For many customer experience (CX) organizations, the question is no longer whether to invest in artificial intelligence (AI). The technology is already part of their operations, customer service strategies, and future plans. Some have moved well beyond initial pilots, scaling AI across channels and workflows while learning firsthand what it takes to make those investments work.
Execs In The Know’s 2026 CX Leaders Trends & Insights: Corporate Edition report puts numbers to that shift. Two-thirds (67%) of contact center organizations now use some form of AI, up from 31% in 2022. Yet only 7% of CX leaders say they are meeting their AI objectives very effectively.
That gap tells the story. Deploying AI is no longer the hard part. Getting it to perform and keep performing is. As AI becomes more embedded in CX operations, the decisions surrounding it are becoming more complex. Leaders are evaluating where automation is producing meaningful results, where human involvement remains essential, how to manage risk, and how to adapt as the technology continues to evolve.
Those decisions require more than a technology provider that can implement a solution. They require partners who understand the realities of running a CX operation and who stay engaged after go-live, when the real work of measuring, tuning, and governing AI begins.
For AI providers, that changes where value gets proven. It’s less about the demo or the deployment and more about what happens in month three, month six, and month twelve.
The Hardest Question Isn’t What AI Can Do
AI capabilities are advancing quickly, but that doesn’t mean every new capability belongs in a CX operation. Organizations increasingly face questions about where AI can improve efficiency, which customer interactions are appropriate for automation, and where a human response will produce a better outcome. They also need to consider how AI affects employees, existing processes, customer trust, and the broader experience.
These decisions aren’t always straightforward. A use case that looks promising in a demonstration may introduce complications when applied to real customer interactions. Automation may reduce contact volume but create additional work when customers need to be transferred to an employee. A tool may perform well in one part of the operation but struggle with the context or complexity required elsewhere.
The challenge isn’t simply identifying what AI can do. It’s understanding where it makes sense, what it takes to make it successful, and how to recognize when a different approach is needed. That requires operational and strategic understanding that goes beyond product knowledge.
RELATED READING: When AI Can’t Resolve the Issue: Rethinking the Human Handoff in Customer Experience →
Organizations Need Help Navigating the AI Decision
As organizations mature in their use of AI, the questions they ask partners are changing, too. They need help evaluating new capabilities, identifying opportunities for improvement, and understanding how different technologies fit within an increasingly complex CX environment. They need partners who can bring perspective on implementation, integration, governance, employee adoption, and performance.
Just as importantly, they need partners willing to offer an honest assessment. Not every challenge requires another AI solution. Sometimes the issue is an outdated process, fragmented data, unclear ownership, or a lack of alignment between technology and the people expected to use it. Adding another tool won’t necessarily solve those problems.
Execs In The Know’s The ROI Imperative: Building the Business Case for Exceptional CX research makes the point: when CX leaders were asked which investments have historically delivered their strongest ROI, process improvement (58%) edged out both AI/automation and self-service tools (54% each), even though 75% expect AI/automation to deliver the strongest ROI over the next 12 to 24 months.
The most valuable AI partners understand that distinction and are willing to say so, even when the right answer is to fix a process before automating it. They can look beyond the immediate opportunity to sell or implement a solution and help organizations determine what will move the business forward.
That kind of guidance becomes especially important as AI investments grow, and the consequences of getting those decisions wrong become more significant.
The Risk of Solving for Technology Instead of the Customer
One of the biggest risks in AI adoption is letting the technology’s capabilities drive decisions that should start with the customer and the business. Automation may be improving efficiency, but is it also making the experience easier? Is the organization resolving customer needs more effectively, or simply shifting work between channels? Are employees better equipped to help customers when AI reaches its limits?
These are important distinctions. An AI strategy that focuses too narrowly on automation rates, cost reduction, or deployment milestones can overlook the outcomes CX organizations are ultimately responsible for delivering. Technology may be performing as designed while the customer or employee experience tells a different story. Our research shows how easily that can happen: among the KPIs organizations use to measure their AI-powered solutions, resolution rate (67%) and containment/escalation rate (58%) lead the list, while just 24% track customer effort.
Partners play an important role in helping organizations see the bigger picture. That means understanding how technology affects the operation as a whole, identifying unintended consequences, and helping teams adjust when the results don’t match the original expectations.
It also means recognizing that AI and human support aren’t competing priorities. The goal is to determine how they work together to deliver better outcomes.
What Makes an AI Partner Valuable After Go-Live?
As AI capabilities become more widely available, access to the technology alone is no longer a meaningful differentiator. A partner’s real value shows over time, through its ability to keep solutions delivering results as customer expectations, business priorities, and technology evolve.
That’s where many CX organizations are still building capability. Eighty-three percent of corporate respondents we heard from said they monitor their AI-powered solutions in some way, but only 42% have a defined process for turning that monitoring data into continuous improvement, with another 44% planning to add one. As Mitch Mann, Vice President, Member Services, put it in the report, AI is not a “set it and forget it” solution.
Making that happen requires an understanding of the environment in which the technology will operate. AI doesn’t exist independently of an organization’s systems, processes, employees, and customers. Successful adoption depends on how those elements work together.
Partners who understand this broader context can contribute in several important ways:
- Prioritization: Helping organizations distinguish between AI opportunities that offer meaningful business value and those that may create unnecessary complexity.
- Readiness: Identifying gaps in data, infrastructure, processes, governance, and workforce capabilities before implementation begins.
- Integration: Understanding how new AI capabilities fit within existing technology environments and operating models.
- Accountability: Agreeing on realistic measures of success before launch, then reporting honestly on whether investments are producing the intended results.
- Adaptation: Turning performance data into continuous improvement as AI capabilities, customer expectations, and business priorities evolve.
These contributions require more than product expertise. They require curiosity, industry knowledge, and a willingness to understand the client’s business before recommending a solution.
The Best AI Partners Stay for the Results
An interesting shift is happening in the relationship between enterprise organizations and their technology providers. As AI moves out of the experimentation phase and into day-to-day operations, the conversation is shifting from who has the most advanced technology to who will help make it work, month after month.
That creates different expectations for partners. The most valuable providers won’t necessarily be those who arrive with the most impressive demonstrations or the longest list of AI capabilities. They’ll be the ones still in the room after launch, reviewing performance, flagging where the human handoff is breaking down, and recommending what to adjust next.
For CX leaders, finding those partners may be just as important as selecting the technology itself. For business partners, the opportunity is to recognize that post-deployment expertise, including measurement, optimization, and governance, is becoming central to the value they bring.
At Execs In The Know, we see this shift firsthand in our research and in our conversations with corporate CX leaders in our community, at Customer Response Summit (CRS), and in our executive virtual roundtables, Rapid-Fire AI Use Case virtual events, and in-person outsourcing briefings. The questions CX leaders are asking about AI have moved from whether to adopt it to whether it’s working and how they will know. The partners who can help answer that second question are the ones those leaders will keep calling.
The AI landscape will keep changing. New tools will emerge, capabilities will expand, and today’s use cases will evolve. But the need for sound judgment won’t go away. In fact, as the number of possibilities grows, having a trusted partner who stays accountable for what happens after go-live may become one of an organization’s most valuable advantages.
Want to hear what corporate CX leaders are asking of their AI partners? Connect with Scott Moberly, Vice President of Partner Advocacy, at [email protected] to explore how your organization can get more involved in the Execs In The Know community. Or download our 2026 Media Kits to discover upcoming partnership opportunities.
