AWS for Software Companies Podcast

New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.Topics Include:Introduction of Camden Swita, Head of AI at New Relic.New Relic invented the observability space for monitoring applications.Started with Java workloads monitoring and APM.Evolved into full-stack observability with infrastructure and browser monitoring.Uses advanced query language (NRQL) with time series database.AI strategy focuses on AI ops for automation.First cornerstone: Intelligent detection capabilities with machine learning.Second cornerstone: Incident response with generative AI assistance.Third cornerstone: Problem management with root cause analysis.Fourth cornerstone: Knowledge management to improve future detection.Initially overwhelmed by "ocean of possibilities" with LLMs.Needed narrow scope and guardrails for measurable progress.Natural language to NRQL translation proved immensely complex.Selecting from thousands of possible events caused accuracy issues.Shifted from "one tool" approach to many specialized tools.Created routing layer to select right tool for each job.Evaluation of NRQL is challenging even when syntactically correct.Implemented multi-stage validation with user confirmation step.AWS partnership involves fine-tuning models for NRQL translation.Using Bedrock to select appropriate models for different tasks.Initially advised prototyping on biggest, best available models.Now recommends considering specialized, targeted models from start.Agent development platforms have improved significantly since beginning.Future focus: "Agentic orchestration" with specialized agents.Envisions agents communicating through APIs without human prompts.Integration with AWS tools like Amazon Q.Industry possibly plateauing in large language model improvements.Increasing focus on inference-time compute in newer models.Context and quality prompts remain crucial despite model advances.Potential pros and cons to inference-time compute approach.Participants:Camden Swita – Head of AI & ML Innovation, Product Management, New RelicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Jon Yoo, CEO of Suger, shares how his company automates the complex & challenging workflows of selling software through cloud marketplaces like AWS.Topics Include:Jon Yoo is co-founder/CEO of Suger.Suger automates B2B marketplace workflows.Handles listing, contracts, offers, billing for marketplaces like AWS.Co-founder previously led Confluent's marketplace enablement product.Confluent had 40-50% revenue through cloud marketplaces.Required 10-20 engineers working solely on marketplace integration.Engineers prefer core product work over marketplace integration.Product/engineering leaders struggle with marketplace deployment requirements.Marketplace customers adopt without marketing, creating unexpected management needs.Version control is challenging for marketplace-deployed products.License management through marketplace creates engineering challenges.Suger helps sell, resell, co-sell through AWS Marketplace.Marketplace integration isn't one-time; requires ongoing maintenance.Business users constantly request marketplace automation features.Suger works with Snowflake, Intel, and AI startups.Data security concerns drive self-hosted AI deployments.AI products increasingly deploy via AMI/container solutions.AI products use usage-based pricing, not seat-based.Usage-based pricing creates complex billing challenges.AI products are tested at unprecedented rates.Two deployment options: vendor cloud or customer cloud.SaaS requires reporting usage to marketplace APIs.Customer-hosted deployment simplifies some billing aspects.Marketplaces need integration with ERP systems.Version control particularly challenging for AI products.Companies need automated updates for marketplace-deployed products.License management includes scaling up/down and expiration handling.Suger aims to integrate with GitHub for automatic updates.Participants:· Jon Yoo – CEO and Co-founder, SugerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

SentinelOne's Ric Smith shares how Purple AI, built on Amazon Bedrock, helps security teams handle increasing threat volumes while facing budget constraints and talent shortages.Topics Include:Introduction of Ric Smith, President of Product Technology and OperationsSentinelOne overview: cybersecurity company focused on endpoint and data securityCustomer range: small businesses to Fortune 10 companiesProducts protect endpoints, cloud environments, and provide enterprise observabilityRic oversees 65% of company operationsPurple AI launched on AWS BedrockPurple AI helps security teams become more efficient and productiveSecurity teams face budget constraints and talent shortagesPurple AI helps teams manage increasing alert volumesTop security challenge: increased malware variants through AIAI enables more convincing spear-phishing attemptsIdentity breaches through social engineering are increasingVoice deepfakes used to bypass security protocolsFuture threats: autonomous AI agents conducting orchestrated attacksSentinelOne helps with productivity and advanced detection capabilitiesSentinelOne primarily deployed on AWS infrastructureUsing SageMaker and Bedrock for AI capabilitiesBest practice: find partners for AI training and deploymentCustomer insight: Purple AI made teams more confident and creativeAI frees security teams from constant anxietySentinelOne's hyper-automation handles cascading remediation tasksMultiple operational modes: fully automated or human-in-the-loopAgent-to-agent interactions expected within 24 monthsCommon misconception: generative AI is infallibleAI helps with "blank slate problem" providing starting frameworksAI content still requires human personalization and reviewAWS partnership provides cost efficiency and governance benefitsParticipants:· Ric Smith – President – Product, Technology and Operations, SentinelOneSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Sam Gantner, Chief Product Officer of Nexthink, reveals how DEX is moving IT from reactive firefighting to proactive problem prevention and transforming enterprise productivity.Topics Include:DEX stands for Digital Employee ExperienceDEX eliminates IT issues preventing employee productivityShifts IT from reactive to proactive problem-solvingEmployees often serve as IT problem alerting systemsBest IT is transparent to employeesDEX solves device sluggishness and slow application issuesNetwork problems consistently appear across organizationsIT teams often lack visibility into employee experiencesMany organizations waste money on unused software licensesDEX Score measures comprehensive employee IT experienceSurveys capture subjective aspects of technology experienceReduction of actual problems differs from ticket reductionNexthink uses lightweight agents on employee devicesBrowser monitoring essential as browsers become application platformsEmployee engagement metrics capture real-time feedbackNexthink rebuilt as cloud-native platform using AWS servicesCompany deploys across 10+ global AWS regions30% of engineering resources dedicated to AI developmentOne customer eliminated 50% of IT ticketsAnother recovered 37,000 productivity hours worth $3M annuallyA third saved $1.3M by identifying unused licensesAI implementation requires dedicated employee trainingGood AI now better than perfect AI neverTechnology adoption is the next DEX frontierDigital dexterity becoming critical for maximizing IT investmentsParticipants:Samuele Gantner – Chief Product Officer, NexthinkSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Linda Ivy-Rosser, Vice President for Forrester, outlines the evolution of business applications and forward thinking predictions of their future.Topics Include:Linda Ivy-Rosser has extensive business applications experience since the 1990s.Business applications historically seen as rigid and lethargic.1990s: On-premise software with limited scale and flexibility.2000s: SaaS emergence with Salesforce, AWS, and Azure.2010s: Mobile-first applications focused on accessibility.Present: AI-driven applications characterize the "AI economy."Purpose of applications evolved from basic to complex capabilities.User expectations grew from friendly interfaces to intelligent systems.Four agreements: AI-infused, composable, cloud-native, ecosystem-driven.AI-infused: 69% consider essential/important in vendor selection.Composability expected to grow in importance with API architectures.Cloud-native: 79% view as foundation for digital transformation.Ecosystem-driven: 68% recognize importance of strategic alliances.Challenges: integration, interoperability, data accessibility, user adoption.43% prioritizing cross-functional workflow and data accessibility capabilities.Tech convergence recycles as horizontal strategy for software companies.Data contextualization crucial for employee adoption of intelligent applications.Explainable AI necessary to build trust in recommendations.Case study: 83% of operators rejected AI recommendations without explanations.Tulip example demonstrated three of four agreements successfully.Software giants using strategic alliances as competitive advantage.AWS offers comprehensive AI infrastructure, platforms, models, and services.Salesforce created ecosystem both within and outside their platform.SaaS marketplaces bridge AI model providers and businesses.Innovation requires partnerships between software vendors and ISVs.Enterprises forming cohorts with startups to solve business challenges.Software supply chain transparency increasingly important.Government sector slower to adopt cloud and AI technologies.Change resistance remains significant challenge for adoption.69% prioritize improving innovation capability over next year.Participants:Linda Ivy-Rosser - Vice President, Enterprise Software, IT services and Digital Transformation Executive Portfolio, ForresterSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

New Relic's Chief Customer Officer Arnaldo (Arnie) Lopez details how their observability platform helps 70,000+ customers monitor cloud performance through AWS infrastructure while introducing AI capabilities that simplify operations.Topics Include:Arnie Lopez is SVP, Chief Customer Officer at New Relic.Oversees pre-sales, post-sales, technical support, and enablement teams.New Relic University offers customer certifications.Founded in 2008, pioneered application performance monitoring (APM).Now offers "Observability 3.0" for full-stack visibility.Prevents interruptions during cloud migration and operations.Serves 70,000+ customers across various industries.16,000 enterprise-level paying customers.Platform consolidates multiple monitoring tools into one solution.Helps detect issues before customers experience performance problems.Market challenge: customers using disparate observability solutions.Reduces TCO by eliminating multiple monitoring tools.Targets VPs, CTOs, CIOs, and sometimes CEOs.Decade-long partnership with AWS.Platform built on largest unified telemetry data cloud.Uses AWS Graviton instances and Amazon EKS.AWS partnership enables innovation and customer trust.Three AI approaches: user assistance, LLM monitoring, faster insights.New Relic AI helps write query language (NURCLs).Monitors LLMs in customer environments.Uses AI to accelerate incident resolution.Lesson learned: should have started AI implementation sooner.Many customers still cautiously adopting AI technologies.Goal: continue growth with AWS partnership.Offers compute-based pricing model.Customers only pay for what they use.Announced one-step AWS monitoring for enterprise scale.Amazon Q Business and New Relic AI integration.Agent-to-agent AI eliminates data silos.Embeds performance insights into business application workflows.Participants:Arnie Lopez – SVP Chief Customer Officer, New RelicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

