AI Will Solve Problems Nobody Could Solve Before, Says Persistent Systems CEO

The world of enterprise technology is buzzing, and for good reason. In a landmark interview published by Brand Finance, Sandeep Kalra — Executive Director and Chief Executive Officer of Persistent Systems — made a striking case: artificial intelligence is not just improving existing solutions, it is opening the door to solving problems that were simply impossible to crack before. This is not a prediction. According to Kalra, it is already happening. And Persistent Systems, which has undergone one of the most dramatic transformations in the IT services industry, is right at the center of it.

From Engineering Firm to AI-Led Powerhouse

Persistent Systems did not become an AI-first company overnight. The journey, as Kalra describes it, was built on deliberate inflection points where the company's founder's intent, strategic clarity, and disciplined execution all came together at the right time. Founded with a clear brand promise — solving complex business problems through high-quality engineering — the company spent years building deep technical expertise before making its boldest move yet: a full pivot to AI-led, platform-driven services.

This was not a cosmetic rebranding exercise. Persistent embedded AI directly into its delivery models, its platforms, and its internal operations. The company became, in Kalra's own words, “customer zero” for its own AI vision — meaning it tested and deployed AI on itself before rolling it out to clients. That kind of internal conviction is rare, and it signals something important: Persistent Systems genuinely believes in what it is building.

AI as Core Enterprise Infrastructure, Not a Side Feature

One of the most powerful insights from Kalra's interview is how he frames AI's role in the modern enterprise. He does not describe AI as a tool or a feature. He describes it as core enterprise infrastructure — something as foundational as electricity or the internet. Enterprises, he argues, now expect AI to be embedded inside their processes with reliability, security, and measurable outcomes. It is no longer enough to run a pilot project or a proof of concept. Real results are expected.

This shift in expectations is what makes the current moment so significant. When AI becomes infrastructure, problems that previously required enormous manual effort, specialized talent that did not exist, or computing power that was not affordable — all of those suddenly become solvable. The barriers that made certain challenges “too hard” or “not worth it” are falling away one by one. And companies like Persistent Systems are positioning themselves to be the ones who help enterprises walk through those open doors.

What “Unsolvable” Actually Means in Business

When Kalra talks about problems that were never solvable before, it is worth pausing to think about what that actually looks like in the real world. Think about large-scale pattern recognition across millions of customer records — something a human team could never complete in a useful timeframe. Think about predicting equipment failures in manufacturing plants before they happen, or personalizing financial advice for millions of retail banking customers at once. These are not fantasy scenarios. These are the categories of problems that AI is actively unlocking right now across industries.

Nowhere is this more visible than in healthcare. AI in healthcare is unlocking better outcomes at a pace that would have been unimaginable a decade ago — from early disease detection to personalized treatment protocols that account for a patient's full genetic and medical history. These are precisely the kinds of high-complexity, high-stakes challenges Kalra is describing when he says AI is solving what was once unsolvable. The enterprise transformation playbook Persistent Systems is executing has a direct mirror in every industry where data complexity was previously a barrier.

The Agentic AI Revolution: Speed, Autonomy, and New Operating Models

One of the most forward-looking parts of Kalra's vision involves what he calls agentic and autonomous AI delivery models. This is the next frontier — AI systems that do not just answer questions or generate content, but that take sequences of actions, make decisions, and complete complex workflows with minimal human intervention. This category of AI is going to redefine what productivity means, what operating models look like, and how fast execution can happen inside large organizations.

For enterprise clients, this is both exciting and daunting. Exciting because the speed and scale of what becomes possible is genuinely transformative. Daunting because it requires rethinking workflows, governance structures, and risk management from the ground up. Persistent Systems' role in this landscape is to be the partner that helps enterprises navigate the transition — not just deploying the technology, but helping organizations think through what responsible AI adoption looks like at every level.

Ecosystem Orchestration: Nobody Wins Alone

A theme that runs through Kalra's entire strategic philosophy is the idea that no single company can deliver enterprise-scale AI transformation alone. Persistent Systems has evolved from being a participant in technology ecosystems to being an orchestrator of them. That means pulling together hyperscalers, specialized technology providers, domain experts, advisory firms, startups, and private equity portfolio companies into cohesive, outcome-driven programmes.

The hyperscaler partnerships in particular are central to Persistent's AI strategy. By working closely with the major cloud platforms, Persistent can co-develop solutions, run joint innovation sprints, and bring clients faster outcomes than they could achieve by working with any single vendor. In an era where AI capabilities are advancing faster than most enterprises can absorb, having a trusted orchestrator who knows how to assemble and manage that ecosystem is enormously valuable.

