# From AI Pilots to AI Products
Why Most AI Initiatives Never Scale—and What the 5% Who Succeed Do Differently
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Abstract
Context: Enterprise AI adoption has reached unprecedented scale. Nearly 90% of organizations now use AI in at least one function, with generative AI pilots proliferating across every industry and geography. Investment intensity continues to accelerate, with organizations committing substantial portions of their technology budgets to AI experimentation.
Problem: Despite this experimentation volume, the overwhelming majority of AI pilots never reach production. MIT's 2025 research found that only 5% of enterprise AI pilots achieve measurable P&L impact. IDC observed that for every 33 AI prototypes launched, only 4 graduate to production—an 88% failure rate. Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by end of 2025. The phenomenon has earned its own label: "pilot purgatory."
Here we argue: That pilot purgatory is not a technical failure but a product failure. Organizations design pilots to demonstrate capability rather than to validate value, integrate workflows, or build adoption. The structural characteristics of successful pilots—technology-centric success metrics, isolated execution, project-based governance—are precisely those that prevent scaling. The 5% who succeed treat pilots as product discovery, not technology demonstrations.
Conclusion: Escaping pilot purgatory requires redesigning how pilots are conceived, measured, and governed. Organizations must shift from "prove the technology works" to "prove the product creates value." This requires product ownership from inception, user-centric success metrics, workflow integration by design, and iteration capability post-launch. The path from pilot to product is not a handoff—it is a continuous evolution that begins before the first line of code.
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1. Introduction: The Pilot Proliferation Paradox
The enterprise AI landscape presents a striking paradox: organizations have never launched more AI pilots, yet the rate at which those pilots reach production continues to decline.
Consider the numbers. In 2025, nearly 90% of organizations report regular AI use, with adoption reaching 72% across multiple business functions. Generative AI pilots have proliferated at unprecedented velocity—a recent Concentrix-Everest Group study of 450+ enterprises found that experimentation rates have never been higher. Investment continues apace, with organizations planning substantial AI budget increases through 2026.
Yet measured by production deployment, the picture inverts. MIT's State of AI in Business 2025 report delivers the sobering verdict: only about 5% of AI pilot programs achieve rapid revenue acceleration. The vast majority stall, delivering little to no measurable impact on P&L. IDC found that 88% of observed AI POCs don't make the cut to widescale deployment. For every 33 AI prototypes a company launched, only 4 graduated to production.
This is pilot purgatory: an organizational state where AI initiatives demonstrate promise in controlled settings but never translate that promise into production value. The phenomenon has become so widespread that industry analysts now treat it as the default expectation rather than an exceptional failure mode.
1.1 The Scale of Stranded Investment
The financial implications are substantial. BCG observed that companies which do scale AI achieve 3x higher revenue impacts (up to 20% of revenue) and 30% higher EBIT compared to those stuck at pilot stage. The opportunity cost of pilot purgatory—measured in foregone efficiency gains, missed revenue opportunities, and competitive disadvantage—compounds annually.
More critically, pilot purgatory creates organizational dysfunction. Teams become cynical about AI initiatives after repeated failures to scale. Executives lose patience with experimentation that produces demonstrations but not outcomes. Technical talent migrates to organizations where their work reaches users. The cumulative effect is an AI capability trap: organizations become increasingly sophisticated at building pilots and increasingly incapable of building products.
1.2 The Misdiagnosis Problem
When pilots fail to scale, organizations typically diagnose technical causes: data quality issues, infrastructure limitations, integration complexity, model performance gaps. These explanations are accurate but incomplete—they describe symptoms rather than root causes.
The fundamental issue is that pilots are designed wrong from inception. They are structured as technology demonstrations rather than product experiments. Their success metrics measure technical achievement rather than user value. Their governance treats scaling as a subsequent phase rather than an inherent design requirement. And their ownership fragments across technical and business functions without anyone accountable for end-to-end value creation.
As Kees Lemmens, CTO (ASEAN) of Microsoft, observed: "Most of these AI initiatives don't fail because of model quality; they fail because the organization isn't ready to use and embrace it." The problem is not AI capability—it is organizational design.
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2. Anatomy of Pilot Purgatory
Understanding why pilots fail requires examining what happens at each stage of the pilot lifecycle—and why the standard approach systematically prevents scaling.
2.1 The Five Failure Modes
Analysis of failed AI pilots reveals five consistent failure modes, each reflecting structural rather than technical deficiencies:
Operational Ops Failure. Most pilots are "scripts put together" lacking observability, rollbacks, or retry mechanisms. They work in controlled demonstration environments but cannot survive production conditions. The handoff from pilot team to operations team fails because operations cannot support or own solutions they didn't design.
