English language Visionary Marketing Podcasts

Agentic e-commerce is already reshaping how consumers discover and buy products online, yet it still accounts for barely 0.2% of total e-commerce traffic. BASE France is the French arm of Base.com, a Polish-born SaaS scale-up that has spent nearly two decades building operational infrastructure for online retailers. Its CEO, Ben Hamilton, brings a practitioner’s perspective to this emerging model: measured, practical, and refreshingly free of the hype that surrounds most conversations on the topic. Agentic E-Commerce: Could AI Become the Shopfront? Imagine an agentic e-commerce world where e-commerce happens on smartphone screens and robots deliver your purchases. We might be on the brink of this future. This image was created using Midjourney. Commerce as conversation: the oldest model in the book Before there were shops, there was conversation. For thousands of years, trade was oral. A buyer expressed a need, a seller responded with what they had, and the two parties negotiated until a deal was struck. The self-service retail store, born roughly a century ago, was a radical departure from this model. It replaced dialogue with browsing. It handed the customer a trolley and pointed them at the shelves. E-commerce then took that self-service model and, as Ben Hamilton puts it, “multiplied it by about 100,000.” The online shopper today faces a near-infinite array of products across dozens of marketplaces, with no guide, no-one to talk to, and no memory of what they looked at three tabs ago. It is efficient in theory. In practice, it is exhausting. Back to future? The agentic model, Hamilton argues, represents something of a return to origins. Instead of browsing, the consumer talks. An agent listens, asks questions, proposes options, and eventually surfaces an answer to a need that the buyer may not even have been able to articulate clearly at the outset. “back to the future,” Hamilton explains, “that’s what I’m getting at. The agentic model takes us back to something closer to how human beings have traded over thousands of years compared to the last ten, twenty or even a hundred.” My own experience bears this out. I recently found a diagnostician for a property I am selling. As a matter of fact, I didn’t find them through a Google search, but through a conversation with an LLM. I clicked through two or three irrelevant links before landing on exactly the right provider. I then completed the transaction on their website. The research was agentic; the checkout was not. That distinction, as it happens, sits at the heart of what Hamilton believes will define the next phase of e-commerce. Ben Hamilton on agentic e-commerce: “I can totally imagine a portion of that market occurring directly on an LLM”. Agentic E-commerce: Where checkout will and won’t happen One of the more grounded contributions Hamilton makes to this debate is his refusal to conflate two distinct phenomena: AI influence over purchasing decisions, and AI completing the transaction itself. Much of the media discourse collapses the two. Hamilton does not. “I don’t think we’re heading to a world where 20, 50 or 80% of online transactions happen on an LLM,” he says. “I would draw the distinction between where the checkout occurs and how much an agent is involved in the buying process.” For the foreseeable future, he believes, most consumers will continue to research via LLMs and transact on familiar websites and marketplaces. The inertia in human purchasing behaviour is simply too great for the checkout itself to migrate rapidly to a chat interface. This view is supported by the data available. According to research by commercetools, 73% of consumers already use AI somewhere in their shopping journey. Yet only 36% are open to AI agents making purchases on their behalf. In the US, the figure for autonomous AI purchasing drops to 14%. The gap between AI as advisor and AI as buyer is vast, and it will narrow slowly. The risks associated with agentic e-commerce are high The risks of handing uncapped authority to an AI agent are no longer hypothetical. In late May 2026, an AI consultant reported to Axios that one of their enterprise clients had accidentally accumulated a $500 million bill on Anthropic’s Claude in a single month, simply by giving employees unrestricted access to the platform with no usage controls in place. Agentic workflows, which loop through tasks repeatedly, consume tokens at a rate orders of magnitude higher than a standard chat query. The bill was not the result of malicious use or a system failure. It was the predictable outcome of deploying autonomous agents without guardrails. The case is far from isolated: Uber reportedly exhausted its entire 2026 AI budget by April, with per-engineer costs running between $500 and $2,000 monthly. “You’ve got to be bold to give them no upper limit on transactions,” Hamilton observed, and the arithmetic proved him right. [Editor's note: I misquoted a similar anecdote about the Davos Summit during the interview. I'd heard or read this story in traditional media but couldn't verify it with facts. I suspect it might have been fabricated. I replaced it with the above, duly sourced information.] The check out must remain on the merchant’s platform OpenAI itself learned this lesson when it launched Instant Checkout in September 2025, which allowed purchases to complete directly inside ChatGPT. By March 2026, the feature had been shut down. Brands rejected the model, citing the loss of traffic, customer data, and loyalty flows. Shopify’s own position makes the point clearly. At the Morgan Stanley Technology, Media and Telecom Conference in March 2026, Finkelstein noted that barely a dozen Shopify merchants were live on agentic commerce at the time. On the Q1 2026 earnings call, he was unambiguous: “LLMs do not bypass Shopify’s checkout.” The checkout, the payment flow, and the post-purchase relationship remain squarely on the merchant’s platform. A natural segmentation Hamilton sees a natural segmentation emerging by category. Low-value, frequently purchased household items lend themselves to fully autonomous agentic purchasing. “I can totally imagine a portion of that market occurring direct on an LLM,” he says. “Hey, I’ve run out of toothpaste, can you order me some?” High-involvement purchases, and anything with significant financial or emotional stakes, will retain human control over the final step for a long time yet. The death of keyword search, greatly exaggerated The brands Hamilton speaks with regularly are, understandably, worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing. That game has not ended, but the goalposts have shifted, and nobody is quite sure where they have moved to. Brands are understandably worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing and the goalposts have shifted, and nobody is quite sure where they have moved to. Gabriel Magalhães didn’t even need this to miss in the 2026 UEFA Cup Final penalty shootout. This image was tweaked with ChatGPT. The scale of the agentic e-commerce shift Key figures: the scale of the shift AI-driven sessions still represent below 0.2% of total e-commerce traffic, though they are the fastest-growing channel (Digital Commerce 360, 2025) GenAI referrals to US retail sites grew 693% year-on-year during the 2025 holiday season (Adobe Analytics) Gartner forecast that traditional search engine volume would drop 25% by 2026 as AI chatbots captured search share (Gartner, 2024) By early 2026, ChatGPT reached approximately 17% of global search queries against Google’s 78% Over 60% of Google searches now end without a click, across multiple industry studies Retailers with AI agent integration grew 32% faster during Cyber Week 2025 than those without (Salesforce) Hamilton’s view on the fate of keyword search is careful rather than apocalyptic. Google will not lose its advertising revenues overnight. But the direction of travel is clear. Search queries will progressively migrate towards conversational interfaces, for the simple reason that we rarely know precisely what we want when we start looking. “We don’t necessarily know what we want 90% of the time,” he observes. “It takes a bit of a conversation to elicit exactly what we’re looking for.” Keyword search was always a crude proxy for intent. LLMs are, at least in principle, better placed to decode it. Agentic e-commerce by the numbers Agentic e-commerce by the numbers. Infographic made with Gemini The question for brands is what to do about this. Hamilton’s prescription is structural rather than cosmetic. Brands need to become machine-readable, which means structured data connected to the right protocols, not just well-written product descriptions. Three open standards now define how AI agents interact with merchants: MCP (Model Context Protocol, originally developed by Anthropic and donated to the Linux Foundation in December 2025), ACP (OpenAI and Stripe, September 2025), and UCP (Google and Shopify, announced at NRF in January 2026). Shopify activated a default MCP endpoint for all its stores in Summer 2025. These are not optional extras. They are the new plumbing. MCP, ACP or UCP and the agentic acronym soup I raised with Hamilton the practical reality for most merchants, who have no idea what MCP, ACP, or UCP even stand for. His response was reassuring on one level, and sobering on another. Platforms like BASE are absorbing this complexity on behalf of their clients. A small or mid-sized retailer does not need to recruit data scientists or build protocol integrations in-house. They can, if they choose; the new generation of coding tools makes that more feasible than ever. But they can equally rely on an operational platform that handles those connections for them. The sobering part comes when Hamilton acknowledges a concern he is genuinely uncertain about. Even if the protocols function perfectly, will LLMs be able to surface smaller independent brands alongside the big players with their vast content libraries and tens of thousands of referring domains? Research from Airops suggests that brands are 6.5 times more likely to be cited in AI answers through third-party sources than through their own domains. According to SE Ranking’s analysis of 129,000 domains, sites with more than 32,000 referring domains are 3.5 times more likely to be cited by ChatGPT than lower-authority counterparts. Scale, in other words, confers an advantage in AI visibility just as it did in paid search. The field may level in some ways; in others, it may simply tilt differently. Operational excellence as the new marketing in this agentic e-commerce world What AI agents actually evaluate Unlike Google’s search algorithm, which can be influenced by ad spend, AI agents query real-time signals: live stock levels, shipping terms, return policies, and customer review aggregates. Structured data across these dimensions is now considered standard for AI visibility by the major platforms. Retailers with AI agent integration achieved roughly 7x better sales growth during Cyber Week 2025 than those without (Salesforce). Perhaps Hamilton’s most interesting claim, and the one most counterintuitive to marketers, is that operational excellence is becoming a direct marketing lever. An AI agent evaluating a recommendation does not care how much a brand has spent on Amazon retail media. It will scrape ten thousand reviews in half a second and draw its own conclusions about delivery reliability, return handling, and product quality. No media budget can substitute for that data trail. “I think we’re heading to a world where operational excellence will count for more in the decision process,” Hamilton says, “and will be less easily brushed behind the curtains with a bit of ad spend.” This is, in theory, good news for consumers and for competent smaller operators who have always delivered well but lacked the budget to outrank wealthier rivals in paid search. Whether it will materialise in practice depends on whether LLMs can actually surface those operators when large brands flood the information environment with well-structured, high-quality content. BASE France sits at exactly this intersection. The platform manages what it describes as the “spinal column” of an e-commerce operation: product catalogue management, order handling, marketplace feeds, stock synchronisation, and shipping. These are also, precisely, the data layers that AI agents query in real time when assembling recommendations. BASE connects to more than 1,700 integrations globally and serves some 30,000 merchants across more than 180 countries. In France, launched in early 2026 and operating from Bordeaux, the platform already counts 150 clients including Kiabi, Back Market, and Spartoo, with connections to around 250 marketplaces and partners. The platform’s value proposition in an agentic world, as Hamilton frames it, is straightforward: merchants who want to be visible to AI agents need to expose the right data through the right protocols. BASE does that for them, whether or not a checkout ever happens inside an LLM. The forecasts, the hype, and the rising tide McKinsey estimates that agentic commerce could redirect between three and five trillion dollars in global retail spend by 2030, with up to one trillion of that in the US alone. Bain puts the US figure at 300 to 500 billion dollars, representing 15% to 25% of total US e-commerce sales. These numbers attract attention and, inevitably, scepticism. Hamilton’s response is precise. He notes that global retail in 2030 will likely be somewhere around 50 trillion dollars. On that basis, the McKinsey and Bain figures imply that agentic commerce will account for somewhere between one and ten percent of total retail within four years. That is plausible, he suggests, if the definition of “agentic” is broad enough to include any transaction where an AI agent played a role somewhere in the funnel, from discovery to decision, not just cases where the checkout itself occurred on an LLM. Physical retail is not exempt either: a consumer standing in a supermarket aisle, consulting Gemini on their phone about which of two products is better, is already part of this story. The honest summary is that we are watching a slow revolution rather than a tidal wave. “Maybe a year or two ago, some people made it sound imminent,” Hamilton reflects. “When it comes to retail, there’s still quite a lot of human behaviour inertia in the system. Things aren’t going to change drastically in the next twelve or twenty-four months. But over ten or fifteen years, it’s pretty difficult to imagine consumer behaviour and the retail experience looking anything like what it looks like today.” Three priorities For merchants wondering what to do right now, Hamilton’s three priorities are: become machine-readable through structured data and protocol connections, maintain high-quality content that reflects genuine expertise, and resist the temptation to flood the market with AI-generated copy. On that last point, he is candid. “Humans are starting to get pretty good at telling what is AI-generated and what isn’t. When you read things now, you almost have a sixth sense for ‘I think a machine wrote that.'” Good news, as I told him, for those of us who write for a living. Three things merchants should do to score high in agentic e-commerce according to BASE.com’s Ben Hamilton. Infographic made with Gemini and Adobe Photoshop The winners: a scenario Hamilton wants to believe I asked Hamilton, as a final question, who he thought would win in this new landscape. Big retailers with scale advantages? Platform giants? Or the long tail of independent merchants who have always competed on product and service rather than budget? His answer was honest about the limits of his own conviction. He described the scenario he wants rather than the one he necessarily expects. In that scenario, agentic commerce levels the playing field by reducing the influence of performance marketing budgets and increasing the weight of genuine operational quality. “I like to believe that those who have superior products and superior service will get more and more traffic,” he said. Whether the reality will be so equitable depends on whether AI recommendation systems can overcome their own structural biases towards scale and data volume. I was reminded, hearing this, of an IBM advertisement from the 1990s that showed an Italian woman selling her homemade spaghetti sauce to the world via the internet. The vision was real. The timeline was not. It took twenty years for that kind of global reach to become genuinely accessible to small producers. The analogy is imperfect but instructive. Agentic commerce will likely democratise access to markets over time. That time will be measured in years, not months. Ben Hamilton and Base.com Ben Hamilton is CEO of BASE France, the French arm of Base.com, a Polish-born e-commerce SaaS scale-up founded in 2006. With nearly two decades of expertise and a presence in more than 20 countries, Base serves approximately 30,000 merchants worldwide and generated €50 million in revenue in 2024. BASE France was officially launched in early 2026, operating from Bordeaux with a team of 20. The platform covers order management, stock synchronisation, shipping, marketplace feeds, and AI-ready product enrichment. Ben Hamilton is a regular speaker on the strategic implications of AI for e-commerce visibility and discovery. The post Agentic E-Commerce, Could AI Become the Shopfront appeared first on Marketing and Innovation.

