję, wspomniane linki do innych rekomendowanych materiałów oraz zapis wideo znajdziesz na stronie porzadnyagile.pl/140. Jacek: I to by było wszystko na dzisiaj. Dzięki Kuba. Kuba: Dzięki Jacek. I do usłyszenia wkrótce. ________ To była pełna transkrypcja odcinka podcastu Porządny Agile. Dziękujemy za lekturę! The post Przewidywalność zespołu first appeared on Porządny Agile.

You meet people who want freedom. Then you meet someone who refuses a boss so much he reverse-engineers his whole life. After Derek left the Army, he started Amazon FBA with nothing but podcasts, stubborn grit, and a few painful months of trying to "make a penny per ASIN."   In this talk, we walk you through his jump from Excel chaos to 45,460 ASINs stored, his real replens, and a system that runs while he sleeps. You'll hear how he fights through IP blocks, bad prep centers, order cancellations, and the mental weight of slow progress.   If you ever wondered what happens when someone treats sourcing like a gym routine, delayed gratification like a religion, and three in the morning like normal business hours, this one shows you.   Special guest at the conclusion of today's show, Jeff Schick of JeffSchick.com answers the question: "What's an Amazon CSM review and is it a big deal?" Use coupon code "MISTAKE" to get your first month of services for only $1 with Jeff and his team!   Watch this episode on our YouTube channel here: https://youtu.be/Sdqq6GfTcxI   Show note LINKS: SilentSalesMachine.com - Text the word "free" to 507-800-0090 to get a free copy of Jim's latest book in audio about building multiple income streams online (US only) or visit https://silentjim.com/free11   SilentJim.com/bookacall - Schedule a FREE, customized and insightful consultation with my team or me (Jim) to discuss your e-commerce goals and options.   My Silent Team Facebook group. 100% FREE! https://www.facebook.com/groups/mysilentteam - Join 82,000 + Facebook members from around the world who are using the internet creatively every day to launch and grow multiple income streams through our exciting PROVEN strategies! There's no support community like this one anywhere else in the world!   ProvenAmazonCourse.com - The comprehensive course that contains ALL our Amazon training modules, recorded events and a steady stream of latest cutting edge training including of course the most popular starting point, the REPLENS selling model. The PAC is updated free for life!   SilentJim.com/kickstart - If you want a shortcut to learning all you need to get started then get the Proven Amazon Course and go through Kickstart.   SilentJim.com/thesystem  - (aka as 3P Mercury) - The complete workflow software we created on our team. "The System" automates your Amazon reselling/wholesale business the same way Khang (the creator) automated his $3million reselling business and made it HANDS FREE!                 ---

#MentorshipPodcast #FinanceCareer #ConsultingJourney #BCG #Vision2030 #CareerGrowth #InternationalBusiness #leadershiplessons Claudio shares his incredible global journey — from starting in European finance and insurance, to working on Vision 2030 transformation projects in Saudi Arabia, and later joining BCG in management consulting. His story is a masterclass in adaptability, mentorship, and lifelong learning across borders.

Topics CoveredAI efficiency vs. AI opportunity in modern B2B orgsUse cases across Claude, Gemini, Copilot, and ChatGPTClaude for Excel outperforming native pluginsAI-powered brand visibility audits (AIO, GEO)Building MVPs from product demos using GeminiAutomating reporting and funnel analysis with ChatGPT & GeminiCustom GPTs for keyword analysis and lead quality reviewsAI system design in regulated or high-security environmentsFramework-based AI prompting for repeatable resultsTesting rigor and prompt engineering for trustable AI outputQuestions This Video Helps AnswerHow can B2B marketing teams use AI to save time and create net-new strategic opportunities?What LLM (Claude, ChatGPT, Gemini, Copilot) is best for specific tasks like Excel, brand analysis, or creative reviews?How do I know if AI-generated reporting is accurate enough to trust?What's a good prompt structure to consistently get usable output from ChatGPT or Gemini?How can I explain AIO (AI Optimization) visibility and results to executives?Jobs, Roles, and Responsibilities MentionedMarketing teamsSales teamsFinance and accounting departmentsHR and admin functionsDemand gen strategistsOperations and supply chain leadersAI consultants and systems integratorsCreative and copywriting leadsIT and cybersecurity teamsExecutive and portfolio leadershipKey TakeawaysAI tools should be evaluated by outcome, not branding—Claude may outperform Copilot in Excel.Reframing workflows to be AI-native rather than AI-assisted unlocks transformational gains.Demand marketers can use LLMs to streamline reporting, extract funnel insights, and improve creative alignment.Framework-driven prompting (like SPEC) helps generate consistent, high-trust outputs.Custom AI workflows (e.g., for lead scoring, brand checks) can scale across clients and teams without deep coding.Generative AI is a tool for internal enablement, not just public content.

Financial consolidation has long been treated as a “back-office necessity,” but in today's complex, data-dense businesses, it's becoming a strategic engine. Craig Schiff, Founder and CEO of BPM Partners joins Melissa to unpack the evolution of consolidation, why it's resurging in importance, and how AI is reshaping both FP&A and controllership. Craig shares what companies are getting wrong, why consolidation and planning belong together and the trends he's seeing as organizations move from legacy systems, Excel-heavy processes and fragmented tools toward unified performance management. He also explains the critical connection between actuals, planning accuracy and strategic speed, and why more CFOs now lead with consolidation at the center of their data strategy. Discussed in This Episode:  Critical features of modern financial close and consolidation software Building a unified finance tech stack AI in performance management: What CFOs need to know The future of close and consolidationFor CFO insights, episode show notes and exclusive blog content, visit thecfoshowpodcast.com.

"I got employee of the year in 2016 for introducing Excel as a project management tool for the construction department in my company."That's the state of technology in the $300 billion store development industry.In today's episode of Bricks & Bytes, we sat down with Genevieve Davis and Alim Uderbekov from Surfaice. These two met on a beach in California (yes, really) and decided to tackle one of the most analog industries out there with AI agents.Here's what we covered:✅ Why companies building hundreds of stores aren't using Procore (hint: it's expensive and they're retailers, not developers)✅ The "McDonald's is a race car" analogy - why 40,000 cookie-cutter restaurants are actually the perfect use case for AI in construction✅ How code is becoming a commodity and why that changes everything about vertical software✅ Why AI won't take your job, but people who know how to use it willLink in comments - give it a listen if you're in retail, store development, or just want to hear how two people turned a beach conversation into a company.Our Sponsors:Aphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk -  “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.comBuildVision -   streamlining the construction supply chain with a unified platform - www.buildvision.ioChapters00:00 Intro03:46 Introduction to Surface and Founders' Backgrounds 06:48 The Vision Behind Surface and AI in Construction 09:41 The Meeting of Minds: Genevieve and Alim's Collaboration 12:45 Current Challenges in Store Development 15:30 AI's Role in Automating Construction Processes 18:32 Efficiency Gains and Time Savings with AI 21:44 The Future of Store Development and Human Touchpoints 24:30 Defensibility and Business Model of Surface 27:25 Funding and Future Plans for Surface 39:13 Investment Insights and Strategic Partnerships 41:31 Customer Engagement and Feedback 44:33 AI in Construction: Transforming Workflows 47:14 Navigating the Go-To-Market Strategy 54:20 Understanding Competition in the AI Space 59:25 Customization and Onboarding Process 01:03:07 Future of AI in Construction

Happy National Regifting Day! We debate whether this holiday is actually placed correctly on the calendar while Mike battles a post Flyers game sickness with some "Immunity Boost" tea. We get a quick update on Dan, who has seemingly cured his post marathon injuries through fasting and is already planning to run Philly again. Mike recaps his very first hockey game experience, he also dives into the high stakes world of sports card collecting.Things get weird when Erin reveals she has been publicly challenged to a race by Summer House star Craig Conover via Instagram. We discuss the diabolical terms of this "6k" challenge and brainstorm ways to make his travel to Pennsylvania as difficult as possible. We also check in on our favorite "fake" podcaster who is somehow charting on Apple with suspicious numbers and discover the electric world of Microsoft Excel Esports, where spreadsheet experts get intro entrances like pro wrestlers. In sports news, we clarify the misleading headlines about the "trailer park" athlete village for the Winter Olympics. Finally, "Tea Time" covers a controversial Turkey Trot prank that left a new girlfriend in tears , and we end with a wholesome clip of a coach's son dancing in the locker room.

Will IRS agents have to watch OnlyFans to police the new “no tax on tips” deduction? Blake and David unpack the wild twist: a study showing that women-led audit teams deliver higher-quality work at lower fees, and why AI could finally kill the billable hour. They hit PCAOB's AI/PE crackdown, KPMG's AI exam cheating, AI‑written financial reports, Meta's off‑balance‑sheet data center, the IRS Math Act, and new child investment accounts. Walk away with policy context and practical tech takeaways.SponsorsOnPay - http://accountingpodcast.promo/onpayACFE - http://accountingpodcast.promo/acfe Cloud Accountant Staffing - http://accountingpodcast.promo/casChapters(01:15) - New Tax Deduction for Digital Content Creators (02:27) - IRS Agents and OnlyFans Content (04:39) - IRS Telework Policy Changes (06:15) - Earmark CPE and Listener Interaction (09:25) - President's Statement on Federal Income Tax (13:15) - IRS Math Act and Senator Justice's Tax Issues (16:03) - Trump Accounts and PCAOB Scrutiny on AI (24:20) - Audit Quality and Gender Diversity (28:30) - KPMG AI Cheating Scandal (30:51) - AI's Impact on Time Savings in Various Industries (32:56) - The Role of AI in Accounting and Its Limitations (34:26) - AI in Financial Reporting and Business Processes (36:44) - The Decline of the Billable Hour Due to AI (47:01) - Meta's Controversial Accounting Practices (50:56) - Pilot's New Partner Program for Accountants (58:58) - Conclusion and Final Thoughts  Show NotesComing soon!Need CPE?Get CPE for listening to podcasts with Earmark: https://earmarkcpe.comSubscribe to the Earmark Podcast: https://podcast.earmarkcpe.comGet in TouchThanks for listening and the great reviews! We appreciate you! Follow and tweet @BlakeTOliver and @DavidLeary. Find us on Facebook and Instagram. If you like what you hear, please do us a favor and write a review on Apple Podcasts or Podchaser. Call us and leave a voicemail; maybe we'll play it on the show. DIAL (202) 695-1040.SponsorshipsAre you interested in sponsoring The Accounting Podcast? For details, read the prospectus.Need Accounting Conference Info? Check out our new website - accountingconferences.comLimited edition shirts, stickers, and other necessitiesTeePublic Store: http://cloudacctpod.link/merchSubscribeApple Podcasts: http://cloudacctpod.link/ApplePodcastsYouTube: https://www.youtube.com/@TheAccountingPodcastSpotify: http://cloudacctpod.link/SpotifyPodchaser: http://cloudacctpod.link/podchaserStitcher: http://cloudacctpod.link/StitcherOvercast: http://cloudacctpod.link/OvercastClassifiedsWant to get the word out about your newsletter, webinar, party, Facebook group, podcast, e-book, job posting, or that fancy Excel macro you just created? Let the listeners of The Accounting Podcast know by running a classified ad. Go here to create your classified ad: https://cloudacctpod.link/RunClassifiedAdTranscriptsThe full transcript for this episode is available by clicking on the Transcript tab at the top of this page

