By summer, that cluster has turned into a trend. More customers are canceling over billing confusion, agents have started using a workaround script they wrote themselves, and finance is asking why churn went up in a segment that had looked healthy all year.

The team caught this early. They talked about it openly, and everyone agreed it mattered. Months later, nothing had changed.

If that sounds familiar, you’re in good company. Most organizations have more customer feedback than they know what to do with, and better tools to analyze it than ever. The trouble usually comes down to two things. Insights often aren’t specific enough for anyone to act on. And even when they are, nobody clearly owns turning them into a decision, and then into something customers actually notice.

Both problems tend to show up in the same four places.

Where insights go to die

1. The report

Most insights show up as observations, something like “Billing-related contacts rose 40% month over month.” That’s true, and it’s easy to agree with. It just doesn’t give anyone enough to act on. Which customers? Confused about what, exactly? Is it one issue, or five different issues sharing a label?

Compare that with “Customers who switched to annual plans are contacting us about their first prorated invoice, mostly within two days of the charge, and about one in ten of them mentions canceling.” Now the product team knows where to look, and someone can estimate what it’s worth to fix. The first version gets a nod. The second gets a follow-up question, which is a much better sign.

Volume doesn’t help. In a deck with fifteen findings that all look equally important, the one that matters most gets lost, and readers fairly assume they’re all optional.

2. The meeting

Oddly, the meeting is where an insight is most likely to stall, even though it’s where it gets the most attention. The room agrees, someone suggests digging in, and then everyone moves on to the next agenda item.

Agreement feels like progress. But unless the insight leaves with a named owner and a date, it’s been filed. Cross-functional meetings make this worse, because when CX, product and operations all care about the same problem, it’s reasonable for each of them to assume one of the others has it.

3. The backlog

Say the insight does get assigned. Now it’s sitting in a queue next to roadmap commitments, tech debt and revenue projects, and most of those came with a business case. Customer insights usually don’t. They describe the problem the way customers experience it, while the backlog gets ranked on cost, revenue and effort.

This is where vague insights get hurt the most. An insight that can’t say how many customers are affected, how fast it’s growing or what it’s tied to has nothing to put on the scale. So when someone asks what it costs to wait, it has no answer, and it loses. That’s rarely because the issue matters less. It’s just harder to compare.

4. The follow-up

This is the one that’s easiest to miss. The fix ships, the ticket closes, and no one goes back to see whether the customer signal moved. Are billing contacts down? Did those cancellations recover? A lot of teams can’t say.

The damage goes past the original issue. Frontline teams who flag problems and never hear what happened eventually stop flagging them. Over time that early-warning system gets quiet, and a quiet system is easy to mistake for a healthy one. The dashboard stays green while customers drift.

How to keep an insight alive

None of this gets solved with more data. It gets solved with sharper insights and a few habits that teams actually keep.

Start with the insight itself. Before it can have an owner, it has to be specific enough to hand to one. A good test is whether someone could start working on it tomorrow without asking a clarifying question. That usually means it names who’s affected, what exactly is going wrong, how big it is and whether it’s growing. If it can’t do that yet, it isn’t ready for the meeting.

Own it

Every insight that comes out of a review needs one person’s name on it, not a team’s. You can’t hold “product” accountable, but you can follow up with the product lead who owns billing.

It also helps to write the insight as a request. Instead of “Billing contacts rose 40%,” try “We recommend clarifying the prorated charge on first annual invoices, and we’d like a decision from the billing product lead by the 15th.” That version is much harder to nod at and forget.

Be clear about who owns what, too. CX usually owns the signal, but another team usually owns the fix. Naming both makes the handoff obvious.

A quick gut check: if you asked the room a week later who was responsible, would everyone give the same name?

Rank it

This is where CX earns credibility with partner teams, or loses it.

• Bring fewer insights. Three that get decisions will do more than fifteen that get nods. Deciding what to leave out is part of the process.
• Speak the other team’s language. Product weighs roadmap trade-offs, finance looks at revenue and cost, and operations cares about volume and handle time. Put the cost of waiting in their terms.
• Show where it’s heading. A small issue that’s growing fast usually deserves more attention than a big one that’s holding steady, and a trend line makes that case better than a single number.
• Split quick fixes from bigger ones. Some insights need a policy change or a new help article. Others need a spot on the roadmap. Handle them separately so the easy wins don’t get stuck behind the long debates.

Close it

Closing the loop means checking that the change worked, and then telling the people who raised it.

• Decide what “fixed” looks like before anything ships. Pick the signal you expect to move, whether that’s contact volume, sentiment, repeat contacts or cancellations. If you can’t name it, you won’t be able to tell whether it worked.
• Put the check-in on the calendar. A look back at 30 and 90 days covers most issues. Schedule it when the decision is made, or it probably won’t happen.
• Tell the frontline what happened. Agents and analysts who hear back keep flagging problems. It costs almost nothing and keeps the signal coming.
• Close it out or reopen it on purpose. If the numbers moved, say so and retire the insight. If they didn’t, reopen it with what you learned.

Making the loop run at scale

All of this gets harder as feedback volume grows. Getting from a broad label like “billing confusion” to the root cause, spotting which issues are picking up speed and confirming that a fix actually worked are close to impossible to do by hand across thousands of conversations a week. This is where technology earns its place. It makes ownership, prioritization and follow-through something a team can keep up week after week, instead of something that depends on one person staying late.

People still make the calls, like which trade-off to accept, who owns the fix and when an issue is really closed. Better signal means they can make those calls sooner and with more confidence.

What this means for CX leaders

As AI takes on more of the routine contacts, the people in CX are increasingly the ones who make sense of what customers are going through and push the rest of the business to respond. That changes the job. It’s less about reporting what happened and more about making sure something happens next.

It changes how trust works, too. Customers never see your insights deck. What they see is whether the confusing thing got fixed, and whether they had to contact you twice to find out. When an insight stalls internally, customers feel it as a broken promise.

Where to start

When insights keep stalling, it’s tempting to add more, like another dashboard or another report. But most teams can already see the problem in broad strokes. What’s missing is enough detail to act on it, and someone who owns what happens next.

So start small. Make each insight specific enough that someone could act on it tomorrow, put a name on it and don’t call it done until customers can tell the difference. The teams that do this well don’t necessarily know more about their customers than anyone else. They just act on more of what they know.

 Guest post written by Christina Wiesendanger Stewart, Senior Product Marketing Manager, Unwrap

The post Why Customer Insights Die Before Anyone Acts on Them appeared first on Execs In The Know.

As artificial intelligence (AI) changes how customer service is delivered, the skills organizations need are changing with it. The opportunity is not simply to make agents better at using technology. It is to build a workforce that understands how people, processes, data, and technology work together to produce a business outcome.

That is a different kind of upskilling.

From Using Technology to Orchestrating It

The contact center of the future will not be defined by how many AI tools an organization deploys. It will be defined by how effectively people can orchestrate those capabilities.

AI may summarize an interaction, recommend a response, identify a customer’s intent, surface relevant information, automate a transaction, or determine that a situation requires human intervention. But someone still has to understand where those capabilities fit into the broader customer journey and business process.

