Why is it so hard to measure the ROI of enterprise AI?
Deploying enterprise AI tools often leads to high token consumption and low utilization without clear business value to show for it. Because the benefits and costs are so hard to measure, companies end up facing a reckoning where they drop their tools entirely rather than renewing them.
What people tried
Every workaround mentioned in the threads below. We haven’t tested any of them — and nobody here is claiming they worked.
- 1abandoning renewals for underperforming AI features
- 2relying on internal dev teams to build alternative custom solutions at a fraction of the cost
In their words
Unedited, most upvoted first, each linked to the thread it came from.
“why is achieving the benefits of a working Agentforce implementation so hard, and why are those benefits and associated costs so hard to measure?”source ↗
“CIOs and CFOs around the world are facing a reckoning for allowing their employees to recklessly consume tokens with no ROI to show for it.”source ↗
“The utilization was not good so we're not renewing.”source ↗
Where this came up
People with this problem also raised
- 2How to choose an AI customer support agent for real use cases
- 3Is enterprise software support actually worth the cost for small teams?
- 4Is it safe to use AI for business financial data?
- 3How to secure and support internal AI apps built by non-technical employees
- 4How to actually use AI for IT work instead of just hype
- 4How to use AI in grant writing without getting flagged