Why agentic AI will grow customer service demand, not shrink it

Nearly every AI business case for customer service begins with the same assumption: customer demand is finite.

Take today’s interaction volume. Automate a percentage of it. Reduce the cost per interaction. Convert the difference into projected savings.

The logic is sound. The assumption deserves another look — and the timing could not be more relevant. A Gartner survey of customer service and support leaders found 85% planned to explore or pilot customer-facing conversational generative AI. That unprecedented scale of experimentation means most enterprises are about to test, in real time, their demand assumptions.

But what if today's interaction volume equals the amount of friction customers have been willing to tolerate and is not about demand at all?

It’s common for customers to do a quick cost-benefit analysis before reaching out to a company. They ask themselves whether the issue is worth enduring hold purgatory? Or searching through a website, navigating a menu, verifying an account, explaining the situation – only to have to repeat every detail a second, third or fourth time? Something our friend Kristen Bell knew all too well before experiencing the NiCE effect.

Sometimes it’s enough to make people want to avoid that scenario altogether.

A customer ignores a minor billing question. A new user gives up on a feature they need help using. A buyer abandons a product comparison. An account owner postpones a configuration change. A traveler accepts an inconvenient option because resolving it would require too much time.

These moments rarely appear in contact center data because they never become interactions. Yet they represent real customer needs, lost opportunities and, in some cases, early indicators of churn.

AI changes the cost of serving that demand. It also changes the cost of expressing it.

When assistance becomes immediate, conversational, contextual and capable of completing an action, customers may find more reasons to engage. What if making customer service dramatically faster, easier, and less expensive causes customers to use far more of it?

Jevons Paradox: Why cheaper AI conversations increase demand

Customer service can take a cue from British economist William Stanley Jevons' observation, giving CX leaders a lens to consider how behavior changes when the price of an activity falls.

In 1865, Jevons examined Britain's industrial growth and dependence on coal. At the time, improvements in steam-engine efficiency were expected to conserve fuel. More efficient engines required less coal to produce the same amount of work. Jevons observed a broader effect: as steam power became more economical, businesses found more uses for it. Adoption expanded across factories, transportation and industry, contributing to greater overall coal consumption.

The idea later became known as Jevons Paradox. When technology sharply reduces the effective cost of using a resource, demand often expands enough that total consumption of that resource increases rather than declines.

We’ve seen efficiency expanding consumption many times. Lower-cost computing increased the number of calculations businesses could perform. A less expensive digital storage solution meant companies could retain far more data. Lower distribution costs helped transform software from a product purchased periodically into a service used continuously.

Each efficiency gain changed the economics of supply. More significantly, each one expanded the range of economically viable uses.

AI is having that effect on customer conversations right now.

What is the abundance economy in customer service?

For as long as contact centers have existed, demand has been rationed by two constraints: human capacity and cost. AI removes both at once, making an entire tier of conversations — too small or inconvenient to justify a human interaction — economically viable for the first time. That shift turns a scarcity economy into an abundance economy: high-quality customer engagement becomes less expensive and available enough to support far more of it than legacy staffing models allowed.

As the marginal cost of a customer interaction decreases so does the threshold for starting one. We’re at the point now where the same customer issue that made a 15-minute call seem daunting may easily justify a 15-second exchange with an AI agent today.

Traditional metrics may miss this hidden demand, because they only measure what customers already tolerated. Abundant service capacity reaches those moments earlier. This shows that real demand has become visible and affordable to serve.

As cost approaches zero, that's an ideal outcome, but it also presents new potential challenges.

Coordinating millions of customer conversations

Scarcity economies are easy to manage because they're small: a modest set of agents, scripts, and systems can keep pace with a few thousand conversations a day. Abundance breaks that arithmetic. Millions of additional viable conversations mean the enterprise is coordinating an expanding web among customers, AI agents, employees, applications and enterprise systems, all needing the same accurate outcome.

That complexity doesn't grow at the same rate as the conversations themselves: each new participant multiplies the relationships that must stay consistent, rather than adding to a list.

That's the paradox at the center of the abundance economy where the same efficiency that makes more conversations possible also makes each one harder to get right, since there are more places for context to be lost and more moments where an answer without orchestrating intelligence can go off the rails.

Why standalone chatbots and point AI solutions fail without scale

The first wave of customer service AI focused on substitution with chatbots answering questions, copilots summarizing calls, classifiers routing intent — each solving a narrow problem well.

