How to Prove AI ROI to Your Board

Enterprises are shifting from measuring AI adoption through prompts and spending to demonstrating business outcomes such as production applications delivered, employee time saved, software costs avoided, and engineering capacity redirected. Drawing on discussions with CIOs, CISOs, and business teams, the author argues that usage metrics can obscure both limited ROI and security risks from employees using unapproved personal AI tools, citing IBM and MIT research on low rates of realized returns and widespread shadow AI use. The proposed approach is a weekly review that tracks applications in production, identifies where adoption is growing or stalled, connects each app to measurable value, and resolves common blockers including missing data access, unclear use cases, and absent sponsors. Examples include nontechnical employees building portfolio, scheduling, and HR tools that reduced manual work or avoided custom software costs. The piece presents Superblocks as a governed platform for enabling non-engineers to build internal AI applications while maintaining centralized access controls, audit logs, single sign-on, and deployment options that keep data within a company’s cloud environment.

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