How Agentic AI In CXM Can Benefit Customers And Brands

From Generative to Agentic AI

Generative AI is good at creating things. It’s reactive. You give it a prompt, it gives you text, images, videos, code, or the perfect email to send to your boss.

Agentic AI goes further. It’s proactive. Think of it as AI that doesn’t just respond to questions but actually takes action. An agent can plan, search, compare, evaluate, decide, transact, and act (somewhat) independently to achieve a goal.

It can decide what to do next, not just how to respond. This shift from static prompts to dynamic actions is a big deal for customer experience management (CXM). Not today – the average consumer is still getting their heads around generative AI, and today’s agents are rudimentary examples of the potential for independent, complex task completion. But when AI agents come into regular use, it’ll upend the dynamics of B2C and B2B CXM.

Is Agentic AI the New CXM Battleground?

CXM strategy has long focused on helping people find the right thing on owned platforms, typically a brand’s website or app.

We mapped journeys. We personalised content. We optimised for search engines and spent loads on search ads. We worked hard to make the journey seamless, and tried to predict the next best action to nudge customers closer to conversion.

Now customers are outsourcing those steps. AI agents are doing the research. Agents are querying sites, evaluating results, and bringing back the best options. Agents are analysing chat histories to learn about individuals’ preferences. In other words, AI agents are becoming the customer’s new front end.

And customers may not even realise it’s happening.

ChatGPT rolled out shopping features in April 2025, the same month that Amazon announced its new in-app agentic AI system. The month before, Google introduced AI Mode, its first foray into multimodal AI-powered search. Users (consumers) didn’t need to do a thing. These new features appeared from one day to the next, integrated into familiar workflows.

This should be a wake-up call for CXM leaders. You no longer only need to optimise your website for humans. You now need to optimise your services, data, and digital touchpoints for machines. Because in a world where your customer’s agent talks to your agent, the company that makes it easiest for those agents to get a good result will win.

How Quickly Will Agents Enter the CXM Conversation?

According to a 2025 Cisco survey, pundits expect agentic AI to handle up to 56% of customer service and support interactions as soon as mid-2026. That could ramp up to 79% by 2035.

Most see agentic AI adoption as a strategic boon. The overwhelming majority predict that agentic AI-led experiences will help the organisation achieve its goals.

‘Help’ is the active word here. Agentic AI won’t take the wheel overnight. The human touch will remain essential in CXM for the foreseeable future. Almost all of Cisco’s survey respondents (96%) say human relationships remain important, and 3 in 4 don’t think AI is capable of replicating human empathy in CX.

“While agentic AI promises unparalleled efficiency, human connection remains irreplaceable…We are all seeking that balance or harmony between agentic AI’s power and the human touch and intelligence that makes customer interactions truly meaningful”

Cisco, The Race To An Agentic Future, 2025.

What Will Agentic AI Actually Look Like?

• IBM’s agentic AI experts talk about human-in-the-loop (HITL) adoption, where agents augment the humans who make the final decisions.
• Shopify wants eCommerce brands to focus on using agents for customer service triage and 24/7 support, freeing up humans to do the big-picture stuff.
• Adobe is rolling out workflow-focused agents that bridge Experience Cloud tools, helping to orchestrate journeys and automate personalisation at scale.
• Salesforce has given enterprise customers the tools to build agents that support employees and customers (it’s already generating $100m/£73m in annual value).

In practice, agentic AI will take myriad forms. Microsoft collected 700+ AI transformation stories, many of them leaning into agents. (And those are only some of the use cases powered by one vendor’s products.) We expect it to pop up everywhere in the coming years, before the hype settles and agents become ingrained in our daily workflows.

“There are some cookie-cutter workflows that AI agents can take over, but the majority of use cases are business or customer-specific. Marketing and sales teams need to put themselves in customers’ shoes to understand what needs to be improved, then decide whether an AI agent is the best tool to improve it. That’s the only way to find valuable use cases, and agents should be considered alongside other solutions, not ahead of them.”

