A Cognitive Revolution Requires More Than Incremental AI: SupportLogic CEO & VMware Vet Krishna Raj Raja

A strategic divide is emerging among enterprises. One camp deploys AI to accelerate legacy workflows, chasing hyper-efficiency. The other sees a more fundamental opportunity: using AI to eliminate and reinvent those processes altogether. The gap between those approaches is quickly becoming a competitive fault line.

We spoke to Krishna Raj Raja, Founder and CEO of SupportLogic. A foundational member of the team at CloudPhysics, Raja’s perspective was forged during his time as the first hire for VMware’s Bangalore office, where he witnessed the industry's transition to a cloud-first world. For Raja, the lessons from that era are a direct blueprint for today. He believes many companies treating AI as a simple efficiency lever are thinking too small—a miscalculation that risks commoditization and a race to the bottom.

“If AI is only making your existing workflows faster, you’re thinking too small. The real advantage comes when you use AI to eliminate the process altogether and redesign the business around what’s now possible,” says Raja. That flawed mindset, Raja notes, is so ingrained that when presented with a tool to eliminate customer escalations, many leaders will instead ask for a better way to manage them.

• Redefine business value: The central risk of an efficiency-only strategy isn't just a lack of imagination, it's economic. Raja warns that the initial productivity gains rarely remain proprietary. “If you only make your operations efficient, what’s to stop your customers from demanding you pass on those savings at renewal time?” he asked. “They’ll argue that you haven't added any new value, so they should get a lower price. It becomes a race to the bottom.” It's a line of thinking that sidesteps the more valuable opportunity to redefine business value and creates a low-cost commoditization trap that can erode margins.

• Persistent AI: True enterprise value, according to Raja, emerges from a model entirely different from the session-based, interactive paradigm of consumer tools like ChatGPT. He describes a persistent, autonomous AI that operates in the background, eliminating the inefficient "swivel chair" work of switching between applications.

Moving from workflow automation to structural redesign represents more than an incremental improvement. For Raja, it marks a new chapter in technological history. He frames the current moment as the third great economic transformation, following the Industrial and Digital Revolutions. He notes that while these waves often began with an efficiency play, their durable, long-term value was ultimately realized by enabling entirely new economic structures and a reinvented business model.

• The Cognitive Revolution: "The true impact of the Industrial Revolution wasn't just making manufacturing faster, it was laying down roads that opened up markets and shifted the world from a local to a global economy. That is the kind of fundamental shift in value generation we should be seeking from this new Cognitive Revolution."

• Revenue-centered mindset: According to Raja, making this shift requires abandoning a "cost center mindset" in favor of a "revenue center mindset." "If you are looking at AI purely inward-looking, with an internal, organizational look and say, 'I'm going to use this to fire my employees, to increase my bottom line,' all of that, in the long term, it may impact your revenue growth." He points to the cautionary tale of Red Robin, which focused on internal cost-cutting, only to see the customer experience suffer and its stock price plummet. Chili's, in contrast, invested in experience and saw revenues climb. The lesson, Raja says, is to reinvest AI-driven efficiency into growth, transforming it from mere savings into a strategic asset.

One path to doing that, Raja says, is to use AI to create a premium, compelling product experience. The other is to use that efficiency as fuel for expansion. By making a service radically cheaper, AI can unlock entire markets of users who were previously priced out. That approach allows a company to create new offerings or enter new geographies that were once cost-prohibitive. But to get to that point, this transformation will require work, as it means confronting the messy reality of enterprise AI adoption and the hurdles that come with it.

• Proving efficiency gains: Raja is pragmatic about the first hurdle: proving that any claimed efficiency gains are even real. "Take AI-assisted coding. Developer productivity seems to increase, but that gain is often erased by the time spent debugging and maintaining the code. When you look at the total cost of ownership, the net gain isn't there. You must first establish that an efficiency gain is realistic and measurable."

• All-in: Before any grand vision can be realized, gains must be governed with proper AI observability, and many companies must first undertake the unglamorous work of building a modern data foundation. This foundational complexity, Raja says, is precisely why a cautious, incremental approach often falls short. "Too many leaders tiptoe into AI, testing the waters with half-hearted pilots. That approach is doomed because they back off at the first sign of pain. Real transformation requires conviction. You have to go all in, endure the painful transition period, and accept the risk. You have to burn the ships and move."

Raja’s call for conviction is rooted in his own experience at the forefront of the last great tech disruption. Drawing from his time at VMware, he recalls a clear dividing line emerging between companies that hesitated to adopt the public cloud and those that mandated a "Cloud First" strategy. The latter group, in many cases, endured a painful and expensive transition to gain a massive competitive advantage, while competitors who stayed on-prem were left behind. The parallel to the global tech scene today, from Silicon Valley to the burgeoning India AI ecosystem, is clear.

"Just as we saw with the Cloud First strategies of the past, enterprises today need to adopt an AI First mandate. Every new application must be AI-native, alongside a radical plan to migrate existing systems. It cannot be AI slapped onto an existing workflow. It has to be fully AI-first," concludes Raja. Such a mandate reflects a growing consensus: a genuine AI strategy is quickly becoming a prerequisite for growth. As Raja's 'Cloud First' analogy suggests, a playbook for this kind of transformation may already exist. The only question is whether today’s leaders have the conviction to use it.