Hey there,
Have you noticed how AI isn’t a side project anymore? It now runs as the strategy under every serious ops and people decision. Progressively turning that reality into something executable. A 30 minute AI leadership debrief, a clear role stack, and an HR radar you can utilize in your next meetings.
Pause for a second and analyze the strategies for constant progress & adaptation within operations during the ongoing recent advancements.
In this issue:
Playbook of the Day
How to Run a 30-Minute AI Leadership Ops Debrief
Goal: Formalize AI ownership across strategy, governance, and operations to scale intelligence from pilots to enterprise systems, slashing risk and unlocking ROI.
Who: COO as orchestrator, plus Chief AI Officer, Head of AI Governance, and Director of AI Engineering/MLOps. Weekly sync, same slot every Thursday.
Before the debrief (10 mins):
Each lead loads 3 bullets into shared dashboard: top AI roadmap priority, live governance gap or risk, one production metric like uptime & infer cost.
COO scans for cross-dependencies or EU compliance flags, & tag anything hitting growth targets, budget drift, or agent orchestra sprawl as “blocker.”
During the 30 minutes:
AI Roadmap in 90 Seconds (5 mins): Each lead should share one win, one problem, and one key metric.
Governance & Risk Review (12 mins): Review blockers, assign one owner and one concrete action per risk.
Scale Plan for Next Cycle (10 mins): Confirm about 3–5 prioritized items, with proper owners and deadlines.
Rules: No tech deep dives, no research tangents. The process is to simply stick to executive accountability for AI as operating system. Outcomes shows as growth tied to models, risks contained, and production being reliable.
Latest News
AI Leadership Stack 2026 for COO Intelligence 📊
Published: 01/22/2026
Christian & Timbers shows how AI has become the operating system of modern enterprises, pushing COOs to clarify ownership of AI strategy, model risk, talent adoption, and production reliability. Laying these specialized 2026 AI leadership stackChief AI Officer, Head of AI Governance and Responsible AI, Chief Agent Officer, Head of Generative AI, Chief Scientist, Head of Autonomous Systems, AI Product Lead, and Director of AI Engineering/MLOps along with mandates, responsibilities, and hiring signals across the US and EU.
Upside: For COOs building a COO intelligence function, it operates as an org design blueprint: consolidate fragmented AI spend under a CAIO, stand up governance leadership to satisfy regulators and customers, and invest in MLOps and agent orchestration so AI moves from pilots to dependable, scaled operations.
Impact: Organizations that treat AI as an enterprise system with clearly accountable executive owners move faster with tighter control, while those that leave AI in test phase accumulate the technical debt, duplicated spend, and mounting governance pressure problems that land squarely in the COO’s lap.
At Davos, AI Is a COO Jobs Lever, Not a Layoff Script 🧠
Published: 01/23/26
Reuters’ Davos coverage shows a clear pivot in the AI debate: top executives now frame AI less as a job‑killer and more as a force for “jobs, jobs, jobs,” especially in energy, chips, and digital infrastructure. Leaders from Nvidia, IBM, BNY, Cisco, and BlackRock highlight concrete ROI. An onboarding cut from two days to 10 minutes and 19 years of work compressed into weeks. Arguing AI is finally paying off in core operations now that it is advancing and efficient in the work field .
Upside: For COO and HR Intelligence, this gives you credible proof points to position AI as a productivity‑and‑growth engine, not a mass‑layoff excuse, while still acknowledging real disruption.
Impact: Union voices and Bill Gates warn that without worker input and smart policy (including ideas like taxing AI activity), AI will still be seen as a threat, so COOs and HR must pair automation wins with visible reskilling, job redesign, and clear communication on how AI changes work.
AI Skills, Wages, & HR Mandate in an AI Economy 📈
Published: 01/23/2026
UNLEASH has recently summarized the IMF’s new report “Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age,” showing that AI is rapidly reshaping which skills are in demand and how much they pay, without guaranteeing broad job growth. The data highlights that 10% of jobs in advanced economies (and 5% in emerging markets) now require at least one new skill, with IT and AI-related capabilities dominating new requirements and delivering wage premiums. Especially for deep AI developer expertise.
Upside: For HR and COOs, this gives a strong, numbers-backed case to treat structured upskilling and STEM/IT training as core strategy: roles with more new skills can pay significantly more, supporting retention and competitiveness where talent is scarce.
Impact: The IMF warns that while AI skills can boost wages, employment effects are uneven and risk reinforcing job polarization, especially for new workers exposed to automation. Making lifelong learning, targeted retraining, & workforce mobility a necessity, not an optional HR project.
Prompt of the Day
HR AI Management Radar Prompt
Trigger Event | Action | Use Case Example |
|---|---|---|
Start of the week. | List AI-related people priorities and key talent risks. | Short list of AI skills gaps, critical roles, and hiring needs. |
Before leadership / ops / tech sync | Capture AI workforce updates and open HR issues. | AI roles to highlight, headcount asks, and change‑management risks. |
When an AI rollout or pilot feels unstable | Summarize adoption blockers and people impacts. | View of resistance pockets and needed enablement or comms. |
After an AI-linked incident or complaint | Record what happened, impact, and HR response. | Inputs for policy tweaks, training, and guardrail updates. |
Month-end / quarter-end planning. | Map AI talent pipeline, L&D plans, and constraints. | 30–90 day roadmap for hiring, upskilling, and engagement moves. |
Prompt
Act as my HR AI management radar. Based on this snapshot of our AI initiatives, workforce skills, and people metrics, (1) flag the top 3 talent, adoption, or culture risks for the next 30 days, (2) show me early warning signals to watch this week, and (3) suggest 2–3 concrete HR actions I can assign today to stabilize adoption, protect employee trust, and close the most critical AI skill gaps.
The goal is to turn data into information, and information into insight.
One last Thing
AI will keep advancing work whether you have an ownership model or not. These stacks offer efficiency and shortcuts for progressive performance. Leverage & utilize as your baseline so every sync, hire, and training sprint moves you from scattered pilots to disciplined intelligence.
Until next edition,
Chloe Rivers
Editor-in-Chief
COO Intelligence
P.S. Interested in sponsoring a future issue? Just reply to this email and I’ll send packages!