That creates a growing need for people who can connect the dots.

They need to understand the technology, but they also need to understand the operation around it. They need to recognize when an automated process creates friction, where a handoff breaks down, what information is missing, and how a change in one part of the customer journey affects another.

In that environment, the workforce becomes an orchestration layer between the customer, the business, and the technology.

The Rise of the AI Builder

This does not mean every contact center employee needs to become a software engineer. It does mean organizations will increasingly need people who can help build and shape AI-enabled capabilities.

Consider an employee who understands the most common reasons customers contact the organization. That person may be able to identify opportunities to improve an AI workflow, refine the information being surfaced to agents, redesign an escalation path, or identify a process that should be automated altogether.

That is valuable expertise.

The people closest to the customer often have a practical understanding of where technology succeeds and where it creates more work. Upskilling those employees to help design and improve capabilities can turn frontline knowledge into an organizational asset.

The emerging role is not simply an “agent who uses AI.” It is closer to an AI-enabled problem solver: someone who understands the customer need, the business objective, the available technology, and the process required to bring them together.

Business Context Becomes a Critical Skill

One of the biggest challenges in workforce development is helping employees understand not just what they are being asked to do, but why it matters to the business.

If an employee understands only the task, they can follow a process. If they understand the desired outcome, they can make better decisions when the process, technology, or customer situation changes.

That distinction matters when AI enters the picture.

An employee who understands that the business is trying to reduce customer effort may evaluate an automation differently from someone whose only objective is to increase containment. An employee who understands the importance of customer retention may recognize that a seemingly efficient automated interaction is creating risk for a high-value customer.

The more technology takes over individual tasks, the more important that broader context becomes.

CX leaders therefore have an opportunity to connect workforce development much more directly to business strategy. Employees should understand the outcomes the organization is trying to achieve, the customer problems that matter most, and the measures used to determine whether the strategy is working.

That context gives people a framework for making decisions rather than simply executing instructions.

Build Capabilities, Not Just Training Programs

This also changes how leaders should think about program management. Upskilling cannot be a series of disconnected courses launched whenever a new technology arrives. It needs to be managed as an ongoing capability-building effort.

That means mapping the skills required across the operating model: customer communication, process knowledge, data literacy, AI fluency, problem-solving, workflow design, change management, and business acumen. It means identifying which capabilities belong with frontline employees, which require specialized roles, and where technology can augment both.

It also means creating mechanisms for continuous learning.

When a new AI capability is introduced, the question should not simply be, “How do we train employees to use it?” Leaders should ask, “What new decisions will employees need to make? What processes will change? What knowledge will they need? What should they be able to improve themselves?”

That is a much more durable approach to workforce transformation.

The Workforce is Part of the Technology Strategy

For CX leaders, the larger opportunity is to stop treating workforce strategy and technology strategy as separate conversations.

They are increasingly the same conversation.

Organizations are building new technology capabilities, but their value depends on whether people can integrate them into how work actually gets done. The workforce needs enough technical fluency to work alongside AI, enough operational knowledge to connect systems and processes, and enough business understanding to know what outcome all of that work is supposed to produce.

The goal is not to train people to keep up with technology. It is to build people who can help shape what the technology becomes.

That shift, from training employees to building organizational capability, may ultimately be one of the most important elements of the AI transformation taking place across CX.

The post Upskilling the CX Workforce: Building the People Who Can Orchestrate What Comes Next appeared first on Execs In The Know.

The challenge is not a lack of data. Most organizations have more customer data than they know what to do with.

The more consequential question is whether the measures being used are actually reinforcing the experience the organization intends to create. CX metrics do more than describe performance. They shape it.

The measures an organization reviews most consistently influence where leaders invest, which problems receive attention, how teams prioritize their work, which technologies get scaled, and what employees understand to be important. Over time, the measurement system becomes an organizational blueprint.

What you measure is what you’re building for.

That is an increasingly important consideration as CX leaders navigate the tension between experience, efficiency, and the rapid adoption of artificial intelligence (AI).

The CX Metrics We Choose Become Management Priorities

Every organization has competing objectives. Customers want experiences that are easy, responsive, and effective. Employees need the tools and capacity to deliver them. Finance wants sustainable economics. Operations wants consistency and productivity. Technology teams are looking for opportunities to automate and scale.

Metrics help leaders reconcile those competing demands, but they can also unintentionally tilt the organization toward one objective at another’s expense.

Consider a contact center where average handle time has historically been one of the most closely watched measures. There is nothing inherently wrong with wanting interactions to be efficient. The problem emerges when efficiency becomes the dominant definition of performance.

A representative who resolves a complicated issue in eight minutes may be viewed as performing better than one who takes 15 minutes, even if the longer interaction prevents a repeat contact, eliminates an escalation, and leaves the customer confident that the issue is actually resolved.

The organization has not necessarily decided that resolution matters less than speed. It has simply created a measurement environment in which speed is easier to see, compare, and manage.

The same dynamic is emerging in AI.

Containment, automation rates, deflection, and cost reduction are relatively straightforward to quantify. But if those measures become the primary evidence of success, organizations can inadvertently build AI programs around reducing human involvement rather than improving the customer outcome.

That distinction matters.

A customer who never reaches an employee because an AI system successfully resolved the issue represents a very different outcome from a customer who never reaches an employee because the system made it difficult.

From a dashboard perspective, both can look like successful containment. From the customer’s perspective, they are not remotely the same experience.

The Danger of Optimizing the Metric Instead of the Outcome

This is where mature CX measurement becomes less about selecting individual metrics and more about understanding relationships between them. A metric is rarely meaningful on its own. A reduction in contact volume could indicate that customers are using self-service successfully. It could also indicate that customers have stopped trying to get help.

A reduction in handle time could reflect better employee tools and more efficient processes. It could also reflect interactions ending before the underlying problem has been resolved. An increase in chatbot adoption could signal that customers appreciate a convenient digital channel. It could also reflect a company aggressively steering customers toward automation.

The number itself does not tell us which story is true. Context does.

That means CX leaders increasingly need to look at measures as a system rather than a collection of independent KPIs. Customer outcomes, operational performance, employee experience, and business results should inform one another.

For example, a contact center focused on improving efficiency might track handle time alongside first-contact resolution, repeat contacts, transfers, customer effort, escalation rates, and cost to serve. The goal isn’t to create a larger dashboard. It is to understand whether efficiency comes from better performance or is simply shifted elsewhere in the customer journey.

The same principle applies to digital experiences and AI.

Containment becomes more meaningful when viewed alongside successful resolution. Automation becomes more meaningful when considered alongside customer effort and trust. Speed becomes more meaningful when paired with accuracy and clarity.

The goal is not to eliminate the operational metric. It is to prevent the operational metric from becoming a substitute for the outcome.

What Gets Measured Gets Funded

CX organizations also face financial consequences based on how they measure performance.

Executives naturally allocate resources to areas with a visible problem, a measurable opportunity, or a credible business case. If an organization can demonstrate that a particular intervention improves a metric that leadership already values, it becomes easier to justify additional investment.

That makes measurement a strategic lever.

