2026-01-21 23:00:00
There are many paths AI evolution could take. On one end of the spectrum, AI is dismissed as a marginal fad, another bubble fueled by notoriety and misallocated capital. On the other end, it’s cast as a dystopian force, destined to eliminate jobs on a large scale and destabilize economies. Markets oscillate between skepticism and the fear of missing out, while the technology itself evolves quickly and investment dollars flow at a rate not seen in decades.
All the while, many of today’s financial and economic thought leaders hold to the consensus that the financial landscape will stay the same as it has been for the last several years. Two years ago, Joseph Davis, global chief economist at Vanguard, and his team felt the same but wanted to develop their perspective on AI technology with a deeper foundation built on history and data. Based on a proprietary data set covering the last 130 years, Davis and his team developed a new framework, The Vanguard Megatrends Model, from research that suggested a more nuanced path than hype extremes: that AI has the potential to be a general purpose technology that lifts productivity, reshapes industries, and augments human work rather than displaces it. In short, AI will be neither marginal nor dystopian.
“Our findings suggest that the continuation of the status quo, the basic expectation of most economists, is actually the least likely outcome,” Davis says. “We project that AI will have an even greater effect on productivity than the personal computer did. And we project that a scenario where AI transforms the economy is far more likely than one where AI disappoints and fiscal deficits dominate. The latter would likely lead to slower economic growth, higher inflation, and increased interest rates.”
Davis does not sugar-coat it, however. Although AI promises economic growth and productivity, it will be disruptive, especially for business leaders and workers in knowledge sectors. “AI is likely to be the most disruptive technology to alter the nature of our work since the personal computer,” says Davis. “Those of a certain age might recall how the broad availability of PCs remade many jobs. It didn’t eliminate jobs as much as it allowed people to focus on higher value activities.”
The team’s framework allowed them to examine AI automation risks to over 800 different occupations. The research indicated that while the potential for job loss exists in upwards of 20% of occupations as a result of AI-driven automation, the majority of jobs—likely four out of five—will result in a mixture of innovation and automation. Workers’ time will increasingly shift to higher value and uniquely human tasks.
This introduces the idea that AI could serve as a copilot to various roles, performing repetitive tasks and generally assisting with responsibilities. Davis argues that traditional economic models often underestimate the potential of AI because they fail to examine the deeper structural effects of technological change. “Most approaches for thinking about future growth, such as GDP, don’t adequately account for AI,” he explains. “They fail to link short-term variations in productivity with the three dimensions of technological change: automation, augmentation, and the emergence of new industries.” Automation enhances worker productivity by handling routine tasks; augmentation allows technology to act as a copilot, amplifying human skills; and the creation of new industries creates new sources of growth.
Ironically, Davis’s research suggests that a reason for the relatively low productivity growth in recent years may be a lack of automation. Despite a decade of rapid innovation in digital and automation technologies, productivity growth has lagged since the 2008 financial crisis, hitting 50-year lows. This appears to support the view that AI’s impact will be marginal. But Davis believes that automation has been adopted in the wrong places. “What surprised me most was how little automation there has been in services like finance, health care, and education,” he says. “Outside of manufacturing, automation has been very limited. That’s been holding back growth for at least two decades.” The services sector accounts for more than 60% of US GDP and 80% of the workforce and has experienced some of the lowest productivity growth. It is here, Davis argues, that AI will make the biggest difference.
One of the biggest challenges facing the economy is demographics, as the Baby Boomer generation retires, immigration slows, and birth rates decline. These demographic headwinds reinforce the need for technological acceleration. “There are concerns about AI being dystopian and causing massive job loss, but we’ll soon have too few workers, not too many,” Davis says. “Economies like the US, Japan, China, and those across Europe will need to step up function in automation as their populations age.”
