Strategic Foresight LLC · An independent Oklahoma research and decision-intelligence firmOklahoma City, Oklahoma
SF-US-2026-102 · Last reviewed · Draft, qualitative desk review
BLUF: Explicit go, hold, redesign and stop gates are likely to preserve flexibility as AI evidence improves.
Explicit go, hold, redesign and stop gates are likely to preserve flexibility as AI evidence improves.
This assessment connects technology diffusion, investment uncertainty, productivity measurement and changing work. to Consultants and client boards deciding when an AI pilot has sufficient evidence for the next commitment. Decisions should be staged around observable evidence rather than a single forecast.
Staged commitments are likely to reduce avoidable lock-in when pilot economics and assurance requirements remain uncertain.
Analyst assessment. Likelihood and confidence are stated separately, following the estimative-language approach described in ICD 203.
Technology diffusion, investment uncertainty, productivity measurement and changing work.
Consultants and client boards deciding when an AI pilot has sufficient evidence for the next commitment.
Illustrative, not empirical
| Driver | Mechanism | Magnitude | Source |
|---|---|---|---|
| Data quality | Unreliable inputs can undermine trustworthy AI. | Case-dependent | NIST AI Risk Management Framework (opens in new tab) |
| Technology change | Innovation changes the options available to decision makers. | Uncertain | GAO-22-3SP: Trends Affecting Government and Society (opens in new tab) |
| Workforce adaptation | Implementation depends on roles, skills and workflow. | Case-dependent | GAO-22-3SP: Trends Affecting Government and Society (opens in new tab) |
| Governance | Accountability and risk management shape responsible deployment. | Case-dependent | NIST AI Risk Management Framework (opens in new tab) |
Illustrative — not an empirical estimate. Source: demonstration assumptions.
Illustrative — not an empirical estimate. Source: demonstration assumptions.
Illustrative — not an empirical estimate. Source: demonstration assumptions.
One-time build
Illustrative — not an empirical estimate. Source: demonstration assumptions.
Payback
Illustrative — not an empirical estimate. Source: demonstration assumptions.
Illustrative — not an empirical estimate. Source: demonstration assumptions.
A thinking aid for applying this report to your own situation. It is separate from the analyst assessment.
What capabilities already support this decision?
Owner: Decision sponsor — to be assigned
What internal constraints could undermine execution?
Owner: Operational lead — to be assigned
What external changes create options worth testing?
Owner: Strategy lead — to be assigned
What external developments could change the value case?
Owner: Risk lead — to be assigned
Illustrative worksheet: your notes stay in this browser tab
| Source | Note |
|---|---|
| GAO-22-3SP · Trends Affecting Government and Society (opens in a new tab) | 2022 trend framework; not a current statistical release. |
| BLS · Oklahoma Economy at a Glance (opens in a new tab) | Public source. |
| OESC · Labor Market Information (opens in a new tab) | Public source. |
| BEA · GDP by State (opens in a new tab) | Public source. |
| GAO-21-519SP — Artificial Intelligence: An Accountability Framework (opens in a new tab) | Public accountability framework for governance, data, performance and monitoring. Not an endorsement of this firm's products. |
| NIST AI Risk Management Framework (opens in a new tab) | Voluntary risk-management guidance, not a certification or claim of compliance. |
Sources provide context. Project-specific claims require additional verification before action. "Last reviewed" records this draft's editorial review, not each source's refresh date.
Sources: GAO-22-3SP · Trends Affecting Government and Society (opens in new tab); BLS · Oklahoma Economy at a Glance (opens in new tab); OESC · Labor Market Information (opens in new tab)
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