The first data framework we’re releasing focuses on customer intent, and that’s deliberate. Brands that want to meet customers on their own terms, through AI‑powered experiences, should organize CX and delivery around intent. Organizing digital transformation and CX around self-sufficient product teams brought value to market quickly. Now, changing expectations around intelligent experiences calls for a new model to deliver smoother, more customer‑focused service.

Why shift? Because customers increasingly expect interactions that work the way people do—fluid, context-aware, and able to anticipate next steps. If you adopt coordination around intent, you’ll improve your CSAT and LTV, and dramatically reduce AI-related risks. All while operationally shipping cross-channel experiences with higher completion rates at lower costs.

The Next Generation of Service

We’ve seen the Future of CX All Along.. in everyday real-life interaction. We jump between topics, carry multiple threads, and infer what’s unsaid. The person across from you knows what you mean and adapts instantly. In service contexts, luxury hospitality has delivered that sort of attentiveness for decades. Digital experiences are catching up.

Tomorrow’s customer will expect experiences that:

• Happen across any channel. A task might start in a social DM and continue by email or SMS. Customers reach out in whatever channel is handy and expect you to carry the thread forward. They shouldn’t have to navigate your org chart.
• Are context-aware. No one wants to repeat the basics. Customers expect you to know their preferences and share context across your brand, even when they talk to different teams.
• Are stackable and parallel. People multitask by default, switch contexts quickly, and run several goals at once. They expect tools and teams to keep pace without friction.

The Limits of Product Thinking

We launch products when we spot an opportunity and mobilize a team to capitalize on it. A focused product team sets a clear goal, ships software, and grows value through the features and touchpoints they control. That model works well, especially in the early stages.

As you scale, multiple product teams deliver value in parallel. That’s usually better than a monolithic release, which slows to a crawl. Product thinking remains the backbone of digital value creation—yet three implicit constraints can block smarter, better experiences if we ignore them.

Products Are Touchpoint-Centric

Product teams are responsible for developing software, including dashboards, websites, mobile apps, and more. They manage their customer funnel through the touchpoints they own. That’s fine until dozens of products compete for attention and space. Customers don’t want to hunt for their path forward.

Products Create Silos

This isn’t a bug—it’s a speed feature. Teams optimize for momentum and control. Data, systems, and customer intelligence are shared when possible, but each cross-product connection is custom, team by team. That case-by-case integration introduces frictions in the customer experience. You can only mix and mash what product teams deem necessary, nothing else.

AI's Variability Changes How We Develop Products

We ship products feature by feature, user story by user story. That suits deterministic software. AI is probabilistic. Users can achieve their goals in many ways, and outcomes vary. Sprint planning, user stories, and QA get strained. There isn’t always a neat “definition of done” for an AI-enabled experience. Instead, you orchestrate a web of agentic tools, data sources, and rules with varying reliability, measured quantitatively over time. It changes how we plan and assure quality. Success shifts from shipping features to meeting Service Level Objectives, measured continuously.

The Power of Intents

In AI-enabled products, customer intent is the underlying goal that a user seeks to achieve. It’s the 'why' behind the query, not just the 'what’. Intent goes beyond keywords. It focuses on the problem the customer needs to solve, the question they need answered, or the task they need to complete.

An intent-centric approach assumes the user didn’t come to use a feature; they came to make progress.

Intent Personalizes Customer Journeys

Identify intent at the first touchpoint and route the customer through the most efficient, personalized path, regardless of how they arrive. An intent to sign up can flow into a streamlined sales path. An intent to cancel can route to retention with targeted offers, all handled by AI behind the scenes.

Intent Enables Fluid, Contextual Experiences

Instead of navigating menus and features, users state intent explicitly or signal it implicitly. An intent-centric system interprets those signals, then orchestrates tools in the background. Customers stay in flow, focused on outcomes—not on operating software.

Intent Helps You Anticipate Next Steps

When you understand the why, you can anticipate the next step. If a marketer asks for campaign reporting, and the system recognizes an optimization intent, it can surface the best-performing ads and offer to reallocate budget. Intent turns software from a reactive tool into a proactive partner. That drives retention and LTV.

Intents Make AI Experiences Safer

Safety is intent‑specific. Use intent specificity as your safety backbone to allow, restrict, or escalate. Each intent has a risk tier, from human escalation, and PII management, to circuit breakers for abuse. You can tie red teaming, monitoring, and audits to intents so governance scales across channels, and be transparent about what was blocked and why.

Getting Started: Identifying Intents

Intent reflects how people think and communicate. You can begin identifying and prioritizing intents now. The Jobs-to-Be-Done (JTBD) framework is a good place to start.

Developed by Clayton Christensen, JTBD shifts focus from demographics and features to the underlying progress customers seek. Customers “hire” products and services to make that progress. The “Job” is essentially an intent. If you haven’t already, use Jobs Theory to frame research and strategy. It’s a meaningful step toward intelligent CX organized around intent.

Alan Klement’s Job Stories extend this approach by replacing personas and user stories with situational intent. They frame activity around context and motivation instead of features and demographics.

It’s worth noting that JTBD includes emotional and aspirational drivers. Intents often operate at a lower level, but the higher-level goals still matter.

Keep Identifying the high-level drivers behind each intent but focus on the immediate goal to shape the journey. High-level jobs form the basis of personalization, build empathy, and tell clearer stories.

Inferring Intents

Unlike motivations, intents are expressible and observable. A user can state an intent, and a system, or a person, can infer it from context. Inferability matters because your organization needs to recognize intent reliably when it happens.

Intent inference is at the core of AI orchestration

AI models are well-suited for intent inference. The ability to recognize specific intent from interactions is a key step toward an intent‑led experience across channels.

With reliable intent inference, you can form teams focused on specific intents. Product teams evolve to focus on intents while CX stays coordinated around intent.

Measuring Intents

You can’t improve what you can’t see. Ask two questions: Does the intent have a clear completion state? Can you observe a user progressing through distinct stages?

Non-measurable intents still matter. Practically we tend to capture those higher-order intents and connects them to measurable ones. That linkage supports personalization and targeting agents without forcing vague goals into ill-fitting metrics.

This is where a dedicated experience data framework shines. It makes intent-centered experiences sustainable and helps intent teams reach their goals.

Structuring experience around intent aligns research, strategy, and delivery with how AI systems observe and measure progress across the data flywheel. In a world where individuals can do anything, anytime, intent provides a reliable way to deliver value at scale.

[Show the flywheel: one with broken intent signals that devolves into noise, and one where intents are identified, inferred, and measured.]

Intent Aligns Customer, AI , and Organization

Product thinking remains essential. Independent teams, customer insight, and incremental delivery continue to create value. To go further, we need a model that matches how people pursue outcomes. Intent is that model.

By centering on intent, you align AI systems, customer outcomes, and strategy—from planning to execution to improvement. Instead of getting lost in implementation details, you focus on what creates value: solving customer problems.

Do that, and you deliver an experience that feels more human, more personal, and more connected—across every channel, for every customer.