Why Enterprise AI Needs Production-Grade Evaluations

The Measurement Problem

Benchmarks tell us how models perform on tests, not how they perform at work.

Enterprises are spending millions on AI systems that look brilliant on paper and feel clumsy in production. Models crush SWE-bench, MMLU, HumanEval, GSM8K, and a dozen other leaderboards—yet inside the business, leaders struggle to point to clear wins.

This disconnect isn't just a minor inconvenience; it's a fundamental misalignment between how we evaluate AI systems and how businesses actually measure success. While model developers optimize for benchmark scores, enterprises wrestle with basic questions: Is our AI deployment actually improving productivity? Which teams are seeing ROI? Where are the failure modes in production?

The core problem is that current AI evaluation methods focus on isolated cognitive capabilities in controlled environments, while real-world enterprise tasks involve messy, interconnected workflows that cannot be reduced to neat test cases. The gap between benchmark performance and production utility is widening—and you can’t close that gap by tweaking benchmarks alone.

The Benchmark Trap

We imported research metrics into business decisions without changing the questions we ask.

Benchmarks came from research, where they do their job well. Fixed test sets and clear scoring rules make it easy to compare models under controlled conditions: MMLU for knowledge and reasoning, GSM8K for math word problems, HumanEval for short coding tasks, SWE-bench for GitHub issues. In that world, a single score is a useful abstraction.

The trouble started when those research instruments quietly became procurement and strategy inputs. Vendors began leading with leaderboard slides. Internal AI teams defended model and vendor choices by pointing at benchmark deltas. Executives, lacking better tools, treated those numbers as proxies for “general capability” and assumed business value would follow.

That’s the benchmark trap: metrics designed to compare models in isolation are now driving multimillion-dollar decisions in messy, high-stakes environments. Benchmarks describe how a model behaves on abstract, one-shot tasks. Enterprises need to understand how AI systems behave in live, multi-step workflows, alongside people and tools, under real constraints.

Case Study: The SWE-bench Paradox

SWE-bench measures an AI’s ability to resolve GitHub issues by generating code patches that make test suites pass. It’s a strong signal of coding capability; above a certain threshold, models clearly show genuine engineering competence.

Yet enterprises that deploy “high–SWE-bench” models often report mixed results. The model can fix small bugs, but:

• It doesn’t follow team conventions.

• It produces patches that are hard to review or reason about.

• It writes weak documentation and confusing commit messages.

What’s missing is everything SWE-bench doesn’t see: code review collaboration, documentation quality, architectural consistency, debugging communication, and the ability to work within team norms. Those are exactly the factors that determine whether AI-generated code delivers value or creates maintenance burden.

SWE-bench isn’t wrong; it’s just narrower than the decisions it’s being used to justify.

Goodhart’s Law: When Metrics Break

Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure.

Even imperfect benchmarks can be useful if they stay honest signals. The deeper issue is that many of our favorite metrics have already crossed that line.

Once benchmark scores influence research prestige, marketing narratives, and contract wins, they stop being neutral measurements and start becoming targets. Training pipelines are tuned explicitly to move numbers on a small suite of public tests. Data curation, fine-tuning, prompting, and scaffolding are all shaped around those metrics.

The result is models that are outstanding at passing specific exams and surprisingly fragile once you step outside that frame: change the format, shift to proprietary data, introduce tools, or ask for multi-step reasoning, and performance can drop in ways the benchmarks never warned you about.

Contamination compounds this. Popular benchmarks increasingly leak into training data, especially coding sets built from public repositories or widely circulated question banks. Apparent breakthroughs can be driven by memorization and benchmark-specific quirks, not robust generalization. Meanwhile, vendors cherry-pick the tests they win, buyers anchor on those numbers, and roadmaps are built around them.

Goodhart’s Law turns benchmarks from rough indicators into distorted mirrors—the more the ecosystem optimizes them, the less they reflect how systems behave in the wild.

The Human Work Analogy

The Convergence Thesis: the measurement system should match the work system.

If benchmarks are misaligned and over-optimized, what should “good” evaluation look like? One useful lens is how we already evaluate another kind of intelligent agent: human employees.

Imagine running performance reviews purely on standardized tests. Engineers are promoted solely on coding challenge scores. Marketers are judged by a verbal reasoning exam. Operations leaders are evaluated on logic puzzles. Those tests say something about raw aptitude, but almost nothing about whether someone can ship a complex project, handle ambiguity, work across teams, or move the metrics the business actually tracks.

In reality, we evaluate people with richer, longitudinal signals: what they deliver, how they collaborate, how they respond to feedback, whether their work shifts revenue, cost, risk, or satisfaction, and how they affect the people around them. An interview exercise or aptitude test might play a role at the beginning, but it is never the full story.

This is the heart of the Convergence Thesis: if we're building toward a future where AI systems perform work alongside human employees—not just as experimental tools, but as integrated contributors to business outcomes—we need to evaluate them using similar frameworks.

The measurement system should match the work system. As AI moves from research labs into production environments, evaluation has to move from exam-style testing toward performance-style assessment: continuous, contextual, and tied to outcomes. Benchmarks can remain as capability screens and regression checks, but they cannot be the primary lens for judging value.

GDPVal: Real Tasks, Still Not Real Work

From exam questions to real tasks—but still outside the flow of work.

GDPVal is one of the first serious attempts to bring benchmarks closer to the real economy. Instead of synthetic puzzles, it focuses on tasks that look like actual work products across major sectors: drafting contract clauses, responding to multi-turn support threads, writing clinical summaries, producing engineering notes, reviewing documents for risk. These tasks are derived from real artifacts created by professionals and graded by domain experts using realistic criteria.

Conceptually, GDPVal shifts the question from “Can this model pick the right answer on a toy problem?” to “Can this model produce a credible deliverable for a realistic professional task?” It’s closer to reviewing a portfolio than grading a multiple-choice exam. And that shift matters: when you evaluate on GDPVal-style tasks, model rankings change, and some leaderboard darlings look less impressive.

But GDPVal still evaluates snapshots, not systems doing ongoing work. It doesn’t capture how a model behaves over weeks of collaboration and iteration, how it interacts with CRMs or ticketing systems, how it respects approvals, compliance rules, and SLAs, or how human workflows adapt around AI assistance. GDPVal answers: Can this model produce a strong one-off artifact for this task?

Enterprises ultimately need to answer: When we embed this system in our real workflows, does it reliably make the business better? GDPVal proves that more realistic, economically grounded benchmarks are possible and useful, while also underlining that even the best benchmarks are still just that—benchmarks.

From Benchmarks to Work-Centered Evaluation

We don’t just need better benchmarks; we need to start modeling AI the way we model work.

The only way to close the gap between impressive benchmark curves and disappointing deployments is to shift from model-centered evaluation to work-centered evaluation—to measure AI systems in the same context, and against the same kinds of metrics, that we use for people and processes today.

Context Evaluations solve this issue. We've built an evaluation platform that live in production, inside real workflows, and treat AI not as a test-taker, but as part of how work actually gets done at a specific company.