PDI Technologies' Steve Antonakakis shares how his company is implementing generative AI across their fuel and retail technology ecosystem through a practical, customer-focused approach using Amazon Bedrock.Topics Include:PDI's COO/CTO discussing generative AI implementationPractical step-by-step approach to AI integrationTesting in real-world settings with customer feedbackAWS Bedrock and Nova models exceeded expectationsEarly adoption phase with huge potentialFuel/retail industry processes many in-person transactionsPDI began in 1983 as ERP providerGrew through 33+ acquisitionsProvides end-to-end fuel industry solutionsOwns GasBuddy and Shell Fuel RewardsProcesses millions of transactions dailyGenerative AI fits into their intelligence plane architectureAWS Bedrock integrates well with existing infrastructureFocus on trusted, controlled, accountable AIProductizing AI features harder than traditional featuresCreated entrepreneurial structure alongside regular product teamsTeam designed to fail fast but stay customer-focusedAI features can access databases without disrupting applicationsCustomers want summarization across different business areasAI provides insights and actionable recommendationsConversational AI replaces traditional reporting limitationsWorking closely with customers to solve problems togetherBeyond prototyping phase, now in implementationAWS Nova provides excellent cost-to-value ratioFocus on measuring customer value over immediate profitabilityRFP use case saved half a million dollarsEarly prompts were massive, now more structuredSetting realistic customer expectations is importantData security approach same as other applicationsTreating AI outputs with same data classification standardsParticipants:Steve Antonakakis – COO & CTO, Retail & Energy, PDI TechnologiesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS partners Braze, Qualtrics, and Tealium share strategies for marketplace success, vertical industry expansion, and generative AI integration that have driven significant business growth. Topics Include:Jason Warren introduces AWS Business Application Partnerships panel.Three key topics: Marketplace Strategy, Vertical Expansion, Gen-AI Integration.Alex Rees of Braze, Matthew Gray of Tealium, and Jason Mann of Qualtrics join discussion.Braze experienced triple-digit percentage growth through AWS Marketplace.Braze dedicating resources specifically to Marketplace procurement.Tealium accelerated deal velocity by listing on Marketplace.Tealium saw broader use case expansion with AWS co-selling.Qualtrics views Marketplace listing as earning a "diploma."Understanding AWS incentives and metrics is crucial.Knowing AWS "love language" helps partnership success.Braze saw transaction volume increase between Q1 and Q4.Aligning with industry verticals unlocked faster growth.Tealium sees bigger deals and faster close times.Tealium moved from transactional to strategic marketplace approach.Private offers work well for complex enterprise agreements.Qualtrics measures AWS partnership through "influence, intel, introductions."AWS relationships help navigate IT and procurement challenges.Propensity-to-buy data guides AWS engagement strategy.Marketplace strategy evolving with new capabilities and international expansion.Brazilian marketplace distribution reduces currency and tax challenges.Partnership evolution: sell first, then market, then co-innovate.Braze penetrated airline market through AWS Travel & Hospitality.RFP introductions show tangible partnership benefits.Tealium partnering with Virgin Australia and United Airlines.MUFG bank case study shows joint AWS-Tealium success.Qualtrics won awards despite not completing formal competencies.Focus on fewer verticals yields better results.Gen AI brings both opportunities and regulatory concerns.First-party data rights critical for AI implementation.AWS Bedrock integration provides security and prescriptive solutions.Participants:Alex Rees – Director Tech Partnerships, BrazeJason Mann – Global AWS Alliance Lead, QualtricsMatthew Gray - SVP, Partnerships & Alliances, TealiumJason Warren - Head of Business Applications ISV Partnerships (Americas), AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Yashodha Bhavnani, Head of AI at Box, reveals Box's vision for intelligent content management that transforms unstructured data into actionable insights. Topics Include:Yashodha Bhavnani leads AI products at Box.Box's mission: power how the world works together.Box serves customers globally across various industries.Works with majority of Fortune 500 companies.AI agents will join workforce for repetitive tasks.Workflows like hiring will become easily automated with AI.Content will work for users, not vice versa.Customers demand better experiences with generative AI.Box calls this shift "intelligent content management."90% of enterprise content is unstructured data.AI thrives on unstructured data.Current content systems are unproductive and unsecured.AI can generate insights from scattered company knowledge.AI extracts metadata automatically from documents like contracts.Automated workflows triggered by AI-extracted data.Box provides enterprise-grade AI connected to your content.AI follows same permissions as the content itself.Customer data never used to train AI models.AI helps classify sensitive data to prevent leaks.Box offers choice of AI models to customers.AI is seamlessly connected with customer content.Administrators control AI deployment across their organization.Partnership with AWS Bedrock brings frontier models to Box.Box supports customers using their own custom models.Box preparing for AI agents to join workforce.Introduced "AI Units" for flexible pricing.Basic AI included free with Business Plus tiers.Both horizontal and vertical multi-agent architectures planned.Working toward agent-to-agent communication protocols.Participants:Yashodha Bhavnani - VP of Product Management, AI products, BoxSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Tech leaders from RingCentral, Zoom and AWS discuss how generative AI is transforming business communications while balancing challenges & regulatory concerns in this rapidly evolving landscape.Topics Include:Introduction of panel on generative AI's impact on businesses.How to transition AI from prototypes to production.Understanding value creation for customers through AI.Introduction of Khurram Tajji from RingCentral.Introduction of Brendan Ittleson from Zoom.How generative AI fits into Zoom's product offerings.Zoom's AI companion available to all paid customers.Zoom's federated approach to AI model selection.RingCentral's new AI Receptionist (AIR) launch.How AIR routes calls using generative AI capabilities.AI improving customer experience through sentiment analysis.The disproportionate value of real-time AI assistance.Economics of delivering real-time AI capabilities.Real-time AI compliance monitoring in banking.Value of preventing regulatory fines through AI.Voice cloning detection through AI security.Democratizing AI access across Zoom's platform.Monetizing specialized AI solutions for business value.Challenges in taking AI prototypes to production.Importance of selecting the right AI models.Privacy considerations when training AI models.Maintaining quality without using customer data for training.Co-innovation with customers during product development.Scaling challenges for AI businesses.Case study of AI in legal case assessment.Ensuring unit economics work before scaling AI applications.Zoom's approach to scaling AI across products.Importance of centralizing but federating AI capabilities.Breaking down data silos for effective AI context.Navigating evolving regulations around AI.EU AI Act restrictions on emotion inference.Balancing regulations with customer experience needs.Future of AI agents interacting with other agents.How AI enhances human connection by handling routine tasks.Impact of AI on company valuations and M&A activity.Participants:Khurram Tajji – Group CMO & Partnerships, RingCentralBrendan Ittleson – Chief Ecosystem Officer, ZoomSirish Chandrasekaran – VP of Analytics, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

CEO Joe Kim shares how Sumo Logic has implemented generative AI to democratize data analytics, leveraging AWS Bedrock's multi-agent capabilities to dramatically improve accuracy.Topics Include:Introduction of Joe Kim, CEO of Sumo Logic.Question: Overview of Sumo Logic's products and customers?Sumo Logic specializes in observability and security markets.Company leverages industry-leading log management and analytics capabilities.Question: How has generative AI entered this space?Kim's background is in product, strategy and engineering.Non-experts struggle to extract value from complex telemetry data.Generative AI provides easier interface for interacting with data.Question: How do you measure success of AI initiatives?Focus on customer problems, not retrofitting AI everywhere.Launched "Mo, the co-pilot" at AWS re:Invent.Mo enables natural language queries of complex data.Mo suggests visualizations and follow-up questions during incidents.Question: What challenges did you face implementing AI?Team knew competitors would eventually implement similar capabilities.Single model approach topped out at 80% accuracy.Multi-agent approach with AWS Bedrock achieved mid-90% accuracy.Bedrock offered security benefits and multiple model capabilities.Question: How was working with the AWS team?Partnered with Bedrock team and tribe.ai for implementation.Partners helped avoid pitfalls from thousands of prior projects.Question: What advice for other software leaders?Don't implement AI just to satisfy board pressure.Identify problems without mentioning generative AI first.Innovation should come from listening to customers.Question: Future plans with AWS partnership?Moving toward automated remediation beyond just analysis.Question: Has Sumo Logic monetized generative AI?Changed pricing from data ingestion to data usage.New model encourages more data sharing without cost barriers.Participants:Joe Kim – Chief Executive Officer, Sumo LogicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