Compliance Crisis. Pilots often lack audit trails, PII protection, or model explainability. Because the integration with human-in-the-loop processes hasn't been worked out, compliance teams red-flag projects, halting progress. This is especially acute in regulated industries where 60% of enterprises lead in pilot count but report the lowest rates of pilot-to-scale conversion.
Siloed Execution. Pilots are frequently "bespoke, isolated projects" with no reusable infrastructure behind them. As organizations attempt to scale isolated projects, complexity and cost multiply, driving abandonment. MIT found that internal builds succeed only one-third as often as purchased solutions—reflecting the absence of productization disciplines in most internal pilot efforts.
Trust and Adoption Gaps. Users aren't incentivized or trained to leverage AI systems. More fundamentally, if users don't trust the output, they won't trust the system, and when they don't trust the system, they won't use it. Pilot teams rarely invest in the change management required for adoption because their success metrics don't require adoption.
Ownership Fragmentation. No single leader owns the end-to-end value chain from model development through production operation through business outcome. Data science owns models, IT owns infrastructure, business owns "strategy," and no one owns whether the initiative actually creates value.
2.2 The Structural Root Cause
These failure modes share a common root: pilots are designed to demonstrate capability, not to create value.
The difference is fundamental. A capability demonstration asks: "Can AI do this task?" A value creation experiment asks: "Will users adopt this AI capability, and will it improve business outcomes?" The first question can be answered in isolation with curated data and controlled conditions. The second requires workflow integration, user research, adoption measurement, and iteration based on production feedback.
Standard pilot methodology optimizes for the first question while providing no pathway to answer the second. This explains why pilots can "succeed" technically while failing to scale—they were never designed to scale.
2.3 The Data Behind the Dysfunction
MIT's research quantifies this structural problem:
- 60% of organizations evaluated enterprise-grade AI tools, but only 20% reached pilot stage and just 5% reached production
- Most failures occur due to brittle workflows, lack of contextual learning, and absence of adaptation mechanisms
- Enterprises (firms with over $100M revenue) lead in pilot count but report the lowest pilot-to-scale conversion rates
- Mid-market companies moved faster—top performers reported average timelines of 90 days from pilot to full implementation
The pattern is clear: more resources devoted to pilots does not translate to more successful products. The constraint is not investment—it is approach.
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3. What the 5% Do Differently
If 95% of pilots fail to create value, what distinguishes the 5% that succeed? Research across multiple studies reveals consistent patterns.
3.1 They Start with Business Outcomes, Not Technology Capability
Successful pilots begin with a precise business problem and measurable KPI, not with a technology to deploy. MIT found this is the single most important differentiator: "A pilot without defined KPIs is a research project, not a business initiative."
The contrast is stark:
| Failed Pilot Approach | Successful Product Approach |
|----------------------|----------------------------|
| "Let's see what AI can do for customer service" | "Reduce average handle time by 20% for tier-1 support inquiries" |
| "We should explore AI for document processing" | "Decrease contract review cycle time from 5 days to 1 day" |
| "The business wants to use generative AI" | "Improve lead qualification accuracy by 30%, measured by conversion rate" |
The specificity matters because it determines what gets measured, what gets optimized, and what defines success. Vague objectives produce vague outcomes.
3.2 They Design for Production from Day One
Successful organizations design pilots with production requirements embedded, not as subsequent considerations. This includes:
Infrastructure requirements. What compute, storage, and integration architecture will be needed at scale? Pilots that work on sample datasets often collapse under production load.
Operational requirements. How will the solution be monitored, maintained, and updated? Pilots without observability become production liabilities.
Integration requirements. How will the solution connect to existing systems and workflows? Legacy integration is "one of the most underestimated challenges"—pristine cloud-based pilots often cannot connect to live Core Banking or ERP systems.
Governance requirements. What audit trails, explainability, and compliance controls are required? Pilots that defer these requirements face compliance crises at scale.
BCG's research found that 62% of AI initiatives in "future-built" companies are already deployed, compared to just 12% for laggards. The difference isn't better technology—it's better planning for production from inception.
3.3 They Assign Single-Threaded Product Ownership
The 5% that succeed assign a single accountable owner for end-to-end value creation—not just model development, not just infrastructure, not just business sponsorship, but the complete value chain from development through adoption through business outcome.
