Foresight Works Tackles the Execution Gap in AI Buildouts

The race to build AI infrastructure is defined, in most coverage, by megawatts announced, leases signed, and capital deployed. What gets less attention is whether any of it actually gets built on time. Atif Ansar, co-founder of Foresight Works, has spent 15 years studying why large projects fail, including through academic work at Oxford University. In my recent conversation with him and Solidigm’s Jeniece Wnorowski, Atif shared an insight from that work that shapes his company’s direction: the limiting factor in the AI data center boom is not technological, but human.

Foresight Works offers a project delivery platform combining AI, proprietary data, and scheduling methodologies developed through Atif’s academic research. The platform is built around the idea that projects are human systems, and that basic psychological biases that can harm outcomes can be overcome with processes that ensure both micro daily commitments and larger goals are met.

The Real Bubble Risk

In discussion about a potential AI infrastructure bubble, the concern usually voiced is about overbuilding. Will the supply of new data center capacity outpace demand? Atif rejects that framing. He points to accelerating demand for AI-enabled services and the contractual structure of hyperscaler leases as evidence that demand is not the problem. The problem, he argues, is delivery.

“These data centers are being built under contract from very high rated companies like hyperscalers,” he explained. “And as a result, they have 15-year leases. So the developers are not taking huge amount of risk.” But that contractual protection cuts both ways. A single month of delay on a 100-megawatt facility can cost $15 million or more in lost early revenue, and service level agreement penalties from hyperscalers compound the damage further. Atif estimates that more than a three-month delay can dissipate the net present value of even a very large build. “The penalties for missing your targets become potentially insolvency causing for companies that are not well managed,” he said.

The Watermelon Problem

One of the more memorable concepts Atif brought to this conversation was what he called the watermelon problem, a phrase for projects that are “green from the outside until they suddenly turn red.” Atif noted that the gap between perceived and actual progress is typically a product of optimism bias and political pressure, not malice. Teams hope they will catch up. But often, they do not.

The interdependencies inside a data center build make this particularly dangerous. A purchase order that should have been issued today, but was not, means a missing piece of equipment six months from now. The delay is invisible until the moment it becomes a crisis. “What lets people down is the accumulation of these very small variances,” he noted. “It’s actually those micro details that make the difference.”

Foresight is designed to function as a control tower, allowing organizations to manage the macro picture while having a handle on micro variances early enough to act on them. In one case, the platform uncovered a five-month delay buried beneath distorted reporting.

Where Schedules Break Down

Atif has identified recurring patterns in how project schedules fail. One is an overemphasis on civil and structural work, which is visually dramatic and easy to represent, at the expense of mechanical, electrical, and plumbing work, which is complex and harder to schedule. “Nearly 60 to 80% of a data center is MEP,” he noted. Treating it in broad strokes rather than mapping it with precision is a reliable path to delay. A second pattern involves compressing commissioning into the final phase of a build, when factory and site acceptance tests should begin far earlier, alongside procurement and equipment delivery.

Foresight’s AI layer helps identify structural gaps in submitted schedules, particularly missing milestones or poorly sequenced dependencies. “We help them look at what milestones they are likely missing or what gates they’re likely missing and help them insert those back in the appropriate places,” Atif said.

A Skills Supply Crisis Beneath the Capital Crisis

The execution problem extends well beyond data centers. Digital infrastructure, the energy transition, and defense spending are all generating enormous project delivery demands simultaneously on top of existing demand from evergreen sectors like pharmaceuticals and civil infrastructure, and the construction workforce is not keeping pace. Atif estimates that data centers represent roughly 10% of the global construction market by value, yet the people working in the industry amount to just 0.05% of the global construction labor force.

“So it’s still a cottage industry in terms of the footprint,” he said. “They need a lot of technology, automation, and AI to simply keep pace. They also need education…and I think that we need to upskill people and train them in the art of becoming better project managers.”

The TechArena Take

While day-to-day execution doesn’t typically garner headlines, Atif's work makes a compelling case that it deserves far more attention from executives committing capital to AI infrastructure. The trillion-dollar build underway globally requires disciplined upfront planning, clear governance, and repeatable processes. The developers who get this right are those who invest in process maturity early, building the kind of credibility that holds up with communities, investors, and hyperscalers alike. Platforms like Foresight Works represent an important step toward making that discipline accessible at scale, at a moment when the cost of getting it wrong compounds with every month of delay.