For example, consider nursing, a profession in which empathy and human presence are irreplaceable. AI has already shown the potential to augment rather than automate in this field, streamlining data entry in electronic health records and helping nurses reclaim time for patient care. Davis estimates that these tools could increase nursing productivity by as much as 20% by 2035, a crucial gain as health-care systems adapt to ageing populations and rising demand. “In our most likely scenario, AI will offset demographic pressures. Within five to seven years, AI’s ability to automate portions of work will be roughly equivalent to adding 16 million to 17 million workers to the US labor force,” Davis says. “That’s essentially the same as if everyone turning 65 over the next five years decided not to retire.” He projects that more than 60% of occupations, including nurses, family physicians, high school teachers, pharmacists, human resource managers, and insurance sales agents, will benefit from AI as an augmentation tool.
As AI technology spreads, the strongest performers in the stock market won’t be its producers, but its users. “That makes sense, because general-purpose technologies enhance productivity, efficiency, and profitability across entire sectors,” says Davis. This adoption of AI is creating flexibility for investment options, which means diversifying beyond technology stocks might be appropriate as reflected in Vanguard’s Economic and Market Outlook for 2026. “As that happens, the benefits move beyond places like Silicon Valley or Boston and into industries that apply the technology in transformative ways.” And history shows that early adopters of new technologies reap the greatest productivity rewards. “We’re clearly in the experimentation phase of learning by doing,” says Davis. “Those companies that encourage and reward experimentation will capture the most value from AI.”
Looking globally, Davis sees the United States and China as significantly ahead in the AI race. “It’s a virtual dead heat,” he says. “That tells me the competition between the two will remain intense.” But other economies, especially those with low automation rates and large service sectors, like Japan, Europe, and Canada, could also see significant benefits. “If AI is truly going to be transformative, three sectors stand out: health care, education, and finance,” says Davis. “For AI to live up to its potential, it must fundamentally reshape these industries, which face high costs and rising demand for better, faster, more personalized services.”
However, Davis says Vanguard is more bullish on AI’s potential to transform the economy than it was just a year ago. Especially since that transformation requires application beyond Silicon Valley. “When I speak to business leaders, I remind them that this transformation hasn’t happened yet,” says Davis. “It’s their investment and innovation that will determine whether it does.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
2026-01-21 22:00:00
Governments plan to pour $1.3 trillion into AI infrastructure by 2030 to invest in “sovereign AI,” with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. This is a response to real shocks: covid-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine.
But the pursuit of absolute autonomy is running into reality. AI supply chains are irreducibly global: Chips are designed in the US and manufactured in East Asia; models are trained on data sets drawn from multiple countries; applications are deployed across dozens of jurisdictions.
If sovereignty is to remain meaningful, it must shift from a defensive model of self-reliance to a vision that emphasizes the concept of orchestration, balancing national autonomy with strategic partnership.
Why infrastructure-first strategies hit walls
A November survey by Accenture found that 62% of European organizations are now seeking sovereign AI solutions, driven primarily by geopolitical anxiety rather than technical necessity. That figure rises to 80% in Denmark and 72% in Germany. The European Union has appointed its first Commissioner for Tech Sovereignty.
This year, $475 billion is flowing into AI data centers globally. In the United States, AI data centers accounted for roughly one-fifth of GDP growth in the second quarter of 2025. But the obstacle for other nations hoping to follow suit isn’t just money. It’s energy and physics. Global data center capacity is projected to hit 130 gigawatts by 2030, and for every $1 billion spent on these facilities, $125 million is needed for electricity networks. More than $750 billion in planned investment is already facing grid delays.
And it’s also talent. Researchers and entrepreneurs are mobile, drawn to ecosystems with access to capital, competitive wages, and rapid innovation cycles. Infrastructure alone won’t attract or retain world-class talent.
What works: An orchestrated sovereignty
What nations need isn’t sovereignty through isolation but through specialization and orchestration. This means choosing which capabilities you build, which you pursue through partnership, and where you can genuinely lead in shaping the global AI landscape.
The most successful AI strategies don’t try to replicate Silicon Valley; they identify specific advantages and build partnerships around them.
