English language Visionary Marketing Podcasts

Visionary Marketing

Visionary Marketing Podcasts in English

  1. JAN 28

    AI Job Impact in the US: the Apocalypse Can Wait

    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 nec

    28 min
  2. JAN 26

    AI is not a tool it’s reshaping our society and economy

    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 po

    26 min
  3. JAN 9

    Private Equity Branding Enhances Valuation Through Storytelling

    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 suffering Tom 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

    43 min
  4. 12/03/2025

    Inside the Ebook Self-Publishing Industry

    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.

    18 min
  5. 10/27/2025

    Web writing : words retain all their magic

    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 AI: why words retain all their magic Niederhoffer is adamant: despite GenAI, the written word retains its magic — image produced with Midjourney Human vs. AI Selim, a seasoned copywriter and author is keen on using what he calls “magic words.” Ask him why and his answer might surprise you in this age of sheer automation. “I’ve already been thinking about magic words for years. I want to dig deeper, show real examples and, most importantly, explain why they actually work.” Pen and paper His approach remains resolutely traditional. “I genuinely work with a pen and paper.” This approach reveals something fundamental about Web writing: it works when you truly understand the psychology behind it, not when you just mechanically apply copywriting techniques. Selim bases his work on well-established principles of persuasion. Selim Niederhoffer still believes in the virtues of word magic in Web writing — image produced with Midjourney “When there’s a principle of persuasion, there’s usually a word that goes with it. Take urgency or scarcity, for example. If something’s rare, that means limited places, running out of stock… that sort of thing. That’s how I build my word cluster. The research then extends to field observation. “I look at what my clients are using, what’s going on at Burger King, McDonald’s, Nike. I check out the major brands too – what they’re doing on YouTube, on LinkedIn.” Eventually, Selim identified 55 magic words but trimmed them down to 50. It’s an approach that perfectly shows what still sets humans apart from machines: the ability to critically analyse and curate with discrimination. However, it’s worth adding nuance. Selim can’t conceal that he genuinely “loves” ChatGPT. As we’ll see later, this raises some legitimate questions. Thank you! the ultimate magic word Among the 50 words he’s analysed, the first is also the simplest: thank you! For Selim, this should be essential for every business. “How many times have you walked out of a shop where the sales assistant just didn’t seem to have noticed you? Whether you bought anything, or didn’t doesn’t make any difference, once the transaction’s done, you’re out the door.” Yet some brands know how to thank their customers. “Nespresso or Apple, for example: the Nespresso employee comes out from behind her counter, she hands you your product. Thank you for your visit. Have a nice day! That’s how it should be.” For Selim, saying “thank you!” is more than just being polite, it’s a way of life. “You can go further: thank you for your visit, thank you for subscribing to the newsletter, thank you for your comment. You need to constantly think in terms of gratitude.” This approach fits into what Gary Vaynerchuk called The Thank You Economy. “We’re in an attention economy,” Selim explains. The stakes are high in Web writing: how do you maintain this human dimension at corporate scale? “For me, the essence of business is that there’s a person in front of you who’s exchanging something with you. That’s really the foundation. But today, how do you keep that at corporate scale?” Data confirms an intuition: gratitude improves customer experience and encourages loyalty. A valuable lesson for all those who practice Web writing and seek to create a lasting connection with their audiences. You need to seek to create a connection with your audiences — image produced with Midjourney AI and Web writing: threat or opportunity? The conversation inevitably turns to artificial intelligence. For Selim, AI is first and foremost an incredible productivity tool. “If I’ve got a newsletter to write, I’ll use AI. Sales pages? AI. The thing is, AI works brilliantly for me. I’ve even become clearer in my writing,” he admits without hesitation. But he’s not entirely starry-eyed about it. He’s identified three major pitfalls that professionals absolutely must avoid. The three pitfalls of AI in copywriting The first pitfall is wordiness. “ChatGPT tends to waffle because it’s been trained on Reddit, Wikipedia, and similar