Behavioral science and AI, applied
Behavioral science consulting for enterprise AI adoption
The Decision Lab is a behavioral science consultancy that helps large organizations get employees to actually use the AI tools they have deployed. We diagnose the behavioral barriers blocking adoption, build programs that clear them, and test those programs against control groups before they scale. At a Fortune 500 HR-technology company, this raised organization-wide AI adoption 35%.
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How we drive AI adoption
Diagnose why adoption is stalling
We map how employees in your organization actually move from first exposure to everyday use of AI, and where they drop off. This combines a review of the adoption research, workforce interviews and surveys, and usage data where available. The output is a ranked set of barriers, segmented by employee group, so you know what is blocking whom.
Build programs that clear specific barriers
Each barrier gets a program built for it and for the segment it affects: hands-on tasks embedded in real workflows for people who need practice, structured usage guidelines for people who are unsure what is allowed, peer challenge series for teams who respond to social proof. We typically design 15 to 20 candidate programs and select the strongest using evidence-based criteria.
Test before you scale
We pilot programs with a defined employee group against a control group for four to six weeks, measuring adoption, confidence, and time to first use before and after. Only programs that show a measurable effect move to rollout. This is what separates a program that drove change from one that rode company momentum.
Hand over something your teams can run
Every program ships with the protocols, guides, and support structures your internal teams need to run it without us. We also train internal champions so adoption keeps compounding after the engagement ends.
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I was blown away with their application and translation of behavioral science into practice. They took a very complex ecosystem and created a series of interventions using an innovative mix of the latest research and creative client co-creation. I was so impressed at the final product they created, which was hugely comprehensive despite the large scope of the client being of the world's most far-reaching and best known consumer brands. I'm excited to see what we can create together in the future.
Heather McKee
BEHAVIORAL SCIENTIST
GLOBAL COFFEEHOUSE CHAIN
RESULTS FROM OUR AI ADOPTION WORK
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Increase in AI Adoption
Organization-wide AI adoption rose 35% at a Fortune 500 HR-technology company after we diagnosed five behavioral barriers and piloted eight targeted programs against a control group.
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Increase in AI Use in Core Work
Use of AI within employees' core job responsibilities rose 23%, measured before and after the pilot programs. This is the number that separates real adoption from experimentation.
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Confidence Gain vs Control
Confidence in exploring AI tools rose 41% in the pilot group while falling 26% in the control group over the same period. The control group is how we know the programs drove the change.
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Employees Now Covered
The programs came with the protocols, guides, and support structures the client's teams need to run them without us, and are now scaling to the company's full 20,000-person workforce.
What an AI adoption engagement looks like
Map the adoption journey
We build an end-to-end map of how your employees move from first exposure to everyday AI use, and identify the barriers and employee segments where adoption breaks down.
Pilot against a control group
We run the selected programs with a defined employee group for four to six weeks, measuring adoption, confidence, and time to first use before and after, compared against a control group.
Build targeted programs
Each program is built to clear one barrier for one segment, from workflow-embedded practice tasks to usage guidelines to peer challenge series, then selected using evidence-based criteria.
Scale what worked
Programs with a measured effect roll out with the protocols, guides, and internal champions your teams need to run them without us.
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our team
Most enterprise AI programs stall for behavioral reasons, not technical ones.
Applied behavioral science for enterprise AI adoption
Enterprise AI adoption has reached 88% of organizations, yet most companies report little or no measurable return. The tools work. People don't use them, or use them for low-value tasks and stop.
The reasons are consistent across the companies we've studied: employees don't have time to learn a new way of working, the tools don't fit existing workflows, usage rules are unclear, people don't trust the outputs, and the tools feel more complex than the task they replace. None of these are solved by buying more licenses or running another training session.
This is the gap between AI investment and AI value, and closing it is a behavior change problem.
SPROUT is our framework for the conditions that determine whether AI adoption sticks inside an organization. It is grounded in the adoption research and validated with survey data from over 20,000 employees. We use it to score where an organization stands on each condition, rank the barriers, and decide which programs to build first.
