Preparing engineering teams for the AI era
How can businesses prepare their engineering teams for the AI-powered future of software development? In this episode of the AI at Scale podcast, Chitra Sukumar, SVP, Digital Engineering and Tech Depth at Schneider Electric, explores how artificial intelligence is reshaping the way software and firmware are developed across industries.
Chitra explains how AI tools are now supporting every phase of development: from code generation and test automation to documentation, reverse engineering, and performance optimization. Furthermore, she emphasizes that while AI can automate many technical tasks, the human role is evolving, not disappearing. Engineers must now master new skills like prompt engineering, critical validation, and system-level thinking.
“The act of programming may become more automated, but understanding complex problems and translating them into prompts is key,” she notes.
AI is no longer optional. Hear from Chitra how to turbocharge digital engineering with AI.
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Transcript
Gosia Gorska: Welcome everyone, this is Gosia Gurska and you’re listening to the Schneider Electric AI at Scale podcast. Today we are diving into the world of digital engineering and how AI is revolutionizing software and firmware development. Our guest is here to walk us through the acceleration we’ve seen, the challenges being addressed, and the possibilities ahead. Let’s welcome Chitra Sukumar, SVP, Digital Engineering and Tech Depth at Schneider Electric.
Chitra Sukumar: Thank you for having me, Gosia.
Gosia: Thanks for coming, Chitra. For a proper introduction, you are driving the digital transformation of R&D across the organization. Your mission encompasses the convergence of PLM platforms across the company, the standardization of tools across the product development life cycle, the integration of AI-driven capabilities for design and development, and the seamless connection between engineering and production platforms to ensure end-to-end traceability and data integrity. Additionally, I know that you are on the board of Schneider Electric President Systems Limited as a Non-Executive Director. Prior to joining Schneider, you spent 23 years at Philips. You began your career as an embedded software engineer for consumer products and progressed to lead the global R&D for the mobile surgery product portfolio within the healthcare domain. You hold an engineering degree in Computer Science from Bangalore University. After this proper introduction, Chitra, is there anything else that is important to know to understand you better?
Chitra: Well, I enjoy travelling, and living in a large and diverse country like India gives me a lot of interesting opportunities to do this over the weekend without too much planning. And I love movies, which is, I think, typical of many Indians.
Biggest technological surprises and GenAI
Gosia: Oh, I’m a big fan of Bollywood actually, which is a secret that I like to keep. But definitely it started a long time ago. At some point it was very popular in Poland and I kept it as a hobby as well. So I enjoy this too. OK, so let’s jump into the topic of our conversation. Let’s start with your amazing career. You have over 25 years in software engineering and R&D. What were the biggest technological surprises or advancements that changed the life of software engineers and R&D engineers during your career?
Chitra: There are many, but I’d like to pick a few. One, I would say cloud adoption becoming mainstream has certainly enabled scalable, on-demand infrastructure. Then mobile phone-based apps have really changed how we interact with the world, especially in the last 10 years. In a country like India, it’s enabled access for many people. AI and machine learning, while not new, have become much more usable with advancements in compute power and large amounts of data. And generative AI advances have certainly turbocharged this trend.
Gosia: So how exactly has the appearance of generative AI impacted software development and R&D? Has it already changed the essence of the job?
Chitra: Generative AI is reshaping our industry in many ways. It started off as coding assistance, and today there are GenAI products for multiple phases of software and firmware development. On code generation itself, AI tools are consistently improving features, providing access to different LLMs, and we’re seeing a rise in agent-based coding systems. We’re also seeing tools for test automation, documentation, fixing code vulnerabilities, log analysis—these all help us develop software and firmware faster. But we must always validate what we generate, ensure it’s secure, and do the necessary checks. The essence of the job—solving user needs—remains, but how the job is done is changing. Developers will need to enhance their skills to use these capabilities, which will also free up time to focus on building value-added solutions for our customers.
Transforming R&D with tech
Gosia: Can you share some insights from Schneider Electric’s R&D digital transformation journey and the key milestones you’ve managed?
Chitra: At Schneider Electric, R&D digitization goes beyond software. We’re a large company with multiple product lines and we grow aggressively through acquisitions. Our businesses have done well, and innovative R&D products are a big part of that success. Some major elements of digitization include standardizing tools across software and non-software spaces, establishing authoritative sources of data, and integrating digital tools across the product development lifecycle—from idea to decommissioning. We’re strengthening our digital twin capabilities, enhancing simulation, model-based system design, code generation, and even carbon footprint analysis. Generative AI is disrupting code generation, but we’re also seeing AI enhance productivity in areas like electronics design.
Gosia: That sounds like a huge scope. You’re supporting all lines of business at Schneider in their digital transformation. How does that add to the complexity of your job?
Chitra: It’s very interesting. We operate on a global scale with four major hubs: France, NAM, China, and India. This gives me amazing opportunities to interact with people across geographies and understand the unique needs of engineers in each region. Some needs are common, others are quite specific. Of course, there’s complexity, but the breadth of the company is also an asset.
Gosia: And on top of that, you’re innovating with AI. Can you give us some examples of how AI is being integrated into Schneider’s software development? What are some of the most impactful AI-driven capabilities you’ve seen?
Chitra: We were early adopters of GitHub Copilot at Schneider. We also implemented guardrails early on to ensure safe usage. Over the past year, I’ve seen engineers use Copilot for reverse engineering code, improving documentation, optimizing performance, creating unit tests, migrating between languages, and improving code review efficiency.