sources. However, in real life, when we write, we cut down to the core, deleting most of what we initially write.” The second limitation is related to syntax. “AI tools have their favourite phrases. It can be a bit clunky. It’s got that ‘ChatGPT-ish’ quality – you know it when you see it. After a while, you can spot AI-written text a mile off.” The third issue, and the most surreptitious one, is lack of personality. “When we just use basic LLMs, we lose our tone of voice, we lose what makes us different, we lose what makes people go ‘Ah! That’s Selim, that’s Yann’. That personal touch. I’d say that’s the biggest danger AI poses for copywriters, Web writers, anyone working in this field.” For him, the key questions are: how do you refine AI, how do you avoid its main pitfalls, how do you stay in control and how do you harness all its power? This evolution shows a profound shift in the profession: the copywriter is becoming an orchestrator of AI platforms. Losing our skills Selim warns against a hidden danger: declining skills. He reminds us that the brain is a muscle, and using it creates connections. However, if we stop using it, those connections are lost. He admits that he has fallen victim to this himself. “I have noticed that my writing isn’t as fluid as it used to be when I’m starting from scratch. Between 2010 and 2022, I was churning out three to five blog posts a week. Now, if I can just knock up a prompt, get a result and tweak it a bit, job done. But it’s less satisfying.” This awareness led him to experiment. “I run A/B tests. Send out version A written by AI, version B written by me, then see who clicks more. I check which headlines work best, which text performs better,” he explains. It’s a data-driven approach that could bruise his ego, he admits with a laugh, but it’s essential for understanding what genuine human value looks like in Web writing. What human added value tomorrow? The final question comes naturally: will humans still add value compared to machines in a year or two? Selim remains cautious about making predictions. “I’m rubbish at forecasting because I don’t spend enough time on it,” he admits candidly. But his reading of the market reassures him. While I can see companies increasingly launching AI training for their staff, there’s always the question of depth. Are they adequately trained and properly supported? Web writing needs remain massive “I meet marketing directors who want me on their pay-roll. We’ve got so much work to get through that we’d need ten people using ChatGPT just to keep up,” he reports. This suggests that AI isn’t so much replacing writers as it is multiplying opportunities for content production. Selim’s thoughts tap into bigger questions about how work is evolving. “The idea of heading towards a society where people become useless doesn’t surprise me. I’ve seen it enough times in sci-fi,” he confides. This vision actually mirrors history: villages like ours in the Pyrenees, with thousands of inhabitants at the turn of the 20th century now down to a few dozens. Economic and technological shifts have always redrawn the employment map. What the future holds for Web writing This conversation with Selim Niederhoffer sketches out the shape of Web writing as it transforms. Words remain magical, but how we summon that magic is changing. AI’s becoming a powerful content production tool, but the real value still lies in properly understanding how persuasion works, in being able to orchestrate these tools cleverly, and in keeping a human tone of voice. Here’s the paradox: just when machines can churn out infinite text, what becomes scarce is quality strategic thinking, subtle psychological understanding, and the ability to inject real personality into content. Selim’s magic words are just a a beginning – they are mere ways of understanding what makes human readers tick. A hybrid Web According to Selim, tomorrow’s Web writing will be hybrid – a collaboration between human and machine. It will demand technical mastery of AI tools, but it’ll be rooted in a deep understanding of how persuasion actually works. In short, words will always be magical, but that magic needs cultivating, training, constant work – otherwise it will erode in the face of automation’s easy appeal. One needs to keep reading, discussing and engaging with real people. That’s how we will train our brains and hang onto that human edge that makes all the difference in Web writing. 10 of Selim’s 50 magic words Here are 10 words taken from Selim Niederhoffer’s book “Les Mots Magiques” (Magic Words), which are effective words to boost your marketing content and encourage action: Please Thank you New Since Out of stock Free Now or never Satisfaction guaranteed or your money back Hundreds of people already trust us First name The post Web writing : words retain all their magic appeared first on Marketing and Innovation.

