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As CTO of Prove AI, Greg Whalen operates at the sharp edge of one of technology’s most urgent challenges: transforming generative AI from impressive prototype to production-grade, trusted enterprise system.
In a landscape defined by rapid model releases and relentless innovation cycles, Whalen’s focus is not on hype or novelty. It is on resilience, governance, and the operational backbone required to make AI systems dependable at scale. For organizations racing to adopt generative AI, his message is clear: technical brilliance alone is not enough. Without observability, telemetry, and engineering-led governance, even the most promising AI initiatives will stall before they reach true enterprise impact.
Whalen’s journey into AI began long before the current generative wave. In the late 1990s, AI was largely academic and computationally constrained. Running even modest machine learning workloads on large datasets was a technical ordeal. Back then, the field revolved around predictive models, not generative systems, and experimentation outpaced practical deployment.
Today, the barriers that once limited AI have largely disappeared. Computational scale, cloud infrastructure, and foundation models have unlocked extraordinary capabilities. But in Whalen’s view, this acceleration has introduced a new and more subtle constraint: operational maturity.
Modern organizations can prototype AI systems quickly. They can even achieve impressive early results. But what they routinely underestimate is the difficulty of moving from “good” to “great.” That final stretch—the transition from promising proof-of-concept to reliable production system—is where most initiatives falter.
One of the most common mistakes enterprises make is assuming that a compelling demo signals readiness. A generative AI system that performs well 80 percent of the time can look transformative. In reality, that often represents only the first 20 percent of the journey.
The remaining work is not about tweaking prompts or swapping models. It is about building a resilient system around a non-deterministic core. Generative models can hallucinate, drift, or behave unpredictably in edge cases. These failures may be statistically rare—but in production, even a handful of severe errors can erode trust.
Organizations that rush to deploy without addressing these edge cases typically encounter a long tail of operational debt. Debugging isolated failures becomes slow and manual. Engineers are forced into reactive firefighting. Confidence declines. Investment dries up.
For Whalen, the solution is straightforward but demanding: design for scale and resilience from the beginning.
In traditional cloud software, observability has become a foundational principle. Teams expect full visibility into logs, metrics, and system behavior. Yet in generative AI, many organizations attempt to manage only the visible outputs—answers, responses, or transactions—while ignoring the deeper system dynamics.
Whalen argues that this is a critical misstep.
Observability in AI systems means more than checking uptime or reviewing user-facing outputs. It means having end-to-end visibility into how prompts are processed, how decisions are made, where failures occur, and how systems recover. It means understanding which components are deterministic and which are not. It means being able to diagnose and remediate issues quickly—without days of forensic investigation.
In generative AI, the stakes are higher. A single off-the-rails interaction can define stakeholder perception, even if thousands of other interactions were flawless. Without robust telemetry and reporting, teams can spend hours dissecting one problematic conversation. Multiply that by several incidents per day, and forward progress grinds to a halt.
Observability is no longer optional. It is the condition for survival.
AI governance has often been treated as a policy exercise. Slide decks are created. Principles are drafted. Committees are formed. But according to Whalen, governance that lives outside the engineering workflow rarely delivers meaningful control.
Effective governance must be operational.
That means the individuals building AI systems must also own their governability. They need clarity around what constitutes acceptable performance, what defines failure, and what “great” truly looks like for the business. Governance becomes embedded in system design—through telemetry, metrics, safeguards, and transparent reporting—rather than imposed externally through abstract mandates.
In practice, this shifts responsibility. Engineers and product leaders must engage directly with business outcomes, cost constraints, and performance trade-offs. They must determine not only how systems function, but how success is measured and reported.
This represents a significant cultural change. Many stakeholders who previously influenced technical direction without deep system knowledge now face a choice: engage with the details of generative AI, or step back from decision-making authority. The pace and complexity of modern AI leave little room for superficial oversight.
Generative AI has redefined the role of the engineer.
Beyond writing code, engineers now make business-impacting decisions about model evaluation, infrastructure cost, and performance thresholds. They must weigh whether to adopt new tools or foundation models—often under tight timelines. They must decide how much to invest in evaluation runs, how to balance quality against token costs, and when to prioritize remediation over innovation.
Unlike traditional software systems, generative AI rarely offers clean, binary signals. There are no simple thresholds that neatly trigger alerts. Performance drifts gradually. Edge cases emerge unpredictably. Engineers must interpret trends, assess severity, and choose the right intervention path.
Without strong observability foundations, this cognitive load becomes overwhelming. With them, it becomes manageable.
A recurring theme in Whalen’s philosophy is the importance of defining “great” early. Enterprises frequently build prototypes and then ask stakeholders whether results are good enough. The feedback is often vague or inconsistent. Expectations remain unclear. Progress stalls.
Instead, leaders should reverse the process.
Before launching a proof-of-concept, teams should define success in concrete, observable terms. What business outcome must improve? At what cost? What failure rate is acceptable? How will performance be measured? How will costs be tracked? What remediation path exists when metrics degrade?
By anchoring AI initiatives in measurable outcomes and instrumenting systems to report on them, organizations dramatically increase their odds of reaching production.
At Prove AI, Whalen and his team focus on providing the infrastructure that underpins trustworthy AI. This includes curated observability stacks built around established tools such as OpenTelemetry and Prometheus, along with dashboards designed to give teams immediate insight into system health and behavior.
The goal is not simply monitoring. It is empowerment. When engineers can see how their AI systems perform, diagnose issues quickly, and demonstrate reliability to stakeholders, they unlock the confidence required for long-term adoption.
In Whalen’s view, trust in AI does not emerge from model sophistication alone. It emerges from disciplined engineering.
For CTOs and engineering leaders, the message is unequivocal: engage deeply.
Generative AI cannot be managed at arm’s length. Leaders must invest time in understanding the tooling, the workflows, and the operational differences that distinguish AI from traditional software. That means allocating meaningful blocks of time to experiment, build, and learn—far beyond superficial exploration.
Simultaneously, they must prioritize telemetry and governance from the outset. Deferring observability until “phase two” invites operational chaos. The longer it is postponed, the harder it becomes to retrofit.
The organizations that succeed will be those that treat generative AI not as a novelty feature, but as a high-availability, business-critical system—designed, instrumented, and governed accordingly.
The pace of AI innovation shows no signs of slowing. New models, tools, and techniques emerge almost monthly. In this environment, chasing every development is impossible. Choosing what to prioritize—and building durable systems around those choices—is the real challenge.
Greg Whalen stands at the forefront of that discipline. By insisting on observability, operational rigor, and engineering-led governance, he is helping enterprises move beyond experimentation toward AI systems that are not only powerful but also provably trustworthy.
In an era where expectations for AI are sky-high and tolerance for failure is low, that difference is everything.