Is your business ready for an agentic AI team?

What are agentic AI teams?

Agentic AI is changing how work happens. AI agents can handle entire workflows and processes more efficiently and effectively than humans. Humans can team with them as managers, overseeing agents' outputs and performing work that only humans can or should do. This pairing boosts productivity and cuts costs while maintaining a human-in-the-loop strategy.

Research from IDC's "FutureScape 2026" report estimated that 40% of all Global 2000 company job roles will "involve working with AI agents, redefining long-held traditional entry-, mid- and senior-level positions" by 2026. Executives are reporting positive results, as PWC's survey found that 66% of executives who adopted AI agents said these agents delivered measurable value through increased productivity.

"As agentic AI becomes more reliable across a multistep workflow and as orchestration technologies become enterprise-grade, we expect AI agents to play a key role in supporting our adjusters," said Vishy Padmanabhan, chief transformation officer at Sedgwick, a global third-party administrator that manages insurance claims, workers' compensation and disability leaves for its enterprise clients.

According to Deloitte's 2026 "State of AI in the Enterprise" report, 85% of companies expect to customize agents to fit the unique needs of their business. Airlines could use AI agents to rebook flights for customers and reroute bags, freeing up time for human agents to address more complex matters. Financial services firms could use agents to capture to-do actions identified in meetings, draft and send reminders about them to participants and track follow-through. Organizations could tailor these teams to meet their precise needs.

However, organizations must prepare in multiple areas before they can successfully deploy AI agents, including technically, culturally and strategically.

Assess business readiness for agentic AI teams

Experts recommend that businesses assess their readiness in the following areas to ensure they can successfully use agentic AI teams to perform work:

1. Strategy

Companies that see benefits from agentic AI deployments have "strategic clarity" and know what they want to achieve with the technology, said Vuk Janosevic, senior director and analyst at Gartner who advises technology leaders on scaling AI-driven growth.

"Some [organizations] are focused on 'Can we build this?' But then they do, and it's then, 'So what?'" he said. "An organization must be clear on what business outcome it's trying to improve, how improvement will be measured and what success will look like over time. Start with outcome definition, not model selection. Start with, 'What do we want to achieve with this digital transformation?'"

2. Leadership

"Leadership is absolutely critical," said Myles Suer, research director at Dresner Advisory.

Suer explained that the executive team and leaders throughout an organization must grasp what AI can do and its business advantages, as well as how to align everyone with the organization's AI strategy. Lead with an understanding of the technology, coupled with a clearly defined implementation strategy, to earn confidence among employees and provide direction for deployment.

3. Data

As with all AI deployments, data readiness is essential for success.

"If an organization's data isn't in sufficient shape, it's really hard to use agentic AI to provide insights or have agents take on jobs," Suer said. According to Suer, his firm's research has found that only a third of organizations have data ready enough to succeed with agentic AI.

Moreover, Padmanabhan said organizations must ensure they continuously monitor their data quality. "There are no high-quality AI outcomes without consistent data and proper governance. This is vital for high-quality agentic AI performance," he explained.

4. Integration with current processes

Agentic AI uses intelligence to automate outcomes, not just tasks. Businesses must know what outcomes they want to achieve with AI agents as well as understand the current processes they use to achieve those outcomes.

"They need to ask: Is there a clear understanding of the process, how it flows, what does that workflow look like and what could it look like?" said Ann Bosche, partner at consulting firm Bain & Company and a member of the firm's technology and cloud services practice.

Bosche recommended that organizations document processes, make them consistent and reimagine them, noting there's no real value in using AI to automate bad processes. Sedgwick's Padmanabhan agreed.

"What is different with [agentic AI] is the impact it can have across a multistep workflow," Padmanabhan said. "This calls for workflow reinvention, not just continuous improvement. We are working closely with our claims examiner front line to redesign the workflow. Some of our clients are now discussing initiatives with us to reimagine aspects of claims handling."

5. Technology and architecture

Technology stacks must be redesigned and rebuilt for agentic AI, which further pressures organizations that still have legacy on-premises compute. Suer said businesses must embrace modern IT architecture, data architecture and software development principles.

Architecture is one specific problem area. According to Janosevic, some organizations are seeing their agentic AI deployments fail because they're focused on task efficiency, not the workflow. Agents need to perform tasks in a clear sequence to deliver an outcome. To ensure they do, organizations need to have architecture orchestration. This is a control layer that coordinates, manages and directs the agents so they work together to complete the multiple tasks of the workflow or process.

7. Change readiness

The ability for employees to change how they work is critical to successfully adopting agentic AI teams.

"You need to have an openness for change," Bosche said. She noted that while some teams embrace AI agents, others quickly follow suit, and some resist completely. There are levels to AI acceptance.

Executives need to foster agility and AI fluency in their workers so they're more willing to learn how to work with digital teammates, Bosche said. This is something that Padmanabhan and his executive colleagues are doing.

"We are very focused on this aspect as a company," he said. "The Quad model -- i.e., Business/Operations/Tech/GTM alignment -- is the engine room that defines the roadmap, priorities and sequencing. We work very hard every day to cascade this alignment mindset across our global teams."

8. Governance and risk control

"As autonomy increases, so does risk," Janosevic said.

Organizations must ensure they have adequate governance and risk controls in place. Janosevic said enterprise leaders must create clear policies that establish what agents can and cannot do and ensure they enforce those policies via tools and technologies.

"You have to have a security and governance layer for agents as they access proprietary data, and you have to create human checkpoints -- human in the loop," he said. Platforms for this layer create audit trails, providing lineage and explainability so organizations can monitor and understand how agents make decisions.

9. Skills

Agentic AI will change the work humans do, and that means they'll need different skills, Suer said. To work successfully with their digital teammates, human workers must develop a combination of business and technology skills, and enterprise leaders should ensure their employees are prepared.

Bosche emphasized the need to have technologists skilled in AI, including those capable of filling the emerging roles like forward-deployed engineer -- a software engineer who works on the front lines of the business to design, customize, deploy and operationalize complex software and agentic AI in particular.

"That person knows the tech, data, what's required, the limitations and the opportunities, where use cases can be deployed and the process," she said.

10. Cost management capabilities

As organizations scale their use of agentic AI, they'll see their costs scale quickly, too.

That necessitates a strong cost management capability, Janosevic said, adding that organizations will want to track costs to determine whether they're getting value from their AI agents.

Pros and cons of agentic AI teams

By all accounts, agentic AI is poised to bring significant changes to the world in the coming years. For those who are ready, agentic AI will bring transformative productivity and efficiency gains.

But it comes with risks, such as producing faulty outcomes and making bad decisions if ungoverned. It will also be disruptive, eliminating certain roles and reshaping those that remain.

Padmanabhan said he and other executives see those pros and cons, which inform how they're moving forward.

"We want AI solutions to enable our adjusters to do what they do best with our claimants and adjust claims diligently and quickly. A thoughtfully designed workflow with agentic AI allows optimization across a complex, multistep workflow, which sometimes spans months in our business," he said.

Padmanabhan stressed that they expect employees to be treated with care. "That is our North Star. AI FOMO is not going to distract us from that mission," he said.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.