From possibility to evidence: what applied AI research is for.

AI creates possibilities. Organisations need evidence about which possibilities deserve their time, budget and trust. Applied AI research connects those two things through a specific problem, a testable hypothesis and a disciplined experiment.

Start with the work

A useful investigation begins with the people doing a task. What information do they use? Where do delays occur? What does a good result look like? Before choosing a model, document the existing workflow and establish a baseline for time, cost and quality.

Turn an opportunity into a hypothesis

“We should use AI” is too broad to test. A more useful hypothesis is: “A source-linked first draft could reduce report preparation time while keeping the rate of material corrections at or below the current level.” This makes the task, expected benefit and quality constraint explicit.

Test against realistic conditions

Use representative examples, including incomplete inputs and difficult cases. Separate examples used to develop the approach from examples used to evaluate it. Record failures as carefully as successes. Measure the complete workflow, including the time people spend checking and correcting outputs.

Make a decision

A research project should support a decision: proceed, revise, or stop. Sometimes the right result is a simpler process without AI. When an approach does work, the next step is a limited implementation with clear ownership, review requirements and monitoring.

This is the approach Bolderank intends to bring to applied AI research: investigate meaningful problems, test assumptions and build on evidence.