AI can now handle a huge range of business tasks – you need to decide which ones are worth implementing.
Sitting down with your team or chatting with an LLM will produce a long list of ideas where AI can help. Few AI use cases on that list will deliver enough benefit to justify the effort, cost, and risk of implementation.
You want to be strategic about your AI usage, finding the business problems where you get a meaningful improvement. We’ve identified five stages that get you there:
Problem → Outcome → Suitability → Priority → Pilot
The Five-Step Framework
Step 1: Define the Business Problem
Don’t start by thinking about how you can use AI. Start by looking at your business processes and identifying bottlenecks and problems. Where do the constraints sit? Where are objectives getting thrown off course?
Ask these questions:
- Where are we losing time or money?
- What are the points of friction?
- Where do bottlenecks and delays occur?
- Where does a lack of information hinder decision-making?
- Which business objectives are a struggle?
- Where is more consistency required?
Remember, this isn’t about finding an AI solution – you’re looking for what to improve.
Create a Targeted Problem Statement
Every business is different, so broad prompts get broad answers. The more specific your prompt, the more relevant the response will be to you. For example, “We want to use AI in customer service” can be interpreted in many ways.
A problem statement gets right into the nuts and bolts of the issue:
“Our service team spends too much time finding information across different systems before it can respond to customers.”
That gives you something to investigate, measure and hopefully solve.
Step 2: Define the Desired Outcome
You’ve pinpointed a problem. Now ask yourself what needs to be improved to make a solution worth the effort.
This involves setting a measurable definition of success. The right metric depends on the problem, but could include:
- Time: Can the process be completed faster?
- Cost: Can costs come down?
- Capacity: Can your team handle more work without adding extra resources?
- Quality: Can you improve quality consistency?
- Customer experience: Can you shorten response or resolution times?
- Revenue: Can conversions, retention, or sales be lifted?
Establish Your Baseline
Measure the current state before any AI implementation. Record the hours if you’re trying to reduce time spent on a task, or the cost if that is the chief metric to improve.
Without your baseline, you can’t prove the return on investment, so this is an important step.
Step 3: Decide Whether AI Is the Right Solution
It might be that AI isn’t the right fit. Having identified the problem, you’ll be able to tell if there’s another way to improve it. It could be conventional automation, or a change to the process itself.
If there are easier ways to solve the problem, then it doesn’t qualify as an appropriate AI use case.
Where is AI most Suitable?
AI is favoured where large volumes of information are involved, and language, classification, prediction and pattern recognition are important.
If a task is highly predictable and governed by set rules, traditional automation may be the best fit.
For examples of where AI agents fit well, see our guide to business processes ripe for AI agents.
Our tip: Don’t try to force AI as a solution to every problem. Once you understand where you need to improve, you’ll have a better idea of what can help.
Is the Data Available?
AI needs good data to be effective. Ask yourself these questions about the data that it will be fed:
- Where does the information live?
- Is it accurate and up to date?
- Is it consistently organised?
- Can the AI system be given appropriate access to it?
Data that’s scattered across disconnected systems can bring a promising use case to its knees.
Can AI Work With Your Existing Systems?
Your business will rely on existing systems like a CRM, accounting software, Microsoft 365 and industry-specific applications. Your AI tool may need to connect to them – you need to assess integration requirements, APIs and permissions.
Step 4: Prioritise Your AI Use Cases
You’ve probably landed on a number of problems where AI could be a viable solution. You need to compare them to decide which are the ones worth prioritising.
How Should You Assess an AI Use Case?
Six factors cover most of what matters.
| Factor | What to assess |
| Business value | How significant is the potential benefit? |
| Data readiness | Is reliable, appropriate information available? |
| Technical feasibility | Can the solution realistically work in your technology environment? |
| Implementation effort | How much time, cost and internal resources will it take? |
| Risk | What happens if the AI produces an incorrect result or takes the wrong action? |
| Time to value | How quickly will the business see a measurable benefit? |
No elaborate scoring system is required – keep it practical and simple.
Compare Business Value Against Feasibility
Business value and feasibility are the best metrics to map your use case against.
| High feasibility | Low feasibility | |
| High business value | Prioritise: strong candidates for your first projects | Plan: valuable, but the foundations need work first |
| Low business value | Consider: possible quick wins if implementation is simple | Avoid: limited return for the effort required |
Don’t Choose the Biggest AI Idea
Too often people get blinded by the enormous value on offer with an ambitious AI use case. Very quickly it becomes apparent that the multiple integrations, sensitive data usage and significant organisational change are overwhelming to deal with on a first-time implementation.
It doesn’t make it a bad idea – it’s just a bad first idea. Start smaller, build practical AI experience, and the more ambitious projects can follow.
Risk Should Also be a Factor
AI Cybersecurity, privacy, access controls, regulatory obligations, human oversight and accountability all belong in the conversation. You need to get your AI security right at the initial stages of configuration – not as a bolt-on later.
Step 5: Pilot the Use Case and Measure the Results
Your pilot will prove whether your AI use case works or not. It allows you to test on a controlled scale before you commit to a broader deployment.
What Should an AI Pilot Measure?
Return to the baseline measurements you took in Step 2. Those need to be measured, and evidence gathered on:
- Time saved
- Implementation and operating cost
- Accuracy
- Output quality
- Employee adoption
- Customer impact
- Errors and exceptions
- Security and operational issues
The results will determine exactly how your AI use case is faring. Then you’re faced with three outcomes.
Scale, Improve or Stop
A pilot has three legitimate outcomes.
- Scale it. Implement it more widely.
- Improve it. The potential is there, but the solution, workflow or underlying data needs work.
- Stop it. The benefit doesn’t justify the cost, effort or risk.
Stopping is not failure – it’s far cheaper to discard an AI use case after the pilot than to persist with something that isn’t working.
Smile IT Can Help Identify AI Use Cases
Leveraging AI in your business isn’t about picking a tool and running with it. There’s a lot more to take into account – from existing technology, business systems, data, integrations and security controls to your business objectives.
At Smile IT, our AI consulting team can guide you through it all and help you identify the most worthwhile AI use cases to pursue. They will assess your environment and have a chat about your business goals. Then they’ll guide you through the planning and implementation of a practical path, from idea to pilot and beyond.
Get in touch today. Call us on 1300 766 720.
