I love what AI promises; I’m allergic to the way it’s often implemented. Too many leaders brag about pilots while their P&L waits for impact. Meanwhile, adoption is exploding: 65% of organizations reported regular gen-AI use in early 2024, rising to 71% by 2025—a near-unheard-of curve for enterprise tech. (McKinsey & Company)
Zoom out and the macro story is even louder: AI could add $15.7 trillion to global GDP by 2030. And firms already leaning in are seeing real productivity lift—surveys in financial services report ~20% improvements as teams redesign work around gen-AI. (PwC)
If the numbers are this strong, why do so many programs stall? Because value doesn’t come from having AI—it comes from governed deployment tied to needle-moving KPIs. That’s the difference between novelty and scale. (This week’s INFOGRAPHIC and CAROUSEL show the model and roadmap I use with clients.)
Let's Make AI Earn Its Keep
AI should compress cycle time, raise conversion, or expand margin—on purpose. The fastest way to get there is to pair a sharp business case with a lightweight, proven governance spine: the NIST AI Risk Management Framework (AI RMF 1.0)—Govern → Map → Measure → Manage. It’s vendor-neutral, regulator-friendly, and built for repeatability. NIST Publications+1
Actionable Steps (Using the NIST AI RMF)
1) GOVERN – Set the rules before you write prompts.
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Publish a 2-page AI Policy v1 (data use, human-in-the-loop, model lifecycle, incident response).
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Stand up an AI Review Board (legal, risk, security, product, data) with clear sign-off thresholds. NIST Publications+1
2) MAP – Tie use cases to money.
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Pick 2–3 use cases that touch revenue or cost (e.g., time-to-quote ↓, ticket deflection ↑).
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For each, document stakeholders, data sources, and potential harms (privacy, bias, IP). NIST Publications
3) MEASURE – Track outcomes, not activity.
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Define business KPIs (cycle time, conversion, NPS, CAC/LTV), model metrics (accuracy, drift), and trust metrics (bias tests, privacy compliance).
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Establish a Quarterly AI Value Review to retire losers and scale winners. NIST Publications
4) MANAGE – Mitigate, monitor, and scale.
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Implement controls (role-based access, red-teaming, prompt guardrails, rollback plans).
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Add monitoring (drift alarms, retrain cadence, post-deployment audits). Then productize what works. NIST Publications
Why this framework? Because adoption is surging and expectations are rising—your stakeholders will ask for proof that AI is safe, effective, and economically justified. McKinsey’s adoption curve and PwC’s GDP outlook tell you where the market is headed; NIST tells you how to scale responsibly. (McKinsey & Company)
Real-World Example
Context: A leadership team had three disconnected pilots (sales emails, support summaries, FP&A variance analysis). Lots of demos, zero P&L impact.
What we did (8 weeks):
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Ran a MAP sprint to score each use case by KPI potential and data readiness.
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Chose one: claims triage to cut cycle time 30%.
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Built Govern/Measure basics: policy v1, review board, baseline metrics, human-in-the-loop.
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Launched a guarded pilot with Manage controls (access, prompt standards, red-team tests).
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Held a Value Review at week 8.
Result: 22% cycle-time reduction, 14-pt improvement in first-pass correctness, and measurable call-center deflection—enough to sunset the two weaker pilots and fund scale-out of the winner. (Productivity deltas mirrored what Bain reports when teams redesign work, not just add tools.) Bain
Real Strategies. Real Results.
AI is not a sidecar. Treat it like any revenue-bearing product: pick high-leverage use cases, govern the work, measure outcomes relentlessly, and scale what pays. The moment you shift from “cool pilot” to “quarterly value review,” AI stops being a cost center and starts acting like a competitive moat.
That's it for this week!
Sam Palazzolo
Real Strategies. Real Results.
PS – Here’s three ways I can help right here/right now:
1 – Catalyst Audit – Identify if your growth plan is globally ready (and where it’s likely to break) – 5 questions / 3 minutes: https://www.sampalazzolo.com/assessments/2148521795
2 – CEO Catalyst Program – The last CEO Cohort is forming for a November 6 launch – Details: https://www.sampalazzolo.com/ceo-catalyst
3 – An Exclusive Executive Luncheon – In Nashville on October 23, I’ll be hosting an exclusive luncheon that will be, in a word, Transformational! RSVP: https://docs.google.com/document/d/1sk9kbnr5pDWlL-n1BHIVIJGkhyk24F129nCZzk6VfUs/edit?usp=sharing
References
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McKinsey & Company. (2024). The state of AI in early 2024 — 65% regular gen-AI use. Link. McKinsey & Company
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McKinsey & Company. (2025). The State of AI: Global survey — 71% regular gen-AI use. Link. McKinsey & Company
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PwC. (2017). Sizing the prize — $15.7T GDP by 2030. PDF. PwC
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Bain & Company. (2024). AI in Financial Services Survey Shows Productivity Gains Across the Board. Link. Bain
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NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). PDF. NIST Publications
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