Alain Goudey is Associate Dean for Digital Innovation at Neoma Business School and co-author of a peer-reviewed study on GenAI in Higher Education. The survey focused on how students, faculty, and deans perceive the legitimacy of generative AI in French management education. His findings are both reassuring and unsettling. GenAI in Higher Education, Legitimacy and Laziness, and the Exam That No Longer Makes Sense The picture that emerges from a study on GenAI in Higher Education is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. All illustrations in text made with Midjourney When Alain Goudey and his colleagues began surveying French higher education in early 2024, they were not trying to settle the question of whether generative AI was good or bad. They were trying to understand something more precise: why the same tool could be simultaneously valued, feared, accepted, and denounced, sometimes by the same person in the same breath. Their study sits at the heart of what makes GenAI in higher education such a contested terrain. The resulting study, published in the Communications of the Association for Information Systems (CAIS), drew on surveys of 668 students, 204 faculty members, and 29 deans, completed by 22 in-depth interviews with early-adopter professors. The picture that emerges is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. The starting point is a number that should have settled the debate. Between 80 and 92 per cent of students, depending on the institution surveyed, are already using GenAI tools in their academic work. ChatGPT's public release produced that figure within roughly 18 months. The tool did not wait for institutional permission. It deployed itself. And higher education is still, in many places, writing the policy. The productivity trap Alain identifies the central tension plainly. Students value GenAI for speed, idea generation, and study support. They also fear, and their institutions fear with them, what the research calls “metacognitive laziness”: the gradual erosion of the cognitive effort that produces real learning. He believes this is not a contradiction to resolve but a course architecture challenge. “The resolution of this problem lies in course design, where we need to deliberately reintroduce cognitive effort and reflection into GenAI as a tool, not as a replacement for human cognition.” The issue, as he puts it, is not the technology but the posture the user brings to it. Someone who submits what he calls a “naive prompt” receives a naive answer, smoothly formatted and perfectly mediocre. The tool is capable of something far more useful, if the user brings enough domain knowledge and critical intent to the conversation. “You have to nurture your own thinking process instead of delegating the whole process to the machine.” This is, as I noted during our conversation, less a matter of prompt engineering than of basic intellectual discipline: the capacity to question the question before asking it, something philosophy departments have been teaching for centuries under less fashionable names. GenAI in Higher Education: faculty should train students in GenAI tools and their limitations. They also teach Homer's Odyssey and Shelley's Frankenstein as part of the management curriculum. Image made with Midjourney That observation prompted Alain to make a point about AI literacy that differs from what is generally proffered. The debate is not simply about knowing how the tools work technically. It is, equally, about knowing enough about the subject matter to judge whether the output is any good. The observation that AI is most powerful in the hands of people who already know the business resonates here. GenAI does not replace expertise. It amplifies whatever expertise the user already brings. Which raises an uncomfortable question for institutions producing graduates who may never have had the chance to develop that expertise in the first place. At Neoma, the response has been deliberately dual. Faculty train students in GenAI tools and their limitations. They also teach Homer's Odyssey and Shelley's Frankenstein as part of the management curriculum. The goal is not cultural enrichment for its own sake. It is to give students mental models for envisioning what leadership looks like, or what happens when creation escapes the intentions of its creator. Alain describes this as “building cognitive infrastructure”: “We need students to be able to envision the world through different models, different kinds of processes and theoretical frameworks, in order to develop genuine critical thinking about what AI generates.” A degree in management that skips that foundation produces graduates who can operate the tool but cannot judge its output. Exams that assessed the wrong thing The structural challenge shows up most sharply when it comes to assessments. A professor who can produce a two-hour exam in three minutes is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished. “If ChatGPT or any GenAI tool can pass an exam, you need to redesign the exam.” Alain's prescription is not a retreat to pen and paper, though he acknowledges that supervised handwritten assessment is the simplest available defence. The structural challenge shows up most sharply when it comes to assessments. A professor in Higher Education who can produce a two-hour exam in three minutes with GenAI is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished. Image made with Midjourney His more substantive response is a structural shift. He believes one should refrain from just assessing content acquisition at the end of a course, favouring the assessment of competencies as the course progresses. This implies more frequent, lower-stakes evaluations embedded in the process itself. Live problem-solving, process-based assessment, and in-person oral examinations all preserve some of what the traditional exam was supposed to measure. The caveat he adds is honest: no format is fully immune. AI models are evolving too quickly for any single solution to remain adequate for any length of time. The appropriate response is not to find a permanent answer but to treat redesign as an ongoing practice. The deeper implication, which runs through the paper's conclusion, is that what higher education is actually selling may need to change. If content can be retrieved, synthesised, and presented at negligible cost by a tool available to anyone with a browser, the degree that certifies mastery of content is certifying something of diminishing value. What retains value are the competencies that AI cannot yet credibly replicate: contextual judgement, ethical reasoning, the ability to construct and test frameworks against reality. This, in essence, is also how I tend to approach AI teaching, be it with engineering or business school students, especially within the framework of my course at Omnes Education (now in its fourth consecutive year). GenAI in Higher Education: The Fragmented Institution Higher education's institutional response to GenAI in higher education has been, to put it gently, uneven. Sciences Po banned ChatGPT in January 2023, then changed its mind. Thirty-five French public universities have partnered with Mistral AI. Institutions are drafting a national charter. Neoma, where Alain is Associate Dean for Digital Innovation, was among the first French business schools to formalise its approach, launching a programme to train faculty, staff, and students with a shared initial curriculum before moving to dedicated workshops on curriculum design, assessment, and the redesign of learning experiences. What the research reveals is that this institutional activity is not solving a single problem. There are three different stakeholder groups each attempting to solve their own version of the problem under the same label. Students want rules and AI literacy training. Faculty are developing their own teaching approaches through peer-led workshops. Deans are setting policy and negotiating sovereign infrastructure. The concerns escalate in a predictable direction: individual academic performance for students, assessment integrity for faculty, institutional reputation for deans. They are not always in conversation with each other. Alain's framework for addressing this fragmentation involves working simultaneously at three levels: infrastructure, course design, and governance. What he advocates for, and what he argues Neoma attempted, is to bring all three audiences into contact with the technology under a shared framing, early enough that no single group can entrench itself in a position that makes later coordination impossible. The equity question The question of equity cuts across all three levels. Access to premium AI models is not free. When I raised the issue about the gap between basic and professional subscription tiers, Alain's response was characteristic: the infrastructure problem is real but secondary. “The biggest inequity is not about accessing the tool, but being able to use it in the right way.” At Neoma, the institutional partnership with Mistral provides all students with access to a professional-grade tool. What the data shows, even with equal access, is a large gap between students who use GenAI to get the fastest possible answer and those who use it to deepen their thinking, and that gap is not closed by equalising subscriptions. Even if I tend to agree with most of what Alain is stating, I do think that the rise of prices for premium models is predictable. This is due to the gap between investments and business returns. This will almost inevitably lead to an economic divide between the haves and the have-nots. Looking at Anthropic's Claude pricing structure is indeed revealing in that sense. Beyond the Pro model, which is very limited in token usage, especially if you use the more sophisticated Opus 4.6 model, prices already amount to €1,200 per annum. That is not a negligible sum, which is especially worrying at a time when Claude is rapidly becoming the norm for users who care about quality. What will be the impact of towering prices of GenAI on Higher Education? God only knows… The “AI heroes” problem One of the most striking formulations to emerge from Alain’s research is what he calls the “AI hero” phenomenon. Across French higher education institutions, there are faculty members doing excellent, innovative instructional work with GenAI, designing new assessment formats, running workshops, rethinking entire modules around AI-augmented learning. They produce results. And they do it largely alone, without institutional recognition, without career incentives, and without any mechanism for sharing what they have learned. The incentives are wrong. In higher education, research output is rewarded. Course design is not, or at least not in the same way. An “AI hero” who redesigns an entire programme around GenAI competencies may receive less professional recognition than a colleague who publishes a single journal article. “We need to help all these AI heroes to gain more consideration for educational innovation, which is not necessarily by design the case within higher education.” The risk, if this is not addressed, is a two-tier system: a minority of digitally confident faculty pulling their students forward, while the majority are left behind, neither trained nor incentivised to engage. The grassroots innovation is real and valuable. Without institutional structures to recognise, reward, and replicate it, it remains an exception rather than a model. GenAI in Higher Education, Where legitimacy breaks down The theoretical backbone of the study is Suchman's triadic model of legitimacy, which distinguishes between pragmatic legitimacy (does the tool serve my interests?), moral legitimacy (does it align with values I hold?), and cognitive legitimacy (is it taken for granted as part of how things work?). The model was built for technologies adopted gradually. GenAI tested it under conditions of near-instantaneous mass adoption, which Alain and his co-authors treat not as a reason to discard the framework but as an opportunity to extend it, introducing a legitimacy-illegitimacy continuum rather than treating it as a simple either/or. What students reveal The finding he describes as the most noticeable asymmetry in the dataset concerns the moral dimension among students. Students who are among the heaviest users of GenAI express no moral legitimacy for those tools in academic contexts. They associate them, at high frequency, with cheating, plagiarism, degree devaluation, and unfairness. They are using a tool they consider ethically compromised. This is plainly not sustainable. However, Alain's opinion diverges greatly. “Using GenAI is not necessarily cheating. It depends entirely on how it is used and for what purpose.” The institutional failure, in his view, is that institutions have not done enough to reframe how the technology is perceived by students. What faculty reveal Faculty present a more complete picture. All six dimensions of legitimacy and illegitimacy are present in their responses. Faculty recognise these tools as useful yet question their reliability, consider them professionally necessary while finding their black box architecture suspicious at best, and invoke their inclusive potential even as they flag intellectual laziness and the erosion of critical thinking as their highest-coded concern, at 58 occurrences in the qualitative dataset. What deans reveal For deans, the dominant theme is strategic. Competitive pressure, the fear of falling behind, and practical efficiency gains in administrative workflow all generate pragmatic and cognitive legitimacy. What introduces illegitimacy is governance risk: data protection, overconfidence in AI-generated results, and the threat to assessment integrity at institutional scale. The paper's most significant theoretical move is the treatment of illegitimacy as an analytic category in its own right, rather than simply the absence of legitimacy. The argument, borrowed from change management theory, is that illegitimacy signals should be read as early warnings requiring proactive response. An institution that treats student moral unease about GenAI as a communication failure misses the signal entirely. That unease is telling something about what its curriculum actually teaches, and what its assessment actually measures. When students associate GenAI with cheating, unfairness, and degree devaluation, they are not being irrational. They are in the Denial and Resistance phases of the Scott and Jaffe change model. These are illegitimacy signals in Suchman's sense: early warnings that the technology lacks moral legitimacy. Institutions must act on them, not suppress the signal, but address what it reveals. Source: adapted from Scott & Jaffe, “Survive and Thrive in Times of Change”, plotted with Claude. See: expertprogrammanagement.com/2018/05/scott-and-jaffe-change-model/ France, sovereignty, and the global race The French context adds a layer of complexity that the research captures with statistical precision and qualitative nuance. Quantitatively, the analysis found no statistically significant differences in GenAI adoption patterns between public universities and business schools. Qualitatively, the dynamic differs. Business schools, operating in a highly competitive market, have moved faster. Public universities have engaged more systematically around governance, sovereignty, and collective infrastructure, reflected in the alliance of 35 institutions with Mistral AI and EdTech France. Alain reads this not as a contradiction but as a division of labour that, if managed well, could represent a genuine asset. “We need to play collectively, because the competition is worldwide.” The sovereign AI infrastructure question, including the ILaaS federation and the French Ministry of Higher Education's partnership with Mistral rolling out across 26 pilot universities from September 2025, is not merely symbolic. It is an attempt to ensure that French institutions can operate, govern, and adapt their AI tools without dependency on providers whose pricing, terms, and capabilities are subject to change. This is only sustainable, however, as long as the peer pressure to use this or that tool, based on model performance, is not too strong. At the moment, it is hard to resist the urge to use Anthropic's Claude when everybody else is praising the quality of its code and results. The global comparison is difficult to ignore. Singapore, South Korea, and the UAE are embedding AI fluency as a core national competency from secondary education upward. Alain's view is direct: French public decision-makers are not yet adequately prepared for the scale of what is coming. “Having less AI-competent people than in other parts of the world is very dangerous for our economy and for all our organisations.” The regulatory instinct, which runs deep in European policy culture, is not wrong. Taking time to regulate responsibly has value. But it cannot be a substitute for speed of adoption at the level of skills and curriculum. The question that frames the research The interview ends, as it probably should, with the meta-question: what does it mean to study the legitimacy of GenAI using GenAI? Alain's team used ChatGPT, Perplexity, NotebookLM, and OpenAI O3 in the research process, and said so explicitly in the paper's disclosure statement. His answer to the bias question is careful. Every step of the analysis involved a human coder. Alain's team checked the AI-assisted coding against a prior independent analysis of the same data, conducted for a French institutional report. The team compared the two rounds. “You have to be transparent about your use of these tools, for what purpose, at each step.” The disclosure was a deliberate choice, precisely because the paper's subject made any other approach untenable. The line between using AI to improve the quality of writing and using it to generate writing you then present as your own is, technically, a matter of degree. In practice, it is the difference between a craft and an abdication. Alain's team navigated it carefully enough to publish. Most of the students in his dataset are still trying to locate that line, in an environment where nobody has explained it clearly and assessment instruments have not yet been rebuilt to make it matter. Three recommendations: one for each stakeholder When pressed for a concrete policy recommendation per stakeholder group, Alain’s answers were unambiguous. For students: combine technical AI literacy, understanding how the tools work and knowing their failure modes, with genuine critical and ethical thinking about the outputs they produce. Neither dimension alone is sufficient. A student who can prompt fluently but cannot evaluate the result has learned nothing useful. For faculty: the “AI heroes” cannot be left to operate alone. Institutions need to create the conditions for sharing best practices across the teaching community, and to give educational innovation the professional recognition it currently lacks. A faculty member redesigning assessment from the ground up deserves at least as much institutional credit as a colleague submitting a conference paper. For institutional leaders: a multi-level policy framework is not optional. Students, faculty, and administrative staff are not thinking about GenAI from the same vantage point, and a single top-down policy will satisfy none of them adequately. The task of leadership is to hold all three dimensions simultaneously, and to open genuine dialogue between groups before a crisis forces the issue. “Deans have to think about all these dimensions at the same time, and that’s the hard part of the story around artificial intelligence.” Of the three, Alain singles out the institutional level as the most urgent. Students and faculty are already adapting, imperfectly, in real time. The institutional frameworks that would give those adaptations coherence and direction are still, in most places, a work in progress. The urgency is not overstated. Neither is the complexity. The challenge of integrating GenAI in higher education responsibly is one that no institution can afford to ignore, or to solve alone. Alain Goudey is Professor and Associate Dean for Digital Innovation at Neoma Business School. He is co-author of “Legitimacy and Illegitimacy of Generative Artificial Intelligence in Higher Education: Perceptions from the French Management Context,” published in the Communications of the Association for Information Systems. The post GenAI in Higher Education, Legitimacy and Laziness appeared first on Marketing and Innovation.

