Why 93% of GenAI Pilots Stall, and How Program Managers Can Fix This
- Ethical Hacking
The Uncomfortable Pattern
Across industries, enterprises are running dozens or hundreds of generative AI (GenAI) pilots. Many look promising in isolation. Most never scale. Internal reviews repeatedly show the same outcome: roughly 9 out of 10 GenAI pilots fail to transition into sustained, enterprise-grade capabilities.
This is not a tooling problem. Model quality has improved rapidly, cloud platforms are mature, and vendors are abundant. The failure pattern is structural and managerial. GenAI is being treated as a series of experiments rather than as a managed program with accountability, funding discipline, and lifecycle control.
Program managers sit at the center of this gap. Pilots usually stall because program mechanics are never put in place. Ownership is vague, risk is deferred, and operating models are undefined. The work remains trapped between innovation theater and production reality.
This article explores why GenAI pilots stall, what trade-offs leaders face, and what experienced program managers have learned from navigating this transition repeatedly.
Why Pilots Stall: The Underlying Dynamics
The Accountability Vacuum
Most GenAI pilots begin with enthusiastic sponsorship from innovation teams, digital labs, or forward-leaning business units. That energy works well for exploration. However, it creates a predictable problem at scale.
When a pilot shows early promise, the question of ownership becomes urgent. Who decides whether to invest in production integration? Who owns the outcome if the system fails? Who manages the model once it is live?
In practice, these questions often go unanswered. Security assumes IT will handle it. IT assumes the business unit that sponsored the pilot remains accountable. Legal waits to be formally engaged. Risk teams stay in observation mode.
One common scenario: a customer service pilot demonstrates that GenAI can draft responses faster than human agents. The pilot team celebrates. Then, someone asks whether the company is comfortable with the AI making commitments on behalf of the brand. They ask whether regulators will accept AI-generated correspondence in certain contexts. They may also ask who will be responsible if the system produces a harmful or misleading response.
No one has clear authority to answer. The pilot stalls.
Success Defined Too Narrowly
Pilots frequently declare victory when the model produces plausible outputs. While this signals the model’s technical feasibility, it says nothing about enterprise readiness.
The gap becomes visible when someone asks tougher questions. Does the output change a business decision, or does it require so much human review that efficiency gains disappear? Is the error rate acceptable given the consequences of mistakes? Can the system be audited if regulators or auditors ask how a decision was made? What happens when the model fails in production, and how will that failure be detected?
A financial services firm ran a pilot using GenAI to summarize loan application documents. The summaries looked reasonable, but when the compliance team reviewed the outputs, they found that the model occasionally omitted critical risk flags that were buried in dense paragraphs. The error rate was low, but the consequences were unacceptable. The pilot had succeeded on its own terms. It failed on enterprise terms.
Without explicit success criteria tied to business outcomes, risk tolerance, and operational realities, pilots linger in ambiguity. Leaders cannot justify the investment to scale because the definition of success keeps shifting.
Risk as a Future Problem
In many stalled pilots, risk discussions are postponed. Privacy implications, bias potential, explainability requirements, data lineage, and regulatory exposure are treated as issues to resolve later, once the technology is proven.
This sequencing backfires. When risk and compliance teams finally engage, they often identify structural issues that cannot be patched. The pilot may have been trained on data that cannot legally be used at scale. The model may lack the explainability that regulators require. The system may introduce bias that creates legal or reputational exposure.
At that point, the choice is rework or abandonment. Momentum collapses either way.
The trade-off here is real. Involving risk teams too early can slow exploration and impose constraints that make experimentation difficult. Involving them too late creates expensive failures.
Risk considerations should be designed into the pilot scope from the beginning, not as bureaucratic gates but as design constraints. This does not eliminate risk completely but makes it manageable.
Episodic Funding and the Valley of Death
Pilots are often funded as one-time experiments. There is a budget for a proof of concept, but there is no committed funding for integration, infrastructure, ongoing operations, or lifecycle management.
This creates a predictable valley of death. A pilot succeeds technically. The team requests funding to scale. Finance asks for a business case. The business case depends on assumptions about adoption, efficiency gains, and cost structures that are difficult to validate before production deployment. Leadership hesitates. The pilot sits in limbo.
One technology company ran a successful pilot using GenAI to generate technical documentation. The pilot cost a modest amount. Scaling required investment in production infrastructure, integration with existing content management systems, and ongoing model maintenance. The business case was positive, but it required multi-year funding commitments. No single budget owner wanted to carry that responsibility. The pilot never scaled.
The funding challenge is not purely financial. It is a signal of organizational commitment. Leaders who fund pilots episodically are implicitly treating GenAI as optional. Leaders who fund programs with committed scale budgets are making a strategic choice.