This week on Mums on Cloud Nine, hosts Heather Black, Kelly-Jace Halls, and Lyn Constantine dive deep into the world of AI productivity hacks for budget planning and finances. As part of our 90 day series on AI, we're exploring how everyday mums can use simple AI tools to save time, lower stress, and take control of their finances. From finding bargain deals online, to streamlining mortgage repayments and bringing financial clarity into family life, our hosts share honest stories, practical tips, and tried-and-tested tricks from their personal experiences. Whether you're a spreadsheet lover, a savvy deal-hunter, or simply looking for ways to overcome money anxiety, this episode has got you covered. We discuss top financial apps like Snoop, Cleo, Plum and Emma, and show how they can help couples and families manage money together. Plus, find out why engaging with AI isn't just about saving cash and time—it's about building confidence, relationships and new career skills for the future. Join us to get inspired and empowered to make smarter financial decisions with the help of AI! Key Points in this Episode: Discover AI hacks that can save you up to 3 hours a week on financial planning and budgeting How to use ChatGPT to find discount codes, the best deals and smarter mortgage repayment plans Making spreadsheets smarter: bridging Excel with AI for financial accuracy Must-try financial apps: Snoop, Cleo, Plum, and Emma (https://emma-app.com/) How AI is supporting couples to manage joint finances and reduce money-related stress Tips for overcoming money anxiety and fear with gentle accountability from finance apps Teaching children about budgeting, saving and the value of money with fun AI tools The importance of developing AI skills for family life and future career opportunities Websites & Apps Mentioned: Emma: https://emma-app.com/ Cleo: https://web.meetcleo.com/ Plum: https://withplum.com/ Snoop: https://snoop.app/ ChatGPT: https://chatgpt.com/  Listen now to start your journey to financial freedom, supported every step of the way by your Mums on Cloud Nine community! Subscribe and connect with us for weekly mindset tips, practical hacks and empowering stories. Let's build a brighter future together. If you enjoyed this episode, please leave a review and share with any mums who want to feel stronger, clearer and more confident about their money and career!

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they'd all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They're all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they're better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.If you're an early or mid-career PM in Silicon Valley, this matters to you. Because here's the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn't whether to use these tools. The question is whether you're using the right ones most effectively.So let me walk you through exactly what I did, what I learned, and what you should do differently.The Setup: A Real-World Test CaseHere's how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that's complex enough to stress-test these tools but straightforward enough that I could iterate quickly.But here's the critical part that too many PMs get wrong when they start using AI for product work: I didn't just throw a single sentence at these tools and expect magic.The “Back of the Napkin” Approach: Why You Still Need to Think“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we're gonna do this more, a little old-school AI approach where we're gonna do some original human thinking.”This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.Your job as a PM isn't to become obsolete. It's to become more effective. And that means doing the strategic thinking work that AI cannot do for you.So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here's what I included:Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.Problem to Solve: What's broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”Key Elements: The feature set and approach.Success Metrics: How we'd measure success.Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that's exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That's all it took to create a foundation that would dramatically improve what came out of the AI tools.Round One: Generating the Full PRDWith my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.ChatGPT: The Reliable GeneralistChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.The document it produced checked all the boxes. It had the sections you'd expect. The writing was clear. But when I read it, I couldn't shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.Here's what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.But here's what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.The problem with generic output isn't that it's wrong, it's that it's invisible. When you're trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company's actual strategy. ChatGPT's output felt like it was written by someone who'd read a lot of PRDs but never actually shipped a product.One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren't wrong, but they're lazy. They don't show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude's output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn't need to be exceptional. Think: internal updates, status reports, routine communications. Don't rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.Gemini: Better Than ExpectedGoogle's Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren't in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.But here's where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren't terrible, but they weren't compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer's brain.For a PRD that you're going to use internally with a team that already understands the context, Gemini's output would work well. The text quality is strong enough, and if you're in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini's output directly into Google Docs and continue iterating there.But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It's good, but not great. It's the solid B+ student: reliably competent but rarely exceptional.Actionable Insight: Gemini is a strong choice if you're working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It's particularly good if you're working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don't expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.Grok: Not Ready for Prime TimeLet's just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.“I don't have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.Actionable Insight: Skip Grok for product documentation work right now. Maybe it'll improve, but as of my testing, it's simply not competitive with the other options. It felt like 1-2 years behind the others.ChatPRD: The Specialized ToolNow this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.Actionable Insight: If you're a technical lead without a dedicated PM, or you're a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It's particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.Claude: The Clear WinnerBut the standout performer, and I'm ranking these, was Claude.“I think we know that for now, I'm gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.Actionable Insight: For any PRD that matters, meaning anything you'll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it's worth using Claude even if you primarily use another tool for other tasks.Final Rankings: The Definitive HierarchyAfter testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here's my final ranking:* Claude - Best overall quality, most compelling mockups, strongest strategic thinking* ChatPRD - Best for structured PRD creation, feels most “human”* Gemini - Solid all-around performance, good Google integration* ChatGPT - Reliable but generic, lacks differentiation* Grok - Not competitive for this use case“I'd probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.The Deeper Lesson: Garbage In, Garbage Out (Still Applies)But here's what matters more than which tool wins: the realization that hit me partway through this experiment.“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I'm sure the output would have been better. But as you can see we didn't really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”And we still got pretty good results.This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what's possible.No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.So the dual strategy is:* Use the best tool available (currently Claude for PRDs)* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.Real-World Workflows: How to Actually Use This in Your Day-to-Day PM WorkTheory is great. Here's how to incorporate these insights into your actual product management workflows.The Weekly Sprint Planning WorkflowEvery PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.Here's an example workflow:Monday morning (30 minutes):* Review upcoming priorities and open your rough notes/outline in Google Docs* Open Claude and paste your outline with this prompt:“I'm preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”Monday afternoon (20 minutes):* Review Claude's output critically* Identify gaps, unclear requirements, or missing context* Follow up with targeted prompts:“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”Tuesday morning (15 minutes):* Generate mockups for any UI-heavy stories:“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”* Even if the HTML doesn't work perfectly, it gives your designers a starting pointBefore sprint planning (10 minutes):* Ask Claude to anticipate engineering questions:“Review these user stories as if you're a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”* This preparation makes you look thoughtful and helps the meeting run smoothlyTotal time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.The Stakeholder Alignment WorkflowGetting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.Here's how:Step 1: Map your stakeholders (10 minutes)Create a quick table in a doc:Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn't allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flowsStep 2: Generate stakeholder-specific communications (20 minutes)For each key stakeholder, ask Claude:“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”Then you'll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.Step 3: Synthesize feedback (15 minutes)After gathering stakeholder input, ask Claude to help you synthesize:“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.The Quarterly Planning WorkflowQuarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.Six weeks before planning:* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)* Don't wait until the last minuteFour weeks before planning:Dump everything into Claude with this structure:“I'm creating our Q2 roadmap. Context:* Business objectives: [paste from leadership]* Customer feedback themes: [paste synthesis]* Technical capabilities/constraints: [paste from engineering]* Competitive landscape: [paste analysis]* Current product gaps: [paste from your analysis]Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:* Strategic rationale (how it connects to business objectives)* Key initiatives (2-3 major features/projects)* Success metrics* Resource requirements (rough estimate)* Risks and mitigations* Customer segments addressed”This gives you a strategic framework to react to rather than starting from a blank page.Three weeks before planning:Iterate on the most promising themes:“Deep dive on Theme 3. Generate:* Detailed initiative breakdown* Dependencies on platform/infrastructure* Phasing options (MVP vs full build)* Go-to-market considerations* Data requirements* Open questions requiring research”Two weeks before planning:Pressure-test your thinking:“Play devil's advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”This adversarial prompting forces you to strengthen weak points before your leadership reviews it.One week before planning:Generate your presentation:“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”Then add your company-specific context, visual brand, and personal voice.The Customer Research WorkflowAI can't replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.Before customer interviews:“I'm interviewing customers about [topic]. Generate:* 10 open-ended questions that avoid leading the witness* 5 follow-up questions for each main question* Common cognitive biases I should watch for* A framework for categorizing responses”This prep work helps you conduct better interviews.After interviews:“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:* Recurring themes and patterns* Surprising insights that contradict our assumptions* Segments with different needs* Implied needs customers didn't articulate directly* Recommended next steps for validation”AI is excellent at pattern-matching across qualitative data at scale.The Crisis Management WorkflowSomething broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.Immediate response (5 minutes):“Critical incident. Details: [brief description]. Generate:* Incident classification (Sev 1-4)* Immediate stakeholders to notify* Draft customer communication (honest, apologetic, specific about what happened and what we're doing)* Draft internal communication for leadership* Key questions to ask engineering during investigation”Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.Post-incident (30 minutes):“Write a post-mortem based on this incident timeline: [paste timeline]. Include:* What happened (technical details)* Root cause analysis* Impact quantification (users affected, revenue impact, time to resolution)* What went well in our response* What could have been better* Specific action items with owners and deadlines* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”This gives you a strong first draft to refine with your team.Common Pitfalls: What Not to Do with AI in Product ManagementNow let's talk about the mistakes I see PMs making with AI tools. Pitfall #1: Treating AI Output as FinalThe biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don't hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.The fix: Always ask yourself:* Does this reflect my actual strategic thinking, or generic best practices?* Would my CEO/engineering lead/biggest customer find this compelling and specific?* Are there company-specific details, customer insights, or technical constraints that only I know?* Does this sound like me, or like a robot?Add those elements. That's where your value as a PM comes through.Pitfall #2: Using AI as a Crutch Instead of a ToolSome PMs use AI because they don't want to think deeply about the product. They're looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.This never works. AI can help you think more systematically, but it can't replace thinking.If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What's our actual competitive advantage?” “Why would customers switch from the incumbent?”), you're using it wrong.The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you're still the one choosing.Pitfall #3: Not IteratingGetting mediocre AI output and just accepting it is a waste of the technology's potential.The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what's weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.The fix: Budget time for iteration. Don't try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.Pitfall #4: Ignoring the Political and Human ContextAI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you're working with.They don't know that your VP of Engineering is burned out and skeptical of any new initiatives. They don't know that your CEO has a personal obsession with a specific competitor. They don't know that your lead designer is sensitive about not being included early enough in the process.If you use AI-generated communications without layering in this human context, you'll create perfectly worded documents that land badly because they miss the subtext.The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.Pitfall #5: Over-Relying on a Single ToolDifferent AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.If you only ever use one tool, you're missing opportunities to leverage different strengths for different tasks.The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.Pitfall #6: Not Fact-Checking AI OutputAI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren't true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.The fix: Fact-check everything, especially:* Statistics and market data* Competitive feature claims* Technical capabilities and limitations* Regulatory and compliance requirementsIf the AI cites a number or makes a factual claim, verify it independently before including it in your document.The Meta-Skill: Prompt Engineering for PMsLet's zoom out and talk about the underlying skill that makes all of this work: prompt engineering.This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there's a steep learning curve, prompt engineering is something you can get good at quickly.Principle 1: Provide Context Before InstructionsBad prompt:“Write a PRD for an AI tutor”Good prompt:“I'm a PM at an edtech company with 2M users, primarily high school students. We're exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.Principle 2: Specify Format and ConstraintsBad prompt:“Generate success metrics”Good prompt:“Generate 5-7 success metrics for this feature. Include a mix of:* Leading indicators (early signals of success)* Lagging indicators (definitive success measures)* User behavior metrics* Business impact metricsFor each metric, specify: name, definition, target value, measurement method, and why it matters.”The structure you provide shapes the structure you get back.Principle 3: Ask for Multiple OptionsBad prompt:“What should our Q2 priorities be?”Good prompt:“Generate 3 different strategic approaches for Q2:* Option A: Focus on user acquisition* Option B: Focus on engagement and retention* Option C: Focus on monetizationFor each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.Principle 4: Specify Audience and ToneBad prompt:“Summarize this PRD”Good prompt:“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”The audience and tone specification ensures the output will actually work for your intended use.Principle 5: Use Iterative RefinementDon't try to get perfect output in one prompt. Instead:First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”Each iteration improves the output incrementally.Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.Strategy 1: The Structured Outline ApproachDon't go from zero to full PRD in one prompt. Instead:* Start with strategic thinking - Spend 10-15 minutes outlining why you're building this, who it's for, and what problem it solves* Get specific - Don't say “users,” say “high school students in the middle 80% of academic performance”* Include constraints - Budget, timeline, technical limitations, competitive landscape* Dump your outline into the AI - Now ask it to expand into a full PRD* Iterate section by section - Don't try to perfect everything at onceThis is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.Strategy 2: The Comparative Analysis PatternOne technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.Strategy 3: The Iterative Refinement LoopDon't treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.After getting the initial PRD, I could have asked follow-up questions like:* “What's missing from this PRD?”* “How would you strengthen the success metrics section?”* “Generate 3 alternative approaches to the core feature set”Each iteration improves the output and, more importantly, forces me to think more deeply about the product.What This Means for Your CareerIf you're an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”Absolutely not. But your role is evolving, and understanding that evolution is critical.The PMs who will thrive in the AI era are those who:* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably producesThink of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.The Real-World Application: What to Do Monday MorningLet's get tactical. Here's exactly how to apply these insights to your actual product work:For Your Next PRD:* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice* Open Claude (or ChatPRD if you want more structure)* Copy your outline with this prompt:“I'm a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”* Review the output critically - Look for generic statements, missing details, or strategic misalignments* Iterate on specific sections:“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”* Generate supporting materials:“Create a visual mockup of the core user flow showing the key interaction points.”* Synthesize the best elements - Don't just copy-paste the AI output. Use it as raw material that you shape into your final documentFor Stakeholder Communication:When you need to pitch something to leadership or engineering:* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)* Compare them for:* Clarity and conciseness* Strategic framing* Compelling value proposition* Addressing likely objections* Blend the best elements into your final version* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.For Feature Prioritization:AI tools can help you think through trade-offs more systematically:“I'm deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”This doesn't replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn't thought of.The Uncomfortable Truth About AI and Product ManagementLet me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.Less valuable:* Writing boilerplate documentation* Creating standard frameworks and templates* Generating routine status updates* Synthesizing information from existing sourcesMore valuable:* Strategic product vision and roadmapping* Deep customer empathy and insight generation* Cross-functional leadership and influence* Critical evaluation of options and trade-offs* Creative problem-solving for novel situationsIf your PM role primarily involves the first category of tasks, you should be concerned. But if you're focused on the second category while leveraging AI for the first, you're going to be exponentially more effective than your peers who resist these tools.The PMs I see succeeding aren't those who can write the best PRD manually. They're those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.Advanced Techniques: Beyond Basic PRD GenerationOnce you've mastered the basics, here are some advanced applications I've found valuable:Competitive Analysis at Scale“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.Scenario Planning“We're considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.User Story GenerationAfter your PRD is solid:“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].' Include acceptance criteria for each story. Organize them into epics by functional area.”This can save your engineering team hours of grooming meetings.The Tools Will Keep Evolving. Your Process Shouldn'tHere's something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.But the core principles won't change:* Do strategic thinking before touching AI* Use the best tool available for your specific task* Iterate and refine rather than accepting first outputs* Blend AI capabilities with human judgment* Focus your time on the uniquely human aspects of product managementThe specific tools matter less than your process for using them effectively.A Final Experiment: The Skeptical VP TestI want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who's brilliant, who's seen a thousand product ideas fail, and who can spot b******t from a mile away?The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.Questions to Consider: A Framework for Your Own ExperimentsAs I wrapped up my Loom, I posed some questions to the audience that I'll pose to you:“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”These aren't rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.For early-career PMs: I'd recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.The Path Forward: Continuous ExperimentationMy experiment with these five AI tools took 45 minutes. But I'm not done experimenting.The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.That's why I created this Loom and why I'm writing this post. Not because I have all the answers, but because I'm figuring it out in real-time and want to share the journey.A Personal Note on Coaching and ConsultingIf this kind of practical advice resonates with you, I'm happy to work with you directly.Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that's leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.Reach out through tomleungcoaching.com if you're interested in working together.OK. Enough pontificating. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