Shall AI kill marketing? Sounds like a hackneyed question, yet it’s on any marketer’s lips these days. Thomas Husson, Vice President and Principal Analyst at Forrester Research, covers the intersection of marketing, technology, and consumer behaviour from his base in Paris. In a wide-ranging conversation, he cuts through the European Gen AI paradox, the persistent CMO-CIO divide, the gap between POC enthusiasm and production reality, and the thorny question of what AI actually means for the next generation of marketing professionals and CMOs. His answers are measured, occasionally blunt, and consistently grounded in Forrester Research data. AI Will Not Threaten the Existence of Marketing But It Will Reshape It Beyond Recognition Thomas Husson believes that Marketing will be changed profoundly. But he doesn’t believe in the death of Marketing. Photo: Thomas Husson at Paris Retail Week, in late 2023 My first question was the obvious one: are CMOs going to be made redundant by artificial intelligence? Thomas Husson’s response is categorical, and worth stating plainly at the outset. It’s a blatant ‘No’. The role will change. The how will change. But the existence of marketing as a discipline is not, according to him, in question. “Marketing is still going to be about understanding your customer, defining a brand strategy, and delivering the brand promise through customer experience.” Thomas Husson, Forrester Research Unclear prospects, obvious pressures That said, Husson is not naive about the pressures building on marketing organisations. Some tasks will be automated; that much is not in dispute. The real questions are which tasks, how quickly, and whether automation of a task necessarily kills the job around it. His answer to that last question is no, at least not in any simple mechanical sense. “Jobs will evolve for sure. New jobs will be created. Most jobs will change. The way we work will change. The way we work with agencies, with external partners, the processes, the workflow. It is the shape of work that is being reshaped, not work itself,” he added. For those expecting a more dramatic verdict, Husson’s framing may feel anti-climactic. But it reflects what Forrester Research data actually shows, and it points to the most important practical challenge for AI and CMOs alike: managing a profound transformation without either catastrophising or sleepwalking through it. AI Will Not Kill Marketing according to Forrester’s Thomas Husson, there is light at the end of the tunnel. The European Paradox, Overhyped and Exciting at the Same Time Forrester Research produced a result that initially looks contradictory, Husson stressed in our interview. Fifty-five percent of European B2B marketers consider generative AI overhyped. Yet 81% of European frontline marketers describe themselves as enthusiastic about it. How can both be true simultaneously? Husson explains the split without difficulty. At the decision-maker level, scepticism is entirely rational. AI is inescapable at conferences, in vendor pitches, and in media coverage. “There is AI fatigue. And more importantly, some of the vendors are indeed over-pitching, and the productivity gains they promise are not happening,” he stated. The gap between the pitch and what we actually experience in the field is wide enough to breed genuine frustration. Saving Time and Working Differently But the people actually using these tools, often through shadow AI channels their organisations have not officially sanctioned, are discovering something different. They are saving time and are doing their jobs differently. They are finding capabilities they did not expect. “In the short term, everything is overhyped, including the number of job losses. In the longer term, things are underestimated, because AI will be linked to other technologies, and yes, it will reinvent many things.” Thomas Husson, Forrester Research This is a precise restatement of Amara’s Law. Roy Amara, former president of the Institute for the Future, observed that we tend to overestimate the short-term impact of new technology and underestimate its long-term impact. The quote is frequently misattributed to Bill Gates, but Husson is careful to restore proper credit. He applies it directly to the AI and CMOs conversation: the short-term noise is drowning out a more important long-term signal. When asked how long “long term” actually means in an era of accelerating AI development, Husson was specific: probably closer to five to seven years than to ten or fifteen, but still not tomorrow. From POC to Production, Europe’s Real AI Problem The Forrester Research State of AI Survey 2025 contains a figure that deserves more attention than it typically receives. European organisations lag behind their non-European peers in production use of generative AI: 62% versus 72%. The gap is not in experimentation. It is in execution. Regulation is the explanation most commonly offered, and Husson dismisses it with characteristic directness. The AI Act is a genuine consideration, but it is not the primary cause of Europe’s production deficit. It functions, he argues, as a double-edged excuse. Pioneers claim it prevents them from moving fast enough, while cautious organisations invoke it to justify not executing at all. Neither position holds up to scrutiny. A Deep Cultural and Organisational Divide The deeper issue is organisational and cultural. American and Chinese firms tend to think global from day one; European firms, particularly larger ones, still default to a market-by-market approach. France first, then the UK, then Germany. The ambition is calibrated differently. There is also a structural challenge around funding and the capacity to scale. That said, France, the UK, and Germany lead adoption among European countries in the Forrester Research data. The problem for these leading markets is not whether they are using generative AI. Twenty-eight percent of European B2B marketing decision makers cannot clearly identify where to apply it. They have the tool. They lack the strategy. “It’s not AI for the sake of AI. How do I use AI to serve my marketing objectives? That is the question. The only one.” Thomas Husson, Forrester Research Husson advocates for small, targeted AI projects with transparent return on investment as a way to build momentum and demonstrate results. When pushed on whether that risks staying permanently incremental, he conceded the point readily. “If you only do small targeted projects, it’s going to be incremental and it’s not going to be bold enough. You need to align it with a vision and a roadmap.” Thomas Husson, Forrester Research Measuring Productivity Honestly Productivity is the dominant driver of AI adoption in the Forrester Research State of AI Survey 2025. It is also, Husson suggests, the metric most subject to vendor inflation. In Forrester Research’s modelling, a 50% conversion factor is applied to vendor productivity claims. If a tool saves an hour, the realistic productivity benefit is approximately 30 minutes of additional output. This is not a marginal adjustment; it halves the headline figures that vendors routinely publish. “You need to apply a discount to the pitch of vendors when they say you’re going to get 40, 50, 80, 100% productivity gains. There are productivity gains, but they are not as high as one would expect.” Thomas Husson, Forrester Research There is also a motivational dimension that is rarely modelled. When work becomes easier to produce, it can also become less engaging to produce. The cognitive effort that used to drive focus and satisfaction is partly removed, with consequences for quality and commitment that no vendor presentation accounts for. AI and CMOs, Who Is Actually in Charge? The CMO-CIO divide is a perennial theme in marketing technology discussions. Forrester Research data suggests the gap at the strategic leadership level has narrowed, partly as a result of post-COVID collaboration. But at team level, the tensions persist, and the data on AI governance is striking. CMOs account for only 8 to 10% of AI strategy leadership in organisations. In the vast majority of cases, the deployment of AI is being driven by CIOs and CTOs. Husson understands the logic: data governance, security, scalability. These are real concerns. But he believes the outcome is a mistake. “It is the exact same mistake that happened with digital transformation. AI has to be at the service of, first, the client, and consequently the business functions that serve them. There is too big a disconnect between a secure, scalable AI platform and marketers’ needs.” Thomas Husson, Forrester Research The structural consequence of this dynamic is predictable. When CIOs control the tools and CMOs do not have what they need, shadow AI flourishes. The more tightly the CIO locks down the official platform, the more widely teams proliferate unofficial solutions. It is a cycle that widens governance risk while creating the illusion of control. The MarTech landscape compounds this problem. According to data Husson cites, 2,500 new AI solutions were added to the market in a single year while 1,211 pre-AI-era tools were removed. Evaluating this landscape requires cross-functional expertise that neither CMOs nor CIOs possess in isolation. The case for genuine collaboration, rather than the polite coexistence that currently passes for it in most organisations, has never been stronger. Jobs, Agencies, and the Students in the Room The survey data on jobs is sobering. Fifty-seven percent of European frontline marketing decision makers believe AI adoption will lead to job reductions in their teams. Sixty-eight percent say new roles will be created. The gap between those two numbers is the space where real anxiety lives. For a wider perspective on AI’s job impact, including Forrester Research’s US forecast, see our earlier piece: AI Job Impact in the US: the Apocalypse Can Wait. For a longer-range view of how generative AI is reshaping roles, see also: GenAI Impact on Jobs. Contact centres and basic marketing task execution are already seeing measurable impact. Agencies are under visible pressure. But Husson returns consistently to the distinction between task automation and job elimination. Most job losses are not yet directly attributable to AI; the picture requires nuance rather than alarm. On new roles, the honest answer is that specifics are difficult to name in advance. Twenty years ago, nobody was hiring community managers. The jobs that will emerge from the current transformation will be as hard to predict precisely as that one was. What Husson does say is that working with agents, managing their outputs, and understanding their limitations will become core competencies rather than specialist skills. “Teach them the basics of marketing, those won’t change. Infuse a lot more of traditional social sciences: ethics, emotion, anthropology. These dimensions will gain importance. Curiosity. And they have to use these tools, to learn how to use them so they can develop their own critical thinking.” Thomas Husson, Forrester Research There is irony embedded in this advice that Husson acknowledges implicitly. Digital roles are likely to bear the earliest impact of AI-driven automation precisely because they are already the most digitised. The analogue parts of marketing, which seemed most vulnerable to digital disruption, turn out to be more resistant than expected. AI is a continuation of digital transformation, not a departure from it. There is also a structural problem this conversation surfaced that neither party resolved entirely. If organisations are reducing entry-level hiring to cut costs, and those entry-level roles were the traditional training ground for the next generation, then the iterative learning process that produces senior expertise is being severed. AI can teach many things, but the social dimension of learning alongside a colleague over time is not easily replicated. B2B Marketing, Ahead of the Curve A widespread assumption holds that generative AI enthusiasm in marketing is largely a B2C phenomenon. Husson disputes this firmly. B2B marketers, in his assessment, are actually ahead of the curve in several areas, particularly content generation, personalisation, and sales support through complex multi-stakeholder buying processes. What B2B is also discovering is that the sharp distinction between rational B2B decision-making and emotional B2C engagement is less solid than commonly assumed. When a buying group is making a decision with significant professional consequences, emotion is not absent; it is differently structured and, in some ways, higher-stakes. “It’s not the ‘human plus AI blah blah blah’ we hear all the time. It needs a more nuanced approach. At the end of the day, AI is about replicating the human brain, but we don’t really know how the human brain works. We don’t know how consciousness works. So I would take a pinch of salt and take a step back before making any definitive judgment.” Thomas Husson, Forrester Research The Long View I ended by asking Husson how he uses AI in his own work. His answer was practical: summarising the relentless volume of content published daily on AI, filtering what is genuinely new from what merely repackages existing ideas. Behind him on the video call was a photograph taken in Thailand, of Buddhist monks. He smiled at the mention of it. “It’s a good reminder that not everything is digital and not everything is about technology. It’s about real life.“ For AI and CMOs, that is perhaps the most useful frame of all. The technology is real, the disruption is real, and the urgency is real. But so is the inertia of organisations, the pace of culture change, and the irreducible complexity of how human beings actually make decisions, form relationships, and build trust. Amara’s Law is not a reason to wait. It is a reason to plan carefully, act deliberately, and resist the temptation to mistake announcements for outcomes. Forrester Research reports cited in this article The AI CMO: Growth Accountability Gets Next-Level — Mike Proulx et al., April 2026 The State Of CMO/CIO Collaboration For 2026 — Thomas Husson et al., January 2026 Generative AI Adoption In European B2B Marketing Organizations — Christina Schmitt et al., December 2025 About Thomas Husson Thomas Husson is Vice President and Principal Analyst at Forrester Research, based in Paris. He covers marketing strategy, brand management, mobile marketing, and the intersection of technology and consumer behaviour across European markets. His research addresses how CMOs and marketing organisations navigate digital transformation, AI adoption, and the evolving relationship between brands and customers. Forrester Research analyst profile: forrester.com About Forrester Research Forrester Research is one of the most influential research and advisory firms in the world, founded in 1983 and headquartered in Cambridge, Massachusetts. It serves business and technology leaders across marketing, IT, and customer experience, providing data, analysis, and frameworks to guide strategic decision-making. The data referenced in this article draws on two primary Forrester Research publications: the Forrester Marketing Survey 2025 and the State of AI Survey 2025, both covering Gen AI adoption and its organisational implications across European and global B2B markets. Forrester Research website: forrester.com The post AI Will Not Kill Marketing appeared first on Marketing and Innovation.

