AIAI readiness assessment: a checklist before you build
An AI readiness checklist for product teams: seven areas to check, how to score a first use case on value and feasibility, and the red flags that mean not yet.
Bilal Khursheed9 min read
AI service
AI strategy that starts with your product, not the technology
Find where AI pays for itself in your product, and what to leave alone.
The problem
Where teams get stuck.
Every team is under pressure to add AI. Few are sure where it would help, what it would cost to run, or how to tell if it is working.
Starting from the technology leads to features nobody asked for. Starting from a real task, with real data, shows quickly what is worth building.
Some problems are better solved without a model at all. You need advice from engineers who will tell you that.
Our approach
What makes this work.
- 01
Understand the work
We look at your product, your users and the tasks that take the most time or cause the most errors.
- 02
Shortlist the use cases
Each candidate is assessed for value, feasibility, data readiness and risk. We say clearly which ones to drop.
- 03
Test on your data
For the strongest candidates we run a small feasibility test on real examples, so the plan rests on evidence.
- 04
Write the plan
You get a prioritised roadmap with architecture, running-cost drivers and a definition of good enough for each feature.
Delivery process
How we deliver it, step by step.
Every engagement follows the same seven stages, from requirements to post-launch support.
- 01
Discovery & requirements
Workshops with your stakeholders to define goals, users, scope and constraints, captured in clear written requirements.
- Goals and success measures
- Requirements and user stories
- Scope, estimate and roadmap
- 02
UX & UI design
User flows, wireframes and interface design, reviewed with you until the product feels right before it is built.
- User flows and wireframes
- Interface design
- Clickable prototype
- 03
Architecture & planning
Data model, system design, integrations and security are settled, and the work is broken into sprints.
- Architecture and data model
- Technical decisions on record
- Sprint plan
- 04
Development
Short iterations with code review on every change and a demo of working software each week.
- Weekly demo
- Shared board and written updates
- Reviewed, version-controlled code
- 05
QA & testing
Manual and automated testing across functionality, devices, APIs and performance, followed by user acceptance testing.
- Test plan and test cases
- Automated regression suite
- UAT support and sign-off
- 06
Deployment & launch
Release through a CI/CD pipeline onto monitored cloud infrastructure, with a go-live checklist and rollback plan.
- CI/CD pipeline
- Monitoring and alerting
- Go-live checklist
- 07
Hypercare & support
Close monitoring and priority fixes straight after launch, then ongoing support, maintenance and improvement.
- Hypercare period
- Support under agreed response times
- Improvement backlog
Deliverables
What you receive.
- A review of your product, data and current workflows
- A ranked list of AI use cases with reasons for and against
- Feasibility findings from tests on your own data
- Recommended architecture and model choices
- Running-cost drivers and how to control them
- A risk and data-handling assessment
- A phased roadmap your team or ours can build from
Technology
What we build it with.
- OpenAI
- Anthropic
- Google Gemini
- Open-source models
- Python
- PostgreSQL
Related work
Where we have done this before.
AI / LegalTech
Client name withheld
Contract analysis with a multi-agent RAG pipeline
Turns 500 to 1,000 page legal contracts into a queryable knowledge base that answers with cited sources.
- Python
- FastAPI
- OpenAI
- Gemini
AI business tooling
Client name withheld
An AI business consultancy chatbot for multi-department strategy
A conversational AI advisor that asks targeted questions and returns recommendations across marketing, HR, finance, operations, strategy and IT.
- OpenAI
- Node.js
- Next.js
From the blog
Related guides.
What we have written about ai strategy & consulting: cost, timelines and the trade-offs behind each decision.
- AI
FAQ
AI Strategy & Consulting: common questions.
Do we need a strategy before building an AI feature?
Not always. If the use case is clear and small, we would rather build a prototype. Strategy work pays off when there are several options, the data is messy, or the feature carries real risk.
Will you tell us if AI is the wrong answer?
Yes. Some tasks are better served by search, rules or a simpler workflow. Saying so early is part of the job.
Should we fine-tune a model, use RAG or stick to prompts?
Start with good prompts. Add retrieval when the model needs your facts. Consider fine-tuning only when tone or format still falls short. Most products never need the third step.
Which model provider should we choose?
We choose per task and design the system so the model can be swapped. Tying a product to one provider is the mistake to avoid.
Do we have to build with you afterwards?
No. The roadmap is written so your own engineers can build from it. Many clients do ask our AI and engineering teams to carry on.
Discuss your AI Strategy & Consulting project with us.
Share a short brief and we will reply with next steps, usually within one working day.