The Operating Model Gap
Even when a pilot works and funding is available, a different question arises: who will run this in production?
Traditional IT operating models are built around systems with predictable behavior. GenAI systems behave differently. They degrade over time as data distributions shift. They require periodic retraining. They produce outputs that need human review in some contexts but not others. They create new categories of incidents that existing runbooks do not cover.
A healthcare organization piloted a GenAI system to assist with clinical documentation. The pilot worked. Yet, when IT reviewed operational requirements, they realized they had no process for monitoring model performance degradation, no escalation path for ambiguous outputs, and no clear answer about who was responsible when the system suggested something that contradicted clinical guidelines.
The lack of an operating model is not a technical problem. It is an organizational design problem. In the absence of clarity, risk-averse organizations default to inaction.
What Experienced Program Managers Have Learned
Program managers who have navigated this transition repeatedly share common observations. They do not claim to have universal solutions. They describe what has worked in specific contexts and where trade-offs remain difficult.
Lifecycle Thinking over Pilot Counting
The most experienced program managers stopped counting pilots as a measure of progress years ago. They learned that pilot velocity without transition discipline creates the illusion of momentum while masking systemic failure.
Instead, they think in terms of lifecycle stages. A use case is qualified before a pilot begins. The pilot has explicit exit criteria. A structured readiness assessment happens before scaling. Production integration includes defined operational handoffs. Continuous oversight is designed into the operating model, not added later.
This does not eliminate risk or slow innovation as much as skeptics fear. It imposes clarity. Many ideas are filtered out early, which frees resources for the initiatives that matter. Pilots that proceed move faster because the path forward is explicit.
Governance as Enabler, Not Obstacle
Governance has a reputation for slowing AI adoption. In practice, weak governance is the actual bottleneck.
When governance is unclear, decisions stall. No one knows who can approve a pilot, who can approve its scaling, or who has the authority to shut down a failing system. Committees form. Meetings multiply. Progress stops.
Experienced program managers apply governance differently. They focus on decision rights, not process documentation. They assign approvals to named roles. They design lightweight controls that focus on outcomes rather than paperwork. They map GenAI risk to existing enterprise risk categories so that executives can make informed trade-offs rather than defaulting to avoidance.
One global manufacturer implemented a simple control: any GenAI system that makes decisions affecting customer commitments requires human-in-the-loop review until the error rate drops below a defined threshold. That single rule unlocked multiple pilots that had been stalled in risk review. It was not permissive. It was precise.
Funding Models That Match Ambition
Program managers who succeed at scaling GenAI change the funding conversation. Instead of requesting pilot budgets and hoping for future approvals, they present phased investment models.
Pilot funding is positioned as validation capital with defined learning objectives. Scale funding is tied to approved business cases with explicit cost and benefit assumptions. Run funding is treated as an operating expense, not a project cost.
This framing reduces surprise and builds executive confidence. Leaders become more willing to invest when costs, ownership, and accountability are explicit from the start.
The Trade-Offs That Remain Challenging
Even experienced program managers acknowledge that certain trade-offs remain difficult.
Speed versus control is real. Moving fast on pilots requires tolerance for ambiguity. Scaling safely requires structure. Finding the right balance depends on risk appetite, regulatory context, and organizational culture.
Innovation versus standardization creates tension. Experimentation thrives on flexibility, whereas enterprise systems require consistency. Different organizations resolve this differently, and no single answer fits all contexts.
Centralized versus distributed ownership is contested. Some organizations succeed with centralized AI centers of excellence. Others empower business units to move independently within guardrails. The right model depends on organizational structure, talent distribution, and leadership philosophy.
What This Means for Enterprise Leaders
The evidence is consistent. Most GenAI pilots do not fail because the technology is immature. They fail because the organizational mechanics required to scale them were never built.
For senior leaders, the implication is direct. Counting pilots is not progress. Accountability, lifecycle clarity, and committed funding are the actual indicators of readiness.
For program managers, the opportunity is significant. The skill set required to translate GenAI ambition into disciplined execution is in short supply. Those who can navigate the organizational complexity, frame the trade-offs clearly, and build the program mechanics that enable scale become central to enterprise transformation.
Closing Perspective
GenAI will not integrate itself into the enterprise quietly. It will either be intentionally managed as a program or perpetually piloted as an experiment.
The organizations that break the 93% failure pattern are not chasing better tools. They are building better programs.
About the Author
Brian C. Newman
Brian C. Newman is a senior technology and AI program practitioner with more than 30 years of experience leading large-scale transformation across telecommunications, network operations, and emerging technologies. He has held multiple senior leadership roles at Verizon, spanning global network engineering, systems architecture, and operational transformation. Today, he advises enterprises on AI program management, governance, and execution, and has contributed to the design and instruction of EC-Council’s CAIPM and CRAGE programs.
