Contact Center AI Adoption Outruns Readiness as Focus Shifts to End-to-End Resolution

Talkdesk research presented at the event found that 98% of companies have deployed AI somewhere along the customer journey, but only 15% combine agentic AI with cross-departmental orchestration. Pedro Andrade, vice president of AI and generative AI business specialist at Talkdesk, said the barriers sit outside the model itself: compliance was cited by 50% of respondents, security by 48% and disconnected systems by 45%. “Adoption is easy. The orchestration is the hardest part,” Andrade said. “Agentic orchestration requires AI to maintain a context across all those systems.” He described customer experience automation, or CXA, as an operating model rather than a product category, coordinating a hybrid workforce of AI and human employees and connecting systems, knowledge and workflows.

The distinction shows up in results, according to Andrade. Companies Talkdesk classifies as CXA leaders report about four times the net promoter score improvement of less mature adopters, 22% versus 5%, while cost-per-contact gains separate the two groups far less sharply. Churn prediction and personalized recommendations show a similar spread. “Savings alone don’t tell the whole story,” Andrade said. “They understate the value that you can get with AI.”

Managing that hybrid workforce is creating a job that did not exist a few years ago. Talkdesk built its CXA Operations Center around the idea that supervisors will evaluate, monitor and course-correct AI agents much as they do human ones, shifting from writing scripts to watching behavior. The measurement framework shifts with it. “Now you don’t measure just average handle time. You are going to measure how much time it takes from opening of a problem until it gets closed,” Andrade said. “Not because average handle time just measures the time of an agent, but what about the rest of the process?”

Andrade said the end state pushes customer service beyond inbound calls into proactive outreach such as service reminders, renewals and collections, because the call is usually the last step of a journey that began somewhere else. Automating one point of failure leaves the underlying process untouched. “Inbound is kind of the last piece of a journey is when everything breaks, people call in,” he said. “Look at the whole spectrum, look at the whole journey and put your journey all on paper and now start thinking about automating the whole journey instead of just having a one-point solution that takes you nowhere.”

In a separate conclusion from the same event, Bob Laliberte, principal analyst for networking and observability at theCUBE Research, and Zeus Kerravala, principal analyst and founder of ZK Research, said the next phase of contact center AI will be measured less by how many interactions machines handle and more by whether customers get their problems resolved. The analysts drew on sessions with Cisco Systems Inc., Talkdesk, Zoom Communications Inc. and Five9 Inc. “Resolution and resolution quality is the new unit of value,” Kerravala said. “Agentic systems should be judged on whether the customer’s needs were completed — and completed actually across the full journey.”

The analysts said organizations will need broader scorecards than containment and deflection alone, covering customer satisfaction, effort, employee productivity, cost and growth. Connected data and governance underpin those results, they said, because fragmented systems and stale knowledge can undermine AI accuracy, create repetitive interactions and accelerate flawed processes. “If you’ve got a broken process, you’re going to get to that bad destination faster,” Kerravala said.

Laliberte and Kerravala recommended starting with a clearly bounded, high-value problem rather than redesigning the entire customer journey at once. Companies can establish baseline metrics, deploy AI against a specific workflow and measure improvement before expanding. Initial deployments still need architectures capable of connecting systems and reusing governance controls as deployments grow. Governance must move beyond a preproduction checkpoint and become an ongoing operating practice involving continuous evaluation, observability, policy enforcement and testing. “If you have the proper governance in place, you can actually move faster with your AI initiative,” Kerravala said. “It should be something that enables adoption, not holds it back.”

AI adoption will also reshape how contact centers divide work between people and digital agents, the analysts said. Human employees are expected to spend more time on exceptions, emotionally sensitive situations and interactions requiring judgment, while AI takes on more standardized processes. Supervisors will need to manage that blended workforce differently, including understanding when AI is working, when it is failing and how work should move between automated systems and people. The analysts recommended selecting one customer journey, documenting the workflow and data requirements, establishing baseline metrics, and testing routine interactions and edge cases before expanding. “AI ROI in CX won’t be determined by the number of bots deployed,” Laliberte said. “It’s going to come from getting to better resolutions, being able to have more capable employees and more efficient operations, and responsible execution at scale.”