Dirk Wybe De Jong, Solutions Director

That said, the stakes are huge. When (not if) tools like ChatGPT, Amazon’s shopping assistant, or a customer’s AI agent start searching, comparing, and deciding, your brand may never even be in the room. You risk losing visibility and loyalty, not to mention valuable customer data.

Similarly, when your colleagues task Adobe’s, Salesforce’s or HubSpot’s agents with customer journey decisions, or palm off creative output to Claude, CoPilot, or GitHub, the quality of those interactions is likely to nosedive. We’ll talk more about that when we discuss governance, guardrails, and GIGO in agentic AI.

Customer Benefits: Designing Journeys For the “AI First” Consumer

AI-Accelerated Journey Mapping

AI agents can analyse huge volumes of customer interaction data to spot friction, run simulations to test for optimal responses, and recommend strategies to fix experience gaps that human analysts can’t see.

What used to take weeks of mapping and analysis can now be done in minutes. You’ll spot pain points faster, amplify moments that matter, and get better at anticipating customer needs.

Hyper-Personal Real-Time Routing

Imagine a shopper asking their AI agent, “Find me a waterproof jacket under £150 that’ll arrive by Friday.” The agent searches multiple brands, compares prices, checks stock, and brings back the best-fit options.

Shopify’s data shows it’s already happening. Entire purchases are happening in a chat window and customers like it. In fact 73% say AI improves the shopping experience. But brands with poor data organisation are missing out.

Even if your product meets all the criteria, the customer may never see your brand. Your data and inventory must be easily understood and surfaced by third-party agents.

Automated and Proactive Multi-Step Purchases

It won’t be long before AI agents are doing your grocery shopping. One that knows your buying habits, preferences, budget, and dietary needs could build a cart every week.

This is a great example of HITL agentic AI. It’s unlikely that AI will be empowered to complete the purchase without your approval. Don’t worry, you can still sneak a treat into your basket. But it can shop the specials and schedule delivery around your calendar.

Brands in this position need to be ready for agent-to-agent conversations. That means exposing structured, reliable data and deploying internal agents to respond to queries.

If you can remove the tedium of shopping for toilet paper, you can expect customers to re-order more often.

24/7 Service With (Almost) Zero Waiting

Air India’s agentic customer support AI handled 4M customer queries with zero human intervention. Ema, a firm focused on agentic AI development, has seen 98%+ accuracy in autonomous ticket resolution.

We’re all familiar with the horror stories of chatbot support agents going rogue. Agents are different in that they’re inherently less rigid in handling customer queries. They can resolve issues autonomously, route complex questions to the right person, or raise tickets if needed. Faster than humans, at higher volumes, and around the clock.

55% of respondents to Cisco’s global survey expect agentic AI to significantly improve vendor performance in support resolution times. The human element is still important, but having AI agents handle easy triage or overnight support could be a win for everyone.

Marketer and Sales Benefits: Automate the Automations

We know almost intrinsically that agentic AI will increase the speed of work in marketing and sales. The question is where and how.
To answer that, we need a new perspective. Agentic AI is not merely beefed-up ChatGPT. It’s an orchestration layer capable of making decisions and carrying out multi-phase tasks. That difference matters.

Evolutions in Real Time

There was a time when marketing automation felt revolutionary. Then came genAI tools. Agentic AI continues what many see as an ongoing evolution in “intelligent automation”. It orchestrates existing automations, taking care of the time-consuming in-between bits.

If your team is typing the same prompt or process 10 times a day to write briefs, analyse performance, or check content quality, it’s probably time to consider how an agent can automate it.

Faster, More Effective Feedback Loops

The core marketing loop of create, test, and refine hasn’t changed. Agentic AI speeds up the process, automating tasks in the messy middle. It can do things like:

• Delegate subtasks to genAI tools to create multimedia content.
• Analyse customer data and interrogate your database to find the best offer.
• Plan and manage multi-channel marketing campaigns by coordinating other AIs.
• Run performance analysis, generate reports, and suggest optimisations.
• Identify new sales prospects, recommend engagement strategies, and even interact.