McKinsey's research confirms: "High performers are three times more likely than their peers to strongly agree that senior leaders at their organizations demonstrate ownership of and commitment to their AI initiatives." This ownership isn't ceremonial sponsorship—it's operational accountability for results.
The ownership model mirrors product management: one leader responsible for whether the initiative creates value, with authority to make tradeoffs across technical and business considerations.
3.4 They Iterate Based on Production Feedback
Successful AI products don't "launch"—they evolve. The 5% build feedback loops into their designs from inception, creating mechanisms to learn from production usage and improve continuously.
MIT found that "the core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time." The solutions that scale are those that learn from users.
This requires:
- Telemetry to understand how users actually interact with AI capabilities
- Feedback mechanisms for users to correct or improve AI outputs
- Iteration cycles to deploy improvements based on production learning
- Continuous discovery to identify emerging needs and opportunities
3.5 They Invest in Change Management as a First-Class Concern
Technology alone won't drive transformation. The 5% recognize that adoption is the binding constraint, not capability.
Successful organizations invest in:
- **Executive sponsorship** for funding and legitimacy
- **AI literacy programs** to ensure adoption across ranks
- **Internal campaigns** that share success stories and build momentum
- **Enabling rather than forcing adoption**—compelling employees to use AI leads to resistance; enabling and rewarding adoption leads to success
As one Fortune analysis noted: "Companies are failing when they lead with AI and finding success when they lead with the problem they're trying to solve."
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4. The Product Disciplines That Enable Scaling
Translating the success patterns of the 5% into operational practice requires specific product disciplines, applied throughout the pilot-to-product lifecycle.
4.1 Discovery Before Development
Before building anything, successful organizations validate that a real problem exists and that AI is an appropriate solution. This requires:
User research. Who experiences the problem? How do they currently address it? What would improvement look like from their perspective? Pilots that skip this step often solve problems users don't have or solve them in ways users won't adopt.
Opportunity sizing. How large is the potential value? What business metrics will change if the solution succeeds? Pilots without clear value sizing cannot attract the sustained investment required for scaling.
Solution fit assessment. Is AI the right solution for this problem? Could simpler approaches work? AI should be deployed where it creates unique advantage, not as a default technology choice.
Teresa Torres's continuous discovery framework applies directly: "Discovery shouldn't be something you do once at the start of a project. It should be a weekly rhythm."
4.2 Hypothesis-Driven Experimentation
Pilots should be structured as experiments testing explicit hypotheses, not as demonstrations of capability. Each pilot should articulate:
- **The hypothesis:** "We believe that [AI capability] will [improve outcome] for [user segment] because [rationale]"
- **The success criteria:** "We will know this is true if [measurable outcome] improves by [threshold] within [timeframe]"
- **The learning agenda:** "Regardless of outcome, we will learn [insights] that inform next steps"
This framing transforms pilots from binary success/failure judgments into learning investments that generate value regardless of outcome.
4.3 Minimum Viable Product Thinking
Rather than building complete solutions, successful organizations deploy minimum viable products (MVPs) that test core value hypotheses with real users.
For AI products, this means:
- Deploying to a limited user segment rather than enterprise-wide
- Starting with human-in-the-loop processes that can catch AI errors
- Instrumenting thoroughly to understand actual usage patterns
- Planning iteration cycles before launch
The goal is not to demonstrate what AI can do but to learn whether users will adopt it and whether adoption creates value.
4.4 Integration by Design
Workflow integration cannot be an afterthought—it must be designed from inception.
This requires deep understanding of:
- How current workflows operate and where AI fits
- What existing systems must be integrated and how
- What human roles remain and how they interact with AI
- What failure modes exist and how they're handled
Deloitte's Tech Trends 2026 report emphasizes: "The challenge isn't technology, it's that enterprises are trying to automate existing processes designed for humans rather than redesigning them for AI-first operations."
4.5 Governance That Enables Iteration
Traditional governance—stage gates, approval committees, compliance checkpoints—often kills AI initiatives by making iteration prohibitively expensive.
Product-led governance enables rather than prevents scaling:
- **Risk guardrails** rather than approval gates
- **Continuous monitoring** rather than periodic review
- **Outcome accountability** rather than compliance certification
- **Escalation triggers** based on signals rather than schedules
The goal is responsible deployment that can evolve with evidence, not frozen artifacts that cannot adapt.
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5. The Pilot-to-Product Transition Framework
Moving from pilot to product requires a structured approach that addresses organizational, technical, and adoption challenges simultaneously.
