CIOs today are navigating a complex paradox: they are expected to modernize legacy-heavy environments, unlock AI-driven value, and strengthen security—all while budgets tighten and risk tolerance shrinks. Incremental change feels safer, but existential reinvention is increasingly unavoidable.
As enterprises grapple with aging infrastructure, AI acceleration, and rising cyber risk, incremental modernization is no longer enough.
Few leaders have a clearer vantage point on this tension than Kim Basile, CIO of Kyndryl, the world’s largest provider of mission-critical IT infrastructure services. Having led Kyndryl’s own radical post-separation transformation as a “Customer Zero,” Basile brings a rare blend of operational empathy and strategic candour to conversations with fellow CIOs.
During her recent visit to India, she spoke with ETCIO. In this interview, she reflects on the ambition gap she sees across enterprises, why data and culture—not technology—determine AI readiness, how customer confrontation led to the creation of Kyndryl Bridge, and why trust has become the true enterprise perimeter.Kyndryl’s scale gives you a panoramic view of enterprise modernization journeys. In CIO-to-CIO conversations, what have you observed about this ambition gap? Where do organizations most often misjudge the leap from incremental change to true reinvention? How do you balance empathy for operational realities with the need to challenge strategic assumptions?
One of the most revealing insights came from our recently published Readiness Report, where five clear themes emerged.
Second, many organizations underestimate their foundational barriers. Around 57% told us that innovation is delayed because of their existing technology stack. Aging infrastructure and accumulated technical debt slow progress far more than leaders initially expect.
Third, there is a widespread tendency to get stuck in pilot mode. Proofs of concept start, stall, and restart—creating a cycle of momentum loss that frustrates teams and dilutes business confidence.
The fourth theme is data. Data sits at the core of everything, yet many enterprises struggle with consistency, structure, and governance across platforms. Without trusted, well-organized data, speed becomes impossible. As the saying goes: garbage in, garbage out.
Finally—and most critically—there is workforce readiness and culture. I strongly believe that technology alone doesn’t transform organizations. People do. CIOs often tell me how difficult it is to balance technology priorities with talent, skills, and change management. But without that balance, transformation simply doesn’t scale.
At Kyndryl, we firmly believe business processes should lead. Technology should enable—not dictate—them. If you modernize tools but retain outdated processes, you defeat the very purpose of transformation. Scale, speed, and pace must move together.
As a CIO, you often speak about being “Customer Zero.” How do you decide which internal innovations are mature enough to externalize into client offerings?
We marked our fourth birthday as an independent company and being Customer Zero is central to how we operate at Kyndryl. In fact, our largest and most defining Customer Zero story was our TSA exit and enterprise-wide transformation following independence.
We reduced our application landscape from over 1,800 applications to just over 300, consolidated 68 data warehouses into one, modernized our network, adopted strategic platforms like Microsoft 365, SAP, ServiceNow, Workday, and Okta—and achieved roughly 50 percent IT cost savings.
That level of transformation isn’t feasible for every organization. But the learnings are transferable. Whether it’s application rationalization, network modernization, or data consolidation, pieces of that journey can be adapted to different enterprise realities.
The real differentiator, however, was culture. We invested deeply in helping employees understand why we were transforming, how it would happen, and what role they played. Once people felt empowered and aligned, technology execution accelerated dramatically.
My personal “aha” moment came with AI. Our ability to pivot quickly into AI initiatives was only possible because our data foundations were already in place. With a simplified stack and structured data, we could rapidly establish governance, control access, and experiment responsibly.
We moved from early Microsoft Copilot pilots to what we now call AI Garage Labs—a sandbox for testing and scaling real use cases. That experience reinforced a powerful lesson: strong data foundations turn experimentation into execution. And that’s how Customer Zero becomes repeatable.
Have you faced a situation where a customer challenged your assumptions, and that confrontation led to a breakthrough in how Kyndryl delivers value?
Absolutely. When your customers are CIOs, they challenge you by default—and rightly so. Trust isn’t assumed; it’s earned.
One of the most important outcomes of those conversations was Kyndryl Bridge, our AI-powered, open integration platform. Customers wanted transparency. They asked: How do we know our infrastructure is healthy? Are we ahead of end-of-life risks? Are vulnerabilities being addressed proactively?
