AI Readiness Assessment
What we do
We provide a rigorous assessment of your organisation's readiness for AI and machine learning adoption - evaluating data quality, governance, infrastructure maturity, team capabilities, and the realistic feasibility of proposed use cases. The output is a prioritised roadmap of where AI will deliver measurable returns for your specific business, where the prerequisites are not yet met, and what concrete steps will close the gap.
Data Quality & Governance Assessment
Systematic evaluation of your data assets against the specific requirements of target AI applications. We assess completeness, accuracy, timeliness, consistency, and labelling quality across your data sources, identifying gaps that must be closed before ML models can deliver reliable results.
Use Case Prioritisation & Feasibility
Objective ranking of AI opportunities using a multi-criteria framework that balances business impact, technical feasibility, data readiness, and implementation effort. We distinguish between opportunities that need ML from those better served by rules-based automation or statistical analysis.
Build vs. Buy Analysis
Honest evaluation of when custom ML development delivers superior results versus adopting existing AI services from AWS, Google, Azure, or specialised vendors. We factor in total cost of ownership, maintenance burden, differentiation value, and data privacy requirements to make recommendations your CFO and CTO can both support.
Organisational Readiness Review
Assessment of your team's ML engineering capabilities, data literacy across departments, and the process changes required to operationalise AI. We identify skills gaps, recommend hiring profiles or training programmes, and define the governance structures needed to deploy AI responsibly.
How we work together
Stakeholder & Objective Mapping
We interview leadership, domain experts, data teams, and end users to understand business objectives, existing pain points, and the specific outcomes you expect AI to deliver. We map these objectives against realistic technical capabilities to filter aspirational ideas from achievable initiatives.
Data & Infrastructure Audit
Systematic assessment of data quality, availability, labelling, governance, and the technical infrastructure required to support ML workloads. We evaluate your data warehouse maturity, feature store readiness, compute provisioning, and MLOps capabilities against the requirements of each proposed use case.
Use Case Scoring & Prioritisation
Each potential AI use case is evaluated against a structured scoring framework covering technical feasibility, data readiness, expected business impact, implementation complexity, and organisational risk. We identify quick wins that build confidence alongside strategic investments that create lasting competitive advantage.
Roadmap & Recommendations
A prioritised implementation plan with clear milestones, resource requirements, technology recommendations, and expected outcomes for each initiative. We include honest assessments of where build versus buy makes sense, estimated timelines for each phase, and the organisational changes needed to support AI adoption.