thedecisionlab.com

The Decision Lab works with client organizations through consulting-led engagements that apply behavioral science and AI to specific problems in the public and private sectors. The work is framed around a question the client is trying to solve, and the engagement typically moves from problem definition to applied research and measurement. Organizations start by describing their problem through the contact form on the site or by emailing [email protected], and a team member follows up.

What an engagement looks like

The site presents the firm's work as a set of problem-led case studies rather than a fixed service menu. Each case study is framed as a question, which shows how the firm scopes work with clients:

The pattern: the client brings a concrete behavioral or adoption problem, and the firm combines research methods with AI to define, test, and measure a response.

The methods clients can expect

Based on the published work, engagements draw on several applied methods:

• Simulated environments — used to test how a platform or intervention affects real decisions, such as purchase behavior, before or alongside live deployment.
• Predictive modeling — used to identify who is likely to drop off (for example, debt repayment dropouts) so interventions can be targeted.
• AI adoption and trust work — helping large workforces actually use AI tools, not just deploy them.
• Decision intelligence — applying AI to personalize decisions at scale, as in the 25M-member care example.
• Applied research and measurement — including published research such as work on sycophancy in LLM advisors and cultural fit in mental-health LLMs.

How the process starts

1. Describe the problem. The contact form asks for your name, work email, and "What are you trying to solve?" The site also lists [email protected].
2. Team follow-up. The site states that filling in the form prompts a member of the team to reach out.
3. Scoping around the problem. Given how the case studies are framed, expect the first conversations to center on the specific decision or behavior you want to change, and which methods (simulation, modeling, research) fit.

Who this fits

The published clients are large organizations — Fortune 500 companies, financial services firms, digital health platforms, and a bank — with problems that involve getting people to adopt a tool, change a financial behavior, or make better decisions at scale. The firm also positions its insights as read by "over 100,000 leaders and innovators," and publishes research and articles on topics like digital nudging ethics, persuasive algorithms, and misinformation defenses.

If your problem is a behavioral or adoption challenge inside a sizable organization, the case-study pattern suggests a fit. If you need a fixed-scope product or self-serve tool, the site does not describe one; the model shown is consulting engagement via inquiry.

Practical notes

• The site does not publish pricing or engagement terms; those would come up in the follow-up conversation.
• The contact form is the stated entry point, so prepare a one- or two-sentence problem statement before submitting.
• Reading the case studies first helps you match your problem to a method the firm has already applied.

The Decision Lab (thedecisionlab.com) is a behavioral science consulting firm that applies decision science and AI to problems in the public and private sectors. It is a good fit to know about if you are looking for a partner to diagnose why people behave the way they do — and to design interventions that change that behavior at scale — rather than a pure research publication or a software vendor.

What the firm actually does

The site frames its work as "frontier behavioral science for the age of AI." In practice, that means combining behavioral science methods with AI and data work to solve applied problems for organizations. The homepage organizes its offer around five areas:

• AI — adoption, trust, and measurement questions around AI systems
• Consulting — engagement with client teams on behavioral problems
• Industries — sector-specific applications
• Resources — research, insights, and case studies
• About Us — who the firm is and how it works

The two entry points it pushes most are "View our work" (case studies) and "How we work" (methodology), which is a useful signal: the firm positions itself on demonstrated outcomes and process, not on a product catalog.

The kinds of problems it takes on

The homepage's featured work gives a concrete sense of scope. Each example is framed as a question, which is a fair summary of how behavioral consulting engagements are usually scoped:

Two things stand out. First, the work spans workforce adoption, consumer influence, health-adjacent personalization, and financial behavior — a wide range, but all of it is about human decisions under uncertainty. Second, the firm publishes its own research alongside client work, including topics like measuring sycophancy in LLM advisors and the ethics of digital nudging. That combination — applied case studies plus methodological research — is the main reason to treat it as a consulting firm with a research arm rather than a content site.

