How to Cut Your Organization's AI Costs and Keep Your Data In-House: Two Solutions
Cheaper, more secure, more private. Two ways to run AI models inside your organization, from a real client case, and what still belongs with the frontier models.
Read →The Third Entity: From HR to H-LLM
Without noticing, we have created a third entity: the LLM. Why managing it is a behavioral challenge, not a prompting one, and where Influence Science comes in.
Read →The Sales Rep Isn't to Blame
When sales don't convert, the rep takes the blame. More often, the problem starts long before the call: in the lead source and the funnel behind it.
Read →How to Build an Affiliate Program That Actually Works: Six Principles
Most companies treat affiliates as a niche. Amazon treats them as a channel. Six principles that decide whether your affiliate program works, after 25 years on both sides of the table.
Read →This Is Not How You Work With Affiliates
The one channel you pay for on results alone is also the one companies squeeze hardest. After 20 years on both sides of the table, why that costs them revenue.
Read →Don't Fall Into the Vibe Coding Trap: What to Know Before Building on Lovable or Base44
Four things I learned first-hand about vibe coding platforms, and why only lightweight projects belong on them.
Read →Branding in an AI World: What GEO Is, and Why AI-Written Content Won't Get You There
The goal is no longer ranking first on Google, but being inside the answer the AI generates. What actually changes, and why generic content won't get you in.
Read →The Five Levels of AI Usage - and Why Most Organizations Are Stuck in the First Two
Two people both say “I work with AI\" and mean entirely different things. One saves ten minutes a day; the other replaced a department. A five-level scale for telling them apart.
Read →Fad or Shift: Three Questions to Ask Before the Market Answers for You
In the age of AI, you don't lose a market for a wrong decision. You lose for a decision that wasn't made.
Read →If AI is writing so much code, where are all the new products?
AI has stripped away the barrier to building products, shifting the real challenge from development to distribution. Success today depends less on code and more on a proven growth engine to reach users without burning capital.
Read →The Change Management Line Item Nobody Budgets For
Every AI transformation budget accounts for infrastructure, licenses, and integration. Almost none accounts for the actual bottleneck: getting the people who'll use the system to trust it enough to change how they work.
Read →Build vs. Buy: The Real Architecture Decision in AI Transformation
The build-vs-buy debate in AI transformation isn't about cost — it's about which parts of your decision-making you're willing to hand to a vendor's roadmap, and which parts have to stay yours.
Read →Why AI Pilots Die in Pilot Purgatory
Most AI initiatives don't fail at the pilot stage — they fail at the stage after it, the one nobody budgets for: the unglamorous work of turning a working demo into a system the business actually depends on.
Read →The Decision Engine — A Mental Model for AI-Era Growth Leaders
Predict. Decode. Decide. A three-stage mental model for where AI systems, behavioral science, and strategic execution truly meet — and why eliminating “decision latency” in your organization is your next competitive edge.
Read →AI Agents Are Not a Headcount Strategy
Most transformation projects fail for the exact same reason: they're framed as a cost-savings move, instead of an aggressive push to accelerate decision-making — which is sometimes very painful. Here's the mindset shift that actually moves the bottom line.
Read →Why Applying Behavioral Science Beats the “More Data” Approach
When growth teams hit a glass ceiling, their first instinct is to ask for more data. The right question is almost always different: which decision is your customer actually trying to avoid, and why?
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