In this hands-on labs episode of This Week in NoCode + AI, JJ sits down with David, CEO and solo founder of Formula Bot, for a practical, live walkthrough of how AI is changing the way teams analyze data.If you want to learn by doing, this episode is for you. JJ and David dive directly into the Formula Bot platform, connect real data sources, and show how AI can act as a true data analyst — answering questions, generating reports, and uncovering insights without complex dashboards or manual spreadsheet work.You'll see how Formula Bot evolved from a simple Excel formula generator into a full AI-powered data analysis platform that integrates with tools like Google Analytics, data warehouses, and more. David walks through key features including data connectors, automated reporting, web scraping, and enrichment — all designed to help teams move faster from raw data to decisions.David also shares his journey bootstrapping Formula Bot to over $1M in revenue as a solo founder, what he learned along the way, and how he thinks about AI competing with (and complementing) tools like ChatGPT.Whether you're in finance, accounting, marketing, or running a small business, this episode shows exactly how AI can simplify data analysis and unlock insights you may be missing today.Links & Resources

Ab wann macht Dadnoises und wie intelligent kann man mit einer Brille aussehen? Wer ist der Excelperte 2025 und wir hart hittet Glühwein bei unseren Bundeswehrsoldaten? Wie groß ist die Gefahr das Amsterdam einen neuen Namen bekommt, wer designed den FIFA peace Pokal und muss Fußball 2.0 erfunden werden? Warum ist Space My Space kein guter Name für eine Umzugsfirma im Weltall und warum hat Japan so kreative Filmnamen? Wieso fällt der Ausflug zum Flugsimulator aus, welche neuen kuriosen isso oder is nich so Fragen haben wir und welcher Monsterdeal erschüttert die Medienwelt? Der Weihnachtsmann hat after credit noch den Arc Raider Nerdtalk platziert :) ZDP wünscht ein frohes Fest!

Se acaba el año. Y no, no basta con mirar el Excel. Hoy no vengo a hablarte de facturación, sino de algo mucho más incómodo… y mucho más útil: cómo has vendido. Qué has hecho, qué has dejado de hacer y qué repetirías sin dudar. En este episodio te propongo un ejercicio de cierre diferente: uno que va más allá de los números, que pone la lupa en tu actitud comercial, en las decisiones que tomaste y en lo que puedes ajustar para vender más el próximo año. Aquí tienes la plantilla para hacer tu balance: https://eticacomercial.com/balance-comercial-anual/ Y tienes muchos más recursos aquí: https://eticacomercial.com/recursos

Se acaba el año. Y no, no basta con mirar el Excel. Hoy no vengo a hablarte de facturación, sino de algo mucho más incómodo… y mucho más útil: cómo has vendido. Qué has hecho, qué has dejado de hacer y qué repetirías sin dudar. En este episodio te propongo un ejercicio de cierre diferente: uno que va más allá de los números, que pone la lupa en tu actitud comercial, en las decisiones que tomaste y en lo que puedes ajustar para vender más el próximo año. 📩 Aquí tienes la plantilla para hacer tu balance: https://eticacomercial.com/balance-comercial-anual/ Y tienes muchos más recursos aquí: https://eticacomercial.com/recursos

This episode is all about one of the smartest Amazon PPC bidding systems you'll ever come across. Priscilla, our guest, has spent years building her own custom bidding logic — not using software, but by creating a super detailed, data-heavy Excel system that she keeps improving over time. She breaks keywords into different behavior groups, figures out how many clicks each match type really needs to get a sale, checks organic rank, placements, inventory signals, and basically connects all those data points to decide exactly how every bid should move. What's cool is that she didn't learn this from any tool — she built it herself after studying a giant Excel file a VA once gave her. If you want to hear how someone can take PPC bidding way deeper than “raise bid, lower bid,” this episode is a super interesting look into how Priscilla thinks and why her approach works so well.We'll see you in The PPC Den!

