Operating Model for AI: The CEO’s Fast-Track to AI-Driven Value
- JPJ
- May 29
- 3 min read
Updated: May 31
Chief executives should launch a stand-alone operating model for AI. Its purpose is twofold: (1) demonstrate near-term gains in revenue, margin and risk mitigation; and (2) establish the controls that will ultimately permeate the full enterprise once AI becomes intrinsic to every capability.
GenAI lifts work output 25 % faster at 40 % higher quality—adopt early or fall behind
Generative AI has moved beyond hype into hard economics. Research indicates that the technology could lift global productivity by the equivalent of US $2.6–4.4 trillion a year, adding 15-40 percent to the total economic prize from artificial intelligence. Capital markets are already pricing in the advantage: companies that lead on digital-and-AI capabilities are delivering two- to six-times higher total-shareholder returns than slower peers, a gap that continues to widen. At the level of individual knowledge workers, a field experiment with 758 consultants showed that those equipped with GPT-4 completed 12 percent more tasks, 25 percent faster and with over 40 percent higher quality than a control group.
CEOs who launch an AI operating model will scale safely and profitably
Despite this upside, most organisations lack the structures to scale AI responsibly: in one cross-industry survey only 17 percent of executives said their company has a documented policy for responsible AI. The practical implication is clear—value will accrue to firms that can move quickly and safely. Doing so requires a top-management–sponsored operating model dedicated to AI.
To create value while containing risk, the AIOM needs four generic but essential components:
Value-tracking KPIs. Metrics that translate experiments into hard outcomes—EBIT uplift, cash release, valuation multiple—keep scarce talent focused on what matters and build investor confidence.
Unified governance and risk policy. A single playbook covering ethics, privacy, intellectual-property protection and model oversight accelerates approval cycles and avoids expensive rework.
End-to-end process blueprints. A clear funnel from idea to scaled deployment turns one-off pilots into repeatable gains in revenue and cost.
Information and information-systems architecture. A cloud-scale data lakehouse, secure GPU fabric and industrialised MLOps toolchain provide the “pipes” for rapid yet compliant roll-out.
Create a simple view of capabilities needed and assign responsibilities across leadership
The AIOM is more than a governance shell; it is also a roadmap that leads the organisation through four cumulative layers of capability:
Foundational Enablers supply clean data, compliant compute and open APIs under clear stewardship.
AI Platform Services—including MLOps, prompt libraries and drift monitoring—industrialise model life-cycles while a business-facing ROI dashboard tracks impact.
Cross-functional AI Utilities such as conversational copilots, intelligent automation and predictive forecasting convert the platform into broad productivity and risk-reduction levers.
Domain-specific AI Products—for example revenue-boosting sales accelerators or supply-chain optimisers—deliver direct top-line growth and margin expansion.
Each successive layer compounds the value of the one beneath it: the first two reduce the unit cost and risk of every future use case; the latter two drive P&L impact.
Schedule a broad roll-out and ensure that AI becomes a reflex, not a project
First, staff the AIOM within ninety days with joint business, technology and risk ownership.
Second, select three high-impact use cases that can prove gains in revenue, cost and risk within six months.
Third, codify the reusable assets—prompts, APIs, governance gates—into the shared platform to shrink marginal cost of future work.
Fourth, scale capability layer by layer, funding expansion from captured EBIT while the risk framework keeps pace.
Finally, once disciplines are embedded, integrate the AIOM into the broader enterprise operating model so that AI becomes a reflex, not a project.
The window to capture an AI valuation premium is open, but it will narrow as the technology commoditises. A CEO-backed, stand-alone Accelerated Intelligence Operating Model offers the fastest and safest path to monetise AI across valuation, growth, margin and risk—before competitors do.
Sources:
Comments