The USD 1 Billion Milestone and What It Proves

Numbers matter when they validate strategy. Persistent Systems crossed the USD 1 billion revenue mark in FY23 — a full year ahead of its own internal plan. That kind of execution is not accidental. It reflects the cumulative effect of years of deliberate brand building, strategic repositioning, and talent investment. The company has also recently been recognized as the fastest-growing IT services brand in the Brand Finance IT Services rankings for 2026, debuting in 22nd place with a brand value approaching USD 989 million following a 22% increase from the previous year.

These figures are not just impressive on their own. They are evidence that the market is rewarding Persistent's approach. Enterprise clients are choosing Persistent not just because they offer AI services, but because they have demonstrated the ability to operationalize AI at scale, take accountability for outcomes, and sustain innovation over time. That is a very different value proposition from the traditional IT services model, and it is clearly resonating.

Talent as a Decisive Factor in the AI Race

Technology alone does not win in the AI era. Kalra is emphatic on this point. Talent readiness — the ability of people inside an organization to adapt, reskill, and take ownership of AI-driven workflows — will be as decisive as the technology itself. This is why Persistent has invested so heavily in Persistent University, its internal reskilling platform, and in broad AI readiness programmes that touch employees across every function and level.

The company has also built a culture of ownership through an inclusive Employee Stock Ownership Plan, ensuring that team members at all levels feel personally invested in the outcomes they are helping to create. When clients engage with Persistent, they are not just accessing software or infrastructure. They are accessing empowered teams who believe in what they are delivering. That human element, Kalra argues, is what ultimately makes the difference between AI projects that succeed and those that stall.

Responsible AI and Governance: Building Trust at Scale

With all the excitement around what AI can now accomplish, it would be easy to overlook the risks. Kalra does not make that mistake. He identifies responsible AI, data modernization, and governance as critical trust anchors for the next phase of AI adoption. As enterprises move from experimentation to production, the questions around data privacy, algorithmic bias, regulatory compliance, and explainability become impossible to ignore.

Consider the consumer side of this equation. A recent trend worth examining is how the majority of Brits are now choosing AI algorithms over traditional GPs for initial health consultations. This reflects just how rapidly public trust in AI has grown. But it also underscores why governance frameworks are non-negotiable. When individuals are making health decisions based on AI outputs, the accountability systems behind those tools must be rock solid. Persistent Systems' emphasis on responsible AI is not a niche enterprise concern — it mirrors a broader societal demand that is playing out across every sector where AI now touches human lives.

Sustainability and AI: Two Priorities, One Strategy

It is worth noting that Persistent's transformation is not purely about commercial growth. The company has embedded sustainability deeply into its strategy, achieving carbon neutrality for Scope 1 and 2 emissions and committing to a Science Based Targets initiative-approved net-zero goal by 2050. Through the Persistent Foundation, the company extends social impact beyond India to the United States, supporting nonprofits in education, veterans' services, animal welfare, and underserved communities.

This matters because AI companies that ignore environmental and social accountability are increasingly finding themselves on the wrong side of stakeholder expectations. Persistent's approach — growing its AI capabilities while simultaneously strengthening its sustainability commitments — is a model that other technology firms would do well to study. Innovation and responsibility are not opposites. According to Persistent, they scale together.

What the Next Three Years Look Like for AI-Driven IT Services

Kalra's outlook for the next three years is both ambitious and grounded. He sees AI continuing its transformation from an experimental technology into an indispensable part of enterprise infrastructure. The companies that will lead in this environment are those that combine AI-led platforms, ecosystem orchestration, rigorous governance, and a genuine culture of ownership. That is not a simple checklist to complete — it is a way of operating that has to be built over years, which is exactly what Persistent has been doing.

For businesses watching from the sidelines, wondering whether the AI wave is real or overhyped, the Persistent Systems story offers a clear answer. The problems being solved today were genuinely impossible just a few years ago. The organizations that embrace this moment with seriousness, strategy, and the right partners will emerge with durable competitive advantages. Those that wait may find that the gap has become too wide to close.

The Verdict: A New Era of Problem-Solving Has Begun

What Sandeep Kalra is describing is nothing short of a redefinition of what is possible in business. For decades, entire categories of challenges were left on the shelf because the tools to address them simply did not exist. AI is pulling those challenges back off the shelf and handing enterprises the means to tackle them. Persistent Systems, through its strategic evolution, its ecosystem approach, its talent investment, and its commitment to responsible innovation, has positioned itself at the exact center of this shift.

Whether you are a business leader, a technology enthusiast, or simply someone paying attention to where the world is heading, the message from Persistent Systems is worth taking seriously. AI is not just changing how problems are solved. It is changing which problems get solved at all. And that, as Kalra would put it, changes everything.

Source & AI Information: External links in this article are provided for informational reference to authoritative sources. This content was drafted with the assistance of Artificial Intelligence tools to ensure comprehensive coverage, and subsequently reviewed by a human editor prior to publication.

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