New research serves as a wake-up call for CIOs and CTOs bleeding budget on implementation.
Enterprise transformation isn’t short on software; it’s weighed down by code sprawl and the ecosystem built to maintain it. Each new bespoke line of code adds a maintenance tail that inflates services spend and tech debt, pulling budgets into integration and upkeep while innovation waits. That strain on IT increases reliance on systems integrators and crowds out software-led change. Artificial intelligence (AI) will compound the problem if it simply generates more code on legacy stacks; however, used within guardrails to refactor, standardize, and assemble governed components, it can reduce tech debt. The current operating model must evolve.
HFS Research, in partnership with Unqork, surveyed 123 large enterprises to understand IT budgets, services-to-software ratios, systems integrator (SI) relationships, AI adoption, and governance patterns. The survey uncovered:
The Bottom Line: As enterprises generate more “AI-assisted” code, they find themselves in an operating model with unintended consequences, such as runaway maintenance costs and increasing tech debt. IT is overwhelmed maintaining the systems they currently run while business leaders demand more, faster from them. Simultaneously, the C-suite is applying mounting pressure for AI implementation—not just pilots, but production with real return on investment.
The way through is a shift in models and architectures. IT needs to flip the services ratio and move from projectized services to productized outcomes in architectures that minimize customer code creation, maximize reuse, and embed governance so that AI reduces, rather than creates more tech debt.
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