Why do I need an AI strategy to achieve AI adoption?

Is AI strategy about implementing an AI chatbot, automating business processes, or training employees? None of it. These may be tactics needed to achieve specific goals for AI adoption, but they are not a strategy.

What is an AI strategy?

To start with, it helps to understand what a strategy is. Strategy is a plan to achieve one or more long-term goals under conditions of uncertainty. It’s a formula for how a business is going to compete, what its goals should be, and what policies and resources are needed to reach those goals.

As Michael Porter, the Harvard Business School professor and famous strategy guru, put it: “Sound strategy starts with having the right goal.”

With that in mind, AI strategy is a plan to be(come) data-driven and to leverage data & AI for business decisions. You need to:

1️⃣ Define clear goals of what “data- and AI-driven” looks like
2️⃣ Identify actions needed to achieve these goals
3️⃣ Mobilize resources to execute the actions

By the way, the terms “data strategy” and “AI strategy” are often used interchangeably. Without data there is no AI. And data alone is like having a fully charged battery and not using it for anything. AI is used to extract insights from data and turn that into business decisions. Whether you call it data strategy or AI strategy, at the end it’s a plan to turn data into business advantage through AI.

Since the launch of ChatGPT, I have experienced several companies rushing to implement AI chatbots to not be left behind. They do that without any strategy in place. Often the IT department implements an off-the-shelf AI chatbot, such as Copilot or ChatGPT, and employees are left to “experiment”.

Even if you just want your employees to experiment with chatbots, define a business objective for that experimentation. Is the goal to improve AI mindset? To identify promising use cases? To compare chatbots? Having a clear goal in place helps to measure it and enables you to define how to take it to the next level.

Michael Porter also said: “The essence of strategy is choosing what not to do.”

Companies often take on too many objectives relative to the resources they have available. With the hype around Generative AI and now AI agents, many executives and business leaders believe they need to do something with AI. Without a clear strategy in place and without explicit choices of what not to do, this leads to many initiatives competing for the same resources.

Just ask yourself: How many AI pilots do you have currently running at your company?

How do I create a sound AI strategy?

Be pragmatic.

While defining the plan with goals, actions, and resources, keep in mind what is required for a successful data- and AI-driven organization. This may initially appear complex, but like with many other complex topics, it helps to break it down into smaller components. Where you start will depend on the current data maturity level in your company.

I have successfully used that pragmatic approach when developing data and AI strategies in large tech and pharma companies. It was always based on the data and AI readiness of the organization. This led to defining eleven building blocks of AI strategy, divided into three levels of maturity: crawling, walking, and running.

The CRAWLING stage covers the basic requirements needed for success with using data for business decisions. This involves executive sponsorship, first business use cases that can be addressed with proofs of concept, and robust descriptive analytics solutions. If you’re dealing with personally identifiable information, this crawling stage also needs to include alignment with data privacy.

Next is the WALKING stage where you expand on your initial success. You start creating in-house analytics teams to build and retain intellectual property instead of outsourcing this to consultants. You improve data and AI mindset. And, you establish clear data governance across all your data assets built around a solid data infrastructure.

In the final RUNNING stage, you develop AI minimum viable products (MVP) and move promising AI solutions from MVP to production. You optimize your AI products, automate them, and embed them into existing workflows. At least at this point you may realize that your internally available data could be enhanced through external partnerships.

AI adoption is built along the three stages. With first business use cases you show how AI addresses business problems. When improving data and AI mindset, your employees understand better how AI can help them. And once AI is embedded into existing workflows, employees understand its full value.

AI adoption is not one individual step. It is built over time. AI strategy defines how you build AI adoption.