Seven digital transformation shifts to address in 2026

Transformation programmes have not stopped. What has changed is what boards will approve, and that shift is sharper in the DACH market than almost anywhere else.

The era of funding a multi-year programme on a narrative is over. The seven shifts below are what replaced it.

1. Strategy is judged by outcomes, not ambition

A transformation strategy used to be a destination — “become a digital business”. That framing survives no serious budget review now, because it cannot be falsified and therefore cannot be finished.

What gets approved instead names a current cost or constraint, a target, and a date: reduce the time to onboard a new client from eleven days to three by Q3.

What this changes in practice: programmes get smaller and more numerous. That is the point. A programme you can finish in two quarters produces evidence that funds the next one.

2. Digitalisation is being reassessed for value, not coverage

The previous decade rewarded breadth — digitise everything, everywhere. The bill arrived as a sprawl of systems each covering one process, none of them talking.

The reassessment underway asks a narrower question: which of these actually changed a cost, a risk or a revenue line? A meaningful share of digitised processes turn out to have added a system without removing any work.

This is uncomfortable but useful, because retiring systems is one of the few moves that reduces cost and complexity at the same time.

3. Transformation is becoming an architecture problem

The most consequential shift. For several years the binding constraint was tooling; now it is structure.

Symptoms that you are here:

• A change that sounds small takes a quarter because four systems have to agree
• Nobody can say confidently which system is authoritative for a given entity
• Integration is handled by connections nobody owns deliberately

None of that is solved by another platform. It is solved by deciding boundaries: what is authoritative for what, how data moves, and who owns each interface. Unglamorous work that determines the pace of everything after it.

4. Data strategy is shifting toward decision enablement

The old data strategy was about collection and storage. The new one is about whether a specific decision can be made better, faster or more consistently.

That reframing kills a lot of projects, which is its value. A dashboard nobody uses to decide anything is a cost, however well built.

The test worth applying to any data initiative: name the decision it improves and the person who makes it. If neither has a name, it is infrastructure looking for a purpose.

5. AI is moving from experiment to production — unevenly

Most organisations have run AI experiments. Far fewer have an AI system in production with an owner, monitoring and a way to switch it off.

The gap between those two states is not model quality. It is the operational work: data pipelines that run without a person, lineage, oversight, and someone accountable when it is wrong. Covered in detail in six AI adoption challenges Swiss companies hit.

Organisations that treat this gap as the project rather than as an afterthought get systems into production. The rest accumulate pilots.

6. Governance is moving to the front of the process

For DACH organisations this shift is not optional and it has dates attached.

The EU AI Act carries obligations for systems placed on the EU market or whose output is used there — which catches Swiss companies serving German and Austrian customers. revDSG/nDSG has applied since September 2023, and GDPR wherever EU personal data is involved.

The change is one of sequence rather than substance. Governance used to be a review at the end. The artefacts it now requires — documented data provenance, a system inventory, defined human oversight — cannot be produced retrospectively, so they have to be designed in.

7. Delivery capacity is being restructured, not just expanded

The final shift is about who does the work. Hiring your way through a capability gap is slow and expensive in Switzerland, and it commits you to a permanent role for a temporary need.

The pattern replacing it separates two things:

• Knowledge of your business — what the data means, which exceptions matter, which decisions count. This stays internal, because it cannot be bought.
• Experience of having done it before — cloud architecture, MLOps, modernization patterns. This is brought in, and it should leave behind a team that can run what was built.

That is the shape of most dedicated team engagements: capacity plus transfer, not a permanent dependency.

What to do with this list

Read it as a diagnostic rather than a plan. Most organisations are already moving on three or four of these and have not started on the rest — and the ones not started are usually 3, 6 and 7, because they produce no visible feature.

Pick the one that is currently costing you the most and make it the next quarter’s work. That is a more useful starting point than a strategy covering all seven.

If you want an outside read on which of the seven is your binding constraint, book a call. No obligation.