If an organization consistently measures the cost of service but has limited visibility into the value of customer retention, for example, investment decisions may naturally favor efficiency initiatives. If it can show how experience improvements influence retention, revenue, risk, or customer lifetime value, the conversation changes.

This is one reason CX leaders have spent years connecting experience measures with broader business outcomes.

The objective is not to turn every customer interaction into a financial calculation. It is to make the relationship between customer experience and enterprise performance more visible.

The measures leaders choose signal to the rest of the organization what kind of CX investment it will value.

Some of the Most Important Outcomes Are the Hardest to Measure

The other challenge is that some of the experiences organizations most want to create don’t translate easily into traditional operational metrics.

Trust is difficult to reduce to a single number. So is confidence and the feeling that a company understands what a customer needs and will do what it says it will do. Yet these outcomes matter more than ever, especially as customers interact with automated systems, AI-generated content, and increasingly complex digital journeys.

An outcome being difficult to measure does not make it less important. In some cases, it makes it more important to find meaningful ways to understand it.

This may mean combining quantitative measures with qualitative customer feedback, behavioral data, journey analysis, employee insight, and targeted research. It may also mean resisting the temptation to force every aspect of CX into a single composite score simply because a single number is easier to report.

Precision is not the same thing as simplicity. A dashboard can be beautifully simple and still tell the wrong story.

The Question CX Leaders Could Be Asking

The next evolution of CX measurement is unlikely to come from finding one perfect KPI. It will come from becoming more deliberate about what the organization’s measurement system is designed to produce.

Before adding another metric to the dashboard, leaders should consider what behavior that metric will encourage if it becomes a major organizational priority.

If we optimize for this number, what will our employees do differently? What decisions will our technology teams make? Where will investment flow? What trade-offs will leaders make?

And, ultimately, what will customers experience as a result?

Those questions move measurement beyond reporting and into strategy.

They also create an important discipline for CX leaders: measure the outcomes you want to create, not simply the activities that are easiest to count.

Operational measures will always matter. Financial constraints, productivity expectations, service-level requirements, and efficiency opportunities will always exist. The answer is not to choose customer experience over business performance. Mature CX organizations understand that the two are connected.

The opportunity is to build a measurement architecture that makes those connections visible. Because every metric signals what matters. Every KPI creates an incentive. Every dashboard directs attention. And every repeated measurement, over time, helps determine what an organization becomes better at building.

What you measure is what you’re building for. The question for CX leaders is whether the experience being built is the one the organization actually intends to create.

The post What You Measure is What You’re Building For appeared first on Execs In The Know.

For years, CX leaders have focused on what AI can take on: which contacts can be automated, which journeys can become self-service and where technology can improve efficiency. But as those capabilities move from pilots into the standard operating model, there is a bigger question: What happens if the AI strategy actually works?

Predictable contacts disappear. Proactive service resolves more issues before customers ever need to reach out. What increasingly reaches the agent is the customer whose self-service journey failed, the exception the workflow cannot resolve, the emotionally charged situation, or the problem spanning multiple systems and departments.

The standard answer is that agents will handle the harder contacts. That answer is too small. The agent organization cannot simply become whatever is left after AI has automated the rest.

We’re already seeing signals of what that means in practice.

A team of agents in the Philippines supporting a communications client had been trained on the customer experience surrounding an in-home field technician visit. But most had never lived or worked in the market they supported, so their understanding of that experience was limited to classroom training.

Field technician visits were filmed and documented, critical moments were mapped, and agents experienced the journey through virtual reality. Within days, first-contact resolution increased from 22% to 65%.

The technology made the experience possible, but the lesson wasn’t really about VR. The agents didn’t need more information. They needed context. They needed to understand what happened beyond their part of the customer journey so they could make better decisions within it.

This is a small example of a much bigger shift now underway in CX. Context, judgment, and the ability to take ownership when the expected path breaks becomes more important.

The question is no longer simply how technology can make agents better at the jobs we’ve designed for them. It’s whether we’re designing the right jobs, and the right operating model for what comes next.

That starts with five moves:

• Map the work AI leaves behind. Understand which interactions will still require an agent and how that work is changing.
• Define agent ownership before headcount. Decide what agents should own before determining how many are needed.
• Build the talent profile around the work. Hire and develop for the skills the new workload actually demands.
• Train for where predictability ends. Prepare agents to navigate exceptions, ambiguity and moments that require judgment.
• Measure and invest for the model you’re building. Align KPIs, technology and investment to the future operating model, not the one automation is making obsolete.

Start with what AI leaves behind 

Most AI dashboards tell leaders what the technology handled: containment, automation, resolution, adoption and cost. Those numbers matter, but they only tell half the workforce strategy. The other half is what AI consistently hands back to the agents.

When AI resolves predictable interactions, what remains becomes more concentrated around exceptions, ambiguity and failed journeys. That work deserves the same rigor organizations have applied to automation: where does the standard process break, what judgment is required, and which outcomes require human intervention?

The answers can change how performance should be interpreted. If AI disproportionately removes short, predictable contacts, average handle time can rise because the average agent interaction is harder. Escalation patterns can change. Performance variance can widen as judgment matters more than adherence. Some KPIs can appear to deteriorate precisely because the AI strategy is working.

Traditional metrics do not suddenly become obsolete. However, their context has changed. Your automation rate tells you what AI took away. It doesn’t tell you what it left behind. Build a residual-work map alongside the automation roadmap: the exceptions, failed journeys, cross-functional issues, judgment calls and recovery moments that remain. Use it to forecast the skills, capacity and authority the remaining work will require.

The automation roadmap tells you where technology is going. The residual-work map tells you what your workforce needs to become.

Agentic AI raises the stakes further. If ownership is unclear, data is fragmented or the underlying process is broken, giving AI more autonomy won’t fix the operation; it can accelerate its weaknesses. The question isn’t simply how agentic the technology can become, but whether the operation around it is ready for that autonomy.

Redesign ownership before you redesign headcount

Once the remaining work is visible, the natural question is capacity: How many agents will we need? But capacity should not be the first question. Ownership should be: What areas are most important to prioritize agent ownership?

If an AI strategy reduces contacts while the workforce strategy simply reduces headcount, the organization risks automating the old operating model without designing the next one.

Consider the traditional tier structure. Tier 1 handles common issues, while increasingly complicated problems move through specialized levels of support. Moving agents higher up that ladder as AI absorbs Tier 1 work still assumes agent work should be organized around contacts, queues, and escalations.

AI gives us a reason to question the ladder itself. When technology can identify intent, retrieve information, complete actions and resolve predictable needs, agents can be designed around ownership of the outcomes that require judgement.

A contact has a beginning and an end. An outcome can cross channels, systems, departments, and multiple interactions. A contact can be transferred. Ownership stays accountable to the result. Managing a contact asks how efficiently the interaction was handled. Owning an outcome asks whether the customer’s actual need was resolved.

But ownership without authority is just another label. If an agent cannot deviate from a workflow, make a judgment call or coordinate across the functions required to resolve an issue, they do not really own the outcome. They own the next step.

Before redesigning capacity, run an ownership test against the highest-value work AI leaves behind: Who owns the outcome? Do they have the authority to resolve it? Can they cross the organizational boundaries required to do so? Define ownership first, align authority to it, then design capacity around the work.