Point AI is built to answer a question rather than coordinate an enterprise. That distinction becomes decisive as conversations become abundant. A standalone chatbot can resolve one request in isolation but typically can't see what a customer discussed with an agent last week or where a related workflow stands.

Stack several such tools, each governed by different rules, and the result is AI that's individually competent but collectively incoherent. Gartner's 2025 research on customer service AI investment captures this shift directly: the most valuable AI use cases are no longer standalone chatbots but AI embedded across the full service operating model.

That's because, in an abundance economy, the scarce resource is the context that makes it useful: a customer's history, the policy that applies, their verified identity, what they're permitted to do, where a workflow stands, what the product can and can't do, and what the organization already knows from prior interactions scattered across CRMs, knowledge bases, billing systems and employees' memories rather than any single point solution.

Without a shared layer to assemble that context on demand, abundance produces millions of disconnected conversations instead of millions of successful outcomes. That's when a platform stops being a preference and becomes a necessity.

4 payoffs from shared intelligence on a CX AI platform

If context is the scarce resource, the organizations that win will treat customer intelligence as shared infrastructure, not a feature bolted onto each channel — an operating system that every conversation draws from and writes back to, so the next interaction is as informed as the first.

McKinsey's 2025 Global Survey on AI shows why most enterprises aren't there yet: 88% report regular AI use in at least one function and 62% are experimenting with AI agents, yet only 23% are actually scaling an agentic system anywhere.

High performers, McKinsey finds, are nearly three times more likely to have fundamentally redesigned workflows around AI rather than layering it on top.

This is the role of a CX AI platform, a connective layer of intelligence, orchestration and governance that lets every AI agent, employee and system operate from the same context, policy and source of truth.

NiCE’s CX AI centers on exactly this — linking intelligence, orchestration and human expertise so AI moves beyond isolated automation toward complete, coordinated outcomes, growing more valuable as volume grows.

At Fabletics, agentic AI authenticates the customer and completes a refund by coordinating the full path to resolution, bringing in humans when discretion or empathy is required. The value isn't a marginally cheaper refund — it's a policy-driven request resolved end to end, at a cost that makes handling it economically viable in the first place.

Tripadvisor took its voice agent, Vesper, from concept to live calls in two and a half months — proof this shift can happen quickly. Built on NiCE AI Agents powered by Cognigy, Vesper captures intent, verifies identity, pulls information from multiple systems in parallel, and hands off full context the moment a human is needed. Early results show a 90% customer sentiment score on the interaction types measured, against 71% for the same interactions handled by people. Every handoff also strips a 2-minute identity-and-intent sequence out of the employee's workload.

Toyota's E-Care shows what proactive engagement looks like at scale: more than 25 AI agents across voice and chat, watching for the moment a vehicle flags an engine warning, then contacting the owner, scheduling service, alerting the dealership and briefing the service team before the car arrives. Under the old model, that interaction often never happened — the warning was ignored, the call put off, the appointment never got booked. Now, 95% of customers who get the outreach book directly with the AI agent, and 98% respond positively. This is an interaction that wouldn't have existed otherwise, with safety, operational and revenue value attached.

As these leading brands highlight, a CX AI platform pays off in at least four ways:

1. Substitution with AI handling existing work more efficiently.
2. Stimulated demand with customers asking questions they would have skipped.
3. Proactive engagement with AI initiating contact before a customer has to.
4. Expanded purpose with service as an entry point into adoption, retention and revenue work.

A CX AI platform also compounds as an intelligence system. What’s fragmented across transcripts and tickets becomes visible across channels so teams see friction as it happens, not after renewal.

But the platform still assumes conversations begin and end inside the enterprise's own channels. The next wave of interaction growth challenges that assumption.

Agent-to-agent AI: How personal and enterprise agents will negotiate directly

Interaction growth will likely accelerate further as customers delegate work to personal AI Agents — systems that monitor subscriptions, travel, or accounts and can initiate a conversation with an enterprise's AI agent directly. A task too small may still be worth pursuing automatically at machine speed. That shift is the inevitable consequence of everything before it: conversations that cost less enable more interactions at a scale outside what the old contact center was built to absorb.

