Day: June 30, 2026

Opus Technologies, the engineering partner to payment providers, banks, and fintechs built around the promise of “Business Value Acceleration,” has brought one of the biggest names in global IT services onto its Board of Directors. Phaneesh Murthy, whose four-decade career helped shape how the modern technology services industry sells, scales, and delivers, now sits on the board alongside Founder and Executive Chairman Ramesh Mengawade and Chief Executive Officer Srini Rajamani.

And for a company whose whole job is modernizing the rails of banking and payments, this isn’t really a flashy headline hire. It’s more like two stories that were always heading toward each other, finally meeting, because both are built on the same belief: real value comes from systematic engineering, not from chasing whatever’s in front of you.

A Career That Mirrors the Industry’s Own Evolution

Here’s the thing about the path that led Phaneesh Murthy to the Opus board. It lines up almost perfectly with how the global services industry itself grew up.

His early years were at Infosys, from 1995 to 2002, where he ran Sales and Marketing worldwide, led Communications, and headed the Product Solutions Group. As the company’s global sales head, he’s widely credited with helping push revenues from roughly two million dollars to around seven hundred million in under a decade. A lot of that came down to the Global Delivery Model he helped pioneer, which used teams spread across time zones to keep work moving around the clock. The trick was that it solved the problem through smart process design, not by asking people to burn themselves out. That’s exactly why it stuck and became an industry standard.

Then he moved into the operator’s seat. As CEO and President of iGate Corporation, now part of Capgemini, he turned his scaling ideas into the Integrated Technology and Operations model, or iTOPS. Instead of treating technology and business process as two separate things you buy, iTOPS rolled them into one delivery engine. And he paired it with a commercial idea that changed what clients expected: you pay for outcomes and productivity, not hours on a timesheet. The numbers backed it up. Over his time there, enterprise value went from about seventy million dollars to somewhere around four and a half to four point eight billion, including the headline-grabbing purchase of Patni Computer Systems, which was actually bigger than iGate when the deal happened.

The Investor and Advisor Years

The next chapter added the things boards really care about: capital discipline and good portfolio judgment. In 2013, Phaneesh Murthy founded Primentor Inc., a strategy consulting firm that helps senior leaders at big organizations create shareholder value and stay nimble when things get uncertain. Since then, he’s served on the advisory board of the global private equity firm Partners Group, and since 2014, he’s been an Operating Partner at New State Capital Partners, focusing on business services, technology, and business process outsourcing.

That investor’s eye is backed up by a real record of creating value from the boardroom. During his time on the board at CSS Corporation, for example, EBITDA grew many times over, which fed into the company’s eventual sale to a private equity buyer. And across more than twenty-five years, he’s structured and run large-scale outsourcing deals for Fortune 500 companies. Those are exactly the kind of complex, multi-year relationships that sit at the center of the banking and payments work Opus does.

More recently, he’s picked up advisory roles across a deliberately mixed bag of technology companies, including AI-first software firm InfoBeans, digital transformation specialist CriticalRiver, and agentic-AI challenger Covasant Technologies. The variety is the whole point. Watching how different organizations adopt AI at the same time gives him a view most single-company executives simply don’t have, and it’s made him pretty sharp about the gap between AI theater and AI that actually moves the needle. He’s been openly skeptical of a market full of chatbots and shallow automation, and far more interested in systems that keep humans in the loop and can genuinely run a real business process from start to finish.

Where His Track Record Meets Opus’s Mandate

Opus builds solutions for banks, payment providers, and fintechs across modernization, cloud, data and AI, financial crime and compliance, and AI-powered automation. Every one of those is an area where the things Phaneesh Murthy spent a career proving carry straight over.

His push for outcome-based commercial models fits neatly with Opus’s whole promise of accelerating, realizing, and maximizing business value instead of just billing for effort. His habit of building capability before the demand shows up speaks directly to the modernization work financial institutions need to do before regulation or competition forces the issue. His iTOPS instinct for fusing technology and operations into one delivery engine is the same instinct behind Opus’s domain-native, AI-augmented approach. And having advised a major bank on process automation, shifting more responsibility offshore, and lifting productivity, he’s got hands-on credibility with exactly the kind of institutions Opus was built to serve.

