As artificial intelligence takes on more front-line tasks, the agent's role is evolving. That shift demands new tools, new training, and a fundamentally different human contribution: less time spent on repetitive execution and more time making the judgment calls that keep work from falling through the cracks between systems.
For many organizations, though, the early reality of AI has been more complicated than the promise. Employees already spend roughly 60 percent of their time on emails, meetings, and follow-ups, leaving little room for strategic work. Rather than removing that burden, AI has often introduced new layers of review, oversight, and coordination. Call it the AI workload gap: technology intended to reduce effort ends up creating additional operational friction. Customer service teams, for example, might find that AI handles routine inquiries with ease but escalates more complex or ambiguous cases, increasing the volume of high-effort interactions that require human intervention.
Research confirms the pattern. AI does not significantly free up employee time; the value emerges from how agents, humans, and systems work together across workflows. RingCentral research found that 96 percent of organizations agree AI agents will be essential to staying competitive, but adoption alone is not the story. The real question is whether those agents can effectively coordinate with people and systems to move outcomes forward.
The Workload Gap in CX
Customer experience is where AI's limits are most visible, because that work rarely happens within a single, contained system. A single customer interaction can span channels, systems of record, routing flows, summaries, follow-ups, escalations, and human handoffs. A chatbot might resolve a basic request but fail to pass full context into a CRM or ticketing system, forcing the next human agent to reconstruct the interaction from scratch. RingCentral data found that nearly 40 percent of organizations cite data integration as a barrier to broader AI adoption, reinforcing that the challenge isn't AI capability but how systems connect and share information.
The cost compounds over time. What begins as a small gap in context at intake becomes repeated clarification, duplicated effort, and slower resolution at every handoff. Instead of accelerating service delivery, fragmented context forces teams to retrace steps rather than close the loop.
Orchestration is what turns AI from a collection of features into an operating model. Agents handle tasks; process orchestration delivers outcomes. That distinction matters because conversations are where the most important context lives. Calls, video, chat, and messaging capture intent, nuance, and edge cases that rarely surface in structured systems alone. Orchestration has to start in the communications layer where work begins and context can be captured before it gets lost. If it isn't, downstream systems, from CRM to analytics, operate with an incomplete picture and every handoff becomes a reset.
A Clear Workforce Shift
Agents will evolve into orchestrators, managing a digital front line of AI and stepping in where human judgment matters most. The role becomes less about answering calls or documenting every interaction and more about overseeing how work gets done across AI and human touchpoints.
Training is evolving alongside it. Instead of memorizing five legacy systems, representatives are learning to guide intelligent assistants, interpret AI outputs, and drive outcomes. The modern CX employee looks less like a task handler and more like an air traffic controller: coordinating chatbots, voice AI agents, and sentiment tools with full authority to step in when a frustrated customer needs a human touch. Humans remain essential, but their value concentrates where it matters most: judgment, empathy, exception handling, and accountability.
The value of orchestration shouldn't be measured by how many tasks AI completes independently but by whether work moves faster and more smoothly across the entire workflow. That means tracking fewer handoffs, faster resolution, improved first-contact outcomes, and less manual follow-up.
Leaders should also assess whether context persists as work moves between systems, channels, and people rather than resetting at every stage. The ROI of orchestration isn't just labor reduction. It's reduced friction, preserved context, and better end-to-end execution.
Organizations need to shift from viewing AI as a tool deployment to designing it as a workflow. The question isn't just where can AI save time but how should work move between AI and humans to produce a better outcome. That means designing for shared context, clear handoffs, and defined escalation paths, not isolated automations.
It also means training employees not just to use AI, but to supervise it, guide it, and step in when real-world complexity demands human judgment. The organizations that gain the most from AI won't deploy the most agents. They'll give every employee and every agent a clear role in moving the work forward.
Carson Hostetter is executive vice president and general manager of AI and CX solutions at RingCentral.