Why agentic AI is the next big shift in CX strategy

As enterprises expand AI adoption across both internal teams and end consumers, agentic AI stands out for its potential to elevate customer experience through autonomous and semi-autonomous actions. By enabling faster, more personalized interactions, agentic AI is driving demand for practical ways to deploy it at scale.

The proof is in the investments. The global autonomous agents market is valued at $4.35 billion in 2025 and is forecast to surpass $100 billion by 2034 — a compound annual growth rate of more than 42%. Microsoft’s earlier projections of a $3.50 return for every $1 spent on generative AI alone point to even greater promise for agentic investments.

Still, as appealing as automation may be for enterprises under constant pressure to do more with less, the risk lies in using it poorly — creating the digital equivalent of phone-tree doom loops or dumb chatbots that frustrate rather than help.

What does agentic AI in customer experience actually look like — and what should enterprises aim to achieve with it? Just as important, where does automation risk adding complexity instead of value? Let’s explore.

Personalized, proactive and predictive

Personalization has long been the holy grail of customer experience. Agentic AI brings that goal within reach by delivering timely, relevant help that reduces effort and prevents repeat contacts or escalations. Its value lies in simple, actionable promises:

• Know the customer.
• Anticipate needs based on recent behavior.
• Offer clear next steps within established policies. 

The payoff is twofold — higher satisfaction and fewer escalations. Here’s what that looks like in practice.

What this looks like for customers 

• Wireless plan usage: Mid-month, a family is trending over their data allowance. They get a clear message with two options of either a temporary boost or plan change, along with price, start date and how to switch back automatically later. No jargon, no call required. 
• Retail delivery slip: When a shipment won’t make the stated window, the customer gets a heads-up with an alternative item or pickup option and a small credit where appropriate. They choose in a tap and get a confirmed delivery date.
• B2B onboarding stall: A new account isn’t using key features by week two. The admin gets a short nudge that explains what’s missing, a 10-minute guided offer to get started and a follow-up scheduled with their CSM only if adoption doesn’t recover.

What marketing leaders can do

• Start with clear customer promises: Define simple, public-facing rules that your teams can actually uphold (e.g., “We’ll notify you before you exceed your plan,” “We’ll propose alternatives if delivery is late”). These set expectations and guide AI behavior. 
• Prioritize moments that matter: Pick three to five points along the customer journey where proactive help measurably changes the outcome (e.g., usage spikes, shipping delays, early-life adoption) and set monthly targets for First-Contact Resolution, time-to-resolution and complaint rate for those moments only.
• Keep offers simple and within bounds: Provide a small number of approved options that customers can easily act on in a few taps (limited-value credits, reschedule windows, plan changes with easy reversion). Ensure that every option can be easily explained in a single sentence.

What to avoid 

• Personalization that adds steps: If the customer has to re-enter details or compare complex choices, it isn’t helpful.
• Proactive messages without an explicit action: Every alert should give the customer one or two decisions to make, not a link to “learn more.”
• Chasing deflection as the goal: Track the outcomes that matter (faster resolution, fewer repeat contacts, higher CSAT/NPS) and let deflection follow.

Personalized, proactive and predictive should mean fewer surprises and faster decisions for customers, not fancy copy. Set explicit promises, focus on a few high-impact moments and keep choices focused. 

Dig deeper: Your website isn’t ready for AI agents — here’s what needs to change

Navigating complex pathways

Customers now engage across multiple channels and devices — often switching between them mid-journey — yet still expect speed, clarity and consistency. Agentic AI adds the most value when customer needs cut across several steps, systems and teams. 

Marketing’s job is to reduce customer effort in these complex middle journeys — such as moves, returns, renewals and plan changes — and to ensure promises are true across every channel. It’s coordination at scale: the assistant understands what the customer is trying to accomplish, closes the gaps, and keeps them moving forward without handoffs.

What this means for customers 

• Telecom move: A customer changes their address and is guided through availability of services, appointment options and any bill adjustments in a single flow. They don’t repeat themselves or call back to confirm anything — they just receive a clear confirmation and next steps.
• Retail return with mixed items: A shopper initiates a return and is told — right at the start — where each item goes, when they will receive the refund and if there are any value-added services. Labels are generated, pickups are offered if needed and status updates arrive automatically.
• B2B software access issue: A user can’t see a feature following an org change. The assistant confirms their role, explains what’s included in their plan and either resolves access or transfers them to the right owner with a succinct summary. The user doesn’t get bounced between IT and the vendor.

What marketing leaders can do

• Prioritize journeys, not channels: Identify the top three multi-step experiences that cause friction and take ownership end-to-end (e.g., onboarding, returns/exchanges, plan changes/renewals). Agree on explicit service promises and enforce them across all channels.
• Set simple rules of engagement: Define what good looks like (time-to-resolution, first-contact resolution, complaint rate) and a handful of plain-language guardrails (what can be offered, when a human steps in, what must be explained). These are leadership decisions, not technical ones. 
• Make progress visible: Review a short dashboard weekly, where customers are stuck, how many cases finish without handoffs and the effect on satisfaction and revenue. Reward fixes that eliminate steps or clarify policies, not just volume processed.

