AI Strategy Commonalities

This post was originally featured in the March 18th, 2026 newsletter found here: INBOX INSIGHTS: AI Strategy Commonalities, B2B Marketing Writing and AI Part 2

The One Thing Every AI Strategy Has in Common

A bartender told me a story recently that I haven’t been able to shake. The parking system in her building is run by an AI startup — a buddy of the building owner. It scans your plate, logs your entry, tracks your space, monitors duration, calculates what you owe, processes the data, generates a record, and… never sends you a bill. Eight steps of sophisticated data collection. And the one thing it was built to do? It doesn’t do that.

I keep thinking about that parking system because it’s the most honest picture of where we are with AI right now. Not the conference keynote version. The real one.

Every failed AI initiative I’ve looked at — every stalled pilot, every abandoned project, every training program that produced certificates but not capability — has the same root cause. Leadership bought the shiny thing to avoid doing the hard thing.

That’s it. That’s the whole pattern.

It shows up everywhere once you start looking. Companies are running AI pilots that collect data, process inputs, generate outputs, and produce beautiful dashboards — but nobody can tell you what business outcome the pilot delivered this quarter. They’re measuring activity, not results. Meetings held, models trained, dashboards built. Check, check, check. Revenue generated? Decisions improved? Crickets.

It shows up in training. The instinct was right — people need to learn how to use AI. The execution was wrong. Most organizations rolled out the same 101 course for everyone: the intern, the VP, the data scientist, and the operations manager all sat through identical training on prompt engineering basics. I say this as someone whose company built generic AI training too. We did it. And then we realized that completion rates tell you nothing about capability. A workshop where the CMO learns to audit AI outputs for brand risk is fundamentally different from one where a junior analyst learns to build a workflow. The hard work is assessing where each person actually is and what they specifically need. Most organizations skipped that step because assessment is slow and messy.

It shows up in infrastructure. Companies want the electric train — the cutting-edge AI, the automation, the intelligent agents — but their data lives in seventeen systems that don’t talk to each other. Their CRM hasn’t been cleaned since 2019. Half their analytics run on spreadsheets one person maintains and nobody else understands. The AI budget got approved. The data infrastructure budget didn’t. And in many organizations, the data team has been raising this flag for years. Leadership just didn’t fund the fix.

And it shows up in talent. AI has automated the entry-level work that used to train future leaders — the research, the data pulls, the first drafts. All the unglamorous tasks that taught junior employees how the business actually works. That pipeline is gone, and nobody has replaced it. We celebrated the efficiency gains without asking where the next generation of leaders comes from.

Four different symptoms. Same disease. Leadership keeps buying solutions to avoid doing the work that makes those solutions actually function.

So before you approve your next AI initiative, answer four questions:

• Who’s building judgment? How are you developing the next generation of leaders now that AI handles their old proving ground?
• Where is each person today? Have you assessed your team’s actual AI proficiency before buying training?
• What’s under the hood? When was the last time someone audited the data infrastructure your AI is supposed to run on?
• What does done look like? Can you articulate the specific business outcome this pilot produces, in one sentence?

If you can’t answer all four, that’s your to-do list. Not another platform evaluation. Not another vendor demo. The work that makes everything else work.

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– Katie Robbert, CEO

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.