Sanjay Mittal, Senior Partner and Industrial Sector Leader, IBM Consulting India & South Asia in conversation with Emerging Manufacturing Magazine
Q. How are Indian manufacturers leveraging AI across their business? What are some trends/patterns you’ve seen in the past year?
What has changed over the last year is that manufacturers are no longer asking, “Where can we pilot AI?” They’re asking, “Where can AI drive measurable business impact?”
We’re seeing strong interest in areas like predictive maintenance, production planning, quality inspection and supply chain optimisation because that’s where the value is tangible. At the same time, many enterprises have realised that scaling AI requires getting their data right. This is consistent with the recent IBM-IndiaAI study, where 57% of surveyed businesses cited data quality as a key challenge to adoption. We’re seeing manufacturers invest heavily in connected operations and modern data foundations as a result.
The third trend is around trust, control and governance. As AI moves closer to core operations, manufacturers are paying far more attention to governance, security and where data resides. In fact, 74% of executives in the IBM-IndiaAI study said data residency is an important consideration for scaling AI.
Overall, industry conversations are clearly shifting from AI experimentation to industrialisation.
Q. How do you view the adoption of agentic AI in this sector? What opportunities do AI agents present?
We’re seeing growing interest in agentic AI as manufacturers look to create more touchless operations across production, maintenance, supply chains and engineering workflows. While adoption is still in its early stages, the conversation has clearly moved beyond experimentation.
What makes AI agents different is that they can move from providing insights to taking coordinated action. For example, an agent could identify a potential production bottleneck, assess its impact on delivery schedules, trigger a maintenance request, and alert the relevant teams – all within the same workflow. In the automotive sector, we’re also seeing interest in using AI agents to accelerate engineering, software development and product support processes.
The opportunity is not just automation; it’s about creating more connected and responsive operations. The manufacturers that realise the greatest value will be those that combine agentic AI with strong data foundations, governance, and operating-model change so these capabilities can scale across the enterprise.
Q. Could you share some examples of how IBM Consulting is helping clients scale AI?
At IBM Consulting, we help clients move beyond AI experimentation to enterprise-scale transformation. What differentiates IBM is our ability to combine deep consulting expertise with technologies such as IBM Enterprise Advantage, IBM watsonx and IBM Sovereign Core to help clients build, govern and scale AI securely across the enterprise.
Take IBM’s own client zero example. By embedding AI and automation across functions including HR, finance, procurement and IT, we have delivered more than $4.5 billion in productivity gains. Those lessons now inform how we help clients scale AI across their businesses.
In the automotive sector, for example, we recently worked with a leading R&D centre to embed AI across both customer-facing and enterprise operations. Beyond integrating generative AI into the in-car infotainment experience, the engagement focused on improving software engineering productivity, making trusted data more accessible, and accelerating decision-making across teams. The result was faster innovation, improved operational efficiency, and a foundation for scaling AI across the enterprise.
Q. What are some best practices that automotive manufacturers should adopt in 2026?
One of the biggest shifts we’re seeing in the automotive industry is that manufacturers are increasingly thinking beyond the vehicle itself and focusing on the entire software-enabled ownership experience. As vehicles become more connected and intelligent, the ability to continuously improve features, services and customer experiences through software is becoming a key differentiator. IBM research suggests digital and software-related revenue streams will account for a much larger share of automotive revenue over the next decade, reinforcing the importance of this shift.
As a result, manufacturers are investing in software-defined vehicle architectures, AI-enabled engineering and manufacturing processes, and the data foundations needed to support continuous innovation. We’re also seeing more focus on breaking down traditional silos between software, hardware and mechanical engineering teams, as these capabilities increasingly need to work together.
Ultimately, the leaders will be those who can move from isolated technology initiatives to connected, software-driven operations. Manufacturers who use AI and data not just to improve products, but to accelerate innovation, improve resilience and create new sources of value over the vehicle lifecycle.
