From IT Control to Technology Operating Model Orchestration

Rather than evolving incrementally from legacy structures, CIOs must deliberately select and design a technology operating model archetype that reflects the organization’s business strategy, risk posture and ambition for technology-enabled growth. Whether emphasizing efficiency, scaling output, enabling business-led innovation or expanding enterprisewide technology creation, each archetype defines a different balance of control, autonomy and collaboration.

Alignment requires ensuring that operating model components work cohesively to support this choice, enabling CIOs to reduce friction, focus investment and orchestrate technology capability at scale. Without this intentional alignment, organizations risk defaulting into fragmented delivery models that undermine both performance and enterprise coherence. 

CIOs cannot optimize for every objective simultaneously and must make deliberate trade-offs between cost, innovation, agility and reliability, aligning these choices to the priorities of the enterprise. The focus of the technology operating model should reflect whether the organization is pursuing efficiency, growth, customer experience or resilience, rather than attempting to achieve all outcomes equally.

Implementing this shift requires building alignment with executive peers, securing sponsorship and empowering teams to adapt within clearly defined boundaries. The scale and pace of change must match the organization’s capacity to absorb it, with clear articulation of expected benefits and ongoing measurement to ensure the operating model delivers tangible value.

Sourcing and shared services are no longer just cost optimization levers, but critical design choices within the technology operating model that determine how value is created and scaled. Shared services and rightsourcing are evolving rapidly, with 71% of IT managers expecting increased use of shared services and 89% anticipating greater reliance on AI, reflecting a shift toward platform-based, AI-enabled delivery. CIOs must therefore move beyond fragmented, bottom-up sourcing decisions and adopt a deliberate, principle-led approach that aligns sourcing with enterprise priorities for value, cost and risk.

This includes focusing outsourcing on nondifferentiating capabilities while retaining strategic capabilities in-house, and increasingly leveraging AI to substitute external labor, with up to 40% of external IT resources expected to be replaced by internally enabled employees. When supported by strong governance, funding transparency and clear decision rights, shared services become a coordinated system that strengthens resilience, improves cost efficiency and accelerates enterprisewide innovation rather than fragmenting it.

Governance structures must become more flexible and adaptive to balance risk management with the need for speed in an AI-driven, distributed environment. Traditional, centralized models cannot keep pace, requiring CIOs to adopt dynamic, principles-based approaches that embed guardrails, decision rights and accountability directly into platforms and ways of working.

Governance must shift from periodic oversight to continuous orchestration, with performance measures aligned to business outcomes to drive transparency and ongoing optimization of the technology operating model. At the same time, the cost of governing AI is rising, with Gartner estimating that by 2028, AI governance costs will offset 60% of the savings generated by agentic AI, reinforcing the need to design governance that scales efficiently while maintaining control and trust.