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:
Enterprises are moving away from monolithic ERP vendor upgrades in favor of modular architectures that allow them to change or modernize components independently while keeping a stable core for essential transactions.
Agentic AI is a timely complement to composability, functioning as a UX and orchestration layer that can coordinate workflows across disparate systems and turn multi-step processes into automated, cross-platform operations.
These dual shifts are finally enabling technology architecture to organize around the business, instead of the business around the ERP. Companies can modernize by reconfiguring and extending what they already have, rather than relying on ERP-centric upgrades.
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.
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.
The agent explosion is coming
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.
The reliability gap that’s holding AI back
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.
A framework for agent reliability: The four quadrants
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:
Models: The underlying AI systems that interpret prompts, generate responses, and make predictions
Tools: The integration layer that connects AI to enterprise systems, such as APIs, protocols, and connectors
Context: Before making decisions, information agents need to understand the full business picture, including customer histories, product catalogs, and supply chain networks
Governance: The policies, controls, and processes that ensure data quality, security, and compliance
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?
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.
Leverage agentic AI without the chaos
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.
A number of startups and universities 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.
ARIA defines an AI scientist as a system that can run an entire scientific workflow, coming up with hypotheses, designing and running experiments to test those hypotheses, and then analyzing the results. In many cases, the system may then feed those results back into itself and run the loop again and again. Human scientists become overseers, coming up with the initial research questions and then letting the AI scientist get on with the grunt work.
“There are better uses for a PhD student than waiting around in a lab until 3 a.m. to make sure an experiment is run to the end,” says Ant Rowstron, ARIA’s chief technology officer.
ARIA picked 12 projects to fund from the 245 proposals, doubling the amount of funding it had intended to allocate because of the large number and high quality of submissions. Half the teams are from the UK; the rest are from the US and Europe. Some of the teams are from universities, some from industry. Each will get around £500,000 (around $675,000) to cover nine months’ work. At the end of that time, they should be able to demonstrate that their AI scientist was able to come up with novel findings.
Winning teams include Lila Sciences, a US company that is building what it calls an AI nano-scientist—a system that will design and run experiments to discover the best ways to compose and process quantum dots, which are nanometer-scale semiconductor particles used in medical imaging, solar panels, and QLED TVs.
“We are using the funds and time to prove a point,” says Rafa Gómez-Bombarelli, chief science officer for physical sciences at Lila: “The grant lets us design a real AI robotics loop around a focused scientific problem, generate evidence that it works, and document the playbook so others can reproduce and extend it.”
Another team, from the University of Liverpool, UK, is building a robot chemist, which runs multiple experiments at once and uses a vision language model to help troubleshoot when the robot makes an error.
And a startup based in London, still in stealth mode, is developing an AI scientist called ThetaWorld, which is using LLMs to design experiments on the physical and chemical interactions that are important for the performance of batteries. The experiments will then be run in an automated lab by Sandia National Laboratories in the US.
Taking the temperature
Compared with the £5 million projects spanning two or three years that ARIA usually funds, £500,000 is small change. But that was the idea, says Rowstron: It’s an experiment on ARIA’s part too. By funding a range of projects for a short amount of time, the agency is taking the temperature at the cutting edge to determine how the way science is done is changing, and how fast. What it learns will become the baseline for funding future large-scale projects.
Rowstron acknowledges there’s a lot of hype, especially now that most of the top AI companies have teams focused on science. When results are shared by press release and not peer review, it can be hard to know what the technology can and can’t do. “That’s always a challenge for a research agency trying to fund the frontier,” he says. “To do things at the frontier, we’ve got to know what the frontier is.”
For now, the cutting edge involves agentic systems calling up other existing tools on the fly. “They’re running things like large language models to do the ideation, and then they use other models to do optimization and run experiments,” says Rowstron. “And then they feed the results back round.”
Rowstron sees the technology stacked in tiers. At the bottom are AI tools designed by humans for humans, such as AlphaFold. These tools let scientists leapfrog slow and painstaking parts of the scientific pipeline but can still require many months of lab work to verify results. The idea of an AI scientist is to automate that work too.
AI scientists sit in a layer above those human-made tools and call ton hose tools as needed, says Rowstron. “But there’s a point in time—and I don’t think it’s a decade away—where that AI scientist layer says, ‘I need a tool and it doesn’t exist,’ and it will actually create an AlphaFold kind of tool just on the way to figuring out how to solve another problem. That whole bottom zone will just be automated.”
That’s still some way off, he says. All the projects ARIA is now funding involve systems that call on existing tools rather than spin up new ones.
There are also unsolved problems with agentic systems in general, which limits how long they can run by themselves without going off track or making errors. For example, a study, titled “Why LLMs aren’t scientists yet,” posted online last week by researchers at Lossfunk, an AI lab based in India, reports that in an experiment to get LLM agents to run a scientific workflow to completion, the system failed three out of four times. According to the researchers, the reasons the LLMs broke down included changes in the initial specifications and “overexcitement that declares success despite obvious failures.”
“Obviously, at the moment these tools are still fairly early in their cycle and these things might plateau,” says Rowstron. “I’m not expecting them to win a Nobel Prize.”
“But there is a world where some of these tools will force us to operate so much quicker,” he continues. “And if we end up in that world, it’s super important for us to be ready.”
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.