Singapore offers a model. Rather than seeking to duplicate massive infrastructure, it invested in governance frameworks, digital-identity platforms, and applications of AI in logistics and finance, areas where it can realistically compete.
Israel shows a different path. Its strength lies in a dense network of startups and military-adjacent research institutions delivering outsize influence despite the country’s small size.
South Korea is instructive too. While it has national champions like Samsung and Naver, these firms still partner with Microsoft and Nvidia on infrastructure. That’s deliberate collaboration reflecting strategic oversight, not dependence.
Even China, despite its scale and ambition, cannot secure full-stack autonomy. Its reliance on global research networks and on foreign lithography equipment, such as extreme ultraviolet systems needed to manufacture advanced chips and GPU architectures, shows the limits of techno-nationalism.
The pattern is clear: Nations that specialize and partner strategically can outperform those trying to do everything alone.
Three ways to align ambition with reality
1. Measure added value, not inputs.
Sovereignty isn’t how many petaflops you own. It’s how many lives you improve and how fast the economy grows. Real sovereignty is the ability to innovate in support of national priorities such as productivity, resilience, and sustainability while maintaining freedom to shape governance and standards.
Nations should track the use of AI in health care and monitor how the technology’s adoption correlates with manufacturing productivity, patent citations, and international research collaborations. The goal is to ensure that AI ecosystems generate inclusive and lasting economic and social value.
2. Cultivate a strong AI innovation ecosystem.
Build infrastructure, but also build the ecosystem around it: research institutions, technical education, entrepreneurship support, and public-private talent development. Infrastructure without skilled talent and vibrant networks cannot deliver a lasting competitive advantage.
3. Build global partnerships.
Strategic partnerships enable nations to pool resources, lower infrastructure costs, and access complementary expertise. Singapore’s work with global cloud providers and the EU’s collaborative research programs show how nations advance capabilities faster through partnership than through isolation. Rather than competing to set dominant standards, nations should collaborate on interoperable frameworks for transparency, safety, and accountability.
What’s at stake
Overinvesting in independence fragments markets and slows cross-border innovation, which is the foundation of AI progress. When strategies focus too narrowly on control, they sacrifice the agility needed to compete.
The cost of getting this wrong isn’t just wasted capital—it’s a decade of falling behind. Nations that double down on infrastructure-first strategies risk ending up with expensive data centers running yesterday’s models, while competitors that choose strategic partnerships iterate faster, attract better talent, and shape the standards that matter.
The winners will be those who define sovereignty not as separation, but as participation plus leadership—choosing who they depend on, where they build, and which global rules they shape. Strategic interdependence may feel less satisfying than independence, but it’s real, it is achievable, and it will separate the leaders from the followers over the next decade.
The age of intelligent systems demands intelligent strategies—ones that measure success not by infrastructure owned, but by problems solved. Nations that embrace this shift won’t just participate in the AI economy; they’ll shape it. That’s sovereignty worth pursuing.
Cathy Li is head of the Centre for AI Excellence at the World Economic Forum.
2026-01-21 21:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
—Mat Honan, MIT Technology Review’s editor in chief
At Davos this year Trump is dominating all the side conversations. There are lots of little jokes. Nervous laughter. Outright anger. Fear in the eyes. It’s wild. The US president is due to speak here today, amid threats of seizing Greenland and fears that he’s about to permanently fracture the NATO alliance.
But Trump isn’t the only game in town—everyone’s also talking about AI. Read Mat’s story to find out more.
This subscriber-only story appeared first in The Debrief, Mat’s weekly newsletter about the biggest stories in tech. Sign up here to get the next one in your inbox, and subscribe if you haven’t already!
A number of startups and university teams that are building “AI scientists” to design and run experiments in the lab, including robot biologists and chemists, have just won extra funding from the UK government agency that funds moonshot R&D.
The competition, set up by ARIA (the Advanced Research and Invention Agency), gives a clear sense of how fast this technology is moving: The agency received 245 proposals from research teams that are already building tools capable of automating increasing amounts of lab work. Read the full story to learn more.