Dive into what's next for enterprise AI in the latest episode of Tech-Driven Business. Mustansir Saifuddin welcomes SAP expert Andrea Haupfear for an in-depth conversation on how SAP is helping organizations turn AI from hype into measurable business value. If you're navigating transformation across finance, supply chain, or operations, this episode is a must-listen. Andrea breaks down what makes enterprise AI different—why trusted data, business context, and governance are non-negotiable—and how SAP is embedding AI directly into business processes to improve speed, accuracy, and ROI. Tune in for a real-world example and practical guidance on how organizations can start small, prioritize high-impact use cases, and prepare for what's coming next with Agentic AI. Andrea Haupfear is a Business Process Architect with over a decade of experience driving digital transformation through artificial intelligence and advanced analytics. She specializes in designing and implementing AI-powered solutions that enhance operational efficiency, decision-making, and adaptability across diverse business environments. Andrea is recognized for her strategic leadership in translating complex technologies into scalable, real-world applications, making her a trusted advisor in navigating change and unlocking value through innovation. Connect with Us: LinkedIn: Andrea Haupfear Mustansir Saifuddin Innovative Solution Partners X:  @Mmsaifuddin YouTube or learn more about our sponsor Innovative Solution Partners to schedule a free consultation.    Episode Transcript [00:00:00] Mustansir Saifuddin: Welcome to Tech Driven Business, brought to you by Innovative Solution Partners. I'm honored to have Andrea Haupfear of SAP. Join me today to break down how SAP is helping organizations leverage AI to drive efficiency, reduce risk and deliver measurable ROI. We'll also look ahead at what's next and how you and your team can prepare as AI moves from experimental to essential for enterprises to thrive. [00:00:35] Hello Andrea. How are you? [00:00:37] Andrea Haupfear: I'm good. How are you? [00:00:40] Mustansir Saifuddin: Doing well, doing well. I'm so excited to have you on our show. So thank you for coming on. Today we would like to talk about the latest SAP's AI journey and the business transformation. And what it really means for SAP customers. How does it sound? [00:00:57] Andrea Haupfear: Sure. No, it sounds great. This is one of my passions that I love to talk about, and so you know, happy and excited to actually share a little bit about what we're doing with AI at SAP and what we've seen in the field with our customers. So, super excited and it's a pleasure being here. [00:01:14] So thank you again for inviting me here. [00:01:17] Mustansir Saifuddin: Awesome. Awesome. Let's get into it, we know we are at an inflection point, right? AI is moving so fast and it's actually turning from experimental to essential, right? For in a lot of different cases. So let's focus in how is SAP's AI strategy fundamentally different from consumer AI? And why does it really matter for enterprises? [00:01:39] Andrea Haupfear: Yeah, absolutely. So a couple of things that I wanna kind of touch on here. So oftentimes, and you mentioned this, right, we, we've used, we've used AI in our personal and daily lives for, you know, the last decade plus, right? I mean, when you think about AI, a lot of people think about Siri or Alexa or ChatGPT, right? [00:02:01] And you know, when I personally think about AI you've got your broad and creative tasks. What we've done in our personal lives, you know, everything from creating a grocery list to, editing a, a photo, right? A family photo. But from a business perspective you know, an enterprise AI, it really has to change those business outcomes. [00:02:26] And really when you think about this, think about, you know, everything from could be closing the books faster or. A faster on time delivery rate or reducing risk in my supply chain. And how do we ultimately do it with the highest level of governance, auditability, cost control. And so SAP's approach is really built around that flywheel of applications, data and that additional layer of artificial intelligence on top of it. [00:02:58] So there's, there's that aspect to it, but then also thinking about it in a couple of other ways of how we're doing this. Is, you know, yes, we're embedding it where your business or where the work happens. So making it easier for our end users to be able to leverage artificial intelligent capabilities and even machine learning capabilities, not just [00:03:23] from a digital assistant or a chat perspective, but how do we integrate it and infuse it within their specific business, day-to-day business processes to make their lives that much easier? But then also thinking about it from a strategic perspective, how can I obtain that high level of ROI by leveraging artificial intelligence. [00:03:47] So we're seeing it in a couple of different flavors from our customers. And then also what we're developing from a, from a product perspective as well. So we're thinking about it from a couple of different angles. [00:04:00] Additionally, thinking about it from a AI operating system for our developers and for our consultants, leveraging the AI foundation on the business technology platform. [00:04:13] So think about what used to be our developers would have to generate thousands upon thousands of lines of code. Now that's no longer the case, right? It can take them you know, a, a minute or so now to develop the, these applications and these lines of code to where it's, it's easier for them to go about their day-to-day jobs and their, their tasks where now they don't have to spend days upon days trying to develop these different applications and agents. [00:04:45] It also lets you and I just mentioned around agents, it also lets you create and govern custom agents to read and write back to SAP and non SAP systems. Thinking about this automation and accountability, not just getting those pointed answers, right? And then last but not least, kind of how I think about this is, yes, you've got your trust. [00:05:09] Think about trust not only in the data from looking at it in your SAP systems, but also think about non SAP systems. Think about your third party applications that you're going in and looking at the data, whether it's geographic information customer sentiment information, or it could even be, asset related sensor information, right? So bringing in that data as well as looking at it from your SAP business data context but then also looking at it with responsibility. So SAP has that responsible AI program in place really aligned to the UN UNESCO principles and the ISO 4 2 0 0 1 certification to really prioritize [00:05:57] those ethics and compliance and human oversight within our, our AI applications such as, you know, AI core and our digital assistant juul. So really taking it from a full spectrum approach and looking at it at the holistic level and how we can bring in artificial intelligence within these, these areas. [00:06:20] Mustansir Saifuddin: I love the way you kind of package it all together, from an overall perspective, especially, you know, the two things that really stuck out to me was. Again, coming from a business side, what is in it for a customer, right? What is the real value? [00:06:35] And you touched upon two things, embedding it. So a person who is currently doing a job and they're used to doing it manually. Now you can. Embed these ai component to their daily work streams, right? And how they can, you know, utilize that. And the second part I really loved it is you talked about ROI really what is the return I'm getting on this investment, right? And then lastly, you talked about data. So let's, let's talk about that. You know, here's the uncomfortable truth. AI is only as good as the data it learns from. We all know that. We all talk about it. And we have always heard this term garbage and garbage out, but what that sort really mean when we are talking SAP AI, making recommendations to customers and we are talking the effect in terms of millions in revenues or supply chain decisions. How would you like to address that? What is, what is SAP's approach on that? [00:07:33] Andrea Haupfear: Yeah, so a couple of things and you know, I've heard that term many times and coming and being an ex consultant. It's, it's definitely right. You're only as good as, as I've heard, you know, as the data that you have. So my thought on this is really when you have an AI agent that recommends, say for example, expediting a shipment or reclassifying a receivable, the truth that it relies on, [00:08:00] is your master data. [00:08:01] It is your transactional history and any sort of process constraints that lie in between. So ultimately, when I think about this, it's not just your, your master data. It's a, it's a multitude of things that ultimately will help the AI model in the end. In the end game to take those three pillars and turn garbage in into good decisions out. [00:08:27] The other piece that, how I think about this are semantics and not necessarily just schemas. So think about when we have some of our solutions such as Business Data Cloud, which carries those semantics and lineage into your AI workloads. So say for example, the customer, the plants, or an open po, it means the same thing everywhere. [00:08:51] And that's really critical for explainability and audit purposes. That's another way how I think about this. And then also just looking at this from a context perspective. So I also think about, you have to train a model when at first go, right? And be able to provide it some context, some instruction, some understanding that says, this is where this particular business process lies. [00:09:17] This is how the process should look and feel. What does good look like? And that's ultimately what we explain and tell our customers is we need to train our model to understand what does good look like, and this is where you have that context, rich retrieval and not just kind of that blind prompting that just says, go do this. [00:09:36] But the model will try to establish what it thinks good looks like, which may not necessarily mean what you think good looks like. This is where SAP HANA Cloud will bring in that vector engine, so those semantic retrievals and documents and notes and images. [00:09:53] Think about a knowledge graph. So it brings in those specific facts and relationships from your ERP, but then also thinking about a rag model as well, reducing those hallucinations and making those citations explainable. So why did the model or an agent be able to go through the process that it did? [00:10:16] Well, that's because there's multiple steps and instructions that the model has to take in order to provide an accurate response. Those are some of the things in, in which we leverage today with our customers and really making it so that way it's, yes, they may not always have the best data, but let's provide additional context to really help, again, make those good decisions coming out. [00:10:41] Mustansir Saifuddin: I liked the way you tied it together, right? We talk about business semantics being so important, BDC, the business data cloud. How is that coming into play in this conversation? And then coming from business semantics into a context. A context really is required for the answers to make sense and be business relevant. [00:11:03] So I really love the way you kind of connected together. Let's zoom in. Let's pick an industry. And there are so many examples that manufacturing, retail, financial services. Can you walk us through one compelling use case where SAP AI is really creating these breakthrough value? [00:11:21] What was the business problem? And how did AI solve it differently? [00:11:26] Andrea Haupfear: Yeah, absolutely. Mentioned at the beginning, SAP is investing heavily within artificial intelligence and machine learning capabilities, not just from an embedded AI perspective, but also think about it from a tailored AI perspective. So I mentioned [00:11:41] Business Data Cloud, being able to pull information and data not only from your internal SAP systems, but also external and third party information. And I wanna give you an example in a use case real world use case of a dairy co-op out of Wisconsin that is actually doing this today [00:12:00] from a very innovative approach. [00:12:02] Their challenge was around their performance at the subcontracting level. Ultimately these guys have a dairy co-op with their local farmers or farm base. They bring in the milk to not only from an internal manufacturing perspective to process out milk cheese, butter whey, et cetera cream, but they also subcontract it out as well. [00:12:28] And so this is really where they wanted to be able to get a better understanding, not just insights perspective on their data at their subcontractors from a yield output perspective. How much dairy, how much cheese was being was an output or yield, but what was going in and then going in versus going out. [00:12:50] And so ultimately what they wanted to be able to do was, yes, be able to look at the yield perspective from an insights, but they wanted to be able to leverage and infuse artificial intelligence from this process to ultimately help with their reduce of shrink. And contributing to a 1% KPI, which ultimately makes up to you and I roughly 10 to $15 million. [00:13:16] Okay. So just to kind of put it in perspective here of how much we're talking about. And so what they did was, yes, we have the insights from an analytics perspective, they wanted to be able to make it easier for their dairy supply chain planners to be able to, in real time through natural language processing, be able to chat with a digital assistant to gain insights around, Hey, what is my yield output for specific plant? [00:13:42] Tell me my highest and lowest plants that had the yield output. Tell me un understanding from a scrapping perspective how much waste is going out. So they wanted to be able to look at these specific metrics and be able to get a better understanding, hey, which particular plant is performing the best versus the worst. [00:14:03] So that way they can help to be able to retain and possibly improve some of these plant relationships going forward. Additionally as kind of that part two, what they wanted to be able to do is they receive manual yield output reports on a weekly basis from these subcontractors. It's typically in a PDF format, Excel, PDF format, and oftentimes these can be miskeyed into their S4 system. [00:14:35] And what they wanted to be able to do is be able to have an a little bit more of an automated process. Of, yes, not only uploading these reports that they, they have to manually key in today, but they wanted to be able to provide some intelligence behind it. And so this is where we've put in outlier detection on these attachments to where now I can see, okay, was there a miskey or an oversight that says, okay, you know, this should have been 2,622 versus 6,222. It can detect those mis keys in real time to say, Hey. For my supply chain planner, this doesn't necessarily look right. It's way outta whack compared to what was previously entered in, in the previous weeks. [00:15:24] You should triple check this, right? So it's, it's being able to provide a little bit more of, think of like big brother watching over you before it actually goes and hits into their ERP system. This is ultimately contributing to their supply chain process and has a direct impact on their KPI metrics that they're leveraging. [00:15:44] In this case, it's it's within shrink, so really getting a, a better handle on that. [00:15:50] Mustansir Saifuddin: No, I think the, the way you explained it, it is a great example 'cause now I can see not only does it apply to this particular industry, but it can cut across multiple industries. [00:16:00] Right. Because the example talked about production at a plant level. At the same time, the supply chain mishaps that can happen. [00:16:08] And usually a human eye can take so much [00:16:11] or can detect so much, but you can't try and put it together in a way that you guide your, your workforce to look at anomalies that can really help you steer the ship in the right direction quickly and efficiently. So that's great. [00:16:26] That really leads into my, my next question is, all of this is great, right? This is happening right now, we can see like the example you use, [00:16:35] right? It is in action. It is in motion, and customers are seeing value. Let's fast forward, where is SAP's AI development heading? You know, let's take a time horizon, 18 to 24 months. What capabilities should organizations be preparing for? Because it is all about future proofing ourselves, right? And how should they architect the solutions today to be ready for that feature, you know, coming up so quickly? [00:17:02] Andrea Haupfear: Yeah, absolutely. So a couple things that that come to mind. So number one and we've all been hearing kind of the next and elitist buzzword is around agents and agentic AI. So really how. I think about agents is how do we provide some of those tasks that, you know, may not necessarily whether they're they're still important, but, you know, maybe take up a lot of our time [00:17:29] but being able to provide and have a AI model behind that. To really free up some of the workload and provide some of our end users more on the strategic front. Freeing up some of that time. So I think in my humbled opinion, Agentic AI but Agentic AI at scale. So a lot of our customers are looking at what we have and this is where we're embedding. [00:17:54] Within our SAP applications agents within each one of our lines of business, but also custom agents. So this is something that is going to be released here in the next next several months, is looking at I have my embedded agents, but if I have very specific and unique, maybe differentiated business processes, how can I be able to integrate and infuse custom agents or an agent within this particular process? [00:18:23] And so this is really where I think is gonna we're really gonna see a lot of value coming in from our customers that says, yes, I can use agents in a multitude of different ways. Second is thinking about as an organization, we're becoming more dynamic and, and open source for data and how we can process it in a business context. [00:18:47] So thinking about, yes, I mentioned the Business Data Cloud, but also you know, strong partnerships. That was just announced with Snowflake as well. Right? So bringing in, yes, not only our internal data, but also our external data as well. How can we take that data and be able to normalize it? As far as from an architect's perspective, here are a couple points that I was kind of thinking about in my mind as we were going through this. [00:19:13] Was around keeping the core clean, right? So making sure that, yes, we're using our, our business technology platform various extensions and agents and skills from from JUUL studio and avoiding really those those drastic upgrades. And also kind of how I'm thinking about this is adopting that data product mindset. [00:19:40] So looking at, and I mentioned this as well, like Business Data Cloud, from looking at semantics and lineage, but also looking at retrieval methods. So vector engines and knowledge graphs. But then also thinking about it from a process perspective and [00:20:00] designing agent guardrails. So making sure that you have a much more standardization of an understanding of those roles and permissions. [00:20:10] Understanding human in the loop checkpoints at what point should be automated versus, okay, we need to have a set of eyes on this to actually be able to say, yep, this looks correct. I think that that's extremely important. [00:20:25] Mustansir Saifuddin: Yeah, for sure. And I think a couple of things that really stuck out for me. One is very near and dear to me, is the data part. And you talked about the partnership with Snowflake coming out recently, and I think it's important, especially when we talk about data for an organization. It's not just SAP data, it's like the overall, right? [00:20:42] You know, this, what does it really make up my organization? So [00:20:45] great approach from SAP, how it's trying to bring in like a business context around it, right? You have information within your ERP, outside of your ERP and then using the BTP platform. and the BDC platform to kind of bring it all together. So I think great segue, especially when we talk about agents and you know, we've all been talking about agents you know, for quite some time now, but now we can see the real value, how we can customize it and bring it together from a data perspective. So great conversation. On a personal note, how are you staying up, you know, on top of all these changes taking place in technology and business? What is your secret sauce? [00:21:26] Andrea Haupfear: Yeah, no, it's tough 'cause it's changing daily, weekly, right. And so being able to stay, have it stay top of mind. This is something that is part of, yes, not only my passion and what I do day in and day out, right, but also looking and getting, keeping educated not only from a process perspective by virtue of, you know, our internal processes, what we have in our products and our product offering, but also external with our clients. [00:21:54] To say, okay, what are they doing today with their processes? And then how can we leverage AI within that? So yes, not only from an internal knowledge sharing perspective, from a functional and technical perspective, but also external as well. And then thinking about external blogs, news sources, those are just kind of some of the things that I try to stay up to date. [00:22:16] You know, as best as I can. [00:22:18] Mustansir Saifuddin: No, I hear you on that. It is, it is a constant learning and I think that's the key, right? Educating [00:22:23] and educating and educating and be able to find your sources. I think that's the key. Great conversation. I know we are at time, what would you take out of this conversation that we just had and want to leave a particular takeaway for our listeners and folks who are interested in this topic? [00:22:42] Andrea Haupfear: Sure. Absolutely. So AI is not just like, and, and I meant we mentioned this earlier on, right? AI is not just in our personal lives, but it's also in our workplace and it's very, very real. We are seeing our companies and our customers take advantage of infusing AI into their business processes and receiving the high ROI in their processes. [00:23:05] The key is to start small. Strategically and identify which areas will have AI and will have that high ROI, but then also have the highest value when it comes from an impact perspective. We see this to where we run this as, as ultimately an ideation sessions with our customers and from the takeaway out of those sessions is they can start to craft an internal roadmap that will lead them to AI success. [00:23:38] And ultimately our organization can help them get there along the way, even whether they're just dipping their toe in the AI pool or some, some customers that we deal with already have strong partnerships with large language model providers or they're partnering with universities, things like that. [00:23:57] The end goal in net net is that [00:24:00] our organization can help to not only help identify those high value AI use cases, but also investing in you to create those. We offer a free proof of concept in as much as eight weeks. You mentioned this, you know how frequently this is changing. [00:24:18] AI is changing. Right? And this is where we can develop these proof of concepts to where you, our customers can be able to realize those value in a very quick and short amount of time. And so that's ultimately where I wanna leave the audience with is that we're, we're seeing AI not just in our personal lives, but also in the workplace. [00:24:39] And we're actually showing them and, having them realize it in real time. So you guys can, can feel free to reach out to me. I think we'll have my contact information at the end of the podcast here. But you know, happy to have further conversations with you and your organization. [00:24:55] I wanna thank you again personally for inviting me to the podcast and to discuss a, a very, very close and passionate topic for me. [00:25:04] Mustansir Saifuddin: It's a pleasure to have you, Andrea, and really, I think it was a great conversation. You touched upon so many different things and I think that was the purpose of this, was to kind of bring light to exactly what's going on in, you know, we talk about AI in general, but what is really happening at the inter-enterprise level [00:25:21] and what is the real value when folks are looking at, from a business perspective. How to increase ROI in this new technology and what does really mean in terms of increasing business revenue and across the board improving efficiencies. Right? So it's all together. But thank you so much for coming on our show. [00:25:42] Andrea Haupfear: Absolutely. Thank you for having me. [00:25:44] Mustansir Saifuddin: Thanks for listening to Drug Driven Business, brought to you by Innovative Solution Partners. SAP is helping customers move from AI experimentation to enterprise value by embedding AI where work happens, grounding it in trusted data and business context. And ensuring governance, auditability, and control. [00:26:10] Andrea's Key takeaway? Start small. Focus on the highest ROI use cases and build a clear roadmap because when AI is tied to real processes and real outcomes, SAP customers can unlock faster decisions, lower risk, and measurable impact. We would love to hear from you. Continue the conversation by connecting with me on LinkedIn or X. [00:26:36] Learn more about innovative solution partners and schedule a free consultation by visiting isolutionpartners.com. Never miss a podcast by subscribing to our YouTube channel. Information is in the show notes.        

