The most important tool in the AI Transformation toolbox
Every AI report recommends reinventing how you work in order to see an ROI from AI, but are woefully short on details on why or how. The answer can be pulled from the worlds of CX and service design.
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“Wait — you mean the decision we make here affects you guys down there?” asked the sales director to the head of customer service, pointing at a mess of sticky notes and arrows on the wall in front of us. “Ohhhh…”
That collective light-bulb moment is why I got addicted to what Forrester calls “journey ecosystem mapping” when I worked there as CX Principal. In every workshop, two things happened: first, the biggest broken internal process becomes blindingly evident. And second, everyone can see the second-order effects of their actions and decisions.
Journey ecosystem mapping, service blueprints… the new trendy name is journey operations. Whatever the label, this process transcends customer touchpoints. Because it maps everything below the line of visibility – all the people, processes and technologies that sit underneath the customer journey – it works just like the “You are Here” sign in an airport. When it’s done collaboratively with a cross-functional team, this process:
helps every team orient themselves to what drives customer value
gives a shared language and source of truth across functions
reveals the broken processes and handoffs that fall between the cracks
provides a shared purpose that breaks down silos
checks multiple goals simultaneously – improving customer loyalty, internal efficiency, and competitive differentiation in one go.
The rest of this article unpacks why it’s now the most important tool in the AI Transformation toolbox, and what an effective Strategic Journey Ops programme includes.
Why AI transformation needs Journey Ops
Let’s first unpack why AI requires a fundamentally different, cross-functional approach that journey ops is well-suited to solve.
The human buffer is gone.
Businesses are great at optimising individual functions, but not how the whole company operates together to create customer value. Historically, it hasn’t been a catastrophic problem because there’s always been a person to pick up the slack: someone who asks the clarifying question, applies some judgment, or sits on a weird case until it makes sense. An enormous part of every business runs on individuals who say, “hang on, this doesn’t look right.” Massively inefficient, but it's worked for decades.
AI doesn’t say “hang on.” It just tries to speed up whatever it’s sitting on top of. If those processes and data haven’t been streamlined first, very expensive problems result.
End-to-end is where the ROI is.
McKinsey found that intentional end-to-end workflow redesign is one of the strongest predictors of substantial EBIT improvements. So how does this connect to customer journeys? The customer experience is where front-of-house and back-of-house meet, and 9 times out of 10, the place with the biggest customer pain is where your biggest internal mess is too.
When you understand which workflows map to your most important journeys, you get the biggest bang for your improvement buck. This approach -- leading with the customer and the job to be done -- also makes it far easier to identify and prioritize data requirements and gaps that functional views miss.
The winners look to growth, not just efficiency.
The same McKinsey study found that top AI performers aim for growth and innovation, not just efficiency. When you start with your priority customers’ goals, lifecycle and journeys, it’s easy to spot meaningful innovation opportunities, repair and reinvent at the same time, and identify new capabilities for building a compounding competitive moat over time.
The hybrid workforce needs choreography.
AI forces us to ask better questions at the journey level. For this specific customer journey or value stream, what capabilities do we really need? What human strengths are essential here versus where we could use AI?
Most companies are asking those questions at the individual task or role level and wondering why the results feel disappointing. The leverage comes from redesigning the entire journey first, deciding the right mix of human and AI capabilities, and then connecting the workflows so the handoffs are seamless. This ensures the hybrid workforce is choreographed to deliver a smooth, purposeful flow across the lifecycle that delivers the value customers need to keep buying from you.
AI “unboxes” the customer experience.
Before AI, customer interactions were bounded by IVRs, checkboxes and pull-down menus. Now a customer can ask a chatbot multi-topic questions that require the AI to pull from shipping, billing, support tickets, inventory, and possibly escalate to a human. You need the right data, context, rules, etc. to make that process less frustrating than the process you’re trying to replace.