CTO Arun Kumar discusses how Socure leverages AWS and generative AI to collect billions of data points each day in order to combat sophisticated online fraud at scale.Topics Include:Introduction of Arun Kumar, CTO of SocureWhat does Socure specialize in?KYC and anti-money laundering checksMission: eliminate 100% fraud on the internetFraud has increased since COVIDSocure blocks fraud at entry pointWorks with top banks and government agenciesCTO responsibilities include product and engineeringFocus on increasing efficiency through technologyTwo goals: internal efficiency and combating fraudCountering tools like FraudGPT on dark webMeasuring success through reduced human capital needsFraud investigations reduced from hours to minutesImproved success rates in uncovering fraud ringsDetecting multi-hop connections in fraud networksQuestion: Who's winning - fraudsters or AI?It's a constant "cat and mouse game"Creating a fraud "red team" similar to cybersecurityPartnership details with AWSAmazon Bedrock provides multiple LLM optionsBuilding world's largest identity graph with NeptuneReal-time suspicious activity detectionBlocking account takeovers through phone number changesSuccess story: detecting deepfake across 3,000 IDsCollecting hundreds of data points per identityChallenges: adding selfie checks and liveness detectionFuture strategy: 10x-100x performance improvementsCreating second and third-order intelligence signalsInternal efficiency applications of generative AIAI-powered sales tools and legal document reviewParticipants:Arun Kumar – Chief Technical Officer, SocureSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Oron Noah of Wiz outlines how organizations evolve their security practices to address new vulnerabilities in AI systems through improved visibility, risk assessment, and pipeline protection.Topics Include:Introduction of Oron Noah, VP at Wiz.Wiz: largest private service security company.$1.9 billion raised from leading VCs.45% of Fortune 100 use Wiz.Wiz scans 60+ Amazon native services.Cloud introduced visibility challenges.Cloud created risk prioritization issues.Security ownership shifted from CISOs to everyone.Wiz offers a unified security platform.Three pillars: Wiz Cloud, Code, and Defend.Wiz democratizes cloud security for all teams.Security Graph uses Amazon Neptune.Wiz has 150+ available integrations.Risk analysis connects to cloud environments.Wiz identifies critical attack paths.AI assists in security graph searches.AI helps with remediation scripts.AI introduces new security challenges.70% of customers already use AI services.AI security requires visibility, risk assessment, pipeline protection.AI introduces risks like prompt injection.Data poisoning can manipulate AI results.Model vulnerabilities create attack vectors.AI Security Posture Management (ASPM) introduced.Four key questions for AI security.AI pipelines resemble traditional cloud infrastructure.Wiz researchers found real AI security vulnerabilities.Wiz AI ASPM provides agentless visibility.Supports major AI services (AWS, OpenAI, etc.).Built-in rules detect AI service misconfigurations.Participants:Oron Noah – VP Product Extensibility & Partnerships, WizSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Ruslan Kusov of SoftServe presents how their Application Modernization Framework accelerates ISV modernization, assesses legacy code, and delivers modernized applications through platform engineering principles.Topics Include:Introduction of Ruslan Kusov, Cloud CoE Director at SoftServeSoftServe builds code for top ISVsSuccess case: accelerated security ISV modernization by six monthsHealthcare tech company assessment: 1.6 million code lines in weeksBusiness need: product development acceleration for competitive advantageBusiness need: intelligent operations automationBusiness need: ecosystem integration and "sizeification" to cloudBusiness need: secure and compliant solutionsBusiness need: customer-centric platforms with personalized experiencesBusiness need: AWS marketplace integrationDistinguishing intentional from unintentional complexityPlatform engineering concept introductionSelf-service internal platforms for standardizationApplying platform engineering across teams (GenAI, CSO, etc.)No one-size-fits-all approach to modernizationSAMP/SEMP framework introductionCore components: EKS, ECS, or LambdaModular structure with interchangeable componentsCase study: ISV switching from hardware to software productsFour-week MVP instead of planned ten weeksSix-month full modernization versus planned twelve monthsAssessment phase importance for business case developmentCalculating cost of doing nothing during modernization decisionsHealthcare customer case: 1.6 million code lines assessedBenefits: platform deployment in under 20 minutesBenefits: 5x reduced assessment timeBenefits: 30% lower infrastructure costsBenefits: 20% increased development productivity with GenAIIntegration with Amazon Q for developer productivityClosing Q&A on security modernization and ongoing managementParticipants:Ruslan Kusov – Cloud CoE Director, SoftserveSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Richard Sonnenblick and Lee Rehwinkel of Planview discuss their transition to Amazon Bedrock for a multi-agent AI system while sharing valuable implementation and user experience lessons.Topics Include:Introduction to Planview's 18-month journey creating an AI co-pilot.Planview builds solutions for strategic portfolio and agile planning.5,000+ companies with millions of users leverage Planview solutions.Co-pilot vision: AI assistant sidebar across multiple applications.RAG used to ingest customer success center documents.Tracking product data, screens, charts, and tables.Incorporating industry best practices and methodologies.Can ingest customer-specific documents to understand company terminology.Key benefit: Making every user a power user.Key benefit: Saving time on tedious and redundant tasks.Key benefit: De-risking initiatives through early risk identification.Cost challenges: GPT-4 initially cost $60 per million tokens.Cost now only $1.20 per million tokens.Market evolution: AI features becoming table stakes.Performance rubrics created for different personas and applications.Multi-agent architecture provides technical and organizational scalability.Initial implementation used Azure and GPT-4 models.Migration to AWS Bedrock brought model choice benefits.Bedrock allowed optimization across cost, benchmarking, and speed dimensions.Added AWS guardrails and knowledge base capabilities.Lesson #1: Users hate typing; provide clickable options.Lesson #2: Users don't like waiting; optimize for speed.Lesson #3: Users take time to trust AI; provide auditable answers.Question about role-based access control and permissions.Co-pilot uses user authentication to access application data.Question about subscription pricing for AI features.Need to educate customers about AI's value proposition.Question about reasoning modes and timing expectations.Showing users the work process makes waiting more tolerable.Participants:Richard Sonnenblick - Chief Data Scientist, PlanviewLee Rehwinkel – Principal Data Scientist, PlanviewSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Executives from DataRobot, LaunchDarkly and ServiceNow share strategies, actions and recommendations to achieve profitable growth in today's competitive SaaS landscape.Topics Include:Introduction of panelists from DataRobot, LaunchDarkly & ServiceNowServiceNow's journey from service management to workflow orchestration platform.DataRobot's evolution as comprehensive AI platform before AI boom.LaunchDarkly's focus on helping teams decouple release from deploy.Rule of 40: balancing revenue growth and profit margin.ServiceNow exceeding standards with Rule of 50-60 approach.Vertical markets expansion as key strategy for sustainable growth.AWS Marketplace enabling largest-ever deal for ServiceNow.R&D investment effectiveness through experimentation and feature management.Developer efficiency as driver of profitable SaaS growth.Competition through data-driven decisions rather than guesswork.Speed and iteration frequency determining competitive advantage in SaaS.Balancing innovation with early customer adoption for AI products.Product managers should adopt revenue goals and variable compensation.Product-led growth versus sales-led motion: strategies and frictions.Sales-led growth optimized for enterprise; PLG for practitioners.Marketplace-led growth as complementary go-to-market strategy.Customer acquisition cost (CAC) as primary driver of margin erosion.Pricing and packaging philosophy: platform versus consumption models.Value realization must precede pricing and packaging discussions.Good-better-best pricing model used by LaunchDarkly.Security as foundation of trust in software delivery.LaunchDarkly's Guardian Edition for high-risk software release scenarios.Security for regulated industries through public cloud partnerships.GenAI security: benchmarks, tests, and governance to prevent issues.M&A strategy: ServiceNow's 33 acquisitions for features, not revenue.Replatforming acquisitions into core architecture for consistent experience.Balancing technology integration with people aspects during acquisitions.Trends in buying groups: AI budgets and tool consolidation.Implementing revenue goals in product teams for new initiatives.Participants:Prajakta Damle – Head of Product / SVP of Product, DataRobotClaire Vo – Chief Product & Technology Officer, LaunchDarklyAnshuman Didwania – VP/GM, Hyperscalers Business Group, ServiceNowAkshay Patel – Global SaaS Strategist, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Ryan Steeb shares DTEX Systems' strategic approach to implementing generative AI with AWS Bedrock, reducing risk while focusing on meaningful customer outcomes.Topics Include:Introduction of Ryan Steeb, Head of Product at DTEX Systems Explanation of insider risk challenges Three categories of insider risk (malicious, negligent, compromised) How DTEX Systems is using generative AI Collection of proprietary data to map human behavior on networks Three key areas leveraging Gen AI: customer value, services acceleration, operations How partnership with AWS has impacted DTEX's AI capabilities Value of AWS expertise for discovering AI possibilities AWS Bedrock providing flexibility in AI implementation Collaboration on unique applications beyond conventional chat assistants AWS OpenSearch as a foundational component Creating invisible AI workflows that simplify user experiences The path to monetization for generative AI Three approaches: direct pricing, service efficiency, operational improvements Second and third-order effects (retention, NPS, reduced churn) How DTEX prioritizes Gen AI projects Starting with customer problems vs. finding problems for AI solutions Business impact prioritization framework Technical capability considerations Benefits of moving AI solutions to AWS Bedrock Fostering a culture of experimentation and innovation Adopting Amazon's "working backwards" philosophy Balancing customer-driven evolution with original innovation Time machine advice: start experimenting with Gen AI earlier Importance of leveraging peer groups and experts Future outlook: concerns about innovation outpacing risk mitigation Security implications of Gen AI adoption Participation in the OpenSearch Linux Foundation initiative Final thoughts on the DTEX-AWS partnershipParticipants:Ryan Steeb – Head of Product, DTEX SystemsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