    10 min
  6. 10/20/2025

    The Truth About the Environmental Impact of AI

    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 environmental impact. It is worthy of note that Yves and I explored Silicon Valley’s infrastructure innovators together through extensive press tours some time ago. This provided us with firsthand insight into the tech industry’s approach to these challenges. The current annotated transcript of our interview is a summary of our thorough, nuanced, and let’s admit it, quite lengthy discussion. You are therefore encouraged to treat this article as your starting point for diving deeper into this extremely complex topic. Exploring the Real Environmental Impact of AI What’s the real environmental impact of AI? An employee keeps watch over the cooling units at Orange’s data centre in Val de Rueil in Normandy, France — Photo antimuseum.com * DCMag is only available in French This post summarises what turned out to be an incredibly rich hour-long conversation. The sheer complexity of this topic forced us to dig into multiple technical, economic, and environmental angles—making any kind of comprehensive analysis near inconceivable. Drawing on his deep expertise in the data centre and AI sectors, Yves Grandmontagne gives us some much-needed factual perspective on a debate that’s often polarised between doomsday scenarios and over-the-top techno-optimism. To tackle this properly, we decided to take recent quotes—both positive and negative—and fact-check them with our expert. Yves’s analysis helps us cut through the noise and understand what’s really at stake in this technological breakthrough. Environmental Impact of AI: Reality Check Time TLDR: Environmental Impact of AI The electricity consumption issue is more nuanced than you think* – AI will represent 20-30% of data centre consumption (not twice that number), and only 2-4% of overall electricity consumption Energy efficiency gains are actually remarkable – Over the last decade: number of data centres x2, floor space x4, but energy consumption up only 6% Beware of dubious comparisons – Comparing a ChatGPT query to Google search is methodologically flawed (completely different technologies and services) Water consumption varies massively by geography – Huge issue in the US, but Europe has been using smarter closed-loop systems for ages Tech innovations look promising – New technologies (direct liquid cooling, immersion cooling) are slashing water and energy consumption AI might actually be part of the solution – Can optimise energy mix management and electricity transport, which is currently our main bottleneck Let’s get some perspective here – Data centre impact remains pretty marginal compared to the chemical industry (32% of French energy consumption) or agriculture *All numbers by Yves Grandmontagne at Data Centre Magazine Bottom line: The impact is “real but massively overstated”—we need to put things in context and remain cool and collected. So here are the quotes about the environmental impact of AI that we wanted to fact-check with Yves. Rumours vs Reality: Decoding the Doomsday Predictions Predictions of Booming Electricity Consumption The first claim I put to Yves Grandmontagne was Synth Media’s prediction [Fr] that “AI’s growth could double data centre electricity consumption by 2026.” His response immediately throws cold water on this alarmist take: “It’s absolutely true that AI is rolling out infrastructure at breakneck speed and eating up more space in data centres. Sure, it’s going to significantly bump up their energy consumption—no question about that. But will consumption actually double? I seriously doubt it. AI should account for somewhere between 20 and 30% of global data centre consumption worldwide.” A data centre server rack — photo antimuseum.com AI should account for somewhere between 20 and 30% of global data centre consumption worldwide This reality check reveals something consistent throughout Yves Grandmontagne’s analysis: the absolute need to put numbers in context. The expert points out that this increase is just part of the natural progression tied to our ever-growing digital habits. “What’s driving this consumption increase is our daily usage, whether that’s for work or for personal reasons,” he reminds us, highlighting our collective responsibility in this evolution. It’s a topic we’ve tackled before with a broader focus on digital consumption. The most striking part of his analysis is about the energy efficiency angle. Contrary to popular belief, data centres aren’t following a consumption curve that mirrors data growth. This improving efficiency is something that gets completely overlooked in public