Key takeaways
- Faster customer experience responses do not necessarily create lower customer effort.
- Customers measure outcomes, not interaction speed alone.
- AI has improved customer understanding, but execution challenges remain.
- Cross-functional coordination is often a hidden source of friction.
- Agentic AI is emerging to close the gap between insight and execution.
- Future CX improvements may depend on improving execution reliability rather than simply accelerating interactions.
Why customer effort remains one of the biggest CX challenges
Differentiation remains surprisingly difficult. According to ISG research, only 20% of enterprises believe their CX meaningfully outperforms that of their competitors.
Despite significant automation improvements in CX, customers continue to report many of the same frustrations.
They still repeat information across channels. They still get transferred between departments. They still wait days for issues to be fully resolved, even after receiving an immediate response.
The disconnect highlights an uncomfortable reality for many organizations: improving interaction speed does not automatically reduce customer effort.
Customers rarely judge an experience based on how quickly a conversation begins. They simply want their issue resolved with minimal friction. A five-minute response that leads to three transfers creates more effort than a longer interaction that solves the problem the first time.
This is becoming increasingly important as organizations invest in AI.
How AI improves customer insight, but not always customer outcomes
Generative AI has dramatically improved the ability to interpret customer interactions, giving agents and teams access to real-time summaries, intent detection, and recommended next steps. But better insight doesn’t automatically create better outcomes.
Customers don’t experience analytics. They experience execution.
ISG describes this as the CX execution gap: the misalignment between an organization’s ability to understand what a customer needs and its ability to reliably deliver the outcome across systems, teams, and policies. Closing that gap requires redesigning where execution responsibility sits.
The hidden operation barriers behind customer service friction
Many customer journeys still require employees to navigate multiple systems, coordinate across departments, interpret policies and manually trigger follow-up actions. As journeys become more complex, execution often becomes the bottleneck.
This is one reason customer effort remains stubbornly difficult to improve. The challenge is not always understanding what the customer needs. Often, it is ensuring the organization can consistently deliver the outcome.
A quick example: a financial services client automated a process that accounted for 40% of logged call volume, expecting a significant reduction in contacts. None materialized. Why? Because the recorded reason for contact — a balance inquiry — was rarely the whole story. Customers were calling to act on that information: to transfer funds, make a purchase, or resolve a connected issue. Automating the stated intent while missing the underlying need produced a technically functional solution that delivered no operational value.
This is exactly the kind of gap that agentic AI, deployed with genuine operational expertise, is designed to close.
What is agentic AI and why it’s important for CX
Agentic AI refers to systems designed to execute defined business processes autonomously within explicit guardrails, coordinating decisions and actions across platforms to achieve a specified outcome. Unlike generative AI tools that summarize or recommend, agentic systems can act — initiating and completing predefined workflows while escalating to human oversight when conditions fall outside established limits.
In a contact center context, that means systems handling classification, eligibility checks, and standard service actions, while human agents focus on exceptions, complex decisions, and interactions that require genuine empathy and judgment.
ISG projects that by 2028, approximately one quarter of enterprises will deploy some form of agentic AI in digital business environments, driven not by experimentation, but by real operational pressure to reduce coordination friction and execution variability.
Importantly, ISG is clear that near-term value will come from selective delegation, not universal automation. The goal is to assign the right work to systems and preserve human judgment for the moments that truly require it.
Why operator expertise is the missing ingredient
Technology alone does not close the execution gap. The most common reason agentic AI deployments underdeliver goes beyond the technology itself. It is the distance between the teams building that technology and the operational reality of how customers really interact with brands.
Experienced agents develop sequencing decisions and handling approaches outside of what’s in a training manual but consistently produce better outcomes. Automation built from observed operational reality performs at an entirely different level than automation just built from documentation.
That distinction is central to Foundever’s approach. It brings operator-level knowledge to every stage of the agentic deployment process, from identifying what should be automated to designing experiences that reflect real customer behavior, to driving continuous improvement after deployment.
The result is agentic execution accountable to measurable outcomes, built to deliver value that compounds over time rather than stalling at proof of concept.
Why CX leaders are shifting their focus from speed to resolution
Forward-thinking CX leaders are beginning to rethink where friction exists inside the service experience. Rather than focusing exclusively on interaction quality, they are examining how work moves through the organization after the customer makes contact. They are looking into questions like:
- Where do handoffs occur?
- Which processes require manual intervention?
- Which workflows depend on tribal knowledge rather than documented logic?
- Where do customers experience delays because systems fail to work together?
These questions often reveal greater opportunities for improvement than another chatbot enhancement or routing optimization project. And increasingly, the answers are pointing toward agentic AI as an operating model transformation.
What’s next for CX in the age of AI
The next phase of CX transformation is more about improving execution. Organizations that approach agentic AI with discipline — starting with bounded use cases, embedding governance from the outset, and building from operational reality rather than documentation — will scale faster and with less risk. For a deeper look at how customer experience is evolving from insight to execution, download the ISG and Foundever whitepaper, “Rethinking Customer Experience in the Age of Agentic AI.”
Frequently asked questions
Why is customer effort important in customer experience?
Customer effort is important in customer experience because it measures how easy it is for customers to achieve their desired outcome. Lower effort is often associated with higher satisfaction, stronger loyalty, and reduced churn.
What causes customer effort to increase?
Common causes of customer effort increasing include multiple transfers, repeated explanations, inconsistent information, delayed follow-up, and disconnected systems that require manual coordination.
Speed can improve satisfaction, but only when it contributes to effective resolution. Fast responses without resolution may increase frustration.
What is agentic AI in customer experience?
Agentic AI refers to systems that can execute defined workflows autonomously within explicit guardrails, going beyond recommendations to complete actions across platforms while escalating to humans when needed.
How does AI help reduce customer effort?
AI can help reduce customer effort by improving intent detection, personalization, knowledge retrieval, and workflow efficiency. However, organizations must also address operational execution to fully reduce customer effort.
What should CX leaders focus on next?
Many CX leaders are shifting attention toward workflow orchestration, cross-functional coordination, and operational execution to improve customer outcomes, treating agentic AI as an operating model transformation rather than a standalone technology upgrade.