The Best AI Strategy Starts with a Business Strategy 

There is no shortage of conversation about AI right now. Every executive team is evaluating where it fits, every software provider is incorporating it into their products, and every board is asking how it will change the business. Those are the right conversations to have. The risk is that organizations become so focused on the technology that they lose sight of the business outcomes they are trying to achieve.

The most successful technology transformations have always started with a business problem, not a technology solution. AI should be no different.

For commercial organizations, the fundamental questions have remained remarkably consistent over time. Which customers represent the greatest opportunity? Where is margin leaking? Which products have pricing power? Where is discounting inconsistent? Which commercial actions will create the greatest financial impact? Those are the same questions leadership teams have been asking for decades.

What has changed is the complexity of answering them.

Manufacturers and distributors now operate in environments with thousands of customers, expansive product portfolios, customer-specific agreements, volatile costs, changing market conditions, and millions of individual transactions. The amount of information available has grown exponentially, making it increasingly difficult for even experienced commercial teams to identify the opportunities that matter most.

This is where AI has the potential to create meaningful value. It can continuously analyze transactional data, detect patterns that would be impossible to identify manually, prioritize opportunities, and help organizations respond more quickly to changing market conditions. Used well, AI allows commercial teams to spend less time searching for insights and more time acting on them.

Technology alone, however, doesn’t produce better decisions.

In our experience, successful commercial margin management requires bringing together two perspectives. The first is technical. It involves analyzing transactional data, measuring price realization, identifying patterns, and quantifying opportunities. The second is business context. It requires understanding customer relationships, competitive dynamics, product strategy, sales behaviors, and the operational realities that explain why the data looks the way it does.

One without the other creates blind spots. Data can highlight pricing differences across customers, but it cannot determine whether those differences reflect poor execution, a strategic decision, or a unique customer relationship. Likewise, deep commercial experience is invaluable, but without analytical rigor it becomes difficult to consistently identify the highest-value opportunities across a complex business.

The organizations generating the greatest value from AI are combining these perspectives. They are using technology to strengthen a well-defined commercial process rather than expecting technology to define the process itself.

Ultimately, we don’t believe AI changes the fundamentals of commercial excellence. It changes the speed, scale, and precision with which organizations can execute those fundamentals. Companies that begin with a clear business strategy, supported by strong commercial processes and informed by both technical analysis and business context, will be far better positioned to realize AI’s potential than those that begin with the technology alone.