Who its content is for

The site states that over 100,000 leaders and innovators follow its insights, and the featured articles are aimed at that audience: ethical lines in digital nudging, the behavioral ethics of persuasive algorithms, a taxonomy for countering AI-amplified misinformation, and behavioral defenses against deepfakes. If you work in product, policy, HR, or consumer research and need to reason about how people respond to AI-mediated systems, the insights section is the low-commitment way in. If you have a specific behavioral problem to solve, the case studies are the better starting point.

How to engage

The contact route is direct: a short inquiry form on the site (name, work email, and "What are you trying to solve?") plus a general address, [email protected]. The form's phrasing suggests the firm expects a described problem rather than a generic request, so come with a concrete behavioral question and, ideally, the metric you want to move.

What to check before deciding

• Scope fit: confirm your problem is a behavior problem, not a pure engineering or analytics one — the firm's value is in the decision layer.
• Evidence for your sector: browse the case studies for an industry close to yours before reaching out; the published set is a sample, not a complete list.
• Research vs. engagement: the insights and research articles are free to read; consulting work is a separate commercial relationship. The site does not publish pricing, so terms are discussed directly.
• Timeline expectations: case studies describe multi-stakeholder programs (a 20,000-person workforce rollout, a 25M-member platform), which suggests engagements sized for large organizations rather than short one-off studies.

The Decision Lab works across a defined set of industries—financial services, HR technology, banking, digital wellness, and mental-health AI—and applies behavioral science and AI to recurring topics such as AI adoption and trust, consumer insights, digital nudging ethics, and misinformation. If your organization operates in one of these sectors or faces one of these problems, the site's case studies and research pages are the fastest way to judge relevance.

Industries with demonstrated work

The site's case studies are the clearest evidence of sector experience. Each pairs an industry context with a specific problem:

The site also references work on personalizing care for 25 million members with a digital wellness platform, and simulated environments for measuring platform influence. These examples suggest the firm is comfortable with both large-scale consumer problems and internal workforce challenges.

Recurring topics across sectors

Beyond industry labels, several themes recur in the firm's insights and research:

• AI adoption and trust — how organizations move employees from awareness to actual use, and how trust is built at scale.
• Consumer insights — how people really decide, including the ethics of persuasive algorithms.
• Digital nudging ethics — where personalization and motivation cross into manipulation.
• Misinformation and deepfakes — behavioral defenses and taxonomies for countering AI-amplified false information.
• Decision intelligence — using AI to personalize services while keeping human decision-making central.

How to check fit for your own case

1. Open the case studies section and scan for your sector or a closely adjacent problem.
2. Read the featured insights for the topics you care about—nudging ethics, misinformation, or AI trust—to see whether the framing matches your needs.
3. If a case study describes a problem similar to yours, note the scale (workforce size, member base) and the method used; that is the strongest signal of relevant experience.
4. Use the contact form on the site, describing what you are trying to solve, to confirm whether your industry is in scope.

A practical caveat: the published case studies skew toward large organizations and AI-adjacent problems. If your issue is outside these sectors—or much smaller in scale—the insights pages may still be useful, but direct sector experience is less evident from the site alone.

The Decision Lab applies behavioral science and AI to practical business and public-sector problems: getting large workforces to actually adopt AI, measuring how platforms influence purchasing, personalizing care at scale, and reducing drop-off in debt repayment. Its work fits organizations facing a gap between having a technology or program and getting people to use it well — typically large enterprises, financial services firms, health and wellness platforms, and public-sector bodies. The firm describes itself as a behavioral science consulting firm that applies decision science and AI to solve pressing problems in the public and private sectors.

The problem types it works on

Based on the firm's published case studies and service areas, its work clusters into four recurring challenge types:

These are not abstract research questions. Each pairs a behavioral question (why people don't adopt, trust, follow through, or choose) with a measurement or modeling problem (how to detect and predict that behavior at scale).

How applied research and predictive modeling support the work

Two methods recur across the examples.

