Synopsis

ITC Infotech's merger with Happiest Minds highlights the trend of mid-market consolidation in response to AI advancements. The global IT market faces challenges, requiring companies to adopt AI-centric capabilities for survival. Enterprises are burdened with legacy systems, which present both challenges and opportunities for funding technological transformations. Companies will need to adapt to the evolving landscape by integrating AI into existing frameworks effectively.

Is ITC Infotech's merger with Bengaluru-based Happiest Minds, coming close on the heels of Persistent's acquisition of Munich-based Nagarro, a signal of mid-market consolidation in the face of AI-led disruption? With the market capitalisation of top global companies down almost 25% since 2026 beginning, the picture becomes blurrier. To compound matters, trading multiples of global IT majors today resemble those of low-end staffing companies not so long ago.



There can be little argument against acquiring new AI-centric capabilities. But the industry needs a cure for its ailment, and scale alone may not provide it. Excitement around LLMs and GenAI notwithstanding, the real story is not the models themselves, but how, where and through whom AI gets embedded in global firms.



Also Read: ITC Infotech to acquire 22.1% stake in Happiest Minds for Rs 1,330 crore; firms to merge

Model differentiation will have to be matched by distribution reach. This is where global IT comes in. The slew of partnerships between frontier model providers such as OpenAI and Anthropic, and IT majors like TCS, Accenture, EPAM, Cognizant and DXC points to this growing realisation.



So, where will funding for this transformation come from? Enterprises carry an estimated $1.5 tn in technical debt, while maintaining legacy systems consumes nearly $500 bn in annual tech spending. This burden is also an opportunity. AI's first meaningful contribution may not be glamorous. It could quietly optimise and automate the legacy layer, with resulting efficiencies funding the next wave of AI adoption. But the IT industry must astutely manage the resulting revenue contraction and redistribution if it's to participate meaningfully in new pools of value.



AI is not a single market but an ecosystem. Value is already being generated across the entire stack – from silicon to data infrastructure, from energy generation to foundation models, and ultimately to enterprise applications.



Much of the early narrative focused on foundation models, creating fears that application software would become commoditised. But there is no 'SaaSocalypse'. Enterprise software is evolving. Some applications will become thinner as intelligence migrates into the platform, while others will become richer by embedding AI deeply into workflows.



The AI-native enterprise is likely to evolve around 4 interconnected layers of systems:



Engagement: Customer-facing interfaces where humans interact with intelligent software.



Agents: Networks of AI agents that reason, collaborate and execute tasks.



Work: Operational platforms and workflow systems that run the enterprise.



Context: Enterprise data, knowledge, governance and memory that ground AI in organisational reality.



The alpha of individual companies may change. But it will still have to be coded to their specific systems and context. This is precisely where IT partners have played a role in the past, and that role is unlikely to diminish in an AI-native world.



Software was always a deterministic science. But in the AI context, software is becoming probabilistic, and increasingly capable of self-improvement. So, can AI-native systems coexist indefinitely with IT infrastructure that was designed around human operators?



The tech for autonomous vehicles has existed for years. Yet, adoption has been constrained by the difficulty of running manual and autonomous cars in the same lanes. Likewise, AI-native systems may never achieve their full potential while operating inside workflows designed for human decision-making. This could mean wholesale reengineering of the systems stack, creating more revenue opportunities for tech-services companies.



Evolution of SaaS offers a useful precedent. Successful SaaS companies largely emerged in 3 categories:



Universal needs: Email, calendars, collaboration and search, now dominated by established tech giants.



New consumer categories: Ride-sharing, food delivery and home-sharing, where agile startups established leadership before incumbents recognised the opportunity.



B2B specialisation: Software built around deep vertical or functional expertise, becoming indispensable to specific industries or business functions.



AI appears to be following a similar trajectory in broad consumer applications such as AI assistants as well as surprising, new categories. The largest long-term opportunity, however, may lie in enterprise AI: domain-specific models, industry-specialised applications and intelligent systems deeply integrated into business processes.



As with SaaS, this layer is unlikely to produce a single winner. Instead, it could spawn hundreds of companies serving specialised markets. IT companies' trajectory will increasingly depend on enabling these domain-specific AI solutions, from multi-step, AI agent-driven loan processing to autonomous cyber defence.



Future of enterprise AI won't be determined solely by breakthroughs in model architecture, but by how effectively these models are integrated into the messy, heterogeneous and highly regulated reality of global business. That is where the global ITeS industry becomes indispensable.



If AI is the engine, IT services provide the road that allows it to move at scale. Some legacy IT players will adapt. Others won't. But successful ones are likely to unlock disproportionate value. Most technology revolutions progress through 3 phases:



Extractive: Foundational capabilities developed.



Distributive: Technologies deployed at scale.



Destructive: Incumbent architectures displaced.



Legacy enterprise software is entering its destructive phase, while AI remains largely in its extractive phase, with rapid advances in models and infrastructure.



Historically, global IT services firms create the greatest value for themselves when an emergent tech enters its distributive phase. This thesis has held true for mainframes, packaged applications, internet, cloud and SaaS. There's no reason to believe that AI will be different. So, the good times for the IT industry in the AI world may actually be around the corner.



The writer is former head of technology services investment banking, Avendus Capital

(Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of www.economictimes.com.)

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