debates about the environmental impact of AI. ChatGPT’s Carbon Footprint: More Context Needed When it comes to the 10,113 tonnes of CO2 equivalent attributed to ChatGPT usage in January 2023 (Basta Media – Data for Good, AI has the potential to destroy the planet), Yves Grandmontagne takes a refreshingly pragmatic approach: “I can’t verify that exact figure. Getting that precise—down to 10,113 tonnes—represents a massive methodological challenge, especially when you’re dealing with AI infrastructures that are distributed systems.” We asked Yves Grandmontagne, editor-in-chief of DCMAG (Data Centre Magazine) to give us the real story about the environmental impact of AI — He gave us facts and figures, which we’ve compiled in the infographic at the end of this post This observation raises a crucial methodological point: just how tough it is to accurately measure the carbon footprint of distributed infrastructures. The expert does acknowledge this pollution is real, but puts it in perspective: “Those 10,113 tonnes of CO2 still represent volumes significantly smaller than what many other industries pump out.” This contextualisation isn’t about downplaying the issue—it’s about keeping things proportional. Yves Grandmontagne reminds us of a basic truth that is often overlooked: “The moment we use our smartphones, we become CO2 producers.” This highlights the inconsistency in criticisms that single out AI from our overall digital consumption. The Google vs ChatGPT Comparison: A Methodological Trap Watch out for convenient shortcuts between pollution and digital tech—they’re everywhere and pretty handy when you want to hide overall industrial pollution — image created with Midjourney MIT’s claim that “a ChatGPT query uses ten times more electricity than a Google search [p 9]” perfectly illustrates the danger of oversimplified comparisons, according to Yves Grandmontagne. His take is particularly eye-opening: “This comparison just doesn’t work. I think we’re making a fundamental methodological error here because we’re comparing two completely different technologies. Google is a search engine that gives you results that you then have to sift through to find what you’re actually looking for. ChatGPT, on the other hand, serves up information that’s already structured and ready to use.” “The more we use computing, and the more we use AI, the more we consume.” User responsibility matters — images antimuseum.com Orange data centre in Val de Rueil This analysis shows just how sophisticated you need to be when evaluating the environmental impact of AI. A ChatGPT query might actually replace multiple Google searches plus visits to various websites. That makes direct comparison pretty meaningless. This distinction will probably become moot anyway with the rollout of Google AI Overviews, which will integrate similar functionality to ChatGPT. The Water Issue: Geography Matters More Than You Think New data centre cooling techniques work in closed loops. Image antimuseum.com Orange data centre in Val de Rueil Massive Geographical Differences One of the most revealing parts of our interview dealt with data centre water consumption. Yves Grandmontagne draws a crucial distinction between American and European practices: In the United States, when you install a data centre, you’re a private company reaching out to other private companies for water on one side, electricity on the other. And the utility companies that manage energy or water are thrilled to have a massive client that’ll absorb a chunk of their production. This geopolitical insight explains those 5.4 million litres of fresh water attributed to ChatGPT-3 training. The issue isn’t the technology itself—it’s local practices and regulations. In Europe, our expert reminds us, “we’ve been developing infrastructure cooling systems that work in closed circuits or are air-based rather than water-based for quite some time”. Take Orange’s data centre in Val de Rueil, for instance—it’s cooled by the crisp Normandy air. The only exceptions are during heat waves, which are naturally time-limited. How Cooling Actually Works Yves Grandmontagne’s technical explanation demystifies the cooling process: “Water acts as a conductor to capture heat.” He then breaks down the dual-circuit system that protects infrastructure while managing thermal exchanges. This approach reveals that warm water discharge from data centres (~20-25°C) stays well below that of nuclear plants (27-35°C for the Gravelines nuclear plant in the North of France). That gives us a useful comparison point for our debate. Warm water discharge from data centres (around 20-25 degrees) stays well below that of nuclear plants (27-35°C for

