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Boards of directors are approving substantial investments in generative AI without fully anticipating the strategic, legislative, governance, and adoption challenges necessary to derive lasting value from the investment. In touch with technology leaders across most major markets, Martha Heller, CEO at technology executive search firm Heller, discusses the conditions companies must establish to create an enterprise AI capability.
The Heller Report: AI continues to dominate business news. What is the reality behind the headlines?
Martha Heller: The AI tail continues to wag the corporate investment dog. I’ve been writing about technology since the late '80s, and even more than “the internet,” generative AI has taken the firmest hold of corporate checkbooks of any emerging tech capability to date. And for good reason. The pace of innovation of AI technologies is unparalleled, and just as the steam engine invention of 1765 propelled us out of farming and into the industrial revolution, so are the technologies behind AI driving us out of the industrial and into the data economy.
Boards that ignore AI will find their companies too far behind to catch up. But boards who invest without understanding the conditions for generative AI will have the equally concerning problem of investments without return.
What are the parameters companies must have in place for generative AI to have a true impact?
It’s a long list!
Game-changing enterprise AI use cases require the following (and more): a significant volume and quality of ingestible data—whether that’s sales, customer, or supply chain data, or ideally, a combination of different data types—standardized business processes, and clear success metrics for those processes. Successful use cases also require a collaborative culture, where technology and P&L leadership work together to make decisions quickly, and the ability to educate a range of employees about how to use the data.
Many companies still in the mode of 'data, data everywhere, with value difficult to reach,' have a long road of data integration, process standardization, and cultural change ahead of them before they can generate high-value use cases.
Generative AI impact also requires a clarity of understanding across the business about what the company even means by AI. Is AI pointing Copilot at a SharePoint file and letting employees do advanced search? Is AI, as Dow Chemical is using it, reducing the company’s patent research process from months to hours? Is AI allowing me to write this article faster? (Not really). When something means everything, it rarely means anything. So this upfront education work at all levels is critical, and yet for many companies, very difficult.
With these obstacles, how should companies think about their evolution to the data economy?
AI is a capability that requires a tremendous amount of change to have a real impact. I’ve conceptualized this change as a “data-to-value journey” which starts with a ton of essentially useless data and ends with a wholesale business model change to a “platform business” or at the very least, new data-driven revenue streams. The journey includes assessing and understanding owned data, evaluating which data is most important, integrating and securing the data, establishing governance over who has access to the data, educating the entire company on the value of the data, defining use cases, developing the solutions, and then of course, driving adoption.
The irony is that most of the factors that make AI valuable are human, and as we all know, humans can be resistant to change. One piece of advice: Don’t bother stepping on the data-to-value journey if you don’t have an effective change management function.
In addition to getting good at change management, what are must-dos before a company can step on the data-to-value journey?
Starting at the top, boards will need to appoint new directors who understand emerging technologies and can envision a very different world. That board member, along with the technology executive management team, will educate the rest of the board on their new data governance role, and help them sort through the hype versus impact.
The CEO and executive team need to be very vocal with their own teams about how AI will impact the growth of the business and cannot relegate “AI strategy” to the tech team. The executive team members are also the drivers of the use cases, which are essential to turning AI from a buzzword to value.
Then on the technology team, data integration and architecture must-have skillsets since the value of generative AI will be higher with high quality data. But as tech teams start building more sophisticated AI models, the emphasis on data integration will decrease, as tech teams start building their LLM and data science muscles.
Any final words of advice to companies in search of AI value?
Your AI use cases need to be connected to the mission or values of your business. You cannot attract the technology, change management, or business skills if you cannot connect people to the why of what you are doing. At this point, it is unlikely you can stay off the data-to-value journey, but to travel it successfully, people – at all levels – must be invested in your new AI-based strategy.
Connect with Martha Heller on LinkedIn and join the conversation.