There’s a nice symmetry at the leadership level, too. Opus was founded by a payments pioneer whose ventures were acquired by global names like Mastercard and Western Union, and it’s run today by a CEO with decades of transformation experience. Adding a director who built his career scaling services businesses from millions into billions, and who now brings a private-equity-trained take on governance and value creation, rounds out a board that’s clearly set up for the company’s next stage of growth.

In the end, board appointments are really just statements about where a company thinks it’s headed. By bringing Phaneesh Murthy into the room, Opus is signaling that it wants to scale with the same discipline he brought to Infosys and iGate, to compete on outcomes rather than inputs, and to treat AI as a genuine engineering capability rather than a marketing line. For a company leading the way in banking, payments, and fintech engineering, it’s hard to think of a better fit.

The Pharmaceutical Industry Is Facing an Innovation Bottleneck

Few industries carry the responsibility that pharmaceuticals do. Every breakthrough has the potential to improve, extend or even save millions of lives. Yet despite remarkable advances in science, one reality has remained stubbornly consistent. Drug discovery is extraordinarily slow, expensive and uncertain.

Bringing a new drug from the laboratory to the patient often takes well over a decade. Industry estimates suggest that developing a single successful drug can cost more than US$2 billion when accounting for failed candidates and clinical development costs. Thousands of compounds may be investigated before one demonstrates sufficient safety and efficacy to reach the market. Most fail long before they ever become medicines.

The challenge facing pharmaceutical companies today is not a shortage of scientific talent. It is the overwhelming complexity of biology itself. Human diseases involve intricate molecular interactions that cannot easily be understood through conventional research methods alone.

During my learning journey under Phaneesh Murthy, one implementation principle consistently emerged across industries. Technology delivers its greatest value when it helps organisations solve problems that cannot realistically be solved through scale alone. Hiring more researchers or increasing laboratory capacity does not necessarily accelerate discovery. At some point, complexity outpaces human capability. This is precisely where artificial intelligence is beginning to redefine pharmaceutical innovation.

Drug Discovery Is Becoming a Data Problem

Traditionally, pharmaceutical research has relied on years of laboratory experimentation. Scientists identify biological targets, test thousands of chemical compounds, analyse results, refine promising candidates and repeat the process continuously until a viable molecule emerges.

While this approach has produced life-changing medicines, it remains largely iterative.

Artificial intelligence changes the starting point entirely.

Rather than examining compounds one at a time, AI systems can analyse millions of molecular structures simultaneously, evaluating their chemical properties, biological interactions and probability of success within a fraction of the time required through conventional methods.

Modern machine learning models can recognise relationships between proteins, genes, disease pathways and molecular structures that would be almost impossible for researchers to identify manually.

The result is a dramatic reduction in the search space.

As Phaneesh Murthy often explains when discussing enterprise AI implementation, organisations should focus on applying intelligence where decision complexity becomes too large for traditional systems. Drug discovery represents one of the most compelling examples of this principle because AI does not replace scientific research. It dramatically improves where researchers begin.

Instead of searching for a needle in a haystack, researchers begin with a much smaller and far more promising set of candidates.

AI Is Accelerating Molecule Discovery

One of the most exciting developments within pharmaceutical research is AI’s ability to generate entirely new molecular candidates.

Historically, scientists searched existing chemical libraries for compounds that might influence specific biological targets. This process depended heavily on previous knowledge and experimental testing.

Artificial intelligence introduces a fundamentally different capability.

Generative AI models can design novel molecules based on desired biological characteristics. Rather than waiting for researchers to discover suitable compounds, AI proposes entirely new molecular structures that satisfy predefined therapeutic objectives.

Researchers then evaluate these AI-generated candidates through laboratory validation rather than beginning with unrestricted exploration.

This significantly reduces both time and cost during the earliest stages of research.

From my experience learning implementation thinking under Phaneesh Murthy, one lesson has consistently shaped my perspective on AI adoption. The greatest business value often comes from improving the earliest decisions within a process because every downstream activity benefits from better starting assumptions.

Drug discovery follows exactly this pattern.

Better molecule identification at the beginning dramatically improves research efficiency throughout the entire development lifecycle.

Clinical Trial Design Is Becoming More Intelligent

Identifying a promising drug candidate is only the beginning.

Clinical trials remain one of the longest, most expensive and highest-risk phases of pharmaceutical development. Recruiting appropriate participants, predicting patient responses and managing trial complexity often take years.

Artificial intelligence is transforming this process as well.

