Tag Archives: AIOperatingModel

Data Mesh was step one. 2026 belongs to agent ecosystems.

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I used to think “more catalogs, better lakes” would get us there. Then I watched agents start acting—not just assisting—and realized our data products weren’t ready for that responsibility.

Here’s the simple truth I’m seeing with executive teams: bad data becomes bad decisions at scale. If our contracts, SLOs, lineage, and internal marketplaces are weak, agents will scale the wrong thing—errors—at machine speed. That’s a board-level conversation, not an IT complaint.

What changes in practice?
We evolve the data operating model from “publish & pray” to agent-grade: data products with p95 latency targets, explicit access scopes, and traceable provenance. Hyperscalers are now shipping real agent runtimes (memory, identity, observability—and billing), which means the economics and accountability just got very real.

How I’m approaching it with leaders:

  • Certify data products for agents. Each product has an owner, SLOs (latency/freshness), and mandatory provenance. If it can’t meet its SLOs, it doesn’t feed agents—full stop.
  • Enforce least privilege by skill. Approvals are tied to the actions an agent can perform, not just the datasets it can see.
  • Make observability a product. Trace every call (inputs, tools, sources, cost, outcome). No trace, no production.

Practical next steps:
Start by mapping your top 10 data products to target agent skills and auditing them. Set SLOs. Assign owners. Then pick one product—implement policy-aware access and lineage capture, record evaluation traces for every agent call, and scale it. Afterwards, launch an internal Agent Marketplace that connects certified skills and certified data products, with change gates based on risk tier.

KPIs I push for:

  • % of agent invocations served by certified data products meeting SLOs (with recorded lineage)
  • $/successful agent task at target quality and latency
  • Incident rate per 1,000 runs (blocked vs executed)

Behind the scenes, the shift that surprised me most wasn’t technical—it was managerial. The winning teams treat this as work redesign: new ownership, new runbooks, new kill criteria. When we do that, agents unlock speed and resilience. When we don’t, they magnify our mess.

If you had to fix just one weak link this quarter—SLOs, provenance, or access controls—which would it be, and why?

Agentic Operating Models: from Pilots to P&L

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We’re past the demo phase. Boards are asking a harder question: how do human-plus-agent workflows show up in cash flow—this quarter? There is a clear answer: The winners don’t “add an agent”; they redesign the work. That means owners, SLAs, guardrails, and value tracking—weekly. Not glamorous, just effective.

Here’s the short playbook I’d bring to the next ExCo:

  • Make Agents products. Name a product owner, publish SLAs (latency, accuracy, human-override rate), and set chargeback so value—and cost—land in the P&L.
  • Design human+agent flow, end-to-end. Pilots fail for organizational reasons. Tie every pilot to a customer metric and a service level from day one.
  • Build guardrails you can audit. Map risks to NIST’s Cyber AI Profile; log decisions, provenance, and incidents. “Trust” that isn’t evidenced will stall at Legal.

Does it pay?  Signals are real but uneven. A European bank modernization program cut 35-70% cycle time with reusable “agent components.” In KYC/AML, agent “factories” show 200-2000% productivity potential when humans supervise at scale. Klarna’s AI assistant handles  ~1.3M monthly interactions (~800 FTEs) with CSAT parity. Yet BCG says only ~5% are truly at value-at-scale, and Gartner warns ~40% of agentic projects could be scrapped by 2027. Operating model discipline determines who wins.

If I had 90 days:

  • 30: Inventory top 5 agent candidates; assign owners; baseline SLAs and override rates.
  • 60: Stand up an Agent Review Board (CIO/CDO/GC/CISO); add release gates and rollback.
  • 90: Ship two agents to production; publish a value dashboard (savings, cycle time, SLA hit rate) and decide scale/retire.

A candid note on risk: labor anxiety and model drift will erase ROI if we skip change management and runtime oversight. Bring HR and the 2nd line in early, and rehearse incidents like you would a cyber tabletop.

If we can’t show weekly value, SLA adherence, and audit-ready evidence, we’re still in pilot land—no matter how advanced the model sounds.

What would make your CFO believe – tomorrow – that an agent belongs on the P&L?