For a long time, digital transformation was treated mainly as a technology question. Which platform should we buy? What should we automate? How do we migrate to the cloud? In 2026, that framing is no longer enough. The real issue is now the operating model: how work flows across teams, how decisions are made, how data moves, where accountability sits, and how humans and AI interact in real workflows. Recent McKinsey research says organizations need to “rewire structures and workflows end to end to scale AI” and shift from traditional functions toward outcome-oriented operating models.
That change is happening because newer technologies, especially agentic AI, do not just support tasks. They cut across workflows. McKinsey’s April 2026 guidance argues that companies need to identify high-impact workflows to “agentify,” modernize the data architecture around them, and redesign how work gets done rather than layer AI onto existing routines. The World Economic Forum is making a similar point, highlighting end-to-end operating model redesign as one of the core principles for adopting frontier technologies at scale.
Technology no longer creates value on its own
A new system can still improve visibility. Automation can still remove manual effort. AI can still generate insight. But none of these automatically improve the business if the operating model underneath remains fragmented. A company can have strong tools and still suffer from slow decisions, duplicated work, siloed ownership, and weak accountability. That is why digital transformation increasingly succeeds or fails at the level of operating design, not software deployment. McKinsey’s recent “agentic organization” research says the best use cases come when an entire workflow is reimagined, especially where work crosses multiple teams and touchpoints.
This is the point many organisations miss. They digitise the old model instead of redesigning it. They keep the same handoffs, the same approvals, the same reporting logic, and the same functional silos, then expect better results because the system looks more modern. The outcome is usually more digital activity, not necessarily better performance. McKinsey’s 2026 research on operations describes the competitive challenge as a “race to rewire,” not a race to buy more software.
AI is accelerating the shift
AI is making the operating-model question harder to avoid. Earlier digital programmes often focused on visibility and analytics. AI agents push closer to execution. They can coordinate tasks, synthesize information, recommend next steps, and sometimes act across systems. Once that becomes possible, the old operating model starts to show its weaknesses very quickly.
If ownership is unclear, the agent does not know where authority ends. If data is inconsistent, outputs become unreliable. If escalation paths are weak, automation creates risk instead of value. That is why McKinsey argues that scaling agentic AI depends on interoperable architectures, governance, and workflow redesign, while its broader organizational work says many roles need “fundamental reshaping” right now.
In other words, AI is not just another tool entering the business. It is exposing whether the business is designed to operate well in the first place.
The operating model is where human and digital meet
This shift also matters because digital transformation is becoming more human, not less. The best operating models are no longer just efficient. They also need to be understandable, governable, resilient, and usable by real people.
The European Commission’s Industry 5.0 framework continues to define modern industrial transformation around human-centricity, resilience, and sustainability, not technology alone. The World Economic Forum likewise emphasizes human accountability, scalable talent systems, and transparency-driven trust alongside operating-model redesign.
That means leaders cannot separate digital design from people design. If roles change, workflows change. If AI takes on more routine execution, human work shifts toward judgment, supervision, exception handling, and orchestration. If teams remain structured around old functional boundaries while work becomes more cross-functional, the organization slows itself down. McKinsey’s research on designing a technology workforce for the AI-first era says companies must rebalance hiring, internal capability building, and team design as agents take on more work.
Why the old functional model struggles
Traditional operating models were often built around functions: IT, operations, finance, quality, HR, supply chain. That structure can work reasonably well when work moves in stable, predictable ways. It struggles more when digital value depends on end-to-end coordination.
Modern transformation often cuts across many areas at once. A workflow may involve customer data, operational systems, approval logic, analytics, compliance, and AI support all in one chain. If each piece is owned separately and managed to local goals, the whole system becomes slow and brittle. McKinsey’s State of Organizations 2026 says companies need to move from traditional functional structures toward outcome-oriented models to scale AI effectively.
That is why digital transformation is now an operating model question. It is about whether the organization is designed around internal boundaries or around real outcomes.
Governance is now part of the model, not an add-on
Another reason operating models matter more is that governance can no longer sit on the edge of transformation. As digital systems become more autonomous and interconnected, governance has to be embedded directly into workflows.
McKinsey’s 2026 agentic AI guidance stresses governance across systems and visibility across workflows. The World Economic Forum’s 2026 materials similarly point to human accountability and trust as core requirements for scaling advanced technologies. That means override rights, auditability, escalation logic, and decision boundaries all have to be designed into the operating model itself.
A business cannot claim to be digitally mature if it still relies on unclear ownership and informal workarounds to control risk.
What leaders should ask now
If digital transformation is now an operating model question, leaders need to start asking different questions.
Not just: What technology should we deploy?
But: Which end-to-end workflows matter most?
Where are the slowest decisions?
Which handoffs create friction?
What should humans own, and what should AI support?
How should teams be structured around outcomes rather than functions?
What governance needs to be built into the flow of work itself?
Those questions are much closer to organization design than classic IT planning. McKinsey’s 2025 and 2026 work consistently points in that direction: the companies capturing value are the ones redesigning workflows, teams, and structures around how work actually gets done.
Conclusion
Digital transformation is now an operating model question because technology is no longer sitting at the edge of the business. It is moving into the core of how work is executed. AI, automation, and connected systems are forcing companies to rethink workflows, team design, accountability, governance, and human roles. The old model of “install the tool and the value will follow” is becoming less credible by the year.
The companies that move ahead will not be the ones with the most platforms or the most pilots. They will be the ones that redesign how work flows across the business, align technology with outcomes, and build operating models that are fit for a more AI-enabled, human-centred, and resilient future.