Thesis: AI has crossed the chasm from novelty to infrastructure. Differentiation now comes from integration quality, not from merely “using AI.” [1]

1) Noise vs. Narrative

Adoption is broad (78% using genAI somewhere), but durable value lags until organizations rewire operating models—workflow redesign, accountable governance, and senior ownership. [2][3]

Executive takeaway: move from proof-of-concept to proof-of-value by hard-wiring AI into processes and P&L accountability.

2) The Dot-Com Analogy—Updated

Like the dot-com era, a few will compound value while many fade. The difference now: winners are built inside the enterprise fabric (data, workflows, controls), not just on the front page. [2][3][4]

3) What the Experts Say (and what it means)

Macro upside: GenAI unlocks $2.6–$4.4T/year across 60+ use cases—if firms redesign work. [4]

Scaling returns: IDC observes material productivity/cost gains where companies make the “AI pivot” to enterprise scale (the oft-quoted $3.7 per $1 comes from commissioned studies and should be treated as directional, not guaranteed). [5][6]

Governance gap: C-suites report adoption outpacing responsible AI practices; firms linking governance to outcomes perform better. [7][8]

4) The Late-Mover Trap

The “profit window” narrows as capabilities commoditize. Late movers face higher change costs and fewer green-field gains. Treat AI like electricity: it’s table stakes—returns accrue from how you build with it. [1][3]

5) The Enterprise Playbook for 2025–26

Upskill at scale: make LLM literacy and agentic-workflow design a core competency. [1][3]

Own your data advantage: prioritize first-party data and capture institutional reasoning (decisions, rationales, playbooks) to compound learning. [1][4][6]

Redesign work, then tool: map value streams, automate decision cycles, and set KPI guardrails (revenue, cost, risk, time-to-decision). [2][3]

Operationalize governance: board-level oversight, model risk controls, auditability; link governance to business outcomes. [7][8]

Platform strategy: assume consolidation; architect for portability, vendor diversity, and secure integration. [2][3]

💬 Reflective Call to Action

AI isn’t the finish line — it’s the foundation.

As leaders, we need to ask:

“What is my organization doing today that my competitors can’t automate tomorrow?”

The organizations that win won’t be the loudest, they’ll be the fastest learners—those that embed intelligence into the DNA of how they operate.

Reflect. Comment. And share this with a leader who still believes AI is optional.

Conclusion

AI won’t crash—undisciplined adoption will. The next decade separates experimenters from industrializers. Your advantage is no longer that you use AI; it’s that your organization learns faster than rivals because you instrumented the work.

References

1. Everyday AI Podcast – Ep 634 “AI Hype Is Over. Here’s What Your Business Should Do Next”
2. McKinsey – The State of AI 2025
3. McKinsey – Seizing the Agentic AI Advantage
4. McKinsey – The Economic Potential of Generative AI
5. IDC – AI Adoption Boosts ROI by $3.7 per Dollar Spent
6. OECD – Emerging Divides in the Transition to AI
7. EY – Responsible AI Pulse Survey 2025
8. EY – AI Adoption Outpaces Governance