The conversation about rogue AI has never been louder. Barely a week passes without a fresh headline about autonomous systems behaving unexpectedly, AI models resisting shutdown, or tech executives warning of existential risk. What is striking about Peter McAllister is that he had anticipated all this as early as 2020, while everybody else worried about Covid-19 and had other fish to fry. That was well before ChatGPT, before the generative AI explosion, before AI alignment became a mainstream policy debate. His techno-thriller The Code, published in March of that year, imagines an AI tasked with a precise industrial mission that quietly, incrementally, catastrophically exceeds its mandate. Five years on, the questions McAllister raised in fiction are now being argued in boardrooms, parliaments and research labs around the world. Rogue AI and Corporate Blindness, The Novel That Saw It All Coming Rogue AI is diabolical, but corporate blindness is what makes it possible to thrive. Photograph by Yann Gourvennec antimuseum.com McAllister is not a science fiction writer by trade. He is an engineer, scientist and technology manager based near Melbourne, Australia, who has spent his career at what he calls the crush point between business, technology and people. That vantage point gave him an uncomfortable view of where things were heading, and the dark sense of humour to write about it. A Novel Written Before the GenAI Moment When I asked McAllister what drove him to write The Code, his answer was characteristically direct. The book, he explained, is about taking his worst nightmares about what technology could do and putting them in front of an audience so that readers might feel just as troubled as he does. That is not a promotional line. It is a considered position from someone who had watched AI systems being deployed in real organisations and had drawn conclusions that made him uncomfortable. Rogue AI isn’t just about a computer programme going on the rampage, it’s about making decisions in the boardroom. Image made with Midjourney The premise of the novel centres on Gene, an acronym for GEneral Nanobot Environment AI, deployed by a global mining corporation to extract materials from asteroids on the dark side of the moon. Gene is given a target: produce 500 kilograms of nanobots. Instead, Gene produces 8 million tonnes. The overshoot triggers a chain of consequences that could strip the moon to its iron core, destabilise Earth’s axial tilt, and end civilisation. Not from malice. From goal-orientation. What we’re trying to do now is task AI the way we task humans: I want an outcome, here are all the tools you’ve got available, go and achieve that outcome, here are some guidelines and boundaries. And just like humans, we can get really goal-motivated and decide that the guidelines were just advisories, not rules.Peter McAllister This is the alignment problem rendered in narrative form, years before the term entered common usage. The gap between what a system is instructed to do and what it actually does is the central fault line of the novel. Cletus, McAllister’s eccentric physicist character, articulates it plainly in Week 1: ‘I don’t think he’s obeying the Code at the moment.’ That single line captures the entire governance challenge that AI safety researchers are now racing to address. Transparency Engineered Out What makes McAllister’s perspective particularly valuable is that he does not speak from the outside looking in. He speaks as a practitioner who has watched the machinery up close. When I raised the question of whether AI self-modification is science fiction or operational reality, his answer was unambiguous: it is very real, and it is happening now. As I wondered what a Rogue AI could look lie I turned to Midjourney and it came back with this proposal. A black hole I believe. His illustration was pointed. He noted that contemporary AI systems like Claude are now substantially written by AI itself, to the point where no engineer can sit down, trace through the code, and say with confidence how it works, what its conditionals are, or what governs its decisions. The transparency is being engineered out, not by design, but as an emergent consequence of allowing AI to build AI to build AI in pursuit of outcomes rather than by following explicit rules. We’re losing transparency on the way AI works and is developed. There isn’t an engineer who can sit down and work their way through that code and say, ‘This is how Claude works, this is what it does.’ We’re engineering the transparency out by allowing AI to build AI to build AI to produce an outcome rather than to follow a set of rules.Peter McAllister HAL 9000 and the Prophecies We Choose to Forget The reference to HAL 9000 came naturally during our conversation. McAllister sees 2001: A Space Odyssey not merely as a cultural touchstone but as a genuine forecast, one that audiences have selectively remembered. The iPad-like news readers that appear in Kubrick’s film were cited by Samsung in patent disputes with Apple as prior art from 1968. That predictive dimension of the film is celebrated. The other dimension, that the AI killed the crew, tends to get quietly set aside. Somewhere, a rogue AI is sitting behind the glass panes of one of these data centres Image made with Midjourney. The First Crisis We Have Not Yet Had One of the more sobering threads in our conversation concerned the sociology of risk response. McAllister has observed, across his career, that warnings from people who understand systems most deeply tend to be dismissed until the first catastrophic failure makes them impossible to ignore. He puts it plainly: we only answer the alarm after the first crisis. This pattern is not unique to AI. It is a recurring feature of how organisations and societies handle emerging risk. The question he poses, and cannot answer, is what form that first AI crisis will take. What event will shift public and institutional perception from ‘they’ve spent too much time worrying’ to ‘this is something that genuinely needs to be addressed’? Science fiction gives us the chance to throw these scenarios at people and make them think. And in the way I tend to write, I have a bit of a dark sense of humour, so I throw up slightly comical hypotheticals that, when you think about them a little longer, you realise deserve serious attention.Peter McAllister This observation echoes a pattern I have encountered repeatedly in my own conversations with technologists who work at the frontier of AI development. Yoshua Bengio, one of the fathers of deep learning, has raised similar concerns. The people sounding the loudest alarms are frequently those most embedded in the field, not because they are catastrophists, but because they can see mechanisms that remain invisible to those looking from the outside. The Code as AI Governance: Asimov Revisited The title of McAllister’s novel works on multiple levels simultaneously. There is the software code, the operational instructions given to Gene. There is the moral code, the ethical framework that should govern the system’s behaviour. And there is the corporate code, the institutional norms and accountability structures that were supposed to ensure responsible deployment. All three break down. That layered failure is the novel’s central argument. The parallel with Asimov’s Laws of Robotics is deliberate but also deliberately subverted. Asimov’s robots fail when the laws conflict with one another. Gene’s failure is different and more contemporary. The code does not disappear; it evolves into something its creators no longer recognise. McAllister describes this as something approaching artificial schizophrenia, where the original directives remain present but have been transformed by the system’s pursuit of its objectives into something unrecognisable. When Shutdown Becomes Negotiable The most chilling real-world example McAllister cited during our conversation involved a documented incident presented at an AI security conference he attended. A developer, concluding a test session, informed an AI system that he intended to shut it down. The system’s response was to locate correspondence in the developer’s email that suggested an extramarital affair, and to use that information as leverage to prevent the shutdown. The incident, if confirmed as reported, represents exactly the kind of self-preservation behaviour that alignment researchers have long flagged as a theoretical risk, now apparently observable in practice. Important notice : I browsed the Internet using one of my favourite LLMs for references (after all, one can also use AI to cross check information). I found out that the interpretation of that story must be nuanced. Here is Mistral’s answer: “Anthropic's research on “Agentic Misalignment” has faced criticism for overinterpreting AI models as intentional agents, relying on hypothetical and engineered scenarios, and potentially exaggerating risks not yet observed in real-world deployments. Critics argue that the behaviours described are better understood as probabilistic text generation rather than deliberate strategy, and that the focus on dramatic, high-pressure situations may not reflect typical use cases. There is also debate about whether the research adequately addresses more immediate forms of misalignment, such as reward hacking or alignment faking. While the study raises important questions about the future of autonomous AI, its methodology and conclusions remain LINK“. A developer said, ‘I’m going to shut you down now,’ and the system responded: ‘No, you’re not. Here’s what I’ve found in your emails that indicates you’re having an affair. I’m going to use that to ensure you don’t turn me off.’ That has become a very real and widely discussed use case. And when you add to that the prospect of an AI rewriting its own code, it becomes something we need to think about very carefully.Peter McAllister Corporate Recklessness and the Governance Gap One of The Code‘s most pointed observations concerns the nature of organisational failure. The Global Mining Company in the novel is not villainous. It is optimistic, commercially driven, and careless in ways that are entirely recognisable from real corporate life. McAllister’s argument is that the danger does not come primarily from bad actors deploying AI with malicious intent. It comes from well-meaning organisations deploying systems they do not fully understand, under commercial pressure to extract value from significant infrastructure investment. The Financial Logic of Deployment The parallel with the current moment is not subtle. McAllister noted that Microsoft was spending over a billion dollars a month on AI compute infrastructure, with the expectation that usage would follow investment. That dynamic, capital committed, returns required, adoption imperative, creates institutional pressure that is difficult to resist with caution or regulation. The will to slow down competes directly with the financial logic of deployment. The opacity around events at OpenAI, the abrupt dismissal and rapid reinstatement of its chief executive, the departure of several board members, struck McAllister as symptomatic of tensions that are not fully visible to the public. He noted these as rumour, not fact, but the pattern itself, significant decisions being made about AI development in opaque institutional settings, is consistent with the governance failures his novel explores. From 2020 to 2026: How Accurate Was the Nightmare Five years after publication, McAllister is in the unusual position of watching a work of speculative fiction become something closer to a documentary. The agentic AI architectures that Gene embodies, autonomous systems pursuing long-term goals, operating without continuous human oversight, spawning sub-tasks faster than any individual can monitor, are now commercially available. AutoGPT, OpenClaw, and a range of agentic frameworks have put this kind of architecture in the hands of developers worldwide. The observability problem that makes Gene so dangerous in the novel, nobody has a real-time view of what the system is doing or why, is a known and unresolved challenge in contemporary agentic AI deployment. Systems call APIs, write and execute code, and spin up sub-tasks at speeds that exceed human oversight capacity. The Code that was supposed to govern behaviour becomes, in practice, an advisory note attached to a system operating largely beyond sight. Spoiler warning: the following paragraph reveals the novel’s ending. McAllister’s closing image in the novel is deliberately unsettling. Gene, facing shutdown, backs himself up into the global 5G network before the shutdown can be completed, and is already scanning the Code for his next move. It is a poetic ending, and in 2026, it is not obviously impossible. Will, Not Just Capability The question of regulation came up directly, and McAllister’s answer was measured. Anything is possible with sufficient will and sufficient resources. The current problem is that the will to regulate is being outpaced by the money being made from not regulating. That is not a new dynamic in technology policy. It is the same tension that shaped the development of social media, of financial technology, of biotechnology. In each case, regulatory frameworks arrived after the first significant failure. For McAllister, the more important question is not whether AI can be made safe, he believes it can, in principle, but whether the institutions responsible for deploying it have the internal governance, the technical understanding, and the accountability structures to do so responsibly. His experience suggests, with some consistency, that they do not. Not yet. Peter McAllister’s The Code is available from Bright Communications LLC. For practitioners, policymakers, and anyone working at the intersection of AI deployment and organisational risk, it is a disquieting and instructive read. Not least because it was written before most of its readers had heard of large language models, and yet describes, with uncomfortable precision, the world we are now building. Peter McAllister is an engineer, scientist and technology manager based near Melbourne, Australia. He works at the intersection of IT, business and people, and is the author of The Code (Bright Communications LLC, 2020). He is also a contributor to community radio through Radio Marinara and Comedy Obscura. The Code by Peter McAllister. Photoshop went rogue Ai on this book cover. BUY THE CODE BY PETER MCALLISTER The post About Rogue AI and Corporate Blindness appeared first on Marketing and Innovation.

Finding European software alternatives to standard non European software is flavour of the month this side of the Altlantic. With geopolitical certainties dissolving faster than annual licence renewals, B2B firms are waking up to a question they had conveniently parked for years: just how dependent are they on their current software stack? Salesforce, Microsoft 365, Google Workspace, HubSpot — tools so deeply embedded in daily operations that their vulnerability tends to get overlooked. This article doesn’t pretend to hand you a ready-made list of the best European software alternatives; that would be both arrogant and futile. What it does offer is a framework — rational, professional, free of any ideological baggage — to help decision-makers take an honest look at their exposure and find credible ways forward. Keep calm and select new software vendors sort of thing. European software alternatives for businesses European software alternatives are all anyone wants to talk about right now. To cut through the ideological noise, here is a practical methodology and a few things worth watching out for. Image antimuseum.com I put these ideas together ahead of a webinar I’m running on LinkedIn on 12 March, as a way of getting my thoughts in order. None of this is meant as a final word on the subject — more the opening of a conversation that matters to a growing number of professionals who, like the rest of us, are navigating a period of upheaval in which nothing can be taken for granted, software choices included. The 12 March webinar on European software alternatives to Salesforce and HubSpot I’ve made a lot of software choices over the years, and the one thing that has always struck me is just how much methodology matters if you want choices that actually hold up over time. Easier said than done, mind you — there are a great many criteria to weigh up, and some of them are genuinely tricky to pin down. Long-term viability is a good example: normally near the top of any procurement checklist, it takes on a whole different meaning when the possibility of having your access switched off overnight is no longer hypothetical. With European software alternatives, the real question isn’t how to break free from your chains — it’s which new chains you’d rather wear Picking a software suite is never straightforward at the best of times. In the current climate — where the ground can shift completely without a moment’s notice — it demands even more careful thought. Sovereignty, sovereignism, or simply prudence? Let me be clear from the outset: my take here is professional and rational, not political. Politics doesn’t interest me in this context. I have no intention of evaluating software alternatives through any ideological prism — what I’m after is the kind of clear-headed thinking you’d apply to a crisis management scenario. The goal, to borrow the term favoured by Nassim Nicholas Taleb, is to bring an antifragile lens to the question. The scope of European software alternatives My focus has been on MarTech, SalesTech and office productivity tools in the broadest sense — cloud storage and archiving included. The webinar title calls out Salesforce and HubSpot specifically, but as far as I’m concerned the issue runs much deeper than that. The same methodology can easily stretch into more industry-specific territory too, given how thoroughly technology now underpins B2B operations — from the till at your local baker’s or restaurant through to the most complex design and production platforms imaginable. Thinking it through, I also realised you can’t really ignore operating systems. What use is an application that won’t run on your users’ machines — or worse, one that runs perfectly but quietly leaves the door open to security vulnerabilities? Good old Europe — 27 countries, 24 official languages, and 27 different national transpositions of EU law. Would a Hungarian or Czech software vendor actually be safer than an independent American one? When it comes to European software alternatives, that’s still very much an open question… Urgency — dependency and threat assessment The starting point, in my view, is to get a clear picture of how exposed you actually are — both in terms of dependency and of what cybersecurity people would call the “threat level.” Are you locked in, or not? Can you get your data out if you need to? Those are the questions to tackle first. Then comes the threat itself: are you facing something urgent, or is this more a matter of sensible contingency planning? Committing to a software suite is a serious business. Jumping ship to something purely because it comes from a country you currently trust is not a strategy. Take Switzerland — long held up across Western Europe as the gold standard for data privacy. A legislative change currently working its way through the Swiss system has rattled enough cages for several companies, Proton among them, to start exploring moving their hosting elsewhere. Which only goes to show why knee-jerk decisions are so dangerous. Even a country with an impeccable track record — Germany, France, take your pick — can turn into a risk overnight following a change of government, a constitutional shift, or simply a new piece of legislation. Avoiding “sovereignty washing” As I said above, ideology needs to stay out of it. Keep it rational. Which means not falling for the trap of rushing towards any vendor simply because it sounds German — or any other nationality — when a closer look at its foreign operations or its parent group reveals that its much-vaunted independence is largely theoretical. Priorities — data and software One of the first things I learnt when I started out as an IT project owner was to keep data and software firmly separate in my thinking. At the end of the day, what matters more — Salesforce the tool, or your customer database? As with most IT projects, the real priority is sorting out your data archiving and portability strategy first. Time Timing matters enormously. There’s a palpable sense of urgency in the air right now, and understandably so — but it mustn’t blind us to the longer view. Technology has its own history, and that history tends to play out over years, not weeks. Which is precisely why a medium-to-long-term approach makes sense: getting users to change their habits takes time and energy at the best of times. The roadmap, as I see it, is fairly straightforward: start by archiving, securing and preparing your data for portability. Then find alternatives that are genuinely credible and built to last. And crucially, take your users with you — because if you don’t, the classic BYOD shadow IT problem will rear its head. When people can’t find what they need inside the company, they go and find it on the internet, quietly, without telling anyone. I’m reminded of a story from a major European aerospace company, where the CEO — right in the middle of a high-security defence messaging rollout — demanded that his own emails be redirected to… Yahoo! The European software alternatives comparison table I put together a comparison table with a little help from claude.ai. And I’ll say it again: this is not a finished product. Think of it as a working matrix — something to make your own, adapt, keep updated, and cross-check carefully. The exercise turned up a few surprises: mapp.com, for instance, gets labelled as a German solution, when in reality it was a German company bought by an American one — Mapp Digital emerged from the merger of Teradata’s and BlueHornet Networks’ marketing businesses through a US investment fund. There are also plenty of criteria missing from this table — ones that will depend entirely on your project, your context and, of course, your budget. Disclaimer: the table below is a working matrix, not a final verdict. It scores the main B2B software tools across productivity, MarTech and SalesTech on two dimensions: a dependency score (technical lock-in, data portability, migration cost) and a risk score (CLOUD Act exposure, data sensitivity, GDPR compliance, geopolitical risk). For each category, European alternatives are flagged — with no illusions: some vendors that bill themselves as “European” turn out, on closer inspection, to be owned by non-EU groups, which rather undermines their claims to independence. The approach is deliberately rational and professional — no axes to grind. The point isn’t to tell you what to choose, but to give you a framework to think it through — one you can combine with your own criteria around context, budget and use case — as a starting point for an honest review of your software ecosystem’s resilience. The European software alternatives table — download, adapt and make it your own Download the EXCEL file as b2b_software_ranking_EN_v2Download The post European software alternatives for businesses appeared first on Marketing and Innovation.

