Do you have an “early days” generative AI strategy?

Priority 2: Align your generative AI strategy with your digital strategy (and vice versa)

If you’re anything like most leaders we know, you’ve been striving to digitally transform your organization for a while, and you still have some distance to go. The rapid improvement and growing accessibility of generative AI capabilities has significant implications for these digital efforts. Generative AI’s primary output is digital, after all—digital data, assets, and analytic insights, whose impact is greatest when applied to and used in combination with existing digital tools, tasks, environments, workflows, and datasets. If you can align your generative AI strategy with your overall digital approach, the benefits can be enormous. On the other hand, it’s also easy, given the excitement around generative AI and its distributed nature, for experimental efforts to germinate that are disconnected from broader efforts to accelerate digital value creation.

To understand the opportunity, consider the experience of a global consumer packaged goods company that recently began crafting a strategy to deploy generative AI in its customer service operations. Such emphasis has been common among companies. The chatbot-style interface of ChatGPT and other generative AI tools naturally lends itself to customer service applications. And it often harmonizes with existing strategies to digitize, personalize, and automate customer service. In this company’s case, the generative AI model fills out service tickets so people don’t have to, while providing easy Q&A access to data from reams of documents on the company’s immense line of products and services. That all helps service representatives route requests and answer customer questions, boosting both productivity and employee satisfaction.

As the initiative took hold, leaders at the company began wondering whether generative AI could connect with other processes they had been working to digitize, such as procurement, accounts payable, finance, compliance, HR, and supply chain management. It turned out that similar generative AI models, with refinement and tailoring for specific business processes, could fill out forms, as well as provide Q&A access to data and insights in a wide range of functions. The resulting gains, in total, dwarfed those associated with customer service, and were possible only because the company had come up for air and connected its digital strategy and its generative AI strategy. In this case, the alternative would have been a foregone opportunity to turbocharge existing digital efforts. In the extreme, siloed digitization and generative AI efforts might even work at cross-purposes. Given how much companies have already invested in digitization, and the significance of generative AI’s potential, there’s no substitute for the hard work of bringing the two together.

A fringe benefit of connecting digital strategies and AI strategies is that the former typically have worked through policy issues such as data security and the use of third-party tools, resulting in clear lines of accountability and decision-making approaches. Such clarity can help mitigate a challenge we’ve seen in some companies, which is the existence of disconnects between risk and legal functions, which tend to advise caution, and more innovation-oriented parts of businesses. This can lead to mixed messages and disputes over who has the final say in choices about how to leverage generative AI, which can frustrate everyone, cause deteriorating cross-functional relations, and slow down deployment progress. These disconnects are easily avoided, though. At another financial services company we know that was seeking to exploit generative AI in the HR function, the CHRO, the CIO, and the CISO came together quickly to assess the new opportunities against the company’s existing data, tech, and cybersecurity policies, providing helpful guidance that maintained momentum.