{"id":84415,"date":"2026-02-18T11:15:22","date_gmt":"2026-02-18T11:15:22","guid":{"rendered":"https:\/\/www.eccouncil.org\/cybersecurity-exchange\/?p=84415"},"modified":"2026-04-01T12:59:28","modified_gmt":"2026-04-01T12:59:28","slug":"why-genai-pilots-fail-how-program-managers-fix","status":"publish","type":"post","link":"https:\/\/www.eccouncil.org\/cybersecurity-exchange\/ai-program-manager\/why-genai-pilots-fail-how-program-managers-fix\/","title":{"rendered":"Why 93% of GenAI Pilots Stall, and How Program Managers Can Fix This"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"84415\" class=\"elementor elementor-84415\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e8d993f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e8d993f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-33f9e70\" data-id=\"33f9e70\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d98a552 elementor-widget elementor-widget-heading\" data-id=\"d98a552\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Why 93% of GenAI Pilots Stall, and How Program Managers Can Fix This<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de0ed1b elementor-widget elementor-widget-post-info\" data-id=\"de0ed1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"post-info.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-inline-items elementor-icon-list-items elementor-post-info\">\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-5dadb57 elementor-inline-item\" itemprop=\"datePublished\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-date\">\n\t\t\t\t\t\t\t\t\t\t<time>February 18, 2026<\/time>\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-45d48a4 elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-custom\">\n\t\t\t\t\t\t\t\t\t\tBrian\u202fC. Newman\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-ca7ce6d elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-custom\">\n\t\t\t\t\t\t\t\t\t\tAI Program Manager\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-19bd879d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"19bd879d\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7580547b\" data-id=\"7580547b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-26abd2e4 elementor-widget elementor-widget-heading\" data-id=\"26abd2e4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Uncomfortable Pattern <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7734eec elementor-widget elementor-widget-text-editor\" data-id=\"7734eec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Across industries, enterprises are running dozens or hundreds of generative AI\u00a0(GenAI)\u00a0pilots. Many look promising in isolation. Most never scale. Internal reviews repeatedly show the same outcome:\u00a0roughly\u00a09\u00a0out of\u00a010\u00a0GenAI pilots\u00a0fail to\u00a0transition into sustained, enterprise-grade capabilities.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is not\u00a0a tooling\u00a0problem. Model quality has improved rapidly,\u00a0cloud platforms are mature, and\u00a0vendors are abundant. The failure pattern is structural and managerial. GenAI is being treated as a\u00a0series\u00a0of experiments rather than as a managed program with accountability, funding discipline, and lifecycle control.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/www.eccouncil.org\/ai-courses\/certified-ai-program-manager-caipm\/\">Program managers<\/a> sit at the center of this gap.\u00a0Pilots\u00a0usually\u00a0stall\u00a0because program mechanics\u00a0are\u00a0never put in place. Ownership is vague,\u00a0risk is deferred,\u00a0and\u00a0operating models are undefined. The work\u00a0remains\u00a0trapped between\u00a0innovation\u00a0theater and production reality.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This article explores why GenAI pilots stall, what trade-offs\u00a0leaders face, and what experienced program managers have learned from navigating this transition repeatedly.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57699785 elementor-widget elementor-widget-heading\" data-id=\"57699785\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why Pilots Stall: The Underlying Dynamics <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6369d017 elementor-widget elementor-widget-heading\" data-id=\"6369d017\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The Accountability Vacuum <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a7b46e6 elementor-widget elementor-widget-text-editor\" data-id=\"7a7b46e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Most GenAI pilots begin with enthusiastic sponsorship from innovation teams, digital labs, or forward-leaning business units. That energy works well for exploration.\u00a0However,\u00a0it creates a predictable problem at\u00a0scale.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">In practice, these questions often go unanswered. Security assumes IT will handle it. IT assumes the business unit that\u00a0sponsored\u00a0the pilot remains accountable. Legal waits to be formally engaged. Risk teams stay in observation mode.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">One common scenario: a customer service pilot demonstrates that GenAI can draft responses faster than human agents.\u00a0The 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">No one has clear authority to answer. The pilot stalls.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32bb25cc elementor-widget elementor-widget-heading\" data-id=\"32bb25cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Success Defined Too Narrowly <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14d4fec elementor-widget elementor-widget-text-editor\" data-id=\"14d4fec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Pilots\u00a0frequently\u00a0declare victory when the model produces plausible outputs.\u00a0While this\u00a0signals\u00a0the model&#8217;s\u00a0technical feasibility,\u00a0it says nothing about enterprise readiness.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">The gap becomes visible when someone asks\u00a0tougher\u00a0questions. 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?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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\u00a0on\u00a0enterprise terms.