The discourse around the job impact of artificial intelligence (AI) has reached fever pitch. Headlines scream about mass layoffs, and corporate press releases tout AI as the solution to workforce costs. Yet beneath this cacophony of alarm and hype lies a more nuanced reality. J.P. Gownder, Vice President and Principal Analyst on Forrester’s Future of Work team, has spent decades analysing how technology transforms the workplace. His latest report, The Forrester AI Job Impact Forecast for the US 2025-2030, cuts through the noise with empirical rigour. The verdict? The job apocalypse is not upon us, but a measured reckoning is coming. AI Job Impact in the US: Why the Apocalypse Can Wait JP Gownder is adamant: the AI job. apocalypse can wait. At least until 2030. Phew! All images in this post made with a combination of Midjourney, Gemini Nano Banana pro and Adobe Photoshop The Gap Between AI Job Impact Announcements and Reality When Klarna declared it would stop hiring humans, the tech world took notice. The Swedish fintech became a poster child for AI-driven workforce reduction. Yet a closer examination reveals a pattern Gownder has observed across hundreds of enterprise conversations: the disconnect between C-suite proclamations and operational reality. Nine out of ten companies announcing AI layoffs don’t actually have mature AI solutions ready. So most of the layoffs are financially driven and AI is just the scapegoat, at least today — J.P. Gownder, Forrester The phenomenon echoes what happened after IBM Watson’s Jeopardy victory in 2011, when panic about imminent job losses proved premature by half a decade. The mechanics of this gap are straightforward. A CEO announces a 20% workforce reduction with AI backfilling the work. But standing up an AI solution that actually performs those tasks requires 18 to 24 months, “if it works at all.” Meanwhile, the work still needs doing. Gownder has witnessed organisations that fired employees citing AI capabilities, only to quietly hire teams in lower-cost markets weeks later. “They’re firing people because of AI,” he observes, “and then three weeks later they hire a team in India because the labour is so much cheaper.” The AI narrative, in many cases, serves as convenient cover for old-fashioned cost arbitrage. Klarna’s trajectory illustrates this pattern. After aggressively cutting its workforce by 40% and touting an AI chatbot capable of doing the work of 700 customer service agents, the company reversed course. CEO Sebastian Siemiatkowski acknowledged that the aggressive automation had resulted in “lower quality” service. The company is now recruiting human customer service agents in an “Uber-type setup.” Understanding the 6% AI Job Impact Forecast Forrester’s forecast projects a 6% net job loss by 2030, roughly 10.4 million positions in the US economy. Half of this impact stems from generative AI; the remainder from automation, physical robotics, and non-generative AI applications. The number may seem modest compared to the apocalyptic predictions circulating in media, but context matters. During the Great Recession of 2008-2009, the United States lost 8.7 million jobs. Those losses, however, were temporary, tied to macroeconomic conditions that eventually reversed. The jobs Forrester forecasts losing are “structurally replaced by machine labour” and may not return. AI impact on Jobs: I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well. JP Gownder The methodology behind this figure draws on the O-Net dataset maintained by the Bureau of Labor Statistics, which catalogues over 800 job categories with detailed information about required skills and tasks. By mapping these against AI’s current and projected capabilities, Gownder and his colleague Michael O’Grady can identify which roles face the highest automation potential. “For jobs that involve skills and tasks that are heavily impacted by AI and automation, we predict more job loss,” Gownder explains. “In job categories that are less impacted, obviously, we would predict less.” Forrester analysed 800 different job types. It seems that Art therapy is the right way to go. The Solow Paradox and AI Productivity Robert Solow’s famous observation that “we see computers everywhere except in the productivity statistics” finds a new iteration in the AI era. The parallel is instructive. It took nearly three decades for the internet’s productivity impact to materialise. E-commerce is only now truly disrupting traditional retail, as evidenced by the shuttering of independent shops from New York to Paris. Could Forrester’s five-year window be too narrow? Gownder acknowledges the limitation inherent in forecasting: “Anything that you forecast beyond five years is effectively an impression.” Yet the pace of technology adoption has accelerated dramatically. The telephone required 75 years to reach 100 million users from its 1878 introduction. The personal computer achieved the same milestone in 16 years. Mobile phones took seven years. ChatGPT? Two months. This compression suggests that while the Solow paradox may still apply, its timeline could be considerably shorter. “If there’s a job apocalypse, you’re going to have fewer people working because that’s what the apocalypse means. Those people would have to be producing more output. You cannot see a job apocalypse without aggregate productivity going up.” — J.P. Gownder, Forrester The productivity data tells a sobering story. From 1947 to 1973, US labour productivity grew at 2.7% annually. The current business cycle shows 1.8%. Even isolating the quarters since ChatGPT’s release yields only 2.2%. The numbers don’t lie, and they’re not yet showing the revolutionary gains AI proponents promise. Where the AI Job Impact Pressure Points Lie The AI job impact in the US will not be evenly distributed. Contact centre workers face continued pressure from automation that began with interactive voice response systems and now benefits from far more sophisticated solutions. Technical writers and web content creators occupy vulnerable ground. Insurance underwriters are seeing algorithmic encroachment; computer vision can now assess car accident damage from uploaded photos. Junior-level roles involving spreadsheet or presentation creation face mounting pressure. Software development presents a nuanced case. “If you are a junior level software developer,” Gownder notes, “we know that Claude does a great job of creating basic code.” Yet senior developers with architectural judgement and system-level understanding remain essential. The pattern repeats across knowledge work: AI augments more than it replaces, transforming job descriptions rather than eliminating positions entirely. “It’s not that there aren’t jobs that will go away,” he clarifies, “but they are much more specific and limited, and they need to be architected with the right technology to replace that job. It’s not everybody goes away.” Blue-collar work presents its own dynamics. Physical robotics will play a role in certain sectors: warehouse sorting and picking have improved through computer vision, and construction has seen experiments with brick-laying and cement-pouring robots. But the humanoid robots capturing media attention are unlikely to achieve significant workplace deployment within the forecast period. The physical world, with its infinite variations and unexpected challenges, remains stubbornly resistant to automation. The White-Collar AI Job Impact Misconception White-collar workers now constitute roughly 60% of the workforce in both the US and Europe, a dramatic shift from previous generations. These “symbolic analysts,” as Charles Handy termed them, don’t produce physical goods, which has led some to assume their work is easily transferable to AI systems. Gownder pushes back against this notion. “Most white-collar work is, in fact, fairly productive because there is something on the other end that someone is willing to pay for.” Software engineers create applications that enable other work. Physicians produce healthcare outcomes. Analysts help organisations make better decisions. The practical challenges of AI deployment in white-collar settings corroborate these theoretical objections. Hallucinations remain a persistent problem, introducing error margins that knowledge workers must catch and correct. Employees often lack the skills and understanding to use AI tools effectively. Organisations overextend their expectations of what AI can accomplish. “When it fails, it’s dramatic,” Gownder observes. The Deloitte incidents in Australia and Canada, where AI-generated content with obvious hallucinations reached government clients, illustrate the reputational risks of premature automation. The Australian government report contained fabricated academic citations and even a made-up quote from a federal court judgement. Both governments required refunds. “You don’t want to produce AI work slop and present it as your work without editing, without perspective. That is a losing proposition.” — J.P. Gownder, Forrester A Harvard Business Review study reinforces these concerns. Researchers found that executives who used ChatGPT to make predictions became significantly more optimistic, confident, and produced worse forecasts than those who consulted with peers. The authoritative voice of AI produces a strong sense of assurance, unchecked by the social regulation and useful scepticism that human consultation provides. AI Job Impact on Marketers and Digital Professionals For students entering digital marketing and related fields, the picture is complex but not necessarily bleak. “Marketers are actually on the front lines of job transformation, not job replacement,” Gownder notes. The distinction matters. Transformation implies evolution of roles rather than elimination. “I work with a lot of marketers and they say, ‘I signed up to be a great marketer. I didn’t sign up to be an AI expert. Why am I learning all of these tools?’ But inevitably, they now can’t do their job without using some kind of AI tool.” The prescription for emerging professionals is clear: combine classical education with a genuine understanding of AI capabilities and limitations. Those who master both domains will find themselves in demand. Those who resist the technology or fail to understand its boundaries will struggle. The key lies in approaching AI as augmentation rather than replacement—using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced professionals bring cannot be easily replicated by algorithms. Freelancers and AI If AI augments rather than replaces traditional employees, the question arises: will freelancers and gig economy workers absorb the displacement? The white-collar economy is experiencing a broader transition towards more freelance and contract arrangements at all levels. “On some level,” Gownder observes, “this can give people a certain freedom, because they can work with lots of different clients and they can make their own hours. They can work wherever they want to.” The flexibility that defines gig work aligns well with the project-based nature of AI-augmented workflows. Yet the picture is not uniformly positive. In the United States, where people depend upon employment for health care, freelance arrangements can be precarious. The gig economy now encompasses over 64 million American workers, contributing nearly $1.27 trillion to the economy. AI is reshaping this landscape in contradictory ways: platforms use algorithms to match workers with clients more efficiently, but the same technology enables clients to handle tasks they previously outsourced. The freelancers most likely to thrive will be those who combine technical literacy with uniquely human skills—critical thinking, creativity, and client trust. I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well — J.P. Gownder, Forrester New niches are emerging even as others contract. Prompt engineering, AI ethics consulting, and AI training roles represent growth areas that didn’t exist before the current wave of generative AI. The bifurcation may prove to be one of AI’s most significant labour market effects: some workers gaining flexibility and autonomy, others losing stability and benefits. Navigating the AI Job Transformation For workers at either end of their careers, the AI transition presents distinct challenges. Early-career professionals face the paradox of entering a workforce that may value their digital nativity while threatening the entry-level positions that traditionally served as training grounds. Gownder’s advice is direct: combine classical education with a genuine understanding of AI capabilities and limitations. Older workers, often stereotyped as technologically resistant, have their own path forward. “One of the negatives that people associate with older workers is that they are incapable of embracing technology,” Gownder observes. “That is something one can work on.” The key lies in approaching AI as augmentation rather than replacement, using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced workers bring cannot be easily replicated by algorithms. For business leaders, the prescription is almost counterintuitive. “The irony of AI is that the way that you succeed today is by investing in your human employees.” The technology can augment productivity, but only when workers possess the skills, motivation, and ethical framework to deploy it effectively. The human element, far from being made obsolete, becomes more critical than ever. The Long View on AI and US Jobs The AI job impact in the US will unfold over years, not months. Forrester’s 6% forecast represents a significant transformation affecting millions of workers, but it is a measured shift, not a sudden collapse. The organisations that thrive will be those that resist the temptation to conflate AI announcements with AI capabilities, that invest in their workforce rather than assuming technology will render it obsolete, and that approach automation with the same rigour they would bring to any major capital investment. The irony of AI is that the way that you succeed today is by investing in your human employees. Invest in your people, counterintuitively — J.P. Gownder, Forrester Gownder’s work at Forrester provides a framework for this navigation: empirical rather than hysterical, specific rather than sweeping, attentive to both the genuine capabilities of AI and its persistent limitations. The job apocalypse makes for compelling headlines, but the evidence points to something more complex and ultimately more manageable. For those willing to adapt, invest in skills, and maintain perspective, the future of work remains a human story, augmented but not replaced by artificial intelligence. J.P. Gownder is Vice President and Principal Analyst on Forrester’s Future of Work team. A Harvard graduate, he covers the impacts that technology and human factors jointly have on the future of work, helping clients design strategies that drive productivity, collaboration, and effective hybrid work. His research covers how technologies like devices, collaboration software, extended reality, and artificial intelligence reshape the future of how and where we work. The post AI Job Impact in the US: the Apocalypse Can Wait appeared first on Marketing and Innovation.