Well, if you thought you were getting your money back after returning something... you're partially wrong! Holiday Headlines today covers the growing number of stores that will charge you a fee for sending your packing back- and how to get around it!  Pick Em' News is back! Raven chose to hear all about the Excel World Championships crowning a new winner. Whatever that means!    I Saw Mommy Kissing Santa Claus is today's Unwrapped with Anna & Raven! You may think its a cute Holiday jingle, but the tune was actually very controversial and caused a lot of drama in the early days.   It's back! Anna and Raven Santa Tipline! Santa needs to know who's naughty, and who's nice. Call and leave a message 24/7 and you may hear yourself on the air. Call 888-702-9646 to leave a message for the Big Guy!     It's time for Student Teacher! Every week Producer Justin joins Anna and Raven to teach them about something new! Today he shares a brief lesson on celebrating Christmas around the world   Producer John visits Anna and Raven to discuss all things' relationships. Johns recent breakup, commitment issues, and how to cope with rough patches around the holidays,  Anna & Raven discuss your “Oh no, I am getting old” moments. Injured backs from a sneeze to Anna's complaints in a restaurant. There really are no boundaries here when aging!   What occupation makes you roll your eyes when someone mentions it? Anna & Raven dive into the jobs out there that everyone agrees are not the best to encounter!  Zak is obsessed with putting up holiday decorations at his home. Not because he feels the magic of the season, but because his new neighbors have gone over the top with theirs, and he's embarrassed that his display is embarrassing. His wife, Amber, says he spent $2200 so far this year, and she's upset about it. Forget about “keeping up with the Jonses”, they just don't have the money for this kind of thing, especially since they need to buy Christmas presents. He argues that these are things they'll have forever, and since there's a line of cars down their street every night, it just makes sense to do it too. What do you think?  Johanna, Gracie, and Jim have a chance to win $1000! All they have to do is answer more pop culture questions than Raven in Can't Beat Raven!   

Discover the voices shaping the future of remote, austere, and expeditionary healthcare. In this special podcast preview, you will hear directly from the expert presenters who will be leading sessions at ⁠Medicine in the Mediterranean⁠, held from 31 January to 2 February.This annual gathering brings together clinicians, researchers, and operational medics from across the globe, professionals delivering care in deserts, jungles, mountains, conflict zones, offshore platforms, and every environment in between.Places are still available, offering a rare opportunity to network with colleagues working at the cutting edge of remote medicine, gain insights from internationally recognised leaders, and strengthen your practice through evidence-based, real-world learning.If your goal is to elevate your capability in challenging environments, this is the forum. Excel by learning from those who set the standard.https://corom.edu.mt/medicine-in-the-mediterranean-2026/

This week on The Group Chat: Sky News Special Correspondent Alex Crawford tells us about reporting from the battle frontlines. And, Ireland's media minister talks tough about online safety.Plus, world Excel champion Diarmuid Early finds that winning formula. Hosted on Acast. See acast.com/privacy for more information.

