Jason M. Riggs on Why AI-Native Product Leadership Is Reshaping Enterprise Strategy

As artificial intelligence shifts from experimentation to operational infrastructure, product leadership inside large enterprises is undergoing a structural transformation. According to U.S.-based AI product executive Jason M. Riggs, the change is not about tools. It is about decision architecture.

Riggs, Chief Commercial and Product Officer at Audivi AI and a former product leader at Qualcomm and GoPro, argues that traditional product management frameworks were designed for roadmap execution, not adaptive systems operating in real time. “In an AI-native environment,” he says, “the unit of value is no longer the feature. It is the quality and velocity of decisions.”

Across sectors including retail, logistics, and financial services, enterprises are embedding machine learning directly into customer-facing workflows. McKinsey estimates that AI adoption in at least one business function now exceeds 50 percent among surveyed organizations. Analysts describe the shift as a move from isolated pilots to embedded operational infrastructure. Yet performance outcomes vary widely. Riggs contends the difference lies in whether organizations treat AI as a bolt-on capability or as a decision system.

From Roadmaps to Decision Systems

Traditional product models emphasize quarterly planning, backlog prioritization, and feature release cycles. That structure weakens when products are driven by live data streams and continuously learning models.

“In AI-native systems, outcomes are probabilistic, not deterministic,” Riggs explains. “You are managing signal quality, feedback loops, and intervention thresholds, not just shipping features.”

He describes the transition as a move from roadmap management to orchestration. In this model, product leaders are responsible for governing model behavior, monitoring performance signals, designing escalation protocols, managing human-in-the-loop controls, and optimizing decision latency.

The central question shifts. Instead of asking whether a feature launched on time, executive teams increasingly ask whether the system is making high-quality decisions at scale.

The MACH-10 Decision Velocity Philosophy

Riggs frames this shift through what he calls the MACH-10 decision velocity philosophy. The principle is straightforward: increase decision density, defined as the number of high-quality decisions made per unit of time, while maintaining governance and strategic alignment.

“Speed without structure leads to volatility,” Riggs says. “Structure without speed leads to stagnation. AI-native leadership requires both.”

Under this approach, product executives function less as backlog managers and more as system stewards. They design feedback mechanisms, define escalation logic, and align AI performance with enterprise risk tolerances.

The implications extend beyond product teams. Finance must account for performance variability. Legal must assess automated decision exposure. Boards must evaluate AI not as an innovation initiative but as an operational layer embedded within revenue-critical workflows.

Why Traditional Frameworks Break

Riggs argues that many organizations underestimate how deeply AI alters accountability structures.

Legacy frameworks assume predictability: defined requirements, scheduled releases, measurable outputs. AI systems evolve continuously. Performance depends on data quality, model retraining cycles, and environmental shifts.

“When your product adapts in real time, you cannot manage it like a static application,” Riggs notes. “Governance models and monitoring systems must be designed into the architecture from the start.”

This demands tighter collaboration among product, data science, engineering, compliance, and operations. The boundary between technical oversight and business decision-making becomes increasingly blurred.

Organizations that fail to adapt often encounter stalled AI initiatives or uncontrolled deployments that introduce operational risk.

Board-Level Considerations

The transformation is increasingly visible in boardrooms. Directors are asking new questions:

How quickly can the system adapt to market signals?

What guardrails govern automated decisions?

Where does human override occur?

How is performance audited?

Riggs believes boards are beginning to view AI systems as core infrastructure, comparable to cloud or cybersecurity investments.

“The conversation is shifting from innovation budgets to operating resilience,” he says. “AI is becoming embedded in revenue-critical workflows. That changes the risk profile.”

Global Enterprise Impact

For multinational firms, the complexity intensifies. AI systems must operate across regulatory environments, cultural contexts, and infrastructure constraints. Product leaders must design for localization, compliance, and scalability from the outset.

Riggs argues that enterprises embracing AI-native leadership early will gain structural advantage.

“The organizations that win will not be the ones with the most models,” he says. “They will be the ones with the most disciplined decision systems.”

As AI reshapes enterprise operations, product leadership is emerging as a strategic control point. The shift from roadmap management to decision orchestration may prove to be one of the defining structural changes of the AI era.

About the Expert

Jason M. Riggs is Chief Commercial and Product Officer at Audivi AI and founder of Perfect Wave AI Ventures. He previously held product leadership roles at Qualcomm and GoPro, where he worked on large-scale consumer and technology platforms. Riggs focuses on AI-native product strategy, enterprise decision systems, and operational governance frameworks. He is the author of The MACH-10 PM: AI-Powered Product Management at Hypersonic Speed.