AI can analyse enormous datasets containing patient demographics, medical histories, genomic information and disease progression patterns to identify the most suitable participants for clinical studies. Rather than relying solely on manual screening processes, researchers can identify patient populations more precisely while reducing recruitment timelines.

AI also enables simulation and modelling of clinical scenarios before trials begin.

By analysing historical trial outcomes alongside biological and patient data, intelligent systems can help researchers optimise study design, anticipate operational challenges and improve trial efficiency.

As Phaneesh Murthy sir suggested during discussions around enterprise transformation, implementation success often depends on improving decision quality before execution begins. Clinical trial design demonstrates this principle perfectly because better planning significantly increases the probability of successful execution.

AI Is Changing How Pharmaceutical Companies Innovate

Perhaps the most important implication of artificial intelligence is that it is reshaping pharmaceutical innovation itself.

Drug discovery has historically been organised as a sequence of relatively independent activities. Biology research, chemistry, laboratory testing, clinical development and commercial planning frequently operated in distinct phases with limited integration.

AI encourages a much more connected approach.

Data generated during laboratory experiments informs predictive models. Clinical outcomes improve biological understanding. Commercial insights help prioritise future research programmes. Every stage contributes intelligence that strengthens the next.

The pharmaceutical organisation gradually evolves into a continuous learning system.

Phaneesh Murthy is of the belief that organisations realise the full value of AI only when intelligence flows across the enterprise rather than remaining isolated within individual departments. Pharmaceutical companies that successfully integrate research, clinical and commercial intelligence will innovate far more effectively than organisations applying AI to isolated functions.

The competitive advantage lies not only in adopting AI.

It lies in embedding AI throughout the innovation ecosystem.

Technology Alone Does Not Create Better Medicines

Artificial intelligence has understandably generated significant excitement across the pharmaceutical industry, but implementation requires discipline.

AI models are only as effective as the scientific data used to train them. Poor quality datasets, fragmented research environments or inadequate governance can produce misleading conclusions. Pharmaceutical innovation still depends on rigorous experimentation, regulatory oversight and clinical validation.

AI should therefore be viewed as an accelerator rather than a replacement for scientific expertise.

Researchers continue to define biological questions, interpret experimental evidence and make critical clinical judgments. Artificial intelligence enhances those capabilities by reducing repetitive analysis and identifying opportunities that might otherwise remain undiscovered.

As Phaneesh Murthy often emphasises in conversations around enterprise technology implementation, successful AI adoption begins with understanding where human expertise creates value and where intelligent systems can amplify it. The pharmaceutical industry illustrates this balance exceptionally well.

Scientific excellence remains essential.

AI simply allows scientists to apply that excellence more effectively.

The Future Pharmaceutical Company Will Be Built Around Intelligence

Over the next decade, competitive advantage within pharmaceuticals will increasingly depend on how effectively organisations combine biological science with artificial intelligence.

Companies capable of identifying promising molecules faster, designing more efficient clinical trials and continuously learning from research data will reduce development timelines while improving innovation outcomes.

The opportunity extends far beyond operational efficiency.

Faster discovery means patients gain access to new therapies sooner. Research investment becomes more productive. Healthcare systems benefit from accelerated innovation.

From my learning under Phaneesh Murthy, one insight has consistently influenced how I think about enterprise transformation. Technology should never be viewed simply as a productivity tool. Its greatest impact comes when it fundamentally changes how organisations solve complex problems.

Drug discovery is undergoing exactly that transformation.

Artificial intelligence is not merely helping pharmaceutical companies conduct research faster.

It is changing how research itself is performed.

The Future of Drug Discovery Will Be Predictive

The pharmaceutical industry stands at one of the most significant technological turning points in its history. Artificial intelligence is enabling researchers to analyse biological complexity at a scale previously unimaginable, transforming molecule identification, clinical trial modelling and scientific decision making.

While the journey from laboratory discovery to approved medicine will always require rigorous validation, AI is dramatically improving the speed and intelligence of every stage leading up to that point.

As Phaneesh Murthy has consistently reinforced throughout discussions on enterprise technology implementation, organisations create lasting competitive advantage when intelligence becomes embedded within their operating model rather than added as a separate capability.

The pharmaceutical companies that embrace this philosophy will not simply discover drugs faster.

They will redefine how medicines are discovered altogether.

This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry-leader, Phaneesh Murthy.

www.phaneeshmurthy.com

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