Anshuman Bhar founded Aays in 2018 with a simple conviction: that data and AI are only as valuable as the decisions they change. An IIT and IIM alumnus who spent his early career across consulting, banking and large enterprises before starting Aays, he brought with him a habit of asking what a number actually means to a business, not just how elegantly it was produced. That instinct has shaped Aays from its first year.
What began as a small team in India has grown, with the trust of clients and the effort of teams, into a firm with a presence across the UK, Singapore and the US, working alongside partners like Microsoft Azure, Databricks and OpenAI, and serving Fortune 1000 enterprises in CPG, manufacturing and supply chain. Aays has been profitable since its earliest days, a choice Anshuman is quick to credit to the discipline of the whole team.
A milestone he speaks of with particular pride in the people involved, more than the deal itself, was the acquisition of Aidetic, a Bengaluru-based AI/ML consulting firm that deepened Aays' work in computer vision, natural language processing, deep learning and agentic AI. He is equally focused today on building centres of excellence in India's emerging Tier-II cities, with a growing base in Dehradun. He sees this less as expansion and more as widening opportunity for talent beyond the usual metros.
Over the years, Aays has supported clients across more than 350 engagements, and been recognised along the way by ISG, Everest Group, the Financial Times and Statista, AIM Research, and Great Place to Work.
What inspired you to establish Aays, and what gap in the data and AI ecosystem were you aiming to address?
During my corporate years, I saw capable teams build genuinely good analytics that never quite changed how a business operated. The gap wasn't ambition or talent, it was in the space between a sharp insight and a decision someone was actually willing to stand behind. We started Aays in 2018 because I wanted to build a firm that stayed close to that space rather than handing it off once the analysis was done.
Aays has delivered significant business impact for organizations worldwide. What has been the key philosophy behind your approach to helping clients become truly data-driven?
I'd be cautious about calling it a philosophy so much, but a habit we've tried to hold onto: start with the business question, and only then reach for the technology. It sounds obvious, but it's easy to lose sight of once a project gets underway. What I'm proudest of isn't any single engagement, it's that our teams have carried that discipline across several hundred client relationships over the years, often quietly, without much need for it to be noticed.
In a world filled with AI hype, how can organizations ensure they remain focused on business outcomes rather than technology alone?
I think the honest answer is that it takes constant, almost boring discipline. Ask what changes in the business if this works, before any model gets built, and keep asking it as the project progresses. Our teams are trained to treat adoption, not a good demo, as the real measure of success. It isn't the most exciting story to tell, but it's precisely this discipline that lets us adopt whatever is genuinely useful next, agentic systems, stronger data foundations, better tooling, etc without ever losing sight of what any of it is meant to achieve for a client.
Having worked across software engineering, corporate finance, consulting, and entrepreneurship, how have these experiences shaped your leadership style?
Looking back, the common thread across all of it was learning to trust outcomes over activity. Those years left me with a learning: build teams that own outcomes for themselves, because the best decisions not necessarily start with me.
What are the biggest opportunities you see for enterprises leveraging AI and analytics over the next few years?
I think the most meaningful shift ahead is AI moving from something that informs a decision to something that meaningfully participates in one, what we call agentic powered decision intelligence. Rather than a dashboard simply flagging a problem, these systems can help identify an opportunity, work through the possible scenarios, and offer a recommendation with the reasoning laid bare, so a leader can genuinely trust it rather than take it on faith. It's still early, and we are building towards it, with a few implementations already under way.
As AI adoption accelerates globally, what common mistakes do organizations make when implementing data and AI initiatives?
Across the enterprises we work with, we often see technology moving faster than the business problem it's meant to solve. Teams rush to adopt models before aligning on the decisions those models are meant to support. In our experience, data and AI initiatives rarely fail because the technology falls short. They fail because trust, adoption, and the underlying data foundation never receive the same attention as the technology itself.
Aays has expanded its presence internationally. What strategies have been most important in scaling the company across different markets?
We moved into the UK, Singapore and the US after we had proven results and clients willing to vouch for us, and we've expanded deliberately, market by market, leaning on partnerships with hyperscalers like Microsoft Azure and Databricks, alongside other ecosystem partners.
How do you foster a culture of innovation and continuous learning within your organization?
One of our major focus areas has been building an engineering Centre of Excellence in a Tier-2 city like Dehradun, with the objective of nurturing and developing AI talent. Our learning culture is centred around AI, Generative AI, and the emerging research areas shaping the future of the industry. We are deliberate about hiring specialists in areas such as AI architecture and Agentic AI, not just to strengthen our capabilities, but to democratise that knowledge across the organisation and establish industry-standard engineering practices.
We place strong emphasis on continuous upskilling. We consistently encourage our teams to pursue certifications by leveraging our partnerships with Microsoft and Databricks and stay current with the evolving technology landscape.
What emerging trends in data, analytics, and artificial intelligence do you believe will have the greatest impact on businesses by 2030?
Agentic AI will become an ordinary part of how enterprises operate, in the way cloud computing did a decade ago. Trust and explainability will matter as much as raw performance, perhaps more, because no enterprise will scale AI it cannot explain to a regulator or its own board. I hold these views loosely as with the pace of change around us, no one can be entirely confident about exactly how this decade unfolds.
What advice would you give to founders and business leaders who want to build successful technology-driven companies in today's rapidly evolving landscape?
Be patient, and be honest with yourself about what you don't yet understand. Build a team that feels genuine ownership of outcomes. And measure yourself by the clients who stay with you for years, not by how impressive any single quarter looks from the outside.