Buildings 2.0 Library
Conversations on the future of the built environment
81 episodes of the Buildings 2.0 podcast, each bound as a short book: the ideas, the numbers and the lines worth keeping.
Season 2, Episode 34 /Jul 28, 2026
Win the AI race by slowing down
Maribeth Martorana, Sapien Advisors
Jul 28, 2026 /AI & Automation
Win the AI race by slowing down
Maribeth Martorana is the founder and CEO of Sapient Advisors, which helps real estate firms turn AI ambitions into working systems. A chartered financial analyst, she built financial data products at FactSet and Moody's before turning to real estate. Her claim is that the winners of the AI race will not be the fastest adopters but the most disciplined operators.
Maribeth Martorana, Founder and CEO, Sapien Advisors
In this conversation
What Maribeth found in the middle market: teams building agents who could not get requirements out of the business.
When everyone has the same reasoning models, what unique data a firm has becomes the competitive question.
Whether clean data still confers an advantage as models get better at structuring messy inputs, and her answer.
Why the organizations that implemented AI well ended up hiring more, not fewer, people.
The solution shouldn't be chasing the problem. We have to start with the business strategy and then everything else aligns with that.
Maribeth Martorana
Chapters
Disciplined operators
Why discipline beats speed in adopting AI.
By the numbers
Enterprise generative-AI pilots that showed no measurable impact on profit and loss
Maribeth says the winners will be the most disciplined operators, not the fastest adopters. Most pilots so far prove her right.
Source: MIT NANDA, The GenAI Divide: State of AI in Business 2025 ↗What unique data do you have?
The competitive question underneath every AI strategy.
Does data cleanliness still win?
Does data quality still win when models can clean it up?
Fewer humans?
Whether AI means fewer people, and why Maribeth says otherwise.
When management hears a data project, their eyes glaze over and they're like, it's never gonna get done.
Maribeth Martorana
Takeaways
The solution should not chase the problem. Start with business strategy, then crawl, walk, run: a surgical pilot, a roadmap, then production and scale.
Agentic systems do not fix a data problem; they steamroll it faster. Documentation, handoffs and data catalogs matter more now, not less.
When an owner hears 'data project,' their eyes glaze over. Pick one use case, write down the definitions, and build confidence before boiling the ocean.
It's the data, the human element, and the AI that come all together, like an intersection.
Maribeth Martorana