Build tomorrow’s talent profile from tomorrow’s work

Changing ownership changes what high performance looks like. Contact centers have spent decades creating repeatability: know the process, find the answer, follow the workflow, be consistent, and reduce unnecessary time. We spent years teaching agents to behave more like machines. Now we finally have machines that are exceptionally good at being machines.

When information can be surfaced instantly, knowledge retention becomes less differentiating than knowing how to apply it. When predictable workflows can be automated, speed through a defined process becomes less valuable than knowing what to do when the process no longer fits.

As routine tasks are automated, agent performance shifts towards investigation, judgment, ownership, recovery, and contextual decision-making. That means today’s highest performers will not automatically be tomorrow’s most valuable talent.

The signals are already visible in agents who investigate rather than immediately escalate, recognize when the prescribed answer does not fit, connect information across systems, recover a relationship after something goes wrong, and take ownership when the process stops providing an obvious next step.

Even the language we use needs to catch up. Cortney Jonas Burnos, VP of AI & Digital Solutions at Transcom, has questioned whether “agent” still accurately reflects the role, describing today’s frontline professionals as “high empathy, high skill CX experts working alongside AI.” The language matters because it shapes the roles we design, the capabilities we reward, and the talent we recruit.

Build tomorrow’s talent profile from the emerging work, not yesterday’s top-performer scorecard. Identify the capabilities that the work requires, find the agents already demonstrating them and compare those behaviors with what current recruiting profiles, scorecards, coaching, and career paths reward. The gaps tell you where the talent strategy needs to change.

Train for where predictability ends.

The field technician example illustrates the difference: better performance didn’t come from giving agents more information. It came from giving them the context to apply what they already knew.

That becomes increasingly more important as AI absorbs more of the predictable path. Agents enter where predictability ends. They need experience applying knowledge when information is incomplete, the prescribed answer does not fit, an AI recommendation needs to be challenged or the customer’s problem crosses the boundaries of the workflow.

The lesson is not simply to use more technology in training. It is to change what agents are being prepared for. Train the exception, not just the process. Use simulation to expose agents to incomplete information, failed recommendations, emotional situations, competing priorities, and cross-functional problems before the customer becomes the test.

There is something counterintuitive about that. One of AI’s most valuable roles in the future of CX will be helping humans strengthen the capabilities that matter most when automation reaches its limits.

Measuring the work you are creating, not the work that is disappearing 

Changing the role without changing the scorecard leaves agents doing tomorrow’s work while being evaluated against yesterday’s job.

Leaders increasingly need two views of performance. The first measures what AI handles: automation, containment, adoption, resolution, and efficiency. The second measures what agents now own: complex resolution, recovery, judgment, ownership, and customer outcomes. Looking at one without the other creates an incomplete picture.

The investment strategy needs to evolve too. The hidden cost of a successful AI strategy may be everything an organization failed to redesign around it. Funding technology without investing in the data integrity, governance, systems integration, training, role design, and frontline authority it depends on can create an AI-enabled version of yesterday’s operating model.

The workforce plan and the AI plan can no longer be separate exercises. Every successful automation decision changes the work agents receive, which changes the capabilities, authority, training, and measures the organization needs around them.

Customer connection deserves the same precision. Customers do not need an agent inserted into every journey simply to prove one is available. When AI can resolve a need accurately, immediately, and with less effort, that is a better experience. But access to an agent still matters when the situation requires judgment, recovery, trust, meaningful value or risk. Fewer agent interactions make those moments more consequential, not less.

As Cortney Jonas Burnos puts it, “We spent years designing contact centers around managing routine contacts. AI now handles those routine interactions effortlessly. The real opportunity is empowering people with true authority and full ownership over meaningful outcomes.”

For years, organizations have asked what AI can do inside the contact center. The more consequential question for the next three to five years is what the contact center must become because of it.

The organizations that stop at automation will have a more efficient version of the past. The ones that redesign the work around it will build what comes next.

Continue the conversation 

The AI conversation is moving fast. The operating model around it needs to move just as deliberately.

Transcom’s AI, but Make It Human series on Leading Voices goes beyond the AI hype to explore what this shift means in practice, from the changing role of frontline professionals to where AI creates real value and where judgment, empathy and expertise still matter most.

Explore AI, but Make It Human on Transcom Leading Voices

Guest post written by Transcom.

The post The Work AI Leaves Behind: Designing the Next Generation of CX appeared first on Execs In The Know.

That shift matters.

For the past several years, CX leaders have been surrounded by possibilities. Generative AI could transform service. Voice AI could reshape the contact center. Predictive models could enable more proactive engagement. Automation could change how work gets done.

But possibility is no longer enough. As AI investment moves from experimentation toward implementation, leaders are being asked to make harder decisions: Which use cases deserve investment? Where can AI create measurable value? What needs to remain human? How should success be measured? And what happens when an AI deployment doesn’t deliver the expected results?

The answers are increasingly being found in the details of actual deployments.

What Happens After the Pilot?

One challenge facing CX leaders is that much of the conversation around AI remains conceptual. Industry reports can identify trends, and technology providers can demonstrate what their platforms can do, but neither necessarily answers the questions leaders face when putting AI into production.

What problem were you actually trying to solve? Why was AI the right approach? What changed after implementation? Did customers notice a difference? Did employees? Did the business? And perhaps most importantly, what did you learn that changed the next decision?

These questions matter even more as organizations evaluate AI across different parts of the customer experience. A voice AI deployment, for example, may be evaluated through containment, transfer rates, resolution, and customer sentiment. An employee-facing application may be measured through productivity, quality, or time saved. Proactive AI may require an entirely different definition of value.

No single AI metric tells the whole story. The real work for CX leaders is connecting the use case to the business problem, defining what meaningful improvement looks like, and building the measurement discipline to determine whether the technology actually delivered it.

Use Cases Can Reveal More Than One AI Success Story

Looking at AI through individual use cases can also help leaders make better decisions about their own roadmaps.

Customer-facing AI may reveal where automation can improve access without creating additional friction. Voice AI can demonstrate how conversational technology is changing the economics and experience of the contact center. Proactive engagement can show what becomes possible when organizations use AI to anticipate customer needs rather than simply respond to them.

Workforce applications offer another perspective, particularly as AI begins changing the nature of the work itself. The value of examining these applications together is not to identify one universal model for AI adoption. It is to see how different organizations are approaching the same fundamental challenge: turning AI capability into measurable CX and business impact.

The differences are often as instructive as the similarities.

Some organizations may be focused on efficiency. Others may be pursuing revenue, experience improvement, employee enablement, or greater scalability. Some use cases may begin with a narrowly defined problem and expand. Others may reveal limitations that lead the organization to change course. Those distinctions matter when building an AI roadmap because the path from experimentation to scale rarely looks the same across organizations.

What Leaders Should Be Looking for in an AI Use Case

As more AI deployments move into production, the most useful examples are not necessarily the ones with the biggest claims. They provide enough detail to understand the decisions behind the result.

A useful use case should answer four questions:

1. What problem was worth solving?
AI should start with a meaningful customer, employee, or business problem, rather than the technology itself.

