Scaling gen AI right takes a certain kind of CIO. Are you one of them?

Set priorities

In successful tech transformations, actions are closely connected to solving business problems. That’s why CIOs need to work with business unit leaders to set priorities and then make those choices work. The principle is to identify use cases where gen AI advances strategy, which may require shutting down dud pilots and doubling down on those that show promise. Affordability must be part of this analysis as well. Because gen AI is still so new, costs can balloon, making it difficult to scale up. One rule to keep in mind is that for every $1 spent on building gen AI applications, about $3 is needed for change management, including training people and actively tracking performance.

It’s also important to resist the temptation to just cut the techs loose. That can lead to multiple, sometimes overlapping, platforms, which is costly in both money and time. The better approach is to build the infrastructure and applications in a way that provide the flexibility to switch providers or models relatively easily.

Don’t treat gen AI as a tech program

Gen AI is a team sport, and the CIO is the head coach. To have real impact, gen AI has to leave the IT function and be imbedded into the business, which means integrating tech with product, risk, legal, and other departments. One important focus for this cross-functional team is to develop and put in place protocols and standards that support scale. There are different ways to develop such teams, and the CIO will have a large say in their composition and mandate. Some companies have started centers of excellence, while others have chosen to have discrete strategic and delivery units. What matters is that the team collaborates well and knows what it’s trying to achieve, with regular check-ins along the way. The CIO needs to ensure the team acts as builders of value, not just managers of work.

The principle to keep in mind is it’s not about creating different pieces, but making sure they all work together. Each use case needs to be able to access multiple models, vector databases, prompt libraries, and applications. That means companies have to manage a variety of sources, such as applications or databases in the cloud, on-site, with a vendor, or a combination, while ensuring resilience and consistency with existing protocols, including access rights.