July 18, 2025 Building A Strategy for GenAI Success In Manufacturing

AI is transforming business operations, yet manufacturers struggle to uncover tangible business outcomes per NTT DATA’s latest report.

By Prasoon Saxena, Global President, Products Industries, NTT DATA

GenAI has opened a new world of opportunities for organizations that have taken the leap into leveraging Al technology to transform business operations. It helps them augment and automate decisions, design better products, discover new revenue streams and optimize their operations. But for those in earlier stages, there remain challenges in translating Al’s potential into tangible business outcomes, without the right vision and support to undertake such a journey. I recently participated in a fireside chat with Blake Moret at the National Association of Manufacturers (NAM). Our aim was to chart some of the key steps in building a successful GenAI strategy that helps businesses evolve successfully.

Why GenAI, and why now?

The progression from traditional Al to GenAI, and now to emerging forms like agentic Al, is opening new opportunities to rethink processes that will enhance operational agility and unlock greater value across the entire value chain. This progression is not solely the result of advances in Al technology. Digital transformation to modernize legacy technology, combined with progressing trends in cost, hypercompute capabilities and connectivity, have made Al technologies more accessible and scalable. This has driven exponential adoption and value generation.

Insights from the recent NTT DATA Global GenAI Report reflect this shift with 94% of manufacturers planning to increase GenAI investments over the next two years. Why? Because manufacturers that successfully integrate Al into their core operations are already seeing gains. These applications range from basic automation of routine tasks to wide-reaching and high-impact applications in engineering cycle time reduction, product formulations, simulated manufacturing optimizations and supply chains optimization that would not have been possible a few short years ago.

From proof of concept to real-world impact

While manufacturers with mature Al foundations are moving confidently into deployment, others are still working to establish the structures and strategies needed to move beyond experimentation. For example, they may be running isolated pilots without a clear roadmap to scale. But a closer look at early adopters reveals clear, repeatable patterns for turning proofs of concept into enterprise-wide impact:

1. They define a clear Al strategy aligned with business goals.
2. They prioritize high-value use cases that can create sustained value, such as supply chain optimization and Al-driven product design
3. They ensure data readiness, recognizing that high-quality, diverse and clean data is essential for Al success.
4. They embed generative Al in their human augmentation strategy, together with human enablement and change management.
5. They integrate Al with existing infrastructure rather than treating it as a standalone initiative.

To accelerate outcomes, many manufacturers are turning to experienced partners that take a business-first approach. These partners offer a full stack of technology capabilities, combined with domain expertise, to achieve slated business goals. They help enterprises through the innovation process and scaling at an enterprise level while keeping the human element at the center of this transformation.

Balancing innovation, risk and investment

CEOs and boards are eager to accelerate GenAI adoption and unlock business outcomes like faster product cycles, smarter supply chains and stronger customer engagement. At the same time, they are weighing the risks associated with not having the right controls in place. As with any innovative technology, experimentation and adoption are driving clarity that alleviates early apprehensions associated with unknowns. Early AI adopters, through learning, acquaintance and the right partnerships, have found successful strategies to balance innovation with risk management.

The core of these strategies is building governance into broader strategic frameworks that connect Al oversight to measurable business value. This means embedding responsible Al practices into how an organization defines goals, deploys solutions and evaluates outcomes. Making governance part of the business plan makes innovation both safer and more scalable.

Best practices for responsible Al adoption include:

• Establishing clear policies on data security, ethical Al usage and regulatory compliance.
• Implementing human-in-the-loop models to ensure oversight of Al-driven decision­ making.
• Investing in explainable Al to enhance transparency and trust among employees and stakeholders.

A strong governance framework not only mitigates risks but also builds confidence in Al’s adoption across the organization. This is especially important for the 76% of manufacturers that still lack a formal AI policy despite the urgent need for ethical and secure Al implementation.

Even so, adoption challenges extend beyond governance. Manufacturers must address organizational readiness, data infrastructure and workforce enablement to scale Al successfully. We have seen the most successful companies employ a three-phase strategy:

1. Foundational readiness

• Assess current IT and data infrastructure for Al compatibility.
• Establish clear business objectives and use-case priorities.
• Ensure leadership alignment and executive sponsorship.

2. Implementation and integration

• Look at Al as a business resource vs a technology
• Deploy Al models in production environments, integrating them with existing processes.
• Prioritize quick wins that deliver immediate ROI while building toward long-term Al maturity.
• Utilize hybrid Al deployment models (such as, a mix of off-the-shelf Al tools and customized solutions).

3. Sustained Al maturity

• Continuously monitor and refine Al models to ensure accuracy and effectiveness.
• Develop in-house AI expertise through training and upskilling programs.
• Maintain a governance framework that evolves with changing technology and regulations.

The workforce factor: Augmentation, not replacement

While most employees don’t need to become Al experts, they do need to understand, trust and collaborate with Al systems as part of their daily work. But NTT DATA research shows two thirds of manufacturers say their employees lack the Al comprehension and coexistence skills they need to work effectively in Al-augmented environments. Moreover, only half of manufacturing enterprises are actively investing in upskilling programs.

By contrast, manufacturers leading in Al adoption establish formal policies and processes that include:

• Employee training programs focused on Al literacy and ethical considerations.
• Al-powered tools that enhance human decision-making, rather than replacing it.
• Change management initiatives to ensure a smooth cultural and operational transition.

Successful Al adoption results from a consistent focus on people, processes and leadership. The manufacturers that succeed with GenAI will be those that empower their workforce to collaborate, rather than compete, with Al.

The road ahead

The factories that thrive in the coming decade will be those that master AI as a force multiplier for everything they do. Success will hinge on fusing digital intelligence with physical production, and human insight with machine learning. NTT DATA is committed to helping the leaders of tomorrow shape factories, supply chains and workforces that can think, learn, and adapt at scale.

About the Author:
Prasoon Saxena is the President of Global Products Business Unit for NTT DATA, Inc. He is responsible for Products business, industry strategy and client satisfaction, globally. He has held an executive position at NTT DATA for over eight years showcasing his ability to deliver business outcomes for his clients. He has over 25 years of experience in the Products and professional services industries. Prior to NTT DATA, he has held leadership positions with Dell Services and Wipro Limited. In 2025, Prasoon was appointed to The National Association of Manufacturers (NAM) Board of Directors. Originally from India, Prasoon lives in Connecticut with his family. He holds a Bachelor of Engineering in Electrical, Electronics and Communications Engineering from Nagpur University.

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