And it works. In one IBM survey of 2.9k execs, 69% said “improved decision-making” was the biggest benefit they’ve seen from AI agents.

Automated, Scalable Quality Assurance

QA is essential, but often repetitive and under-resourced. Now you can train agents to check every email or campaign asset for:

• Broken links.
• Personalisation errors.
• Missing opt-outs.
• Inconsistent messaging.

It’s not glamorous work. But it’s one of the best pilot use cases for agentic AI in CXM, because it delivers almost immediate ROI.

Segment-of-One At Scale

Pretty soon, we’ll see a raft of agentic AI tools being used to create unique user journeys en masse. There are already tools like Salesforce’s Einstein Copilot capable of combining live CRM data with product inventory, purchase history, and behaviour to recommend the next-best offer for individual customers at scale.

Once more vendors introduce similar journey orchestration capabilities, customer experience gaps will all but disappear. Tasks that frustrate marketers – record de-duplication, database calls, automation software troubleshooting, communication delays – will be automated by agents.

Behind-the-Scenes Operational Efficiencies

83% of the execs IBM surveyed said they expected AI agents to improve output and process efficiency as soon as 2026. 71% believe agents will be able to adapt autonomously to changing workflows.

Agents will work behind the scenes to:

• Triage internal/IT support tickets.
• Find info buried in internal docs.
• Prep tender and RFP responses.
• Schedule sales calls and follow-ups.
• Optimise ongoing projects and workflows.

Naturally, this leads to concerns about job losses. These fears aren’t totally unfounded, but mass layoffs are unlikely.

One-third of organisational leaders plan to use AI to reduce headcount. About the same (32%) plan to increase headcount to support new ways of working. Meanwhile, 45% say they’ll maintain headcount and use AI as ‘digital labour’, while 47% will upskill their workforce in AI.

Getting Started is Low Stakes, But You Need a Strategy

Building the actual agentic AI systems is becoming faster and more accessible. Tools like N8N, Microsoft Copilot, Zapier, Make, and Pipedream have smashed the barriers to entry. What once took months of application development can now be prototyped in days or even hours.

This speed means that, although AI tools are being superseded almost daily, the short shelf life of any one tool is low-risk. It can be rebuilt or adapted quickly.

What’s more important is the strategy guiding your organisation’s AI adoption. Before rushing out to build an agent, ask a few key questions:

• What problem are we solving, and is it worth solving with AI?
• Is our data clean, structured, and accessible enough for an agent to use?
• Who will maintain the prompt, the logic, and the knowledge base once it’s live?
• How will we measure value for the customer and for the business?
• Are customers actually asking for this?

Once you start unpacking those questions, more will arise. Especially around data hygiene, security, and quality control.

“Don’t let your business become a hammer in search of a nail. Challenge whether agentic AI really is the best tool for the job, or whether another solution is better. The only way to get clarity on that is to put yourself in the customer’s shoes and find use cases that are specific to your business.”

Dirk Wybe De Jong, Solutions Director

The Consultant’s Role in Launching Agentic AI in CXM

The hardest part of working with agentic AI in CXM is figuring out what’s worth building. Actually building the thing is easy after that.

Automation should add value for your customers and your business. Sometimes that value comes from saving time. Sometimes it’s about removing complexity. Sometimes it’s about being available on demand, ready to solve the customer’s problem.

But spotting those opportunities – and making sure the AI doesn’t cause more problems than it solves – takes experience. As CXM consultants, we help teams cut through the hype, get clarity on what matters, and build AI into their customer experience strategy in a way that’s smart, sustainable, and human.

Does this all sound too good to be true? Don’t worry, we’ll get to the risks of rushing into agentic AI. Subscribe to our newsletter below or join our LinkedIn community to get notified when we publish new insights.