The CIO Professional Network, the private CIO, CISO, and CTO community for The National CIO Review, gathered for a a Roundtable discussion led by Lonnie Garris on how organizations can adopt AI in a way that creates business value without treating workforce displacement as the default outcome.
Building on his cybersecurity background, Lonnie introduced the idea of developing a CION-led AI implementation framework to help organizations use AI with structure and accountability.
The project would turn member experience into a shared resource based on an existing governance framework that aligns stakeholder needs with business, IT, cybersecurity, architecture, and workforce goals. By applying this model to AI, the group could help organizations define why they are using AI, where it fits, what risks need to be managed, and how employees can remain involved.
While the group discussed, members helped shape direction by sharing insights into their own challenges and successes implementing AI, as well as how they are managing risk.
Why It Matters: Although many organizations want to use AI, many need a clearer path because they do not have room to experiment and absorb failed pilots. By working together towards a CION-built framework, members will help guide AI adoption for organizations by connecting use cases to business goals and enterprise risk.
- Define What the Framework Should Help Leaders Solve: Lonnie framed the project around the question, “How can AI improve productivity without reducing the conversation to workforce cuts?” He encouraged the group to think about a resource that helps organizations make better decisions before AI use becomes scattered across teams. Because large companies often have more room to experiment and recover, the framework could be especially valuable for smaller companies and public agencies that need a clearer starting point.
- Use a Governance Model to Guide AI Adoption: The group will follow the existing structure of another framework that tightly connects stakeholder needs and technology goals. By applying the model to AI implementations, organizations avoid disconnected experiments and focus on use cases that serve a clear purpose. For example, one member connected this initiative to their own organization’s growth plans, noting that their company needs AI objectives that make sense for all aspects of the business before acquisition activity adds more pressure.
- Integration and Architecture Workstream: The group posed that the project would consist of two direct work groups to start the project. The first would focus on the technical foundation needed to bring AI into the organization responsibly, including how tools are approved, how agents are designed, how workflows are monitored, and how security requirements are built into the process.
- Human Element Workstream: The second group will focus on the human side of AI adoption, including how employees are trained and kept involved as work changes. Another attendee shared how their startup used AI to extend resources and increase output when funding pressure forced the team to rethink its approach. While AI eventually became a major part of the development process, he emphasized that value came through helping the existing team accomplish more. Because of his results, he challenged the idea that AI removes the need for new talent, noting that their more junior employees are still playing an important role in reviewing AI-supported work and supporting production needs.
- Build Governance Before AI Use Spreads Informally: Several members noted that employees will use AI with or without a formal program, forcing governance to be an early priority for organizations. Tiffany Kane described this challenge between encouragement and governance mainly occurring when resources are limited, and leaders are still learning AI’s capabilities. Tim Ryan added, sharing that his own organization incorporated AI governance into its company risk matrix to raise awareness and prompt stronger attention to data access.
- Show How AI Can Support Growth and Efficiency: Attendees also discussed how AI can help organizations grow without repeating legacy processes or adding headcount in the same pattern. Dave Hatz shared that his organization is growing quickly and wants to understand how AI can help the business expand without duplicating its current employee base. He referenced finance in particular as an area where older processes are still influencing how work is done and brought attention to the fact that AI has caused organizations to take a long-overdue review of team operations. From a public sector standpoint, Rafael Pimentel Pinto shared that while agencies may measure value differently than private companies, they still need better ways to work within limits.
- Avenues for Member Collaboration: This initiative is calling on members willing to share their experiences to help turn their lessons into a useful resource for AI implementation. Those with more developed AI programs can offer insight into their process and what other organizations may be able to apply. Just as important, members earlier in their AI journey can help identify challenges the framework should address.
To get involved with this project and contribute to future conversations shaping the AI implementation framework, join the CIO Professional Network.