We work with Chief AI Officers and transformation leads who have deployed tools and need adoption numbers to move, CHROs and heads of L&D responsible for workforce readiness, CIOs and CTOs whose AI investments are being judged on usage rather than deployment, and product leaders launching AI features and seeing low uptake.
Find out why AI adoption is stalling in your organization
Run the free SPROUT diagnostic on your team at thedecisionlab.com/tools/ai-adoption-toolkit, or talk to us about a full adoption engagement.
We’ll never share your info with anyone.
CASE STUDY
Increasing AI adoption across a Fortune 500 workforce
A Fortune 500 HR-technology company had deployed AI tools across the organization, supported by training and internal communications, but usage remained low. We built an end-to-end map of how employees moved from first exposure to everyday AI use, combining a 48-source review of the adoption literature with workforce interviews, surveys, and expert consultation. The map isolated five barriers and two employee segments with opposite failure modes: enthusiasts who were experimenting but hitting friction, and skeptics who distrusted AI's value and stalled at first contact.
Our mission is to translate cutting-edge science into powerful tools, enabling passionate leaders to place intentional bets on innovation.
Dan Pilat
Co-Founder & Managing Director
MEET OUR EXPERTS
Managing director
Marielle MontenegroMarielle has led a number of large product mandates for multinational clients, focusing on the intersection of human centered design and behavioral science. Her work within highly complex team structures and organizations has made her a world expert of the integration of behavioral science into product - and expertise which she speaks about at conferences around the world.
Director
Turney McKeeTurney is a Director at The Decision Lab, where he brings a multidisciplinary lens to solving complex public health challenges. With a background in cellular biology and pharmacology, he explores how behavioral science can support healthcare systems and policies around the world. Before joining TDL, Turney worked at the intersection of healthcare and technology as a competitive and business intelligence analyst, helping organizations navigate emerging trends and strategic decisions.
Managing director
Sekoul Krastev, PhDSekoul is a decision scientist focused on using scientific rigor to create innovation within digital environments. His work, which has been featured in peer review journals, conferences and publications focuses on next-generation products that are not only innovative but also deeply integrated with an understanding of user psychology to drive desired behaviors and outcomes.
Everything we do is based on five core principles (SPICE): Socially Conscious, Pragmatic, Inventive, Catalytic, Evidence-Based.
Dr. Sekoul Krastev
Co-Founder & Managing Director
AS FEATURED IN
Related AI adoption resources
faq
Common questions about AI adoption consulting
What is AI adoption strategy consulting?
AI adoption strategy consulting helps organizations move from deploying AI tools to having employees use them consistently in their work. It focuses on the human side of AI implementation: diagnosing why people are not using the tools, designing programs that change behavior, and measuring whether adoption actually increased.
Why do employees not use AI tools after they are rolled out?
The five most common barriers are time constraints, poor fit with existing workflows, unclear rules about what is allowed, low trust in AI outputs, and perceived complexity. Which barrier dominates varies by employee segment, which is why blanket training programs usually underperform.
How is a behavioral science approach different from change management?
Traditional change management runs communications and training and measures completion. A behavioral science approach diagnoses the specific barriers stopping specific groups, builds programs targeted at each, and tests them against a control group before scaling. The difference shows up in the numbers: you know whether adoption moved because of the program or in spite of it.
How do you measure AI adoption?
We measure adoption directly: frequency of use, use within core job responsibilities, time to first use, and self-reported confidence and ease, collected before and after each program and compared against a control group. We do not rely on training completion or satisfaction scores.
How long does an AI adoption engagement take?
Diagnosis typically takes four to six weeks. Program design and a controlled pilot take another six to eight weeks. Organizations usually have pilot results within one quarter and a rollout plan for the following one.
What size of organization do you work with?
Our AI adoption work is built for enterprises with 2,000 or more employees, where adoption problems are large enough to segment and pilot properly. We have run programs scaling to workforces of 20,000.
Do you work with companies outside North America?
Yes. We are headquartered in Montreal and work with clients across the United States, Canada, Europe, and international organizations.
