AI can be applied to almost any business process: from customer service to procurement, from content production to HR. That makes prioritising difficult. Without clear criteria, you choose the most visible application or the one currently in the media, not necessarily the one that delivers the most for your organisation.

Structured prioritisation looks at three factors: the scale of the problem AI can solve, the technical feasibility of the solution and the strategic relevance to your organisation. The combination of those three determines which applications come to the top of the list.

The first step is mapping the processes in your organisation that are time-consuming, repetitive or error-prone. Ask teams what they spend the most time on and what they would most like to see automated. That produces a long list.

Then assess which of those processes have a clear, repeatable structure. AI works best on tasks with a consistent pattern: processing documents, answering questions, generating text, summarising data. Tasks that require a great deal of exception handling and judgement are harder to automate.

For each potential AI application, estimate the potential impact on two dimensions: time saving and quality improvement. How many hours per week are currently spent on this process? What does an error in this process cost? How much is a ten percent improvement worth?

Be realistic. AI rarely improves everything by one hundred percent and there are implementation costs and learning periods. Estimates of fifty percent time saving sound optimistic but are achievable in certain cases, such as summarising reports or producing first drafts of standard documents.

Not every AI application is equally straightforward to implement. Feasibility depends on the availability of data, the complexity of integration with existing systems and the technical capacity of your organisation.

A simple chatbot for frequently asked questions can be set up quickly. An AI system that supports purchasing decisions based on historical data requires weeks or months of preparation. Prioritise applications that can be implemented relatively quickly while still having significant impact.

A classic prioritisation tool is the impact-feasibility matrix: you plot all potential applications on a diagram with impact on the vertical axis and feasibility on the horizontal axis. Applications in the top right have high impact and are highly feasible. Those are the ones you tackle first.

Applications in the bottom left have low impact and are difficult to implement. Leave those aside. The other two quadrants require a judgement call based on strategic relevance.

A common mistake in AI prioritisation is that it becomes a management exercise without input from the people who work with the processes every day. Employees know exactly where the bottlenecks are, what is repetitive and where errors easily creep in.

Involve them early in the prioritisation phase. Not just for better input, but also to build buy-in for the implementation. AI applications that employees perceive as threatening fail more often.

If you do not have sufficient insight into AI possibilities yourself, it is wise to bring in external expertise. Not to outsource the decision, but to get a realistic picture of what is possible and at what cost.

Mach8 helps organisations conduct these kinds of prioritisation exercises: from process inventory to drawing up a prioritised AI roadmap.

The organisations that get the most out of AI do not start with the most advanced technology. They start with the right questions: where are our bottlenecks, what is feasible and what does it deliver? Those questions lead to the applications that genuinely add value.

Want to know how Mach8 helps your organisation identify the most promising AI applications? Get in touch or view our AI agents service.