The MIT Wake-Up Call
MIT’s recent report revealed that 95% of generative AI pilots inside companies are failing. For many, this felt like the bubble was already bursting. But the reality is not that AI itself is broken—it’s that companies are approaching adoption without structure.
Executives approve budgets to “test AI” without defining success metrics or considering how the technology fits into workflows. Teams are left with tools that look great in a demo but fail to produce measurable results in practice. The outcome? Nine out of ten pilots never get past the trial phase.
Why Most Pilots Collapse
There are several patterns behind the staggering failure rate:
Lack of Clear Goals: Too many pilots start with the vague directive to “try AI.” Without KPIs, such as hours saved, errors reduced, or cost avoided the pilot quickly loses direction.
Workflow Misalignment: AI isn’t plug-and-play. If the tool doesn’t match the way employees already work, adoption plummets. Engineers, accountants, and project managers won’t bend their process to fit a poorly integrated system.
Misplaced Budgets: MIT’s research shows most AI funding is directed toward sales and marketing. Yet the biggest ROI lies in back-office and operational efficiency places where automation can save money, cut errors, and speed up delivery.
No ROI Validation: Boardroom demos are impressive, but without validated case studies, organizations can’t justify scaling beyond the pilot.
Trust Issues: In industries like civil engineering, trust is non-negotiable. If the system can’t prove its accuracy, it won’t be used no matter how flashy it looks.
How Ferris Breaks the 95% Rule
Ferris was built with these pitfalls in mind. Civil engineering projects don’t have room for failed experiments, so we designed a model that ensures adoption translates into measurable results.
White-Glove Onboarding
Instead of handing over log-ins and leaving teams to figure it out, Ferris embeds with clients to understand their workflows. We configure the platform to match how engineers actually work, not the other way around.
Structured Pilot Programs
Our pilots aren’t open-ended experiments they are structured projects with clear milestones and defined ROI metrics. By the end of a pilot, clients have proof: reduced submittal review time, verified calculations with higher accuracy, or dollars saved through avoided errors.
Proof Before Scale
We start small, validating one workflow or project. Once the ROI is undeniable, expansion happens naturally. This staged approach prevents wasted investment and builds internal trust.
Building Trust in a High-Stakes Industry
Engineering doesn’t reward guesswork. One mistake can cost millions. That’s why Ferris is built to validate calculations, accelerate reviews, and provide reliable answers from complex drawing sets. Our clients don’t just get a tool they get confidence that Ferris is a trustworthy member of the team.
This focus on trust and proof is why Ferris clients expand their pilots into full rollouts, rather than abandoning them like the 95%.
The Bigger Picture
The MIT report isn’t proof that AI is failing. It’s proof that sloppy adoption is failing. The technology works but only when implemented with rigor.
At Ferris, we’ve taken that lesson to heart. By combining hands-on onboarding, structured pilots, and validated ROI case studies, we’ve created a model that doesn’t just avoid the 95% it sets a new standard for how AI should be adopted in civil engineering.
AI won’t transform this industry because of hype. It will transform it because companies like ours prove, project by project, that the return is real.
Leave A Comment