Unpacking Strategic Journey Ops for AI Transformation
If you’ve started your agentic AI journey, you might have jumped straight into workflow design. It’s a good move, and you can certainly tackle the low-hanging fruit first. But if you’re taking a workflow-first approach, you’re leaving a lot of money on the table.
When you blend AI transformation with customer-centric practices like strategic personas and Journey Ops, you’re more likely to realize an ROI fast without even adding AI. That’s why I’m focusing my company on building a Strategic Journey Ops practice for AI – starting with the intersection of customer and business value and backing into your top priorities for repair and reinvention. Here’s what it includes:
1. Customer Value Clarity
This is its own discipline, and massively underestimated. Without shared clarity on your best-fit customers, it will continue to fragment your business even as you try to unify it. One of my clients had over 80 personas, each built by a different function and BU for individual use cases. We got them down to a core 8 customer archetypes; designing them together ensured they shared the same needs for one value proposition, while clarifying important differences. These became the single source of truth that informed every function and – most importantly – cross-functional journey redesign.
2. Journey/Workflow Portfolio Management
Every journey is simply how a customer solves for a “job to be done,” like set up an account, make an appointment or file a claim. There can be tens or even hundreds of journeys that sit under the end-to-end customer lifecycle. That’s where the Portfolio Map comes in: a comprehensive catalogue of all journeys across the lifecycle.
It can’t be a one-and-done exercise, but rather a living dashboard for tracking repairs and redesigns over time. You don’t have to get fancy, by the way; a Miro board is fine. Here’s what I view as critical to track (and am starting to build a product to do this specifically for AI transformation:)
Journey health/satisfaction informed by voice-of-customer data
Ops health: How well are we delivering that journey? Usually the biggest customer and employee pains are intrinsically linked.
Redesign priority and progress: How important is it? Where is it in the queue?
Associated AI workflows: Have they been defined and mapped for your priority journeys?
Ongoing metrics to measure performance and flag issues (see #5)
3: Journey Ops Mapping
With customer clarity and the portfolio map in place, this is the standing practice: tackling the list of journeys in order of priority, with the right cross-functional team members mapping the customer journey plus the operations underneath. You can deliver a sizable ROI before AI even enters the picture by just cleaning up the friction, waste and workarounds in your current process — which gives you better margin and loyalty-based revenue in one shot.
4: AI workflow mapping and redesign
Workflows are the more granular maps that show the tasks, decisions, handoffs, accountability, etc. and take the journey ops map to a whole new level of detail that's necessary for AI. There are two main types of workflows for AI, and serious players will invest in someone to do this work. The roles are still emerging, but they’ll have titles like AI Business & Process Architect or AI Workflow Architect.
End-to-end, or cross-functional workflows — These span multiple functions/departments like the full onboarding journey that touches sales, product, finance, compliance, and support. You’ll only do a handful of these because they're major initiatives.
Functional workflows — These live mostly inside one department or team. For example, how the finance team processes invoices, or how the support team handles ticket routing. There are enough of these to justify a full-time role in larger companies.
5: Strategic measurement
With the first four capabilities running, you still don’t know if any of it worked unless you’re measuring the right things in an integrated way. A support agent that deflects 80% of tickets looks like a win on a dashboard right up until those same customers call back next week about the same problem, angrier.
Effective measurement clearly links operational wins (leading indicators) to downstream financial performance (lagging indicators). And even more importantly, you’re looking at performance holistically. Costs can be offset by top-line growth. Increased margins can be sabotaged by delayed customer churn.
Bottom line
You can absolutely start small with a single integrated journey / workflow mapping exercise to solve the biggest high-friction areas.
But when you step back to see the bigger strategic picture – your priority customer, value proposition and lifecycle – and prioritise the areas that impact both customer and business value, you’ll see exponentially better ROI over time.
If you’re curious, I’m happy to have a no-strings conversation.
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Source: McKinsey, “The State of AI in 2025: Agents, Innovation, and Transformation” — published November 5, 2025, based on a survey of roughly 2,000 respondents across 105 countries.
