Customer journeys are dead in the agentic AI era

It’s time to declare the death of the traditional customer journey. The familiar linear marketing funnels and journey maps – where we nudge customers step by step from awareness to purchase – are quickly becoming relics. In their place, a new paradigm is emerging: the holistic customer “mission.” This shift isn’t just semantics; it signals a seismic change in how we approach marketing automation and personalization in the age of intelligent AI agents. According to recent insights from BCG, companies must “build for journeys, but design for missions” as AI agents begin to take over many customer interactions. In other words, instead of orchestrating siloed campaigns, brands need to think about serving broader customer goals – their missions – in a seamless, proactive way.

From Funnels to Missions: Why the Old Model No Longer Fits

For decades, marketers have relied on funnels and mapped out linear customer journeys. But real customers don’t live their lives in neat funnels – and with AI in the mix, they won’t have to. A recent article I read that agentic AI will make the traditional marketing funnel obsolete. The answer increasingly looks like “yes.” In the near future, customers will delegate tasks to personal AI agents acting on their behalf. These agents can research options, compare offers, negotiate, and even execute purchases – fundamentally upending the way we think about guiding a customer along a preset path

Consider this contrast: A traditional funnel might involve a consumer manually searching “mid-size hybrid SUVs,” comparing models, visiting dealer websites, etc. An AI-driven customer mission, by contrast, would simply be “buy the perfect car for me.” The AI (a network of autonomous agents) will handle the complex sub-tasks: finding models that fit the customer’s needs, checking inventories, securing financing, negotiating price, and scheduling test drives or delivery – all with minimal human involvement As BCG researchers note, companies must be ready to serve these complex, data-driven missions (“buy the perfect car for me”) instead of separate journeys (“show me mid-size hybrid SUVs”). In short, the focus shifts from managing steps to fulfilling outcomes.

This “mission” mindset is a game-changer for customer experience. Rather than pushing customers down a marketing pipeline, the goal is to help customers achieve their broader objectives as effortlessly as possible. It’s a holistic, goal-centric view of CX that aligns perfectly with the rise of agentic AI.

Agentic AI: The New Engine of Customer Experience

What’s enabling this dramatic shift is the maturation of agentic AI – AI agents that can autonomously perceive, decide, and act. Unlike the chatbots of old that were “reactive, transactional, and channel-bound” modern AI agents are goal-driven co-pilots that orchestrate end-to-end solutions across channels. “The future of CX isn’t omnichannel – it’s agentic orchestration across dynamic, context-rich multichannel ecosystems.” In practical terms, this means an AI agent doesn’t just answer a customer’s question on one channel – it carries context with it, switches mediums as needed and completes tasks proactively without dropping the thread.

Today’s customers bounce fluidly from a web chat, to email, to a phone call, to a WhatsApp message. They expect a seamless continuity of experience, but most companies’ systems can’t handle that – context gets lost, and customers repeat themselves. Agentic AI changes the equation. Because these agents retain memory of the entire interaction and have the autonomy to take actions, they can manage a customer’s mission holistically. For example, an AI agent handling a support case might simultaneously check backend systems, auto-file a refund, schedule a follow-up call, and update the customer via email – all in one coherent workflow Enterprises adopting such autonomous, multichannel AI workflows have seen 60–80% reductions in resolution times and significant gains in customer satisfaction through faster, more proactive service

Crucially, these AI agents don’t just respond – they act on behalf of the customer. They can make decisions and execute transactions under predefined guardrails. McKinsey calls this the shift from generative AI to truly agentic AI: “a goal-driven approach, where the AI is capable of making decisions and taking actions on behalf of the human. Early examples are already here. Klarna, for instance, introduced an AI assistant that handled the workload of 700 agents in its first month, cutting average resolution time from 11 minutes to under 2 minutes – all while maintaining customer satisfaction levels on par with human service. These AI “co-pilots” not only resolve issues faster, but even help customers discover products and make recommendations in real time

The implication for executives: customer experience is poised to leap forward on two fronts – higher personalization and lower cost-to-serve – thanks to agentic AI. BCG goes so far as to call this the “holy grail” of CX, where companies deliver “a vastly superior customer experience at a radically lower cost-to-serve” simultaneously Little wonder they dub this the “golden era” of CX opening up

Personalization 2.0 – Finally Getting to 1:1 at Scale

Perhaps the most exciting impact of this shift is on marketing personalization. As a marketer, I’ve long aspired to true 1-to-1 communication – treating each customer as a “segment of one.” In reality, most personalization has been clunky and superficial. We blast out “Dear [Name]” emails with product recommendations based on broad segments or past purchases. This often misses the mark. "Most marketing today is surface-level personalization” that fails to adapt to a customer’s changing context or intent Customers notice the gap: we send the same leggings promotion to a runner looking for marathon gear and to a fashion browser casually eyeing athleisure, leaving them feeling unseen

The root problem is that traditional marketing automation tools were built for segments, not individuals. Even “journey builder” systems that allow branching paths are fundamentally rule-based – a marketer has to anticipate every scenario and hard-code the next step. This approach breaks down in complexity. You can’t realistically predefine thousands of micro-decisions (optimal send time, ideal content, best channel, etc.) for millions of customers in real time. True personalization means making all those decisions dynamically for each user, which is impossible with manual journey mapping – the combinatorial explosion is just too much.

