Fragmented ownership is quietly weakening your digital experience

Our spring 2026 digital leadership study showed that in Finnish consumer companies, digital experience is not automatically a part of the core company strategy. Digital leaders struggle with fragmented or unclear ownership, and development is driven by optimizing existing bits, rather than maximizing customer and commercial outcomes. 

This article dives into why this is so problematic, and the fixes you can initiate today. 

Most digital experience issues arise from how teams collaborate – or fail to do so 

Companies often handle customer journeys across multiple internal teams. In our spring 2026 survey on Finnish digital leaders, 36% said their digital channels were still optimized independently.

The problem is that users don’t mentally separate the app, website, or email. They experience a brand through the customer journey designed to achieve a specific outcome.

This journey often cuts across separate units and several services. If each team only controls its own service and metrics, these seams can be felt by the client. When isolated excellence trumps focus on key business drivers, such as sales, upsell, or customer retention, local optimisation can easily work against the whole. 

A screen can become clearer, but make the journey more confusing. The service team can reduce handling time – by pushing the customer into another channel. While the individual moves make sense, the journey doesn’t. 

Research on journey management and coordination tells us that companies simply do better when they can create consistent value across internal boundaries, monitor journeys as journeys, and coordinate decision-making across functions. Stronger journey-management capability correlates positively with better performance. 

Broader research on coordination across functions points in the same direction: brands perform better when they intentionally align around the market and the customer, rather than leaving this to chance. So – what’s the exact cost of our current reality where this doesn’t happen?

Fragmented digital ownership creates two costs

The first cost is the one visible to the customer, meaning extra effort such as repeated logins or unnecessary questions. The second cost is less visible, but can be even more damaging; it’s slower improvement.

When a broken journey crosses five teams, fixing it becomes negotiation. Whose backlog does the issue enter? Who has the authority to change the rule, the data dependency, or the service process? A small problem becomes a month of internal ping-pong.

The other (poor) option is parallel tracks. If teams can’t easily collaborate, they naturally start developing overlapping solutions to the same problem – which is a huge waste of resources.

In today’s digital landscape, competitive advantage is more and more about learning faster than others. The companies that organize around customer outcomes and prioritize speed will safely build better products, reduce waste and respond faster to the rapidly changing customer behaviour. 

AI will not fix a fragmented operating model

Fragmentation is also where many AI initiatives run into trouble. AI can make parts of your system faster, helping teams reduce work and improve quality. But if the underlying ownership model is disconnected, AI doesn’t magically create end-to-end accountability. 

In some cases, it actually makes fragmentation more efficient. Recent DORA research suggests that AI can improve local measures while simultaneously harming delivery throughput and stability. As one team produces more output faster, downstream teams are faced with more exceptions, more noise or more poorly governed decisions. 

The organisation feels more active, but it’s not necessarily more effective.

The question is not whether AI improves a task – the better question is whether it improves the journey as a whole. Does the customer get to the outcome with less effort, less uncertainty and fewer handoffs? Does the organisation learn faster from what happens? Can teams change the experience safely without waiting for a steering round every time something touches another function?

Without these fundamentals, AI becomes another layer on top of an already complex system.

End-to-end accountability has to be practical

The answer to all of this is not a grand reorganization – which oftentimes seems out of reach. Most companies just need a clearer way to own and improve the core journeys that matter.

End-to-end accountability means that one named person is responsible for the performance of a journey across channels, and this person has to have real operational leverage and a cross-functional team.

The work can’t be scattered across disconnected roadmaps, so the team has to have a shared backlog for the journey. Dependencies have to be explicit, and friction, issues, experiments and decisions reviewed together.

If the journey is broken because of policy, a budget constraint, or a data dependency, the team also needs a route to resolve it quickly.

How and what to measure?

The metrics you use must naturally also change. If every function is measured only on its own local performance, the journey will keep fragmenting. 

A better setup combines one trusted journey metric, one business outcome and one measure of improvement speed. 

For example: can the customer complete the journey without assisted fallback, does the journey convert or retain, and how long does it take to move from an issue to a live customer-visible fix?

The speed metric matters more than many leaders expect, revealing whether the organisation can actually improve, or just discuss improvement. To dive deeper into metrics specifically, read more about getting started and choosing the right metrics on our blog.

Start with one journey that matters

The practical starting point for optimizing around customer outcomes is choosing one high-volume, high-friction journey with clear commercial importance. Don’t pick the easiest journey, or the most politically convenient one – choose one where the seams are painful and the business loss is real.

Name an owner for the journey, define the outcome, and bring all the relevant functions into a single operating rhythm. To make decisions on fixes, testing, or things to escalate, you need to review friction weekly. 

Make your standard clear: where possible, improvement should happen in days, not weeks or months by default.

To clarify, this doesn’t remove complexity. Your legacy systems will still exist, as will risk, budgets and competing priorities. The point is not to pretend these constraints disappear, but to stop letting them hide in the voids.

Finally, when your one journey begins to show measurable improvement, you can start repeating the model. You’ve optimized the cadence, metrics, decision rights and backlog structure, so they become a reference point for your other customer journeys.

That’s how you move digital from a series of disconnected projects to a unified, scalable, commercial capability.

The companies that learn faster will win

While customers pay in effort, the internal cost of fragmentation is slower learning, weaker conversion, and higher service cost. Research on this is clear: if responsibility is diffused and measurement is unclear, improvement slows down fast.

The organisations that improve their digital experiences fastest will be the ones that see the whole journey clearly, give someone real accountability for it, and create a system for making it better.

This work is most likely uncomfortable – but it’s often the difference between a digital experience that keeps accumulating cost and one that keeps accumulating value.

 

Sources

Primary academic backbone: Lemon & Verhoef; Sousa & Voss; Homburg & Tischer; Arkadan, Macdonald & Wilson.
Broader organisational alignment: Kohli & Jaworski or Narver & Slater on market orientation and interfunctional coordination.
Speed and learning: DORA metrics/capabilities plus Kohavi or similar online experimentation research.
Executive/practitioner framing: McKinsey customer journey, governance and operating model articles.
AI: DORA 2024 and 2025.