Artificial intelligence is usually framed as a technical disruption. Faster code. Smarter models. Cheaper experimentation. Inside companies actually deploying AI at scale, the impact shows up somewhere else first.
It shows up in how decisions get made.
As execution cycles collapse and iteration becomes cheap, long-standing white-collar roles built around coordination and process are starting to crack. The earliest role to feel that pressure is not engineering. It is product leadership.
The Shift in Brief
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The Old Constraint: Engineering scarcity and slow feedback loops.
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The New Reality: Abundant execution and compressed time to clarity.
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The Bottleneck: Shifted from technical output to human judgment.
“What AI is doing inside organizations isn’t mysterious,” says Jason M. Riggs, a product and business leader who focuses on execution speed and decision quality in AI-enabled teams. “It’s crushing the time to clarity. When teams can build, test, and learn in days, the bottleneck stops being output and starts being judgment.”
For years, product leadership evolved to manage scarcity. Engineering capacity was limited. Feedback loops were slow. Mistakes were expensive. In that environment, roadmaps mattered. Alignment mattered. Process created safety.
AI removes those constraints.
The Efficiency Reckoning
This shift is already visible in how AI-native companies are scaling. Across the market, a growing number of startups are reaching meaningful revenue milestones with far fewer people than traditional SaaS organizations required just a few years ago. Smaller teams are producing outsized output by collapsing handoffs and eliminating layers designed to manage slower execution.
This pattern mirrors what analysts at McKinsey and Gartner have been observing. According to Gartner's recent 2025 research, 95% of decisions that currently use data will be at least partially automated by the end of this year. Furthermore, they predict that by 2028, 15% of day-to-day work decisions will be made autonomously through agentic AI, up from nearly 0% in 2024.
The result is not just efficiency. It is structural change. Leaders are discovering that the mechanisms designed to govern product development no longer keep up with reality. Roadmaps expire before they are approved. Planning cycles lag behind live data.
“The problem isn’t that teams are moving too fast,” Riggs says. “The problem is that leadership systems are still optimized for a world where moving fast was dangerous.”
The VC Perspective: Investing in Leaner Logic
From a venture capital standpoint, the “product manager as coordinator” is increasingly viewed as a liability rather than an asset.
“We are no longer funding headcount; we are funding high-velocity decision engines,” says a general partner at a top-tier Silicon Valley firm. “In the ZIRP era, a large product team was a status symbol. Today, when I see an early-stage company with layers of product operations and coordination roles, it raises questions about how quickly leadership can translate signal into action.”
Venture firms are shifting their diligence toward what some describe as decision velocity. They look for leaders who can synthesize AI-generated signals and pivot in real time, rather than those who excel at maintaining a long-range backlog.
Why Product Leadership Breaks First
Product leadership sits at the intersection of strategy and feedback. In AI-native environments, risk surfaces faster and resolves earlier.
“The old job was about avoiding mistakes,” Riggs explains. “The new job is about shortening the time it takes to know whether you’re right or wrong.”
When execution is slow, delay feels prudent. When execution is fast, delay becomes the most expensive mistake a team can make. Leaders who rely on process to manufacture certainty turn into bottlenecks.
A Note of Caution: The Case Against Moving Too Fast
Not everyone views this acceleration as an unqualified advantage. Some management experts warn that rapid iteration can increase organizational risk, overwhelm teams with incomplete data, and lead to reactive decision-making. From this perspective, speed threatens to erode strategic coherence, replacing deliberate planning with constant motion.
Riggs argues that this critique misunderstands what has materially changed.
“Speed isn’t the defining variable,” he says. “Time to clarity is. AI doesn’t just make teams move faster; it allows them to find out whether they’re right or wrong sooner. When clarity arrives earlier, delaying decisions doesn’t reduce risk. It compounds it.”
In other words, the risk profile has inverted. What once protected organizations from costly mistakes now delays learning. In environments where feedback is immediate, waiting for certainty can be more dangerous than acting on incomplete information.
Closing the Gap: The Transition to Continuous Discovery
To navigate this transition, modern leaders are moving away from the quarterly roadmap entirely because it’s tool that assumes a static future. Instead, they are adopting continuous discovery cycles. In this model, the ritual shifts from a massive planning meeting to a daily synthesis of AI-driven user data and rapid prototypes.
By using AI to simulate user responses or generate "disposable code," leaders can test multiple directions in the time it previously took to document one. The tool of choice is no longer the Gantt chart. It is the decision log, where the focus is on how quickly the team invalidated a wrong direction to find the right one. This reframes the leader’s role from project gatekeeper to hypothesis tester.
The New Leadership Mandate
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From Roadmaps to Pulse: Replace static six-month plans with real-time signal-to-action loops.
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From Safety to Speed: Prioritize the cost of indecision over the cost of a reversible mistake.
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From Managing to Owning: Shift focus from coordinating handoffs to being the primary filter for judgment.
The Mandate for the Modern Leader
What is happening to product leadership is the first visible example of a broader transition across white-collar work. Roles built around coordination and documentation were designed for environments where information traveled unevenly. AI collapses those distances. The cost of being wrong drops, but the cost of being indecisive spikes.
In a world of abundant execution, the only remaining scarcity is high-quality judgment. AI has solved the problem of how to build. It has only made the problem of what to build more urgent. For the modern leader, the mandate is clear: stop managing the process and start owning the result.
Expert Perspective Jason M. Riggs is a product executive and operator who has led product and commercial teams at GoPro, Qualcomm, and PAR Technology, and currently serves in a senior product and commercial leadership role at Audivi AI. His work focuses on how AI compresses decision cycles inside organizations and reshapes leadership expectations as execution speed accelerates.
Sources:
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Gartner Top Strategic Technology Trends for 2025: Agentic AI
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Gartner: 5 Data and Analytics Actions for Your Data-Driven Enterprise (2024-2025)
