For more than two decades, I have been immersed in aerospace delivery: programs, reviews, design changes, late night calls with customers, and early morning conversations with engineers across regions. What keeps me energized is not the hype around the latest technology - it is the pride in product engineering.

Cyient has always been, at its core, a product‑engineering company. We help our customers design, build, operate and maintain safety critical complex systems that perform safely and reliably for years. This is an enduring foundation - without a robust and reliable product, there is nothing to build an ‘intelligence layer’ on.

Over the last decade, the language around us has shifted: ‘digital transformation’, ‘AI‑first’, ‘autonomous’ everything. These changes matter, and we are already engaged with them. But they can also create what I think of as ‘phases of confusion’ inside engineering organizations. People start to wonder: “Is what I know still valuable?”, “Does my product experience still matter?”

My answer, very simply, is that natural human intelligence and product know‑how must always come first.

Human Intelligence Comes First

When we talk about ‘intelligence’, the first thing we should think of is the human intelligence of engineers: instinct, judgment, and the ability to connect dots from years of experience and from various fields.

Whatever intelligence exists in a machine, model or automated workflow has been codified by people. Sometimes tools surprise us, but we must never make human intelligence subservient to machine output. In Aerospace & Defense, where safety and traceability are non negotiable, this is not philosophy; it is an engineering requirement.

For my teams, I frame it like this:

The first time you are given a piece of work to execute, follow the process as defined. Because the process has learning and years of experience codified leaving little chance for failure.  

The second time you are given same type of work again, take a step back and start to challenge the process in an informed way.

The third time and beyond, improve the process: remove redundant steps, combine steps or re‑sequence activities and bring in new tools and technologies to make the workflow more efficient.

A machine will execute what you give it. The decision to question, refine and redesign comes from human intelligence. These are the behaviours that must be cultivated if you want Embracing Intelligence to be integral to the way you work.

Embracing Existing Intelligence

One of the privileges of leading Aerospace & Defense Global Delivery at Cyient is the depth of organizational memory I witness every day. In some domains we have decades of experience: design practices, failure modes, customer preferences, certification learnings, etc. This knowledge sits in drawings, documents and, above all, in the minds of people – which we call tacit knowledge.

So, the first step in Embracing Intelligence, is to embrace existing knowledge.

This means asking engineers to follow established practices before they start reinventing them. It means understanding why a process looks the way it does – what constraints it solved at the time – before we change it. It means respecting the work of colleagues who built the original product or process under very different state of technology and understanding of the operating environment.

This is not resisting change, it is making sure that change is grounded in understanding, not just the latest technology trend. This way, you can decide where new forms of intelligence – analytics, automation, AI – truly add value, versus where they might introduce unacceptable risk.

Automation As a Second Skill

If embracing existing intelligence is the first building block, the next is the idea of embracing a “second skill”.

I often say: “You might be a stress analyst, a product designer, or a customer‑support engineer – that is your primary skill, and you must become better at it every day – but your second skill should increasingly be automation”.

This does not mean everyone becomes a programmer. It means that whatever your discipline, you build enough automation capability to make your own work – and your team’s work – better. We know that when people hear ‘automation’, they sometimes worry that they are automating themselves out of relevance. My experience is the opposite: when engineers automate aspects of their work, they free up time and energy for more complex and challenging problems – and their value increases, not decreases. Engineers love solving problems and enjoy challenges in their work. And they find it intellectually boring to execute known and matured processes every day.

Intelligence Expands the Design Space

To put that in context: in the past, a stress analyst might only have the tools and time to simulate a few design variants for a critical component. Today, with automation, high performance computing and AI assisted optimization, we can run simulations across hundreds – or even tens of thousands – of configurations in the same time window. That shift from ‘a few’ to ‘thousands’ is where AI genuinely expands the scope of what our engineers can do. It takes away mechanical effort, so their intelligence is focused on framing the problem, setting constraints, exploring the entire design space and interpreting the patterns that matter.