Consider what becomes routine once personal and enterprise AI agents are common. An airline’s operations agent contacts a hotel’s booking agent to extend a stranded traveler’s reservation after cancellation. A bank’s fraud agent coordinates with a customer's insurance claims agent to open a related claim without the customer repeating the story twice. A connected vehicle’s onboard agent negotiates a service appointment directly with a dealership’s scheduling agent. A procurement agent renegotiates supplier terms with a supplier’s own commercial agent ahead of renewal, escalating to a human only if the two can't converge.

None of this is futuristic at this moment. What's new is that both sides of an exchange may be AI agents, acting for a human and an enterprise, without either watching in real time.

What AI agent governance requires in identity, consent, and auditability

When one AI agent acts for a customer and another for an enterprise, the question is no longer just “was the answer correct?” It becomes who authorized this exchange, whose identity is represented, what was the agent permitted to agree to, and does a human need to approve it before it's final?

Without guardrails, this is a new category of risk since most enterprise AI programs have so far kept a human on at least one side of every exchange.

Authorization and identity must work in both directions. Consent must be scoped and revocable, authorizing an agent to reschedule a flight not renegotiate a mortgage.

Policy must be enforced inside the exchange itself since no human reviews every handshake, and auditability must be built in so decisions can be reconstructed later. Human oversight must be engineered on a unified foundation.

This gap shows up in the data: Adobe's 2026 AI and Digital Trends report finds 63% of organizations expect agentic AI to free employees for more strategic work, but just 39% have a shared customer data platform capable of supporting it, and nearly one-third report misalignment between executives and the practitioners who must govern these systems.

How an agentic engagement plane offers a trusted coordination layer

Agentic AI native at the core of a platform changes the equation. Enterprises can now run AI agents, human teams, workflows, data, and systems as one intelligent operating model. NiCE’s Agentic Engagement Plane is built for a world where the parties to an interaction are a shifting mix of personal and enterprise AI agents, human experts and business systems.

Where the CX AI platform assembles the context an enterprise needs to serve a customer, the agentic plane is the layer that governs how that context — and the authority to act on it — is shared with outside agents, enforcing exactly those safeguards in real time rather than after the fact. That coordinating plane is what Opus Research calls “the clipboard for the age of AI magic” in its Why Customer Experience Needs an AI Agent Control Plane report.

That has to work across organizational boundaries that have never integrated before. The standards are still being written, but the direction is visible enough to build toward now.

In a world with more AI agents, the plane will become a basic requirement so abundant engagement stays trustworthy and coordinated as the operating layer behind customer outcomes, as invisible to customers as payment networks are to commerce today.

Will AI always increase customer service demand?

When we talk about what Jevons Paradox means in relation to AI in customer service, we should be clear the economic theory doesn't guarantee AI increases demand everywhere.

Better products may resolve issues before they need support, and some customers will prefer self-service with no conversation at all. Quality also determines the outcome: an inaccurate or intrusive AI experience suppresses demand by eroding trust, and disconnected, ungoverned interactions create outright risk. It depends on accurate knowledge, effective orchestration, human involvement and strong governance.

As McKinsey's survey shows, most organizations still have work to do to scale AI enterprise-wide. Leaders should prepare for growing demand rather than assume volume stays fixed.

How CX abundance defines the next era of customer service

Efficiency and cost-per-resolution metrics still matter, but they capture just part of the picture.

A complete business case also asks how much additional demand becomes viable when service is immediate and scalable, and how much of it becomes coordinated outcomes rather than disconnected noise.

That means tracking experience (is service trustworthy?), outcomes (adoption, retention, recovery?), prevention (what was avoided?), intelligence (what conversations taught the business), and, increasingly, coordination — whether agents, employees and systems share one governed view of the truth.

For decades, interaction volume was treated as a measure of demand. It may be better understood as a measure of how much friction customers were willing to endure. The next era of customer service will be defined not just by how many needs an enterprise can resolve, but by how well it can coordinate an abundance of them into outcomes customers can trust.

AI will make customer service efficient and save costs. CX abundance will make service bigger. Orchestration will make service cohesive. Outcomes will make it more valuable.

Three questions can help leaders act on that shift now:

• Where is customer context still fragmented across disconnected tools?
• Which AI investments are optimized for containment rather than outcomes?
• And is the organization ready to govern AI agents that act on a customer's behalf, and go beyond just answering their questions?

Enterprises that start answering those questions now can start building the CX AI coordination layer that customer service abundance requires instead of spending time managing the disconnected conversations.