Connect AI Quality to User & Business Outcomes

Our analysis of research from MIT NANDA, RAND, Gartner, Deloitte, Zapier, and S&P Global shows that only a fraction of enterprise AI pilots reach scaled production or prove sustained business impact. Links.

Why?

One of the common problems the analysis reveals is that AI product teams can't easily connect AI quality to user behaviour and measurable business results. And when those signals are untracked or live in separate tools, teams can't prioritize improvements and struggle to justify further AI investments.

For example, AI observability tools show AI teams how a model responded, including input, output, model & tool calls but they can't tell them whether the output was useful to a user. Think of how useful the answers to the following questions would be for them and their executive stakeholders:

• Which AI initiatives are delivering the highest business impact?
• Did users use all AI agents and complete the workflow faster?
• How many users accepted the answers or the recommendations? Did conversions increase?
• How much value did the complete AI-powered workflow deliver?

Here's our short breakdown of why this is a problem:

1. There is no way to measure AI's business impact

   Teams don't have a way to measure what business value AI products and features deliver.

   Depending on your business, some important metrics to measure could be:

   • Task completion, conversion, and drop-off
   • Cycle time
   • Accepted, corrected, or challenged outputs
   • Abandonment, retries, and cancellations
   • Escalations, support requests, and complaints
   • Feature adoption

   The AI may complete the task after mistakes, retries, wrong tool calls, unnecessary loops, corrections, etc., but was it a good user experience? Only the users can tell.

   These metrics connected to your AI products can tell you which AI workflows provide the most value and which ones need improvement.

2. User signals and AI traces are disconnected

   When AI fails to deliver value, users take actions like correcting an answer, retrying a task, abandoning the workflow, or contacting support, but these signals are often untracked or live in different systems from the AI traces. Teams might be able to see the user’s reaction or the AI’s behaviour, but struggle to connect the two.

   As an example, a team may see completion fall without knowing which AI behaviour changed. They may see an evaluation score improve while users correct more and more answers, or take longer to finish the task.

   One customer explained it to us this way:

   "It is not always a thumbs down. Closing the window or ignoring the response can tell us it was not good. Copying the response can be a positive signal."

   AI observability metrics like retrieval accuracy and output groundedness alone are insufficient to indicate if the AI system helps users or the business.

   Teams often find problems through ad-hoc customer complaints, human escalations, repeated questions, corrections, or abandoned tasks.

   These signals tell you that something may have gone wrong. They do not tell you which part of the AI workflow caused it.

3. Expert judgment is not captured as institutional memory

   Domain experts are the best people to determine if the AI's work meets the required criteria. But their decisions often stay in spreadsheets or support tickets.

   Teams don't have a standard way to codify this expert judgment as institutional memory. That shared standard should ideally be reusable across teams, models, projects, and AI products.

   But we often see teams defining the same quality processes again.

4. Companies don't have a sovereign way to control AI quality

   AI quality standards become tied to a model provider, observability tool, vector database, or another part of the AI tech stack.

   AI executives are concerned that their data, expert knowledge, quality standards, and deployment choices aren't easily transferable across models, tools, projects, and providers.

   And so, changing part of the AI stack can mean losing or rebuilding human review history, judges, guardrails, evaluation datasets, and quality processes.

5. Teams cannot tell whether a fix improved the business outcome

   A team may find a poor answer, then optimize the prompt, and confirm that the answer now looks better. That does not show whether the user's workflow improved.

   Tracking it back to show improvement in business outcomes that first exposed the problem might be a more robust approach.