Kia America’s head of IT, Subba Kethu, has laid out a blunt playbook for enterprise modernization that rejects the big-bang replacement mentality. In a new CIOReview leadership perspective published August 1, 2026, Kethu argues that legacy systems like AS/400 workloads, on-premises SAP installations, and heavily customized Windows-dependent applications are not obstacles to innovation—they’re the bedrock that keeps business processes earning revenue every day. His core message: measure modernization against business outcomes, not the volume of retired infrastructure.

The case for keeping your old iron (and code)

Kethu draws a sharp line between age and obsolescence. A platform can run on decade-old hardware and still be remarkably reliable at executing the transactions that matter. The real risks, he says, are not the servers themselves but the undocumented dependencies, brittle interfaces, inconsistent data ownership, and unsupported infrastructure that accumulate around them over years.

“Legacy platforms often contain institutional knowledge, business logic and data that power the enterprise,” Kethu writes, noting that Kia North America still runs critical workloads on platforms like AS/400. The issue is not that these systems are old; it’s that they become difficult to scale, expensive to maintain, or unable to support new digital capabilities.

This distinction matters for Windows administrators. Many enterprises run Windows Server as a frontend or middleware layer for older ERP systems. An IBM i (AS/400) workload may feed transaction data to a .NET application hosted on Windows Server, which in turn exposes APIs for a modern web portal. Ripping out that aging back end without a proven replacement can destabilize everything downstream—even if the Windows layer itself is up to date.

Kethu’s framework points to a staged approach: modernize the experience layer first without disrupting the transactional core. Kia’s paper-to-digital eDelivery platform, built with Infosys, is a case in point. It digitized a formerly manual vehicle-delivery process for dealers and customers while leaving surrounding systems untouched. “That is the more practical form of modernization: improve a visible process while avoiding unnecessary disruption to every system around it,” notes the Forum’s analysis of Kethu’s contribution.

Cloud isn’t a destination—it’s a capability decision

Kethu describes a multi-cloud strategy where AWS hosts customer-facing applications and Azure supports AI and generative-AI work. But he warns against treating cloud migration as a finish line. “Moving a tightly coupled application from a virtual machine in a corporate data center to a virtual machine in the cloud can preserve every existing operational weakness while adding consumption-based spending,” the Forum piece observes.

Instead, Kethu frames cloud as a modernization enabler. The question isn’t “Can we lift and shift this server?” but “Can this workload now recover in another region? Can it scale to meet demand spikes without an emergency procurement cycle? Can we instrument performance and security consistently?”

For Windows teams managing hybrid environments, this means identity architecture, endpoint compliance, privileged-access controls, and patching cannot become afterthoughts just because a workload moved to Azure. Microsoft’s own documentation on Azure Copilot stresses that the tool operates within a user’s existing permissions and policy boundaries. Its value, therefore, depends on identity hygiene, least privilege, role design, and accurate resource governance. AI doesn’t compensate for an overly broad administrator role or an unowned subscription.

ERP modernization must be more than an end-of-support panic

Kethu identifies SAP S/4HANA as Kia’s “digital backbone” for future automation and analytics. For many organizations, however, ERP modernization is triggered by an impending end-of-support deadline, not a business case. That’s a dangerous trap, he implies.

A move to S/4HANA, Dynamics 365, or another modern ERP platform forces an enterprise to confront process variation, data quality, custom code, and regional operating differences that may have accumulated for decades. Finance close, procurement, logistics, warranty processing, and dealer incentives are all interdependent. Every interface has an owner; every exception is a potential failure point; every cutover changes who can see, approve, and correct business data.

Kethu’s practical solution is to establish a modernization portfolio rather than a single program, organized around three priorities: running, improving, and transforming the business. “It is a useful budget lens because it prevents innovation funding from silently cannibalizing the operational teams that keep the company running,” the Forum analysis explains. Under this model, some investments protect reliability (patching, backup testing, identity hardening), others improve efficiency (integration, automation, platform upgrades), and a smaller set pursues transformative capabilities (AI-assisted support, predictive analytics, digital customer channels).

