In enterprise boardrooms across the world, the pressure to invest in artificial intelligence has become a top-down mandate. The fear of being left behind can easily overshadow the discipline of strategic planning that traditionally defined a methodical enterprise IT approach. The central question is no longer if a company should adopt AI, but how it can do so with a clear purpose that separates tangible value from technological hype.
For IT leaders like Ganesh Ariyur, the answer lies in a foundational return to first principles. As the Global Head of Enterprise Technology Strategy & Transformation at Gainwell Technologies, a $3 billion PE-backed healthcare technology company, and active Forbes Technology Council member, Ariyur operates where the stakes are highest. His work is guided by a simple but powerful philosophy that cuts through the noise: technology does not create value; strategy and people do. This perspective, which Ariyur expanded on in a recent LinkedIn post, reframes the AI conversation away from tools and toward outcomes.
AI is not the strategy: "AI itself can never be a strategy; true strategy is rooted in business advancement," Ariyur said. "AI is a great enabler, but IT leaders should never think in terms of models or tools first. The best results come from thinking about the business outcomes first."
Ariyur warned that even the most promising AI tools are set up to fail if they originate in a silo. He pointed to a common pitfall that has doomed countless transformation projects long before AI entered the mainstream.
Plotting a path through pain points: "It should never be IT-driven. That's the number one factor in failure," he said. Though Ariyur is a technology leader, he is adamant that the business leaders must be the executives championing any new initiative. His approach is to go directly to his C-Suite peers and ask a simple question: "What are your top three challenges facing your organization?" By understanding their pain points first, he can then identify how technology can serve as the solution.
Before any AI model can deliver on its promise organizations must confront a more fundamental challenge: their own data. He pointed to an industry-wide problem that is the single biggest cause of failure for AI initiatives.
Fix your data first: "85% of AI failures are related to poor data quality or lack of 'AI-ready' data," he said. "If you don't have quality data, you are going to run into this all the time."
Accumulated tech debt: The issue, he notes is rooted in years of accumulated technical debt that hinders a unified data structure. Ariyur uses a simple analogy to explain the problem: "When I say 'sales,' is it really sales? When the data moves from one system to another, it should be consistent and have the same definition." The solution is to empower a data steward to create a simplified data landscape and architecture with a strong foundation upon which to build.
In a highly regulated industry dealing with sensitive patient health information, Ariyur’s team takes a cautious and deliberate approach to innovation. Rather than attempting a massive, "big bang" rollout, he champions a more methodical path.
Start small, scale fast: "I would say start slow, start small, get that win, and generate buzz," he advised. "Let's identify the low-hanging fruits, get some quick wins under our belt, and then expand." He pointed to a successful AI pilot in Gainwell’s HR department where internal "buzz" created tangible quick adoption. The project's success led the company's CFO to request a strategic roadmap to expand the initiative across the organization.
Ultimately, this business-first, outcome-driven approach isn't just good practice, but a requirement. Operating in a private equity-owned environment means every investment must answer to the bottom line. Efficiency and impact are non-negotiable, and that focused push drives IT leaders like Ariyur to innovate more efficiently than ever.
How to pitch a project: "Before embarking on any initiative, I'm always asked two things: 'Where is the value, and how much cost are you saving?'" This reality shapes how he pitches every project. Rather than asking for money to implement AI; he presents a business plan to solve a problem, where AI is simply one of the tools. It’s a mindset that transforms technology from a cost center into a collaborative engine for growth. "And at the end of the day, it's one team, rooted in collaboration and a successful partnership where the business strategy always comes first."
