The challenge for most enterprises is not generating AI ideas. It is deciding which ideas deserve investment now, which need preparation, and which should not start.
Begin with business outcomes, not technologies
Frame use cases around measurable business problems: revenue leakage, service time, forecast accuracy, procurement effort, knowledge access or employee productivity. “Use generative AI” is not an outcome.
Score value and feasibility separately
High value does not imply high feasibility. A use case can be strategically attractive but blocked by poor data, integration complexity or process instability. Separating the two dimensions prevents enthusiasm from hiding execution risk.
Add risk and adoption to the score
Enterprise AI introduces considerations such as privacy, explainability, regulatory exposure, security, human oversight and behavioural change. A technically feasible solution may still be a poor first initiative if adoption or governance effort is disproportionate.
Map dependencies
Some use cases create foundations for others. A knowledge-assistant initiative may depend on document quality and access controls. A predictive use case may depend on data standardisation. Sequence the portfolio so enabling work is visible rather than treated as an unexpected delay.
Create three decision buckets
A practical portfolio can be expressed as Start Now, Prepare Next and Do Not Start Yet. The third category is important: it protects capital and management attention until the prerequisites are in place.
Revisit the portfolio
Prioritisation is not a one-time workshop. Data conditions, regulations, model capability and business priorities change. Establish a governance cadence for reviewing the portfolio and moving use cases between buckets as evidence changes.
Frequently asked questions
How many AI use cases should a company start with?
There is no universal number. Start with a manageable portfolio that has clear owners, measurable outcomes and realistic dependencies.
Should quick wins always come first?
Not necessarily. Quick wins are useful, but they should contribute to strategic learning or value rather than becoming disconnected experiments.
Who should own prioritisation?
It should be cross-functional, combining business, technology, data, risk and change perspectives.
Turn the idea into an operating system.
Use SIRTIKA™ to diagnose the gap, build the right architecture and govern adoption.