Agentic AI is creating a new kind of team for the workforce.
Many organizations are now deploying AI agents. Of the 300 senior executives surveyed in PwC’s 2025 AI Agent Survey, 79% said their company has an AI agent in place. These autonomous AI systems, like human AI systems, independently process tasks and make real-time decisions to deliver desired outcomes. This automation, combined with human expertise and oversight, can deliver valuable ROI to your business.
Learn more about the emergence of agent AI teams and deploy them in your enterprise with our expert readiness assessment.
What is Agent AI Team?
Agent-based AI is changing the way we work. AI agents can handle entire workflows and processes more efficiently and effectively than humans. As managers, humans can team up with agents, oversee their output, and perform tasks that only humans can or can do. This combination increases productivity and reduces costs while maintaining a human-involved strategy.
Research from IDC’s FutureScape 2026 report estimates that by 2026, 40% of all jobs at Global 2000 companies will “involve collaboration with AI agents, redefining long-standing traditional entry-, mid-, and senior-level positions.” PWC research found that 66% of executives who adopted AI agents said the agents delivered measurable value through improved productivity, and executives reported positive results.
“As agent AI increases reliability across multi-step workflows and orchestration technology becomes enterprise-grade, we expect AI agents to play a key role in supporting adjusters,” said Vishy Padmanabhan, chief transformation officer at Sedgwick, a global third-party management company that manages insurance claims, workers’ compensation and disability leaves for commercial customers.
According to Deloitte’s 2026 State of AI in the Enterprise report, 85% of companies expect agents to be customized to the unique needs of their business. Airlines can use AI agents to rebook customers’ flights or reroute baggage, freeing up time for human agents to deal with more complex issues. Financial services companies can use agents to capture to-do actions identified in meetings, create and send reminders to participants, and track follow-through. Organizations can tailor these teams to meet their exact needs.
However, before organizations can successfully deploy AI agents, they must prepare in multiple areas: technical, cultural, and strategic.
Assess the business readiness of your agent AI team
Experts recommend that companies assess their readiness in the following areas to ensure they can successfully use agent AI teams to get the job done:
1. Strategy
Vuk Janosevic, a senior director and analyst at Gartner who advises technology leaders on expanding AI-driven growth, said companies that are realizing the benefits of implementing agent-based AI are “strategically clear” and know what they want to accomplish with the technology.
“Some [organizations] We focus on “Can we build this?” “Organizations must be clear about what business outcomes they are trying to improve, how improvement will be measured, and what success will look like over time. Start with defining outcomes, not choosing a model. Start with ‘What do we want to achieve with this digital transformation?’
2. Leadership
“Leadership is absolutely critical,” said Miles Suhr, research director at Dresdner Advisory.
Suhr explained that executives and leaders across the organization need to understand what AI can do, its business benefits, and how to align everyone with the organization’s AI strategy. Understand the technology and lead with a clearly defined implementation strategy to gain employee trust and provide direction for implementation.
3. Data
As with all AI deployments, data preparation is critical to success.
“If an organization’s data isn’t well-formed, it’s very difficult to use agent AI to provide insights or get agents to take on work,” Suer says. Suer said his company’s research found that only one-third of organizations have enough data to succeed with agent AI.
Additionally, Padmanabhan said organizations need to continuously monitor data quality. “You cannot achieve high-quality AI outcomes without consistent data and proper governance, which is essential for high-quality agent AI performance,” he explained.
4. Integration with current processes
Agentic AI uses intelligence to automate not just tasks but outcomes. Companies need to understand what outcomes they want to achieve with AI agents and the current processes they are using to achieve those outcomes.
“They need to ask: Do they have a clear understanding of the process, what its flow is, what that workflow looks like, and what it could look like?” said Anne Bosch, a partner at consulting firm Bain & Company and a member of the firm’s technology and cloud services practice.
Bosch recommended that organizations document, make consistent, and rethink their processes, noting that there is no real value in using AI to automate bad processes. Sedgwick’s Padmanabhan agreed.
“What’s the difference?” [agentic AI] This is something that can impact entire multi-step workflows,” Padmanahan said. “This requires continuous improvement as well as reinvention of workflows. We are working closely with the front lines of claims examiners to redesign workflows. Some of our customers are currently discussing with us efforts to reimagine aspects of insurance claims processing.”
5. Technology and architecture
Technology stacks will need to be redesigned and rebuilt for agent AI, putting additional pressure on organizations still using traditional on-premises computing. Suhr said companies need to adopt modern IT architecture, data architecture and software development principles.
Architecture is one particular problem area. According to Janosevic, some organizations fail to implement agent AI because they focus on task efficiency rather than workflow. Agents must perform tasks in a clear order to deliver results. To ensure this, organizations need architectural orchestration. It is a control layer that coordinates, manages, and directs agents, allowing them to work together to complete multiple tasks in a workflow or process.
7. Preparing for changes
The ability for employees to change the way they work is critical to the successful implementation of agent-based AI teams.
“You have to be open to change,” Bosch says. He noted that some teams will embrace AI agents, others will quickly follow suit, and others will resist them completely. There are levels of acceptance of AI.
Bosch said executives need to foster agility and AI fluency in their workforces and encourage employees to more actively learn how to collaborate with digital teammates. This is what Padmanabhan and his fellow executives are working on.
“We as a company are very focused on this aspect,” he said. “The Quad Model, or Business/Operations/Technology/GTM alignment, is the engine room that defines the roadmap, priorities, and order. We work very hard every day to cascade this alignment mindset across our global teams.”
8. Governance and risk management
“As autonomy increases, so does risk,” Janosevic said.
Organizations must ensure proper governance and risk management. Janosevic said company leaders need to create clear policies on what agents can and cannot do and ensure those policies are enforced through tools and technology.
“When agents access proprietary data, there needs to be a layer of security and governance, and you need to create human checkpoints, checkpoints involving humans,” he said. Platforms at this layer create an audit trail, providing systematicity and explainability so organizations can monitor and understand how agents make decisions.
9. Skills
Agentic AI will change the way humans work, Suhr says, meaning humans will need different skills. To successfully collaborate with digital teammates, human employees will need to develop a combination of business and technology skills, and company leaders must ensure their employees are ready.
Bosch emphasized the need to have engineers skilled in AI. That includes technicians who can fill new roles like forward-deployed engineers. Engineers are software engineers who design, customize, deploy, and operate complex software, especially agent AI, on the front lines of business.
“That person knows the technology, the data, what is needed, the limitations and opportunities, and where and the process by which use cases can be deployed,” she said.
10. Cost management function
As organizations expand their use of agent AI, costs will also grow rapidly.
This requires strong cost management capabilities, Janosevic said, adding that organizations need to track costs to determine whether they are getting value from their AI agents.
Advantages and disadvantages of agent AI teams
By all accounts, agent AI is going to revolutionize the world in the coming years. For those who are ready, agent AI offers revolutionary productivity and efficiency gains.
However, this comes with risks, such as producing erroneous results and making wrong decisions if uncontrolled. It would also be disruptive, eliminating certain roles and restructuring those that remain.
Padmanabhan said he and other executives are aware of these advantages and disadvantages, which will influence how they proceed.
“We want our AI solutions to empower adjusters to do what’s best for claimants and adjust claims diligently and quickly. Carefully designed workflows using agent AI allow our business to optimize across complex, multi-step workflows that sometimes span months,” he said.
Padmanabhan stressed that he expects his employees to be treated respectfully. “That is our North Star. AI FOMO will not distract us from that mission,” he said.
Mary K. Pratt is an award-winning freelance journalist focused on covering corporate IT and cybersecurity management.
