Misha Khurana, Product Strategy and Operations Lead at Google.
Enterprise adoption of AI has accelerated dramatically in the past 18 months, driven by the maturation of foundation models and an influx of capital into generative tools. Yet beneath the momentum, many AI initiatives are quietly failing—not due to model performance, but due to an inability to demonstrate attributable business value.
Gartner anticipates over 40% of agentic AI programs will be abandoned by 2027 for this reason. In my experience across enterprise product teams and early-stage ventures, I’ve found that most AI rollouts falter at the same bottleneck: measurement infrastructure that lags behind model deployment.
The Metric Misalignment In AI Rollouts
Most AI tools are launched with surface-level telemetry: prompt volume, average completion time or user engagement deltas. While these proxy KPIs offer directional insights, they rarely capture whether a system delivers outcome-level improvements such as reduced support costs, shortened lead qualification cycles or increased throughput per knowledge worker.
This disconnect emerges partly from legacy analytics paradigms, which treat AI features as “add-ons” rather than integrated capabilities. Instrumentation is often retrofitted post-launch, leading to shallow insights and optimization loops that reinforce usage, not impact.
For example, an LLM-powered support agent might optimize for reducing human escalations. But without instrumentation that ties responses to actual case resolution, CSAT uplift, or recontact rate, it’s impossible to assess the system’s effectiveness beyond surface metrics.
Framework: From Proxy KPIs To Value Loops
To close this gap, I advocate a shift from passive measurement to value loops: closed systems that connect AI feature performance to downstream business outcomes via embedded instrumentation, counter-metrics and cohort-based attribution.
Here’s a four-part approach I’ve seen work in enterprise settings:
1. Instrument early, not after.
AI features should ship with embedded observability: latency, hallucination rate, override frequency and user confirmation behavior. These are foundational for building trust signals.
2. Define counter-metrics.
Every success metric needs a paired risk metric. For instance, optimizing for ticket deflection must be paired with recontact or escalation rate to avoid false positives.
3. Adopt cohort-based impact attribution.
Longitudinal tracking of user cohorts pre- and post-AI exposure helps isolate contribution effects. This is especially useful in internal tooling (e.g., sales productivity or developer experience).
4. Close the loop with feedback.
Prompt refinement based on failure cases and qualitative feedback (e.g., rejection reasons, override paths) should feed back into prompt engineering or fine-tuning cycles.
This framework moves beyond “was the AI used?” to “did the AI system change outcomes meaningfully, and in what direction?”
What AI Tools Still Get Wrong
The broader issue isn’t technical, it’s strategic. Too many AI product efforts still confuse model sophistication with business relevance. Tools get built with complex pipelines and impressive demos, but without a mechanism to verify if they move the needle for users or the business.
Even internally, I’ve seen sophisticated generative tools fail to gain traction because they lacked functionality as well as clarity on how success would be defined or detected. Measurement was an afterthought.
Conversely, some of the most successful deployments are surprisingly lightweight—an auto-summarizer embedded in CRM notes, a dynamic retrieval system for internal policy content. What they have in common isn’t technical novelty, it’s a rigorous focus on measurability and business fit.
Closing Thought: AI Isn’t a Strategy. Outcomes Are.
The next wave of AI differentiation won’t come from better models. It will come from better systems thinking—embedding observability, designing value loops and defining what success actually looks like before shipping.
Teams that measure what matters, and evolve based on what they learn, lead. The rest are building impressive AI features that will likely disappear.
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