How to Measure AI Value Beyond Adoption Metrics | Snowflake

How to Measure AI Value Beyond Adoption Metrics | Snowflake

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Oct 23, 2026 / 11 min read AI & ML Copy post link Open in Claude Open in ChatGPT

Beyond Adoption: How to Measure Whether AI Is Changing the Work That Matters

Siddharth Dwivedi +2

Consider two organizations that each log 1,000 AI interactions in a week. In the first, most of those interactions are explanations and lookups. In the second, people are building data pipelines, investigating anomalies and running multistep workflows.

Most enterprise AI dashboards would score those two organizations identically. The first generation of AI developer metrics measured access and activity: how many people have a license, how many are active and how many prompts they send. But these metrics cannot tell you whether AI is driving meaningful work outcomes.

Answering that question means tracking what people use AI for and whether that is moving. Here is the framework we use to measure AI value and what we found when we applied it to two of our own teams.

A four-part framework for measuring AI value

1. Start with the work AI is doing

Asking an assistant to explain a SQL statement is a different request from asking an agent to build a pipeline or investigate a failure. Group the work the AI is doing into three levels:

Foundational work: The AI explains code, answers questions and helps someone understand an unfamiliar concept.

Applied development: The AI writes and modifies code, explores data, troubleshoots and builds reports or applications.

Agentic work: This is a multistep process. The AI reasons across context, uses tools and helps finish an outcome instead of answering just one question.

This breakdown describes the work rather than the volume, which gives you something to check. If AI is creating real capacity, the hours saved should show up in the agentic level.

2. Measure adoption and fluency separately

The next question is whether higher-value usage has spread beyond just a few people. Two dimensions cover that:

Adoption: This measures how broadly AI is used across the eligible population.

AI fluency: This shows how sophisticated the work that people delegate to AI has become.

Together these dimensions describe four positions.

Figure 1: Measuring AI adoption and AI fluency

3. Use industry benchmarks for context

A number on its own is hard to act on. If 35% of your eligible developers actively use an AI coding agent, that figure alone will not tell you whether 35% is good. It depends on what comparable organizations are doing, and expectations vary by industry. A software company and a regulated insurer are not working from the same baseline.

Peer benchmarking gives you a relative view on both dimensions. Instead of reporting that adoption is 35%, it can tell you whether that puts you among the emerging, developing, proficient or leading organizations in your industry.

The same applies to fluency. One organization may have broad adoption but little agentic work. Another may have a handful of users doing sophisticated work that has not spread. Those two findings call for different responses.

4. Look at the gap between your best users and everyone else

Your most advanced users are the signal worth watching.

In most organizations, a few people find effective AI workflows before their colleagues do. They learn to hand over larger tasks, provide better context and combine tools. Treat those patterns as a roadmap, not a collection of anecdotes.

The practical question is how to move a behavior from one person to 100. Four tactics worked for us:

Have power users teach, not just demo: A hands-on workshop led by someone on the team who already gets results beats a vendor training session. Watch for whether weekly usage holds a month later, not whether the room was full. A workshop that changes habits looks different from one that produces a spike.

Turn their workflows into recipes and templates: When someone solves a problem well, have them write it down as something a colleague can run rather than read. The test is whether the next person executes it without asking the author a question.

Run a regular show-and-tell: Provide a standing slot where people walk through a workflow they built, including the things that did not work. The failures travel better than the successes, because they tell everyone else which problems are worth attempting.

Make the library findable: This is the step most teams skip. Once you have dozens of reusable workflows, discovery, rather than creation, becomes the constraint, and a library nobody can search is a library nobody reuses.

The aim is to move proven practice from a few people to many, and the mechanism matters more than the enthusiasm.

What to do with the answer

Measurement earns its keep when it points to an action instead of a status. There are three common positions, and each suggests a different course of action:

Sophisticated but narrow: Early adopters use agents for advanced work, and most eligible people have not started. Remove access friction, share internal examples, and expand enablement.

Broad but shallow: Many people use AI, mostly for explanations and simple code generation. Show them how to hand over larger tasks through examples, templates and hands-on training.

Broad and sophisticated: More adoption is no longer the priority. Find the repeatable workflows, build reusable agents, and measure outcomes such as development velocity or operational efficiency.

The same metrics support different recommendations depending on where you sit.

Applying the framework to our own teams

We spent two quarters using this framework on Snowflake CoCo, our agentic AI development assistant, inside Snowflake. Two teams with very different work landed in a similar place, and in both cases the gains came from agentic workflows rather than foundational use.

Product data science: Automating a diagnostic runbook

Our product data science team runs thousands of data pipelines. In the first quarter of FY 27, those pipelines needed attention 960 times, which is normal at that scale. Each case took a skilled engineer about 30 minutes to diagnose. That works out to roughly 160 hours a month of expert time spent on diagnosis.

CoCo now performs that diagnosis. Across the Q1 and Q2, working alongside the team CoCo has:

Improved more than 500 pipelines

Generated more...