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59daed01 elementor-widget elementor-widget-heading\" data-id=\"59daed01\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Risk as a Future Problem <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b6cff12 elementor-widget elementor-widget-text-editor\" data-id=\"7b6cff12\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">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\u00a0later, once\u00a0the technology is proven.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">This sequencing backfires. When risk and compliance teams finally engage, they often\u00a0identify\u00a0structural 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">At that point, the choice is rework or abandonment. Momentum collapses either way.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">The\u00a0trade-off\u00a0here is real. Involving risk teams too early can slow exploration and impose constraints that make experimentation difficult. Involving them too late creates expensive failures.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">Risk considerations\u00a0should be designed\u00a0into\u00a0the\u00a0pilot\u00a0scope from the beginning, not as bureaucratic gates but as design constraints. This\u00a0does not\u00a0eliminate\u00a0risk\u00a0completely but\u00a0makes\u00a0it\u00a0manageable.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-531503c0 elementor-widget elementor-widget-heading\" data-id=\"531503c0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Episodic Funding and the Valley of Death <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4801addd elementor-widget elementor-widget-text-editor\" data-id=\"4801addd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">This creates a predictable valley of death.\u00a0A pilot\u00a0succeeds 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\u00a0validate\u00a0before production deployment. Leadership hesitates. The pilot sits in\u00a0limbo.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">One technology company ran a successful pilot using GenAI to generate technical documentation. The pilot\u00a0cost\u00a0a\u00a0modest amount. Scaling required investment in production infrastructure, integration with existing content management systems, and ongoing model maintenance. The business case was positive, but it\u00a0required\u00a0multi-year funding commitments. No single budget owner wanted to carry that responsibility. The pilot never scaled.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">The funding challenge is not purely financial. It is a\u00a0signal\u00a0of 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-738145aa elementor-widget elementor-widget-heading\" data-id=\"738145aa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Operating Model Gap <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d57e7c1 elementor-widget elementor-widget-text-editor\" data-id=\"2d57e7c1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Even when a pilot works and funding is available, a different question arises:\u00a0who will run this in production?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">Traditional IT operating models are built around systems with predictable behavior. GenAI systems behave differently. They degrade over time as data\u00a0distributions 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">A healthcare organization piloted a GenAI system to\u00a0assist\u00a0with 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4235909e elementor-widget elementor-widget-heading\" data-id=\"4235909e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Experienced Program Managers Have Learned <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6bf85dce elementor-widget elementor-widget-text-editor\" data-id=\"6bf85dce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span class=\"TextRun SCXW154048862 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW154048862 BCX0\">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\u00a0<\/span><span class=\"NormalTextRun SCXW154048862 BCX0\">remain<\/span><span class=\"NormalTextRun SCXW154048862 BCX0\">\u00a0difficult.<\/span><\/span><span class=\"EOP SCXW154048862 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-386f077c elementor-widget elementor-widget-heading\" data-id=\"386f077c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Lifecycle Thinking over Pilot Counting <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-55bcbb5e elementor-widget elementor-widget-text-editor\" data-id=\"55bcbb5e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">This does not\u00a0eliminate\u00a0risk or slow innovation as much as\u00a0skeptics\u00a0fear. It imposes clarity. Many ideas are filtered out early, which frees resources for the initiatives that matter. Pilots that\u00a0proceed\u00a0move faster because the path forward is explicit.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-377feae8 elementor-widget elementor-widget-heading\" data-id=\"377feae8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Governance as Enabler, Not Obstacle <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4921b889 elementor-widget elementor-widget-text-editor\" data-id=\"4921b889\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Governance has a reputation for slowing AI adoption. In practice, weak governance is the actual bottleneck.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">When governance is unclear, decisions stall. No one knows who can approve a pilot, who can approve\u00a0its\u00a0scaling, or who has\u00a0the\u00a0authority to shut\u00a0down\u00a0a failing system. Committees form. Meetings multiply. Progress stops.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19ea6d7d elementor-widget elementor-widget-heading\" data-id=\"19ea6d7d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Funding Models That Match Ambition <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ba0efd4 elementor-widget elementor-widget-text-editor\" data-id=\"7ba0efd4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">Pilot funding is positioned as validation capital with defined learning\u00a0objectives. 