From cost management to practical implementation, Sage's Amaya Souarez shares invaluable insights on building AI-powered business tools that deliver measurable value to customers.Topics Include:Amaya Souarez introduced as EVP Cloud Services at SageOverview of Sage: offers accounting, finance, HR and payroll tech for small businessesCompany emphasizes human values alongside technology developmentAmaya oversees core cloud services and operations across 200+ productsSage Co-Pilot announced as new AI assistant – helping automate invoicing and cash flow managementCommon misconceptions with Generative AIAI solutions aren't always solution to every problemCompares AI hype to previous blockchain enthusiasmEmphasizes starting with clear use cases before implementationDifference between task-based and reporting-based use casesPartnering with AWS to build accounting-specific language modelsDifferent accounting terminology varies by countryUsing AWS Bedrock and Lex for a domain-specific language model developmentMultiple AI models may be needed for single solutionCustomer feedback drives project funding decisionsAI development integrated into regular product roadmapsFocus on reducing cost per user for AI featuresSuccess story: reducing 20-hour task to 5 minutesTracks AI usage costs per customer interactionEarly Gen AI hype caused confusion in the marketPlans to make domain-specific models available via APIWill offer language models on AWS MarketplaceEmphasizes practical AI application over blind implementationParticipants:Amaya Souarez - EVP Cloud Services and Operations, SageSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Box's Chief Product Officer Diego Dugatkin discusses how the enterprise content management platform is leveraging AI through partnerships with AWS Bedrock and continuing to innovate for their customers.Topics Include:Introduction of Diego Dugatkin as Box's Chief Product OfficerBox provides cloud content management for enterprise customersFocus on Intelligent Content ManagementBox serves 115,000 customers including 70% of Fortune 500Company manages approximately one exabyte of enterprise dataBox expanding product portfolio to offer more customer valuePartnership with AWS Bedrock for AI implementation announcedCollaboration with Anthropic for LLM technology integrationBox offers neutral approach letting customers choose preferred LLMsCommon misconceptions about generative AI capabilities and limitationsGenerative AI helps accelerate contract analysis and classification processesBox Hubs enables content curation and multi-document queriesSuccess measured through hub creation and query accuracy metricsLong-term AWS partnership continues expanding with new technologiesAmazon is major Box customer while Box uses AWSAPI integration important for third-party developer implementationsAI development exceeding speed expectations in efficiency improvementsChallenges remain in defining AI agent roles and capabilitiesContent strategy crucial for deploying intelligent content managementCompanies must prepare for AI agents in workplaceFlexibility in tech stack recommended over single-vendor approachNext 12-24 months will see accelerated industry changesBox maintains innovative culture through intrapreneurship approachCompany regularly hosts internal and external hackathonsFocus on maintaining integrated platform while acquiring companiesPartnership between Box and AWS continues growing strongerParticipants:Diego Dugatkin – Chief Product Officer, BoxSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Through case studies of Graviton implementation and GPU integration, Justin Fitzhugh, Snowflake's VP of Engineering, demonstrates how cloud-native architecture combined with strategic partnerships can drive technical innovation and build business value.Topics Include:Cloud engineering and AWS partnershipTraditional databases had fixed hardware ratios for compute/storageSnowflake built cloud-native with separated storage and computeCompany has never owned physical infrastructureApplications must be cloud-optimized to leverage elastic scalingSnowflake uses credit system for customer billingCredits loosely based on compute resources providedCompany maintains cloud-agnostic approach across providersInitially aimed for identical pricing across cloud providersNow allows price variation while maintaining consistent experienceConsumption-based revenue model ties to actual usagePerformance improvements can actually decrease revenueCompany tracked ARM's move to data centersInitially skeptical of Graviton performance claimsPorting to ARM required complete pipeline reconstructionDiscovered floating point rounding differences between architecturesAmazon partnership crucial for library optimizationGraviton migration took two years instead of oneAchieved 25% performance gain with 20% cost reductionTeam requested thousands of GPUs within two monthsGPU infrastructure was new territory for SnowflakeNeeded flexible pricing for uncertain future needsSigned three to five-year contracts with flexibilityTeam pivoted from building to fine-tuning modelsPartnership allowed adaptation to business changesEmphasizes importance of leveraging provider expertiseRecommends early engagement with cloud providersBuild relationships before infrastructure needs ariseMaintain personal connections with provider executivesParticipants:Justin Fitzhugh – VP of Engineering, SnowflakeSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

In this AWS panel discussion, Naveen Rao, VP of AI of Databricks and Vijay Karunamurthy, Field CTO of Scale AI share practical insights on implementing generative AI in enterprises, leveraging private data effectively, and building reliable production systems.Topics Include:Sherry Marcus introduces panel discussion on generative AI adoptionScale AI helps make AI models more reliableDatabricks focuses on customizing AI with company dataCompanies often stressed about where to start with AIBoard-level pressure driving many enterprise AI initiativesStart by defining specific goals and success metricsBuild evaluations first before implementing AI solutionsAvoid rushing into demos without proper planningEnterprise data vastly exceeds public training data volumeCustomer support histories valuable for AI trainingModels learning to anticipate customer follow-up questionsProduction concerns: cost, latency, and accuracy trade-offsGood telemetry crucial for diagnosing AI application issuesSpeed matters more for prose, accuracy for legal documentsCost becomes important once systems begin scaling upOrganizations struggle with poor quality existing dataPrivacy crucial when leveraging internal business dataRole-based access control essential for regulated industriesAI can help locate relevant data across legacy systemsModels need organizational awareness to find data effectivelyPrivate data behind firewalls most valuable for AICustomization gives competitive advantage over generic modelsCurrent AI models primarily do flexible data recallNext few years: focus on deriving business valueFuture developments in causal inference expected post-5 yearsComplex multi-agent systems becoming more importantScale AI developing "humanity's last exam" evaluation metricDiscussion of responsibility and liability in AI decisionsCompanies must stand behind their AI system outputsExisting compliance frameworks can be adapted for AIParticipants:Naveen Rao – VP of AI, DatabricksVijay Karunamurthy – Field CTO, Scale AISherry Marcus Ph.D. - Director, Applied Science, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Suresh Vasudevan, CEO of Sysdig, discusses the evolving challenges of cloud security incident response and the need for new approaches to mitigate organizational risk.Topics Include:Cybersecurity regulations mandate incident response reporting.Challenges of cloud breach detection and response.Complex cloud attack patterns: reconnaissance, lateral movement, exploit.Rapid exploitation - minutes vs. days for on-prem.Importance of runtime, identity, and control plane monitoring.Limitations of EDR and SIEM tools for cloud.Coordinated incident response across security, DevOps, executives.Criticality of pre-defined incident response plans.Increased CISO personal liability risk and mitigation.Documenting security team's diligence to demonstrate due care.Establishing strong partnerships with legal and audit teams.Covering defensive steps in internal communications.Sysdig's cloud-native security approach and Falco project.Balancing prevention, detection, and response capabilities.Integrating security tooling with customer workflows and SOCs.Providing 24/7 monitoring and rapid response services.Correlating workload, identity, and control plane activities.Detecting unusual reconnaissance and lateral movement behaviors.Daisy-chaining events to identify potential compromise chains.Tracking historical identity activity patterns for anomaly detection.Aligning security with business impact assessment and reporting.Adapting SOC team skills for cloud-native environments.Resource and disruption cost concerns for cloud agents.Importance of "do no harm" philosophy for response.Enhancing existing security data sources with cloud context.Challenges of post-incident forensics vs. real-time response.Bridging security, DevOps, and executive domains.Establishing pre-approved incident response stakeholder roles.Maintaining documentation to demonstrate proper investigation.Evolving CISO role and personal liability considerations.Proactive management of cyber risk at board level.Developing strong general counsel and audit relationships.Transparency in internal communications to avoid discovery risks.Security teams as business partners, not just technicians.Sysdig's cloud security expertise and open-source contributions.Participants:· Suresh Vasudevan – CEO, SysdigSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