Applied research and measurement. The firm publishes research on how AI systems behave and how people respond to them — for instance, work on measuring sycophancy in LLM advisors using an LLM-as-judge framework with simulated users, and analysis of cultural fit and safety in mental-health LLMs. This research function feeds the consulting side: before you can change a behavior, you need a defensible way to measure it. The "simulated environments" example follows the same logic — testing a platform's influence on purchases in a controlled setting rather than guessing from outcomes alone.

Predictive modeling. The debt repayment case shows the pattern: identify the behavioral signals that precede dropout, model them, and intervene before the person disengages. The same approach underlies personalization at scale — segmenting a large member base by predicted needs rather than treating everyone identically.

When this kind of engagement makes sense

Consider this type of work if you can answer yes to most of these:

• You have already deployed a tool, platform, or program, and the problem is now human behavior — adoption, trust, follow-through, or choice — not the technology itself.
• Your population is large enough that measurement and modeling pay off (the published examples run from thousands to tens of millions of users).
• You need evidence, not just a campaign: you want to know whether an intervention actually changed behavior.
• The decision affects real outcomes — purchases, care, debt repayment, workforce productivity — where a small shift in behavior has measurable value.

If your problem is purely technical (infrastructure, model performance) or your audience is too small to measure, a generalist consultancy or in-house team is likely a better fit.

How to start a conversation

The site directs prospective clients to a contact form asking for name, work email, and a description of "what you are trying to solve," with a team member following up; there is also a direct email address, [email protected]. The most useful first step is to frame your problem in behavioral terms — who needs to do what differently, and how you would know it worked — since that is the shape of question the firm's case studies address. Pricing and engagement terms are not published on the site, so scope and cost need to be established directly with the team.

The Decision Lab publishes two main streams of free-to-read editorial content: Insights (short analytical articles on behavioral science, AI, and society) and Research (longer studies and frameworks, often produced with partner organizations). Both are accessible from the site's navigation and are written for a general audience of leaders, researchers, and practitioners rather than as peer-reviewed academic papers. The site also offers a subscribe option for updates, though the available page evidence does not specify pricing or login requirements for any content.

Insights: Short-Form Analysis

The Insights section is where the site's editorial voice is clearest. Topics sit at the intersection of behavioral science, technology, and public life. Current featured pieces include:

These are useful if you want a conceptual grounding before a design, policy, or product decision — for example, if you are drafting an ethics review for a nudging feature and need a vocabulary for the motivation/manipulation distinction.

Research: Applied Studies and Frameworks

The Research stream is more technical and often tied to client or partner work. Two examples visible on the site:

• Measuring Sycophancy in LLM Advisors: An LLM-as-Judge Framework with Simulated Users — a methodology piece on detecting when AI advisors tell users what they want to hear, using simulated users and LLM-based evaluation.
• The Adaptation Dilemma: Cultural Fit Does Not Guarantee Safety in Mental-Health LLMs — an argument that cultural alignment in mental-health language models is not sufficient for safety.

These suit readers evaluating AI systems — for instance, a team building an LLM-based advisor who needs an evaluation approach for sycophancy, or a product group assessing safety claims for a health-adjacent model.

Case Studies Alongside the Editorial Content

The site pairs its insights with impact case studies that show the same methods in organizational settings, including:

• Preventing debt repayment dropouts using predictive modeling (American Financial Solutions)
• Building AI trust across a 20,000-person workforce at a Fortune 500 HR technology company
• Measuring a platform's real influence on purchasing behavior through simulated environments
• Personalizing care for 25 million members using decision intelligence

Reading a case study next to the related insight piece gives you both the mechanism and an example of it applied — useful when you need to justify a behavioral-science approach to stakeholders.

How to Decide What to Read

• If you need concepts and framing for a design or policy question, start with Insights.
• If you need methods or evaluation approaches for an AI system, start with Research.
• If you need precedent for a client-facing proposal, read the case studies.
• If you want ongoing updates, the site offers a subscribe option; the available evidence does not state terms, so check the subscription page directly before assuming access conditions.

For direct contact or content requests, the site lists [email protected] and a contact form.