In this powerful episode of RISE Urban Nation, host Taryell Simmons sits down with Dr. Sherece Y. West-Scantlebury—philanthropic visionary, equity advocate, transformational strategist, and retiring President & CEO of the Winthrop Rockefeller Foundation.With more than 33 years of leadership in public policy, community development, and values-aligned investing, Dr. West-Scantlebury reflects on her journey shaping systems across Arkansas and the nation.Discover the untold stories behind statewide initiatives such as ALICE in AR, Excel by 8, ForwARd Arkansas, and the Arkansas Enterprise Capital Grant Fund—and how Dr. West-Scantlebury leveraged philanthropy, equity, and lived experience to build lasting, generational impact.Whether you're a nonprofit CEO, emerging executive coach, philanthropic leader, or community advocate, this episode offers a masterclass in courageous leadership, legacy building, and systems transformation. Links & Resources:

In this episode, Sasha Orloff speaks with Renato Villanueva, Founder and CEO of Parallel, about his journey from finance professional at Divvy to raising $2.4 million from Bain Capital and K5 Tokyo Black for an AI-powered FP&A platform that helps founders model financial scenarios and make confident growth decisions. Renato shares lessons on building products founders are passionate about rather than forced wedges, nurturing investor relationships, and how Parallel's approach has enabled customers to achieve significant growth—including one company that scaled from planning one sales hire to four, ultimately raising one of Utah's biggest Series A rounds. -- SPONSORS: Notion Boost your startup with Notion—the ultimate connected workspace trusted by thousands worldwide! From engineering specs to onboarding and fundraising, Notion keeps your team organized and efficient. For a limited time, get 6 months of Notion AI FREE to supercharge your workflow. Claim your offer now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://notion.com/startups/puzzle⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Puzzle

In this powerful episode of We Chat Divorce, Karen and Catherine sit down with Susan Guthrie—one of the nation's leading family law attorneys, top divorce mediators, and a recent guest on the Oprah podcast—to break down the realities of navigating divorce in today's turbulent economic climate. With inflation, job uncertainty, rising debt, and shrinking safety nets affecting families across the country, divorce has become more complicated, more expensive, and more emotionally overwhelming. Susan brings 35 years of experience to this conversation and explains why fear and confusion escalate when people attempt to negotiate or compromise without verified financial information. Together, Karen, Catherine, and Susan expose some of the biggest misconceptions surrounding mediation, financial disclosures, attorney roles, and the belief that a net-worth statement or Excel spreadsheet is “good enough.” They outline the critical need for accurate, neutral financial clarity—something the MDS Financial Portrait™ uniquely provides—to prevent stalled negotiations, runaway legal fees, and emotionally driven decisions that people regret later. Whether you're considering divorce, preparing for mediation, or already working with an attorney, this conversation offers the actionable guidance needed to move forward with confidence, strategy, and peace of mind. Connect with Susan Guthrie Explore her podcast and resources: DivorceAndBeyondPod.com Use her new AI-powered episode search tool to find topics like alimony, gray divorce, co-parenting, and more. Resources Mentioned MDS Financial Portrait™⁠ — the gold-standard tool for financial clarity in divorce Free Financial Assessment — learn where you stand and what steps make sense MDS Community — expert-led Live Q&A events and support Ready to Take the Next Step? Whether you're just beginning the divorce process or have been stuck in uncertainty for years, financial clarity is your strongest advantage. Start with the Free Financial Assessment, explore the Divorce Financial Portrait™, and step into mediation or legal discussions with confidence—not fear. Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover the voices shaping the future of remote, austere, and expeditionary healthcare. In this special podcast preview, you will hear directly from the expert presenters who will be leading sessions at ⁠Medicine in the Mediterranean⁠, held from 31 January to 2 February.This annual gathering brings together clinicians, researchers, and operational medics from across the globe, professionals delivering care in deserts, jungles, mountains, conflict zones, offshore platforms, and every environment in between.Places are still available, offering a rare opportunity to network with colleagues working at the cutting edge of remote medicine, gain insights from internationally recognised leaders, and strengthen your practice through evidence-based, real-world learning.If your goal is to elevate your capability in challenging environments, this is the forum. Excel by learning from those who set the standard.https://corom.edu.mt/medicine-in-the-mediterranean-2026/

Excel Data Visualization & Dashboards: Turn Raw Data into Executive-Ready StoriesExcel is the foundational tool for analysis, but simply having data isn't enough; you need to tell the story behind the numbers.In this episode of What's New at CFI on FinPod, CEO Tim Vipond introduces the new Excel Data Visualization and Dashboards course. Learn how to transform raw data into clean, clear, and powerful visuals that drive business decisions, no matter your industry.This course is a masterclass in building executive-ready dashboards from scratch, making it essential for FP&A, Marketing, Operations, and all analytical roles.This episode covers:The Power of Excel: Why Excel remains the ultimate "blank canvas" for visualization and the foundational skill set for tools like Power BI or Tableau.Mastering the Visual Toolkit: Learn to build and use advanced charts like Waterfall Charts (for variance analysis), Combo Charts (for margin vs. revenue), Sparklines, and Football Field Charts (for valuation ranges).End-to-End Dashboard Creation: Gain the confidence to plan, set up, and build complete, beautiful dashboards that are clearly sectioned, titled, and formatted for maximum impact.Highlighting Insights: The critical skill of moving beyond just building a chart to actively using color, arrows, and annotations to highlight the specific insights that drive business change (e.g., maximizing margins or accelerating growth).Developing Taste: Tim shares career advice on how to develop "good taste" in data visualization by actively seeking out and being inspired by varied internal and external reports (pitch decks, board reports, operations decks).

Discover the voices shaping the future of remote, austere, and expeditionary healthcare. In this special podcast preview, you will hear directly from the expert presenters who will be leading sessions at Medicine in the Mediterranean, held from 31 January to 2 February.This annual gathering brings together clinicians, researchers, and operational medics from across the globe, professionals delivering care in deserts, jungles, mountains, conflict zones, offshore platforms, and every environment in between.Places are still available, offering a rare opportunity to network with colleagues working at the cutting edge of remote medicine, gain insights from internationally recognised leaders, and strengthen your practice through evidence-based, real-world learning.If your goal is to elevate your capability in challenging environments, this is the forum.Excel by learning from those who set the standard.https://corom.edu.mt/medicine-in-the-mediterranean-2026/

Todd Curtis, CEO of YNAB, shares how customer support became the foundation for sustainable growth. Learn why they embedded support in product teams and used it as a training ground for company-wide leadership. Here's the original YNAB Support Ethics document (long since replaced with expanded versions)https://2760806.fs1.hubspotusercontent-na1.net/hubfs/2760806/YNAB%20Support%20Ethic.pdfDiscover how YNAB's “education first” approach shaped not only how they help customers, but also how they build their own business. If you've ever wondered how support can become a secret engine for company growth (and even leadership) this is the episode that shines a light.(01:39) The Excel spreadsheet that started it all(05:16) Early support workshops and one-to-one coaching(07:15) “Five case Monday” and whole-team support(10:31) Why YNAB sees itself as an education company(12:47) Crafting the YNAB Support Ethic(17:27) Embedding support specialists in product teams(22:36) Support as a pipeline for talent across YNAB(28:43) Navigating major product and branding changes(33:09) Moving from “budgeting” to “planning” – and why words matter(34:01) Living core values inside and outside the company(36:57) Balancing customer experience with sustainable business growthSupport is about more than technical issues: YNAB's support team isn't just resolving sync errors and resetting passwords — they're helping people change their mindset about money, and get their financial life back. The most impactful conversations in the queue often go beyond troubleshooting to coaching, education, and emotional support.Empathy drives long-term success: By noticing and celebrating deeper customer interactions (not just ticket volume), YNAB builds trust and loyalty. Expanding the definition of support work makes for a more satisfying role, stronger customer relationships, and helps grow the business.Support Talent Powers the Company: At YNAB, support is a launching pad for roles across product, marketing, and operations. Hiring for values, communication, and willingness to learn pays off when support pros bring customer focus and cross-functional skills company-wide.

Cristina ha 54 anni, è ingegnera e vive a Roma con suo figlio. Cresciuta in una famiglia benestante, lei e sua sorella non hanno mai dovuto rinunciare a nulla di essenziale, ma a stabilire cosa fosse davvero necessario era il padre, un uomo molto attento al risparmio. Così, una volta adulta, Cristina punta dritta all'autonomia: a 26 anni si laurea e trova lavoro in un'azienda, e a 27 anni si sposa e compra casa. Dopo appena tre anni, però, arriva il divorzio, e quel mutuo, che doveva essere un investimento condiviso, resta tutto sulle sue spalle.Intanto passano gli anni, e a 39 Cristina diventa madre. Da sola. Senza un compagno con cui condividere la responsabilità, può contare solo su sé stessa. In questo nuovo equilibrio, Cristina può contare su una pianificazione finanziaria eccellente: «Io ho da sempre un file Excel in cui inserisco tutte le spese fisse, le entrate e le previsioni di uscite». Ma non solo: col tempo, Cristina inizia ad affidarsi sempre più spesso ai servizi di rateizzazione. Le usa per tutto, anche per spese piccole: un paio di scarpe, un pantalone, acquisti di pochi euro che, messi insieme, però, creano un puzzle difficile da tenere sotto controllo. «C'erano dei periodi che ero sempre su Zara, K-way… con tutte queste rate puoi comunque fare una previsione di spesa, però ti può sempre capitare la spesa straordinaria. Quindi, da monoreddito, mi ero iniziata a trovare in difficoltà». Così, col tempo, Cristina capisce che rateizzare tutto può anche diventare una trappola sottile: si perde il conto, si agisce d'impulso, si compra senza riflettere davvero. Oggi, ha trovato un nuovo equilibrio: le rate restano una risorsa preziosa, ma solo quando servono davvero. Per capire come trasformare la dilazione in uno strumento davvero sostenibile, nella puntata c'è Martina Moraschi, esperta di educazione finanziaria di Sella Personal Credit. Questo podcast è una co-produzione di Rame e Sella Personal Credit.

