Supercharging product portfolio performance with generative AI

When generative AI tools do the heavy lifting, companies can optimize their product portfolios faster and more effectively.

Product portfolios tend to grow. Companies add new products and variants all the time to meet specific customer needs, pursue new market opportunities, or exploit technological advances. They can also be reluctant to kill off older items, for fear of upsetting long-standing customers.

Finding the sweet spot in a portfolio is an ongoing challenge. Offer too little choice and customers will choose to play elsewhere, costing the company revenue. Offer too much and the cost and operational complexity of managing the assortment becomes a drag on profitability. Worse, companies can fall into both traps at once: offering too many of the wrong products, resulting in a toxic combination of high costs and lost sales.

Get the balance right, however, and the rewards can be significant. Top performers in product portfolio management enjoy three sources of competitive advantage. First, they improve overall cost efficiency by limiting the costs of maintaining unprofitable and obsolete products. Second, they focus more attention and resources on innovation, developing products and services that propel growth and profitability. Finally, they can improve resilience by simplifying component inventories and reducing supplier-related risks. Now, generative AI gives the capability to get it right without needing to dedicate huge amounts of resources.

Why optimal portfolios are elusive

Product portfolio optimization is difficult because it requires companies to balance so many variables. They must understand how different products and combinations of products meet different customer needs, how those products compare to competitive offerings, and how much each product and variant costs to manufacture, deliver, and support. They also need to integrate the varied, and sometimes conflicting, viewpoints of different stakeholders across the business. Analyzing these variables is further complicated by recent trends, including increased product customization, the interplay of hardware and software in modern product designs, and the need to manage complex supply chains that are subject to geopolitical disruptions.

Then there are network effects. Multiple products often reuse the same underlying components, manufacturing assets, or code. Removing a single product from the portfolio typically does little to reduce the underlying complexity of the company’s operations. Instead, portfolio optimization requires removing a carefully selected cluster of products so that the cost savings to the company’s operations outweigh the potential loss of revenue (Exhibit 1). Identifying the right clusters is a daunting task: in a portfolio of several thousand reasonably complex SKUs, the number of potential options quickly exceeds the number of particles in the universe.

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