AI Strategy
Teach leaders how to build an enterprise AI strategy aligned with business strategy.
An enterprise AI strategy is not a list of AI projects. It is a small number of hard choices, including what the enterprise will not do, that trace back to how the business wins and what constrains it. Strategy decides where and why AI is used; adoption decides whether that becomes changed work and measured results, and an organization needs both. The level of ambition should fit the business strategy, the organization's readiness, its budget and its appetite for risk.
The choices follow from there. Opportunities are found by walking down from the strategy to where value leaks and testing AI against the best alternative. Buying is the default; the strategic work is naming the few capabilities worth owning and keeping a way out. Ownership is placed capability by capability, centralizing what gains from scale and federating what depends on domain context, on a shared platform no larger than repeated demand justifies. Data, talent, the operating model and decision rights then decide whether the choices can be executed. Because rivals can buy the same AI, advantage needs a gap in cost or value and something that keeps it open; unique data defends a lead only when more of it keeps improving the product.
Business Value of AI supplied the test of what a choice is worth. This module joins the choices into one coherent chain. Enterprise AI Use Cases then shows where that chain meets the work in each function.
Questions this module answers
- What makes an AI strategy a strategy rather than a portfolio of projects?
- How should AI ambition be set against business strategy, readiness and risk appetite?
- Which AI capabilities should the enterprise build, buy or partner for, and where should ownership sit?
- What data, talent, operating model and decision rights does execution require?
- Where can AI create an advantage that competitors cannot quickly copy?
The chapters
Strategy foundations
Defines an AI strategy as choices traced to the business strategy, separates it from adoption and sets the level of ambition.
- 001 What Is an Enterprise AI Strategy? An enterprise AI strategy is not a list of AI projects. It is a few hard choices, including refusals, that trace back to the business strategy. 11 min read
- 002 AI Strategy vs AI Adoption Strategy decides where and why the enterprise uses AI; adoption decides whether that becomes changed work and measurable results. You need both. 12 min read
- 003 Start With Business Strategy AI should serve the business strategy, not become one. Start with how the business wins and what constrains it, then ask whether AI changes that. 12 min read
- 004 Defining AI Ambition AI ambition is the choice of how much AI should change the business; the right level fits strategy, readiness, budget and risk appetite. 13 min read
Strategic choices
Finds strategic opportunities and decides sourcing, the placement of ownership and the scope of a shared platform.
- 001 Finding Strategic AI Opportunities Strategic AI opportunities are found by walking down from the strategy to where value leaks, sizing the pool roughly, and testing AI against the best alternative. 12 min read
- 002 Build vs Buy vs Partner Buying is the default; the strategic work is naming the few capabilities to own, deciding layer by layer, and keeping an exit. 12 min read
- 003 Centralized vs Federated AI Place AI ownership capability by capability - centralize what gains from scale and reuse, federate what depends on domain context, and redraw as strategy and maturity change. 12 min read
- 004 AI Platform Strategy An enterprise AI platform exists to create leverage; build the smallest shared one that repeated business demand justifies, run it as a product and judge it by the effort it removes. 12 min read
Enterprise capability
Sets out the data, talent, operating model and decision rights that turn the choices into execution.
- 001 Data Strategy for AI Having data is not enough. AI advantage comes from usable data for the outcomes you chose - so fix the gap that blocks the most value first. 12 min read
- 002 AI Talent and Capability Strategy A hiring number is the last output of an AI talent strategy. Blueprint the capabilities, set role skill targets, measure the gap, then build, own, buy or borrow. 10 min read
- 003 AI Operating Model An approved AI strategy changes nothing until someone owns each part of executing it. The operating model is that system of owners, rights, funding and routines. 11 min read
- 004 AI Decision Rights and Accountability When AI takes over part of a judgment, decision rights move. Make each move deliberate - many responsible, one accountable, authority scaled to the stakes - because vendors and agents never hold the accountability. 13 min read
Competitive advantage
Tests where AI can create a defensible advantage and joins every choice into one executive strategy.
- 001 AI and Competitive Advantage When every rival can buy the same AI, customers keep most of the gains. Advantage needs a gap in cost or value plus a guard that keeps it open. 11 min read
- 002 Proprietary Data, AI Moats and Differentiation Unique data is a head start, not a moat. It defends a lead only if more of it keeps improving the product, and you may learn from it. 11 min read
- 003 Module 04 Synthesis — The Executive AI Strategy An executive AI strategy is one chain of choices, from business problem to advantage, in which every choice fits the others. 11 min read
After this module you can
- Write an AI strategy as a few choices and refusals that trace back to the business strategy.
- Set an AI ambition that fits the organization's readiness, budget and risk appetite.
- Decide build, buy or partner layer by layer, with an exit for each dependency.
- Assign ownership and decision rights so that one person is accountable for each AI-supported judgment.
- Test whether a claimed AI advantage, including one based on proprietary data, can be defended.