Digital transformation roadmap: where should you start?

The management of a company is complaining about delayed purchasing approvals and is thinking of buying an approval software. When the process map is drawn, this is what appears: requests are not waiting at every step, they are waiting at a single desk. Because no amount limit is defined, every request goes to the general manager. The solution is not new software; it is defining an approval rule based on amount. Software only makes sense once this rule is clear, as a tool that applies it.

A digital transformation roadmap is a workable plan that analyses a business’s current processes, data and systems and decides which improvement is made in which order. Its starting point is to clarify the process before the technology, and the need before the investment.

A roadmap in six steps

A transformation roadmap usually consists of the following six topics; YazSis Digital Transformation consulting covers these topics too. For each one, we have also added the concrete output to expect:

1. Process discovery. The current flow, the actors, the data used, the decisions and the exceptions are made visible. The difference between the flow on paper and the flow that actually runs also comes out at this stage. Output: a one-page map of today’s process.
2. Bottleneck analysis. Points with waiting, repeated work, errors and dependence on individuals are identified. Output: a list showing where the problems pile up.
3. Integration roadmap. The order and manner in which systems will be connected is planned. Output: a plan showing which data flows from where to where.
4. Automation roadmap. Work that can be bound to rules is prioritised. Output: an ordered list of the steps to be automated.
5. AI opportunity map. Practical areas where AI can really create business value are selected. Output: a short, reasoned list of opportunities.
6. Data and reporting architecture. A reliable data source, permissions and a management view are designed. The goal is not to increase the number of reports but to make management look at the same figure from the same source. Output: a structure defining who sees which data in which report.

The quality of the first two steps determines everything that follows. In process discovery, you need to talk not only to managers but to the people who do the work every day, because the process on paper and the process that actually runs are often not the same. Exceptions, workarounds and habits described as “everyone does it this way” hold the real cause of the bottleneck. Any planning done without collecting this information rests on assumptions.

What should come first?

Not every bottleneck has the same priority. When ranking them, four criteria are assessed together:

• Business impact: once it is solved, what changes in terms of time, errors or customer experience?
• Risk: what happens if it is not changed, or is changed wrongly?
• Feasibility: are the required data, user habits and technical groundwork ready?
• Dependency: is this step a prerequisite for another? For example, reporting or AI work does not give meaningful results before the data source is cleaned up.

This assessment usually produces two groups: steps that bring quick benefit with little effort, and bigger steps that need longer preparation. Quick wins build the team’s confidence and habits before moving on to the bigger steps. For example, putting a single approval flow under a rule meets far less resistance than changing the process structure of the whole company, and the result is visible immediately.

Sometimes the solution is not new software, but the right process design.

Where do automation and AI come in?

Automation is useful for work that can be bound to rules: the trigger is clear, the decision rule can be written and exceptions are few. AI makes sense where a rule alone is not enough, such as reading documents, searching for information or spotting patterns. Not every step needs AI; often, simplifying the process and putting the data in order first makes the later AI work more efficient too. That is why the opportunity map is one of the later steps of the roadmap, not the first. The same question applies to AI: at which decision point, with which data and under which human control will it work? If there is no answer to these three questions, the opportunity is not ready yet.

Common mistakes in transformation

• Starting with the tool. If software is chosen before it is clear which problem is being solved, the new tool carries the old problem with it.
• Transforming everything at once. Changing several processes at the same time tires users and makes it harder to understand what works.
• Ignoring the data. A report or AI project built on data whose source is not reliable produces wrong decisions.
• Leaving users out of the process. A flow designed without feedback from the person working in the field goes back to the spreadsheet after a while.

What outputs should a roadmap deliver?

• A visual map of today’s process and data flow.
• A reasoned, ranked list of bottlenecks.
• An order of implementation with dependencies shown and quick wins separated out.
• Steps with a clear scope for integration and automation.
• Measurement indicators to track the result of each step.

You can read about the work that runs from process discovery to data and reporting architecture on the Digital Transformation page, and we can put together a workable roadmap for your business together.