What to avoid 

• Channel-by-channel fixes. Enhancing chat or email alone rarely helps if the journey itself is broken.
• Over-personalization without clarity. A chatty tone doesn’t make up for unclear policies or next steps.
• Measuring deflection instead of outcomes. Measure faster resolution and fewer repeat contacts. Treat deflection as a byproduct, not the goal.

For marketers, the lever is orchestration, not new scripts. Select the journeys that matter, set customer-centered rules and promises and inspect progress weekly. If you reduce steps and prevent handoffs with AI, customers will notice and results will improve.

Dig deeper: 6 common agentic AI pitfalls and how to avoid them

Combining human and agentic

The best customer experiences balance AI’s speed with human judgment. Teams need clear rules for when automation should take the lead and when a customer service representative should step in — a simple principle helps: automation handles the routine, people handle exceptions and money and emotion.

What this means for customers

Here are examples of moments where clear rules of engagement determine whether the system should defer to a person or proceed with automation.

• B2B renewal change: The assistant identifies periods of under-use and recommends downshifting to a smaller package. When the customer raises contract questions, a customer service manager joins in with a summary that has already been prepared. Any follow-up paperwork and confirmations are handled automatically.
• Airline disruption: The assistant rebooks the traveler and presents seat choices. When the AI recognizes a family with special-needs concerns, it automatically queues the traveler to an agent who can assist the customer in finalizing the seating and arranging special care. The system’s AI automatically handles confirmations and receipts.
• Healthcare plan questions: The assistant’s first questions to a customer seeking to change plans are to narrow the field by doctors and medications the customer wants covered. When the customer expresses cost concerns, a licensed representative joins in with a clear script prepared. After the conversation, the assistant automatically completes the enrollment process and documents the choices.

What marketing leaders can do

Success in combining human and agentic approaches requires several components. Leaders can start with a few areas.

• Set handoff rules: Establish simple triggers to bring a human into a customer conversation (e.g., a high-dollar-impact issue is raised, the system has detected repeated friction in a process step or a sensitive topic arises). Make it easy for customers to ask to speak to a person at any time.
• Make the handoff frictionless: Require that the system always present the customer with a one-screen summary of the conversation following every human handoff: the customer’s goal, steps completed, current options and next steps. 
• Measure the blended experience: Track key outcome metrics by customer journey path (AI-only, human-only and blended) and favor journeys that reduce customer effort and accelerate resolution, not just ones that reduce handle time. 

What to avoid 

Of course, success also means knowing what doesn’t belong in an optimal customer experience. Leaders should also guard against pitfalls that can quickly derail efforts to make automation customer-centric.

• Forced containment: Barring a customer from accessing a person is an experience design failure that erodes trust and often results in complaints through other channels.
• Context resets: If a customer has to repeat information or choice selections after being handed off, the system — not the customer — is the problem.
• Scripted empathy: Use agents when costs, risks or emotions are high. Use AI to support a human conversation, not to substitute for it.

Winning with a hybrid approach: Blend AI and people so AI clears routine friction points fast and people handle the moments that carry risk or require judgment. 

Establish unambiguous rules for handoffs, insist on clean transfers with complete context and manage outcomes defined by customer effort and speed of resolution, not deflection.

Dig deeper: Customer experience management in the age of agentic AI

Success requires mindset shifts

To harness the power of an agentic CX approach, marketers can begin by addressing the three mindset shifts needed to succeed:

From campaigns to continual service 

CX is a system that’s always on, watching for signals and intervening within policy, not a series of periodic pushes. Let teams set their own event triggers, consent rules and action limits, then iterate each week based on FCR, time-to-resolution and complaint rate. 

From content-first to policy-and-data-first 

Ensure that you have consented identity, entitlements and up-to-date policies before creative. Design flows that can confirm eligibility, ground responses with authoritative sources and execute in clear guardrails (e.g., credits up to $25, 7-day reschedule window). This will lower rework, boost auditability and make personalization reliable across channels and languages.

From automation for deflection to automation for outcomes

Optimize agent interactions for measurable customer outcomes, not just containment. Set clear targets and compare the performance of AI-only, human and hybrid paths. Assist agents to accelerate human work, require escalation when risk or ambiguity crosses thresholds and recycle corrections back into prompts, policies and tools during scheduled review cadence.

Embracing these shifts helps keep control in the hands of marketing and CX leadership while delivering faster resolution, lower effort and consistent experiences at scale.

Dig deeper: 60% of shoppers expect to use AI agents in the next 12 months

Making agentic AI work

Agentic AI should make it easier and faster to meet customer needs — nothing more complicated. The path is straightforward: 

• Define clear promises.
• Orchestrate complex end-to-end journeys.
• Combine automation with human judgment where dollars, risk or emotion run high.

When senior leaders stay focused on these three levers, customers will feel the impact first — and the metrics will follow.

CMOs and CX leaders own the rules of engagement, not the plumbing. 

• Set simple guardrails — what the assistant can offer, when a human steps in.
• Align teams around a few high-value journeys to improve. 
• Review outcomes weekly. 

Good means fewer handoffs, shorter cycles, clearer choices and steady gains in satisfaction and revenue. Keep the playbook narrow, adjust based on data and scale only what measurably improves.

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