From hard-coded credentials to boardroom buy-in, join four tech security leaders from Clumio, Mongo DB, Symphony and AWS, as they unpack how building the right security culture can be your organization's strongest defense against cyber threats.Topics Include:Security culture is crucial for managing organizational cyber riskGood culture enables quick decision-making without constant expert consultationMany security incidents occur from well-meaning people getting dupedPanel includes leaders from AWS, Symphony, MongoDB, and ClumioMeasuring security culture requires both quantitative and qualitative metricsBoard-level engagement indicates organizational security culture maturitySelf-reporting of security incidents shows positive cultural developmentSecurity committees' participation helps measure cultural engagementHard-coded credentials remain persistent problem across organizationsInternal audits and risk committees strengthen security governancePublic security incidents change board conversations about prioritiesLeadership vulnerability and transparency help build trustBeing pragmatic beats emotional responses in security leadershipSecurity programs should align with business revenue goalsCustomer security requirements drive program improvementsExcessive security questionnaires drain resources from actual securitySecurity culture started as exclusionary, evolved toward collaborationFinancial institutions often create unnecessary compliance burdenEarly security involvement in product development prevents delaysSecurity teams must match development team speedTrust between security and development teams enables efficiencySmall security teams can support large enterprise requirementsVendor partnerships help scale security capabilitiesProcess changes work better than adding security toolsSecurity leaders need deep business knowledgeTechnical depth and breadth remain essential skillsEvangelism capability critical for security leadership successInfluencing without authority key for security effectivenessCrisis moments create opportunities for security improvementSocializing between security and development teams builds trustDEF CON attendance helps developers understand security perspectiveBug bounty programs provide continuous security feedbackRegular informal meetings between teams improve collaborationBuilding personal relationships improves security outcomesModern security leadership requires balance of IQ and EQParticipants:Jacob Berry – Head of Information Security, ClumioGeorge Gerchow – Interim CISO, Head of Trust, Mongo DBBrad Levy – Chief Executive Officer, SymphonyBrendan Staveley – Global Sales Leader, Security Services, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS executive Giancarlo Casella explains how organizations can navigate global privacy regulations and achieve compliant international expansion using AWS's privacy reference architecture.Topics Include:Welcome to executive forum on security and Gen AIIntroduction of Giancarlo Casella from AWS Security Assurance ServicesAWS helps organizations with compliance and audit readinessGlobal expansion requires understanding local privacy lawsGermany and France interpret GDPR differentlyGermany has Federal Data Protection Act (BDSG)France focuses on consumer privacy through CENILRisk of non-compliance includes fines and reputation damagePrivacy laws existed in only 10 countries in 2000EU Privacy Directive of 1990 was prominentBy 2010, forty countries had privacy lawsHIPAA and GLBA introduced in United StatesNow over 150 countries have privacy regulations75% of world population under privacy laws soonRegulations are vague and open to interpretationGDPR example: encryption requirements lack specificityNeed right stakeholders for privacy complianceLegal team must lead privacy interpretationEngineering implements technical privacy aspectsRisk and compliance teams coordinate evidence gatheringData Protection Officer oversees entire programCIO, CTO, CISO alignment creates strong foundationSecurity transforms from bureaucratic to revenue enablerAWS develops cloud-specific privacy reference architectureIndustry standards provide guidance frameworksAWS privacy reference architecture focuses on cloud specificsData minimization and individual autonomy are keyCase study: Middle Eastern AI company expands to CanadaCompany used CCTV at gas stationsCreated privacy baseline and roadmapData flow documentation essential for complianceContinuous compliance strategy helps enable successAligning stakeholders across different organizational linesFuture of US federal privacy regulation discussedDiscussion of responsible AI usage requirementsParticipants:Giancarlo Casella - Head of Business Development and Growth Strategies, AWS Security Assurance ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Haggai Polak – Chief Product Officer, Securonix and a veteran cybersecurity expert examines how artificial intelligence, quantum computing, and resource constraints are fundamentally transforming the threat landscape for security leadersTopics Include:AI transformation of cybersecurity landscape from past tactical focusCISO accountability and regulatory pressures increasing significantlyAttack surface expanding beyond traditional network boundariesQuantum computing threatens current cryptographic protectionsDefenders remain understaffed and outmatched against sophisticated threatsSecuronix leads SIEM/SOAR space with 1000+ global customersWorld Economic Forum identifies misinformation/disinformation as major crisisAI benefits attackers more than defenders currentlySmall/medium enterprises falling below cyber poverty lineAI enables faster, more sophisticated malware developmentDeepfakes caused $25M loss in Hong Kong CFO impersonationDigital tsunami: broadband, IoT, cloud everywhere expanding attack surface50+ democracies face election security challenges in 2024Cloud intrusions increased 75% between 2022-2023Quantum-resistant cryptography transition needed within 10 yearsSEC regulations require specific cybersecurity incident disclosure guidelines4 million unfilled cybersecurity positions globallyCybercrime-as-a-Service growing, estimated $1.6B annual revenue81% of organizations faced ransomware attacks in 2023Insider threats increasing with remote work adoption30,000+ vulnerabilities published last year, half critical/highMean time to exploit now 44 daysSecuronix Eon leverages AI to increase analyst efficiencyDark web selling corporate credentials for $10,000Balance needed between protection and detection/response investmentsParticipants:Haggai Polak – Chief Product Officer, SecuronixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Dr. Yanbing Li, Chief Product Officer at Datadog, outlines how the company has integrated AI and automation into its incident response framework, helping customers manage both traditional security challenges and emerging AI-specific risks.Topics Include:Introduced talk about incident response and CISO liabilityDatadog founded 14 years ago for cloud-based developmentPlatform unifies observability and security for cloud applicationsCurrent environment has too many fragmented security productsSEC requires material incident reporting within four daysDatadog's incident response automates Slack room creationResponse team includes Legal, Security, Engineering, and ProductSystem tracks non-material incidents to identify concerning patternsReal-time telemetry data drives incident management automationOn-call capabilities manage escalation workflowsDatadog uses own products internally for incident responseCompany focuses on reducing time to incident detectionAI brings new risks: hallucination, data leaks, design exploitationBits.ai launched as LLM-based incident management co-pilotTool synthesizes events and generates incident summariesBits.ai suggests code remediation and creates synthetic testsSecurity built into AI products from initial designPrompt injection prevented through structured validation approachSensitive data anonymized before LLM processingEngineering and security teams collaborate closely on AILLM observability becoming critical for production deploymentsCustomers need monitoring for hallucinations and token usageDatadog extends infrastructure monitoring into security naturallyCompany maintains strong partnership with AWSQ&A covered Bits.ai proactive capabilities and enterprise differentiationParticipants:Yanbing Li – Chief Produce Officer - DatadogSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Sanjay Kalra of Zscaler and Randy DeFauw of Amazon Web Services explore the hidden dangers of generative AI security—from invisible text manipulation and deep fakes to data poisoning and dark AI models—while offering practical strategies for protecting your enterprise in this era of generative AI.Topics Include:AI security threats grouped into data, malicious use, trust/safetyData security critical for SaaS-based AI servicesModel training data vulnerable to poisoning and manipulationGenAI lacks traditional data deletion capabilitiesAccess controls difficult once data becomes model embeddingsPrompt injection attacks becoming widespread, with libraries available onlineDeepfake scams increasing in sophistication and frequencyAI enhancing phishing attacks with better written contentDark AI models emerging specifically for malicious purposesModel hallucinations being exploited for security attacksAI accelerating analysis of stolen dataShadow AI usage by employees poses security risksExisting vendor AI integration creating unexpected security challengesFine-grained access controls essential for AI applicationsPII protection critical in both inputs and outputsComprehensive prompt and response logging necessaryInvisible text manipulation emerging in resumes and RFPsModel fine-tuning can compromise built-in security guardrailsMulti-language inputs create new security considerationsCompetition-sensitive content requires careful AI managementAI firewalls needed for input/output monitoringRegular security testing required for AI modelsAI compliance standards emerging globallyMulti-modal AI creating new security challengesBrowser isolation helping control AI application usageParticipants:Sanjay Kalra – Product Management at ZscalerRandy DeFauw – Senior Principal Solutions Architect, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