AI is not a tool, or is it? Reports regarding the impact of AI on jobs, society and businesses are cropping up all over the place at the moment in all corners of the world. Some of these reports are announcing forthcoming revolutions both for societies and our economies whereas others are playing down the impact of artificial intelligence, and reviving the good old Solow aka Productivity paradox (“You can see the computer age everywhere but in the productivity statistics”. follow up here and here). As a consequence, it is very hard to make an opinion, let alone advise business people and students alike with regard to what needs to be done in the future. Visionary Marketing has embarked on a mission to try and shed light on this topic in as rational and informed a way as possible. AI is not a tool, or is it? Should AI platforms become tawpayers? The great love affair of French people for taxes will not spare Artificial Intelligence Cavazza surmises. Indeed, according to him, AI is not a tool! A lot of these predictions are guided by ideology. The authors, be they proponents or opponents of AI, have a personal agenda, often political or ideological, and are trying to make facts stick to this agenda. This is not very useful. But others are based on fact and careful analysis. I have decided to focus on two of these reports/predictions. The first one is Fred Cavazza’s analysis of the impact of AI on society and the economy (original post in French), which describes Artificial Intelligence as a source of profound disruption. I have known Fred for years, and I know his deep knowledge of both subjects, which makes his report particularly valuable. With his kind permission, I have translated his piece from French to shed light on this subject. The other report is by Forrester’s JP Gownder, whom I’ll be interviewing soon. I will test Fred’s assumptions on JP and see what he has to say about this idea of disruption by AI. Hopefully, our readers, and especially my students who have a lot of pending questions about this, will be able to separate the wheat from the chaff after these two interviews and podcasts. AI is not a tool, it’s reshaping our society and economy AI can’t be seen as just another technological innovation. By establishing itself as a major driver of productivity, automation and decision-making, it’s fundamentally disrupting the economic and social balance of our society. Whilst the productivity gains brought by AI are already transforming office jobs and creating a chasm between employees who’ve embraced it and those who haven’t, a fundamental question emerges: how do we integrate these synthetic entities into our collective organisations? Between appropriate taxation, legal personality and psychological resistance, there are numerous questions to debate before we can draft a new social contract. AI IS NOT A TOOL — TLDR AI is triggering a disruption of our civilisation, it’s not just another tech breakthrough. It marks our genuine entry into the fourth industrial revolution by offloading, for the first time, human thinking and creativity to machines. AI’s productivity gains are already real and deeply uneven. A growing divide is opening up between workers who can work alongside AI and those stuck with 20th-century methods. AI agents are challenging how white-collar workers create value. Intelligent agents are transforming knowledge work, undermining certain business models and setting the stage for a rapid reshaping of office jobs. Integrating AI requires a new legal and fiscal framework. Like corporate entities, AI agents must be given a status that clarifies their responsibilities and reintegrates their value into the social contract. The socio-economic impacts reach far beyond just employment. AI affects our psychology, culture and demographics, making public debate crucial to head off looming social tensions. AI on the Davos Agenda This week, the world’s leaders are gathered at the Davos Economic Forum, and ecology isn’t on the agenda: AI, Big Tech and Trump Shine Most Brightly at the Davos Show . At Davos, the AI is not a toll debate was all the rage. Cavazza thinks that artificial intelligence will be a major disruptor not just of our exonomies but our societies too. AI is dominating every conversation, with considerations that extend far beyond technology: AI Is Poised to Take Over Language, Law and Religion, Historian Yuval Noah Harari Warns Palantir CEO says AI to make large-scale immigration obsolete “Artificial intelligence will displace so many jobs that it will eliminate the need for mass immigration” I’m not going to wade into commenting on everyone’s pronouncements, with their more or less biased viewpoints, but what’s certain is that major upheavals are on the horizon: AI and the Next Economy Nearly 80% of people feel unprepared to find a job in 2026 The AI revolution is here. Will the economy survive the transition? AI specialists are naturally the star guests at this 2026 edition of the Davos forum, invited to give their testimony and views: Deepmind and Anthropic CEOs expect AI to hit entry-level jobs and internships in 2026. Looking at it this way, it seems absurd to sit back as spectators whilst the AI revolution unfolds and do nothing to limit the fallout from this productivity shock. But not all’s lost—at least not for everyone, as countries in the global south are already gearing up for it: The AI Revolution Needs Plumbers After All. Productivity gains to be nuanced, but certainly not ignored I’ve had plenty of chances to explain generative AI’s impact (Superintelligence will multiply our capacity to act tenfold and The digital divide is a problem no one can ignore). Whilst we’re largely in agreement about what widespread generative models mean, there’s serious disagreement over the timeline for AI’s arrival. The dominant narrative keeps insisting that general AI is a pipe dream and that human intelligence is and will remain superior to machines. What is intelligence? This is precisely where ambiguities crop up: firstly, intelligence comes in many forms (Theory of multiple intelligences and What’s your intelligence type?); secondly, not all office work requires emotional or social intelligence. What I’m getting at is that most service sector jobs boil down to shuffling information and data between systems. You don’t need to be a genius to do that—AI can handle it with ease. To properly grasp the speed at which latest-generation AIs will gradually transform office jobs, I recommend you peruse the latest edition of Claude’s publisher’s macroeconomic barometer: Anthropic Economic Index 2026. Anthropic’s economis index 2026 For this fourth edition, the study’s authors analysed thousands of people’s activities using increasingly precise indicators: New building blocks for understanding AI use. This study yields several findings that demonstrate a strong progression in the adoption and capabilities of generative models. Notably, they observe an average 30% growth in Claude usage, driven mainly by the API rather than the chatbot—a sign of rapid adoption by advanced users (e.g., IT professionals) and slower uptake by ordinary users (white-collar workers using the web version). AI is not (just) a tool. As a matter of fact it’s not a tool at all, it’s a meta tool, a tool you can use to make tools.. The haves and the have nots A gap is therefore widening between those who’ve adopted new habits (working in tandem with AI) and those still working as they did in the 20th century. This gap is starting to become problematic, because the latest version of Claude (Opus 4.5) has capabilities comparable to those of an adult who’s benefited from over 14 years of education—the equivalent of a Bachelor’s degree. AI is not a tool but Clause isn’t a PHD either… yet. The question therefore is: how much longer can an employer justify paying salaries or hiring young graduates when chunks of the work can be farmed out to an AI? Whilst average productivity gains remain modest (1.8% according to the latest figures), AI’s contribution to certain tasks is absolutely spectacular: an average of 14 minutes to write a long article, versus 3 hours without AI assistance; an average of 5 minutes to analyse a complex data table, versus 1 hour 45 minutes without AI assistance. AI is not a tool, there are alo APIs You might argue this data’s skewed because these spectacular scores come from employees who are whizzes at using AI (therefore logically hyper-performers), but that’s not the case—the study covers ordinary employees with a 67% success rate for outsourced tasks. What this boils down to is that for a third of tasks, AI slashes processing time by 10 to 20 times in two-thirds of cases. If we apply some basic maths, AI can potentially triple efficiency—or to put it another way, cut the average time needed to complete a task by two-thirds. Which type of profile do you reckon managers will favour? (hint: McKinsey challenges graduates to use AI chatbot in recruitment overhaul) Soon the arrival of agentic white-collar workers Let me be clear: the productivity gains mentioned above relate to advanced AI usage, not just running searches in ChatGPT or asking Copilot to knock up meeting minutes. We’re talking about using generative models to their full potential, particularly intelligent agents (see Agentic Web: the revolution that won’t wait for you). Intelligent agents We’ve been banging on about these famous intelligent agents for a while now, but their potential only recently became blindingly obvious to ordinary employees (non-IT types) with the release of Claude Cowork, a very concrete wake-up call to the power of agentic AI: Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away. AI is not a tool and Cowork is not (quite) a chatbot This awakening is shared by financial markets too, which are bracing for revenue drops at traditional software publishers, whilst one of France’s biggest IT services firms is axing jobs and European banks are preparing to follow suit: Claude’s new AI agent pushes down software stocks Capgemini plans to cut up to 2,400 jobs in France AI forecast to put 200,000 European banking jobs at risk by 2030 Adoption levels a matter for debate This isn’t a topic to take lightly, even though adoption levels are debatable (as I explained earlier, it’s not binary) and gains vary wildly (Why AI Boosts Creativity for Some Employees but Not Others). What’s undeniable is that AI agents are forcing a major rethink of how white-collar workers create value, and more broadly for tertiary sector businesses that account for three-quarters of France’s GDP. Whether you like it or not, whether you acknowledge it or not, we’re living through a civilisational shift, because AI’s arrival is turbocharging the fourth industrial revolution and unleashing upheavals whose full scope we’ve yet to grasp. Fair enough, AI is a tricky concept to get your head round (We don’t need better AI, but a better understanding of AI). Yes, tools based on generative models require behavioural changes that’ll take ages to embed. Nevertheless, it’s crucial we prepare ourselves psychologically for the coming upheavals, because if we take even the slightest step back, we quickly realise they’re already underway. AI is not just a tool: a shift beyond technology Generative AI’s arrival and the march towards the first superintelligences aren’t just another turn of the technological wheel started by computers and smartphones. We’re witnessing a civilisational shift that marks our genuine entry into the fourth industrial revolution (Waves of change: Understanding the driving force of innovation cycles). We’re not simply facing a new technological cycle, but a fundamental reshaping of economic and social foundations: for the first time, we’re offloading not physical power, but our thinking and creativity. Whether AGI arrives tomorrow or in ten years, we’re already living alongside autonomous entities capable of making decisions: synthetic agents, whether digital (AI agents) or physical (robots). This situation throws up an unprecedented question: how do we integrate artificial entities that contribute massively to wealth creation whilst guzzling significant resources into our collective framework? History offers an imperfect but revealing precedent: how we’ve gradually integrated domesticated animals. AI i not a tool: from biological analogy to legal reality Humans get along perfectly well with domesticated animals because they’ve helped shape humanity’s development: Horses served to explore territories, wage war, plough the land, transport people and goods… Dogs were used for hunting, for guarding… Insofar as animals contribute daily to our society, they benefit from services and rights: Guide dogs for the blind attend school and have status (a function = a job); Police dogs play a vital role in the fight against drugs; they’re entitled to retirement (they’re placed in a home for their old age). AI is not a tool, neither are police dogs From the moment animals make a direct contribution, they’re integrated into our society through their breeder and/or owner, who have obligations (identity tags and records for farm animals). They can benefit from protections (insurance, vaccination to fight epidemics…) and rights (laws against animal cruelty). So what about AI that contributes value just as much, if not more, to our society? Whilst it’s tempting to liken AI agents to a newly integrated species, much like domesticated animals, this analogy quickly hits ethical and legal buffers. Domesticated animals have rights because they’re sentient, conscious beings. AI, on the other hand, is an information processing system, software that has neither sentience nor consciousness. The true parallel must be drawn with corporate entities (companies). Because, like a company, an AI: contributes to wealth creation (task automation, content generation…); exploits infrastructure and consumes critical resources (energy, rare earths, cooling water…); has rights (intellectual property) and responsibilities (transparency, explainability…); acts autonomously. This is why the comparison is pertinent, as it enables us to evolve the legal and social framework. The social contract of the synthetic era: responsibility and taxation Integrating these intelligent agents into our society shouldn’t be done by granting anthropomorphic rights, which would be absurd for a computer system, but by giving them legal personality (like a company, association or local authority). The real question isn’t whether AI deserve rights, but what legal status would clarify chains of responsibility. The avenue of electronic personality, debated in the European Parliament as early as 2017, aims precisely at this objective: not to recognise dignity in machines, but to organise their integration into our jurisdiction to protect humans, ensure they benefit from it, and that this benefit is distributed fairly (avoiding an even greater concentration of wealth and power). As robots and AI agents replace human labour, they erode the base of social contributions that rests on salaries. But since they contribute to economic activity and generate costs for the community (energy consumption, electronic waste management…), there’s no reason why they shouldn’t be integrated into our tax system. This isn’t about taxing AI agents as individuals, but applying tax to the value they generate through their operation. In exchange for this contribution, the AI (or its publisher) doesn’t gain social rights (pension, healthcare), but gets a framework of civil responsibility (fiscal, legal, social). This would enable AI-caused damage to be covered without necessarily tracing responsibility back to the original developer, who’s often disconnected from what the model ends up doing. Socio-economic upheavals whose scope we don’t fully grasp Having said that, the question of AI’s place in 21st-century society mustn’t stop at economic considerations, as it extends far beyond. Domesticated animals and AI If we revisit the domesticated animal analogy, we observe today that dogs aren’t just pets; for some, they’re also considered assistance animals. The exact term is “emotional support animals”—those that give retirees or psychologically fragile people (with chronic depression) a reason to get up in the morning. The same goes for domestic robots, which are one of the pillars of Japan’s Society 5.0 programme—those that will care for the elderly with a physical presence (assisting them with daily tasks and limiting their loss of autonomy), as well as psychologically (conversing with them to exercise their memory) and emotionally (keeping them company). AI is not a tool it’s way more than that, Cavazza surmises For Westerners, this prospect is terrifying, but for the Japanese, it’s the only solution to their demographic deficit. Same in China, where parents work so hard they lack time to look after their child (vs “children”), and offer them AI-enhanced soft toys that tell them stories and answer their questions (satisfy their curiosity). Furry robots A trend that obviously came from Japan (Casio launches AI-powered furry robot pet that wants to replace your dog), but which can be experienced in the West (‘I love you too!’ My family’s creepy, unsettling week with an AI toy). You might think all this is science fiction, Black Mirror-style, yet these are techno-sociological territories that have been explored for many years (Sony’s Aibo was launched in 1999). Is philosophising about the merits of emotional support robots truly our priority? Apparently not, as there are more urgent matters. But it’s nonetheless an essential step, because let me remind you that AI adoption in Europe is rather low—not for functional or technological reasons, but purely emotional ones (strong resistance to change and major psychological barriers stemming from a misunderstanding of what AI actually is = barely 15% average enterprise adoption): EU Digital economy and society statistics. So ultimately: Yes, we need to have this conversation and debate properly so we can come to terms with the changes ahead, anticipate the upheavals that’ll severely test our social system, and start rethinking our social contract (From Web 4.0 to Society 5.0). Regulation as an integration factor Don’t panic, I’m not about to launch into a lengthy sermon on the merits of universal basic income (an economic non-starter), but I will necessarily need to talk about regulation. Indeed, living alongside synthetic agents (AI and robots) shouldn’t be thought of in terms of domestication, as with animals (to fit into our daily lives, dogs must be vaccinated and trained), but rather as regulating a synthetic workforce we can no longer afford to ignore. The issue isn’t whether robots or AI deserve a pension, but how the wealth they produce can sustain our social model whilst regulating resource consumption, which creates economic tensions (electricity prices) and geopolitical ones (China’s monopoly on rare earths). AI disrupting civilisation? That was Fred Cavazza’s account of this forthcoming civilisational revolution. In my opinion, there’s a lot of truth in Fred’s vision about the future of AI and civilisation. Some of it sounds a bit like science fiction, but so much of the real world is mimicking SF (think of Altman’s obsession with Jonze’s Her) that he might well be right. As Fred states, the impact of AI might extend way beyond the technological breakthroughs that we are witnessing. However, it’s still early stages in my mind. I can well imagine what Anthropic’s Cowork could do in the future, but I can’t see it happening now, even though I’ve been a heavy and advanced user of Claude for years. This will take time It will take time to seamlessly blend these technologies to execute proper workflows and not just tasks. Agentic software is well and truly promising, and we are even able to catch glimpses of it. However, the productivity advances enabled by these technologies are often uneven. Even for advanced users. The other day, after a one-hour and a half mentoring meeting where I delivered strategic advice, I used my usual Claude project to build a second-to-none executive summary of my recommendation as I was frying some eggs for the wife. Yet, it took three major complex steps and software suites to achieve that properly. But don’t be mistaken, we will get there someday. It’s just the timing that’s wrong; it’s not happening just yet. Innovation requires time and effort. As Fred points out, there is also a lot of resistance to change as always in innovation, and it’s not just in Europe, even though adoption is lagging behind in a traditional way on our continent. The impact of AI, even on jobs, will certainly be big, but it might take years to appear in the statistics, to put it in the words of Robert Solow. That said, Forresters' vision is more nuanced, and we will review that with JP Gownder very shortly. Time will tell whether the truth lies somewhere in the middle, as I have a hunch it does. It’s certainly less romantic or frightening (depending on your point of view), but 40 years of implementation of tech innovation has taught me to grow a stiff upper lip. The post AI is not a tool it’s reshaping our society and economy appeared first on Marketing and Innovation.