    23 min
  7. 09/30/2025

    Is the AI Bubble About to Burst?

    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 very critical of the situation. For instance, Ed Zitron predicts that the AI bubble will burst in Q4 2025. All this is fueling intense, rather than rational, debate. I wanted to confront these concerns with the expertise of Bernhard Schaffrik, Principal Analyst at Forrester Research. His analysis is insightful and nuanced. In his mind, there will be some sort of correction, but at the same time, GenAI is too popular to disappear. When Will the AI Bubble Burst? Is the AI bubble about to burst or is GenAI here to stay? Forrester’s Schaffrik predicts corrections but says GenAI is too popular to go — photo by Forrester.com Forrester’s Bernhard Schaffrik is recognized as one of the most insightful experts in artificial intelligence. He provides a nuanced analysis that transcends simple financial considerations. His perspective on the AI bubble burst scenario offers first-hand insights for understanding where this transformative technology is truly heading. The AI Bubble: Financial Reality, Technological Continuity The question of a potential AI bubble burst cannot receive a univocal answer. As Bernhard Schaffrik rightfully points out, it all depends on one’s perspective. This duality of vision probably constitutes one of the keys to understanding the current situation and the likelihood of an AI bubble burst. Like us, Schaffrik righfully points out that the main issue with AI is societal and philosophical — image generated with Adobe Firefly “It’s almost impossible to get a one-sentence response from an analyst. Allow me two sentences. Number one is, of course, it always depends on the role or the profile you’re asking. If we are talking about financial investors, then yes, there are strong signals of this being a bubble because there is so much money being pumped into it—more than $120 billion US dollars in capital expenditure on AI infrastructure alone, just by the Magnificent Seven tech providers. So that bubble could burst,” explains Forrester’s expert. This assessment gains particular relevance when considering Google’s $9 billion AI data center investment in Oklahoma for advanced AI training infrastructure. This financial perspective, however, tells only part of the story. Technological adoption follows a different logic from financial markets, as Schaffrik confirmed during our exchange about the AI bubble burst potential. “But now, if you put yourself in the shoes of enterprise decision makers, tech decision makers, also AI users, there are many who would say, ‘I don’t care if that bubble bursts, the technology is there, and it won’t go away.’ “Regardless of the amounts all the financial transactions surrounding the AI industry, people are actually using this technology. And they like what they are seeing. It might not be the disruptive, transformative value some are surmising. It’s probably more incremental than that, but the adoption of that technology is undeniable.” The Revenue Challenge: A $25 Billion Gap to Bridge Fortune’s analysis reveals a concerning gap between current investments and generated revenues. To justify current investments, AI companies would need to generate $40 billion in annual revenue, while they currently produce only $15 to $20 billion. Schaffrik doesn’t believe in an AI bubble burst right now — image made with Adobe Firefly I was wondering whether this $20-25 billion gap could represent a systemic risk that could trigger an AI bubble burst. Schaffrik remains relatively optimistic on this point: “There is still enough money in that market to back these revenue gaps at least for a while. And what I’m also seeing is that especially when it comes to the largest enterpriseson the planet, they are convinced to continue using that software. And if it comes at a premium which is decent, arguably, maybe a couple percentage points higher than what they are paying today for the software, then this seems to be acceptable.” This acceptance of additional costs by large enterprises stems from the incremental value they perceive, even if it hasn’t yet reached the promised transformation level that might prevent an AI bubble burst scenario. LLM Regression: A Warning Signal? A particularly troubling element in the current ecosystem is the recent NewsGuard study revealing that major LLM systems are no longer progressing but regressing, generating more hallucinations and errors. This observation raises fundamental questions about current technology maturity and its impact on AI bubble burst predictions. “I’m not saying that LLMs and generative AI are progressing in a linear fashion nor that this