This illuminating conversation with CyberArk's SVP of Finance, Nili Serr-Reuven, reveals how the 25-year-old cybersecurity leader successfully transformed from a traditional software company to a SaaS business model in just five quarters - far faster than the industry standard of 2-2.5 years - while maintaining strong margins and customer trust throughout the transition.Topics Include:Introduction to SaaS transformation challenges and opportunities.Tomaz Perc introduces Nili Serr Reuven from CyberArk.Overview of CyberArk's 25-year history and milestones.Transition from a perpetual model to SaaS.CyberArk's accelerated transformation in just five quarters.Challenges of shifting from product-centric to customer-centric.Importance of market research and peer consultations.Key role of cross-functional collaboration in success.Explanation of "swallowing the fish" in SaaS.Managing short-term revenue drops during SaaS transformation.CyberArk's 70% SaaS revenue share post-transformation.Impact of global economic challenges on business strategy.CyberArk's robust demand for identity security solutions.Strategic leadership's role in transformation execution.CyberArk's disciplined financial planning during uncertainty.Establishing KPIs like ARR and customer satisfaction.Managing rising cloud costs with FinOps practices.CyberArk's approach to pricing and packaging SaaS solutions.Leveraging acquisitions to speed up SaaS capabilities.Impact of transformation on CyberArk's finance department.Evolution of finance roles to support SaaS growth.Communication with investors during transformative periods.The importance of cultural shifts in transformation success.Continuous learning, transparency, and collaboration as cornerstones.Advice for future SaaS leaders: plan, communicate, adapt.Participants:Nili Serr Reuven – SVP Corporate Finance, CyberArkTomaz Perc – SaaS Business Lead, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Soumya Banerjee, Associate Partner at McKinsey and Company, shares a comprehensive data-driven exploration of how generative AI is transforming the cybersecurity landscape, revealing emerging threats, organizational challenges, and strategic opportunities for security professionals.Topics Include:AI's transformative potential in cybersecuritySurvey of 500 cybersecurity professionalsGenerative AI's impact on security landscapeRising sophistication of phishing attacksThreat actors leveraging generative AIDeepfake technologies circumventing biometric controlsCybersecurity companies' valuation and growthPlatform versus point solution debatesExpanding cybersecurity attack surfacesCloud security emerging as top priorityAI use cases in threat detectionGenerative AI risks for organizationsSecuring AI investments and budgetsData protection and sensitive information challengesRegulatory scrutiny of AI technologiesTalent gaps in cybersecurity sectorEvolving cyber insurance risk modelsIdentity and access management trendsAPI and machine identity securityLLM prompt and data protectionEnterprise strategies for AI adoptionEmerging technologies for cybersecurity defensePartnerships between cybersecurity vendorsDisclosure risks in generative AIFuture of cybersecurity technology landscapeParticipants:· Soumya Banerjee – Associate Partner at McKinsey and CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Brendan Ittelson, Chief Ecosystem Officer of Zoom and Fedrico Torreti of AWS share how Zoom and AWS are leveraging generative AI to revolutionize application development, enhance cross-app personalization, and streamline user experiences with intelligent communication tools.Topics Include:Introduction of speakers and session overview.Generative AI's disruptive impact across industries.Reimagining customer experiences with generative AI.Driving productivity through AI-powered applications.Challenges faced by application developers with AI integration.Importance of AI as a collaborator, not replacement.Cross-functional workplace complexity with multiple apps.Reducing task redundancy via generative AI automation.Case study: AI accelerating creative project briefings.Business outcomes achieved through thoughtful AI implementation.McKinsey and Gartner projections on generative AI's potential.Top use cases: R&D, customer operations, sales, marketing.Bridging data silos for richer user experiences.Security and compliance challenges in AI implementations.Zoom's federated model for adaptable AI architecture.Meeting summaries powered by Zoom AI Companion.Expanding generative AI into chat, whiteboards, voicemails.Vision for AI amplifying, simplifying, and delegating tasks.Integrating external data for personalized user experiences.Open platform approach for seamless data exchange.AI Companion empowering users with actionable insights.Role of AWS in enabling AI-first solutions.Addressing notification overload with smarter AI design.Enhancing end-to-end workflows with unified AI tools.Encouragement for developers to embrace thoughtful AI adoption.Participants:Brendan Ittelson - Chief Ecosystem Officer, ZoomFedrico Torreti - Head of Product, AppFabric, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Jonathan Shulman, Senior Partner at McKinsey & Company, highlights the transformative potential of AI in the software industry and the evolutions needed to capture emerging market opportunities.Topics Include:AI's transformative potential in the software industry.Why AI is a massive business opportunity.Software industry evolution: Mainframe to Cloud SaaS eras.Potential entrance into a new AI-driven era.AI spend forecast: $15B to $200B by 2026.Most AI spend repurposed from existing IT budgets.Legacy software spend likely shifting towards AI.Importance of targeting specific, high-impact AI use cases.Key areas disrupted: sales, marketing, software engineering.AI's adoption rates vary by industry and function.Four waves of AI: predictive to agent-based.Most companies are still in early AI stages.Prioritize building agentic, end-to-end AI solutions.Winning companies invest disproportionately in AI innovation.Position offerings to tap into AI-specific budgets.Deliver complete workflows, not isolated point solutions.Generative AI accelerates development and iteration cycles.Scaling AI pilots remains a major industry challenge.Tool fragmentation undermines productivity and innovation.Change management critical for successful AI integration.Rethinking team roles and processes for AI deployment.Consumption-based pricing models gaining industry traction.Shift from perpetual to subscription to consumption models.Balancing value-driven and cost-efficient consumption pricing.AI market poised to redefine IT and business landscapes.Participants:Jonathan Shulman – Senior Partner, McKinsey and CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS's Miguel Álava and BoardWave's Phill Robinson explore how European software companies can overcome market fragmentation, leverage cloud and generative AI, and adopt global strategies to scale competitively.Topics Include:Introduction of Miguel Álava and Phill RobinsonOverview of BoardWave's mission and communityChallenges for European software companies in scaling globallyComparison of US and European software market dynamicsCloud adoption benefits for European software scalabilityModern licensing models to centralize operations in EuropeImpact of fragmented European markets on growthCOVID-19's effect on sales strategies and efficiencySelling enterprise software across Europe via cloud toolsRole of centralized marketing in global competitivenessAdvantages of targeting the US before EMEA expansionBoardWave's "Voyager" model for scaling internationallyImportance of solving universal versus local market problemsUsing cloud infrastructure to penetrate diverse marketsGenerative AI's role in product innovation and scalingGenerative AI's transformational impact on global software industryEuropean software companies' opportunity to lead in AIBuilding a collaborative European ecosystem for innovationLessons from Silicon Valley's collaborative success modelBoardWave's mentoring programs for European software CEOsAWS's support for cloud adoption and business scalingThe evolving role of Chief Product Officers (CPOs)AI's potential to enhance cross-market product functionalityStrategic next steps for scaling European software businessesVision for Europe as a software superpower by 2034Participants:Phill Robinson – Founder, BoardwaveMiguel Alava – EMEA ISV General Manager, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Richard Borstein of RingCentral and Richard Sonnenblick of Planview discuss how AI-driven innovations enhance customer and employee experiences, and unlock organizational growth through cutting-edge tools and strategies.Topics Include:Importance of integrated communication tools for businesses.Challenges caused by disconnected communication platforms.Role of data in enhancing business operations.How RingCentral addresses communication and data integration issues.Benefits of real-time conversational intelligence in organizations.Leveraging AI to transform communication into actionable insights.Unlocking customer and employee voices through AI.How AI identifies patterns in customer interactions.Overview of RingSense for Sales AI tool.Real-world success story with RingSense for Sales.Streamlining customer interactions using AI-powered analysis.Enhancing employee productivity with AI-driven tools.AI solutions for faster, accurate information searches.Overview of RingCentral's Ring CX contact center solution.Improving customer satisfaction through AI-powered call analysis.Case study: Success with Ring CX at Worldwide Express.Features and benefits of RingCentral Events platform.Integrating event tech with existing customer workflows.Personalizing events with branding and engagement tools.PlanView's use of AWS to drive innovation.Solving governance challenges with PlanView's solutions.How generative AI accelerates productivity and decision-making.Making every user a power user with AI.Practical examples of generative AI in project management.Unlocking growth with next-gen AI-driven business tools.Participants:Richard Bornstein - Chief Business Development Officer, RingCentralRichard Sonnenblick Ph.D – Chief Data Scientist, PlanviewSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.Topics Include:Introduction to MongoDB's vector search and AWS BedrockCore concepts of vectors and embeddings explainedHigh-dimensional space and vector similarity overviewEmbedding model use in vector creationImportance of distance functions in vector relationsVector search uses k-nearest neighbor algorithmEuclidean, Cosine, and Dot Product similarity functionsApplications for different similarity functions discussedLarge language models and vector search explainedIntroduction to retrieval-augmented generation (RAG)Combining external data with LLMs in RAGMongoDB's document model for flexible data storageMongoDB Atlas platform capabilities overviewUnified interface for MongoDB document modelApproximate nearest neighbor search for efficiencyVector indexing in MongoDB for fast queryingSearch nodes for scalable vector search processingMongoDB AI integrations with third-party librariesSemantic caching for efficient response retrievalMongoDB's private link support on AWS BedrockFuture potential of vector search and RAG applicationsExample use case: Metaphor Data's data catalogExample use case: Okta's conversational interfaceExample use case: Delivery Hero product recommendationsFinal takeaways on MongoDB Atlas vector searchParticipants:Benjamin Flast - Director, Product Management, MongoDBSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.Topics Include:Introduction by Dave Gildea, VP of Product at Druva.Focus on building generative AI applications.Emphasis on cost and speed optimization.Mention of Amazon's Matt Wood keynote.AI experience with kids using "Party Rock."Prediction: GenAI as future workplace standard.Overview of Druva's data security platform.Three key Druva components: protection, response, and compliance.Druva's autonomous, rapid, and guaranteed recovery.Benefits of Druva's 100% SaaS platform.Handling 7 billion backups annually.Managing 450 petabytes across 20 global regions.Druva's high NPS score of 89.Introduction to Dru Investigate AI platform.Generative AI for cybersecurity and threat analysis.Support for backup and security admins.Simplified cybersecurity threat detection.AI-based natural language query interpretation.Historical analogy with Charles Babbage's steam engine."Fail upwards" model for LLM optimization.Using small models first, escalating to larger ones.API security and customer data protection.Amazon Bedrock and security guardrails.Testing LLMs with Amazon's new prompt evaluation tool.Speculation on $100 billion future model costs.Session wrap upParticipants:· David Gildea - VP Product Generative AI, GM of CloudRanger, DruvaSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Urmila Kukreja of Smartsheet and Nick Simha of AWS discuss leveraging Amazon Q's Retrieval-Augmented Generation (RAG) solution to enhance productivity by enabling employees to quickly access relevant information within secure, integrated workflows like Slack, improving efficiency across the organization.Topics Include:Introduction by Nick Simha, AWS.Overview of Amazon Q's role in data analytics and Gen AI.Gen AI's impact on productivity, ~30% improvement backed by Gartner study findings.General productivity improvement seen across various departments.Amazon Q's developer code generation tool – rapid developmentGen AI and LLMs' challenges: security, privacy, and data relevance.Foundation models lack specific organizational knowledge by default.Empowering Gen AI to grant system access can cause issuesPrivacy concern: Sensitive data, like credit card info, can be central in data breachesCompliance is critical for organizational reputation and data integrity.Data integration techniques: prompt engineering, RAG, fine-tuning, custom