In this episode of the AI Innovators™ Interview Series, we spoke with Nirmal Jingar, Engineering and AI Strategy Leader for Platforms and Modernization at Wayfair, a leading home furnishings ecommerce platform with more than 15,000 employees and over $10 billion in revenue. Nirmal leads supply chain engineering teams and has delivered more than $300 million through supply chain optimization and platform modernization. He has championed generative AI adoption, accelerated software development through AI coding pilots, and reduced technology costs by more than 60 percent.
Nirmal also works as an advisor outside of Wayfair, helping companies scale their tech teams and build long-term strategies. He is a member of the Forbes Technology Council and the Mass Technology Leadership Council CAIO Peer Group.
In this conversation, he shared insights on leading AI engineering teams, building trust with engineers, measuring AI ROI, and how leaders can get started with AI in their organizations.
Below are the highlights from our conversation.
Engineering Background and Leading AI at Enterprise Scale
Nirmal discussed how his extensive software engineering experience prepared him for leading AI initiatives at Wayfair.
He said, “I have been in the software industry over 18 years. I’ve worked with like smaller companies, medium scale, large companies, you can just name it. Have seen different kind of projects, initiatives. So I’m aware of like all the pain points, friction points and cross-functional collaboration issues, stakeholder issues. So you just name it. And all of those issues and the experience really helped me to pinpoint the specific initiatives, specific projects and specific phases of the projects like where we can implement the AI, where it will work effectively and where it will not work effectively.”
He explained the scale of the systems he manages: “In Wayfair, I am owning and managing supply chain tier one systems. Mostly in the supply chain, one of the example from one of my system is we generate billions of routes every day to move the customer order from point A to point B.” He added, “One of my other systems, we take care of the inventory positioning, like where in which warehouse we should position the inventory so we can deliver the customer order faster.”
Building Trust With Engineering Teams
When asked how he helps engineers get comfortable using AI without fear of replacement, Nirmal explained the importance of messaging and experimentation.
He said, “When we talk about AI, overall in the industry, everybody is scared and there is anxiety like that AI will replace that side. So I think in a different way, the way I lead my teams, I first build the trust, not focusing on the tools.” He continued, “So start with like very clear objective and messaging like why we need to use the AI, where we are using the AI. And the AI is really here to help you and to move faster, not to replace your thinking and not to replace your job, right?”
Nirmal emphasized the value of small experiments: “I also encouraged teams to start with a low risk experiment first. Rather than coming up with a really large scale AI implementation that required a lot of effort, human and the AI spending. So I suggest start with something smaller, share some real example, come up with the real example or problem that you can solve. And then you iterate on it.”
He also encouraged open sharing and involvement from leadership: “I also invite the leadership openly to the demo. So one of the example in my last demo, I invited one of our engineering director to talk about it, like, okay, how you are using the AI, like how AI is affecting you. So the director was able to demo showcase in front of everyone, the whole group.”
Skills Engineers Will Need in an AI-First World
When asked what skills will matter most for engineers, Nirmal emphasized fundamentals.
He explained, “I still think the strong computer science fundamental, problem solving, critical thinking, and the domain knowledge, all of these skills are very, very important, right?” Nirmal illustrated his point by comparing two engineers: one who thoughtfully used AI and one who rushed in without judgment. He concluded, “So of course the engineer one. So I still think the strong computer science, fundamental critical thinking, system thinking, using the right judgment is very critical. So if you combine these skills with AI, then the magic happens.”
Measuring ROI and Success in AI Initiatives
Nirmal shared how he measures ROI at enterprise scale.
He stated, “The way I measure is like I measure in terms of like a longer term benefit and longer term gains. Right. I do not focus on the short term gain gains because if we think at the larger scale and the enterprise level, the real ROI takes time.” He explained that meaningful ROI at scale can take time to emerge: “At the enterprise scale and for the big initiatives. It takes easily like two or three years to see the real impact and the real ROI.”
He outlined the approach to tying ROI to business outcomes: “The way to get the actual ROI is like either you come up with a very specific use case with business outcome first, and then you calculate the ROI for that business outcome.” He also cautioned against using AI simply because of hype: “We don’t have to rush for AI, right? There are already a lot of tools and workflows exist which solves your problem without AI. So you have to be really careful. Like don’t use the AI just for sake of AI and with hype, right? Make sure AI is solving the right problem.”
Advice for Leaders Getting Started With AI
When asked what advice he would give to leaders who want AI impact but do not know where to start, Nirmal focused on education and strategic experimentation.
He said, “Make sure you understand what is the AI, right? That’s really important, right? Make sure you understand what’s the difference between large language models, small language models, smaller models.” He noted that many executives lack basic AI literacy: “Most of them don’t know what is Notebook LM, right? That’s really concerning.”
For executives and boards, he advised education: “Take like two days off, some time, take some course, just the executive level course for two days bootcamp and understand everything. What is AI, what is agentic AI and all the terms and all the things that you know, right? That’s really important.”
He also encouraged starting small: “Pick up one real pain point and one problem, which already exists in your company or organization, and then you reiterate on it. Once you start seeing the results as we discussed earlier, then you can keep on expanding.” Nirmal emphasized that board understanding matters: “If your board doesn’t understand, it’s the responsibility of the executive C-suite team to make sure they educate the board.”
Challenges and Opportunities for AI in Business
Nirmal discussed how challenges and opportunities are intertwined.
He said, “The way I think, I think the challenges and opportunities come together at the same time.” He compared two engineering teams: one that used AI as a thinking partner and one that treated AI like an answer machine. He shared the consequence for the latter: “They were not validating the output. The context was missing, the data was missing, the critical thinking was in the backseat. Instead of reducing the work, the AI created an overburden for them.” He continued, “It created more confusion within the team.”
He also emphasized the importance of learning culture: “If you don’t create that culture within the company, within the organization, and if you don’t create the culture from top to bottom, they are not going to learn. And if they don’t learn, they will use the AI without governance.”
Nirmal acknowledged the rapid pace of change: “Things are moving very, very fast. So that’s why it’s like learning, reiterating, experimenting is really critical.”
Closing Wisdom on AI and the Future of Work
In closing, Nirmal shared a powerful perspective on how leaders should approach AI’s impact on jobs and the future of work.
He said, “Don’t ask what jobs will be replaced. Don’t ask what AI will replace. Ask what constraint can be finally removed. Ask what problems you can solve and that you are waiting and those used to take months and years.”
This mindset shift captures his message on focusing on what AI enables rather than what it takes away.
Watch the Full Interview
To hear Nirmal Jingar’s full conversation on AI engineering leadership, trust building, measurable AI impact, and the future of work, watch the complete interview below.
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