Kyndryl Bridge was built to answer those questions. Today, it delivers nearly 15 million actionable insights across more than 1,200 customers—covering asset health, patching, vulnerabilities, and risk exposure.
What makes it powerful is that customers see exactly what we see. It’s not just a tool; it’s an operating model. As patches or vulnerabilities emerge, Kyndryl Bridge pinpoints the affected assets and routes insights to the right teams.
Over four years of refinement, it has become highly targeted—cutting noise and focusing attention on what truly matters. That transparency builds trust and removes friction from the relationship because customers know we are aligned with their priorities.
Many enterprises underutilize their core platforms. How does Kyndryl unlock hidden value in systems like SAP to drive efficiency and innovation without new investments?
Internally, we use a concept we call “batteries included.” It refers to the capabilities already embedded within strategic platforms that organizations often overlook—especially newer AI-driven features released through updates and feature packs.
We take a structured approach. Through user forums, working groups, and functional-technical partnerships, we ensure users understand not just how platforms work, but what more they can do.
A recent example is our migration to SAP RISE. While technically complex, it was executed over a single weekend with no business disruption. Crucially, finance teams worked alongside IT throughout the process. That ensured visibility, alignment, and a focus on business functionality—not just infrastructure.
We apply the same model across Workday, Okta, and Microsoft 365. These platforms succeed when IT and business teams operate as equal partners. Transformation is not IT for IT’s sake—it must enable measurable business outcomes.
In an era where interconnectivity fuels both growth and cyber risk, what governance principles help Kyndryl protect trust at scale?
The threat landscape is evolving rapidly—AI only accelerates that complexity. The most important starting point is visibility: knowing what you have, where it is, and how it behaves.
From there, you need an action-oriented environment—tools and people capable of responding in real time. At Kyndryl, this is powered by a combination of advanced platforms and highly skilled talent.
Equally important is collaboration and intelligence sharing—internally, with customers, and across the industry. Security improves when insights are shared.
Platforms like Kyndryl Bridge allow us to understand impact instantly—what’s affected, where, and how to remediate—without lengthy analysis cycles that are often slowed by technical debt. That maturity has fundamentally changed how we manage risk.
With trust becoming the new perimeter, how do you operationalize it across hybrid, cloud-native, and legacy environments?
Trust begins—and ends—with the business. Our recently announced agentic framework is not just a technical model; it’s a methodology. It starts with Kyndryl Vital, where we work with customers to define the business problem first—before any technology discussion.
Technology is not the challenge. Applying it meaningfully is.
Through forward-deployed engineers, we co-create solutions with customers—mapping processes, outcomes, and risks end to end. Technology is layered only after clarity on business intent.
We also invest heavily in skills and enablement. AI training at Kyndryl is role-specific—from foundational learning to advanced, customer-facing use cases. This ensures teams feel confident, credible, and empowered.
Experimentation is encouraged—but with discipline. We believe in failing fast, but not failing endlessly. Proofs of concept must translate into business value.
Looking ahead to 2026, what AI-related risks and trends should organizations actively prepare for?
Two foundational things stand out: continuous monitoring and governance.
Every AI use case at Kyndryl goes through an AI governance board. This isn’t about slowing innovation—it’s about establishing guardrails. We validate data access, business value, and risk exposure quickly, in real time.
AI agents must be registered, tracked, monitored, and approved before entering production. Teams dedicated solely to agent governance ensure data access is appropriate and tightly controlled.
The future demands real-time oversight. Organizations that lack visibility into their AI agents—or where they pull data from—will struggle to manage risk effectively.
Finally, what innovations from Kyndryl should customers look forward to in 2026 and beyond?
Our focus is clear: helping customers become AI-ready—securely and at scale.
We will continue to modernize technology stacks using AI, drawing from our own Customer Zero experiences. AI transformation is not theoretical for us; it’s operational.
With the right people, platforms, and governance, we believe Kyndryl is uniquely positioned to guide enterprises through the next phase of transformation—where AI moves from experimentation to enterprise impact.