Welcome to RIMScast. Your host is Justin Smulison, Business Content Manager at RIMS, the Risk and Insurance Management Society.   In this episode, Justin interviews Andréia Stephenson, BSc SIRM, Enterprise Risk Analyst at London Metal Exchange, about her shift from a Bachelor of Science in biology to a risk analyst and risk professional. Andréia speaks of her passion for data and the importance of communicating at all levels of your organization. She regards working for different organizations with good leaders as a way to learn risk frameworks and gain foundational knowledge. She shares views on how risk analysts can influence risk culture. She also tells how she uses AI as an assistant. Listen for thoughts on building a risk-aware culture by asking leaders the right questions.   Key Takeaways: [:01] About RIMS and RIMScast. [:17] About this episode of RIMScast. Our guest today is Andréia Stephenson, BSc SIRM, Enterprise Risk Analyst at London Metal Exchange. She will discuss her career and the evolving role of the Risk Analyst. But first… [:43] RIMS-CRMP and Some Exam Prep Courses. From December 15 through the 18th, CBCP and RIMS will present the RIMS-CRMP Exam Prep Boot Camp. [:53] Another virtual course will be held on January 14th and 15th, 2026. These are virtual courses. Links to these courses can be found through the Certification page of RIMS.org and through this episode's show notes. [1:07] During the interview with Andréia, you will hear her reference the RIMS CRO Certificate Program in Advanced Enterprise Risk Management, which is hosted by the famous James Lam. Andréia is an alum of the program. [1:23] You can enroll now for the next cohort, which will be held over 12 weeks, from January through March of 2026. Registration closes on January 5th. Or Spring ahead and register for the cohort held from April through June of 2026. Registration closes on April 6th. [1:39] Links to registration and enrollment are in this episode's show notes. [1:46] Justin shares that RIMS suffered a tremendous loss in December. Chief Membership Experience Officer, Leslie Whittet, with RIMS for almost three years, tragically passed away due to injuries she sustained in an accident. She was walking her dog when she was struck by a truck. [2:18] Some of the RIMS staff, including CEO Gary LaBranche, knew Leslie from years prior. We are all shocked and saddened. Leslie was a remarkable association leader with 30 years of experience. [2:33] Gary LaBranche had the privilege of working alongside Leslie Whittet at the Association for Corporate Growth for nine years. For the last three years, Justin has had the pleasure of working with her at various RIMS events and seeing her weekly on our remote calls. [2:50] Leslie was always a source of positivity, inspiration, and creativity. She was just a wonderful person who will be deeply missed. Her memory is certainly a blessing. [3:03] RIMS will celebrate her memory at the Chapter Leadership Forum in Orlando in January. If you have any questions, please contact Josh Salter, jsalter@RIMS.org. Tributes are pouring in on LinkedIn and various networking groups. [3:22] If you have memories and photos you'd like to share, we encourage you to do so to honor her memory. [3:29] It wasn't easy to speak these words or read them, so I want to take a brief moment of silence to honor Leslie before we go any further. [3:44] On with the show! Our guest today is Andréia Stephenson. She comes to us all the way from London, where she's an Enterprise Risk Analyst for the London Metal Exchange. [3:57] You may know her a little bit from some promotional videos we've done on social media, promoting the James Lam CRO Certificate Course. In getting to know her, I was struck by how enthusiastic she was about her role as a Risk Analyst for years. [4:14] Many risk professionals begin as risk analysts; others, like Andréia, can make a thriving career of it. She's here to share some tips on how to do that, where ERM fits into the mix, and where she believes the role of the risk analyst will be going in the near future. Let's get started… [4:36] Interview! Andréia Stephenson, welcome to RIMScast! [4:47] Andréia may sound familiar to you because she did a testimonial on LinkedIn for RIMS for the James Lam CRO Certificate course. Justin says she was great to work with. That's how she and Justin met, and that's why she's here. [5:19] Justin notes that his voice is lower from "shouting" during the ERM Conference. Andréia looks forward to the RIMS ERM Conference 2026. [6:09] Andréia shares an overview of her career. She started at O.R.X., an operational risk data exchange association, where she learned all the principles of risk management. It gave her a strong background in operational risk. [6:36] From there, she went to London to go into a second-line risk management function as an analyst at a wealth management investment firm, then she went to a small investment bank, then to another wealth management firm, and now, to the London Metal Exchange. [7:00] They were all analyst roles, primarily operational risk, but also enterprise risk management. Risk has been part of her life for the last 10 years. The foundation was set by O.R.X. She holds the company close to her heart. [7:28] Andréia loves data. It's incredibly important for driving analysis. She says any analyst who doesn't love data is not an analyst! Data structure and data quality are very important for risk analysis, or any analysis. You need to love data to be able to do good risk management. [8:13] Andréia says that working in different organizations is important for risk management. It helps you connect the dots between the components of a risk management framework. [8:28] When Andréia started at O.R.X., she understood all the components, but she didn't join the dots until she went into the industry, hands-on, in the deep end, trying to figure out an RCSA, a KRI, or a KPI. Then, all the components of risk management started to make a bit more sense. [8:53] Andréia has always been fortunate to have worked with several exceptional leaders, each of whom had a kind of superpower in risk management that influenced her approach and understanding of risk. [9:07] Andréia's first manager at O.R.X. was tough and meticulous. She had a deep understanding of corporate governance and the boundaries between the risk types: strategic, financial, and non-financial. [9:22] At the time, Andréia didn't really appreciate how valuable the discipline was. She didn't understand yet. In hindsight, it gave her a strong foundation. Another CRO she worked with taught her the importance of communication in risk. [9:46] Aside from his technical ability, he understood stakeholder management at every level of the organization and how to translate the risk concepts for different audiences and build alignment. [10:00] Then she had a head of risk who was incredible with data, with an exceptional ability to quantify risk using analytics and evidence. Having a science degree, numbers were not Andréia's strongest area, but working with someone who pushed her helped her to become stronger. [10:25] Andréia thinks that working in risk in different organizations can help you build those thoughts. [10:32] Andréia has a Bachelor of Science degree in biology from the University of Bath in England. She's happy she decided not to pursue biology and took the risk road, instead. [10:55] Justin tells of recently having Kellee Ann Richards-St. Clair on the show. She's on the RIMS Strategic and Enterprise Risk Management Council. Kellee Ann started in Chemistry.l She moved into Energy and Power and became the de facto ERM Manager for her organization. [11:15] Kellee Ann and Andréia channelled other areas of knowledge to apply them to risk. For Andréia, the statistical side of biology has been helpful in risk management. James Lam states in his CRO Certificate program that risk is probability and statistics. Risk management isn't easy. [12:19] Andréia believes that legacy tools and practices fall short when they are disconnected from the organization's purpose, vision, mission, and strategic objectives. GRC systems have different modules: an RCSA module, a budding issue module, and an incident module. [12:49] Andréia hasn't seen a system that can connect the dots well. Risk practitioners don't always know how to connect the dots, either. An RCSA becomes isolated from the risk itself because people don't understand the context of those risks. [13:17] Working with business senior leaders to understand the context of your organization will help you to provide more valuable use of those tools and practices. [13:32] Andréia explains RCSA. It stands for Risk and Control Self-Assessment. It's a thought process. You sit down to understand what's most important to you, how much you care about it, and what you have in place to protect what's most important to you. [13:55] Andréia says the way we try to document that thought process is quite heavy. The industry requires that process to be complicated. Andréia recommends simplifying it. [14:20] To simplify it, have a process that's more sensible. The industry requires you to do assessments for inherent risk and residual risk. First, determine if a risk is important to you. If it's not important, why are you assessing it? [15:09] Andréia thinks the industry makes it difficult by requiring organizations to assess risks in a certain way, when it doesn't actually make sense. Managers have to have the courage to say it doesn't make sense for the organization, let's try a simpler approach. [15:34] Andréia uses screens, but sometimes pen and paper will do. Having that brainstorming session with the business really helps in trying to understand the purpose of what you do for your organization and where you fit in the strategic purpose of the firm. [15:51] What is most important to you, as opposed to thinking of everything that could go wrong? Risk is not only about negative outcomes but also about opportunities. [16:09] Quick Break! RISKWORLD 2026 will be held from May 3rd through the 6th in Philadelphia, Pennsylvania. RISKWORLD attracts more than 10,000 risk professionals from across the globe. It's time to Connect, Cultivate, and Collaborate with them. Booth sales are open now! [16:31] General registration and speaker registration are also open right now! Marketplace and Hospitality badges will be available starting on March 3rd. Links are in this episode's show notes. [16:44] Let's conclude our Interview with Andréia Stephenson! [17:14] Beyond documenting risk, Andréia thinks a risk analyst can shape an organization's risk-aware culture by asking questions. The quality of the questions they ask helps drive culture. [17:31] When an analyst consistently probes assumptions, highlights all the inconsistencies they find, or asks what this means in practice, that behavior encourages others to think more critically about risk and about what they are doing. [17:50] Good questions change behaviors. They prompt people to pause and reflect rather than to operate in autopilot, which we all sometimes do. [18:04] Andréia says analysts can contribute by making risk information simpler, clearer, and more accessible, looking for ways to simplify their reports and focusing on the most important things, day-to-day, for their objectives, and having a less bureaucratic process. [18:41] Andréia suggests having the courage to speak up when processes don't make sense in the second line of defense to help as much as possible the first line. [18:51] Risk analysts can influence and change behavior by building truthful and meaningful relationships with people, caring about the business, listening to the business units, taking their feedback to heart, and helping them to change the difficulties they encounter in risk. [19:19] Andréia works in the second line of defense. She works with a lot of first-line business units. For them, it's a burden when the risk team, the CRO, or the processes change. The risk analyst needs to help them minimize that burden. It's important to be conscious of that. [19:57] Andréia says when she goes into a new organization, the first thing she does is to understand the current state. What risk practices do they have? How do they operate? After a month, she has figured out how the organization is and how they make decisions. [20:17] When she has a suggestion, Andréia puts herself on the line for it. More often than not, it has worked out positively because she had good managers who could listen to her ideas for improvement. [20:41] If something doesn't make sense, you have to be true to yourself and say this process is lengthy, or this document is enormous; let's try to simplify it. Never be afraid of providing views for improvements, so long as you have one and have thought about it. [21:16] Andréia believes in passion for what you do. You need to be passionate, and if you're not, find your passion. For Andréia, it has always been to be a professional analyst and risk professional. That passion, in turn, drives your curiosity. [21:40] Look for ways to improve and learn. Working hard is really important, even with AI. Working hard drives good results. Data literacy is very important. Understand the basic principles of data and the basic tools that allow you to do data analysis. [22:04] Think, pause, and reflect. What does that data mean? What do those patterns mean? [22:10] Andréia stresses communication. She says she's still working on her communication skills. She is very direct at work. Sometimes that directness can seem abrupt. If something doesn't make any sense, she will put her hand up and say, This doesn't make any sense! [22:41] Having the soft skill to be able to communicate at all levels of the organization is important. That will set an analyst apart. [23:33] Andréia says AI is everywhere. She uses AI all the time for work and for her personal life. In her experience, AI is most powerful as a sounding board, a thought partner, and a colleague. It helps you explore ideas, structure problems, and challenge assumptions. [24:07] The analyst is the one who provides context and judgment. AI can help you generate lots of possibilities, but it can't decide what makes sense for your organization or for you. A critical mindset is very important. [24:25] Analysts need to treat AI as an extension of their thinking process, not as a replacement for it. You are the Quality Control. You are always the one accountable for the output. AI doesn't understand your business, your culture, or your strategic priorities, but you do. [24:48] There's always the risk that if you rely on AI without applying your own insight, the output will sound sort of right but not add any value. It may be technically correct, but contextually useless. [25:12] If analysts don't know how to extract, refine, and apply what the tool gives them, it won't move the needle in a meaningful way. [25:21] Analysts should work in different places, understand what a good framework is, get certifications, work with risk professionals, work to think about problems you haven't come across before, use critical thinking, and use AI to help perform the mechanical parts of your job. [25:51] Always rely on your judgment, your relationships, and your understanding of the business you are in. [26:04] Justin shares that philosophy. He uses AI as a sounding board, to help him if he's stuck on an idea, to help him expand it. If he likes it, he'll go with it. He takes the output as a template and refines it. [26:31] Andréia says it's almost like having an assistant. If it gives you something different than what you asked for, you can restate your question. [26:41] Justin's daughter is getting into advanced math in middle school. He doesn't remember a lot of it. He's asked ChatGPT to help him come up with math questions for his daughter. It has been invaluable for that. [27:20] Andréia uses it for formulas in Excel. She says, You still have to know what you want. You can prompt it to help you remember how to do something. Justin says you need the foundational knowledge. [27:45] Andréia says foundational knowledge is what will set people apart in their profession, whatever profession it is. She would much rather know what she knows than have AI do something and not feel comfortable with it. The foundation is really important. [28:08] Special thanks again to Andréia Stephenson for joining us here on RIMScast! Keep an eye out for her on LinkedIn in those super cool CRO Certificate Program promotional videos. [28:21] Remember, we have two more cohorts coming up, one in January and one in April. Links are in this episode's show notes.  [28:29] Plug Time! You can sponsor a RIMScast episode for this, our weekly show, or a dedicated episode. Links to sponsored episodes are in the show notes. [28:57] RIMScast has a global audience of risk and insurance professionals, legal professionals, students, business leaders, C-Suite executives, and more. Let's collaborate and help you reach them! Contact pd@rims.org for more information. [29:15] Become a RIMS member and get access to the tools, thought leadership, and network you need to succeed. Visit RIMS.org/membership or email membershipdept@RIMS.org for more information. [29:33] Risk Knowledge is the RIMS searchable content library that provides relevant information for today's risk professionals. Materials include RIMS executive reports, survey findings, contributed articles, industry research, benchmarking data, and more. [29:49] For the best reporting on the profession of risk management, read Risk Management Magazine at RMMagazine.com. It is written and published by the best minds in risk management. [30:03] Justin Smulison is the Business Content Manager at RIMS. Please remember to subscribe to RIMScast on your favorite podcasting app. You can email us at Content@RIMS.org. [30:15] Practice good risk management, stay safe, and thank you again for your continuous support!   Links: RIMS-CRO Certificate Program In Advanced Enterprise Risk Management | Jan‒March 2026 Cohort | Led by James Lam RIMS-Certified Risk Management Professional (RIMS-CRMP) RISKWORLD 2026 Registration — Open for exhibitors, members, and non-members! Reserve your booth at RISKWORLD 2026! The Strategic and Enterprise Risk Center RIMS Diversity Equity Inclusion Council RIMS Risk Management magazine | Contribute RIMS ERM Special Edition 2025 RIMS Now RISK PAC | RIMS Advocacy | RIMS Legislative Summit SAVE THE DATE — March 18‒19, 2026 Statement on the passing of RIMS Chief Membership Experience Officer Leslie Whittet Upcoming RIMS-CRMP Prep Virtual Workshops: "CBCP & RIMS-CRMP Exam Prep Bootcamp: Business Continuity & Risk Management" December 15‒18, 2025, 8:30 am‒5:00 pm EST, Virtual RIMS-CRMP Exam PrepJanuary 14‒15, 2026, 9:00 am‒4:00 pm EST, Virtual Full RIMS-CRMP Prep Course Schedule See the full calendar of RIMS Virtual Workshops   Upcoming RIMS Webinars: RIMS.org/Webinars   Related RIMScast Episodes: "James Lam on ERM, Strategy, and the Modern CRO" "RIMS ERM Global Award of Distinction 2025 Winner Sadig Hajiyev — Recorded live from the RIMS ERM Conference in Seattle!" "Presilience and Cognitive Biases with Dr. Gav Schneider and Shreen Williams" "Risk Rotation with Lori Flaherty and Bill Coller of Paychex" "Energizing ERM with Kellee Ann Richards-St. Clair" "Talking ERM: From Geopolitical Whiplash to Leadership Buy-In" with Chrystina Howard of Hub "Tom Brandt on Growing Your Career and Organization with ERM" "Risk Quantification Through Value-Based Frameworks"   Sponsored RIMScast Episodes: "Secondary Perils, Major Risks: The New Face of Weather-Related Challenges" | Sponsored by AXA XL (New!) "The ART of Risk: Rethinking Risk Through Insight, Design, and Innovation" | Sponsored by Alliant "Mastering ERM: Leveraging Internal and External Risk Factors" | Sponsored by Diligent "Cyberrisk: Preparing Beyond 2025" | Sponsored by Alliant "The New Reality of Risk Engineering: From Code Compliance to Resilience" | Sponsored by AXA XL "Change Management: AI's Role in Loss Control and Property Insurance" | Sponsored by Global Risk Consultants, a TÜV SÜD Company "Demystifying Multinational Fronting Insurance Programs" | Sponsored by Zurich "Understanding Third-Party Litigation Funding" | Sponsored by Zurich "What Risk Managers Can Learn From School Shootings" | Sponsored by Merrill Herzog "Simplifying the Challenges of OSHA Recordkeeping" | Sponsored by Medcor "How Insurance Builds Resilience Against An Active Assailant Attack" | Sponsored by Merrill Herzog "Third-Party and Cyber Risk Management Tips" | Sponsored by Alliant   RIMS Publications, Content, and Links: RIMS Membership — Whether you are a new member or need to transition, be a part of the global risk management community! RIMS Virtual Workshops On-Demand Webinars RIMS-Certified Risk Management Professional (RIMS-CRMP) RISK PAC | RIMS Advocacy RIMS Strategic & Enterprise Risk Center RIMS-CRMP Stories — Featuring RIMS President Kristen Peed!   RIMS Events, Education, and Services: RIMS Risk Maturity Model®   Sponsor RIMScast: Contact sales@rims.org or pd@rims.org for more information.   Want to Learn More? Keep up with the podcast on RIMS.org, and listen on Spotify and Apple Podcasts.   Have a question or suggestion? Email: Content@rims.org.   Join the Conversation! Follow @RIMSorg on Facebook, Twitter, and LinkedIn.   About our guest: Andréia Stephenson, BSc SIRM, Enterprise Risk Analyst, London Metal Exchange   Production and engineering provided by Podfly.