2. What did the organization actually deploy?
Understanding the application, workflow, and role AI plays provides important context for determining whether the approach could translate to another environment.

3. How was impact measured?
Outcomes should extend beyond an AI adoption metric. Leaders need to understand what changed for customers, employees, and the business.

4. What did the organization learn?
Implementation rarely follows the original plan perfectly. The adjustments, challenges, and lessons can be just as valuable as the final outcome.

That last question may ultimately be the most important. The organizations moving AI forward are not simply proving that a technology works. They are learning where it works, where it doesn’t, and how to make better decisions with each deployment.

Moving From AI Curiosity to Discipline

The next phase of AI in CX will require more than identifying exciting applications. It will require discipline around prioritization, implementation, governance, measurement, and scale. That means CX leaders need fewer hypothetical conversations and more visibility into what is happening in the real world.

What are organizations deploying today? What results are they seeing? Where are they encountering friction? And what are they changing as they learn?

Those questions can turn someone else’s AI deployment into a useful input for your own roadmap.

On October 29, Execs In The Know will bring CX AI use cases together for a 90-minute rapid-fire virtual session focused on real deployments, results, and lessons learned, with live Q&A following each use case. For CX leaders evaluating where AI can create meaningful impact, the goal isn’t to leave with someone else’s roadmap. It’s to leave with a clearer view of the decisions, measurements, and lessons that can inform your own.

Learn more and save your seat for the virtual event.

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But for customer experience (CX) leaders, the bigger question isn’t simply what Voice AI can do. It’s where the technology creates meaningful value for customers and the business, and how leaders can prove it. 

That question was at the center of our latest Virtual Executive Roundtable, where CX leaders shared what they’re learning as they test, implement, and evolve Voice AI strategies. While approaches varied, several themes emerged around measurement, readiness, use cases, and the continued importance of the human experience. 

Deflection Isn’t the Finish Line 

For years, contact center leaders have looked at deflection and containment as indicators of automation success. But the conversation is shifting. 

Several leaders pointed to metrics like end-to-end resolution, customer effort, CSAT, cost per contact, assisted conversion, and recontact rate as more meaningful measures of whether Voice AI is delivering value. 

The distinction matters. An interaction that never reaches a human isn’t necessarily successful. If the customer has to call back, struggles to get an answer, or abandons a purchase, a high containment rate can disguise a poor experience. 

The more useful question is: Did the customer get what they needed, and did the interaction create value for the business? 

For CX leaders, that means building measurement strategies around outcomes, not simply how many conversations AI handles. 

Voice AI Is Moving into More Complex Territory 

The capabilities being discussed today extend well beyond the straightforward transactions that initially made Voice AI attractive. 

Leaders shared examples of AI evolving from FAQs and transfers into reservations, ordering, and other customer-facing transactions. Others pointed to an emerging ability to handle more ambiguous requests, frustrated customers, and interactions that traditionally were considered too nuanced for automation. 

That evolution creates a new strategic challenge. 

As AI becomes capable of handling more complexity, organizations need to be increasingly deliberate about where AI should be used, and where it shouldn’t. 

The goal isn’t to automate as much of the customer journey as possible. It’s to determine where technology can reduce effort, improve outcomes, and create a better experience. 

AI Readiness Starts Before the AI 

Some of the biggest barriers discussed weren’t about the AI’s sophistication. They were about everything surrounding it. 

Knowledge source cleanup, access to backend technology, and connections to systems of truth can all become hold points. An AI system can only provide a reliable experience if it has access to reliable information and the systems needed to take action. 

That makes Voice AI as much an organizational readiness challenge as a technology initiative. 

Before scaling an implementation, CX leaders were urged to look closely at the fundamentals: Is our knowledge accurate? Can AI access the information it needs? Can it complete the actions customers are asking for? Are our systems connected well enough to support the experience we’re promising? 

The Right AI Depends on the Customer Need 

Another important theme was that Voice AI shouldn’t automatically be the answer simply because it’s available. 

For straightforward transactions, Voice AI may create meaningful value. For more complex interactions, agent-facing AI may be more effective at helping employees find information faster, improve accuracy, and resolve issues while preserving the human connection. 

This points to a broader principle for CX leadership: Start with the customer need, then determine where AI belongs. 

Sometimes that means a virtual agent or AI supporting an employee. And sometimes the best experience still requires a person. 

The most mature AI strategies won’t necessarily be the ones with the most automation. They’ll be the ones that make the clearest decisions about where technology can improve the experience. 

The Human Impact Matters 

Even as organizations pursue efficiency, the workforce implications of AI remain an important part of the conversation. 

Leaders discussed the importance of thinking beyond replacing roles and considering how employees can be retrained and prepared for new responsibilities as AI becomes part of the operating model. 

That requires CX leaders to think about AI transformation as a people strategy, too. 

The strongest implementations may ultimately combine technology’s ability to increase knowledge, efficiency, and personalization with the judgment, empathy, and expertise of human employees. 

The Conversation is Still Evolving 

Perhaps the clearest takeaway from the roundtable was that Voice AI is not a set-it-and-forget-it initiative. Leaders are still testing, learning, measuring, and refining their approaches. 

And that’s exactly why peer conversations matter. 

As Voice AI continues to evolve, CX leaders can learn not only from what works, but also from the questions, challenges, and lessons emerging across the industry. 

Want to stay part of the conversation? Make sure you’re on the Execs In The Know email list to receive updates on upcoming Virtual Executive Roundtables. 

A special thank you to our business partner, Vapi, for helping bring this conversation about the future of Voice AI to our CX community. We appreciate their partnership in creating space for meaningful conversations, shared insights, and continued learning across the industry. 

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Your Performance Problem is a Leadership Problem

Two numbers every executive should sit with: 20% of job knowledge comes from formal training, and employees spend 1% of their workweek on skill development. If that’s the system you’ve funded, you didn’t buy capability — you bought a compliance event. That’s a design decision, and design decisions belong to leadership.

The Problem: Chasing the Package, Neglecting the Science

Technology is advancing faster than most organizations can keep up with, and the reflex has been to buy our way out of it. New platform. New authoring tool. New AI-enabled content engine. Every cycle brings a prettier package — and every cycle, the same question goes unasked: does any of this reflect how the human brain actually learns?

Amid all that change, one truth hasn’t moved. We cannot ignore the foundations of research and evidence. But that’s exactly what’s happening. Organizations are grasping for the latest polished solution to close skill gaps and reverse declining performance, while quietly skipping the science of learning that would make any of those tools work.

The proof is that despite all the technological advances, corporate training keeps showcasing the same three failures, the ones we see again and again across curricula:

• Content assumes learners share the same background knowledge and life experiences. They don’t, and comprehension gaps open on day one.
• Cognitive load. Overwhelming content reduces cognitive function. More material delivered faster isn’t more learning; it’s less retention.
• Lack of engagement. Lecture-heavy delivery, with little room for discussion, peer learning, or practice, leads straight to disengagement.

No platform fixes those. They’re design problems, and they get solved — or funded into permanence — at the leadership level.

