IT leaders must bring a pragmatic technology lens to gen AI’s potential — and its pitfalls — by optimizing their strategies for use cases first.
Hardly a day goes by without some new business-busting development on generative AI surfacing in the media. And, in fact, McKinsey research argues the future could indeed be dazzling, with gen AI improving productivity in customer support by up to 40%, in software engineering by 20% to 30%, and in marketing by 10%.
Still, it’s worth remembering that we’ve seen this movie before, with companies piling into exciting new technologies with a melee of premature experiments and pilots. CIOs and CTOs have a crucial role in avoiding those pitfalls when it comes to gen AI. They can bring a pragmatic technology lens to determine when and where gen AI can generate the greatest value — and where it is not the best option.
Doing so requires developing use cases based on a deep understanding of the unit economics of gen AI, the resources needed to capture those benefits, and the feasibility of executing the work given existing capabilities. With AI increasingly viewed as a business accelerator and disruptor, this complex equation is a challenge CIOs must get right.
Gen AI archetypes: Takers, shapers, and makers
One key question CIOs face in determining the best strategic fit for gen AI in their enterprise is whether to rent, buy, or build gen AI capabilities for their various use cases. The basic rule is to invest in creating a unique gen AI capability only when there is a proprietary advantage. We’ve found it helpful to think in terms of three archetypes:Takers use a chat interface or an API to quickly access a commodity service via a publicly available model. Examples include GitHub Copilot, an off-the-shelf solution to generate code, or Adobe Firefly, which assists designers with image generation and editing. This archetype is the simplest, both in terms of engineering and infrastructure needs, and is generally the fastest to get up and running. It does not allow for integration of proprietary data and offers the fewest privacy and IP protections. While the changes to the tech stack are minimal when simply accessing gen AI services, CIOs will need to be ready to manage substantial adjustments to the tech architecture and to upgrade data architecture.
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