AI changes that. Instead of manually building ever-more convoluted journeys, marketers can hand the reins to AI decisioning agents that learn and optimize on the fly. I call this a fundamentally different approach: marketers define high-level goals and guardrails, then AI agents test, learn, and decide the best action for each customer at each moment. These agents use techniques like reinforcement learning and contextual multi-armed bandits to constantly improve outcomes for each individual. The result is communications tailored at the one-to-one level – the right message or offer, via the right channel, at the right time for that specific customer’s state. And the system keeps learning with every interaction, something static segments could never do.

We are finally seeing the promise of 1:1 personalization become attainable. For example, a tier-1 bank in Europe that I’m familiar with has begun experimenting with an AI-driven “personalized customer concierge.” Instead of relying on a traditional customer data platform (CDP) and pre-set journeys, the AI agent can pull in data on demand from various internal systems – from transaction history to web behavior to call center logs – and craft individualized recommendations in real time. Early trials indicate higher product uptake and engagement when customers receive advice or offers uniquely tailored to their immediate context (like a timely mortgage rate update right when they start home-browsing, rather than a generic monthly newsletter). This kind of responsiveness simply wasn’t feasible with batch data pipelines and static campaigns. It underscores how AI agents, with real-time decisioning, might render old-school segmentation and even centralized data platforms less critical. When an agent can fetch whatever data it needs at the moment of interaction, the heavy lifting of assembling a 360° customer profile in a CDP beforehand starts to look less vital. In fact, industry observers are already asking whether the martech stack as we know it will be disrupted – perhaps even “the end of the traditional Martech stack” in an agentic world.

Rethinking Data and Platforms in an Agentic World

The rise of agentic AI also forces a re-examination of data strategy and customer ownership. Who controls the data and the interaction? Increasingly, it may be the customer (through their personal AI) rather than the brand. Innovations like Tim Berners-Lee’s Solid pods hint at a future where individuals hold their own data in decentralized stores and AIs act as gatekeepers to that data. In such a scenario, a corporate marketing team might not get direct access to a person’s info via a CDP or CRM – they’d have to appeal to the person’s AI agent, which fiercely protects the user’s preferences and privacy. Marketing could become less about wooing human eyeballs with emotional storytelling, and more about meeting the “strict, data-driven criteria” of algorithms acting in the customer’s best interest. If millions of personal AI agents band together to negotiate or optimize on behalf of consumers, they could flip market power on its head – instantly switching loyalties to brands that offer better value and filtering out those that don’t.

For companies, this means our data platforms and personalization engines must become far more adaptive. The days of hoarding data in one place “just in case” might wane. Instead, success will come from being able to plug into these AI-driven ecosystems – supplying real-time data or APIs that allow customers’ agents to get what they need. In the interim, enterprises can leverage agentic AI internally to break down their own data silos. An AI agent working for a bank, for example, can pull a customer’s info from the retail banking system, the credit card system, and the mortgage system in milliseconds during a single interaction – accomplishing in real time what a traditional customer data platform tries to pre-compute. This on-demand orchestration is precisely why some experts hint that the traditional CDP could become obsolete when faced with AI that fetches and fuses data dynamically.

A New Playbook for the AI-Agent Era

Shifting from journeys to missions and from campaigns to autonomous personalization is no small change. It requires rethinking processes, teams, and success metrics. In my experience, leaders who begin adopting this new playbook are seeing encouraging results, but it demands a bold vision and willingness to transform. As one McKinsey expert observed, “It’s not about automating tasks anymore. It’s about redesigning how work is done… The future is not about managing AI but collaborating with AI agents that think, act, and optimize in real time.” In practical terms, that means breaking down organizational silos (marketing, sales, support, IT need to work hand-in-hand), and upskilling teams to work alongside AI. It also means revisiting what we measure. We may care less about email open rates or app MAUs, and more about higher-level outcomes like cost-to-serve, customer lifetime value, and mission success rate for customers.

What should an executive do next? A few no-regrets moves are emerging. Start with focused pilots that put an AI agent on one well-defined customer mission – this lets you prove value and understand the impact on your tech stack and teams. Reimagine your role in the customer’s mission – ask how your brand can plug into a broader goal (like “help me live a healthier life” rather than just “sell insurance”) and design services around that. And importantly, prepare your data and IT architecture for convergence. Ensure your systems can integrate via APIs and share data fluidly, because agents thrive on connectivity. Leading companies like Amazon and Klarna are already showing what’s possible by marrying internal efficiency with external AI assistants to delight customers. The payoff can be huge – Klarna’s AI assistant improvements are projected to yield $40 million in incremental profit in its first year.

The bottom line: Customer journeys as we knew them are fading. The future belongs to companies that can understand and support customer missions, leveraging AI agents to deliver truly personalized, frictionless experiences. It’s a future where marketing is less about one-size-fits-all journeys and more about millions of unique journeys unfolding in real time – each led by an intelligent agent collaborating with your brand. For those ready to embrace this shift, a golden era of customer experience awaits. Brands that don’t adapt, however, risk being left behind as customers’ AI co-pilots steer them elsewhere. As an executive, the choice is yours: cling to the old funnels, or help your customers complete their missions. The latter promises not only deeper loyalty and satisfaction, but also the efficiency and growth that come from finally delivering the right message or service at the right time – for each customer, every time.