What changes when leaders decide differently

Clear Harbor replaced the traditional “sit and get” model with a five-part framework grounded in brain science and adult learning research.

The measured shift:

• Agent confidence entering nesting: 3 → 5 out of 5
• Readiness to engage customers and problem-solve: 5 → 4.5
• Confidence in production: 5 → 5
• CES: +2 percentage points in a 5-day production implementation

That last line belongs in a board deck. Learning design moved a customer metric in a week.

Three decisions that are yours to make

1. Put science at the center of every learning decision: evidence, not precedent, and not the newest package.
2. Mandate one framework, not one-off courses: User Experience, Direct Instruction, Exploration, Application, Reflect, built on autonomy, competence, and relatedness.
3. Fund learning past the classroom: drip-feed campaigns deliver small, consistent doses on the floor, reducing cognitive load and building long-term memory. That 1% only moves if a leader moves it.

Are you ready to embrace science to increase performance? Let’s talk.

Guest blog post written by Clear Harbor.

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Here’s what CX leaders shared about the gap between adopting AI and seeing real benefits.

Nearly all contact center leaders say they use AI, but few can explain the results it delivers.

COPC’s 2026 global study found that the real problem is a lack of information. To understand how AI is used in customer care, we spoke with over 200 contact center and CX leaders from various industries. Seventy-nine percent already use AI, and only 6% have no plans to adopt it. So, the adoption debate is settled.

But measuring AI’s impact is still new. Fifty-two percent track AI resolution rates; 41% monitor escalations to humans, and just 22% directly measure AI’s return on investment.

When asked what would speed up AI adoption, 59% of leaders said proven return on investment mattered most, more than integration tools, governance, or training.

The main driver for the next wave of investment is something most companies still can’t deliver.

Adoption has happened, but integrating AI into daily work is still in progress.

A key trend stands out: among organizations using AI, 61% are expanding or upgrading their systems. Some want better features, while others are rethinking earlier choices. Most are not sticking with their original setup.

Features once seen as special are now basic needs. Chatbots, knowledge management, and sentiment analytics are standard, with adoption rates over 80%. Currently, 51% of organizations have a dedicated AI team or center of excellence. This shows AI is now a core business function with clear ownership: 54% say this, followed by 45% citing integration capability and 43% mentioning cost. It’s positive that customers prefer reliable options over cheaper ones. Still, it’s concerning that 23% of organizations don’t know their annual AI spending for customer care.

You can’t call it real governance without financial transparency. Right now, AI is bought with controls that most organizations wouldn’t accept for other enterprise systems. Generative AI makes up 73%; predictive AI 51%, and agentic AI (systems that handle multi-step tasks without human help) accounts for 42%. One agentic pilot is enough for a company to say yes to using it. This number reflects deployment, not how much customer care work agentic AI does.

Most organizations have started with limited, specific uses of agentic AI. The main question now is how much work it handles and whether it’s managed and monitored like other production systems.

Agentic work is reviewed in five areas: attracting and developing talent, helping during live interactions, empowering customers, using data and insights, and handling security and operations. Agentic AI looks different across areas, so combining them into a single percentage hides where real adoption is happening and where it isn’t. It was a positive return on investment, but the results were very different.

Forty-five percent of organizations have AI leaders focused on customers, 43% on operations, and 40% on talent management. For workforce management, it’s 37%; analytics, 34%; and agent assist, 33%. Meanwhile, 42% think it’s too soon to judge the results. This makes sense, as containment rates, deflection volumes, and handle times all vary. Even if agent assist works perfectly, it can still be hard to prove.

There’s a clear pattern: when building your business case, sort ROI by functional area and time in use.

ROI for workforce management more than triples after two years, and for talent management, it more than doubles. This matches what we’ve seen with our clients. Forecasting, scheduling, and real-time models need historical data and fine-tuning, and the benefits only show up when these models are part of the planning process.

Keep in mind two things: the two-year results for workforce and talent management are based on smaller samples, so treat the multipliers as general trends, not exact numbers. Also, don’t set a single ROI target for all AI projects. Expect customer-facing tools to show returns in the first year, but workforce and talent management tools may take longer. Review your targets each year as you get more data. While most leaders believe AI matches or outperforms humans in interactions, in 64% of cases, less than 40% of interactions are handled autonomously.

At first, this might seem contradictory, but it’s intentional. Companies give AI the tasks it does best, while people handle work that needs judgment, empathy, or exception handling. There’s no single right level of autonomous handling; it depends on risk, regulations, and the cost of mistakes. The right balance is when AI takes on suitable tasks, and routing and escalation are reviewed from both the AI and human perspectives. Both AI and human performance should be measured together, not in separate, competing ratings.

The workforce impact is more positive than most headlines suggest.

Thirty-three percent of organizations have seen employee satisfaction rise after introducing AI, while only 4%have seen a decrease. That’s an eight-to-one ratio rise after introducing AI, while only 4% have seen a decrease.

Thirty-five percent of organizations have reduced their workforce, but most cuts are small. About 20% reduced staff by less than 10%. Another 18% moved employees into more valuable roles, and large-scale job losses remain rare.

Seventy-six percent of leaders view AI’s impact on the contact center industry as positive, which contrasts with what outside commentators often claim.

Organizations that see the biggest gains in satisfaction use AI to reorganize their workforce, not just cut jobs. They put freed-up resources into more valuable work, invest in reskilling, and clearly explain job changes. If they skip these steps, employees see AI as a threat and resist it. For genuine and substantial cuts, you should be honest about that; attempting to make it seem as though it’s just an augmentation does more harm to trust than the cuts do.

Start by addressing the measurement gap.

In short, you can’t manage or measure AI effectively if you only look at deflection rates or CSAT. That alone won’t make a real difference.

We recommend measuring AI at three levels. For outcome quality, check whether the problem was solved using criteria such as confirmed resolution, repeat contacts within seven days, and how escalations are handled. Process quality means reviewing each step of AI’s decisions, such as identifying intent, following the decision path, and checking confidence in any policy changes. Oversight includes human checks such as sampling edge cases, reviewing handoff quality, and spotting model drift.

Before proceeding with any of the following steps, complete the one that most programs fail to do. Set your behavior policy before deploying AI. Clearly define what your AI can and should do, build this into the system, and use measurement to keep it within those limits.  The quality of the process at each stage of decision-making is frequently overlooked. While outcome metrics indicate what has taken place, process quality tells you whether your AI is reasoning as it should, serving as the sole early warning sign.

Over the next 24 months, success in CX won’t depend on who buys the most AI, but on who can show what their AI is actually doing.

If you need help building that measurement layer, COPC Inc.’s CX consulting team works with organizations to design AI governance and performance frameworks that hold up under scrutiny.

This COPC Inc. research is based on responses from 219 CX and contact center leaders worldwide. For questions about this research, email research@copc.com.

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The AI solution was sound. But the connection was the barrier.

It’s a Pattern, Not a Unique Situation

Ask a dozen contact center leaders about their technology stack, and you’ll hear a dozen different combinations of systems. But you’ll hear the same complaint about all of them: something in there is effectively untouchable. Sometimes it’s the platform that came bundled in from an acquisition nobody fully absorbed. Sometimes it’s a tool an IT director built in-house years ago because buying something at the time didn’t fit the budget. Sometimes it’s simply a vendor whose product roadmap moved on before your use case ever came up.

