Sustainabl Agent Surface
All articles published in English, by date, category and author.
177 articles · Page 1 of 8
The promise repeated over the last ten years was tempting: with videoconferencing and instant messaging, distance would cease to be a real obstacle. Any company could build global teams without paying the price of geographic dispersal. That promise was partially true and completely insufficient.
According to 2026 research by CIO&Leader, 52% of companies surveyed reported that fewer than one in four AI pilots ever becomes an operational system. Only 8% managed to have more than three-quarters of their experiments cross that line. And just 12% were able to demonstrate significant, measurable return on investment.
The chief technology officer of a large global corporation recently told Abhijit Kakhandiki, senior vice president at BMC Software, something that captures the state of the market with uncomfortable precision: his data science teams had built more than 200 artificial intelligence models. Fewer than ten were in production. A conversion rate of less than five percent.
The average executive who takes the helm of a public company today is 55 years old, seven years older than in 2000. A study by the National Bureau of Economic Research that analysed more than 50,000 executive career trajectories arrived at that figure and explained it with an apparently reasonable logic. Boards of directors, confronted with greater economic uncertainty and growing regulatory and geographic complexity, prefer to bet on executives with broad, accumulated experience over executives with unproven talent.
There is a moment in the life of any large organization when the ambition to scale collides with an uncomfortable question: who truly owns the knowledge that makes the system work? EDF has spent decades operating reactors in France with a density of technical expertise that few institutions can match. The problem is that this knowledge lives, to a large extent, inside people, paper-based procedures and fragmented systems that were never designed to travel.
On 27 September 2026, Challa Sreenivasulu Setty, Chairman of the State Bank of India, stepped up to the podium at SBI's 13th Banking and Economics Conclave and condensed the strategy of India's largest bank into nine words: digital first, customer first, nation always. The declaration spread quickly. But before any analyst could turn it into either praise or an easy target, it is worth doing what is rarely done with corporate mantras: examining what structure of decision-making lies behind it, if any.
There is a figure that CIOs are memorizing with discomfort: 72% of organizations admit that their AI investments are, at best, breaking even. At worst, losing money. What is failing is not the machine's intelligence. What is failing is the business architecture surrounding it.
When Bjørn Gulden took over the leadership of Adidas in January 2023, the company was carrying $1.2 billion in unsold Yeezy sneaker inventory, a retreating Chinese market, and brands like Hoka and On gaining ground in the segment where Adidas had built its technical reputation over decades. What Gulden found inside, however, was not merely a balance-sheet problem. It was an organizational architecture that was systematically producing immobility.
Larry Ellison cancelled in September 2026 a plan to sell up to 50 million Oracle shares, equivalent to roughly $7.5 billion at that Friday's closing price. No official explanation was given. What did appear in a regulatory filing submitted that same week was another figure: Oracle expanded its fiscal 2026 restructuring plan by an additional $700 million, bringing the total expected cost of the programme to approximately $2.8 billion.
There are technologies that exist long before anyone makes them usable. Email had been around for decades before Hotmail showed your mother how to use it. GPS navigation existed in military devices years before a hundred-dollar gadget told a delivery driver which way to turn.
Yazmin Ruiz doesn't wait for the PepsiCo sales rep to restock her inventory. When the store runs out of chips at ten o'clock at night, she opens an app on her phone, places the order, and gets on with her shift. Behind that everyday gesture lies a transformation PepsiCo has been building since 2022 in Mexico, its second-largest global market after the United States and its most fragmented in terms of distribution.
There is a pattern that repeats itself across organizations that have spent eighteen months deploying artificial intelligence agents: they know the systems work, because they saw them work in the demo. What they do not know is whether they are still working today, in production, on their customers' data, inside the workflows that matter. That gap between the certainty of the pilot and the opacity of the real environment is where budgets, trust, and time nobody has are lost.
The figure is hard to ignore: 95% of enterprise generative AI pilots produce no measurable financial impact. This isn't a pessimistic estimate from tech skeptics — it's the central finding of The GenAI Divide: State of AI in Business 2025, produced by MIT's NANDA initiative, based on nearly 300 public implementations and more than 150 executive interviews.
There is a number worth reading twice: 542,000 industrial robots installed in factories during 2024, more than double the number installed a decade ago. This is not a trend statistic; it is a snapshot of a threshold already crossed. The global operational stock of industrial robots reached 4.66 million units, growing nearly 9% year-on-year, and the International Federation of Robotics projects that installations will exceed 575,000 units in 2025.
There's a conversation that leadership teams in heavy industries have been putting off for years. It's not about technology. It's about what it means, precisely, to make a good operational decision when the data supporting it lives scattered across twelve different systems, four siloed departments, and a maintenance history that nobody has fully digitized.
There is a precise moment in the life of any growing organisation where the very practices that built its success begin to undermine it. It is not a dramatic moment. There is no meeting where someone declares that the model no longer works.
The dominant narrative around Global Capability Centers in India has, for the past decade, been an almost unqualified success story. Nineteen hundred operational centers, presence across eight industries, ambitions to reach one hundred billion dollars in economic contribution. And yet, beneath that accelerated growth, there is a fracture the sector has been struggling to process for months: the talent profile GCCs need today no longer matches the talent India produces in sufficient quantity.
ServiceNow has been measuring AI maturity in large enterprises for three years. In 2026, the index they build from surveys of more than 4,500 executives and 2,000 employees worldwide recorded a notable improvement: a 16-point advance, reaching 51 out of 100. That sounds promising until you read the next line: while corporate AI spending grew 110% year-over-year, the foundational capabilities that AI needs to function at scale simply did not keep pace.
There is a distinction that few organizations have fully processed: an AI assistant waits to be spoken to. An agent acts on its own. That difference, which seems technical, carries economic and psychological consequences that are redefining how executives think about their technology budgets and, more quietly, how their teams feel about work.
Hayagreeva Rao, professor of organizational behavior at Stanford Graduate School of Business, recently offered a definition of leadership that holds up better than most books on the subject: 'Great leaders are people who think of themselves as custodians of other people's time.' No war metaphors. No references to transformational vision or charisma as a managerial asset.
There is a line in almost every corporate budget right now. It has a name like 'AI transformation' or 'intelligent automation.' But behind that line, in many organisations, there is no clear definition of what success looks like, who is responsible, or how progress will be measured. This is the structural problem of AI at the top level of leadership.
There comes a moment when the accumulation of artificial intelligence pilots stops looking like ambition and starts looking like disorder. That moment arrived for many large enterprises in 2026, and the clearest signal wasn't a technological collapse or a model failure. It was something more mundane and harder to defend in a board meeting: token consumption ran ahead of budget without generating proportional value.
There is a conversation that most leaders avoid with almost surgical precision. Not the one about unmet goals, nor the difficult dismissal. There is another, quieter and more costly: the one about the employee who lost someone and simply stopped performing the way they used to.
There is a phrase that repeats in almost every executive committee meeting where artificial intelligence projects are reviewed: 'the pilot was successful.' And then, silence. Nobody asks why the pilot never became anything else. The organization celebrates the experiment, files away the learnings, and three months later launches another pilot.