By Tim Beerman, Chief Technology Officer, Ensono
Hi Alan,
Like countless other organizational leaders right now, you’re struggling with what my colleague Bryan Doerr, in his article on page 6, refers to as “managing the collision of ideas” sparked by generative AI. Unlike previous tech revolutions, the democratized nature of generative AI makes it accessible to everyone in the organization versus a specialized, siloed few, and applicable in every business function—which means more ideas coming more quickly and from more sources than ever before, and more competition for attention as well as resources.
Both Bryan’s article and “The AI Adoption Blueprint” by Sean Mahoney and John Treadway offer excellent perspectives and guidance on the larger question of how to approach AI adoption, and I highly recommend you read both. The blueprint, in particular, is going to give you a structured, detailed, step-by-step strategic approach that will set your company up for long-term success. However, building out the teams and processes outlined there does take time and effort, and the flood of requests and demands isn’t likely to stop while you’re doing it.
In the meantime, the following streamlined approach can help you separate the signal from the noise, meet business needs and score some quick AI wins, without inadvertently draining resources or opening your company to undue risk.
1) Create and disseminate internal AI usage guidelines
If you haven’t already published some kind of AI usage guide for your teams, take this critical first step as soon as possible. People in your organization are almost certainly already using generative AI tools. That’s a good thing—you want your teams enabled to use this groundbreaking technology—but you also want them to fully understand the cautions they need to take. Establishing clear rules and guardrails early on will help people develop good AI usage habits and is key to minimizing risk. This can be a living document that evolves and expands over time, as new technologies come online and new learnings emerge. (See also, “Leveraging your brand as you dive into the technological unknown,” The Maven Report, Spring 2023.)
2) Build mechanisms to collect, present and evaluate potential use cases and benefits
Until you have more formal structures and processes in place, a simple online form can function to capture ideas and requests, and a small steering committee can serve as an interim evaluating body. The form questions should be specific and challenging enough to produce a fairly complete business case for adopting the proposed application—and make it easy to filter out obvious non-contenders. The evaluating team should have a very clear and aligned understanding of your business priorities and goals, as these will serve as the criteria by which ideas are considered.
3) Vet each viable use case through the triple lens of harm prevention, cost optimization and revenue opportunity
Generative AI use cases are generally going to fall into one of three categories:
Applications for internal workforce usage
Applications integrated into foundational models
Applications that can enhance existing platforms
While you likely have needs on all three of these fronts that could be effectively addressed by generative AI, assigning some level of prioritization to each of these categories, if possible, can be a helpful first step in determining whether and how much to invest in a given application.
From there, every proposed AI solution should be rigorously subjected to three questions:
What risks does it present to our brand, clients and/or associates?
The risks associated with AI are very real and can be hugely damaging. A thorough consideration of how a proposed application could make any aspect of your business vulnerable, and what level of risk you’re willing to assume, is essential.
Will it help us maximize current spend?
From accelerated employee productivity to improved decision-making and accuracy, AI applications hold great potential to help you leverage existing resources to derive increased business value.
Will it drive future growth?
AI applications may serve as a powerful growth engine by enabling you to evolve current services to add more client value or even build out entirely new revenue-generating capabilities.
Any green-lit generative AI application will need to reflect a balance of these three factors.
Getting that calculus right requires deep understanding and alignment among the decision-makers on business needs and goals. You’re far from alone in having limited funding for generative AI investment. Decisions will always have to be made; that’s easier to do and defend when they’re driven by established KPIs.
We’re all still at the very beginning of the generative AI revolution. Excitement is running high, and there’s a lot of pressure on leaders to act. Applying this simple framework in tandem with building out a broader and more robust adoption strategy will help you sort through the volume and variety of ideas about what you could do with generative AI to discern what you really should do to best serve your people, clients and business outcomes.
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