5.1 Phase 1: Foundation (Weeks 1-4)
Establish strategic intent. Identify 3-5 high-value use cases tied to business outcomes. Appoint executive sponsors. Define success metrics and readiness criteria.
Assess readiness across three pillars:
- Data readiness: quality, lineage, governance, accessibility
- Technology readiness: infrastructure for scalable training, serving, monitoring
- Talent readiness: skills across data engineering, MLOps, compliance, domain expertise
Assign product ownership. Designate a single accountable leader for end-to-end value creation.
5.2 Phase 2: Validation (Weeks 5-12)
Deploy minimum viable product. Launch to limited user segment with human-in-the-loop safeguards.
Instrument for learning. Implement telemetry to understand usage patterns, feedback mechanisms for user corrections, and metrics dashboards for outcome tracking.
Iterate rapidly. Deploy improvements weekly based on production feedback. Document learnings regardless of outcome.
Validate scaling path. Confirm that technical architecture, operational processes, and governance frameworks can support enterprise deployment.
5.3 Phase 3: Scaling (Weeks 13-26)
Expand deployment systematically. Move from pilot segment to additional user groups, measuring adoption and outcomes at each stage.
Build organizational capability. Train users, develop support processes, create documentation and knowledge bases.
Establish operational excellence. Implement MLOps practices for continuous monitoring, model retraining, and incident response.
Measure and communicate value. Track business outcome metrics, calculate ROI, and communicate results to maintain executive support.
5.4 Phase 4: Optimization (Ongoing)
Continuous improvement. Maintain discovery cadence to identify new opportunities. Iterate based on user feedback and changing requirements.
Expand scope. Extend successful patterns to additional use cases and business domains.
Build reusable infrastructure. Create platforms and tools that accelerate future AI products.
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6. Case Study: From Pilot Purgatory to Production Value
A large Southeast Asian bank launched an AI initiative for document processing—specifically, automating the review of loan applications. The pilot demonstrated impressive technical results: 90%+ accuracy in extracting key fields, significant reduction in processing time under controlled conditions.
Yet six months after pilot "completion," production deployment remained stalled.
6.1 Diagnosis
Analysis revealed the classic pilot purgatory failure modes:
- **No production design.** The pilot ran on cleaned, standardized documents. Production documents were messy, inconsistent, and came from multiple legacy systems with incompatible formats.
- **No operational capability.** The pilot team had built a script, not a service. No monitoring, no error handling, no maintenance procedures.
- **No user adoption.** Loan officers hadn't been consulted during design. They didn't trust the AI outputs and were manually re-checking everything, eliminating efficiency gains.
- **No compliance approval.** The pilot lacked audit trails required by regulators. Compliance had blocked deployment pending months of remediation.
6.2 The Pivot
The bank shifted from a POC mindset to a product mindset:
Product ownership. A single leader was assigned accountability for end-to-end value creation, with authority to make tradeoffs across technical and business considerations.
Outcome focus. Success metrics shifted from "accuracy percentage" to "time-to-decision reduction" and "loan officer adoption rate."
Production design. The team rebuilt with production requirements: integration with legacy systems, comprehensive error handling, monitoring and alerting, audit trails for compliance.
User-centric design. Loan officers were involved in design decisions. The system was positioned not as "replacing" officers but as "augmenting" them with AI assistance. Traceable citations showed where AI conclusions came from, building trust.
Feedback loops. Users could flag errors and corrections, which fed back into model improvement.
6.3 The Result
Once employees saw the system was transparent and traceable, they shifted from skeptics to "AI Champions," driving peer-to-peer training and widespread adoption.
Time-to-decision decreased by 60%. Loan officer satisfaction increased. And the bank had built organizational capability—a repeatable approach—for future AI products.
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7. Implications for Leaders
7.1 For Product Leaders
Own AI products end-to-end. Don't accept fragmented ownership where data science builds models and "someone else" worries about adoption. If you own outcomes, you must own the complete value chain.
Apply product disciplines rigorously. Discovery, hypothesis testing, MVP deployment, iteration—these disciplines work for AI as they work for any product. Don't let the technology mystique prevent applying proven approaches.
Design for production from day one. Every technical decision in a pilot should ask: "Will this work at scale?" If the answer is unclear, the pilot is accumulating technical debt that will prevent scaling.
7.2 For Executives
Demand outcome metrics. Don't accept "95% accuracy" as success. Ask: "What business outcome improved? By how much? How do we know?" If teams can't answer these questions, the pilot hasn't validated value.