technology will be disruptive in any way, despite the promise. As we have seen with emerging technologies for decades and even centuries, it takes breakthrough technological revolutions rather than evolutions to fulfill such promises,” analyzes Schaffrik. This vision of the current limitations of AI doesn’t diminish Bernhard’s long-term optimism: “But I’m also convinced that these breakthroughs will happen, not within the next seven, eight, nine, 12 months, but maybe in the long term. Something else will be coming up.” Energy Efficiency: The Achilles’ Heel of AI One of Schaffrik’s most compelling criticisms concerns the energy efficiency of current systems. His comparison between the human brain and data centers is striking and relevant to understanding whether we’re facing an AI bubble burst. “If we look at the amount of energy our brains are requiring to create a certain inference, and how much an LLM would require to achieve the same result with electricity, this cannot be the way forward.” This energy inefficiency constitutes a major barrier to scalability and will require significant technological breakthroughs to overcome, potentially influencing AI bubble burst timing. Pilot Failures: Business as Usual or Red Flag? The 95% failure rate of corporate AI pilots revealed by MIT research might seem alarming and suggestive of an impending AI bubble burst. Yet Schaffrik places this figure in its historical context: “It’s quite normal. As an analyst covering innovation management, what I have been observing over time is that about 10% of all innovation-related minimum viable products, proof of concepts, pilots, will turn into a product.” The problem would rather lie in unrealistic expectations: “Everybody rushed at it because one believed that since it’s accessible through natural language, it should  be easier to deploy, to implement, and there are no drawbacks and negatives. That might explain that the failure rate is slightly higher than with technologies we saw in the past.” This assessment aligns with Gartner’s prediction that 30% of GenAI projects will be abandoned after proof of concept by 2025 due to poor ROI and unclear business value. AGI: The Next Revolution in Motion Despite current limitations, Schaffrik maintains his bold prediction from his July 2025 analysis “Demystifying Artificial General Intelligence” that Artificial General Intelligence (AGI) represents “the biggest change in tech we have ever seen and is starting right now.” This vision, which could influence AI bubble burst scenarios, is structured around three maturity stages. “Competent artificial general intelligence is lurking around the corner. Our prediction is that between 2026 and 2030, we will see competent artificial general intelligence. You could think of it as your first trustworthy AI agent. You might not want to give it your car keys or your wallet, but it might do amazing things.” The subsequent stages would unfold over more distant horizons: independent AGI within the next five to ten years, And lastly, strategic AGI in a more distant future. Current AI Limitations: Experience versus Training A crucial point raised in our discussion concerns the difference between training and experience. As I pointed out to Schaffrik, experience develops critical thinking that current LLMs don’t yet possess, which could impact AI bubble burst predictions. “We might get to a point where most of us humans wouldn’t be able to tell if on the other side, a human or a machine is interacting with us. There will be areas where we will still be able to tell. But experience is something we could at least partially solve with more data and better data,” states Schaffrik. The solution, according to him, lies in massive data collection: “So much of the billions of investment money flowing now into all these big companies is also to collect and curate data also from the physical environment. Bringing all this data together, will create something that mimics experience.” The Human Factor: What’s Left for Us? The philosophical question of what humans will do when machines surpass us in thinking capabilities represents Schaffrik’s personal concern regarding potential societal implications of advanced AI, regardless of any AI bubble burst. “That’s my personal doomsday scenario, I must say. It’s not good for us humans to just idle around. So it’s not so much a technical conversation, but more a political, societal, psychological and philosophical one. So I’m sure we are far away from this, but we are getting there.” Leadership and Preparation: The Governance Challenge Regarding leadership in this transformation, Schaffrik acknowledges the complexity: “Rulers are supposed to rule