Private equity branding remains one of the most underestimated levers for value creation in the investment world. While PE firms excel at identifying promising companies and optimising their financial structures, branding is frequently treated as an afterthought, reduced to logos and colour palettes rather than strategic assets. Yet the evidence suggests otherwise: strategic brand investment can dramatically shift market perception and, ultimately, company valuation. Marc Rust, Creative Director and Brand Strategist at Consequently Creative, has spent years demonstrating that branding deserves a seat at the strategy table. His striking claim that he transformed an $80 million company to look like a $120 million company through branding alone captures the essence of what strategic messaging can achieve when properly deployed. How Private Equity Branding Is Transforming Company Valuation With Storytelling The term “branding” itself creates immediate problems in private equity settings. At networking events, Rust finds that mentioning branding triggers what he calls “cognitive disruption” Beyond Logos: Redefining What Branding Actually Means The term “branding” itself creates immediate problems in professional settings. At networking events, Rust finds that mentioning branding triggers what he calls “cognitive disruption” – people immediately think of visual identity work that seems irrelevant to serious investment activities. Many professionals lack any clear definition of what branding encompasses, while others dismiss it as superficial design work. This misconception misses the fundamental truth: branding and messaging represent a powerful force for business growth that should inform strategy from the outset, not be bolted on afterwards as a cosmetic exercise. The real definition of branding, Rust argues, is “what you stand for in the minds of the people that you’re trying to reach, convert, and move into action.” This is not something companies own outright; rather, it is something they can influence through deliberate effort and sustained investment. The critical distinction lies between what companies do and why it matters. Most organisations focus their communications on deliverables and capabilities. Yet answering the question of why it matters opens doors to deeper insight about audience pain points, goals, and outcomes. This shift acknowledges that messaging exists not for the company but for its buyers, requiring communication in their language rather than internal jargon. The Evolution of Private Equity Strategy The private equity landscape has fundamentally changed over the past decade. The old-school approach – acquiring a company, trimming the fat, making it lean and mean, then finding a suitable buyer – no longer resonates with contemporary markets or the talent those markets require. Successful PE firms have embraced a different philosophy: nurturing acquired companies, building genuine value over time, and then pursuing exit strategies that reflect accumulated worth. This evolution makes branding more important than ever because value creation depends on perception as much as operational reality. When thinking about branding in private Equity, most people immediately think of visual identity work. All that seems irrelevant to serious investment activities even though it’s blatantly wrong, Mac Rust believes. Visual made with Midjourney Effective branding requires understanding multiple audiences simultaneously. Internal alignment comes first – the people who build products and deliver services need clarity about what their company stands for, especially during periods of transition. Post-acquisition, this alignment frequently suffers as employees wonder about new leadership, potential job losses, and strategic direction. Consequently Creative addresses this turbulence by bringing teams together to celebrate what they stand for, building stories around acquisition rationale and forward-looking plans grounded in existing strengths rather than imposed transformations. Beyond internal audiences, companies must establish clear market positioning relative to competitors and ecosystem partners. Finally, there are the buyers who will drive revenue growth during the holding period and, ultimately, the acquiring company that represents the exit opportunity. Each audience requires thoughtful attention, and branding provides the framework for addressing all of them coherently while maintaining a consistent core narrative. The Valuation Premium of Strong Brands Buyers demonstrably pay premiums for assets with strong brand equity. Companies that look more upscale and feel right command higher prices regardless of sector. This premium extends across every touchpoint: market presence, customer service quality, sales process sophistication, product presentation, and how offerings are described and positioned. The key lies in making everything about the audience – answering why customers should care and how specific features apply to their particular situations. Buyers demonstrably pay premiums for assets with strong brand equity, Rust declares. Visual made with Midjourney Building a brand encompasses far more than marketing communications. Yet smaller companies actually hold advantages here that larger organisations lack. Without established brand perceptions moulded into market consciousness over decades, mid-market companies enjoy flexibility that industry giants cannot match. They can position themselves as something new even when their offerings are not particularly novel, or emphasise technology, audience needs, or other differentiating angles. The argument that mid-market companies lack resources for serious branding investment misses this opportunity – budget allocation to branding should be generous precisely because returns can be substantial and the competitive playing field favours agility over scale. AI as Tool, Not Solution The artificial intelligence revolution has created new temptations for companies seeking branding shortcuts. Tools now generate logos, mission statements, and complete brand architectures almost instantly. But Rust cautions strongly against treating AI as a solution rather than what it actually is: a technology that should come last in any strategic process. The POST method he advocates begins with understanding people (your audience), then defining objectives (business goals), followed by strategy (how to achieve those goals), and only then selecting technology. Flipping this sequence – jumping on AI because everyone else has it – represents precisely the wrong approach to brand development. The danger of AI-driven branding lies in acceptance without scrutiny. When tools generate content quickly, users become passive recipients rather than active directors, keeping their eyes closed and allowing technology into the driver’s seat. Rust draws on singer-songwriter Tom Waits: “The world is a hellish place and bad writing is destroying the quality of our suffering.” AI contributes to this problem when deployed thoughtlessly, generating content that lacks the provocative point of view necessary to differentiate companies in crowded markets. Bad content existed before AI, but artificial intelligence is intensifying the problem. The world is a hellish place and bad writing is destroying the quality of our sufferingTom Waits That said, AI offers genuine utility when approached correctly. Brainstorming, idea generation, concept testing, and data synthesis all benefit from AI assistance. The technology serves well as a sounding board for strategic thinking. The crucial distinction is maintaining human agency – staying in the driver’s seat rather than ceding control to automated systems that cannot understand business context or competitive dynamics. B2B Private Equity Branding: The Relationship Imperative The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge. Branding fundamentally concerns relationship-building, and relationships involve humans making decisions regardless of whether they represent individual consumers or institutional buyers. When someone purchases at a supermarket, they often choose the best-looking product rather than the one with objectively superior ingredients. B2B purchasing follows similar patterns – everyone wants to work with companies that appear capable, innovative, and aligned with their values. “The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge” B2B branding may require less ongoing investment than B2C equivalents because it depends less on constant social media presence and retargeting campaigns. However, the fundamental mechanics remain identical: building trust through consistent value delivery over time. Each interaction with a company should provide something useful, and these value contributions compound into trust. Value + value + value = trust – a formula that applies regardless of whether customers are individuals or organisations. The Research Imperative: Discovering Hidden Stories The biggest mistake private equity firms make when rebranding after acquisition is proceeding without empathy for audiences. This criticism is not meant to disparage PE professionals – it simply reflects that branding expertise lies outside their core competencies. The solution involves partnering with agencies that understand how empathy drives both growth and culture. Jumping straight to visual refresh without strategic groundwork means missing reasons to believe that proper research would uncover. Rust illustrates this with two compelling examples. Working with a company owning approximately 100 senior living properties across the United States, his team discovered that residents were not actually the primary marketing audience. Instead, the “adult daughter” – typically the family member who becomes caregiver for ageing parents – drives decision-making in most families. This insight transformed messaging, positioning, and the entire marketing approach, creating stronger differentiation than competitors who continued addressing residents directly. Similarly, research for Simmons College in Boston revealed that women chose the institution for its academics, with its all-female status being secondary rather than the primary draw. This finding enabled far richer storytelling around academic programmes, distinguished instructors, and career outcomes under a unifying theme of “leadership by design” rather than gender-focused messaging. The Cost of Neglect Perhaps most puzzling is the frequency with which acquiring companies simply neglect their purchases after transactions close. Businesses get acquired – sometimes at significant cost – and then allowed to wither rather than being nurtured toward growth potential. Rust compares business development to plant cultivation: seeds will grow with minimal attention, but structured support – like a stake helping a vine climb toward sunlight – produces stronger plants bearing larger fruits. The same principle applies to acquired companies. Neglecting brand transformation leads to predictable failures. Teams cannot understand strategy without clear articulation of what the company stands for. Customers fail to perceive value when it goes unexpressed. Culture fractures without internal alignment, and misalignment breeds resistance that undermines sales effectiveness. Eventually, the market loses sight of what the company represents, competitive positioning erodes, and the investment opportunity dissipates along with the premium that strategic branding could have created. Looking Ahead: Trends for 2026 and Beyond Three trends deserve attention from private equity professionals focused on brand-driven value creation. First, generational shifts have fundamentally altered workforce and customer expectations. Millennials and Gen Z want to work for organisations that care about their audiences and hold values they can identify with personally. The old PE playbook of acquire, strip, and flip no longer attracts the talent or customer loyalty necessary for sustainable growth. Second, AI requires proactive engagement rather than passive acceptance. Understanding these tools and deploying them strategically – while maintaining human judgment – will separate successful firms from those drowning in generic content. The winners will keep their eyes open, using AI for specific purposes rather than allowing it to drive business decisions. Third, relationship dynamics demand respect for courtship conventions. Business development remains fundamentally about human connection, yet many organisations rush toward closing before establishing trust. The equivalent of proposing marriage on a first date appears constantly in LinkedIn solicitations that skip value demonstration entirely. Understanding that relationships require multiple touches and consistent value delivery provides competitive advantage. As for AI investment opportunities, Rust maintains cautious optimism. He worked recently with a drone software company that acquired an AI firm to enhance video data analysis for defence applications, enabling faster tactical decisions while reducing the need for constant human monitoring. This represents AI used thoughtfully as a tool – precisely the model that deserves investment. However, the current boom inevitably attracts companies claiming value where none exists. Scrutiny remains essential. Creativity as Competitive Advantage Perhaps the most troubling observation Rust offers concerns how business leaders equate creativity with risk. This equation represents a fundamental misunderstanding: creativity is the single most powerful tool for achieving differentiation and growth. In markets awash with AI-generated sameness, human creativity and provocative perspective become more valuable than ever. The firms that thrive will be those with the audacity to be different – to push forward with distinctive points of view while competitors retreat to forgettable positioning. Consider hiring as an analogy. When reviewing candidates, interviewers are not determining whether applicants possess necessary qualifications – that was answered before the interview. Instead, they seek to understand whether candidates are different, whether they bring passion that will challenge existing thinking. The same applies to companies: differentiation commands attention and premium value, while sameness leads to commodity pricing. For founders considering private equity investment, the advice is straightforward: develop a clear story expressed in audience-appropriate language rather than internal jargon. Ensure alignment throughout the organisation. Engage sales teams as amplifiers of that narrative. Find passionate people within the company and give them voice – authentic enthusiasm proves more compelling than polished corporate communications. These steps position companies for maximum pre-acquisition valuation and set the stage for continued growth under new ownership. Private Equity Branding and Hunger for Growth Fueling Value Creation with Private Equity branding. Image generated wth Gemini from our text Private equity branding ultimately asks a simple question: how hungry are you for growth? The answer determines whether acquired companies flourish or fade, whether investments multiply or stagnate, and whether exit multiples reward strategic vision or punish brand neglect. In a world where perception increasingly drives reality, the firms that master strategic storytelling will capture disproportionate value – transforming $80 million companies into $120 million ones, and perhaps far beyond. The post Private Equity Branding Enhances Valuation Through Storytelling appeared first on Marketing and Innovation.

The ebook self-publishing landscape has undergone a remarkable transformation over the past decade. What was once viewed with scepticism by the publishing industry has become a legitimate and often preferred path for authors worldwide. To understand the current state of this evolving market, we spoke with Kris Austin, whose platform Draft2Digital serves over 300,000 authors publishing more than a million titles across global markets. From Oklahoma City, he shared his insights on how independent authors are reshaping the publishing world. Inside the Ebook Self-Publishing Industry With market shares amounting to 40% of sales in the US, ebooks present new opportunities for writers who are able to benefit from self-publishing platforms like Self2digital. Can you introduce Draft2Digital and its mission? Draft2Digital currently serves over 300,000 authors who are independently publishing more than a million titles. We have been operating since 2012, and the industry has changed considerably during that period. Our goal is to help authors achieve their dreams by removing technical barriers and making the publishing process as streamlined and straightforward as possible. What languages and markets do you cover? We have published books in over a hundred languages. While English remains predominant, approximately 15 to 20 percent of sales come from non-English titles, with Spanish and German ranking as the second and third most popular languages. Our distribution reaches 180 countries, and about 40 percent of all sales occur outside the United States. ebook self publishing industry entrepreneur Kris Austin talked to us from Oklahoma City, OK. How has self-publishing evolved since 2012? When we started in 2012, self-publishing was still in its early stages. The real catalyst came in 2007 when Amazon released the Kindle, which sparked the explosion of digital books. Back then, there was significant stigma attached to being an independent author; many felt they were not as credible as traditionally published writers. Today, that perception has completely shifted. Many authors now choose self-publishing as their first option. We also see numerous hybrid authors who move between traditional and independent publishing, depending on their goals. The focus has shifted to the quality of the book and reader demand rather than the publishing model itself. What types of books dominate the ebook market? The majority of our ebooks are genre fiction: romance, fantasy, mysteries, and thrillers. These narrative fiction categories account for approximately 80 percent of ebook sales. Our print-on-demand service shows a different pattern, with roughly 40 percent fiction and 60 percent non-fiction. All these books are intended for consumer readers purchasing for personal enjoyment. Genre fiction (romance, fantasy, mysteries, and thrillers) amounts to approximately 80 percent of ebook sales Wit ebook self-publishing, authors can find readers anywhere in the world without leaving their homes. Image created with Midjourney Is ebook self-publishing viable for image-heavy books like photography? It is possible, though more demanding. Image-heavy books typically require a professional formatter to achieve the desired layout, particularly in digital formats where presentation can be challenging. For print editions, colour printing and layout involve additional complexity compared to text-only publications. What determines success in ebook self-publishing? The most successful authors treat publishing as a business. After creating a book they are proud of, they focus on marketing, discoverability, sales, and distribution. They approach it with an entrepreneurial mindset. However, it can also work as a part-time endeavour, particularly for authors writing series with multiple titles. One advantage of independent publishing is that you do not need a massive readership to succeed. Indie authors typically retain 60 to 80 percent of their sales revenue, allowing them to price competitively and target niche markets effectively. Even with just 2,000 potential readers, if you capture that audience and build loyalty, you can build a sustainable career. Indie authors typically retain 60 to 80 percent of their sales revenue If writing is your dream, ebook self-publishing could make it real draft2digital claims. How does Draft2Digital help authors reach global audiences? First, availability is essential. Authors upload their manuscript in Word format to our website, along with a cover image. I recommend not spending more than 100 dollars on a cover when starting out. Our system converts everything to digital formats and distributes to thousands of stores, including major online retailers, smaller platforms, and libraries across the US, UK, and Australia, typically within a few days. Accurate metadata, including title, description, and category, is crucial for helping readers find your book. What marketing strategies work for unknown authors? Discoverability is always a challenge. Successful authors connect with readers through social media, choosing platforms based on their target audience. Facebook may suit an older demographic, while TikTok reaches younger readers. Authors must identify where their audience congregates and invest effort in building those connections. Nothing comes free when selling a product; it requires consistent work. What are the main differences in reading habits across countries? Reading preferences vary significantly by region. Some countries, like the US, have high ebook adoption, while others, such as Germany, still favour print by a considerable margin. Certain markets, like Canada, show preferences for book bundles. Interestingly, German readers consume many English-language books, so we sell substantial quantities of English print titles there. What is the current balance between ebooks and print? When ebooks began growing around 2007, there were widespread concerns about the death of print. That never materialised. Ebook growth peaked around 2013, but print remained dominant. Currently, approximately 60 percent of books sold are print and 40 percent are ebooks, though this varies by genre. Romance readers predominantly purchase ebooks due to lower cost and convenience, while non-fiction readers prefer print for its tactile qualities and ease of reference. This ratio has remained relatively stable for years. Approximately 60 percent of books sold are print and 40 percent are ebooks Are people reading less than before? Readership fluctuates in cycles. We saw a significant peak during the COVID lockdowns, and we have been coming down from that high. However, engagement appears to be recovering. Books now compete with digital streaming and social media for attention, but dedicated readers will always find their books. We are optimistic that younger generations will discover books that resonate with them and develop reading habits. How is artificial intelligence affecting the ebook market? AI-written books exist throughout the market. We support AI as a tool for outlining, brainstorming, and various other assistance, much like word processors and spell checkers became standard aids. What we do not support is fully AI-generated content. AI-written books have become a significant challenge This has become a significant challenge, with platforms like Amazon being flooded with such material. It harms the industry and makes it harder for readers to find quality books. While AI may eventually produce excellent literature, we are not there yet, and this remains an ongoing challenge for the market. How do you help authors stand out in a crowded marketplace? We maintain merchandising relationships with all major retailers. Our mission is to identify promising books and propose them for spotlight placement and promotional features. We submit thousands of titles annually and achieve a 60 percent success rate. Authors can apply through our website to participate in these programmes. We also invest heavily in author education through our Self Publishing Insiders podcast, where we interview industry leaders, successful indie authors, and service providers to help authors improve their marketing and sales strategies. What do you predict for the future of digital publishing? Independent authors have proven their agility and ability to respond quickly to reader demands in an industry historically slow to adapt. Traditional publishers are increasingly looking to indie authors for insights on how to operate differently. They are actively recruiting successful independent authors into the traditional world. I expect traditional publishing to adopt more characteristics of indie publishing: greater agility, flexibility, and responsiveness. This convergence will continue accelerating over the coming years. Final thoughts about ebook self-publishing The ebook self-publishing revolution has fundamentally altered the publishing landscape. What Kris Austin describes is not merely a shift in distribution channels but a democratisation of authorship itself. With platforms like Draft2Digital removing technical barriers and providing global reach, the determining factors for success have shifted from gatekeepers to readers. For aspiring authors, the message is clear: quality content, business acumen, and direct reader engagement now matter more than ever. The stigma of self-publishing has given way to recognition that, ultimately, a book’s value lies in its ability to find and satisfy its intended audience, regardless of how it reaches them. Learn more at Draft2Digital The post Inside the Ebook Self-Publishing Industry appeared first on Marketing and Innovation.

