Developing a strong use case for AI Challenge 2026
This guide will help you create a compelling AI use case for the AI Challenge 2026 and design your own AI Agent by applying pre-built public sector AI components developed by Storm ID within their agentic workflow engine platform. It breaks down five key points to help you shape your idea in line with the 2026 judging criteria - Social Impact; Responsibility, Ethics & Trust; Scalability & Reusability; Innovation & Applicability; Feasibility & Value for Money; and Sustainability.
The AI Challenge has quickly become a practical platform for Scottish public sector organisations to explore how AI can help solve real-world problems. Previous years have shown the strength, ambition and creativity already present across Scotland's public services, from improving access to cultural heritage and parliamentary engagement to supporting complaints insight, tourism planning and service improvement.
This year feels especially important. Across Scotland, the Budget, Spending Review and Public Service Reform agenda are all pointing in the same direction: services need to become more joined-up, more sustainable and more responsive to people's needs. The Public Service Reform White Paper places a clear emphasis on releasing capacity back to the workforce, automating tasks rather than jobs, and freeing skilled staff to focus on the work only they can do. A strong 2026 entry should connect directly to that ambition.
Start with a clear problem
Begin by identifying a genuine challenge or need. For example, you might target reducing backlogs, improving a failing service, removing bottlenecks, or improving the experience for citizens. Think about the areas of your organisation where there is scope for improvement through automation, and what the expected benefits will be, particularly in terms of capacity returned to your workforce.
A strong use case explains how it will make things better - will it save time, cut costs, improve decision-making, or help citizens? Think about how you will measure success (for example, 30% faster processing of applications, or a measurable number of staff hours released back to higher-value work).
Understand the existing process
Once you have a clear understanding of your priority business challenges, hone in on the current process or processes involved to gain a deeper understanding of the existing tasks. This could be done by speaking to stakeholders involved in delivery, identifying quick wins and surfacing the major pain points where time and effort are being lost today.
Match tasks to AI components
Now that you understand the current processes involved in your priority areas, you can choose from a suite of pre-built components to design your AI workflow. Each component represents a specific action or task. You simply determine which pre-built AI component is most relevant to each task you are aiming to automate in your workflow.
The pre-built components to choose from are:
Assess
Examine input to understand its content, context, or quality. E.g. check a form submission contains all information needed.
Validate
Ensure input meets specific criteria or standards.
Compare
Identify similarities or differences between inputs.
Rewrite
Rephrase content while preserving its meaning.
Classify
Assign categories or labels to content.
Prioritise
Rank tasks or items by importance or urgency.
Redact
Remove or mask sensitive or unnecessary information.
Generate
Create new content based on input or prompts.
Evaluate
Measure the quality or accuracy of input or output.
Summarise
Condense content into key points or main ideas.
Analyse
Break down content to uncover patterns or insights.
Transcribe
Convert speech or video into written text.
Convert
Transform data into a different format or style. E.g. video or image to text.
Define the end to end AI workflow
Once you have assigned AI components to each step in the process you want to automate, you can chain them together to define the end-to-end workflow.
For example, if your use case was automating public consultation analysis, then your end-to-end workflow and components might look like this:
Summarise
Extract key themes or sentiments from feedback.
Classify
Categorise responses (e.g., support, oppose, neutral).
Analyse
Identify trends or patterns across the citizen input to inform policy decisions.
Feasability Assessment
Once you have a good understanding of your new target AI workflows, you need to check how feasible it will be to implement in the context of your organisation and align it to AI best practice. Some things you will want to check are:
Data availability and quality are key factors - ask subject matter experts internally whether you have access to the relevant data needed and whether it is of sufficient quality.
Ethics, responsibility and compliance should be considered - are there risks around bias, privacy, or transparency? If your idea involves processing personal data, you need to consider how to protect it and ensure that you comply with the UK GDPR and the Data Protection Act. Your approach should also align with the principles set out in the Scottish Government's AI Playbook and the Scottish AI Register.
Scalability and reuse matter as much as the initial use case - entries that build on reusable components, shared capabilities or repeatable delivery patterns are more likely to deliver wider value across the public sector.
Sustainability and value for money - consider operational sustainability, maintainability, and how your approach aligns with Net Zero and carbon reduction ambitions, alongside a realistic view of cost and deliverability.