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">This framing reduces surprise and builds executive confidence. Leaders become more willing to invest when costs, ownership, and accountability are explicit from the start.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53a66b94 elementor-widget elementor-widget-heading\" data-id=\"53a66b94\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Trade-Offs That Remain Challenging <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-412f1ac7 elementor-widget elementor-widget-text-editor\" data-id=\"412f1ac7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">Even experienced program managers acknowledge that certain trade-offs remain difficult.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">Innovation versus standardization creates tension. Experimentation thrives on flexibility, whereas\u00a0enterprise systems require consistency. Different organizations resolve this differently, and no single answer fits all contexts.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ce7a1df elementor-widget elementor-widget-heading\" data-id=\"4ce7a1df\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What This Means for Enterprise Leaders <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-296a9ad0 elementor-widget elementor-widget-text-editor\" data-id=\"296a9ad0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">For senior leaders, the implication is direct. Counting pilots is not progress. Accountability, lifecycle clarity, and committed funding are the actual indicators of readiness.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-49890d3a elementor-widget elementor-widget-heading\" data-id=\"49890d3a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Closing Perspective <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-414e66c8 elementor-widget elementor-widget-text-editor\" data-id=\"414e66c8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span data-contrast=\"auto\">GenAI will not integrate itself into the enterprise quietly. It will either be intentionally managed as a program or perpetually piloted as an experiment.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p><p><span data-contrast=\"auto\">The organizations that break the 93%\u00a0failure pattern are not chasing better tools. They are building better programs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-60368b8b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"60368b8b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2648f7d0\" data-id=\"2648f7d0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-24a62986 tags-cloud elementor-widget elementor-widget-heading\" data-id=\"24a62986\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">About the Author <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-3d98cc54 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d98cc54\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-58bd63ae\" data-id=\"58bd63ae\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7896a5a0 elementor-widget elementor-widget-image\" data-id=\"7896a5a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"811\" height=\"541\" src=\"https:\/\/www.eccouncil.org\/cybersecurity-exchange\/wp-content\/uploads\/2026\/02\/Picture1d.jpg\" class=\"attachment-full size-full wp-image-84413\" alt=\"Brian\u202fC. Newman\" srcset=\"https:\/\/www.eccouncil.org\/cybersecurity-exchange\/wp-content\/uploads\/2026\/02\/Picture1d.jpg 811w, https:\/\/www.eccouncil.org\/cybersecurity-exchange\/wp-content\/uploads\/2026\/02\/Picture1d-300x200.jpg 300w, https:\/\/www.eccouncil.org\/cybersecurity-exchange\/wp-content\/uploads\/2026\/02\/Picture1d-768x512.jpg 768w\" sizes=\"(max-width: 811px) 100vw, 811px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7bdf3be2 elementor-widget elementor-widget-heading\" data-id=\"7bdf3be2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Brian\u202fC. Newman<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e13afeb elementor-widget elementor-widget-text-editor\" data-id=\"3e13afeb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4>Educator &amp; consultant<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-48abcf94\" data-id=\"48abcf94\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-70c5654c elementor-widget elementor-widget-text-editor\" data-id=\"70c5654c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span class=\"TextRun SCXW137714789 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW137714789 BCX0\">Brian\u202fC. Newman\u202fis 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\u202fadvises\u202fenterprises on AI program management, governance, and execution, and <\/span><span class=\"NormalTextRun SCXW137714789 BCX0\">has\u00a0<\/span><span class=\"NormalTextRun SCXW137714789 BCX0\">contributed\u202fto the design and instruction of EC-Council\u2019s CAIPM and CRAGE programs.\u202f<\/span><\/span><span class=\"EOP SCXW137714789 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fc0dc44 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc0dc44\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e380c85\" data-id=\"e380c85\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4fcecc2 elementor-widget elementor-widget-html\" data-id=\"4fcecc2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Person\",\n  \"name\": \"Brian C. 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Many look promising in isolation. Most never scale. Internal reviews repeatedly show the same outcome:\u00a0roughly\u00a09\u00a0out of\u00a010\u00a0GenAI pilots\u00a0fail to\u00a0transition into sustained, enterprise-grade capabilities.\u00a0 This is not\u00a0a tooling\u00a0problem. Model quality&hellip;<\/p>\n","protected":false},"author":33,"featured_media":84582,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":true,"_eb_attr":"","footnotes":""},"categories":[13073],"tags":[],"class_list":{"0":"post-84415","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-program-manager"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.13 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Why 93% of GenAI Pilots Fail &amp; How Program Managers Fix It<\/title>\n<meta name=\"description\" content=\"93% of GenAI pilots fail, but program managers can fix them. 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