The "slope of enlightenment" is steeper for AI because the barrier to entry isn't the technology—it's the Data Integrity Debt you've been accruing for the last decade.
Most C-suite leaders I speak with are currently caught in a pincer movement. On one side, boards are demanding an "AI story" to satisfy investors. On the other, vendors are promising plug-and-play transformation.
In my time at Gartner, we watched the Hype Cycle play out across cloud, big data, and blockchain. AI is different. If your underlying data is siloed or inconsistent, AI won't fix it—it will simply automate the chaos at scale.
The Three Fallacies of the AI Hype Cycle
The Plug-and-Play Myth
The assumption is that AI is a layer you "add" to your existing stack. In reality, AI is a mirror. An LLM trained on bad internal documentation doesn't provide insights; it provides confident hallucinations. High strategy requires acknowledging that your output is only as mature as your data governance.
Overestimating Tech, Underestimating Architecture
The hype focuses on the Model. The reality is that the model is only 10% of the solution. The other 90% is the data pipeline and integration architecture. Leaders are spending millions on tokens while their data remains trapped in legacy ERPs that don't talk to each other.
Solving for the "New" instead of the "Necessary"
The Hype Cycle drives organizations toward "shiny object" use cases while ignoring the boring, high-yield applications in supply chain or predictive maintenance. High strategy isn't about doing what's new; it's about doing what is architecturally sustainable.
Assessing Actual Readiness
When I audit a program, I move past the vendor demos and look at three specific pillars:
- Structural Readiness: If your AI needs data from Finance and Operations, but those departments haven't shared a table in a decade, your program is dead on arrival.
- Semantic Readiness: If "Net Revenue" is defined differently in three regions, your AI will produce three different (and wrong) answers.
- Ethical Readiness: Governance isn't a legal checkbox; it's the operational guardrail that prevents a pilot from becoming a PR nightmare.
I recently worked with a global utility provider wanting an AI-driven "Customer Experience Cloud." They had the budget, but no single source of truth for customer identity. We paused the transformation for six months to fix master data management. It wasn't the "sexy" work, but it was the only work that mattered.
The Hard Truth for Leadership
The Hype Cycle tells you when to get excited. High strategy tells you whento get to work. If you haven't audited your data foundation in the last 24 months, you aren't ready for AI—no matter what the vendor's slide deck says.
The organizations that recover are the ones willing to have the honest conversation early: is our roadmap a strategy, or just a wish list?