Every information technology leader I talk to has an artificial intelligence story about customer experience. Almost none has an outcome story.
That gap is now measurable. Talkdesk Inc., in partnership with NewtonX, surveyed 252 director-level-and-above leaders in customer experience, IT, operations and AI strategy at midmarket and enterprise organizations across four regions. The resulting benchmark, The State of Agentic Automation in CX, concludes that IT should reframe how it plans its next CX investment: Adoption is no longer a differentiator, and execution is the new battleground.
The headline numbers make the point. Ninety-eight percent of organizations use some form of AI in customer experience. Generative AI is at 74%, and scripted AI is at 65%. But only 24% use agentic AI, which refers to systems that take a goal, reason through it, and act across systems. And just 15% pair agentic AI with the orchestration required to resolve a customer need end to end. Meanwhile, 81% are piloting or deploying AI agents, yet only 19% have scaled beyond a single use case, and nearly 80% have fewer than 10 AI automations in production.
Pedro Andrade, vice president of AI at Talkdesk, presented the findings in an analyst briefing and outlined the state of the industry. “AI adoption is no longer a question. Now the question is whether organizations can orchestrate all the elements needed to deliver measurable customer outcomes — agents, humans, data, knowledge, workflows, and governance — all in one package.”
That gap carries a dual cost, as Talkdesk describes it in announcing the research: Companies pay for isolated AI tools while still absorbing the operational cost of customer requests those tools never complete. Talkdesk founder and Chief Executive Tiago Paiva calls the result “a false sense of progress,” arguing that widespread deployment masks how few organizations can quantify AI’s impact.
Routing is not resolving
The most useful distinction in the research is between activity that resembles orchestration and true orchestration. Sixty-four percent of organizations run specialized AI agents for functions such as identity verification, billing, or technical support. Yet only 35% have AI that can maintain customer context and act across systems to drive true resolution. If every handoff forces you to rebuild context and memory, you have built a smarter switchboard, not an outcome engine.
“Routing work is not the same as resolving it,” Andrade said. He used a car accident as an example: One event triggers a tow provider, a claim, a repair shop and a rental — multiple departments, multiple systems, one expected outcome. Fifteen percent of companies can automate that journey end to end.
The research organizes this into a five-stage maturity curve with two chasms. The first is the agentic threshold: moving from scripted and generative AI to AI that acts. Fifty-six percent fall below it. The second is the CXA unlock — moving from isolated agents to autonomous cross-system execution. Twenty-nine percent are Agentic Scalers with one of the two capabilities; 15% are CXA Leaders with both.
The spread between those top two tiers is where budget attention should be focused. CXA Leaders are roughly four times more likely than Scalers to report major CSAT or NPS gains — 22% versus 5%. Thirty-eight percent already resolve over 40% of customer issues autonomously, while 60% of Scalers still resolve fewer than 20%.
Leaders are also about twice as likely to run revenue-oriented automations, such as churn prediction (51% vs. 28%) and personalized recommendations (44% vs. 19%). The difference is not in tool count. It is in how much meaningful work the AI is permitted and able to do.
The blockers are infrastructure, not intelligence
This is squarely in IT’s lap. When asked what limits their ability to automate CX, respondents cited compliance (50%), security (48%), disconnected systems (45%), legacy infrastructure (44%), insufficient skills (41%) and unclean or non-unified data (40%). None of the top barriers stems from AI models.
Fragmentation carries a tangible cost. Human agents lose an average of 28% of their time to system switching, data re-entry, and searching for customer context. That climbs to about 35% in the least mature organizations and falls to roughly 25% in the most mature — about 10% of employee capacity reclaimed. Respondents estimate that incomplete context, repeated explanations, and manual escalations affect roughly one in four customer interactions. And 94% of organizations lack AI-assisted knowledge management.
That knowledge gap is the one Andrade said he hears most often in customer meetings: “It’s not about actually having it. It’s more about how I make it ready to be consumed by AI.” Only 64% of CXA Leaders have knowledge embedded in the workflow or AI-assisted — and they are the leaders.
The lesson generalizes. If your environment is too fragmented for humans to work efficiently, it will be too fragmented for AI agents to operate safely.
What IT pros should do
- Audit for resolution, not deployment. Count how many issues close without human intervention and how many require context to be rebuilt at a handoff. Deployed-agent counts are vanity metrics. Ten agents that hand off are worth less than two that resolve.
- Fix data and knowledge before adding agents. Only 2% of organizations report fully unified data. Move from “some integrations” to “mostly integrated” across the systems your top journeys touch, and move knowledge out of static repositories and into the workflow. As Andrade noted, “operationalization” — not launching the first pilot — is the real challenge.
- Pick journeys, not use cases. Choose two or three high-volume journeys that span departments, then map every system, approval, and exception within them. That surfaces compliance and legacy issues while the blast radius is still small.
- Build measurement in from day one. Only 5% of companies can clearly quantify AI’s business impact. Andrade called measurement the report’s biggest miss: knowing what to track, which KPIs matter and having the plumbing to track them. Tie automations to first-contact resolution, autonomous resolution rate, cost per contact and CSAT before go-live, not after.
- Treat agents like employees. Nearly one in five organizations already view AI agents more as labor than as technology, and 99% believe a hybrid workforce is beneficial — yet 52% cite trust in AI decision quality as their top concern, including 46% of the most mature organizations. The most mature are also more than 10 times as likely to run AI and people as a single unified operation. Define escalation paths, QA frameworks, observability and accountability for agents as you would for a new team.
- Skip the generic roadmap. Maturity varies sharply by vertical. Retail leads, with 24% at the leader stage for repeatable, high-volume journeys. Healthcare adopts at market rate but has the most siloed data and the weakest payoff. Financial services have the best-integrated data, yet only 9% are leaders, held back by legacy cores. “CXA roadmaps cannot be generic,” Andrade said. Design yours around your industry’s actual constraints.
Eighty-three percent of organizations expect AI to resolve more issues autonomously within two years. That will happen only when someone has done the unglamorous work of connecting systems, grounding knowledge, and governing autonomy. That someone is IT.
Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.