—Will Douglas Heaven
—Cathy Li is head of the Centre for AI Excellence at the World Economic Forum
Governments plan to pour $1.3 trillion into AI infrastructure by 2030 to invest in “sovereign AI,” with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines.
This is a response to real shocks: covid-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine. But the pursuit of absolute autonomy is running into reality: AI supply chains are irreducibly global. If sovereignty is to remain meaningful, it must shift from defensive self-reliance to a vision that balances national autonomy with strategic partnership. Read the full story.
Thanks to genetic science, gene editing, and techniques like cloning, it’s now possible to move DNA through time, studying genetic information in ancient remains and then re-creating it in the bodies of modern beings. And that, scientists say, offers new ways to try to help endangered species, engineer new plants that resist climate change, or even create new human medicines.
Read more about why genetic resurrection is one of our 10 Breakthrough Technologies this year, and check out the rest of the list.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The White House wants Americans to embrace AI
It faces an uphill battle—the US public is mostly pretty gloomy about AI’s impact. (WP $)
+ What’s next for AI in 2026. (MIT Technology Review)
2 The UN says we’re entering an “era of water bankruptcy”
And it’s set to affect the vast majority of us on the planet. (Reuters $)
+ Water shortages are fueling the protests in Iran. (Undark)
+ This Nobel Prize–winning chemist dreams of making water from thin air. (MIT Technology Review)
3 How is US science faring after a year of Trump?
Not that well, after proposed budget cuts amounting to $32 billion. (Nature $)
+ The foundations of America’s prosperity are being dismantled. (MIT Technology Review)
4 We need to talk about the early career AI jobs crisis
Young people are graduating and finding there simply aren’t any roles for them to do. (NY Mag $)
+ AI companies are fighting to win over teachers. (Axios $)
+ Chinese universities want students to use more AI, not less. (MIT Technology Review)
5 The AI boyfriend business is booming in China
And it’s mostly geared towards Gen Z women. (Wired $)
+ It’s surprisingly easy to stumble into a relationship with an AI chatbot. (MIT Technology Review)
6 Snap has settled a social media addiction lawsuit ahead of a trial
However the other defendants, including Meta, TikTok and YouTube, are still fighting it. (BBC)
+ A new study is going to examine the effects of restricting social media for children. (The Guardian)
7 Here are some of the best ideas of this century so far
From smartphones to HIV drugs, the pace of progress has been dizzying. (New Scientist $)
8 Robots may be on the cusp of becoming very capable
Until now, their role in the world of work has been limited. AI could radically change that. (FT $)
+ Why the humanoid workforce is running late. (MIT Technology Review)
9 Scientists are racing to put a radio telescope on the moon
If they succeed, it will be able to ‘hear’ all the way back to over 13 billion years ago, just 380,000 years after the big bang. (IEEE Spectrum)
+ Inside the quest to map the universe with mysterious bursts of radio energy. (MIT Technology Review)
10 It turns out cows can use tools
What will we discover next? Flying pigs?! (Futurism)
Quote of the day
“We’re still staggering along, but I don’t know for how much longer. I don’t have the energy any more.”
—A researcher at the National Oceanic and Atmospheric Administration tells Nature they and their colleagues are exhausted by the Trump administration’s attacks on science.
One more thing
Palmer Luckey has, in some ways, come full circle.
His first experience with virtual-reality headsets was as a teenage lab technician at a defense research center in Southern California, studying their potential to curb PTSD symptoms in veterans. He then built Oculus, sold it to Facebook for $2 billion, left Facebook after a highly public ousting, and founded Anduril, which focuses on drones, cruise missiles, and other AI-enhanced technologies for the US Department of Defense. The company is now valued at $14 billion.
Now Luckey is redirecting his energy again, to headsets for the military. He spoke to MIT Technology Review about his plans. Read the full interview.
—James O’Donnell
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ I want to skip around every single one of these beautiful gardens.
+ Your friends help you live longer. Isn’t that nice of them?!