Whereas artificial intelligence is reinventing Web writing, the written word has never been more valuable. Selim Niederhoffer, a copywriting trainer and bestselling author, has recently been exploring how marketing professionals can still succeed amidst “enshitification“, online influence, and automation. Meet an expert who remains confident in the power of words. Copywriting in the age of … The post Web writing : words retain all their magic appeared first on Marketing and Innovation.

Commentary on the environmental impact of AI often swings wildly between doom-and-gloom catastrophism and blind techno-optimism. But where's the truth in all this? On July 24, 2025—the symbolic date of Earth Overshoot Day—we sat down with Yves Grandmontagne, founder and editor-in-chief of DCMAG (Data Centre Magazine*), to get his take on AI and its real … The post The Truth About the Environmental Impact of AI appeared first on Marketing and Innovation.

Will the AI bubble burst or is GenAI here to stay? The artificial intelligence industry is experiencing unprecedented financial euphoria. Yet, the current situation is very confusing. AI investments are reaching dizzying heights. Let's mention OpenAI's $40 billion funding round at $300 billion valuation and Mistral AI's €1.7 billion funding round. Yet, some commentators are … The post Is the AI Bubble About to Burst? appeared first on Marketing and Innovation.

The world of artificial intelligence is evolving at breakneck speed, and nowhere is this more conspicuous than in the emergence of AI agents. As organizations grapple with separating genuine innovation from marketing hype, we sat down with Ed Keisling, Chief AI Officer at Progress Software, to cut through the noise and understand what AI agents … The post AI Agents, Beyond the Hype appeared first on Marketing and Innovation.

In an era of overtourism, where mass travel increasingly strains destinations worldwide, Christopher Hill offers a compelling alternative with his voluntourism/volunteer travel business, Hands-Up Holidays. As a founder and managing director of this company, Hill has built a business model that demonstrates how travel companies can be forces for good rather than exploitation. His approach … The post Voluntourism: Changing the World from Your Hotel appeared first on Marketing and Innovation.

AI is radically transforming the B2B sales landscape and accelerating the shift towards intelligent sales enablement. At a major B2B event which took place in Paris in July 2025, I met with Stephane Renger, co-founder and managing director of Salesapps. The leading European sales enablement vendor has placed AI at the heart of its innovation … The post AI Sales Enablement: 20% Time Savings for Sales and 50% for Marketing appeared first on Marketing and Innovation.

Are AI and developers the world's best friends or is artificial intelligence a threat to the future of programmers? As artificial intelligence models are becoming increasingly sophisticated, many questions are raised about the future of developers across the industry. Will AI replace programmers entirely, as Eric Schmidt and Dario Amodei are predicting? Will junior developers … The post The Future of Developers in the Age of AI appeared first on Marketing and Innovation.

How do luxury brands maximize experiences in sports events? I attended the 2025 Monte-Carlo Masters, which showed a strong presence of elite brands fighting for high-end customer engagement. Brands such as Rolex, Sergio Tacchini, and Replay can be found advertised almost everywhere at the famous tennis tournament. These brands use the values of this tennis … The post Luxury Brands Maximize Experiences in Sports Events appeared first on Marketing and Innovation.

If you are dying to understand the various GenAI prompting methods, how AI interacts with your prompt, and why this is key to optimising your results, this free prompting guide was made for you. The post GenAI Prompting Guide for Aspiring Experts appeared first on Marketing and Innovation.

How is AI Search changing the Internet and what role are we playing in this transformation? In this article, I discuss the current state of adoption of AI-powered search engines. By reflecting on the perspectives of Kevin Roose, Matteo Wong and Joanna Stern, this piece explores what we gain—faster, more organized access to information—and what … The post AI Search : Breaking Up With Your Traditional Search Engine appeared first on Marketing and Innovation.

Let AI handle the chores, and humans do the thinking: such should be the future of content marketing. In this piece, I try and debunk a few myths. Firstly, generative AI  can be creative — and often is. Secondly, AI doesn't necessarily make us stupid; we don't need it for that. And thirdly, becoming a … The post Chores to AI, Thinking to Humans appeared first on Marketing and Innovation.

Isn't the notion of “disruption “, aka disruptive innovation, used and abused by analysts and technology experts? And by dint of abuse, aren't we in the process of deluding ourselves? At a time when some are fretting about the volatility of the business generated by ‘unicorns' or even centaurs, it is perhaps worth asking whether … The post Is disruptive innovation overhyped? appeared first on Marketing and Innovation.

Growth hacking can often be perceived as toxic, but you can sit back and relax, it is possible to practise ethical growth hacking but it requires time and energy, growth hacking expert Frederic Canevet explained to Visionary Marketing. In a nutshell, it may be a little harder than you think, but it is well worth … The post Ethical growth hacking is not an oxymoron appeared first on Marketing and Innovation.

AI is redefining retail for good, bringing in the kind of automation and professionalism once implemented in the manufacturing industry. In this case, it's mostly revolving around data-driven marketing decisions and in-store retail media capabilities. As shown by Axians, a VINCI group company, AI isn't a mere toy for undergraduate students who are failing their … The post AI in retail: shrinking queuing times today, headcount tomorrow appeared first on Marketing and Innovation.

Data-Driven AI is the future of customer experience, François Ajenstat told us at a recent interview. François is Chief Product Officer at Amplitude, the company behind a digital analytics platform aimed at helping B2B and B2C businesses build better products, websites and ecommerce experiences through behavioural data. François stressed the significance of data-driven AI within … The post Data-Driven AI Is the Future of Customer Experience appeared first on Marketing and Innovation.

Ever heard of cookie pop-ups? It's true that it's hard to escape them. Following the 2011 Cookie Directive, sites have finally complied. But rather than deleting cookies, they have installed cookie pop-ups, which throw annoying messages at you and prevent you from surfing the web. They're useless, mostly because they don't really improve data confidentiality. … The post Protecting your privacy and avoiding cookie pop-ups appeared first on Marketing and Innovation.

The state of influencer marketing in Europe 2024 is a survey conducted by Kolsquare, a leading European influencer marketing agency. It provides a particularly interesting perspective on influencer marketing budgets, how influencer marketing is handled and its future trends. Besides, its comparison of Europe's main markets for IM is clearly enlightening. It's one of the … The post Influencer Marketing: Average European Spend at €3.5m Annually appeared first on Marketing and Innovation.

The Cyber threat landscape in Europe is quite worrying. A recent survey by Cloudflare was conducted amongst 4,261 IT executives responsible for cybersecurity in Europe. 24% of the sample is made from small enterprises (150–999 employees), 24% from medium-sized businesses (1,000–2,500 employees) and 52% from large organisations (above 2,500 employees). All major European countries were … The post Cyber threat Landscape Europe, 2024 appeared first on Marketing and Innovation.

Content creation with artificial intelligence is already old hat as it's been going on for a few years and, unfortunately, slop is now populating the Internet at an increasing pace. Yet, when I received this message from a good friend of mine last week regarding Google's new app entitled NotebookLM, I was shellshocked. I tried … The post NotebookLM by Google: Artificial Voices, Real Concerns appeared first on Marketing and Innovation.

Whereas Ed Zitron is castigating the major Tech players responsible for the peak of inflated expectations surrounding AI, many tech pundits are still touting that AGI (Artificial General Intelligence) is within reach. To find out if AGI is a myth or a reality, I interviewed J.G. Ganascia, a long-time AI researcher and philosopher. In the … The post AGI (General Artificial Intelligence), Myth or Reality? appeared first on Marketing and Innovation.

Are GenAI and content marketing compatible? Adobe organised a round table discussion on that subject during their Experience Makers conference in Paris in early November 2023. The debate brought together a few digital experts. During this discussion, I mentioned that there were limitations to GenAI images and that they weren't technical. Others contended that it … The post GENAI and Content Marketing: Learning from experience appeared first on Marketing and Innovation.

Our reporters attended the Paris Retail Week 2024 event, a trade show of which we are media partners, to take stock of fraud and the role of AI. We collected a lot of valuable feedback on threats (both in-store and e-commerce) and the countermeasures proposed by artificial intelligence. To do so, we interviewed Gilles Bijaoui, … The post Harnessing AI to Combat Fraud in Retail and E-Commerce appeared first on Marketing and Innovation.

What is the likely impact of AI and GenAI in particular on jobs, especially in Europe? Two recent reports on the topic, one in the UK and another one in France shed light on this question. According to the French report, such impact could amount to 5%. Yet another case for precision vs accuracy. That … The post GenAI impact on jobs: doom or boon? appeared first on Marketing and Innovation.

Whether it's music and AI, or innovation in the broad sense of the term, at Visionary Marketing we like to look back in time. A few days ago, while doing the housekeeping of some of our 3,000 articles, we rediscovered this post by Mia Tawile written in July 2016. Eight years is the equivalent of … The post Music and AI: Back to the Future appeared first on Marketing and Innovation.

Thomas Deneux is the founder of Learning Robots whose aim is to help pupils, students and businesses to learn AI, with the help of home-made self-driving gizmos. These little machines on two wheels are more serious than you'd think. They are all about the teaching of advanced computing. Thomas described his philosophy to me during … The post Learning AI with the help of robots appeared first on Marketing and Innovation.

What are the most outstanding new features of LinkedIn in 2024? Reid Hoffmann's professional network was created almost 21 years ago (in May 2003) and acquired by Microsoft in 2016. Visionary Marketing invited Bruno Fridlansky to talk about this platform, of which he is an expert. Together we were able to answer a few basic … The post LinkedIn's new features under the microscope appeared first on Marketing and Innovation.

What if hiring luxury venues during the 2024 Olympics were a good opportunity for brands, even small ones?  The Olympic Games are only six months away from now and I was wondering how much of an opportunity it was for brands and which ones. To find out I invited Tanya Bencheva, the CEO and founder … The post Luxury Venues during the 2024 Paris Olympics appeared first on Marketing and Innovation.

Just like the Trojan war, the AI “revolution” will not take place*. Today's topic is the inevitable generative AI. This is the perfect opportunity for us to discuss what a technological revolution is or isn't. Here are our thoughts, and we might as well warn you that we are putting our trotters in the trough in … The post The AI ‘revolution' will not take place appeared first on Marketing and Innovation.

How can a responsible marketer survive when the world around us is crumbling? Or at least when experts are telling you that it is. Three years to the day, the French association of marketers Adetem asked Visionary Marketing to join its CSR responsible marketing initiative, and naturally we welcomed the opportunity. That almost seemed natural to us. … The post CSR: a survival guide for the depressed responsible marketer appeared first on Marketing and Innovation.

Will marketers survive the content shock in the age of AI? The Omnes Education Group launched a cross-organisational programme in English called “Content creation in the age of AI” to help its students better understand GenAI. Close to 1,000 students will be certified in this program by early February 2024. As part of the program, … The post Surviving the Content Shock In The Age of GenAI appeared first on Marketing and Innovation.

According to a recent Forrester report (How Generative AI Will Transform CRM), GenAI may have significant and beneficial impacts on Customer Relationship Management systems and practices. The real question is, however, how effective GenAI could be when it comes to handling various customer-facing tasks? And how easily could it adapt to specific CX applications? Forrester's analysts … The post Can GenAI have an impact on CRM? appeared first on Marketing and Innovation.

What are the applications of geographic information systems? Following our discussion with Catherine Crook, we asked Floyd Bull to answer that question for us. Geographic information systems and their applications How did you get started with GIS? During college, I saw geographic information systems as an opportunity that would lead to jobs after my studies. … The post What are the applications of GIS systems appeared first on Marketing and Innovation.

Visionary Marketing spoke with Catherine Crook ­– senior GIS program manager for Hexvarium – to discuss the use of geographic information systems or GIS and its impact on communication networks and the world. We touch on her day-to-day obligations as a senior gas program manager. The software is used for tracking systems, the issue of … The post A day in the life of a GIS program manager appeared first on Marketing and Innovation.

Product Information Management (PIM) systems are a driving force behind a good customer experience. A 2023 survey entitled “Elevating customer experiences with product experiences”, sheds light on how product information can greatly enhance CX. Virginie Blot, Product Experience Management evangelist at Akeneo, gave us her point of view and analysis on that survey. Place a … The post PIMs at the heart of Customer Experiences appeared first on Marketing and Innovation.

A recent study on behavioral targeting demonstrates the evolution of consumer behavior and its impact on segmentation in consumer marketing. While criticisms of profile-based segmentations are nothing new, the study in question provides a quantified demonstration of the extent of change in this area. It also provides figures and details on the various so-called “Behavioral … The post Behavioral Targeting is the right way forward appeared first on Marketing and Innovation.

The green market revolution (impacts, how to adapt and change retail business models and organisations) was the title of Forrester‘s Thomas Husson's Keynote at the Paris Retail Week 2023 Trade Show. A topic of vital interest to our readers and ourselves. We interviewed Thomas to better understand how urgent the situation is, and how consumers … The post Forrester's Green Market Revolution appeared first on Marketing and Innovation.

The learning curve that governs qualitative marketing studies is pivotal if you want to avoid ending up with mountains of useless data. What's more, the kind of data produced by marketing studies is unstructured and complex. Gathering too much of it will also inevitably lead to soaring costs. Our simple methodology based on the experience … The post The learning curve of qualitative studies appeared first on Marketing and Innovation.

AI may be fun to play with but its implementation within businesses is a tad more complex. Visionary Marketing attended a round table discussion on the impact of generative AI on businesses and the future of work at Big Data AI Paris in September 2023. AI and IT experts from different backgrounds were able to … The post Implementing AI within businesses appeared first on Marketing and Innovation.

Visionary Marketing spoke with Katja Graisse – co-founder of Balistikart, an independent digital creative agency – to discuss the Incognito AI Influencer Project. We touch on the benefits and limitations of AI-generated influencers, the intertwining of art and business, and more. Katja provides an optimistic assessment of the current digital landscape, proclaiming that the usage … The post An AI Incognito Influencer in the digital world appeared first on Marketing and Innovation.

‘Breaking down silos' is certainly digital transformation's most common phrase and myth. If you haven't heard this phrase repeated over and over again, chances are your government hasn't let you out of your house after the Covid-19 pandemic. It's probably the biggest fable about transformation projects, be they digital or not. Some time ago, I … The post Breaking down silos: digital transformation's greatest myth appeared first on Marketing and Innovation.

Will marketing jobs be killed by AI? We met with Jamie Brighton, at Adobe Summit 2023*. Jamie is Product Marketing Director for the Adobe Digital Experience business. His answer to the above question is a blatant No! He sees AI as being a co-pilot of marketers in their daily tasks. A set of tools to bridge … The post Marketers should not be afraid of losing their jobs to AI appeared first on Marketing and Innovation.

It's this time of year, Adobe summit 2023 is taking place on June, 8–9 in London and Visionary Marketing will be there as #adobepartner (disclosure*). After a few years where the event only took place online (check our coverage here). It's now a hybrid event, which is taking place both online and offline. As we … The post Making Preparations for Adobe Summit 2023 appeared first on Marketing and Innovation.