training.RAG (Retrieval Augmented Generation) balances cost and accuracy effectively.Implementing RAG requires complex, resource-heavy integration steps.Amazon Q simplifies RAG integration with "RAG as a service."Amazon Q's Gen AI stack overview, including Bedrock and model flexibility.Amazon Q connects to 40+ applications, including Salesforce and ServiceNow.Amazon Q respects existing security rules and data privacy constraints.Plugin functionality enables backend actions directly from Amazon Q.All configurations and permissions can be managed by administrators.Urmila Kukreja from Smartsheet explains real-world Q implementation.Smartsheet's Ask Us Engineering Slack channel: origin of Q integration.Q integration in Slack simplifies data access and user workflow."Ask Me" Slack bot lets employees query databases instantly.Adoption across departments is high due to integrated workflow.Future plans include adding data sources and personalized response features.Session wrap upParticipants:Urmila Kukreja – Director of Product Management, SmartsheetNick Simha - Solutions Architecture Leader - Data, Analytics, GenAI and Emerging ISVs, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Harold Rivas – Chief Information Security Officer at Trellix, discusses the role of generative AI in cybersecurity, focusing on Trellix's adoption of AI for threat detection and model governance, while emphasizing the importance of privacy, responsible innovation, and cross-functional collaboration.Topics Include:Introduction to generative AI and its impact on cybersecurityHarold's background in financial services and cybersecurity rolesTrellix's focus on product feedback through the Customer Zero ProgramOverview of machine learning's role in anomaly detection at TrellixDevelopment of guided investigations to assist security operations teamsGenerative AI's growing importance in cybersecurity at TrellixLaunch of Trellix WISE at the RSA Conference in 2024Addressing the overload of security alerts with AI modelsIntegration of various AI models like Mistral and AnthropicReducing anomalies and workload for security operations teamsImportance of privacy in generative AI adoption and data governanceChallenges with GDPR and CPRA regulations in AI implementationFocus on privacy frameworks like the NIST Privacy FrameworkNeed for multi-stakeholder involvement in AI governanceDiscussion on model governance inspired by financial services practicesImportance of inventorying and testing AI models for securityBenefits of an AI Center of Excellence (AICOE) within organizationsModel governance in generative AI for regulatory and business outcomesThe impact of AI on labor, jobs, and decision-making processesAddressing cyber risk and threat modeling in AI environmentsThe double-edged sword of AI in offensive and defensive cybersecurityMITRE Atlas framework's role in AI-driven cybersecurity strategiesPotential negative consequences. Auto dealership hacked – Chevy Tahoe sold for $1Importance of vulnerability management and developer trainingEvolution of AI security tools and responsible use of generative AICollaboration, governance, and agility in AI adoption across organizationsQ&A 1: Outcomes and responsibilities an generative AI COE should have?Q&A 2: Model governance and financial implicationsQ&A 3: CISO response to model development, compliance and learning with customer dataQ&A 4: Thoughts and suggestions for rating systems for modelsQ&A 5: Selecting and evaluating modelsQ&A 6: Advice and experience for model deployment and technical controlsQ&A 7: Human reviewing AI responses to ensure accuracyQ&A 8: Will AI help avoid major outages in the future?Q&A 9: How to test and see maturity of models?Session wrap upParticipants:· Harold Rivas – CISO at TrellixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Executive leaders from Arctic Wolf, Docker and Illumio share insights on fostering a strong security culture, balancing innovation with security, and addressing challenges in data protection and AI model development.Topics Include:Overview of security culture in different company teamsImportance of guidelines and secure IT infrastructure for AI modelsChallenges of accessing customer data while maintaining securityNeed for anonymization in early AI model developmentDocker's open-source ecosystem and security integrationDogfooding own products to ensure product reliability and trustworthinessIllumio's high customer trust and responsibility for strong security practicesBalancing security awareness with development speed at IllumioGamifying security training to increase awarenessInterlocking with customers to enhance security understanding for developersEmbedding security into the development process from the startIllumio's approach to security in agile, cloud-native developmentAdapting customer success strategies for evolving security needsRise of non-developers using AI in enterprisesEducating business leaders on security best practicesScaling customer enablement and education through community engagementChallenges of placing security responsibilities in the developer workflowArctic Wolf's AI strategy for secure developmentUse of anonymized data in secure AI model trainingGenerative AI's potential to augment human creativity and efficiencyPanelists' views on private AI and segmented model developmentMeasuring security culture progress with gamification and development metricsAddressing human factors in cybersecurity and social engineering threatsEmphasizing resiliency and containment in preventing widespread cyberattacks.Participants:Dean Teffer – Vice President of Artificial Intelligence, Arctic WolfDixie Dunn – VP of Customer Success, DockerMario Espinoza – Chief Product Officer, IllumioBrian Shadpour – General Manager, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Hear the generative AI journeys of Cohere, Epiq, and Forcura, including their market assessments, use case prioritization, responses to ethical and security considerations, while discussing generative AI's impact on healthcare, legal industries, and business applications.Topics Include:Panel Introductions by David CristiniWhere is Focura at in their AI journeySummary of Epiq's AI journey to dateCohere's AI journey to dateWhere did each company begin and assessing the market opportunitiesPrioritizing of use cases for EpiqFocura's quick focus and results with generative AISimplifying healthcare and improving patient experience with generative AIHow do experiments and proof of concepts develop into production?Indicators that Cohere uses to identify customers ready to move fastUsecases that allows Forcura customers to move forwardGuidance on engaging the Executive Team – getting Executive alignmentHow are legal and healthcare customers responding to AI solutions and challengesChanges of priority from customer advisory panelsEvolving questions and concerns of functionality and dataSome customers reporting AI evaluation is slowing them downUsecases that are easier to start off with to gain trust and tractionDealing with AI concerns of ethics, security and privacy – managing objectionsUnderstanding ethics concerns – privacy can often be about where data residesCustomers often want “traceability”Accuracy and reducing hallucinations – AI comes with risk, business have to decide on business riskFuture facing – what are we excited about?Generative AI is excellent at translation services – ROI is excellentBusiness applications and social impact of generative AIParticipants:MaryAnn Wofford – VP of Sales, CoherePaul O'Hagan - Senior Director Product Management – AI Platform, EpiqAnnie Mueller Erstling – COO, ForcuraDavid Cristini - Director, ISV Sales North America, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------J.B. Brown, VP of Engineering at Smartsheet, shares how integrating Amazon Q with Smartsheet's flexible work management platform has streamlined productivity and enhanced employee support through AI-driven automation.Topics Include:Introduction by J.B. Brown, VP of Engineering at Smartsheet.Story about improving productivityContext about Smartsheet as an enterprise-scale work management platform.Examples of Smartsheet use in healthcare, TV streaming, and small businesses.Focus on not changing how companies work, offering flexibility.Integration with popular enterprise tech stack tools like Okta and Slack.Automations in Smartsheet for notifications and data synchronization.Smartsheet's customer base includes large enterprises and small businesses.Overview of Smartsheet's scale: 15 million users and $1 billion revenue.Smartsheet's employee support system, including 270+ "Ask Us" Slack channels.Mention of AWS and the introduction of Amazon Q Business.Building a Smartsheet Q Business app for streamlined employee support.Setting up an Amazon Q Business app with proprietary data sources.Implementation of Slack integration for Smartsheet employee support.Example of AI summarizing Slack threads for improved efficiency.Demo of Amazon Q Business outperforming human experts in knowledge retrieval.Emphasizing the value of reducing response time and decision-making delays.Future development plans: Smartsheet-Amazon Q connector.Using AI to interrogate and manage Smartsheet project data.Invitation to AI-minded Smartsheet customers to test the new connector.Participants:J.B. Brown - VP of Engineering at SmartsheetSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS's Shahid Mohammed and Darktrace's Michael Beck discuss how generative AI innovations are transforming cybersecurity by both enhancing defences and introducing new, sophisticated threat management strategies.Topics Include:Shahid Mohammed introduces himself as a lead solution architect at AWS.Mike Beck is Global Chief Information Security Officer at Darktrace.Darktrace specializes in AI-driven cybersecurity solutions for digital environments.Darktrace secures multiple digital data pots: email, network, cloud, SaaS, and endpoint.The conversation focuses on innovation in cybersecurity through AI.Mike emphasizes the benefits of Gen AI despite its security risks.Gen AI enables more complex, targeted attacks against organizations.Attackers use Gen AI to tailor attacks through phishing and deepfakes.Gen AI increases phishing complexity by eliminating common detection cues.Data privacy risks arise when large models process sensitive business data.Businesses must be mindful of AI's impact on data sovereignty and security.Shahid compares the cybersecurity space to an arms race due to Gen AI.Mike stresses the importance of choosing the right AI for each task.Darktrace uses unsupervised machine learning and Gen AI together for defense.AI is essential for scaling cybersecurity efforts given today's threat complexity.Darktrace relies on AWS cloud for compute power, scaling, and innovation.AWS infrastructure helps accelerate Darktrace's R&D and operations securely.Security leaders should implement Gen AI policies and training.Mike advises technical controls and monitoring for safe Gen AI use.Gen AI is here to stay, but businesses must handle its security implications carefully.Participants:Michael Beck – Global CISO - DarktraceShahid Mohammed – Solution Architect Manager – Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Daryl Martis of Salesforce and Rashna Chadha of AWS share how Amazon Bedrock integrates with Salesforce to enhance AI applications, highlighting the partnership's strategic benefits, AI model customization, and secure deployment options.Topics Include:Introduction to Amazon Bedrock and Salesforce partnership.Overview of Amazon Bedrock as an API-based generative AI service.The strategic collaboration between AWS and Salesforce.Salesforce Data Cloud and its integration with Amazon Bedrock.Overview of Salesforce Einstein and its use of Amazon SageMaker.Recent AI launches between Salesforce and AWS, including Slack AI and MuleSoft.Use cases of AI services like Amazon Textract within Salesforce.Bringing Your Own Large Language Model (BYO LLM) with Bedrock.Foundation models offered by Amazon Bedrock (Anthropic, Cohere, Llama).Overview of security, privacy, and compliance in Bedrock AI services.Salesforce Data Cloud's unified customer data and real-time AI capabilities.Bedrock's support for custom AI model evaluation and metrics.Consumption models in Bedrock: on-demand vs. provision throughput.Bedrock's agent capabilities for real-world applications like scheduling.Demo of using Amazon Bedrock models within Salesforce.Participants:Daryl Martis – Director of Product Management, Einstein AI - SalesforceRashna Chadha – AI/ML Specialist – Principal Solution Architect – Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Ashish Arora, Head of Engineering & Machine Learning, Product Analytics at Autodesk, shares how personalized AI-powered experiences and real-time data can drive product adoption, featuring insights into Autodesk's transformation journey, leveraging machine learning, and delivering actionable recommendations to millions of users.Topics Include:Real-time data description and examplesLeveraging AWS for real time and generative AI servicesAutodesk's journey leveraging AWS to transform architecture and deliver personalized insightsOverview of Autodesk and product portfolioUtilizing data gathered for customersPersonalized data-driven insights for customers on Autocad and other productsDescriptive insights: providing usage data for customersPrescriptive insights: Making recommendations to customers based on their workflowsPredictive insights: Using ML to recommend products and featuresAutodesk processes 100+ billion events across all products, delivered 350+ million insights to customers, served to 3.5 million customersExample walkthrough – RachaelArchitecture of Autodesk data and insight processLeveraging LLMs – Sagemaker and BedrockBringing it altogetherSession wrap upParticipants:Ashish Arora – Head of Engineering & ML, Product Analytics - AutodeskBrian Slater – Principal Solutions Architect – Amazon Web Services