    27 min
  8. 09/25/2025

    AI Agents, Beyond the Hype

    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 really mean for businesses today. Ed brings a unique perspective, having taken on his new role in February 2025 at a time when the industry is proclaiming this as “the year of agents.” His insights reveal both the tremendous potential and the current limitations of this transformative technology. As always, time is of the essence. AI Agents, Beyond the Hype Preogress Software’s Ed Keisling did a great job debunking the myths surrounding AI Agents and showing what the future holds beyond the hype – photo Progress Software. The Rise of the Chief AI Officer: A Strategic Imperative The creation of Chief AI Officer roles across the technology industry signals more than just a trend—it represents a fundamental shift in how businesses view artificial intelligence. As Ed explains, “AI needs to be a strategic pillar of a business to drive innovation and growth. It really reflects the pace at which technology is evolving, and having somebody that is accountable to follow all these latest updates and really look at it through the lens of new risks and opportunities.” This observation resonates with the broader digital transformation patterns we’ve witnessed over the past decade. Just as Chief Digital Officers emerged to guide organizations through the digital transformation revolution, Chief AI Officers are now stepping up to navigate the AI transformation. The role isn’t merely about implementing technology—it’s about strategic thinking, risk assessment, and identifying genuine business opportunities in a rapidly changing landscape. AI agents: the promise with tools like Manus is that they would behave like your favourite dog. Go search, Rover…! — photo by antimuseum.com Defining AI Agents: Beyond the Buzzwords One of the most persistent challenges in the AI space is the confusion surrounding terminology. AI agents, in particular, have become an overloaded term that means different things to different people. Ed provides valuable clarity by positioning agents on a spectrum of AI capabilities. “When generative AI came out, it was generally reactive,” Ed notes. “We would go to ChatGPT, provide a prompt, and it would generate a response based on its training patterns. Agents are moving along that spectrum in terms of capabilities—they have the ability to perceive their environment, access to audio, video, documents, and crucially, the ability to reason, plan, and learn from their actions.” Unfortunately, Rover isn’t always willing to search in the right direction… — photo by antimuseum.com Traditional automation relies on strict rule-based systems—the digital equivalent of if-then-else logic. Chatbots, while more sophisticated, remain predominantly reactive. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances The evolution doesn’t stop there. Ed introduces the concept of “agentic AI“—a broader paradigm where multiple agents collaborate, passing context between each other to accomplish complex tasks. This represents the holy grail of AI automation: systems that can dynamically adapt to real-time situations without constant human intervention. Reality Check: Why Perfect Automation Remains Elusive Despite the exciting potential, Ed provides a sobering reality check about current capabilities. His observation about the Pareto principle in AI is particularly insightful: “AI is the ultimate manifestation of the 80/20 rule. You can very rapidly get to value with 20% of the work achieving 80% of the results, but actually getting it to work 100% of the time is still very, very difficult.” This phenomenon explains why AI demonstrations look so compelling while real-world implementations often fall short of expectations. The gap between proof-of-concept and production-ready systems remains significant, requiring careful planning, clean data, and well-defined business processes. As always, I should add, “the more it changes, the more it stays the same,”as the French poet would have it. RAG Technology: Making AI Practical for Business While pure AI agents may still be evolving, Progress Software’s acquisition of Nuclia, an agentic RAG (Retrieval Augmented Generation) provider, demonstrates a more immediate and practical application of AI technology. Ed explains the fundamental problem RAG solves: “Large language models have been trained on the entirety of the Internet, giving them broad general knowledge, but they don’t have access to data stored behind firewalls or on personal computers.” This limitation is critical for businesses. While public AI models are impressive, their real value emerges when they can access and reason about proprietary business data. RAG technology bridges this gap, allowing organizations to leverage AI’s reasoning capabilities while grounding responses in their specific knowledge base. The practical implications are significant. As Ed points out, “Small to medium-sized businesses have lots of unstructured data—audio, video, log files, recordings, PDFs, charts—that represent proprietary business value, but they have no way of indexing or finding or correlating the data within it.” RAG technology makes this data accessible and actionable. It’s high time to stop AI-Washing Ed Keisling advises — image created with Midjourney Separating Innovation from AI Washing Ed’s experience at the AI4 conference provides valuable insights into the current state of the AI industry. His observation about AI washing is particularly relevant: “There was a lot of AI washing—companies that weren’t sure they understood the problem to be solved, with very thin wrappers around language models to solve point problems. It felt like a hammer looking for a nail.” The key differentiator, according to Ed, lies in problem-solving focus rather than technology-first thinking. “AI allows you to solve old problems in a new way and to make seemingly impossible problems possible. You’re thinking about how to drive outcomes—making developers more productive, automating tedious workflows, providing better insights that weren’t possible before.” This perspective aligns with successful technology adoption patterns throughout history. The most successful implementations focus on specific business outcomes rather than showcasing technological capabilities. Real-World Value: ShareFile’s Document Intelligence Progress Software’s ShareFile platform provides concrete examples of AI delivering measurable business value. The platform serves client-facing teams in regulated industries—doctor’s offices, law firms, and tax accountants—where document management is critical but time-consuming. The AI implementations are practical and measurable: “We can create curated lists of documents appropriate based on your situation, and as you upload documents, we can figure out which document relates to which checklist item. We’ve measured this at being three and a half times faster.” More importantly, the system addresses security concerns that many organizations face: “We have capabilities that scan for social security numbers, personal information, and credit card information that you didn’t want to upload. This single capability flags around 35,000 documents a week.” These examples demonstrate AI’s sweet spot: automating routine tasks while enhancing security and accuracy. The value isn’t just in speed—it’s in freeing professionals to focus on high-value work instead of admin tasks. The Human Factor: Reskilling Rather Than Replacing One of the most contentious aspects of AI adoption concerns workforce impact. Ed’s perspective is refreshingly pragmatic: “This is a fundamental reshaping of how business is done—a new skill and opportunity for people to grow, learn, and reinvent themselves. There aren’t experts who have been doing this for five or ten years. If you have the headspace and desire, you can become that expert.” This view positions AI as an enabler rather than a replacement. The technology’s real power lies in eliminating organizational silos and enabling individuals to accomplish more with the right tools and training. “One person is now going to be capable of doing multiple things with the right prompts, giving them opportunities to do more and drive more value for the organization.” The message is clear: organisations with infinite backlogs of valuable work don’t need to fear AI displacement. Instead, they should focus on upskilling their workforce to leverage these new capabilities effectively. Looking Forward: Practical Adoption Strategies Ed’s recommendations for AI adoption focus on practical, incremental approaches rather than transformative leaps. “There’s enormous green space for individuals to become fully enabled with AI. The majority of people using AI today are using it in a Google-like fashion, but they haven’t taken time to understand how to correctly prompt agents or use advanced capabilities.” The most successful implementations start with individual productivity tools—document summarization, email assistance, and internal search capabilities—before advancing to more complex agentic systems. This approach allows organizations to build AI literacy while demonstrating concrete value. In Conclusion, Embracing Reality Whi

    28 min

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