Sustainabl Agent Surface
All articles published in English, by date, category and author.
177 articles · Page 3 of 8
The Digital Evolution Index 2026, produced by Digital Planet at the Fletcher School of Tufts University together with Via Science Inc., is not just a ranking of 125 countries. It is an X-ray of how the map of the digital economy has ceased to be a single one. During the first twenty-five years of the digital era, the operating assumption was simple: the world was converging.
There is a form of business failure that never appears on AI adoption dashboards. It is not measured in processed tokens or active users. It manifests when a perfectly trained model delivers results that no one inside the organization can consistently trust.
There is an image that persists in the corporate memory of Silicon Valley: the label of a company that was once the undisputed king of semiconductors, now fighting to reclaim its throne under the radical leadership of Lip-Bu Tan — a CEO who cut Intel in half to make it worth five times more.
There is a gap between what executives say they believe about artificial intelligence and what their organizations actually do with it. It is not a knowledge gap. It is a strategic attention gap, and it carries a cost that few boards of directors have honestly quantified.
Three quarters of the venture capital raised in the last year went to just five companies. Not five sectors. Not five categories. Five companies. That figure, stated bluntly by Niko Bonatsos of Verdict Capital at a recent TechCrunch panel in Athens, captures more precisely than any market report what is happening in global venture capital: an unprecedented concentration that coexists, paradoxically, with a narrative of distributed innovation and open opportunity.
The official picture of corporate AI adoption looks tidy: approved investments, pilot projects underway, dashboards full of productivity metrics. But there is a layer those reports never capture, and that is precisely where real risk accumulates. Gartner's Hype Cycle currently places generative AI in the 'Trough of Disillusionment', the third of five stages where expectations begin to be measured against concrete results.
There is a pattern that repeats itself frequently enough to deserve attention: an organisation announces a digital transformation, allocates budget, hires consultants, implements platforms and, two years later, discovers that almost nothing changed where it mattered. Processes are still slow. Frontline teams did not adopt the tools. And leadership, which managed everything from control dashboards, cannot precisely explain what went wrong.
More than $1.5 trillion in enterprise software valuations evaporated over the last two years. Not for lack of investment in artificial intelligence, but because the investment landed in the wrong place. This is the paradox that defines the current moment: companies have never spent so much on AI and, at the same time, it has never been so hard to show where the value actually is.
There is a widespread way of getting AI wrong in business. It consists of measuring the maturity of a system by how many jobs it managed to eliminate. That metric doesn't measure maturity: it measures speed without governance, which is exactly the condition that precedes the most costly collapses in critical systems.
There is a logic that for decades seemed immovable in the oil industry: value lay in crude oil, in geology, in whoever controlled the subsoil. Repsol has just proven that this logic has visible cracks. The company launched industrial-scale production at its second plant dedicated exclusively to 100% renewable fuels, located at its industrial complex in Puertollano, in the Ciudad Real region of Spain.
The paradox is on the table from the very first moment. A company that operates manufacturing plants with decades of history, that distributes beverages and snacks at a global scale, and that has spent over a century building mass consumer brands, has just publicly declared that its competitive edge in talent doesn't come from knowing how to program language models. It comes from hustle.
Private markets have spent a decade promising sophistication without always delivering it on the operational side. Funds are growing in size, structural complexity, and number of investors. Evergreen and semi-liquid vehicles are proliferating.
Stellantis bets 60 billion euros to recover from the worst loss in its history When a company loses 22.3 billion euros in a single year, the next move cannot be incremental.
The conversation about artificial intelligence in large enterprises follows a comfortable script: evaluating platforms, approving budgets, designing pilots. Meanwhile, inside CRM systems, customer service operations, and financial approval workflows, AI agents are making decisions without anyone knowing exactly how many there are, what data they touch, or what they do when no one is watching. That is the uncomfortable fact the industry has been elegantly avoiding for months.
There's a narrative that has dominated boardroom conversations and venture capital funds for two years: artificial intelligence will devour enterprise software the same way software devoured analog business models. It's a powerful image. And like every powerful image that circulates without friction, it deserves pressure before it dictates investment decisions with real consequences.
There's a scene that repeats itself in almost every mid-sized company I know. The technology team presents an artificial intelligence pilot. The initial numbers look promising. The board approves the investment. And six months later, the pilot is still just a pilot.
The dominant narrative about artificial intelligence and business has a structural bias that is rarely named: it is built almost exclusively around companies with more than 500 employees. Not because large corporations are more interesting, but because for technology vendors they represent more predictable contracts, relatively shorter sales cycles, and recurring revenue streams that justify sales and marketing spend. The logic is understandable from the seller's economics. The problem is that this logic has distorted the reading of where real work happens in the economy.
There is a silent pattern that economic history has repeated at least twice before the era of artificial intelligence. First with industrial electrification, then with personal computers. In both cases, the technology arrived decades before its impact appeared in productivity statistics.
There is a statistic that has been circulating in boardrooms for decades without provoking the discomfort it deserves: between 60 and 75 percent of major organizational transformation processes fail or fall well short of their stated objectives. The data is not new. What is new—or should be—is starting to take it seriously as a symptom of something structural in the way leadership conceives of change.
There is a fact that should make any executive who has approved an artificial intelligence budget in the last two years uncomfortable: the United States, the country that builds the world's most powerful models, ranks 24th in global AI adoption. Its rate is 28.3%. The problem is not technological. It never was.
The conversation around enterprise artificial intelligence security tends to converge on the same points: poorly trained models, hallucinations, algorithmic bias. While technical teams debate model architecture, sensitive data is already traveling to external servers, agents are operating with excessive privileges, and no one has updated identity management frameworks to include entities that make decisions without any human overseeing them in real time. The gap is not technical in origin. It is behavioral and organizational.
Sarvam AI, Ebix's X Pay and AuthBridge's AuthLead address different business problems. Evaluating them requires separating stated capabilities, verified results and metrics suited to each business.
The image that best describes the state of artificial intelligence in businesses during 2025 is not one of a technology that failed. It is one of a technology that was used without real commitment. According to an MIT report published that year, 95% of generative AI pilots never reached production with measurable impact.
Generative artificial intelligence reached most organizations not through the technology department, but through the back door of productivity applications. Microsoft 365 Copilot, Gemini, and assistants integrated into collaboration platforms were activated in corporate environments where employees were already working — and with that began a silent experiment whose terms nobody had fully negotiated. The problem is not with the language models. It's with what those models find when they connect to a real organization.