How To Solve The AI Use Case Dilemma
There’s no shortage of ambition when it comes to AI. Every organisation wants to use artificial intelligence to improve operations, reduce pressure on staff, and find new efficiencies. Across all sectors, conversations are happening about how AI can power every process, operation and task.
But for all that enthusiasm, many leaders are quietly asking the same question:
Where do we actually start?
It’s a familiar story. Dozens of ideas. Competing priorities. Limited time and budget. Everyone agrees AI is part of the future, but few know which project should come first.
That’s what we call The Use Case Dilemma — the point where AI ambition meets decision paralysis.
In this short blog, we break down how to solve The Use Case Dilemma and identify where should focus all that enthusiasm.
The First Project Matters More Than You Think
For many organisations, the conversation about AI starts with excitement and ends with confusion. Everyone sees potential, but few know what to prioritise.
The truth is, the first AI project sets the tone for everything that follows. If it works, it gives teams the confidence to go further. If it fails, it risks colouring every future conversation with doubt.
The stakes are higher than they seem. That first project is more than technical pilot, we’d even go as far to say it defines your organisation’s AI culture going forward. It defines how your people, teams, and executives feel about AI as a whole.
What a Good Use Case Looks Like
There’s no shortage of good ideas around applications of AI. What’s missing is a framework to turn those ideas into value.
A strong first AI use case has a few simple characteristics:
✅ It’s small enough to deliver quickly but big enough to prove the concept.
✅ It uses accessible, safe data — not sprawling, unstructured datasets.
✅ It solves a human problem that staff actually feel day to day.
✅ It creates measurable benefit whether that’s time saved, or improved experience.
✅ It builds confidence in a project everyone can get behind.
Top Tip: The best early projects are modest in scope but powerful in impact. They prove that AI can work — and that it can be done ethically, responsibly, and with measurable value. Aim to validate feasibility and value in weeks, not months. Proof matters more than perfection.
A Real Example: Saving 2,000 Days a Year
A recent example from University Hospitals Coventry and Warwickshire shows the power of starting small.
The Trust’s HR team was overwhelmed, with 400+ mails and ~200 calls a day, long response times, and limited capacity to focus on complex staff issues.
Working with SCC, they implemented an AI-powered virtual assistant to handle routine HR queries. This resulted in:
• 550 conversations handled in the first 7 weeks
• Projected saving of 2,080 working days a year
• Response times reduced from days to minutes
This is a great example of a modest, targeted project with outsized impact.
And most importantly, one that showed staff what AI can really do when applied practically and ethically.
Read the full case study here: https://www.scc.com/testimonials/university-hospitals-coventry-and-warwickshire-nhs-trust/
Solving the Use Case Dilemma with Structure
To move beyond idea overload, organisations need a repeatable, evidence-based approach to prioritising AI opportunities.
SCC’s methodology helps you identify and focus on the projects that will deliver early value through three key filters:
1. Feasibility
Can it be done safely, responsibly, and efficiently?
This means assessing data readiness, process complexity, and risk tolerance before making any investment.
2. Value
What measurable benefit will it create — for customers, staff, or budgets?
“Value” could mean a number of things but it must be defined upfront.
3. Prioritisation
Once feasibility and value are clear, the next question is when to act.
The right use case is one that can be delivered quickly, with visible results and minimal dependencies.
Together, these filters turn a long list of ideas into a short list of high-impact opportunities.
In other words: from “everything everywhere” to “this, now.”
Avoiding the Common Pitfalls
There’s a reason many AI strategies never leave the page.
- They aim too high, move too fast, or depend on systems that aren’t ready.
- They underestimate data complexity or overestimate staff readiness.
- They forget that technology success depends as much on people and process as it does on the algorithms themselves.
Find out more about these common pitfalls and other costly mistakes in this blog.
From Pilot to Proof of Value
Once the right use case is identified, the next step is to test and prove it quickly.
A well-scoped PoV project:
• Tests the concept safely and at low cost
• Generates measurable evidence for future investment
• Builds staff confidence and technical capability
It’s not about ROI on day one — it’s about proving AI can work here, with our data, for our people.
SCC supports organisations to design, build, and deliver these early PoV projects — ensuring they’re aligned with governance, scalable by design, and built to last.
Start Smart, Scale Fast
Every organisation has a long list of things AI could do. And The Use Case Dilemma isn’t unique to one sector but rather the universal challenge of AI adoption.
But it’s important to not view The Use Case Dilemma as a barrier. It’s your chance to bring clarity to your strategy, take a step back and start small with a focused project that works, proves value, and earns trust.
Next Steps
Identify, assess and prioritise your highest‑value AI opportunities, quickly.
You’ll leave with:
- A prioritised use case shortlist (Feasibility × Value × Priority)
- A PoV candidate with success metrics and a delivery approach
- A practical plan for governance, timelines, and next steps