Assign single-threaded ownership. The fragmented ownership model guarantees pilot purgatory. Designate leaders accountable for end-to-end value, with authority commensurate with responsibility.
Fund the full lifecycle. Pilots require discovery, development, deployment, adoption, and iteration. Funding only development creates pilots that can't scale. Budget for the complete journey.
Measure pilot-to-production conversion. Track how many pilots reach production and what value they create. If the conversion rate is low, fix the structural problems rather than launching more pilots.
7.3 For Technical Leaders
Build for operations. The gap between "working script" and "production service" is where most pilots die. Invest in MLOps, observability, and operational capability from inception.
Design for integration. Legacy system integration is consistently underestimated. Understand integration requirements early and design for them explicitly.
Enable iteration. AI products must evolve. Build deployment pipelines that enable rapid updates. Implement feedback loops that capture user corrections. Create monitoring that detects drift.
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8. Conclusion: Pilots Are Not Products
The path from pilot to product is not a handoff—it is a transformation. Pilots and products differ in fundamental ways:
| Dimension | Pilot | Product |
|-----------|-------|---------|
| Goal | Demonstrate capability | Create value |
| Success metric | Technical performance | Business outcome |
| User integration | Optional | Essential |
| Production design | Deferred | Embedded |
| Ownership | Fragmented | Single-threaded |
| Timeline | Fixed endpoint | Continuous evolution |
Organizations that treat pilots as precursors to products—assuming successful demonstrations automatically lead to successful deployments—will remain in pilot purgatory. The 95% failure rate is not bad luck; it is the predictable result of a structurally flawed approach.
The 5% who escape pilot purgatory do so by rejecting the pilot paradigm entirely. They don't run pilots—they build products, starting with discovery, designing for production, measuring outcomes, and iterating based on feedback.
The question for leaders is not "How do we run better pilots?" but "How do we build AI products that create value?" The answer requires different methods, different metrics, different ownership, and different governance.
Pilot purgatory is a choice. Escaping it requires choosing differently.
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Extended References
- MIT NANDA Initiative. (2025). *The GenAI Divide: State of AI in Business 2025*. Massachusetts Institute of Technology. Primary source for pilot failure statistics and success factors across 150 interviews and 300 public AI deployments.
- McKinsey & Company. (2025). *The State of AI in 2025: Agents, Innovation, and Transformation*. McKinsey Global Institute. Comprehensive survey of 1,993 participants revealing scaling challenges and high-performer characteristics.
- Boston Consulting Group. (2025). *The Widening AI Value Gap: Build for the Future 2025*. BCG research showing future-built firms achieve 1.7x revenue growth and 3.6x TSR versus laggards.
- Concentrix & Everest Group. (2025). *Turning AI Ambition into Enterprise-Scale Impact*. Study of 450+ enterprises identifying that only 27% have successfully moved GenAI from testing to implementation.
- IDC & Lenovo. (2025). *AI POC to Production Study*. Research finding 88% of observed AI POCs don't reach production deployment.
- Gartner. (2025). *Predicts 2025: AI Projects and Production Scaling*. Prediction that 30% of generative AI projects will be abandoned after proof of concept.
- KPMG. (2025). *From Pilots to Production: How to Scale Your AI*. Framework for addressing technical, operational, and cultural scaling barriers.
- Microsoft. (2025). *Enterprise AI Maturity in Five Steps*. Guide from Microsoft Digital AI Center of Excellence on pilot-to-production transformation.
- Torres, T. (2021). *Continuous Discovery Habits*. Product Talk LLC. Framework for continuous discovery applicable to AI product development.
- Cagan, M. (2018). *Inspired: How to Create Tech Products Customers Love*. Wiley. Product management principles applicable to AI product ownership and value creation.
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Appendix
A. Pilot-to-Product Diagnostic
Use these questions to assess whether your AI initiative is positioned for production:
Outcome Clarity
- [ ] Is there a specific, measurable business outcome defined?
- [ ] Is there an accountable owner for that outcome?
- [ ] Are success criteria quantified and timebound?
Production Design
- [ ] Will the technical architecture scale to production volumes?
- [ ] Are operational processes defined (monitoring, maintenance, incident response)?
- [ ] Is legacy system integration planned and feasible?
- [ ] Are compliance and governance requirements addressed?
User Integration
- [ ] Have users been involved in design?
- [ ] Is the workflow integration clearly defined?
- [ ] Are adoption metrics tracked?
- [ ] Is change management planned?
Iteration Capability
- [ ] Are feedback mechanisms built in?
