The CIO role is expanding from technology infrastructure into workforce design, AI governance and business operating models as artificial intelligence becomes embedded across enterprises. New global research from Thoughtworks, based on a survey of 3,200 CIOs across 10 countries, finds that 89% of CIOs now see themselves as more responsible for redesigning workforce workflows and labor models than managing core IT infrastructure. The research also shows a widening gap between AI adoption and the governance structures intended to control it.
Artificial intelligence is changing more than enterprise software. It is beginning to reshape who does the work, how decisions are made and where accountability sits—and that is pulling CIOs into responsibilities traditionally associated with operations, HR and executive leadership.
Research from technology consultancy Thoughtworks illustrates the shift. Its survey of 3,200 CIOs across 10 countries found that 89% globally agree their role now carries greater responsibility for redesigning workforce workflows and labor models than for managing core IT infrastructure.
The finding suggests that the traditional CIO mandate is being rewritten as enterprises move from experimenting with generative AI to embedding AI into everyday business processes.
In Singapore, 84% of CIOs reported a similar expansion of their responsibilities.
AI adoption is moving faster than governance
The organizational shift is happening while many enterprises struggle to keep governance frameworks synchronized with AI deployment.
Thoughtworks found that 88% of CIOs globally say AI adoption is progressing faster than their organization’s governance structures can adapt. In Singapore, the figure is 80%.
That gap creates a difficult position for technology leaders. CIOs may be expected to ensure AI systems are secure, compliant and reliable even when individual business units can independently select and deploy AI tools.
The research found that 90% of CIOs believe central IT would ultimately be held responsible for security breaches or compliance failures caused by AI systems purchased independently by business units.
At the same time, 37% of CIOs said they feel personally accountable for AI-related security incidents, 35% for data privacy breaches and 34% for reputational damage caused by AI misuse.
The result is an emerging accountability mismatch: decision-making can be distributed across the organization while responsibility for failures remains concentrated on technology leadership.
AI governance is becoming a workforce problem
Thoughtworks argues that governance cannot be separated from workforce design.
As AI systems automate tasks or alter workflows, organizations have to determine which decisions remain with employees, which can be delegated to AI and where human review is mandatory.
That makes AI governance more than a technology-control function. It becomes an organizational design exercise.
The distinction is particularly important for enterprise AI agents. Unlike conventional software, AI agents can interpret information, generate recommendations and potentially execute multi-step actions. As these systems move deeper into business workflows, organizations need clear decision rights and escalation paths.
The challenge is compounded by the fact that AI capabilities are being introduced across departments rather than solely through central IT.
Thoughtworks found that globally, the CEO has the greatest influence over AI decisions at 23% of organizations, followed by central IT or technology leadership at 21%. Executive leadership teams account for 11%, while dedicated AI roles account for 10%.
AI budgets show a similarly fragmented structure. Twenty-two percent of CIOs report budgets are managed centrally by IT, while another 22% say IT and business units share responsibility. Business units independently control AI budgets at 20% of organizations.
Executive or board-level management accounts for 19%, while 17% say their AI budgeting model is still evolving.
The CAIO adds another layer of complexity
The emergence of the Chief AI Officer is not resolving the question of ownership.
According to Thoughtworks, 70% of surveyed organizations have already hired a CAIO, while another 26% are considering the appointment.
Yet there is little consensus about how the CAIO should work alongside the CIO.
Thirty-six percent of respondents say the CAIO functions as an extension of the CIO’s centralized strategy. A nearly comparable 35% say the CAIO operates independently with equal or greater influence across the enterprise.
Another 29% describe the CIO-CAIO relationship as a source of organizational friction or unclear boundaries.
The numbers point to a broader enterprise AI architecture problem: organizations are creating new leadership structures before they have fully established how AI responsibilities should be distributed.
For technology leaders, that can mean managing AI strategy without controlling every AI deployment.
Singapore highlights the preparedness challenge
Singapore’s results provide a useful example of how the global transition varies by market.
While 36% of Singaporean CIOs describe their organizations as very prepared to govern AI consistently across business functions, 58% say they are somewhat prepared.
Visibility into independently adopted AI tools is also lower than the global result. Seventy-eight percent of Singaporean CIOs report full or high visibility, compared with 89% globally, while another 21% report moderate visibility.
Organizations are responding partly through skills development. In Singapore, 30% identify upskilling technology staff among actions taken or planned, while 20% cite upskilling the broader workforce.
That emphasis reflects a key reality of enterprise AI adoption: governance systems cannot compensate for employees and managers who do not understand how AI should be used.
The CIO is becoming an enterprise AI architect
The Thoughtworks research points toward a CIO role that looks increasingly different from its traditional infrastructure-focused definition.
The modern technology leader may need to coordinate AI strategy, data governance, workforce transformation, cybersecurity, model risk and business-unit adoption simultaneously.
That does not necessarily mean central IT should control every AI decision. Instead, organizations need clearer rules around who can select AI systems, who owns the associated data, which decisions require human approval and who is accountable when an AI deployment fails.
The shift also changes the meaning of AI transformation. Deploying an LLM or AI agent is becoming the easier part. The harder challenge is redesigning the organization around the technology.
As Thoughtworks’ findings suggest, the next stage of enterprise AI will depend less on identifying a single executive owner and more on establishing clear decision rights, shared governance and workforce capabilities across the organization.
Market Landscape
Enterprise AI is moving into a governance and operating-model phase. Early deployments focused heavily on model selection and experimentation; organizations are now confronting questions around data ownership, AI budgets, workforce redesign, security and accountability.
The growing presence of CAIOs illustrates the change. Dedicated AI leadership can accelerate transformation, but without clearly defined responsibilities it can also create overlap with CIOs, chief data officers, business leaders and risk functions.
For enterprise AI platforms and AI agent providers, this means governance capabilities are becoming increasingly important alongside model performance. Organizations will need visibility into independently deployed AI tools, controls around sensitive data, monitoring of AI activity and mechanisms for human oversight.
The competitive opportunity therefore extends beyond LLMs and infrastructure into AI governance, enterprise orchestration, workflow automation and workforce technology.
Top Insights
- CIO responsibilities are expanding beyond IT infrastructure, with workforce workflow and labor-model redesign becoming increasingly central to enterprise AI transformation.
- AI adoption is outpacing governance, creating accountability gaps when business units deploy AI tools independently but central IT remains responsible for failures.
- CAIO appointments are accelerating, yet organizations remain divided over whether the role should complement or operate independently from the CIO.
- AI governance increasingly overlaps with workforce design, requiring organizations to clarify where human judgment remains essential as AI agents take on more work.
- Skills development remains critical, particularly as enterprises need employees capable of using AI responsibly rather than relying exclusively on centralized technology controls.
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