AI won’t fix your messy data—govern it first

The strongest warning in Kethu’s playbook targets the current rush to deploy generative AI: modernize data before trying to scale AI. That’s not a blanket objection to copilots or retrieval-augmented generation (RAG). It’s a recognition that these tools magnify existing information-management problems.

RAG grounds a model’s responses in enterprise documents, knowledge bases, and other approved data sources. If the source content is stale, duplicated, poorly classified, or visible to the wrong audience, the AI layer will make those weaknesses easier to discover and potentially harder to contain. Microsoft’s guidance for Copilot Studio emphasizes the importance of data handling, compliance boundaries, and access controls when building generative answers around organizational knowledge.

Kethu says Kia began its Copilot strategy with a targeted pilot, measured productivity outcomes, and then expanded through structured training, communication, and governance. A sensible first wave of AI work focuses on bounded tasks: internal policy lookup, service-desk knowledge retrieval, document summarization, or developer assistance. These reveal whether the organization’s data and governance are ready for wider deployment and provide a baseline for measuring time saved, error rates, and user adoption.

Governance: the accelerator, not the gatekeeper

Governance has a reputation as the department that says no. Kethu reframes it as the mechanism that lets an enterprise say yes repeatedly without reinventing security, privacy, procurement, and ownership decisions for every new idea.

For AI programs, the minimum framework should establish who can approve a use case, which data classes may be used, how access is enforced, what human review remains necessary, how outputs are logged or retained, and how a deployment can be suspended if it produces harmful or unreliable results. For cloud and application modernization, similar standards should cover landing-zone design, identity, network controls, backup objectives, architecture exceptions, and vendor accountability.

The Hyundai Motor Group’s 2026 Global IT Forum (noted in the Forum analysis) saw Kethu emphasize the need for more process documentation, open problem-sharing, and a better way to share solutions across the wider organization. That’s not bureaucratic overhead: in distributed enterprises, reuse depends on teams being able to find a proven pattern, understand its assumptions, and adopt it without recreating it from scratch.

“The real modernization deliverable, then, is not simply a migrated workload or an AI pilot,” the Forum piece concludes. “It is a repeatable operating model that allows the next workload and the next pilot to move faster with fewer surprises.”

What Windows shops should do this month

Kethu’s playbook translates into immediate, practical steps for IT leaders managing Windows workloads in hybrid environments:

  1. Inventory business processes, not just servers. Before deciding to replace an aging AS/400 or an old SAP instance, ask which business processes it supports and what would break if it went offline. This exercise often reveals that the system is more deeply integrated than realized.

  2. Separate the experience layer from the transactional core. A dealer portal can move to Azure or AWS while retaining integrations with an on-prem ERP. Windows administrators can drive this by modernizing the front end with .NET Core or Power Apps without touching the back-end databases until ready.

  3. Get your data house in order before any AI project. If you can’t reliably find a policy document or a service manual, a Copilot won’t either. Start with a data hygiene sprint: clean up file shares, SharePoint libraries, and knowledge bases, and lock down permissions.

  4. Define governance guardrails now. Even if you’re only piloting Copilot, decide who can approve a use case, what data sources are off-limits, and how outputs will be audited. Microsoft provides templates via Purview and Azure Policy—use them.

  5. Budget across run, improve, and transform. Don’t let innovation spending starve patching cycles, backup testing, or identity hardening. A balanced portfolio keeps the lights on while you build new capabilities.

Bottom line

The organizations that get modernization right won’t be defined by how quickly they abandon legacy platforms, but by how reliably they turn existing systems into foundations for what comes next. Kethu’s message is a sobering one for teams tempted by big-bang rip-and-replace: protect the dependable systems that earn revenue, expose their value through modern interfaces, and retire only what no longer earns its place.