Decision Workspace

Enterprise AI Governance

Single source of truth for this strategic initiative — objectives, risks, expert guidance and the path to a decision.

Confidence
71%
Status
In Review
Budget
$1.6M / 12mo
Target decision
Q1 2026
Business objective

Establish an enforceable AI risk and compliance framework before enterprise Copilot and model deployments scale beyond pilot.

Decision summary

AI adoption is accelerating across trading, research, and operations without unified governance. Data leakage, model drift, and regulatory ambiguity create board-level exposure. This decision defines the policy, control, and oversight model for all AI usage at Wood Mackenzie.

Objectives
  • Define acceptable use policies for GenAI and classical ML
  • Implement model inventory, risk scoring, and approval gates
  • Align with emerging EU AI Act and SEC disclosure requirements
  • Establish AI ethics review board with cross-functional representation
Key risks
High
Regulatory uncertainty — EU AI Act timelines
High
Shadow AI usage in business units
Medium
Overly restrictive policy stifling innovation
Medium
Integration with existing data governance tooling
Timeline
Aug 05
Decision opened
Sep 18
Policy draft v1 complete
Oct 30
Legal & Risk review
Nov 25
Pilot control framework rollout
Jan 20
Board policy approval
Stakeholders
EV
Elena Voss
Chief Data Officer — Owner
PR
Priya Raman
CISO
SB
Sarah Brennan
General Counsel
MH
Marcus Hale
Head of Infrastructure
Expert recommendations
HM
Dr. Hannah Mehta
Lead Analyst — Cybersecurity

"Wood Mackenzie's OT footprint and energy threat profile push the recommendation toward CrowdStrike Falcon Complete + Charlotte AI. Confirm Defender economics for non-trading endpoints."

JO
James Okafor
Principal Analyst — Enterprise Architecture

"Wood Mackenzie's OT footprint and energy threat profile push the recommendation toward CrowdStrike Falcon Complete + Charlotte AI. Confirm Defender economics for non-trading endpoints."

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