Tech Trends 2026 (chapter: The agentic reality check: Preparing for a silicon-based workforce)

Source: Deloitte Insights; Deloitte Tech Trends report (72 pp.) · 2025-12

Despite its promise, many agentic AI implementations are failing. But leading organizations that are reimagining operations and managing agents as workers are finding success.

Leading organizations are discovering something different: True value comes from redesigning operations, not just layering agents onto old workflows. This means building agent-compatible architectures, implementing robust orchestration frameworks, and developing new management approaches for digital workers.

Agentic AI has captured the attention of enterprises with its compelling promises of autonomous operation and intelligent execution.

Three fundamental infrastructure obstacles may prevent organizations from realizing the full potential of agentic AI.

Data architecture constraints: Current enterprise data architectures, built around extract, transform, load (ETL) processes and data warehouses, create friction for agent deployment. The fundamental issue is that most organizational data isn’t positioned to be consumed by agents that need to understand business context and make decisions. In a 2025 Deloitte survey, nearly half of organizations cited searchability of data (48%) and reusability of data (47%) as challenges to their AI automation strategy.

Governance and control frameworks: Enterprises struggle to establish appropriate oversight mechanisms for systems designed to operate autonomously. Traditional IT governance models don’t account for AI systems that make independent decisions and take actions.

Leading enterprises don’t simply layer agents onto existing workflows. Instead, they redesign processes to leverage the unique strengths of agents. This requires taking a step back and examining end-to-end processes rather than finding automation opportunities within current operations. Agents can handle a range of transactions, communicate with each other, and collaborate to achieve a business outcome, but only when the underlying processes are structured to support these capabilities.

Most businesses’ existing processes were designed around human staff. Agents operate differently. They don’t need breaks or weekends. They can complete a high volume of tasks continually. When organizations realize this, the opportunities for process redesign become compelling. That’s why enterprises that are succeeding with agentic AI are looking at their processes from end to end.

Enterprise software and services company HPE is developing an AI agent with exactly this kind of process redesign in mind. “We wanted to select an end-to-end process where we could truly transform rather than just solve for a single pain point.

A: We now have a dozen agentic proofs of concept, all going after composite problems like quoting or end-to-end remediation of a customer issue across domains, including entitlements, billing, and logistics. We’re very focused on ROI. We don’t do science projects. We have agentic technology emerging across sales, services, supply chain, and engineering, areas that have a material impact on the company’s financial performance.

We’ve probably tapped into 20 digitized enterprise processes. Before the end of 2025, we will have live autonomous systems that are more than likely working across domains as first-generation tools, which sets us up for a very good year next year to significantly expand the use of agents.

A: In the front end of our process, we require material ROI signed off by the finance partner and the head of that business unit. That discipline has kept experiments as experiments, and production only happens if there is solid ROI.

We also realized that you apply AI to processes, not to people, organizations, or companies. We expect you to be very clear about the processes you’re improving.

At Toyota, teams are using an agentic tool to gain better visibility into the estimated time of arrival of vehicles at dealerships and will soon start using agents to resolve supply issues. The process used to involve 50 to 100 mainframe screens and significant hands-on work from supply chain team members.

Going forward, the team plans to empower agents to identify delays in vehicle shipments and draft emails to try to resolve the issue.

This evolution represents a fundamental reimagining of what work means, how it’s performed, and who performs it. At the heart of this shift is a recognition that AI agents and human workers have different skill sets. While agents excel at defined processes, humans remain essential for navigating the shifting ground of business requirements and complex problem-solving scenarios.

• Compliance and governance: Humans increasingly focus on validation, oversight, and building guardrails for agent operations.

“It’s hybrid by design,” she says.

Successful deployments focus on specific, well-defined domains rather than attempting enterprise-wide automation. Broad automation remains possible but requires multiple specialized agents working in an orchestrated fashion rather than single, monolithic solutions.

Organizations face critical build-versus-buy decisions that often depend on technical maturity and specific use case requirements.

As agents operate continuously, poorly configured agent interactions can trigger cascading actions like unpredictable resource consumption and ballooning costs, making cost management critical.

• What agents will be deployed, and what functions will they perform?

• What are the cost profiles relative to human employees?

• Which processes will be automated and at what level of efficiency?

• What will be the optimal mix of human and digital workforce over the next four years?

• Will agents eventually take over entire operational areas beyond the five-year horizon?

Organizations should define clear boundaries for agent decision-making through graduated autonomy levels, with appropriate human oversight triggers. The autonomy spectrum progresses through three distinct phases.

Success requires deploying “agent supervisors”— humans who enter workflows at intentionally designed points to handle exceptions requiring their judgment. This isn’t simply about checking agents’ work, but about strategic handoffs of work at critical decision points. Over the coming years, as AI technology improves, potentially to the point of reaching artificial general intelligence, organizations should be able to let agents work more independently. Leaders should continually assess the state of AI capabilities to ensure they are delegating responsibilities that agents are suited to handle.

As agents mature within job functions, organizations will need equally mature approaches to managing them. This will likely require an entirely new framework for managing agents that not only leans on traditional human resource management concepts for areas where agents share similarities with human workers, but also diverges to account for their unique characteristics. Some areas of focus for HR, such as workplace culture, employee loyalty, and worker motivation, won’t be applicable to agents but will remain key pillars of how organizations manage their human staff. Other features of worker management can be extended to apply to agents, even if they look slightly different.

Onboarding: Just as with human workers, agents will require onboarding processes that train them in the enterprise’s unique data and operations.

Performance management: This may be one of the areas where managing agents diverges most from traditional human resource management. Organizations will need systems to prove what agents did, why they made specific decisions, and under whose authority they acted. This requires digital identity systems, cryptographic receipts for transactions, and immutable logs for every agent action. As agents roll out across businesses’ operations, they will create too much data for human managers to evaluate, which may drive a need for additional agents that manage performance.

In an agent-driven environment, systems generate vast amounts of data describing actions taken and outcomes. Today, most agents do not train on their own output data, but in the future, this digital exhaust of silicon workers can become a valuable trove of insights that allow agents to learn and improve. Going forward, the key differentiation lies in how organizations channel this byproduct to reinforce agent learning and capabilities.

Examining the future of system modernization, early evidence suggests that a hybrid approach is most likely to prevail, where agents extend the useful life of legacy systems, while organizations pursue the selective modernization of critical business processes. This approach allows organizations to realize immediate value from agentic capabilities while maintaining strategic flexibility for future technology decisions.

The key to success lies in recognizing that agentic transformation is not about replacing humans with machines, but about creating new forms of human-AI collaboration that leverage the unique strengths of both human and silicon-based workers. The organizations that figure out how to drive this collaboration effectively will define the future of work itself.

A: Leaders in many organizations aren’t clear on what this means. There tends to be a lot of handwaving and statements like “AI will do stuff” or “you’ll manage a bunch of agents.” But that doesn’t happen without rethinking and redoing the way organizations operate.

I find it’s not actually a technology problem. It’s a process problem. It means you have to understand the jagged frontier.

A: There’s a “using AI” skill that we don’t exactly know how to measure or train for yet. It probably involves agency and willingness to experiment, being incentivized properly, and being a subject matter expert in your field.