+ Brb, just buying a pharaoh headdress for my cat.
+ Consider this your annual reminder that you don’t need a gym membership or fancy equipment to get fitter.
2026-01-21 19:20:51
This story first appeared in The Debrief, our subscriber-only newsletter about the biggest news in tech by Mat Honan, Editor in Chief. Subscribe to read the next edition as soon as it lands.
Hello from the World Economic Forum annual meeting in Davos, Switzerland. I’ve been here for two days now, attending meetings, speaking on panels, and basically trying to talk to anyone I can. And as far as I can tell, the only things anyone wants to talk about are AI and Trump.
Davos is physically defined by the Congress Center, where the official WEF sessions take place, and the Promenade, a street running through the center of the town lined with various “houses”—mostly retailers that are temporarily converted into meeting hubs for various corporate or national sponsors. So there is a Ukraine House, a Brazil House, Saudi House, and yes, a USA House (more on that tomorrow). There are a handful of media houses from the likes of CNBC and the Wall Street Journal. Some houses are devoted to specific topics; for example, there’s one for science and another for AI.
But like everything else in 2026, the Promenade is dominated by tech companies. At one point I realized that literally everything I could see, in a spot where the road bends a bit, was a tech company house. Palantir, Workday, Infosys, Cloudflare, C3.ai. Maybe this should go without saying, but their presence, both in the houses and on the various stages and parties and platforms here at the World Economic Forum, really drove home to me how utterly and completely tech has captured the global economy.
While the houses host events and serve as networking hubs, the big show is inside the Congress Center. On Tuesday morning, I kicked off my official Davos experience there by moderating a panel with the CEOs of Accenture, Aramco, Royal Philips, and Visa. The topic was scaling up AI within organizations. All of these leaders represented companies that have gone from pilot projects to large internal implementations. It was, for me, a fascinating conversation. You can watch the whole thing here, but my takeaway was that while there are plenty of stories about AI being overhyped (including from us), it is certainly having substantive effects at large companies.
Aramco CEO Amin Nasser, for example, described how that company has found $3 billion to $5 billion in cost savings by improving the efficiency of its operations. Royal Philips CEO Roy Jakobs described how it was allowing health-care practitioners to spend more time with patients by doing things such as automated note-taking. (This really resonated with me, as my wife is a pediatrics nurse, and for decades now I’ve heard her talk about how much of her time is devoted to charting.) And Visa CEO Ryan McInerney talked about his company’s push into agentic commerce and the way that will play out for consumers, small businesses, and the global payments industry.
To elaborate a little on that point, McInerney painted a picture of commerce where agents won’t just shop for things you ask them to, which will be basically step one, but will eventually be able to shop for things based on your preferences and previous spending patterns. This could be your regular grocery shopping, or even a vacation getaway. That’s going to require a lot of trust and authentication to protect both merchants and consumers, but it is clear that the steps into agentic commerce we saw in 2025 were just baby ones. There are much bigger ones coming for 2026. (Coincidentally, I had a discussion with a senior executive from Mastercard on Monday, who made several of the same points.)
But the thing that really resonated with me from the panel was a comment from Accenture CEO Julie Sweet, who has a view not only of her own large org but across a spectrum of companies: “It’s hard to trust something until you understand it.”
I felt that neatly summed up where we are as a society with AI.
Clearly, other people feel the same. Before the official start of the conference I was at AI House for a panel. The place was packed. There was a consistent, massive line to get in, and once inside, I literally had to muscle my way through the crowd. Everyone wanted to get in. Everyone wanted to talk about AI.
(A quick aside on what I was doing there: I sat on a panel called “Creativity and Identity in the Age of Memes and Deepfakes,” led by Atlantic CEO Nicholas Thompson; it featured the artist Emi Kusano, who works with AI, and Duncan Crabtree-Ireland, the chief negotiator for SAG-AFTRA, who has been at the center of a lot of the debates about AI in the film and gaming industries. I’m not going to spend much time describing it because I’m already running long, but it was a rip-roarer of a panel. Check it out.)
