Artificial intelligence (AI) is no longer an experimental novelty technology. Enterprises throughout the world are eagerly integrating AI technologies into all aspects of their organizations to improve productivity and efficiency. However, doing this creates a complex challenge for enterprises because they must ensure their AI systems are safe, compliant, and trustworthy to operate at a much larger scale.
What would happen if a company unknowingly deploys an unsafe AI system across every layer of its organization? The consequences could be devastating for the business and potentially its customers.
AI Governance Is Fundamental to Business Operations
The newest AI models are more autonomous and less transparent, prompting companies to increasingly depend on them more for critical decision-making duties within their organizations. That is why companies have a huge responsibility to ensure their AI systems behave the way they are supposed to. If an enterprise cannot prove what its AI system is doing, it won’t have any control over it.
Traditionally, companies relied on standard policy documents and one-time quality control audits to help verify that their AI systems were functioning correctly. Today, companies need continuous, verifiable proof that their AI systems are acting as they were intended. This verifiable proof must span across multiple domains of the organization, including its regulatory compliance, security, performance, and bias.
Greg Whalen, the Chief Technology Officer of Prove AI, envisions a future where AI observability is as fundamental to business operations as it is to cybersecurity and financial controls. He has spent decades of his career developing real-world, mission-critical systems. Now, as a product and engineering team leader at Prove AI, Whalen is designing one of the industry’s first AI observability tools designed specifically for production-scale AI deployment.
As a result, enterprises can build more trust among investors, regulators, and customers because they will be able to see verifiable proof of what AI is doing in their organizations – and be able to diagnose and remediate issues faster and with greater accuracy.
Why Greg Whalen Is the Go-To Authority on Debugging Generative AI Solutions
CTO Greg Whalen of Prove AI has become the go-to authority on how to build AI that is not just powerful but provable.
Whalen’s education includes an MS Degree in Computer Science from Columbia University and a BA Degree in Computer Science and Music from Wesleyan University. After entering the workforce, Whalen built a career leading engineering teams and data platforms at a global scale. His work background includes:
- CTO of Xendit (Southeast Asian Payment Gateway)
- General Manager for Amazon Work Mail at AWS
- APAC Director of Data Science Business at Pivotal
- VP, CTO, and Product Leader at Experian, Derm101, and Cheetah Mail
- Researcher of AI and Machine Learning at Columbia University’s Natural Language Processing Group
Whalen is a man of many talents. Not only is he proficient in AI, but he is also proficient in music and Chinese. This rare combination of talents gives him a unique way of thinking about complex systems and how they perform in the real world. These skills allow him to help enterprises confidently move generative AI from the experimentation stage to the production stage with total clarity and control.
In 2026, Whalen urges all other CTOs to focus on the following three priorities in their respective enterprises:
1) Observe AI systems to see how they behave
2) Develop a modern data strategy that includes machine-generated data and synthetic data
3) Build teams that deeply understand generative AI and its tooling
Developers won’t need to delegate AI tooling or just ship features. Engineers and their CTOs will now work closely together when bringing generative AI from the experimentation stage to the production stage. They will build systems that are observable, auditable, and resilient in real-time.
“Developers need visibility into what their systems are doing, and CTOs need confidence that those systems can be trusted in production,” said Whalen. “This is why observability and compliance are now shared responsibilities, not separate concerns.”
Whalen believes most of the current AI data and reliability challenges will be solved over the next four years by multi-agent systems capable of reasoning, collaborating, and operating at scale.
How to Fix the Telemetry Problem
Enterprises are eagerly competing to deploy the newest and best generative AI technologies. Unfortunately, there is one fundamental problem that keeps most applications from ever making it to the production stage, and it’s that engineering teams are flying blind.
Whalen believes enterprises are building generative AI telemetry without a purpose. Because of this, developers have difficulty troubleshooting hallucinations, latency, drift, and compliance failures. It forces engineering teams to spend more time guessing what went wrong rather than building new features. That is one of the biggest reasons why few generative AI applications ever successfully make it from the demo stage to the production stage.
Telemetry refers to the collection, processing, and analysis of operational data signals from within AI systems to ensure the AI model is functioning correctly. Unlike traditional software, generative AI systems utilize real-time context, user behavior, embeddings, and prompts to generate fluid, probabilistic outputs. The ability to capture, store, and analyze such data requires open, flexible telemetry pipelines that can evolve rather than remain fixed or locked down to a single use case.
“Every GenAI deployment is unique,” said Whalen. “If you don’t control your telemetry stack, you don’t control your system. Teams need the freedom to adapt how they capture and analyze data as their AI evolves.”
A purpose-built genAI telemetry capture is how you fix the problem. Developers will no longer feel blinded when diagnosing hallucinations, compliance issues, drift, and latency. They will have complete operational control of their telemetry stack, essentially allowing them to control their AI systems. A comprehensive genAI telemetry system is the backbone of any good generative AI application. It is how you move it from a demo to production.
