We have too many AI tools

Common questions from Omaha leaders

How does this happen?

Bottom-up adoption without governance. Each team buys what helps them most quickly — marketing buys an AI copywriter, engineering buys Copilot, ops buys a meeting-summary tool, sales buys a call-recording AI. Six months later, you have 8 tools, three of which overlap, none of which share data. Express-Harris 2026 found only 36% of companies provide an approved AI tool list, which is the structural cause.

Should we consolidate to one tool?

Usually no — but down to 2–4. The pattern that works: one general-purpose enterprise AI (ChatGPT Enterprise, Claude Enterprise, or Microsoft Copilot for Business) plus 1–3 specialized tools where the specialization actually justifies a separate vendor. Going to one tool tends to break workflows that depend on the specialized one; going to many tools is the sprawl problem you started with.

How do we consolidate without killing adoption?

Run the consolidation as a 90-day workflow, not a fiat. Audit current use, talk to the teams using each tool, identify which workflows depend on which features, then pick the consolidation set with team input. The teams whose tools are sunset need a clear migration path with hands-on help. Top-down 'we're killing your tool' announcements without migration support is how you push usage back into shadow AI.

What about cost?

Sprawl typically costs 2–4x what consolidation costs once you tally per-seat licenses across all tools. The cost case is real but rarely the load-bearing argument; governance and quality consistency usually matter more for leadership signoff.

How does this connect to AI policy?

Directly. The approved tool list is the artifact that prevents future sprawl. Once you've consolidated, the policy locks in what's approved (with a process for adding new tools that goes through IT/Security/Legal). Without the policy lock-in, sprawl returns within 12 months.