And, okay. Sigh. Donald Trump.
The president is due here Wednesday, amid threats of seizing Greenland and fears that he’s about to permanently fracture the NATO alliance. While AI is all over the stages, Trump is dominating all the side conversations. There are lots of little jokes. Nervous laughter. Outright anger. Fear in the eyes. It’s wild.
These conversations are also starting to spill out into the public. Just after my panel on Tuesday, I headed to a pavilion outside the main hall in the Congress Center. I saw someone coming down the stairs with a small entourage, who was suddenly mobbed by cameras and phones.
Moments earlier in the same spot, the press had been surrounding David Beckham, shouting questions at him. So I was primed for it to be another celebrity—after all, captains of industry were everywhere you looked. I mean, I had just bumped into Eric Schmidt, who was literally standing in line in front of me at the coffee bar. Davos is weird.
But in fact, it was Gavin Newsom, the governor of California, who is increasingly seen as the leading voice of the Democratic opposition to President Trump, and a likely contender, or even front-runner, in the race to replace him. Because I live in San Francisco I’ve encountered Newsom many times, dating back to his early days as a city supervisor before he was even mayor. I’ve rarely, rarely, seen him quite so worked up as he was on Tuesday.
Among other things, he called Trump a narcissist who follows “the law of the jungle, the rule of Don” and compared him to a T-Rex, saying, “You mate with him or he devours you.” And he was just as harsh on the world leaders, many of whom are gathered in Davos, calling them “pathetic” and saying he should have brought knee pads for them.
Yikes.
There was more of this sentiment, if in more measured tones, from Canadian prime minister Mark Carney during his address at Davos. While I missed his remarks, they had people talking. “If we’re not at the table, we’re on the menu,” he argued.
2026-01-21 00:14:14
The story of enterprise resource planning (ERP) is really a story of businesses learning to organize themselves around the latest, greatest technology of the times. In the 1960s through the ’80s, mainframes, material requirements planning (MRP), and manufacturing resource planning (MRP II) brought core business data from file cabinets to centralized systems. Client-server architectures defined the ’80s and ’90s, taking digitization mainstream during the internet’s infancy. And in the 21st century, as work moved beyond the desktop, SaaS and cloud ushered in flexible access and elastic infrastructure.
The rise of composability and agentic AI marks yet another dawn—and an apt one for the nascent intelligence age. Composable architectures let organizations assemble capabilities from multiple systems in a mix-and-match fashion, so they can swap vendor gridlock for an à la carte portfolio of fit-for-purpose modules. On top of that architectural shift, agentic AI enables coordination across systems that weren’t originally designed to talk to one another.
Early indicators suggest that AI-enabled ERP will yield meaningful performance gains: One 2024 study found that organizations implementing AI-driven ERP solutions stand to gain around a 30% boost in user satisfaction and a 25% lift in productivity; another suggested that AI-driven ERP can lead to processing time savings of up to 45%, as well as improvements in decision accuracy to the tune of 60%.
These dual advancements address long-standing gaps that previous ERP eras fell short of delivering: freedom to innovate outside of vendor roadmaps, capacity for rapid iteration, and true interoperability across all critical functions. This shift signals the end of monolithic dependency as well as a once-in-a-generation opportunity for early movers to gain a competitive edge.
Key takeaways include:
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
2026-01-20 23:00:00
AI agents are moving beyond coding assistants and customer service chatbots into the operational core of the enterprise. The ROI is promising, but autonomy without alignment is a recipe for chaos. Business leaders need to lay the essential foundations now.
Agents are independently handling end-to-end processes across lead generation, supply chain optimization, customer support, and financial reconciliation. A mid-sized organization could easily run 4,000 agents, each making decisions that affect revenue, compliance, and customer experience.
The transformation toward an agent-driven enterprise is inevitable. The economic benefits are too significant to ignore, and the potential is becoming a reality faster than most predicted. The problem? Most businesses and their underlying infrastructure are not prepared for this shift. Early adopters have found unlocking AI initiatives at scale to be extremely challenging.