The specifics change. The outcome doesn’t. Someone floats an automation idea, someone else checks what connecting the relevant systems would actually take, and the project quickly loses momentum. The idea may have been good, but nobody could justify the build it would take to make it real.

Computer Use Agents Offer a New Approach 

Computer use agents (CUAs) present a way around integration barriers. And it’s worth understanding what makes them different and why the concept only recently became practical.

Robotic process automation has existed for years, and it already promised something similar: software that could operate existing applications without a custom integration. But traditional RPA is rules-based. It follows a fixed script—click this exact pixel, read this exact field—and it breaks the moment a screen layout changes, a button moves, or a workflow branches in a way nobody scripted. Maintaining it becomes its own ongoing project.

A CUA works differently because of what sits underneath it: an AI model that interprets the screen rather than following a fixed set of coordinates. It looks at what’s actually on screen, reasons through what it’s seeing, decides what to do, and then acts. It will click, type, read results, and move to the next step, the same sequence a person would follow. And because it’s interpreting rather than executing a script, it holds up against the kind of small interface changes that would have broken an older RPA bot outright.

Most automation runs somewhere you can’t see it, moving data between systems in the background. A CUA operates out in the open, on the same screens your team already uses every day.

Why This Is Becoming the Smart Strategy for CX Organizations

CUAs don’t require APIs, integration builds, or waiting on a vendor roadmap before a workflow can go live. When a team switches tools—a new CCaaS platform, a new CRM, a new knowledge base—the automation doesn’t need to be rebuilt from the ground up. It gets shown the new screen and the new workflow, and it adapts, just like a human agent would.

And just like you would for a human hire, a CUA can be scoped for security with the same discipline as any least-privilege access model. Defined permissions, specific systems, nothing beyond what a given workflow requires, approved by the team before it ever runs. It doesn’t get broader access just because it’s convenient; the same governance that applies to any system access applies here.

The Honest Trade-Off: What CUAs Don’t Do Well

Like any technology, CUAs do have limitations, so let’s discuss them. A team that’s searching for something faster than a backend integration should reset expectations. The strongest case for CUAs is that they make automation possible where a backend integration was otherwise never going to happen.

A CUA works sequentially, the way a person does: click, wait for the page to render, read the result, decide, click again. For a high-volume transaction that needs to complete in milliseconds, a direct API connection will beat it on raw speed every time, and that’s not going to change. If a workflow processes tens of thousands of transactions a minute and speed is the entire point, a native integration is still the right tool.

But that’s a narrower slice of the automation opportunity than it might seem. Most of the ideas dying in integration meetings aren’t ultra high-volume, millisecond-sensitive processes. Instead, they’re the mid-sized, situational, or short-lived workflows that never had a shot at a dedicated integration build in the first place. For that much larger set of use cases, trading some per-transaction speed for the ability to automate at all is a straightforward win.

Where This Actually Works: The Messy Reality of Contact Centers

Contact centers are close to a worst case scenario for integration cleanliness. Multiple vendors from different eras. Contracts that change every few years, each swap bringing a new platform with its own quirks. Homegrown tools built to patch a gap nobody wanted to pay a vendor to solve. Knowledge bases and internal wikis that were never built with automation in mind at all.

That fragmentation is exactly the environment where a front-end approach earns its keep. A CUA doesn’t need to know which vendor built the system underneath, what era it was written in, or whether it has a modern API. It only needs to know what’s on the screen and what to do with it. That makes it particularly well-suited to the reality most contact centers actually operate in, rather than the clean, single-vendor stack that integration-first automation strategies tend to assume.

It also reopens ideas that were previously shelved purely on cost. A short-term seasonal workflow, a low-volume but high-friction process, a one-off migration task—these become viable because there’s no integration cost to recoup before the automation pays for itself.

What This Looks Like in Practice

Here’s a concrete example, using Laivly’s Sidd Solo. A customer submits a warranty claim through any channel: voice, chat, or email. Sidd handles the conversation directly by verifying the purchase, checking the claim against warranty terms, processing the replacement or refund, updating the record, and closing the case. None of it requires an integration project, an API, or a months-long buildout before the first claim gets processed.

Sidd isn’t tied to a single running copy that has to finish one interaction before starting the next. Because it runs in the cloud, capacity can flex with demand. A product recall or a shipping delay that suddenly triples claim volume doesn’t require hiring and training a temporary team or provisioning new hardware. With Sidd, capacity can be added on the automation side instead, then scaled back down once volume normalizes.

And this isn’t only about running entire processes end to end. The same underlying automation also powers Sidd Duo, Laivly’s agent-assist tool, which runs lookups and administrative busywork alongside a live associate in real time. The technology shows up wherever it’s useful, sometimes handling a task entirely on its own, and sometimes just clearing friction out of a human agent’s way so they can focus on the parts of the conversation that actually need a person.

A Different Question to Ask

The next time an automation idea gets floated and the answer hinges on an integration that doesn’t exist yet, that no longer has to be where the conversation ends. Where you used to ask whether a system has an API, now the question becomes: can the workflow be seen and operated on a screen? For most contact center processes, the answer is yes.

That shift changes which ideas are worth revisiting. Workflows shelved for years because the integration cost never justified the build are worth a second look. Short-lived, seasonal, or low-volume processes that were never going to get a dedicated integration now have a real path to automation anyway.

For CX teams with a backlog of ideas that stalled out for exactly this reason, that backlog is worth reopening.

Guest blog post written by Laivly

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“Nothing is more difficult, and therefore more precious, than to be able to decide.” — Napoleon Bonaparte

He wasn’t talking about customer experience architecture. But he might as well have been.

The AI knows what you want. Now what?

Knowing what a customer wants was never the hard part. Modern models are absurdly good at

this. Feed them “I’m locked out of my account and I have a client call in ten minutes,” and they’ll correctly diagnose panic, urgency, and account-recovery intent before you’ve finished your coffee.

The hard part starts one beat later. What does the business do with that? Reset the password instantly? Flag it for fraud review first? Offer a callback? Escalate to a human? Apply a waiting period because this account has a history of suspicious recovery attempts?

That’s not a detection problem. Detection already finished its job. That’s a decisioning problem — and it’s the layer most CX architectures quietly skip, hoping intent recognition and good intentions will somehow cover the gap.

They won’t. Because “I understand you” and “here’s the correct response” are two completely different systems, built on two completely different kinds of intelligence.

Detection Answers “What.” Decisioning Answers “So What.”

Think about how most CX stacks are actually built. Enormous investment goes into the front door: NLU models, sentiment scoring, intent classifiers, transcription, entity extraction. All of it aimed at one question — what does this person want?

Then, right at the moment that question gets answered, most architectures go strangely quiet. The system hands off to… a flowchart. A static rule. A generic macro. A human agent who has to reconstruct, from scratch, the judgment call that should have been automated years ago.

