Artificial intelligence is already reshaping how work gets done, but not in the way most people imagine. The popular narrative of AI as a “co-worker” oversells its role and misunderstands its limits. AI systems are not peers; they are instruments: programmable, bounded, and entirely dependent on human judgment. Their impact will come not from collaboration, but from how effectively organizations learn to design, deploy, and govern them.
In IDC’s FutureScape Future of Work 2026 research, we see this transformation unfolding unevenly but decisively.
Agentic AI tools are advancing faster than most enterprise structures can adapt. The technology is ready to execute, but organizations are still learning how to make it productive, ethical, and sustainable.
The success of AI at work will depend less on technical power and more on the human systems built around it, including the processes, accountability, and oversight that turn automation into advantage.
Tools for all: From developers to line-of-business teams
The notion that AI is joining the workforce as a co-worker misreads its function. AI systems are tools used by both deeply technical developers and employees across business functions. IDC forecasts that by 2026, 40% of G2000 job roles will involve direct interaction with AI systems.
For developers, this means designing, securing, and maintaining the architectures that make AI dependable and compliant. Their work defines the system’s boundaries. For business users, AI will become part of daily operations; refining analysis, monitoring performance, and automating repeatable steps. The distinction is not about hierarchy but about fluency: the ability to use AI effectively without mistaking its capabilities for comprehension.
AI agents can recognize patterns within data and past interactions, but they lack awareness of intent, nuance, or institutional goals. They do not understand the broader organizational context in which decisions are made or the values that guide them. Their reliability depends on the quality of input and the competence of the person providing it.
The practical challenge ahead is building fluency across these roles. Developers must ensure systems perform as intended, while managers and employees must learn to apply them responsibly. The future of work will depend on dual skill sets: technical mastery and the human capacity for context, critical thinking, and ethical judgment.
As these technologies become embedded in daily operations, the question is no longer who uses AI, but how its presence changes the shape and distribution of work itself.
Redefining work and work roles
Agentic AI is reshaping the workforce through both elimination and creation. Some roles are being reduced or retired as AI systems take on repetitive functions that can be performed more efficiently and at lower cost. At the same time, new roles are emerging to oversee AI operations, manage governance and compliance, and translate technical performance into business outcomes.
IDC predicts that by 2027, half of all AI-enabled enterprise applications will require new oversight positions dedicated to governance, risk, and accountability.
These jobs will not replace those lost on a one-to-one basis. Instead, they redefine where expertise and decision-making authority reside. As tasks once distributed across many functions consolidate into automated systems, organizations must confront how to rebalance reporting structures, reassign responsibilities, and revise expectations.
This transition requires more than reskilling; it demands structural redesign. Organizations must let go of long-held roles that no longer add distinct value while supporting employees in taking on new responsibilities within evolving positions. Those that handle the shift effectively will go beyond adding job titles or automation layers. They will create clearer accountability and stronger alignment between human oversight and machine efficiency.
These shifts in responsibility and reporting require an organizing mechanism. One that ensures both governance and innovation evolve together.
Building structure: Centers of Excellence as engines of transition and trust
As organizations adapt to these changes, structure becomes the mechanism that determines whether transformation succeeds or stalls. The emergence of AI and Agentic Centers of Excellence (CoEs) marks a deliberate move from experimentation toward an integrated model for governance, innovation, and value creation.
First, CoEs help organizations make the transition from traditional automation to new ways of working. Rather than viewing AI as a replacement for existing tools, CoEs define how it should extend enterprise capability. They guide the redesign of processes, clarify which roles will evolve or disappear, and establish the standards that ensure AI is deployed responsibly and with purpose.
Second, CoEs serve as hubs for systemic, cross-functional governance. They bring together data, risk, technology, and human resources leaders to define how AI is used, monitored, and improved across the organization. This alignment prevents fragmented implementation and enforces accountability for outcomes, ensuring that AI decisions are traceable, ethical, and compliant.
Finally, CoEs sustain focus on innovation and long-term value. Internally, they drive a culture of continuous improvement through shared learning and benchmarking. Externally, they ensure AI investments translate into measurable client outcomes; improved service quality, faster delivery, and more adaptive customer engagement.
IDC’s Future of Work 2026 research shows that organizations with mature AI or Agentic CoEs are 20% more capable of competing on innovation, speed, and service excellence. These centers are not symbolic. They are the connective tissue linking technology capability to human expertise, operational discipline, and customer trust.
Redesigning work for a new conversation
The integration of agentic AI is not simply a technical evolution. It is an organizational negotiation.
C-suite leaders often approach AI with a “more, faster” mindset; a drive for scale, speed, and measurable productivity. Employees across functions experience the same transformation from a different vantage point: one that involves redefining responsibilities, acquiring new skills, and navigating the uncertainty of work that no longer looks or feels the same.
Bridging these perspectives will determine whether agentic AI delivers on its potential. The future of work depends on an ongoing dialogue between those who set direction and those who carry it out. It requires leadership that sees AI not only as an efficient tool but as a catalyst for redesigning how work is structured, supported, and rewarded.
The successful adoption of agentic AI will depend on a deliberate partnership between leadership and the workforce; a recognition that innovation and adaptation must advance together.
The future of work will not be defined by speed alone, but by how organizations align ambition with understanding, progress with purpose, and productivity with shared accountability.
Access the full IDC Future of Work 2026 report to explore all predictions, and visit our FutureScape content hub for additional insights and resources.