This insights post is a summary of the blog post published by Roger Hurni of Off Madison Ave.
How to integrate AI through human Behavioral Marketing intelligence and Practical Critical Thinking (PCT)
If you’ve lived through an AI pilot that looked “successful” in a demo but died quietly in the real world, you already know this truth: AI ROI is rarely a capability issue. It’s a human behavior issue.
Harvard Business Review recently pointed to a painful gap between hype and outcomes: one MIT-linked estimate suggests the vast majority of AI initiatives fail to deliver intended value, and a Boston Consulting Group (BCG) survey shows most companies still aren’t seeing tangible returns.
That’s why I keep coming back to a simple premise from my work and my Better Thinkers Win speaking series: technology doesn’t create value, behavior change does. And if behavior is the bridge from insight to impact, then companies need two things to cross it:
- Behavioral intelligence (how people actually decide, act, resist, comply, and adapt), and
- Practical Critical Thinking (PCT) (a repeatable way to pressure-test assumptions, reduce noise, and translate AI into action without self-deception).
Below is a field-ready playbook to integrate AI the way humans work and not the way strategy decks pretend they work.
Step 1: Stop “implementing AI.” Start engineering behavior change.
Most AI roadmaps start with tools: copilots, agents, models, platforms. But ROI starts with a different question:
What do we need people to do differently? Specifically, in a real moment that matters.
Pick a narrow “moment of truth” (a decision, a handoff, a triage step, a customer interaction) and define the behavior in observable terms:
- Who acts?
- What do they do?
- When do they do it?
- What do they stop doing?
- What outcome changes if they do?
BCG found that only 26% of companies have built the capabilities to move beyond proofs of concept and generate tangible value. However, 74% have yet to show tangible value. That’s not just a tooling gap; it’s a translation gap from pilot to lived workflow.
Behavioral Marketing lesson: map the friction. Where does the behavior break? Is it in their motivation, their ability, or the prompt? If any one of those is missing, adoption becomes optional and optional becomes invisible.
Step 2: Design AI for real users, not “ideal” users.
A core consideration is that leaders treat adoption like a technology purchase instead of a behavioral change problem.
Real humans are busy, defensive about competence, and allergic to extra steps. So build your AI workflow with these realities:
- Minimize cognitive load. If it adds decisions, it adds resistance.
- Put AI where work already happens. Switching contexts kills usage.
- Use “good friction” when the stakes are high. Sometimes slowing people down prevents bad automation and blind trust.
One practical principle: Don’t optimize for “seamlessness.” Optimize for “rightness.” If the AI is making a recommendation that could carry risk, a small pause or checkpoint can improve outcomes dramatically.
Step 3: Build trust with transparency before you ask for compliance.
People don’t resist AI because they hate innovation. They resist AI because it threatens certainty, autonomy, and identity.
You need to use transparency and explainability as trust builders. One way to do this is to help users as the right questions so they know how the AI arrived at a recommendation. This greatly reduces anxiety.
Translate that into usage behaviors:
- Define inputs used (at a human-readable level).
- Ask for reasoning or justification.
- Evaluate when the AI is guessing vs. retrieving vs. calculating.
- Learn to override output and take a different direction.
Trust takes time and it takes experience.
Step 4: Make adoption a habit, not a training event.
Training is not adoption. Training is information. Adoption is the behavior.
If you want repeat usage, use behavior sequencing to develop a habit.
- Start with tiny commitments (use the AI for the first draft, don’t run the whole workflow through AI).
- Use prompts at the point of need (embedded cues).
- Note feedback (time saved, errors avoided, quality improved).
- Create social proof (teams copy what respected peers actually do).
In the HBR article they stated that MIT-related reporting highlights that only a small minority of pilots produce measurable returns, and many efforts stall due to workflow brittleness and misalignment with daily operations.
That’s your warning label: if AI is a separate “thing,” it will remain a separate “thing.” Value shows up when AI disappears into the way work gets done.
Step 5: Measure what matters: behavior leading indicators, not vanity metrics
If you only measure “model performance,” you’ll miss the real story.
Track three things:
- Adoption behaviors: frequency, depth of use, repeat usage, drop-off points
- Decision quality: cycle time, error rates, escalation rates, consistency
- Business outcomes: revenue lift, cost reduction, retention, risk reduction
And don’t ignore incentives. If people are punished for slowing down, they’ll rubber-stamp AI. If they’re punished for asking questions, they’ll quietly bypass the tool.
Step 6: Operationalize PCT: Pause, Challenge, Test
This is the simplest way I know to keep AI integration honest.
- Pause: Step off the problem long enough to see it clearly.
- Challenge: Pressure-test assumptions (including your own excitement).
- Test: Run small experiments in real workflows; measure behavior change.
AI rewards speed. But ROI rewards clarity. PCT is how you trade impulse for impact.
The Bottom Line
AI isn’t failing because it can’t generate answers. It’s failing because organizations can’t reliably convert answers into action.
If you want ROI, stop treating AI as a technology rollout. Treat it as a behavioral transformation, guided by Behavioral Marketing intelligence and protected by Practical Critical Thinking.
That’s how you move from pilots to performance. That’s how you avoid the Information–Action Fallacy I wrote about in my book: Outthink. Outperform. And that’s why, in the end, Better Thinkers Win.
(And if you want to go deeper, these ideas connect directly to the frameworks in my book Outthink. Outperform. After all outperforming has never been about having more information. It’s about thinking better with what you have.)
Reference Links:
How Behavioral Science Can Improve the Return on AI Investments
AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value