These are essential activities, and when engineers spend less time on them, they can focus more on building core features for customers. Copilot also helps new team members—whether fresh graduates or experienced hires—learn new programming languages and understand code blocks. It’s also great for prototyping, allowing teams to try ideas and fail fast.
Empowering engineers through change
Gosia: And how would you assess the change in terms of the heart of the job right now? Do you see that employees in your teams are already used to treating AI as an assistant that helps with daily tasks? Or is there still a learning curve where we’re figuring out the benefits, limitations, and where we should be cautious?
Chitra: I think there are a few steps to it. I wouldn’t say everyone is comfortable with it yet—it’s a process. Some people are very enthusiastic and quick to try new things. So we’re doing multiple things: showcasing capabilities, sharing examples, and encouraging learning. Some people are further along in the journey, and when others see what’s possible, they’re more likely to try it too. As you mentioned, we need to be very aware of cyber and data risks. This is something we’re very intentional about. My team focuses heavily on this when selecting tools. But every engineer must also be mindful—these tools generate code, but that doesn’t mean we can skip reviewing and validating it. The human-in-the-loop element remains critical. That’s part of the education: these tools bring great benefits, but also risks we must manage.
Gosia: That’s very insightful. I’d like to dive a bit deeper into the challenges. In such a complex job, what are the biggest challenges you’ve faced in driving digital engineering and tech debt management? How have you addressed them?
Chitra: If we take the context of AI, it’s a big buzzword. There are so many tools being released—every day, a new one claims to solve a different problem. So one challenge is choosing the right tools to evaluate and assess for Schneider. It’s a noisy market. The second challenge is helping people along the AI journey. For some, it’s exciting. For others, it brings fear or uncertainty. We need to work on change management, teaching teams how to use these tools effectively. The earlier we get comfortable, the easier it is to manage the change. The third is training and reskilling. People need new skills. The outcomes may still be about delivering value to customers, but how we do it is changing. Engineers need to be comfortable with things like prompt engineering and best practices. Even experienced developers have a lot to learn to make the most of these tools. And again, I can’t stress enough how top-of-mind cyber and data risks are for us when evaluating tools.
Gosia: I see. And through your career, you’ve probably hired a lot of people. With the rise of GenAI and more mature AI tools, have you started hiring for different competencies? Has the foundation of engineering roles changed?
Chitra: We’re still in the process of that change. I wouldn’t say it’s completely transformed how we hire, but it’s become more important to assess higher-order skills at every level. Design thinking, problem-solving—these are critical. The act of programming may become more automated, but understanding complex problems and translating them into prompts is key. Prompting is about asking the right question. So we look for people who are curious, adaptable, and willing to learn. The pace of change is so fast—just look at the last year and a half. We need people who are open to experimenting, failing fast, and moving on. These attitudes are more important than ever.
Gosia: And speaking of change, what are the changes you’ve noticed in the software engineering tools we have at our disposal? For those not closely following the space, how fast are these tools advancing, and what are the biggest new developments you’ve observed?
Chitra: When these applications first appeared, they were mostly coding assistants. But in just about a year, we can now do much more—generate design from code or vice versa, perform reverse engineering, create and run unit and functional tests, and manage code across multiple files. These are just improvements we’ve seen in tools like GitHub Copilot. Looking ahead, we’re seeing big shifts with agentic AI, which I believe will significantly change how we develop software.
Gosia: Can you expand a bit on that—how does the future look for software development?
Chitra: What I mentioned earlier were the basics of the development process, and AI is already taking care of those. This enables engineers to focus more on understanding customer needs and architecture. But the early capabilities we’re seeing with agentic AI are impressive. We still need to figure out how best to deploy it. The technology is evolving so fast—we can write and deploy code very easily now. Prototyping will become much faster. But we don’t yet fully understand the guardrails and risks. Cybersecurity, system connections, and risk management will be critical, especially for teams like mine. We also need to think about scaling. If anyone can create applications, how do we maintain them? How do we scale them? What resources do they consume? These are challenges we’ll need to address, alongside the efficiency benefits.
Engineering the future with AI
Gosia: Yes, definitely a lot of learning ahead. We still have time for one more question. Could you share a particular project you’re proud of?
Chitra: One fun project has been deploying generative AI at Schneider. We have many different skill sets—software engineers who are at the cutting edge, and others with deep domain knowledge who’ve been with us for years. Helping everyone learn these new skills has been a fascinating change management journey. We’ve tried many things: making tools available, running hackathons, co-creation sessions, and competitions. With each step, we’ve seen how people adapt. Scaling up the use of tools like Copilot has been very interesting. It’s also been exciting to see how other engineering companies and tool providers are advancing, especially in areas like electronics design.
Gosia: Thank you so much, Chitra, for all the insights. Is there any final remark or advice you’d like to share with others working in software development or R&D—how to prepare for this fast-changing future?
Chitra: I’m 100% sure this will significantly impact all our jobs. Today we focused on software, but this will affect every role. We owe it to ourselves and our companies to understand what these tools can bring, to learn, adapt, and adopt them as best we can. The key is to get the most out of these tools while safeguarding against risks. So I’d say: keep a learning mindset and an open mind. That will be critical.
Gosia: Thank you so much for the conversation. It was a pleasure to talk with you, Chitra.
Chitra: It was a pleasure to be here.
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