Companies are investing heavily in AI, but the returns aren’t materializing. According to recent research from Boston Consulting Group, 60% of companies report minimal revenue and cost gains despite substantial investment. However, the leaders reported they achieved five times the revenue increases and three times the cost reductions. Clearly, there is a massive premium for being a leader.
What separates the leaders from the pack isn’t how much they’re spending or which models they’re using. Before scaling AI deployment, these “future-built” companies put critical data infrastructure capabilities in place. They invested in the foundational work that enables AI to function reliably.
To understand how and where enterprise AI can fail, consider four critical quadrants: models, tools, context, and governance.
Take a simple example: an agent that orders you pizza. The model interprets your request (“get me a pizza”). The tool executes the action (calling the Domino’s or Pizza Hut API). Context provides personalization (you tend to order pepperoni on Friday nights at 7pm). Governance validates the outcome (did the pizza actually arrive?).
Each dimension represents a potential failure point:
This framework helps diagnose where reliability gaps emerge. When an enterprise agent fails, which quadrant is the problem? Is the model misunderstanding intent? Are the tools unavailable or broken? Is the context incomplete or contradictory? Or is there no mechanism to verify that the agent did what it was supposed to do?
The temptation is to think that reliability will simply improve as models improve. Yet, model capability is advancing exponentially. The cost of inference has dropped nearly 900 times in three years, hallucination rates are on the decline, and AI’s capacity to perform long tasks doubles every six months.
Tooling is also accelerating. Integration frameworks like the Model Context Protocol (MCP) make it dramatically easier to connect agents with enterprise systems and APIs.
If models are powerful and tools are maturing, then what is holding back adoption?
To borrow from James Carville, “It is the data, stupid.” The root cause of most misbehaving agents is misaligned, inconsistent, or incomplete data.
Enterprises have accumulated data debt over decades. Acquisitions, custom systems, departmental tools, and shadow IT have left data scattered across silos that rarely agree. Support systems do not match what is in marketing systems. Supplier data is duplicated across finance, procurement, and logistics. Locations have multiple representations depending on the source.
Drop a few agents into this environment, and they will perform wonderfully at first, because each one is given a curated set of systems to call. Add more agents and the cracks grow, as each one builds its own fragment of truth.
This dynamic has played out before. When business intelligence became self-serve, everyone started creating dashboards. Productivity soared, reports failed to match. Now imagine that phenomenon not in static dashboards, but in AI agents that can take action. With agents, data inconsistency produces real business consequences, not just debates among departments.
Companies that build unified context and robust governance can deploy thousands of agents with confidence, knowing they’ll work together coherently and comply with business rules. Companies that skip this foundational work will watch their agents produce contradictory results, violate policies, and ultimately erode trust faster than they create value.
The question for enterprises centers on organizational readiness. Will your company prepare the data foundation needed to make agent transformation work? Or will you spend years debugging agents, one issue at a time, forever chasing problems that originate in infrastructure you never built?
Autonomous agents are already transforming how work gets done. But the enterprise will only experience the upside if those systems operate from the same truth. This ensures that when agents reason, plan, and act, they do so based on accurate, consistent, and up-to-date information.
The companies generating value from AI today have built on fit-for-purpose data foundations. They recognized early that in an agentic world, data functions as essential infrastructure. A solid data foundation is what turns experimentation into dependable operations.
At Reltio, the focus is on building that foundation. The Reltio data management platform unifies core data from across the enterprise, giving every agent immediate access to the same business context. This unified approach enables enterprises to move faster, act smarter, and unlock the full value of AI.
Agents will define the future of the enterprise. Context intelligence will determine who leads it.
For leaders navigating this next wave of transformation, see Relatio’s practical guide:
Unlocking Agentic AI: A Business Playbook for Data Readiness. Get your copy now to learn how real-time context becomes the decisive advantage in the age of intelligence.
