AI READINESS
AI Readiness Framework for SMEs
This is for businesses that want to move beyond experimentation and build AI into how they operate. Experimentation without structure wastes budget. This framework gives you a repeatable approach that connects AI to commercial outcomes from day one.
Stage 1. Commercial Alignment
AI must link to money. Before you touch any tool, define where the return comes from.
Define your commercial targets
- Revenue growth opportunities
- Cost reduction targets
- Risk reduction areas
Map each to a specific use case.
| Area | Use Case | Outcome |
|---|---|---|
| Sales | AI lead scoring | Higher conversion |
| Operations | Automated reporting | Time saved |
| Support | AI chatbot | Faster response |
Stage 2. Data Readiness
Focus on access, not perfection. Perfect data is a myth in most SMEs. Usable data is achievable in weeks.
You need
- Central visibility of key data
- Basic structure
- Ability to extract or export
Prioritise fixing these first:
- Duplicate records
- Missing fields
- Siloed systems that cannot talk to each other
Stage 3. People and Capability
Train for usage, not theory. Most AI training programmes teach concepts. Yours should teach people how to use tools on real work today.
Build these capabilities
- Prompting skills
- Tool selection judgment
- Output validation
Create internal AI champions. Write simple guides. Run training on real use cases, not hypotheticals.
Stage 4. Technology and Integration
Audit what you already have before buying anything new.
- CRM
- Finance platform
- Marketing tools
Add AI where it fits those systems. Avoid tool sprawl. One connected workflow beats five disconnected ones.
Stage 5. Governance and Risk
Set clear rules before staff start experimenting. The cost of a data breach or a poor AI decision far exceeds the cost of writing a one-page policy.
Your governance framework needs
- Data usage rules: what can go into public AI tools
- Approval levels: who can deploy AI in client-facing work
- Audit trails: how decisions are recorded
Define clearly
- What is allowed
- What is restricted
- Who is accountable for outcomes
Stage 6. Execution Model
Run AI like a product pipeline. Each use case is a project. Each project has an owner, a test, and a decision point.
The cycle
- Identify use case
- Build test
- Measure impact
- Scale or stop
Repeat continuously. AI implementation is never finished.
Stage 7. Procurement and Vendor Selection
Do not buy tools blindly. Most vendors will tell you their platform solves everything. Your job is to test that claim before you commit.
- Define the requirement before you look at any vendor
- Compare at least three options
- Avoid long contracts in the first year
- Check data handling, model transparency, and exit terms
Stage 8. ROI and Funding
Track everything from day one. You will need this data to justify scaling and to report to stakeholders.
Track these three metrics
- Time saved per process
- Cost reduced per output
- Revenue generated or protected
Use this data to build the case for the next phase of investment.
Stage 9. 90-Day Implementation Plan
Weeks 1-2: Audit and Select
- Audit AI readiness across all 8 stages
- Select 3 high-value use cases
- Assign owners and define success metrics
Weeks 3-6: Build and Test
- Build minimum viable versions of each use case
- Test with real data
- Document results
Weeks 7-12: Deploy and Measure
- Deploy what works
- Measure against original targets
- Decide what scales and what stops
SME AI Readiness: Advanced Checklist
Commercial Alignment
- AI is linked to a revenue or cost target
- Use cases are prioritised by commercial value
- Each use case has a defined outcome and owner
Data
- Data is accessible without major effort
- Key data quality issues have been identified
- A plan exists to resolve the top three issues
People
- Staff are trained on real use cases, not theory
- Internal AI champions are identified and active
- A simple internal guide exists for AI usage
Technology
- Existing systems have been audited
- Integration or export capability is confirmed
- Tool sprawl is being actively managed
Governance
- An AI usage policy exists and is communicated
- Risks have been assessed and documented
- Accountability for AI decisions is assigned
Execution
- A repeatable test and deploy process exists
- Results are tracked against commercial targets
- A review cadence is in place
Procurement
- Vendors are assessed against defined requirements
- Data handling and exit terms are reviewed
- No unnecessary tools have been purchased
ROI and Funding
- Impact is measured in time, cost, or revenue terms
- Results are used to inform investment decisions
- A business case has been prepared for scaling
Ready to build this properly?
Book a full AI Readiness Assessment. Get a structured roadmap. Implement with board-level support.