Use Cases & AI Capabilities
Choosing where to start – and what AI must actually be able to do
Why this cluster matters
Many AI initiatives fail not because they lack ambition –
but because they start in the wrong place.
Common failure patterns:
- Teams pick use cases that are too broad or too abstract
- Leaders choose “cool” AI demos instead of valuable workflows
- Capabilities are described in vague terms like “intelligence”
- Pilots try to do too much, too soon
The result is predictable:
AI looks impressive, but nothing changes in day-to-day work.
This cluster exists to force two critical disciplines:
- Choosing the right places to start
- Being precise about what AI must actually do
Without these, strategy remains theoretical and architecture becomes guesswork.
What this cluster covers – and what it doesn’t
Covered in this cluster
This cluster focuses on two tightly connected elements of the AI Implementation Canvas:
- Use Cases – where AI can deliver value first
- AI Capabilities Needed – the atomic skills the system must demonstrate
Together, they answer the question:
What should this AI system do first – and what must it be capable of to do it reliably?
Explicitly not covered here
This cluster does not cover:
- Strategic goals or KPIs
- Data sourcing or architecture design
- Governance, risk, or pilots
Those are addressed in other clusters.
Here, the focus is scope and feasibility.
The core thinking model
Use cases are not features
A use case is not:
- “AI summarization”
- “Chatbot”
- “AI insights”
Those are features or outputs.
A use case is an archetype of work – a recurring pattern where AI can reliably improve outcomes.
Good use cases:
- Are information-heavy
- Are repetitive or structured
- Have clear inputs and outputs
- Can be measured
Examples of real use cases:
- Knowledge retrieval across internal documents
- Classification and routing of incoming requests
- Structured extraction from unstructured text
- Drafting standardized documents for review
If the work pattern is unclear, the use case is not ready.
Capabilities are not models
One of the most common mistakes in AI planning is confusing models with capabilities.
Saying:
- “We’ll use GPT-4”
- “We need a more advanced LLM”
…says nothing about what the system can actually do.
Capabilities are atomic skills such as:
- Extraction
- Classification
- Reasoning
- Planning
- Evaluation
- Orchestration
Capabilities define limits.
They determine what the system can and cannot be trusted to handle.
Use cases: where to start
The principle of low-hanging fruit
Early AI use cases should:
- Be feasible within weeks, not months
- Touch real workflows
- Be visible enough to build trust
- Fail safely if something goes wrong
This is why strong starting points tend to be universal and industry-agnostic.
Common first-wave use cases include:
- Knowledge access & retrieval
- Summarization of long materials
- Document drafting and templating
- Data extraction and structuring
- Classification and routing of inputs
These are not “small” problems – they are high-volume bottlenecks.
The danger of starting too big
When teams start with:
- End-to-end automation
- Autonomous decision-making
- Cross-department AI systems
They often:
- Overestimate AI capability
- Underestimate integration complexity
- Create risk before trust exists
A good use case is not the most ambitious one –
it is the one that proves value fast and credibly.
AI capabilities: defining what the system must do
Why capability clarity matters
Every use case implicitly assumes certain capabilities.
For example:
- Document review requires extraction + evaluation
- Ticket triage requires classification + routing
- Planning workflows requires reasoning + sequencing
If capabilities are not made explicit:
- Architecture decisions become arbitrary
- Failures are blamed on “the model”
- Scope creep becomes inevitable
Capabilities turn use cases into engineering constraints.
Atomic capability thinking
Effective AI systems are built from small, well-understood skills.
Typical core capabilities include:
- Information extraction
- Classification & routing
- Data structuring
- Analysis & interpretation
- Reasoning & deduction
- Planning & sequencing
- Evaluation & validation
- Orchestration & tool use
The key discipline is not listing everything –
it is selecting only what is essential for the chosen use cases.
Key decisions this cluster forces
By the end of this cluster, teams must be able to answer:
- Which specific workflows will AI touch first?
- Why these use cases – and not others?
- What inputs and outputs define success?
- Which AI capabilities are strictly required?
- Where is precision mandatory vs. “good enough” acceptable?
If these decisions are fuzzy, pilots will expand uncontrollably.
How this cluster connects to the rest of the canvas
This cluster translates strategy into scope.
- Strategy defines why
- Use cases define where
- Capabilities define what must work
From here:
- Architecture & Data are designed to support required capabilities
- Pilots are scoped around a small number of use cases
- KPIs are attached to concrete workflows, not abstract AI performance
Without this cluster, AI initiatives either overreach – or stall.
Where to go next
Once use cases and capabilities are clear, the next question is inevitable:
What kind of system do we need to make this work reliably?
That question is addressed in the next cluster:
Architecture & Data Foundations
→ Explore how AI systems are structured, grounded, and scaled
Final note
Strong AI initiatives do not start with technology.
They start with choosing the right work – and being honest about what AI can actually do.
Precision here saves months later.
