Digital labor in the workforce of tomorrow

Agentic artificial intelligence (AI) is a reality, adoption has begun, and the market agrees.

In 2025, a formal technical study defined strategies for strategic adoption of agentic AI in defense environments while the U.S. Department of Defense (DOD) Chief Digital and AI Office (CDAO) awarded ~$200M in contracts to leading frontier AI contractors, such as OpenAI, to build advanced agentic workflows aligned to national security missions.

The Food and Drug Administration (FDA) deployed an internal agentic AI tool across the agency to orchestrate tasks, including meeting management, pre-/post-market reviews, and inspections.

And, as recently as February 2026, Kelly Fletcher, Chief Information Officer (CIO) of the U.S. Department of State, announced plans to "roll out agentic AI," building on successful generative AI pilots, with the aim of reducing administrative overhead and simplifying agency workflows. 

Boston Consulting Group recently reported that 76% of executives now view agentic AI more as a coworker than a tool; 35% of companies have begun using agentic AI; another 44% plan to deploy it soon, but few have restructured for it. In addition, 250% more respondents expect AI to have greater decision-making authority within three years. In addition, Bain & Company forecasts that agentic AI could account for 15–25% of U.S. e-commerce sales by 2030, amounting to $300–$500 billion.

From generative AI to agentic AI to digital labor

Generative AI powered by large language models (LLMs) enables humans to draw on voluminous data to both create and analyze text and images, build code, automate certain functions, and more. Agentic AI, which includes LLM technology, is a task-oriented autonomous system with reasoning and decision-making capabilities.  

Agentic AI can handle tasks with minimal human intervention, from simple back-office transactions to complex mission-critical processes such as managing supply chains. Agentic AI adoption by agencies will scale into the tens, hundreds, and thousands over the next few years. That adoption will create a new workforce known as digital labor, or the next AI frontier.

Digital labor will evolve into an integral organizational asset working alongside humans and enabling us to focus on strategy, judgment, creativity and mission stewardship. This is a story of workforce augmentation, not displacement. Humans will move up the value chain, taking on roles that require critical thinking, oversight, and strategic vision, while digital labor handles the procedural heavy lifting with speed and precision.     Indeed, workforce transformation is integral to a scalable AI implementation. 

The new paradigm presents leaders with an opportunity to transform their organizations and prepare for a new reality. This blog, one in a series on agentic AI, provides the foundation for that preparation.

Three shifts to watch

As organizations become more confident in implementing agentic AI, there will be several clear signs marking their journeys. Here are some developments to watch for and corresponding recommendations to consider.

1. Rebalancing tasks between agents and humans

Agents taking on more tasks and complex workflows will result in humans’ responsibilities changing to critical thinking and agentic oversight. Let’s take a cybersecurity example, detecting insider threats, to make the point. Today, analysts manually parse logs and correlate alerts, which can be a slow, error-prone process. With agentic AI, agents monitor user activity, detect anomalies, and escalate high-risk cases. Analysts move from alert triage to validating critical alerts, investigating complex cases, and shaping security policies.

Recommendations:
•    Map workflows now to identify tasks for agents vs. those better suited for humans.
•    Develop governance protocols for agent-human collaboration, including communication standards and escalation paths.
•    Pilot hybrid workflows in low-risk areas to validate efficiency and trust before scaling.

2. AI and a shift in talent strategy

As agencies mature and adopt AI, the focus of roles and job responsibilities will shift.  The new paradigm will require different skill sets for agentic oversight (monitoring KPIs, traceability, and agent modifications), productivity expectations, and organizational structures and hierarchies.  

In our cybersecurity example, human analysts will transition to roles such as agentic oversight specialists, responsible for validating agent outputs, ensuring traceability, and modifying agent workflows reflecting a changing threat landscape. New roles include a digital labor manager who oversees the performance of cybersecurity agents, sets productivity benchmarks, and ensures compliance. Another new role is an AI governance lead who defines accountability frameworks, performance guidelines, and escalation paths for agent-driven decisions.

Recommendations:
•    Update job descriptions emphasizing digital fluency and AI governance skills.
•    Create career pathways for AI orchestrators who specialize in managing autonomous systems.
•    Establish performance metrics that measure human-AI collaboration outcomes, not just task completion.
•    Create job responsibilities for agents and agentic workflows.

3. A new partnership between IT and program

Agentic AI will blur traditional boundaries between IT and mission/program teams. Historically, IT has been the enabler providing infrastructure and tools while programs owned the business processes. With digital labor, this separation collapses because AI agents operate inside workflows, not just as tools.

With our insider threat detection example, an agent flags anomalous behavior in privileged accounts. The IT team ensures the agent’s anomaly detection model is functioning and secure, while the cybersecurity program validates the flagged case, determines if escalation is warranted and updates policies for future detection. Together, they decide whether the agent needs retraining or workflow adjustments to reduce false positives.

Recommendations:
•    Form AI Operations Councils with IT and program leaders to jointly manage digital labor.
•    Establish robust AI governance frameworks to ensure transparency, accountability, and risk mitigation as digital labor scales alongside human work.
•    Develop shared accountability frameworks for agent performance and mission alignment.
•    Train both teams in agentic oversight, including technical integrity and ethical governance.

When, not if

The rise of digital labor is already underway. Agentic AI will transform federal operations, creating a workforce where humans and machines collaborate seamlessly. Success will hinge on foresight; leaders who invest in governance, reskilling, and reshaping relationships with IT today will define enhanced operations and mission outcomes tomorrow. 

Learn more about how CGI can help you build your own pathway to incorporate agentic AI.