In This Article
- How rising complexity makes agent capability more valuable
- Why workforce scale needs to become more adaptable
- How technology orchestration depends on talent orchestration and frontline AI fluency
- How the scorecard should change as the mix of work changes
- What an evolved operating model looks like for contact centers and BPOs
Much of the conversation around AI in customer experience has focused on how much work can be automated and what that means for headcount. Those are important questions for today’s organizations, and it leaves the question of what happens to the human work that’s left.
As AI handles more routine questions and predictable transactions, the interactions reaching people become more differentiated. Agents inherit more exceptions, complex problems, and sensitive situations where the obvious answer has already failed. Human contact volume may decline even as the expertise required to handle each interaction increases.
For an industry built around managing human work at scale, that has implications well beyond staffing. Leaders now have to decide how work should move between agents and AI, where each creates the most value, and how to make them effective together. This shift toward orchestration will ultimately prove more consequential than the headcount discussion alone suggests.
These seven shifts help explain where that transformation is leading.
1: AI is Automating Predictable Work First
Password resets, order-status inquiries, basic account questions, and straightforward troubleshooting tend to follow established paths. They are natural places for automation to create value. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention.
As those interactions leave the human queue, the work that remains becomes more concentrated in exceptions and escalations. A customer may reach an agent only after self-service has failed, sometimes with a more complicated problem and considerably less patience.
That changes how organizations should evaluate automation. The number of contacts AI resolves is only part of the picture. They need to understand what removing those contacts does to the complexity of the work people handle. Lower volume does not necessarily mean an easier operation to run.
2: Human Capability Becomes More Valuable
Consider an unusual fraud case, a customer thinking about cancelling, or a sensitive financial situation. Resolving those emotionally driven interactions require judgment, empathy, product knowledge, and commercial awareness that a script alone cannot provide.
Organizations may need fewer people for certain processes while needing greater capability from the people who remain. That should influence who they recruit, train, and develop agents’ careers. Workforce planning must account for the expertise required, alongside the number of people available.
The value of an agent’s contribution can increase as the work becomes harder. Realizing that value requires preparing and supporting agents for the work they are actually inheriting.
3: Scale Needs to Become Adaptable
The ability to recruit, train, and manage large workforces has been a defining strength of contact centers and BPO providers for decades. That capability still matters, especially when it allows an organization to respond quickly as customer needs evolve.
AI places a different demand on that scale. How quickly can teams develop new skills, move into more complex workflows, and work effectively with new technology? How well can an organization retrain and redeploy people as automation changes the mix of work?
A large workforce primarily executing standardized processes is exposed to automation. The more valuable capability is a workforce that can keep learning and adapting. Scale earns its value through what the organization can do with it.
4: Technology Orchestration Requires Talent Orchestration
An AI-enabled customer journey may involve several systems and people working together. An AI agent might begin the interaction, a knowledge system provides context, and workflow automation completes part of the process. When the situation requires judgment or care, a human specialist may take over.
Making that work requires clear decisions about who or what handles each task, when responsibility moves, and what information follows the customer. Client applications and compliance requirements also have to fit into that design.
Technology teams can provide powerful capabilities. Operations must understand where those capabilities belong, how to evaluate their output and when to intervene. Orchestration therefore requires people with the skills and operational knowledge to manage the workflow as a whole. Installing technology is just one part of that responsibility.
5: Frontline Teams Need Practical AI Fluency
That responsibility cannot sit exclusively with technology teams or a centralized transformation group. Those closest to customers need enough AI fluency to recognize an inaccurate or incomplete response, understand when a human should take control, and use the tools without surrendering their own judgment.
They also need a way to improve what they see. Frontline employees encounter recurring questions, unnecessary steps, and various exceptions every day. Those observations can reveal where knowledge needs updating, where a workflow is failing, and where automation could help.
Contact center and BPO leaders don’t need every agent to become an AI engineer. But they do need to give frontline teams the skills and mechanisms to act on their experience. Done well, that creates a distributed network for continuous improvement within the operation.
6: The Scorecard Must Change with the Work
While average handle time, occupancy, adherence, and cost per contact remain useful measurements of success, the challenge is interpreting them when the mix of human work changes.
Suppose automation removes a substantial share of simple interactions, and average handle time rises for the people handling what remains. That alone does not establish that performance has deteriorated. The remaining contacts may require more investigation, judgment, or care. Organizations need to distinguish a change in the work from a change in how well people perform it.
Traditional efficiency measures should sit alongside successful resolution, first-contact resolution, customer effort, and total time to customer outcome. AI accuracy and the quality of the handoff to a person deserve attention too, as does revenue protected or generated where it is relevant to the work.
In addition, it’s key to pay attention to how quickly the organization improves. When an employee identifies a recurring problem, how long does it take for that insight to become better knowledge, a stronger workflow or more effective automation? That is a critical way to think about innovation velocity. Efficiency remains essential, and the scorecard should reflect the agent’s work.
7: The Operating Model Has to Evolve
Taken together, these shifts reach well beyond the agent’s role. More demanding human work changes the skills needed. Adaptable teams need ongoing development. Orchestration requires closer coordination between technology and operations, supported by measures that show how the whole customer journey performs.
Supervisors may spend more time coaching, analyzing exceptions, and improving workflows. Operations leaders may take greater responsibility for automation performance alongside human performance. Technology teams will need to work closely with the frontline, and workforce planning will increasingly need to consider the combination of human and machine capacity.
Eventually, improving the existing operation is no longer the complete assignment. Organizations must ask what the operation should look like now that these capabilities exist.
Prepare for the Next Era of Work
AI will eliminate some work. It will also change what remains, affecting how organizations hire, develop, deploy, and measure teams. These shifts are an operating-model discussion as much as a technology discussion.
For decades, contact centers and BPO providers have become exceptionally good at managing human work at scale. The next era will require the industry to become equally good at orchestrating human expertise and artificial intelligence together.
Contact a member of our team to be ready for what comes next.
