AI gives organisations new ways to interact with their business information. At KOKAI, we believe that making those interactions useful starts with clear definitions, well-structured semantic models and a practical understanding of what the organisation wants to achieve.
Organisations are exploring how AI can help employees ask questions, analyse performance and work with planning solutions.
For businesses using Microsoft Fabric and Power BI, an important part of that opportunity sits in the data foundation they already have.
The semantic model connects data with business meaning. Its relationships, measures and definitions shape how information is interpreted and used.
When AI becomes another way to work with that information, the clarity of the model matters.
Give AI the business context
A question such as “How are sales developing?” contains several assumptions.
Which sales measure should be used? What is the comparison period? Are returns included? Does the question refer to actuals, budget or forecast?
People familiar with the organisation may understand the intended meaning. AI needs that context to be made explicit.
Preparing a model for AI means examining how clearly the business is represented, then testing whether the selected AI tools interpret it appropriately.
KOKAI’s approach to AI readiness
We help organisations prepare new and existing semantic models for their intended use of AI.
The starting point is a business conversation: what would you like people to be able to do?
From there, we examine the relevant foundation:
Data quality and structure: whether the information is consistent and suitable for the intended task.
Business definitions: whether measures, terminology and calculation rules are clearly described.
Context: how periods, scenarios, assumptions and relationships should be interpreted.
Access and ownership: who can use the information and who is responsible for its definitions.
Validation: how AI responses and proposed changes perform against agreed business requirements.
This makes AI readiness a practical process tied to a defined use case, with continued testing as the organisation and technology evolve.
Supporting users and solution builders
AI can support different parts of the same working environment.
Conversational AI can help users explore business information through questions. AI-assisted development can help specialists design, configure and troubleshoot solutions.
Aimplan AI Assistant illustrates the second opportunity. It uses model structure and Aimplan configuration to propose planning solutions, with review and approval before changes are implemented.
These capabilities make professional judgement central to the process. Requirements must be understood, proposals evaluated and results checked over time.
Connecting the foundation with planning
A well-prepared semantic model also supports opportunities beyond AI.
With KOKAI 4t, organisations that meet the required prerequisites can use their existing Power BI semantic model as the starting point for budgeting and forecasting with Aimplan.
AI readiness and planning readiness have different requirements. Both benefit from clear business logic and a shared understanding of the data.
Start with your next step
Your organisation may already have a semantic model or be preparing to build one. In either case, considering the intended use of AI helps establish what the foundation needs to support.
At KOKAI, we bring together business understanding and expertise in Microsoft Fabric, Power BI and Aimplan to help you assess that next step.
Have you started looking at what your data model needs to support AI and agents, or is the focus still on reporting?
Contact KOKAI to discuss your ambitions and what needs to be in place.