- [ ] Can updates be deployed rapidly?
- [ ] Is there telemetry to understand usage?
- [ ] Is there budget for post-launch iteration?
B. Pilot vs. Product Comparison
| Characteristic | Pilot Approach | Product Approach |
|---------------|---------------|------------------|
| Primary question | "Can AI do this?" | "Will users adopt this and create value?" |
| Success metric | Model accuracy | Business outcome improvement |
| Data environment | Curated, clean | Production, messy |
| User involvement | Demo audience | Co-design partners |
| Integration | Simulated | Live systems |
| Operations | Ad hoc | Productionized |
| Timeline | Fixed end date | Continuous evolution |
| Ownership | Team delivers, business accepts | Single owner for value |
| Governance | Stage gate approval | Continuous monitoring |
| Learning | Pass/fail judgment | Hypothesis validation |
C. Common Failure Patterns and Remediation
| Failure Pattern | Symptoms | Remediation |
|-----------------|----------|-------------|
| Operational Fragility | Works in demo, fails in production | Design for operations from day one; invest in MLOps |
| Compliance Blocker | Pilot blocked by risk/legal/compliance | Include compliance requirements in pilot scope |
| Adoption Resistance | Users don't use deployed system | Involve users in design; invest in change management |
| Integration Failure | Cannot connect to production systems | Assess integration requirements early; prototype integration |
| Ownership Vacuum | No one accountable for outcomes | Assign single-threaded product owner |
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Glossary
Pilot Purgatory: The organizational state where AI initiatives demonstrate promise in controlled settings but never translate that promise into production value, typically characterized by repeated pilots that fail to scale.
Single-Threaded Ownership: Assignment of one leader accountable for end-to-end value creation, with authority to make tradeoffs across technical, operational, and business considerations.
Production Design: The practice of embedding production requirements—scalability, integration, operations, compliance—into pilot design from inception rather than deferring them as subsequent concerns.
MLOps: Machine Learning Operations; the practices and infrastructure required to deploy, monitor, maintain, and update machine learning models in production environments.
Change Management: The structured approach to transitioning individuals, teams, and organizations to a desired future state, particularly important for AI adoption where user behavior change is required.
Shadow AI: Unsanctioned use of AI tools by employees, typically consumer tools like ChatGPT used for work tasks without organizational approval or oversight.
AI Factory: An organizational model where centralized governance, security, and responsible AI policies (the "hub") enable distributed domain teams (the "spokes") to build AI products under consistent frameworks.
Minimum Viable Product (MVP): The smallest version of a product that can be deployed to real users to test core value hypotheses and generate learning.
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Author's Notes: Lessons from the Pilot Graveyard
*I have reviewed hundreds of AI pilots across my career. The pattern is depressingly consistent: impressive demonstrations that never reach production, talented teams that never create value, substantial investments that never return.*
*The most instructive conversations happen with the teams behind failed pilots. They rarely blame technology. Instead, they describe organizational dysfunction: unclear ownership, misaligned metrics, deferred production requirements, absent change management, governance that prevented iteration.*
*One conversation crystallized the problem. A data science lead showed me a model with excellent performance metrics—accuracy, precision, recall all impressive. I asked how many users were using it in production. The answer: none. The model had been "completed" for eight months but was still waiting for IT integration, compliance approval, and "business readiness."*
*I asked who was accountable for getting it to production. The answer was illuminating: "Well, we own the model, IT owns integration, compliance owns approval, and the business owns readiness." In other words, no one owned the outcome.*
*This fragmented ownership is the root cause of pilot purgatory. It allows each function to succeed by its own metrics while the initiative fails by the only metric that matters: value creation.*
*The solution is not more pilots, better technology, or increased investment. It is structural: single-threaded ownership, outcome-based metrics, production design from inception, and governance that enables iteration.*
*I've seen organizations escape pilot purgatory. Without exception, they did so by fundamentally changing how they approach AI initiatives—not as technology demonstrations but as product development. They assigned owners accountable for value. They measured business outcomes, not technical performance. They designed for production from day one. They iterated based on user feedback.*
*The technology is not the hard part. The organizational transformation is. But organizations that make that transformation—the 5%—create substantial value while competitors remain trapped in perpetual experimentation.*
*The choice is stark: transform your approach or continue piloting indefinitely. Pilot purgatory is optional.*
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*This article is the second in the Product × AI series. Previous: "AI Strategy Is Product Strategy." Next: "AI Value Realization vs AI Adoption: Why Usage Doesn't Equal Impact"*