If we have selected the wrong experience for you, please change it above.
The next AI advantage starts with CIO + CHRO co-ownership
An enterprise AI strategy must move beyond deployment to rethink how work gets done
Most organizations have deployed AI, but few have unlocked its full potential. The difference lies in the organizational ecosystem around it: workflows, roles, governance and processes that determine whether AI investment becomes business value.
Leaders who redefine what work gets done and redesign how it gets done can shape the next era of competitive advantage. Others risk scaling cost and complexity without capturing returns. For chief information officers (CIOs), the pressure is becoming personal as AI infrastructure, licensing and talent spend rise. And for chief human resources officers (CHROs), pressure involves defining the future workforce while keeping up with continual tech-driven change.
TAKEAWAYS
- AI value requires work redesign of what work is done and how; not tool deployment alone.
- Investment is rising, but measurable impact remains uneven.
- The greatest gains come from combining human judgment with machine scale.
- Technology and HR leaders must co-own the transformation.
- Adaptive organizations will be better positioned to keep creating value as AI evolves.
The AI value paradox: Investment is rising, outcomes are uneven
Organizations are investing heavily in AI platforms, copilots and automation capabilities. A 2025 Deloitte survey of companies using AI found that 84% increased their AI investments over the previous year, and 74% hoped to grow revenue through their AI initiatives.1 Yet only 34% reported transformative impact.2
That gap reflects a fundamental challenge in enterprise AI strategy. Many organizations are layering new technology onto old ways of working. Of survey respondents, 37% were using AI only at a surface level, with little or no change to existing processes,3 and 84% had not redesigned jobs or the nature of work around AI capabilities.4 The result is incremental improvement at best—in some cases, existing problems become more complex.
But the constraint today is no longer access to technology, it is the organization’s ability to absorb, scale and apply it effectively.
From Humans + Machines to Humans × Machines
The greatest value comes when AI amplifies human performance rather than simply replacing human effort. That requires leaders to define what work needs to be done, how it should be performed, and how responsibilities should be divided between people and machines.
Work is increasingly distributed across four categories:
- Machine-led work: Systems execute tasks independently.
- Machine-first work: AI produces an initial output for people to refine.
- Human and machine collaboration: People and technology iterate together.
- Human-led work: Judgment, context and accountability remain essential.
The goal here is not simply efficiency. It is to automate where speed and scale matter, to augment human judgment with real-time data and predictive capabilities, and to free people to focus on higher-value contributions. This is a work design challenge, not only a technology exercise.
The technology organization is the first test case
Within many businesses, their technology organizations are experiencing the AI productivity paradox firsthand. Teams can accomplish more through automated coding, faster testing, improved monitoring, and streamlined service operations. At the same time, demand is expanding for data infrastructure, AI integration, real-time analytics and intelligent products.
Starting with the technology group, several shifts are emerging:
- Leaner, outcome-focused teams are replacing functional silos.
- Value is evolving from production to judgment, increasing demand for cross-disciplinary technologists who can orchestrate AI and agentic solutions.
- Business units and end users are resolving more of their own needs, while technology becomes the platform and agentic nerve center that enables, governs and scales AI-augmented capability.
- Governance is becoming a design principle, with greater focus on transparency, equity, ethics and accountability.
- Hiring and upskilling are moving toward AI fluency, systems thinking and effective use of AI tools.
To help visualize the technologist + HR connections needed, some organizations are using digital organization twins to test workforce, workflow and technology configurations before implementing changes at scale.
The CIO-CHRO partnership is essential to unlocking AI value
The CIO has a dual mandate. As a builder, the CIO develops the platforms, data foundations and AI capabilities the enterprise depends on. As an orchestrator, the CIO helps lead the transformation required to realize those capabilities.
The CIO and CHRO should jointly:
- Redesign work and determine which tasks to automate, augment or keep human led.
- Evolve roles, career paths, reskilling priorities and hiring profiles.
- Build AI capabilities through coordinated learning, performance management and culture change.
- Govern responsibly as AI systems take on more execution and decision-making authority.
This is the foundation of AI workforce transformation and a practical AI operating model.
Leading with tech + talent co-ownership
A fundamental shift in how work is performed, AI is not simply another wave of automation. Organizations that realize its potential will redesign work around AI, make that redesign shared ownership between technology and HR, and continue adapting as technology evolves.
The leaders who act now will be better positioned to define the next chapter of AI and future of work leadership.
End Notes
1. Jim Rowan, Beena Ammanath, Nitin Mittal and Costi Perricos, The State of AI in the Enterprise: The untapped edge, Deloitte US, January 2026, p.10.
2. Deloitte, The State of AI in the Enterprise, p. 11.
3. Ibid.
4. Deloitte, The State of AI in the Enterprise, p. 13.
