Why do we need an AI strategy?
This is a valid question, and one I have frequently encountered while helping business leaders leverage different technologies. Over my 20-year career, I have witnessed numerous technology waves, from ERP systems and digitalization to cloud technologies, e-commerce, mobile applications, RPA, data & analytics—and most recently AI.
On the brink of these technology disruptions, I’ve been asked to support in creating a strategy for utilizing these technologies. Yet, quite often, I have been asked why we need a separate technology strategy at all. Shouldn't a company just have one strategy—the business strategy?
Over the past years, this question has been asked in the context of AI. Why is a separate AI strategy needed if an organization already has a solid business strategy? To answer this, let's first define what a strategy actually is. A good strategy typically consists of:
Goals and direction
Choices and priorities
A roadmap for execution
I would argue that today many organizations have moved past the initial hype of AI experiments and are now at the point where they need to pause and figure out a shared direction. To distinguish it from the core business strategy, some call this "direction" an "AI vision", some an "AI roadmap", and others an "AI implementation plan" to emphasize that they want to achieve concrete results. Regardless of the name, companies need a comprehensive, organization-wide roadmap for scaling AI—and I am used to calling it an AI strategy.
From scattered experiments to a shared direction
Many organizations are no longer wondering whether they should use AI; it is already being used in various forms. Marketing leverages Generative AI for content creation, customer service pilots chatbots, analytics teams build predictive models, and IT develops the technical foundations.
Individual experiments can be highly successful, generating interesting results and helping the organization learn rapidly what the technology can do. This experimental phase is highly beneficial—even mandatory. It is nearly impossible to define a strategy and a clear direction for something you don’t quite understand yet.
However, the outcome of this experimental phase is often that AI is utilized in silos. Its impact on the broader business remains limited, and the true potential of the technology is not fully realized. When different business units develop AI from their own isolated starting points, the organization ends up doing many things simultaneously, but without a unified direction or prioritization. Investments fragment, technological foundations may diverge, and the actual business benefits may remain limited.
The CEO expects value, IT thinks about architecture – how to pull the strings together?
AI simultaneously impacts multiple roles within an organization. Business leaders think about which processes would benefit the most. The CIO considers how new solutions integrate into the current architecture and what tech investments are required. HR ponders competence development and evolving roles. Legal and compliance evaluate risks and regulations. Meanwhile, the CEO expects concrete results, accelerated strategy execution, and measurable business value.
All these perspectives are justified. Tying them together is exactly where an AI strategy steps in. Its purpose is to look at the organization holistically and define where AI investments should be targeted, and what that requires from the organization.
An AI strategy can be outlined through two main dimensions:
Value Drivers: The areas or processes where data and AI can create the most business value.
Value Enablers: The capabilities required to actually realize that value.
The primary priority should be defining the value drivers. The core starting point here is the business strategy and its objectives. The goal is not to identify as many AI use cases as possible, but to identify the most valuable ones. Strategy is about making choices: deciding what to focus on and why.
However, value cannot be created without enablers. The systematic use of AI requires changes in organizational capabilities: data must be accessible and high-quality, the tech architecture must support new solutions, skills must be upgraded, roles need to be redesigned, and AI implementations must be managed systematically. Without these enablers, AI initiatives remain isolated point-solutions, and the organization fails to build scalable capabilities.
AI strategy must not live in a vacuum; it is an extension of business strategy
A technology strategy can never be an isolated plan living its own life parallel to the rest of the business. Its job is not to redefine the company's goals or build alternative visions of the future. The sole purpose of an AI strategy is to define how AI can best support the overarching business strategy, create value, and clarify what the organization must do to succeed with AI.
Without a strong link to the business strategy, AI becomes a detached phenomenon. There can be a lot of talk, and many business areas running pilots, but the impact on the top and bottom lines remains moderate. An AI strategy should be viewed as a management tool: Where are we, where do we want to go, how do we get there—and above all, how do we accelerate the execution of our business strategy?
A good strategy forces choices and acts as a development compass
An AI strategy should not be a highly technical document. If a strategy presentation starts talking too much about language models, data warehouses, development environments, integrations, or AI agents, it has likely drifted too far into solution design. A strategy document needs to set the ambition level, crystallize the target state and outline the plan for what will be done next. While technology can't (and shouldn't) be entirely avoided, an AI strategy should talk much more about euros than bits.
Conversely, a bad AI strategy often contains too much of everything. It might be full of exciting opportunities and transformation hype. A good strategy does the opposite: it limits options. It forces you to make choices, set clear goals, and define metrics for success. It identifies a few key initiatives that actually move the needle for the business. At the same time, it makes visible what executing these initiatives demands in terms of data, technology, talent, and leadership. Strategy is not a wish list of things you could do. It is a firm decision on what will actually be done.
Even the most beautifully documented strategy can end up gathering dust in a desk drawer if it doesn't change what the organization does on a daily basis. A proper AI strategy brings the organization a unified view of where value is created, defines the concrete initiatives to invest in, and highlights the capabilities that must be built to succeed.
When that happens, your AI strategy is no longer just a deck of slides. It becomes the first step toward purposefully managed transformation.
