From pilot fatigue to production: How to make AI work for your business

“AI pilots are everywhere but business impact often isn’t.” This quip captures a frustrating reality: Despite significant investment, most AI proofs of concept and pilot projects never deliver real value at scale.

This “pilot fatigue” is widespread. Over 70% of AI initiatives stall before moving beyond the pilot phase, with . Every stalled pilot represents wasted investment, lost time and missed opportunities in an era where AI could be a decisive competitive differentiator. The question for executive leadership is clear: How do we break out of pilot purgatory and achieve tangible ROI?

Why AI pilot projects often fail to scale

The core issue isn't AI technology itself but its integration into the broader business. Common barriers include:

• Lack of clear business outcomes: Projects often begin as tech explorations without defined outcomes or specific problems to be solved. Without clear ROI, management halts projects. AI for AI’s sake rarely secures a production budget; projects must tie to real business problems and deliver on KPIs such as increased revenue or cost savings.
• Foundational data and integration challenges: AI needs clean, complete and accessible data. While pilot projects use static datasets, scaling requires robust data pipelines and quality management. Forbes suggests up to 85% of AI projects fail due to poor data quality. Integrating AI with complex legacy systems also presents significant hurdles.
• Governance, compliance and trust issues: “Soft factors” such as regulatory compliance, ethical implications (bias) and data security frequently impede progress. Without robust, responsible AI practices (explainability, bias testing, security controls) and a framework for accountability, organizations hesitate to roll out solutions.
• Talent and change management gaps: Scaling AI demands interdisciplinary expertise and effective change management, underpinned by leadership courage to guide teams through unfamiliar territory. Many organizations face a skills gap. Employees, as users, require training, buy-in and confidence; without these, even deployed AI may sit unused.

These roadblocks are often interconnected, ensuring pilots remain pilots.

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From experimentation to execution: Shifting the mindset

Breaking free from pilot purgatory requires treating AI projects as strategic business deployments, not isolated experiments. The shift is from “Let’s see what this cool AI can do” to “Let’s solve XYZ business problem, and we believe AI is the optimal solution.”

Abhijit Dubey, NTT DATA’s CEO and Chief AI Officer, has emphasized this human-centric, value-focused approach, saying that successful AI deployment is about creating a new relationship between technology and people, not replacing people with technology. This means engaging users early, designing integrated solutions and having a systematic plan from idea to production.

Leading organizations establish “AI factories” with clear stage gates, filtering out weak projects and concentrating resources. Executive sponsorship and cross-departmental collaboration are nonnegotiable, with business owners championing projects. Organizations with high AI success rates consistently demonstrate strong C-suite support and dedicated AI governance boards. AI projects must be managed with the same rigor as any business-critical implementation.

Five pillars to make AI work (and scale) in your business

Organizations successfully transitioning AI from pilot to production consistently leverage five core strategies:

1. Start with value, not technology: Prioritize business value. Instead of “What can we do with computer vision?”, rather ask, “Where is a critical business problem or opportunity that AI can address?” — and only then, “What is the right, fit-for-purpose technology to solve it?” Identify use cases with clear, measurable ROI and define success metrics upfront.
2. Take an end-to-end perspective: Develop the solution within the context of the full process and the broader business model. Solving only one step often creates downstream constraints, disjointed experiences or inefficiencies elsewhere, resulting in limited enterprise value. Solving with a comprehensive, end-to-end view significantly increases the ability to deliver measurable business value.
3. Build scalable foundations: A successful pilot project is merely the first step; the foundation for scaling must be robust. This pillar focuses on technology and process readiness: data infrastructure, architecture and machine-learning operations (MLOps). Ensure data is accessible, high-quality and updated in real time. Invest in necessary cloud or edge infrastructure. Implement MLOps pipelines (DevOps for AI) for model versioning, retraining and performance monitoring, ensuring reliable, continuously improving deployed models. Integration is also key; design AI solutions to plug seamlessly into existing workflows.
4. Embed responsible AI: Even a powerful, well-integrated AI will fail if people don’t trust it. This pillar involves embedding responsible AI practices and clear accountability frameworks. Responsible AI encompasses governance, ethics (fairness and transparency) and risk mitigation (privacy and security). Addressing these early, with compliance teams involved, clears the path for scale.
5. Drive user adoption through bold leadership and change management: AI only delivers value when people use it. Effective adoption requires courageous leadership to steer the organization through uncertainty, communicate a clear purpose and build confidence in the technology. Preparing employees, clarifying evolving roles and investing in training are critical. Explainability — showing why the AI makes a decision — is crucial for building trust.

