Operating Model

The operating model describes how companies turn AI adoption from separate pilots into a managed system: from idea and AI product selection to implementation and confirmed impact.

Сигнал рынка

• BCGAI Risk Management Needs a Better Model (2026)
• McKinseyHow organizations are rewiring to capture value (2025)
• BainWhy Agentic AI Demands a New Architecture

In their 2025–2026 publications, the major consulting firms describe a similar management logic.

BCG suggests not forcing every AI initiative through one heavy committee, but routing them along different lines by level of risk and novelty: self-service for simple cases, trust-but-verify for medium risk, strategic review for significant ones, and prohibited for what is unacceptable. McKinsey brings the focus back to the workflow: which process improves, who owns the impact, what data is available, where a human-in-the-loop check is needed, and how the result will be measured.

Bain adds an architectural layer: agentic AI cannot be scaled on top of fragmented architecture, inconsistent access, tool sprawl, and opaque cost economics. The operating model must therefore connect portfolio, governance and control, orchestration, data, delivery, and impact confirmation into a single management layer.

The section answers five base questions.

Principles

What rules underpin AI adoption and help avoid the chaos of failed pilots, duplicated solutions, wasted budgets, and tech hype — without losing speed and agility in change.

Roles

Who owns what in the AI function: strategy, infrastructure, products, initiatives, training, promotion, and result confirmation.

Entities

What the operating model actually manages: AI initiatives, AI products, business funnel, delivery tracks, gates, artifacts, risks, and impact.

Processes

How AI initiatives and AI products move from emerging need to adoption, operations, and scaling — taking initiatives into work, managing product and project portfolios, prioritization, and training.

Rituals

Where regular decisions are made: what to take on, what to stop, which products to grow, where blockers are, and which impact is confirmed.

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Core idea of this section

The AI function should be neither an experiment lab nor internal outsourcing, but a management layer that helps the company adopt AI safely, manageably, and measurably.