AI Readiness
Check
Data & AI Assessment: Evaluate use cases, data readiness and organisational requirements before launching AI projects
The AI Readiness Check helps identify which AI ideas offer genuine business value, whether your data, architecture and business context are ready to support them, and which organisational requirements still need to be addressed.
The result is a documented assessment with prioritised use cases, clearly identified gaps and an actionable roadmap, including a proposal for a pilot project.
Why an AI Assessment
Makes Sense
Almost every organisation has ideas for using AI. But it is often unclear which of these actually require AI and which could be addressed more effectively through traditional Business Intelligence or automation.
At the same time, many pilot projects struggle because of the underlying data rather than the AI model itself. Common obstacles include poor data quality, missing business context and architectures that are not designed to support AI applications.
The AI Readiness Check provides a reliable starting point.
It combines a business perspective with an assessment of your existing data landscape, data quality and semantics. It also considers the organisational requirements around governance, skills and ongoing operations.
The Information Lab brings years of project experience in Business Intelligence, Data Engineering and enablement. As an ISO 27001-certified company, we follow established information security standards, including when reviewing internal data, systems and documentation.
OUR APPROACH
Three Perspectives for a Clear Direction
The AI Readiness Check assesses your organisation from three complementary perspectives. Each component delivers measurable, documented findings that feed directly into your roadmap.
We tailor the areas of emphasis to your organisation’s current situation and requirements.
Business Value & Use Cases
Interviews and facilitated workshops with management, business departments and data teams.
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Use Cases & Prioritisation
Structured identification of opportunities across Business Intelligence, automation and AI.
Deliverables: A use case matrix and 3–5 detailed use case profiles, including baselines, target values, processes, risk assessments, estimated effort, business value and feasibility.
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Decision-Making Processes & Target Audiences
Analysis of existing information flows and how decisions are made in practice.
Deliverables: Defined personas and their access requirements, an assessment of existing dashboard usage, and an evaluation of opportunities for AI agents to reduce manual workload.
Technical Foundation & Data Readiness
Assessment of the existing data landscape and an architecture workshop with IT and engineering teams.
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Architecture & Modernisation
Review of source systems, data pipelines, platforms and semantic layers.
Deliverables: Documented current-state architecture, a future-ready target architecture with technology recommendations, a gap analysis and reliable cost estimates for each scenario.
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Data Quality & Lineage
Assessment of data completeness, consistency, redundancy and timeliness, as well as manual handovers and potential points of failure in existing data flows.
Deliverables: A data quality scorecard with indicators for each data object, a pipeline maturity assessment, the five most significant risk factors and prioritised corrective actions.
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Semantics & Context Provisioning Validation of metric definitions, metadata, business rules and unstructured knowledge to support reliable AI models.
Deliverables: A semantic context inventory, guidance for the business logic layer and a testing framework for AI queries in production.
Governance & Operations
Assessment of organisational structures, policies and operational requirements.
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Governance & Regulatory Compliance
Practical guidelines for access control, information security, data protection (GDPR) and compliance.
Deliverables: Governance-Checkliste mit Ampellogik und Handlungsfeldern für Rechts- und Datenschutzabstimmungen.
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Operations & Costs
Quality measurement, feedback loops for context engineering and ongoing operational costs.
Deliverables: An outline operating model, reliable cost estimates for ongoing operations and an evaluation framework for proofs of concept and pilot projects.
How the AI Readiness Check Works
Price: From €3,990, depending on scope and complexity
Scoping Discussion
Together we define the objectives, current situation, stakeholders and priorities for the assessment.
Workshops & Interviews
Business workshops with management and business departments, alongside an architecture workshop with IT and the data team.
Analysis
Assessment of data quality, context, governance, skills and the operating model.
Findings & Roadmap
Presentation of the maturity assessment, recommendations and roadmap, including a pilot proposal.
DELIVERABLES
What You’ll Receive
You receive actionable deliverables that management, business departments and IT teams can use to plan and implement their next steps.
Tool-Agnostic and Compatible with Your Existing Systems
The assessment starts with your existing technology landscape rather than a particular product. Whether you use Tableau, Power BI, Snowflake, dbt, Alteryx or other systems, we evaluate what is actually required for your prioritised use cases. The target architecture outlines multiple options, including the estimated effort and costs for each.
Who Is the AI Readiness Check For?
- Executive Leadership & Management looking to prioritise AI investments based on business value and feasibility
- Business Departments with specific ideas but uncertainty about their practical implementation
- IT & Data Teams that need a reliable target architecture to support AI initiatives
- Organisations Running Initial AI Pilots that want greater clarity on data quality, governance and costs before scaling up
Request an AI Readiness Check
In a short scoping discussion, we’ll review your current situation, objectives and requirements, and recommend the most suitable scope for your assessment.
Jens Chowdry
Head of Sales
Startseite- & Serviceskontakformular ENG
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Frequently Asked Questions
Do We Already Need Specific AI Ideas for the Assessment?
No. Existing ideas can be included, but the assessment is particularly useful for systematically identifying, evaluating and prioritising potential use cases. It often reveals that some challenges can be addressed more effectively through Business Intelligence or automation than through AI.
Who Needs to Be Involved from Our Organisation?
For the business perspective, we recommend involving representatives from management and relevant business departments. IT and data teams should contribute to the technical assessment. For governance-related topics, it is also useful to involve data protection specialists and, where appropriate, the legal department at an early stage.
How Do You Handle Our Data and Documents?
Agentic Analytics enables business departments to query organisational data using natural language and initiate analyses automatically through a “Chat with Your Data” approach. The Information Lab supports the technical and organisational implementation of these solutions, using technologies such as Tableau Next, Tableau MCP, our own TabCP Insight Agent and Snowflake CoWork. This makes it significantly easier for people across an organisation to access and work with data.
What Happens After the Assessment?
Providing the technology alone is not enough to ensure successful adoption. AI Literacy describes an organisation’s ability to use artificial intelligence critically, confidently and safely. Through practical training sessions and workshops, internal teams develop the skills to interpret AI-generated results, recognise common risks such as bias, and make responsible, data-informed decisions.