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Real-world proof: NTT DATA overcomes pilot-project fatigue

NTT DATA helped a global insurer transform their claims review process, a prime example of moving an AI solution from pilot project to production. Property and casualty claims adjusters faced hundreds of pages of medical records per case, leading to time-intensive manual sifting, delays and human error. The insurer knew AI could help but was stuck in “pilot mode.”

NTT DATA built a GenAI-powered chat assistant for the claims adjusters. This domain-trained AI, using our GenAI TechHub tools and accelerators, integrated with existing systems (deployed in the client’s secure Azure OpenAI environment) and parsed over 500 electronic medical records. Adjusters could now ask natural language questions and receive instant, accurate answers with reference links to source documents, building crucial trust and transparency.

The pilot project showed immediate, compelling results: Adjusters’ productivity improved by roughly 70%, turning hours of manual review into minutes, while decision-making accuracy and speed increased. This convinced leadership to greenlight a full rollout. NTT DATA industrialized the solution by integrating it with the claims management system, setting up access controls and establishing MLOps for monitoring and retraining. A comprehensive architecture plan and roadmap for future enhancements were also provided, creating an “AI playbook.”

Today, the AI assistant is in daily use worldwide — a true production deployment. Enterprise-wide adoption is high, with adjusters praising its speed, accuracy and user-friendly interface. What began as a single-team pilot project is now a standard tool for thousands, with similar GenAI assistants being explored for underwriting and customer service.

Business impact is tangible: By slashing manual research time, the insurer estimates handling higher claim volumes without adding headcount, translating into multimillion-dollar operational savings. Improved accuracy reduced rework and enhanced customer satisfaction. The AI solution started paying for itself within months of full rollout.

This case study illustrates how NTT DATA helps clients break pilot-project purgatory by:

• Focusing on a clear business problem: Speeding up claims reviews with measurable KPIs
• Rolling out a rapid but rigorous pilot project: Delivering a working proof of concept quickly, using accelerators and prioritizing data security
• Compiling a scalability and adoption plan: Planning for scale and user adoption from the start, including trust-building features and training
• Focusing on responsible AI and governance: Early involvement of compliance and IT, with features such as source citations and a controlled cloud environment

As Dubey has observed, successful AI projects require connecting the technology to real business outcomes and building trust with the people who use it. This case hit both marks, embedding AI sustainably and building internal confidence for broader AI adoption.

From pilot projects to impact

Making AI work for your business means treating AI projects as strategic business transformations, not science experiments. Pilot-project fatigue signals a need for mature approaches. Organizations overcoming this fatigue follow a consistent playbook:

• Target meaningful problems and measure outcomes
• Lay the groundwork to scale (data, integration and MLOps)
• Inspire confidence (ethics and governance)
• Drive adoption (leadership and change management)
• Build scalable forward-looking capability (“AI factory” and innovation funnel)

The payoff is immense. NTT DATA’s research indicates leading AI organizations focus on high-value use cases with an end-to-end approach. They’ve woven AI into their business strategies and operations, moving beyond isolated pilot projects.

For organizations stuck in proofs of concept, the message is encouraging: You can break the pilot-project cycle. With a value-first vision, foundational investments and responsible innovation, AI can transition from lab curiosity to a dependable engine of business value. It’s time to move beyond experiments and make AI work for your business — responsibly, at scale and with real, measurable impact. Each pilot project graduating to production is a strategic step toward a smarter, more competitive enterprise.