How Can Agentic AI Transform Enterprise AI Workflow Automation?

Agentic AI has quickly become one of the biggest conversations in enterprise technology. The idea is compelling: AI that can take a goal, determine what needs to happen next and carry work forward across systems with less human intervention.

If you're making decisions about technology investment and enterprise transformation, though, capability is just part of the question. You also need to decide where greater autonomy adds value, how much authority agents should have and what governance is needed to support them.

Agentic AI expands the role AI can play in your enterprise. These systems can participate in a broader sequence of work by planning steps, using tools, interacting with systems and taking permitted actions toward a defined objective.

That creates new possibilities for AI workflow automation, along with important decisions about process design, governance, risk and organizational readiness.

How Does Agentic AI Enable AI Workflow Automation?

Agentic AI expands AI workflow automation and allows systems to interpret context, determine next steps and take permitted actions across a workflow with greater autonomy.

An AI agent can use available tools or data to complete multiple steps toward a defined objective within the permissions it's been given. Consider a service incident. An agent could gather relevant information, check known issues, update a ticket, route work based on established criteria and escalate an exception to the appropriate employee.

The larger enterprise opportunity lies in connecting those capabilities to how work moves across your organization. Most business processes span applications, data sources and decision points. Agentic AI gives leaders an opportunity to examine that flow from end to end and identify where greater autonomy could improve throughput and service quality.

Where Can Agentic AI Create Practical Enterprise Value?

The strongest use cases begin with a business outcome and a well-understood workflow.

That focus could have a significant impact on ROI. Gartner research on agentic AI opportunities predicts that by 2028, 80% of tangible ROI from agentic AI will come from specialized, domain-specific agents. This reinforces the value of applying agentic capabilities to clearly defined business processes.

But identifying the right use case is only the beginning. Realizing that value also depends on how effectively AI is integrated into the work itself. McKinsey research found that while 80% of organizations report individual productivity gains from AI, only 37% report positive EBIT impact:

AI adoption is scaling rapidly, and individuals are already seeing meaningful productivity gains, but enterprise value has not kept pace.

Greater autonomy needs a business case that's grounded in measurable value, whether that means improving cycle time, increasing capacity or delivering a better service experience. Processes requiring coordination across multiple steps or systems are a useful place to look.

In IT operations, for example, an agent might gather context around a service request, query approved knowledge sources, initiate permitted actions and escalate cases outside its authority. Other agents could support approval processes, employee requests, document workflows or customer operations.

But process knowledge is important here. Leaders need to understand how work is completed, including its dependencies, exceptions and decision points, before determining where autonomous action belongs. Otherwise, automation can end up shifting existing friction rather than eliminating it.

For transformation leaders, a more useful question is: Where would greater autonomy materially improve the performance of an end-to-end process?

How Does AI Workflow Automation Change With Agentic AI?

AI workflow automation allows AI to execute or coordinate work across a process, while agentic capabilities give systems greater latitude to determine and carry out appropriate next steps within defined boundaries.

Traditional workflow automation remains highly effective when rules and sequences are predictable. Agentic workflows become useful when the path requires more context. An agent might interpret a request, gather information, select an approved tool, determine the next action and involve a person when a decision exceeds its authority.

At enterprise scale, that creates an orchestration challenge.

Organizations may have agents with different specialties operating across multiple platforms and systems. Some steps may be autonomous. Others will rely on deterministic automation or human judgment. Leaders need visibility across that environment and clear decision rights as work moves among people, agents and technology.

This is an area Experis is actively addressing through ExcelerateAI, a suite of AI capabilities that helps enterprises move from experimentation toward execution. Within this suite, ExcelerateWorkflow, powered by IBM® watsonx Orchestrate™, specifically focuses on agentic automation and orchestration within a governed enterprise environment.

ExcelerateWorkflow brings Experis implementation expertise and specialized talent together with orchestration technology to help organizations:

• Govern agent activity with greater visibility into agents, ownership, access and controls.
• Orchestrate work across the enterprise by connecting agents with people, systems, data and workflows.
• Operationalize high-value workflows by integrating AI into enterprise operations and moving priority use cases beyond pilots.
• Optimize performance over time by monitoring workflows and adapting them as business requirements evolve.

The value of this approach is becoming more important as enterprise AI activity grows. Your organization needs the governance, integration and human oversight to make those workflows work reliably within your existing operating environment.

How Should AI Governance and Risk Management Evolve?

As agents move further into execution, AI governance needs to account for what they are authorized to access and do.

Every production agent should have a defined purpose and accountable owner. Its access should correspond to the work it performs, and leaders should establish where an agent can act independently, where approval is required and when an issue must be escalated.

Observability becomes important too. You need enough visibility into agent activity to understand how workflows are progressing, monitor performance and intervene when necessary.

Existing enterprise controls remain relevant. Identity and access management, cybersecurity, data governance, compliance and technology lifecycle practices all have a role as organizations introduce agents into production environments.

Many organizations may benefit from gradually increasing autonomy. An agent can begin by recommending actions while a person retains approval authority. Greater autonomy can follow as your organization develops confidence in the workflow, controls and performance.

Risk management also continues after AI deployment. Processes, data, integrations and business requirements change. Ongoing visibility shows how agents perform as that operating environment evolves.

Is Your Enterprise Ready for Agentic AI Deployment?

Enterprise readiness for agentic AI depends on a valuable use case, a well-understood workflow, reliable systems and data, clear governance and people who are prepared to operate differently.

Before scaling AI deployment, executive teams should be able to answer five questions:

• Do we understand the business outcome? Define what your organization wants to improve and how success will be measured. Adoption by itself says little about whether an agent is producing business value.
• Do we understand the workflow? Map the process from end to end, including systems, dependencies, exceptions and approvals. Some workflows may need to be redesigned before additional automation can improve them.
• Are our data and systems ready? Agents need reliable access to the information and tools required to perform their work. Fragmented architecture, data quality problems or brittle integrations can limit performance and scale.
• Have we defined the boundaries of autonomy? Establish where agents can act independently, where people retain approval authority and how exceptions are handled. Those boundaries should reflect the potential business impact of each decision.
• Are people prepared for the new workflow? Agentic AI can change responsibilities, handoffs and where employees spend their time. Change management needs to address how the redesigned workflow operates, when people should intervene and where accountability sits.

Turning Agentic AI Into Enterprise Execution

The next phase of agentic AI will be shaped by what organizations can successfully operationalize.

That means choosing workflows where greater autonomy can produce measurable value, establishing the controls those workflows require and designing an environment where people and agents can effectively work together. As agent activity expands, governance, orchestration and visibility will become more important to keeping that environment manageable.

Key Takeaways

• Agentic AI can move work forward across enterprise workflows by interpreting context, determining next steps, interacting with approved tools and systems and escalating exceptions that require human judgment.
• To determine where agentic AI can create practical value, identify processes where greater autonomy could improve speed, capacity or service.
• Build governance into AI deployment from the beginning. Align agent access, ownership, monitoring and oversight with existing enterprise controls.
• To prepare people for redesigned workflows, make responsibilities and handoffs clear so employees understand when to intervene and how their work will change.

Putting these principles into practice across real enterprise workflows can get complicated quickly. The right expertise can help your organization determine where to start, put the right controls in place and move promising use cases into production.

Through ExcelerateWorkflow, Experis combines AI agent orchestration technology with implementation expertise, specialized talent and human oversight to help organizations govern, integrate, deploy and manage AI-enabled workflows.

Find out where and how agentic AI can create practical value across your enterprise.

Learn more about our AI services, or connect with our team of AI and technology experts to start turning high-value use cases into enterprise operations.