What turns a kid from Brooklyn into a leader who builds and rebuilds big businesses, then starts over again by choice? Meet Mike Tepedino, Founder and Managing Partner of Blue Light Capital, whose path runs from six gritty years in loan workouts on the “what can go wrong” side of real estate became the foundation for everything that followed.We get into the leap to brokerage, the Chinese-restaurant meeting that set up HFF's rise, and the 2019 sale to JLL for about $2 billion, where scale brought new tools and reach but also a fresh look at purpose and timing.Now Mike is building again with Blue Light Capital, stepping into an underserved middle-market bridge space in the roughly $15 to $50 million range, and pairing that with BLNext, a nonprofit that trains college and pro athletes for real estate careers with 150 hours of Excel and Argus plus one-to-one mentors who have walked the path.If you care about discipline, people, and timing, this conversation delivers hard lessons and practical wisdom with zero fluff. Watch the full episode to hear how Mike thinks about focus, why small habits like handwritten notes still matter, and what it takes to start again when everyone thinks you are done. 00:00 – Welcome to the Show00:44 – Who Was “Little Mike”? Early Life & Personality01:38 – Two Defining Moments That Changed His Life03:01 – Discovering Discipline at 1903:48 – Being Forced Out of His Comfort Zone05:41 – Fear of Failure & The Shoebox of Quotes08:15 – Falling in Love With Real Estate as a Kid09:54 – The Mentor Who Changed Everything12:18 – Lessons in Marketing, Relationships & Handwritten Notes16:30 – The Dark Side of Real Estate: Learning From Bad Loans20:51 – Becoming a Broker & Betting on Himself23:02 – The Life-Changing Dinner With Mark Gibson28:53 – Scaling From 50 People to 1,000+30:51 – Surviving the 2008–2009 Financial Crisis33:59 – Joining JLL & Operating at Global Scale35:56 – Knowing When It's Time to Walk Away39:41 – Launching Blue Light Capital45:11 – BLNext: Mentoring the Next Generation of Leaders49:47 – Giving Back, Purpose & Legacy51:57 – How to Connect With Mike & Final ThoughtsMike Tepedino on Socials: IG: @blnextLinkedIn: https://www.linkedin.com/in/mike-tepedino-bluelight/Blue Lights LinkedIn: https://www.linkedin.com/company/blue-light-re/?viewAsMember=trueBL Next LinkedIn: https://www.linkedin.com/company/bl-next/?viewAsMember=trueBlue Lights Website: https://bluelightre.com/BL Next Website: https://blnext.org/Jon on Socials: IG: @thejonschultzpodcastX: @JonSchultzPodLinkedIn: https://www.linkedin.com/company/the-jon-schultz-podcastwww.jonschultz.com

A clear plan, a humble posture, and one simple patio moment—that's how a baton gets passed and a church keeps moving. We sit down with James Grogan, lead pastor of EastLake Church in San Diego, to explore what healthy succession actually looks like when a larger-than-life founder hands leadership to a different kind of leader, and why clarity and security beat charisma and ambiguity every time.James traces his path from a vibrant Illinois pastor's home to two summers of nonstop preaching reps, then into the diverse fabric of South San Diego. He shares why he paused planting to learn inside EastLake's thriving culture—and how that decision compressed hard lessons into an apprenticeship that still bears fruit. We dive into the written, date-bound succession plan that worked because both leaders were secure: the outgoing pastor gave authority away freely, and the incoming pastor didn't grasp for titles or control. The result was continuity, trust, and momentum.Send us a textWe want to help you find your next steps in ministry.Connect here with EXCEL. Ministry Partner: Christian Community Credit Union

In this inspiring episode of the Featured Mentor Podcast, we sit down with Arthur, a 28-year-old professional whose path from Brazil to the U.S. and Europe reveals what it really takes to build an international career in finance. From early days at a prep school to studying at Wharton and landing roles at Bank of America and Goldman Sachs, Arthur opens up about the challenges of ambition, adapting across cultures, and learning to define success on his own terms. Listeners will gain insight into: How early mentorship and family values shaped Arthur's global outlook The realities of navigating investment banking and private equity interviews Lessons in resilience, personality, and authenticity in high-pressure careers Perfect for students, young professionals, and anyone redefining what success looks like in global finance and leadership.

From studying Business Data Science to landing a role in investment banking at Centerview Partners, this is my honest story of how I discovered my path, the mistakes I made, and what I wish every student knew before starting their career. In this video, I share how I transitioned from college to corporate life — the lessons I learned outside the classroom, why real-world experience matters, and how small opportunities can lead to big growth. Whether you're a university student, career changer, or just curious about finance and personal growth, this episode will give you insight, motivation, and practical steps to help you find your direction.

Kara Tsuboi covers today's top tech stories. Michael Dell makes one of the largest donations to go directly to the American people. One of the fastest-growing college majors is now artificial intelligence. The esports champion of Excel is crowned in Las Vegas.

Unlock the power of Microsoft Copilot in this beginner-friendly training session led by Liz and Rachel. Learn what Copilot is, how it integrates with Microsoft 365 apps like Word, Excel, PowerPoint, Outlook, and Teams, and how it can streamline your workflow — from summarizing information to building full documents and presentations. The hosts walk through real examples, including creating a project spreadsheet, generating a presentation, and formatting accessible content with AI-powered assistance. They also cover different Copilot plans, tips to avoid AI "hallucinations," how to review sources, and where to access Copilot on the web or as a Windows app. By the end, you'll feel confident exploring Copilot's capabilities to save time, boost productivity, and enhance the quality of your work.

In this inspiring interview, Jonathan explains how he reinvented his career after the Army. Get real advice on networking, job searching, mindset, and using modern resources to accelerate your career.