Hey there,
When every AI update sounds impressive but delivers unstable and unreliable outcomes, a concise 10–15 minute debrief that starts with data quality, clear metrics, and competent leaders can become your most practical AI strategy this year.
Take a look at how a simple weekly debrief can make enterprise AI feel far less annoying and messy to deal with.
In this issue:
Playbook of the Day
How to Run a 15-Min Practical Enterprise AI Debrief
Goal: Change AI from pilots and trends to practical, measurable value by fixing data first and building from a solid foundation.
Who: COO, CIO/CDO, and 1 data/AI lead per major business line. Same time every week.
Before the debrief (5 mins):
Each lead adds 2–3 bullets to a shared doc: State data gathered (gaps, quality issues), AI use cases in play, and where value is or isn’t showing up.
Host highlights anything tagged: Highlight these to section; “data quality risk,” “no measurable outcome,” and/or “exec mandate with no roadmap.”
During the 10 minutes:
Data Reality (4 mins): Each lead shares biggest data win, biggest data gap, and one at-risk metric.
Risks to AI Value (3 mins): Review items tagged “data risk” or “no result” and set one action each.
Fix-First Plan (3 mins): Host recaps 3–5 must-do data fixes before new AI work, with clear owners.
Rules: No altercations, and no new tools. Stay focused on fixing data now and using AI only where it proves value.
Latest News
Data-First AI for Business Foundation 📊
Published: 02/03/2026
CEO of SENEN Group, Ronnie Sheth, has warned enterprises to fix data quality before AI takes hold. Gartner stated that bad data costs at $12.9M yearly in waste. Too many rush AI adoption, now firms prioritize data fixes first, evolving from raw chaos to predictive analytics and AI schemes following SENEN.
Upside: COOs get a clear sequence; data strategy, governance, and operating models, to unlock accurate AI outputs without the $12.9M sinkhole, turning experimental agents into value driving initiatives that expand reliably across ops.
Impact: Practical AI grounded in clean data shifts enterprises from hype to measurable gains, dodging readiness traps and positioning ops leaders to deliver ROI that executives demand in this ‘make-or-break year.’
🛡️ AI Biases Sneaks Into Corp Recruitment
Published: 01/26/26
Major retailers like Etsy, Target, Walmart partner with Gemini/Copilot and ChatGPT utilizes AI for Website shopping; Amazon's Rufus, Walmart's Sparky personalize sites. Adobe: AI traffic increasing 758% the year after another, and Deloitte: 81% of execs fear loyalty decreasing by 2027. Hosanagar warns risk.
Upside: Tap third-party AI partnerships to engage shoppers anywhere via chats, phones, stores. Strengthening sales as adoption grows (per Kearney). Proprietary tools and frontline AI provide insights, alerts, pitches to fight data loss.
Impact: Merging AI’s speed with governance, across the staff training, data rules, proactive agents become more efficient. Evading Google/OpenAI dominance, and pioneer in the better and autonomous shopping era.
Retailers Bets on Agentic AI Commerce 🛒
Published: 02/27/2026
SHRM's summarized the highlights AI has offered as a strategy: $4.48B ‘Humans&’ startup from Google, xAI, Anthropic pushes human and AI collaborate. Workday shows 85% are time savings but still a manageable 40% rework, limits gains to 14%; HR roles like AI trainers & governance managers increase.
Upside: COOs can harness investor-backed human and AI collab tools to lessen rework via targeted upskilling, while new roles strengthen governance against biases and displacement scenarios from WEF's possible four AI futures.
Impact: Leaders blending AI's speed with human judgment. Through job redesign, ethics guardrails, and readiness investments. Will capture real productivity, dodge risks, and stabilize teams for whatever AI scenario.
Prompt of the Day
The COOs AI Value Risk Radar Prompt
Trigger Event | Action | Use Case Example |
|---|---|---|
Start of the week. | 10–15 minute AI debrief: review key initiatives, data health, and one value metric per use case. | Short list of near‑term AI value risks: fuzzy outcomes, missing baselines, or “pilot” work with no owner. |
Before leadership/ops sync. | Pull a one‑page AI value snapshot: data quality flags, at‑risk metrics, and stalled experiments. | Risks to spotlight in the meeting and sharp questions for each data/AI lead about what’s blocking ROI. |
When a project feels unstable. | Run a mini “data‑first check”: clarify source data, quality issues, and what business metric should move. | Clear view of where bad data, proxy metrics, or vague success criteria are hiding behind a shiny demo. |
After a surprising incident. | Capture what happened, which datasets and models were involved, and how the issue was handled. | Root‑cause themes on data quality, governance gaps, or workflow design, plus 2–3 fixes to prevent repeats. |
Month-end / quarter-end planning. | Map upcoming AI work against data readiness, ownership, and governance checkpoints before green‑lighting new pilots. | AI “risk map” for the next 30–90 days and a short list of must‑do data fixes before new spend. |
Prompt
“Act as my AI value risk radar. Based on this snapshot of our AI initiatives, data health, and recent incidents, (1) highlight the top 3 risks that could hurt AI value or reliability in the next 30–90 days, (2) point out any signs this week that a use case looks impressive but lacks solid data or metrics, and (3) recommend 2–3 simple actions I can assign today to improve data quality, ownership, and governance before we approve new work."“
We don’t think ourselves into a new way of acting, we act ourselves into a new way of thinking.
One last Thing
Enterprise AI should leave a trail of hard numbers, not just pilots and publish releases. A short but consistent debrief that turns data gaps into one owned next step is how success turns in.
Treat this AI improvement scheme as your way to govern and set boundaries before AI messes up. Making room for a more durable, and defensible AI outcomes.
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!