It also highlights how Embracing Intelligence is inherently practical. It is not about replacing human intelligence; it is an invitation to extend it. Pairing product ‘know‑how’ with automation skills creates a delivery organization that can handle more complexity without burning people out. We’re no longer limited to asking, “Can I test two or three configurations?”; now we can ask, “What does the landscape look like if I scan the entire design space?”

Making Intelligence Visible

The third element I emphasize is the need to make intelligence ‘visible’.

Intelligence is not useful if it stays locked inside people’s heads. We employ different ways of bringing ideas and problems out into the open: ‘Idea Trees’ in our office environments, open innovation challenges, and the simple expectation that every engineer should contribute at least one idea to improve a process, a product or the experience of work.

One of my favorite rituals is something we call ‘Wednesday Matinee’. Once a week, for 15–20 minutes, we bring teams together to share examples and thinking on technology, innovation, leadership practices from another industry and then ask, “What could we borrow from this and apply in our own context?”

The best ideas often come from this cross‑domain exposure. Customers notice this too: they’ve seen our Idea Trees in our delivery centers, participated by posting an idea or a problem, or taken the practice from one of our sessions back into their own organizations. This is Embracing Intelligence in action: a culture tangible enough to be observed and emulated.

Explainable Intelligence in a Safety-Critical World 

Aerospace & Defense highlights a very important aspect of the purpose of ‘intelligence’, and that is ‘explainability’.

We do not have the luxury of deploying opaque systems and hoping for the best. When an aircraft is in service, every decision that affects safety, performance or compliance must be traceable. Regulators, certification bodies and customers will ask: “How did you arrive at this result? Can you repeat it? What data was used? Can you trace it back to your input and assumptions?”.

That does not mean AI has no role, it means we must be selective and disciplined about where and how we use it.

Deterministic, well‑bounded automation that behaves predictably is already accepted. AI tools and technologies that assist engineers by providing early alerts to issues before they occur – surfacing patterns, highlighting anomalies, or accelerating documentation – are increasingly welcome, so long as the engineer remains in control and the outputs can be explained.

Used this way, AI does not just make existing steps faster; it allows us to cover more scenarios, see subtler trends and test ideas that would have been impractical a few years ago.

Embracing Intelligence in Aerospace & Defense has clear constraints: we must favor explainable, governed intelligence over black‑box models that cannot be interrogated. ‘Human‑in‑loop’ is more than a buzz phrase - it is how responsible engineering will continue to operate in safety‑critical domains. 

Taking the Lead Turns Ideas into Operating Norms

In every organization innovation and culture-change programs and initiatives can start strongly, but falter because of a lack of sponsorship and follow through. The reality is that to create an intelligence operating system you need leadership commitment.

Embracing Intelligence functions as an operating system, with leaders treating it as part of how they run their teams: recognizing examples of intelligent behavior, protecting time for learning and idea generation, and being consistent in how we respond when people challenge a process or propose a new way of working.

If engineers feel that their intelligence is recognized, their ideas matter, and that they can grow by adding new skills rather than abandoning their core strength, they are far more likely to stay and build. 

The Future of Delivery in an Expanded Intelligence Era

When I look ahead, I don’t see a choice between engineering pride and AI‑enabled progress. I see a bigger, more interesting role for delivery teams. If we are Embracing Intelligence correctly – with human judgement first, explainable AI where it fits, and a culture that brings ideas out into the open – we will be able to explore more designs, compress more cycles, and anticipate more issues than we could in the past.

In Aerospace & Defense this matters. Our customers are asking us to help them do things that are both safer and faster, more efficient and more sustainable. These things are only possible if we combine decades of product know‑how with new tools that expand the space of what is feasible, without compromising traceability or trust.

When customers think of Cyient, I want them to see not only a reliable execution partner, but an ‘intelligence partner’ with teams of engineers who respect foundational knowledge, embrace new forms of intelligence responsibly, and bring the best of human and machine understanding and capability to their most critical programs.