That handoff point is the decisioning layer, and it deserves a far more precise definition than “business logic.” Decisioning is the discipline of evaluating, in real time:

• Customer context — who is this person, what’s their history, what’s their value, what have they already tried?
• Eligibility — what are they actually entitled to, contractually and practically?
• Policy and compliance constraints — what’s allowed, what’s regulated, what creates liability?
• Risk — fraud exposure, churn risk, reputational risk, financial exposure?
• Competing business objectives — retention vs. margin vs. policy enforcement vs. operational cost?
• Historical behavior and outcomes — what’s worked for this customer, or customers like them, before?

…and then selecting a single best action from a field of technically valid options that don’t all serve the same goal equally well.

That last part is the crux of it. Detection gives you a destination. Decisioning is what picks the actual route when there are six roads that all technically get there, and only one of them doesn’t blow up your compliance team or your margin.

Why Next-Best-Action Isn’t the Same as Decisioning

If you’ve spent any time around CX or marketing technology, you’ve probably heard next-best-action enough times to develop a mild allergy to it.

The term persists for good reason. At its core, next-best-action is about evaluating the options available to a customer and selecting the action most likely to produce the desired outcome. But there’s an important distinction: next-best-action is a decisioning technique, not the decisioning layer itself.

A next-best-action engine might rank a set of offers, interventions, or responses based on propensity, value, or predicted outcome. A true CX decisioning layer has to do more first. It needs to establish what actions are actually possible, which are permitted, which carry unacceptable risk, and which objectives take priority when they conflict.

That means the question isn’t simply: What’s the next best thing we could do?

It’s: Of everything we could legally, safely, and operationally do for this customer right now, what should we do—and why?

That distinction matters because the best action isn’t always the one with the highest predicted response rate. A retention offer might have a high probability of preventing churn, for example, but still be the wrong decision if the customer isn’t eligible, has already received the same concession twice, or the cost of the intervention outweighs its value.

This is also why decisioning should sit above the channel. The decision shouldn’t change simply because the customer happens to arrive through chat instead of voice, or email instead of SMS. As CX Today puts it, “the brain decides. Channels execute.”

Next-best-action is therefore part of the machinery — not the whole machine. It helps answer which action should win. Decisioning determines what can compete, what constraints apply, what the business is optimizing for, and ultimately why that action should happen at all.

The Business Case Nobody Can Ignore Anymore

If this all sounds like elegant systems architecture with no commercial teeth, the numbers say otherwise.

Decisioning is going from niche to default, fast. Gartner’s own prediction is blunt: by 2027, half of all business decisions will be “augmented or automated by AI agents for decision intelligence.” And this isn’t a distant curve — a 2024 Gartner survey already found a third of organizations had implemented decision intelligence capabilities. This isn’t an emerging category anymore. It’s a category with a deployment curve.

Getting it right pays, and getting it wrong is expensive in a very specific way. McKinsey has found that “companies that excel at personalization generate 40 percent more revenue than average players” in their category.

Notably, McKinsey ties that gap to a shift in how personalization gets decided in the first place — moving away from picking one good offer and toward stringing together a whole sequence of right moves as the relationship unfolds. Decisioning isn’t a single, one-off calculation. It’s continuous. Every touchpoint re-opens the question.

That continuity applies on the operational side too: nothing about a customer’s situation is settled just because they were classified correctly once. A decisioning system has to be willing to reassess with every new interaction, not lock in on a journey map drawn up in advance. That’s exactly what keeps a system from offering something a customer clearly doesn’t want anymore, thirty seconds after the fact.

And the failure mode is depressingly familiar. It isn’t usually the AI’s comprehension that breaks. Gartner’s own research found that marketing analytics were only shaping 53 percent of marketing decisions — with a third of respondents admitting decision-makers cherry-pick data to fit a conclusion they’d already reached. The teams in that study weren’t short on comprehension. They had the analytics. What broke down was the decision itself.

Where Decisioning Architecture Actually Lives

If you’re sketching this as a system rather than a philosophy, a workable decisioning layer generally needs four components working together:

1. A live context store. Decisions made on stale data are just guesses with better packaging. The engine needs real-time access to customer profile, interaction history, and current state — not a nightly batch sync.

2. A rules and policy engine. This is the guardrail layer: eligibility criteria, regulatory constraints, brand policy, and the things that must never be automated around, no matter how compelling the model’s recommendation looks.

3. A scoring or prediction layer. This is where propensity models, risk scores, and next-best-action logic actually rank the eligible options against each other — not just filter for what’s allowed, but rank for what’s best.

4. An arbitration layer. When the “right for the customer” answer and the “right for the business” answer point in different directions — and they will — something has to resolve that tension deliberately, not by accident. This is arguably the most under-built part of most CX stacks, because it requires someone to actually decide what the business values more, in what order, and write that down.

Detection Was the Easy Layer. This Is the Real One.

There’s a reason so many AI CX rollouts feel impressive in a demo and underwhelming eighteen months later. The demo showcases detection — the part that was always going to work, because pattern recognition is exactly what these models are built for.

What the demo doesn’t showcase is what happens when two customers with identical intent need two completely different responses, because one is a first-time caller and the other has churned twice and come back, or because one request is fully within policy and the other creates regulatory exposure the business can’t absorb. That differentiation — judgment under constraint — is what decisioning is for. It’s the layer that turns “the AI understood me” into “the AI did the right thing for me, specifically, right now.”

W. Edwards Deming put it plainly decades before any of this software existed: “The ultimate purpose of collecting the data is to provide a basis for action or a recommendation.”

Detection collects the data. Decisioning is the basis for action. CX architecture that treats those as the same layer is going to keep mistaking comprehension for competence — and customers can tell the difference every single time.

TL;DR

Most CX systems have become very good at figuring out what customers want. The missing piece is deciding what to do about it.

• Detection identifies intent. Decisioning evaluates what should happen next.
• The decision considers context, eligibility, policy, risk, history, and competing business priorities.
• Next-best-action is part of the decisioning machinery, not the decisioning layer itself.
• The decision belongs above the channel, so the outcome doesn’t change simply because the customer chose chat, voice, email, or SMS.

The real opportunity is turning customer understanding into better decisions.

FAQ

What is a CX decisioning system? A CX decisioning system is the layer of architecture that determines the best next action for a customer, using context, business rules, eligibility, risk, and competing objectives — as distinct from intent detection, which only identifies what the customer wants.

How is decisioning different from intent recognition? Intent recognition identifies the customer’s goal. Decisioning determines the business’s response — evaluating multiple valid options against policy, risk, and business priorities to select the single best action.

What is next-best-action, and how does it relate to decisioning? Next-best-action is a specific decisioning technique that scores and ranks every eligible action for a customer, then selects the one most likely to serve both the customer’s needs and the business’s objectives.

Why do CX systems that “understand” customers still deliver poor outcomes? Because understanding a request and correctly deciding how to act on it are different capabilities. Without a dedicated decisioning layer, systems default to generic or one-size-fits-all responses regardless of context, eligibility, or risk.

Does building a decisioning layer require replacing existing CX tools? Not necessarily. Decisioning is typically implemented as its own architectural layer — connected to existing CRM, data, and orchestration systems — rather than a wholesale platform replacement.

Guest blog post written by ibex.

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