The man who made India digital isn’t done yet
Nandan Nilekani can’t stop trying to push India into the future. He started nearly 30 years ago, masterminding an ongoing experiment in technological state capacity that started with Aadhaar—the world’s largest digital identity system.
Using Aadhaar as the bedrock, Nilekani and people working with him went on to build a sprawling collection of free, interoperating online tools that add up to nothing less than a digital infrastructure for society, covering government services, banking, and health care. They offer convenience and access that would be eye-popping in wealthy countries a tenth of India’s size.
Many Americans agree that it’s acceptable to screen embryos for severe genetic diseases. Far fewer say it’s okay to test for characteristics related to a future child’s appearance, behavior, or intelligence. But a few startups are now advertising what they claim is a way to do just that.
This new kind of testing—which can cost up to $50,000—is incredibly controversial. Nevertheless, the practice has grown popular in Silicon Valley, and it’s becoming more widely available to everyone. Read the full story.
—Julia Black Embryo scoring is one of our 10 Breakthrough Technologies this year. Check out what else made the list, and scroll down to vote for the technology you think deserves the 11th slot.
Five AI predictions for 2026
What will surprise us most about AI in 2026?
Tune in at 12.30pm today to hear me, our senior AI editor Will Douglas Heaven and senior AI reporter James O’Donnell discuss our “5 AI Predictions for 2026”. This special LinkedIn Live event will explore the trends that are poised to transform the next twelve months of AI. The conversation will also offer a first glimpse at EmTech AI 2026, MIT Technology Review’s longest running AI event for business leadership. Sign up to join us later today!
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Europe is trying to build its own DeepSeek That’s been a goal for a while, but US hostility is making those efforts newly urgent. (Wired $) + Plenty of Europeans want to wean off US technology. That’s easier said than done. (New Scientist $) + DeepSeek may have found a new way to improve AI’s ability to remember. (MIT Technology Review $)
2 Ship-tracking data shows China is creating massive floating barriers The maneuvers show that Beijing can now rapidly muster large numbers of the boats in disputed seas. (NYT $) + Quantum navigation could solve the military’s GPS jamming problem. (MIT Technology Review)
3 The AI bubble risks disrupting the global economy, says the IMF But it’s hard to see anyone pumping the brakes any time soon. (FT $) + British politicians say the UK is being exposed to ‘serious harm’ by AI risks. (The Guardian) + What even is the AI bubble? (MIT Technology Review)
4 Cryptocurrencies are dying in record numbers In an era of one-off joke coins and pump and dump scams, that’s surely a good thing. (Gizmodo) + President Trump has pardoned a lot of people who’ve committed financial crimes. (NBC)
5 Threads has more global daily mobile users than X now And once-popular alternative Bluesky barely even makes the charts. (Forbes)
6 The UK is considering banning under 16s from social media Just weeks after a similar ban took effect in Australia. (BBC)
7 You can burn yourself out with AI coding agents They could be set to make experienced programmers busier than ever before. (Ars Technica) + Why Anthropic’s Claude Code is taking the AI world by storm. (WSJ $) + AI coding is now everywhere. But not everyone is convinced. (MIT Technology Review)
8 Some tech billionaires are leaving California Not all though—the founders of Nvidia and Airbnb say they’ll stay and pay the 5% wealth tax. (WP $) + Tech bosses’ support for Trump is paying off for them big time. (FT $)
9 Matt Damon says Netflix tells directors to repeat movie plots To accommodate all the people using their phones. (NME)
10 Why more people are going analog in 2026 Crafting, reading, and other screen-free hobbies are on the rise. (CNN) + Dumbphones are becoming popular too—but it’s worth thinking hard before you switch. (Wired $)
Quote of the day
‘It may sound like American chauvinism…and it is. We’re done apologising about that.”
—Thomas Dans, a Trump appointee who heads the US Arctic Research Commission, tells the FT his boss is deadly serious about acquiring Greenland.
One more thing
BRUCE PETERSON
Inside the fierce, messy fight over “healthy” sugar tech
On the outskirts of Charlottesville, Virginia, a new kind of sugar factory is taking shape. The facility is being developed by a startup called Bonumose. It uses a processed corn product called maltodextrin that is found in many junk foods and is calorically similar to table sugar (sucrose).
But for Bonumose, maltodextrin isn’t an ingredient—it’s a raw material. When it’s poured into the company’s bioreactors, what emerges is tagatose. Found naturally in small concentrations in fruit, some grains, and milk, it is nearly as sweet as sucrose but apparently with only around half the calories, and wider health benefits.
Bonumose’s process originated in a company spun out of the Virginia Tech lab of Yi-Heng “Percival” Zhang. When MIT Technology Review spoke to Zhang, he was sitting alone in an empty lab in Tianjin, China, after serving a two-year sentence of supervised release in Virginia for conspiracy to defraud the US government, making false statements, and obstruction of justice. If sugar is the new oil, the global battle to control it has already begun. Read the full story.
—Mark Harris
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ Paul Mescal just keeps getting cooler. + Make this year calmer with these evidence-backed tips. ($) + I can confirm that Lumie wake-up lamps really are worth it (and no one paid me to say so!) + There are some real gems in Green Day’s bassist Mike Dirnt’s favorite albums list.
Wondering if AI voice agents could improve and scale your customer service? Want to know what it takes to implement AI voice assistants in your business? In this article, you’ll discover how to deploy AI voice agents that handle real customer interactions while avoiding common pitfalls. How AI Voice Agents Help Businesses AI voice agents […]
