Presented by Snowflake
As AI moves into core business operations, leadership teams are shifting focus from user headcounts and token consumption to a more consequential question: How efficiently do compute, data and context translate into business results?
Effectively measuring the ROI of AI demands a rigorous operational discipline centered on what we call intelligence efficiency — one that connects the models and resources organizations use to the value they ultimately create.
Why model lock-in is the wrong bet
In discussions around enterprise AI architecture, leaders are often pressed to decide between standardizing on proprietary models or committing to open source.
Standardization can simplify architectures and scaling, but in a market moving this quickly, the best model for a given use case today may not be the best one three months from now. This framing looks at model selection as a binary decision, missing how fluid the ecosystem actually is. With the rise of open-source innovation, capability is no longer restricted to a single model provider.
As inference spend scales, it’s natural for leadership teams to seek predictability through consolidation. However, applying a rigid, single-model mandate treats AI like static software rather than the dynamic innovation it is.
Consider how organizations build high-performing teams. No executive tries to staff every role with the most expensive specialist available, nor do they hire the lowest-cost candidate just to minimize expenses. They match the right expertise to the work at hand. Enterprise AI demands that same operational discipline. Just as we wouldn’t use the same database or computing setup for every workload, we shouldn’t use the same approach for every AI task.
Modern data architectures should treat model selection as a dynamic matching problem depending on how hard the task is, how fast it needs to be, how much it costs, and how sensitive the data is.
Of course, dynamic matching is only as reliable as the evaluations driving it. Generic academic benchmarks tell us very little about how a model performs on real-world enterprise workloads. To route queries with confidence, organizations need specialized, domain-specific evaluation frameworks — such as open-source agentic benchmarks for data engineering (like Data-Eng-Bench) or tailored internal eval suites — to quantify true task proficiency before establishing routing policies.
This is why an intelligent routing layer is emerging as a critical control plane for enterprise AI. Informed by enterprise context, it evaluates incoming tasks in real time and directs them to the optimal model based on capability, latency, cost, and policy requirements. Decoupling applications from specific model APIs prevents technical debt, allows seamless model updates, and routes simpler tasks to lower-cost models while reserving top models for complex work.
But reducing inference costs is only one dimension of intelligence efficiency. The larger goal is to connect these technical decisions to how AI changes the work itself — and ultimately to the outcomes the business cares about.
Measuring intelligence efficiency
Leaders looking for one universal ROI metric for AI won't find it. AI doesn't create value the same way across every part of the business, and forcing it into a single number hides more than it reveals. A better approach tracks three layers, each showing how technical investment turns into results.
1. Adoption: This layer answers a basic question: are teams actually incorporating AI into their work, and how deeply? Daily active users and token volume show experimentation, but they don't show depth. Depth shows up as interactions moving from simple prompts to multi-step reasoning, AI getting embedded into daily tools and workflows rather than used as a one-off, and teams reaching for specialized tools and structured outputs instead of default chat interfaces. That depth tells you where training gaps are and how fast the organization is building capability. It doesn't tell you about ROI on its own though, as high engagement isn't the same as business impact.
2. Workflow Transformation: Here the question is how AI changes the work itself, and the metrics will vary by business function. In engineering, that's release velocity, review throughput, and defect density. In revenue operations, it's time saved on data entry, pipeline velocity, and response turnaround. In support, it's first-contact resolution and handle time. In finance and legal, it's how fast planning cycles and contract reviews close. Directionally, all of these metrics are measuring whether less human intervention is required to get the same or better quality outputs, moving toward workflows AI can run end-to-end.
3. Business Impact: This is the ultimate test of intelligence efficiency. Workflow gains only matter if they add up to something the business cares about. Faster development should get products to market sooner. Time saved for sales should translate into more pipeline and more closed deals. Operational efficiency should show up in margin, retention, or customer experience. The job is connecting what AI improves day to day to the outcomes leadership actually tracks.
Taken together, these layers connect the economics of the AI stack to the economics of the business. The goal isn’t simply to minimize the cost of individual queries or maximize AI usage, but to understand whether AI investment is producing better work and, ultimately, better business outcomes.
Where this goes from here
The companies that win the next phase of AI won't be the ones chasing token volume, and they won't be the ones capping spend so tightly they can't experiment. They'll be the ones building flexible architectures with intelligent model routing, using the full range of available models instead of betting on one, and tying compute spend directly to business outcomes. That’s the shift at the heart of intelligence efficiency, treating AI not as a resource to consume, but as a capability to continuously optimize for impact.
Anahita Tafvizi is Chief Data and AI Officer at Snowflake.
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