A panel discussion on presenting on security and data to Boards of Directors, focusing on metrics, ROI, generative AI, securing intellectual property, and strategic board engagement.Topics Include:What security metrics are most valuable for Board of DirectorsArticulating ROI of security programQuantifying benefits of security programSales enablement of the security teamDriving efficiencies within the business with generative AI and moreQuantifying business and template-based reporting to Boards with Diligent softwareLeveraging consultants and 3rd parties to leverage messages to Board of DirectorsManaging and communicating data posture and risk managementWorking with data leadership, securing intellectual propertyChallenges of labelling dataEducating boards on software architecture principles and generative AIHigh focus and techniques for securing IPEnrolling Boards with generative AI use cases and innovationCreating communities of excellenceGamifying security and generative AI to increase internal knowledgeQ&A 1: Taking action on a summarized documentQ&A 2: Mental model for evaluative SaaS partnersQ&A 3: Using the board to influence budgetary decisionsSession wrap upParticipants:Satheesh Ravala - Chief Technology Officer, DiligentJosh Blackwelder – Deputy CISO, Sentinel OneRobert Huber - Chief Security Officer, Head of Tenable Research, President Tenable PublicKaren Henlsey - Principal WW Data & Generative AI Strategist, AWS

Lacework's VP and Head of Engineering Arash Nikkar, and Product and Security Software Engineer Teddy Reed, discuss their work with generative AI and Amazon Bedrock, focusing on threat detection, chat interface improvements, and the rapid development of AI-driven solutions.Topics Include:Introducing Lacework with Arash NikkarWorking with AWS and BedrockFocusing on threat detectionLacework ingests over a trillion events each dayComposite alerts for detecting anomaliesDeveloping and improving the chat interfaceTeddy Reed talking chat interface improvementsReviewing the architectureFast engagement with generative AI / AWS BedrockThe rapid delivery with Bedrock – took 20% of expected timeBest practice – build in time to review and verify responsesMeasuring customer's engagement and return frequency and feedbackUnderstanding customer sentiment and topic analysisAchieving 60% completed results and feedbackTransparency of source, responseEnabling assistant to have more access to data from LLMUsing AWS' model evaluator, raising the accuracy scoresMoving everything over to Bedrock for multiple reasonsFamiliarity of AWS tools helpful for all development and security teamsAlways challenging assumptions – is chat interface the right interface?Q&A 1: Using vector database for analysis, using results as system promptQ&A 2: Approaching cost-optimization and balancing ROIQ&A 3: Using as automated remediation for findingsQ&A 4: Data source Bedrock in leveragingSession wrap upParticipants:Arash Nikkar – VP, Head of Engineering – LaceworkTeddy Reed – Product and Security Software Engineer - Lacework

Massimo Ghislandi from AWS interviews Frank Contrepois, Chief Innovation Officer of Strategic Blue and Dvir Mizrahi, Head of FinOps for WIX sharing perspectives and best practices for FinOps, implementation, challenges and future FinOps trends for software companies.Topics Include:FinOps is a new approach to managing cloud finances, bridging the gap between finance, engineering, and business stakeholdersThe implementation of FinOps can take a top-down or bottom-up approach, requiring collaboration and a common language across teamsShifting the conversation from "costs" to "investment" and "efficiency" has been an effective strategy for driving cultural changeFocusing on automation, governance, remediation, and anomaly detection can have a greater impact than just cost observabilityEducating finance teams on technical cloud concepts and vice versa is crucial for building trust and alignmentThe future of FinOps may involve expanding beyond just cloud to encompass all IT assets and aligning with a company's overall values and business objectivesPotential changes in legislation or accounting practices related to cloud could drastically impact the FinOps landscapeParticipants:Frank Contrepois – Chief Innovation Officer, Strategic BlueDvir Mizrahi – Head of Financial Engineering (FinOps), WIXMassimo Ghislandi – ISV Marketing Leader, EMEA, AWS

We feature a panel of executives from ASAPP, Glean, Smarsh, Socotra and AWS sharing essential strategies, including the role of Generative AI, trust-building, and addressing legal challenges to secure executive buy-in for transformative initiatives.Topics Include:Introductions to panelExample of ideal Executive SellerDepth of business acumen and problem solving are important skills for Executive SellersFraming a customer problem and create the argumentMaking selling to a C-Suite a team sportKeeping sellers and produce C-suite relevantRevolution and renaissance positioningTrust with the C-suite – do salesperson have a texting relationship w decision makers?How is Generative AI driving customer's satisfaction and dissatisfaction?Data is the new oilDefining the business problem for insurance industryHelping customers reimagine business with Generative AI80% of customers report the chatbot makes them madThe data journey is the precursor to the Generative AI journeyThe top unique legal challenges with Generative AILegal now brought into early stage of sales processTurning legal concerns into an opportunityThe book “Never Split the Difference” by Christopher VossCalls to action from each panellistSession wrap-upParticipants:Daniel Rood – Senior Vice President Marketing, ASAPPAJ Tennant – Vice President Sales & Success, GleanNeva DePalma – General Council, SmarshEkine Akuiyibo – Chief Business Officer, SocotraLauren Larscheid – Sr. Sales Leader, Business Applications, AWS

Today Anthropic's Zach Witten takes us on a deep dive into Anthropic's cutting-edge AI models—Claude Haiku, Sonnet, and Opus—exploring their safety-first approach to generative AI and sharing essential tips for prompt engineering.Topics Include:Introductions, about Anthropic3 models: Haiku, Sonnet and OpusScaling laws for hardware, data and computeCompeting to be safest AI solutions, safety-first organizationLeader in jailbreak resistanceInterpretability features and breakthroughs for AI modelsBasics of prompt engineeringImproving prompts with ClaudeDetails matter – small changes to spelling, context will greatly improve resultsSystem prompt – role setting will improve results (i.e. “You are an expert mathematician…” for math queryBe clear and direct – use XML tags where possibleEncourage Claude to think step-by-step – answering fast comes with accuracy riskUse examples to provide additional clarity to ClaudeBonus tips for image-based prompt engineeringQ&A 1) Who wrote the meta-prompts in the cookbook?Q&A 2) Guidance for writing prompts for prompt generatorQ&A 3) Best practices for tabular and structured dataQ&A 4) Maintaining “tone” across hundreds/thousands of responsesQ&A 5) Reverse engineering a prompt

Today, Dr. Ratinder Paul Singh Ahuja of Pure Storage takes us through how his organization is leveraging Amazon Bedrock to enrich the digital experience for end users and increase returns for the company.Topics Include:IntroductionsGenerative AI activity within Pure StorageOwnership and expertise of team, role of generative AIRapid changes require dedicated team reporting to office of CTOWhere Pure Storage is investing – Productivity gains, Customer Experience, R&DVision starts with the needs of the business usersGenAI Ops – Content, Voctor databases for ingestion, query strategies & securityExample – Purchase Acquisition ProcessFeatures for a mobile experienceConfigurations and logs are getting more enhanced with generative AIDefining schema and improving large language model for more accurate responsesSecurity models and deploymentSummary and session wrap upQ&A 1) How did the team get trained to use the generative AI tools and systems?Q&A 2) How did the team reduce the hallucinations?Q&A 3) How did you decide on which foundation models to use?Q&A 4) The ROI of this particular use caseQ&A 5) Models working with poly-morphic evolving inputsSession conclusion

Federico Torreti and Tom Sly of AWS discuss how application developers can leverage Generative AI to enhance cross-app experiences, minimize custom integrations and increase the value-add of enhanced communication features.Topics Include:Federico Torreti intro: What is AWS' vision for applicationsFocus of Machine Learning & AIAbility to build rapid prototypes, experimentationCambrian explosion of SaaS applicationsThe challenge of toggling of multiple applicationsBringing the world of disconnected appsData and focusing on the primary value for customerGenerating more content will raise number of alerts and potential of burnoutTom Sly intro: Being delighted by businessesCustomers expect personalization from businessesMore than 150,000 customers leverage AWS for communications strategyLeveraging messaging to increase customer delight2 examples: restaurant and medical needSession wrap upParticipants:Federico Torreti – Head of Product – Amazon Q, Amazon Web ServicesTom Sly - Director, Special Projects, Amazon Web Services

Sara Yamase, Partner at Simon-Kucher & Partners, Head of Software, Internet and Media Practice shares guidance and industry examples on generative AI pricing and GTM strategies.Topics Include:What percentage of companies are developing Gen AI features for customersProjected returns on Gen AI investmentArticulating the value is 1st step in building pricing and monetization strategyAlmost half of VC funding in last 5 quarters were for AI solutionsGetting through the hype cycle and trough of disillusionmentEducation, Marketing, Food industry examples of companies clarifying value of generative AI to customersHaving strong data sets for identifying trendsPricing tenets for generative AIIndirect monetization – more usage, faster, greater productivity, etcStandalone Generative AI software with bespoke pricingAbility to scale pricing per customer usageSubscription selling for generative AI softwareChoosing pricing model for generative AI solutionsSummaries and session wrap up