At Farnborough International Airshow 2026, HCLTech hosted a panel discussion on how AI is transforming the aerospace and defense value chain. Moderated by Matthew Cordner, Principal A&D Business Architect at HCLTech, the session brought together Arjun A. Sethi, Chief Growth Officer, A&D, Public Sector and Private Equity at HCLTech, Sayan Bose, Global Head, A&D and Manufacturing at SAP, and Kris Ganase, Executive VP and Chief Strategy Officer at Acron Technologies.

The discussion focused on a central question for aerospace and defense leaders: where does AI create real business value in an industry defined by complex engineering, long production cycles, supply chain pressure, certification requirements and strict regulation?

“The thing that has materially changed in the industry is that we produce a significant amount of data,” said Sethi. “What is core to producing business value in the aerospace industry is really connecting all of that data through the three bedrocks—engineering,  manufacturing and operations.”

That connected view is crucial because AI cannot deliver enterprise-wide impact if it is treated as a stand-alone IT topic. In aerospace and defense, value is created across the full lifecycle: engineering, production, supply chain, certification, maintenance, repair and overhaul, sustainment and operations. AI needs to be embedded into those workflows and connected to the data that supports them.

Sethi described this as the “gritty middle,” where practical AI-enabled transformation can make a measurable difference. This includes MRO scheduling and repair, production engineering and operations and the detailed process controls that determine whether engineering intent can be executed correctly on the shop floor.

“We are looking at the core processes and identifying where AI can enable and automate them,” said Sethi. “That is where there is maximum business value.”

HCLTech’s iMRO Digital Core is one example of this focus, supporting more connected maintenance, repair and overhaul operations through native integration with SAP. The same principle applies in production engineering and operations, where the goal is to connect engineering design, manufacturing readiness and operational execution.

AI needs to be built into the flow of work

For Bose, the opportunity is closely tied to how AI becomes embedded into enterprise platforms and everyday process execution. Across Farnborough International Airshow, he said, the same questions kept coming up: can AI help with planning, product design, product simulation, variant analysis, manufacturing operations, engineering, supply chain, aftermarket and sourcing?

SAP’s focus, he explained, is on helping aerospace and defense organizations use AI within the business processes they already run.

“What we are doing is making it easier for manufacturers to use AI in day-to-day business process execution,” said Bose. “When AI is embedded in core business processes, from planning and product design to the handover to manufacturing for a specific program, it is helping and working alongside manufacturing engineering and design engineering. That is what we call an autonomous enterprise.”

The same applies to data. Aerospace and defense organizations do not only depend on data from their own enterprise systems. They also depend on information from tier one, tier two and wider supplier networks. Bringing that data together is critical to improving visibility, planning and execution across the value chain.

“It’s not just about the data and cleaner data coming in from your enterprise systems,” said Bose. “It’s actually the data coming in from your tier ones, tier twos and your tier-end suppliers.”

Certification is a major value opportunity

Ganase brought a practitioner’s perspective from Acron Technologies, particularly from its avionics business. He said he had initially been skeptical of AI but quickly saw its potential in engineering development.

In safety-critical aerospace environments, engineering and certification require large volumes of documentation, traceability, verification and compliance evidence. These are labor-intensive activities, but they are also essential.

“I’d say the biggest competitive advantage that we have seen, and we’re seeing it already, is the reduction in engineering development time,” said Ganase. “It’s taking us maybe 5% of the time it used to take us to do certain tasks.”

He pointed specifically to DO-178 artifacts used in software certification, including requirements, code, verification, documentation and traceability. AI can help accelerate these tasks while allowing engineers to focus on the areas where human expertise and safety-critical design judgment matter most.

“That’s been where careful implementation of AI has really helped us,” said Ganase. “It allows our engineers to focus on our differentiators, which is safety-critical design, while AI takes care of the boilerplate tasks.”

For Ganase, the business outcome is clear: faster development, faster certification and lower cost. That is especially important when new aircraft programs can take more than a decade to move from concept to certification.

Physical AI and workforce augmentation

The panel also discussed the emerging role of Physical AI in aerospace and defense. Bose pointed to humanoids, quadrupeds, warehouse automation and AI-enabled inspection as examples of how intelligence could move into physical environments.

“There is another dimension of AI called Physical AI evolving for aerospace and defense,” said Bose. “Aerospace and defense needs more talent and a larger workforce. Physical AI can help augment that talent on the shop floor and in the factory,” said Bose.

The aerospace industry also needs to consider kinetic AI, including autonomous vehicles and electric vertical take-off and landing aircraft. Together, Agentic AI, Physical AI and kinetic AI point to a wider AI landscape across aerospace, from enterprise operations to next-generation platforms.

AI can reduce the learning curve

AI also has an important role to play in workforce productivity and knowledge transfer. Aerospace and defense organizations depend on specialist engineering knowledge, technical documentation and accumulated domain expertise. As experienced talent becomes harder to access, AI can help make that knowledge more available to the next generation of engineers, planners and operators.

“As you well know, at HCLTech, we’ve developed our own platform. It’s called Adonis,” said Sethi. “We do a lot of work in converting that legacy paper technical manual into its digital queryable format, compliant to S1000D and every other regulation that is out there.”

This is already visible in HCLTech’s work with a leading in-flight entertainment and communications systems company, where HCLTech used Adonis to help automate technical documentation workflows, convert older PDF files into editable XML formats and align manuals with aviation standards, including S1000D and ATAiSpec2200.

For Bose, the wider point is that AI should make enterprise systems more intuitive for the next generation of talent.

The biggest impact will be speed to market

For Sethi, the biggest opportunity is compressing the long timeline from engineering design to market introduction.

“The Holy Grail in the aerospace industry has traditionally been reducing the time it takes to move from engineering design to market,” said Sethi. “AI truly enables that. For me, that is the single biggest impact.”

Bose focused on secure, governed and accessible AI, as well as the role of AI in scenario planning across the value chain. Aerospace and defense organizations need to ramp up production, but they also need to manage risk across suppliers, programs and operations.

“If AI can help us to do real stochastic planning and not only look at scenarios, but work with different scenarios to mitigate the risk, that’s the game changer for aerospace and defense,” said Bose.

Ganase emphasized that regulation will be central to how quickly AI can transform the industry. AI may help companies move faster, but regulators will still need to validate the tools and the outputs.

“We can develop whatever we want as quick as we want, but if the regulatory authorities are not going to keep pace with what we’re doing, we’re wasting our time,” said Ganase.

Traceability will determine trust

Aerospace and defense leaders cannot rely on black-box decision-making. If AI supports a decision, organizations need to explain how that decision was made, what evidence supported it and how it aligns to safety, quality and regulatory standards.

“For any certification, one of the core tenets is traceability,” said Sethi. “AI allows you that traceability. It allows you to create that entire audit trail that is necessary to give you the history to where things started from.”

Aerospace and defense organizations need AI systems that can support faster execution while maintaining the deterministic standards required in a highly regulated industry.

Sethi also cautioned against relying too heavily on open frontier models for regulated aerospace use cases.

“By nature, they are probabilistic,” said Sethi. “And we are in a highly regulated industry, which is deterministic.”

This is why enterprise-specific models, secure cloud environments and governed data foundations will become increasingly important. Aerospace and defense organizations need AI that is powerful, controlled, explainable and aligned to industry requirements.

The opportunity is measurable business value. The challenge is scaling AI responsibly across an industry where safety, resilience, traceability and trust are essential.