Business Intelligence and AI: Connecting Dashboards, Data, and Business Knowledge

Business intelligence helps organizations understand performance. Artificial intelligence can extend that capability by making business data and internal information easier to investigate. Together, they offer a practical way to support decisions: consistent reporting for recurring needs, flexible analysis for new questions, and access to the documentation that provides business context.

Paragon Consulting Services develops business intelligence solutions using Tableau and Microsoft Power BI, along with custom AI data assistants and AI knowledge assistants. These services can be implemented separately or combined around an organization’s requirements.

The starting point is the business need. Which decisions require better information? Where does analysis take too long? Which systems and documents contain the information employees need? Answering those questions establishes a useful direction for both BI and AI.

Tableau and Power BI Consulting: Building Useful Business Intelligence

Effective dashboards do more than display figures. They organize information around the decisions people are responsible for making. Executives need a consistent view of performance. Department leaders need to identify changes and exceptions. Analysts need enough detail to investigate the results behind a summary.

Paragon designs dashboards, reports, and visual analytics in both Tableau and Power BI. The choice should reflect your existing environment, reporting requirements, and users.

A BI initiative should establish which measures matter, how they are calculated, and how users will interpret them. Revenue, margin, inventory availability, and delivery performance can all be defined differently across departments. Agreeing on those definitions is essential before presenting them as shared measures of business performance.

Dashboard development also requires attention to the level of detail, reporting periods, refresh requirements, and the ability to move from a summary to supporting records. A useful report should make those boundaries clear so readers understand what the figures represent.

AI Data Assistants: Expanding Access to Business Analysis

A dashboard provides a repeatable view of an established reporting need. New business questions often require a different combination of filters, comparisons, and detail. An AI data assistant provides another way to investigate those questions.

Paragon’s AI data assistants support questions in everyday language and can return written responses, reports, tables, or charts. Users can ask follow-up questions without writing SQL or knowing the database structure.

For an organization evaluating this approach, the central requirement is accurate interpretation of its data. An assistant needs appropriate context for business terminology, relationships between records, date ranges, and calculation rules. A request for sales analysis, for example, must distinguish between orders, shipments, and invoices if those records represent different measures.

Responses should be tested against known results before broader use. Useful validation includes checking calculations, confirming that the correct records were included, and identifying questions that require clarification. Clear presentation of the analysis scope helps users assess whether an answer addresses their actual business need.

AI Knowledge Assistants: Making Internal Information Easier to Find

Business information also exists outside databases. Procedures, policies, contracts, and other documents explain obligations, responsibilities, and established ways of working. Finding the right information can be difficult when it is spread across multiple locations or document versions.

Paragon develops AI knowledge assistants that help users locate and summarize internal content, with references to the source material.

Source references are valuable because a summary should be reviewable. Employees may need to consult the complete procedure, confirm a contract clause, or establish whether a policy applies to a particular situation. Document ownership and version management remain important: an assistant’s usefulness depends on the relevance and currency of the information available to it.

Business Intelligence for IBM i and Mixed Technology Environments

BI and AI services should be evaluated in the context of the systems an organization already uses. Important business records may reside in an ERP application, operational databases, departmental systems, or cloud services. A reporting strategy needs to account for those sources and the relationships between them.

Paragon works across IBM i, Windows, Linux, and cloud environments. IBM i customers can also run the complete AI solution within their IBM i environment.

For companies researching IBM i business intelligence, the planning questions include how source data will be accessed, whether analysis should use live or prepared data, and how reporting activity will affect operational workloads. Similar questions apply to other environments. The objective is to make information accessible while maintaining a dependable foundation for analysis.

How BI Dashboards and AI Assistants Work Together

Business intelligence and AI serve different reporting needs. Dashboards provide consistency for measures reviewed regularly. Conversational analysis helps users explore questions that arise between reporting cycles. Knowledge search provides access to relevant written material.

A useful approach is to identify which questions belong in recurring reports and which require flexible investigation. When an analysis becomes a routine business requirement, it can be formalized into a dashboard or report with an agreed definition, schedule, and audience.

This also creates a disciplined way to expand reporting. Teams can explore a requirement, evaluate the result, and determine whether it warrants ongoing development. The decision to add a dashboard component is then based on an established need.

Planning a Business Intelligence and AI Project

Begin with a defined business objective and an audience. Establish the information required, the systems or documents that contain it, and the criteria for a useful result. A focused initial scope makes it easier to validate the work and assess its value.

• Define the reporting or analysis need and how success will be measured.
• Confirm data definitions, source quality, and document currency.
• Determine who may access each type of information.
• Choose the appropriate dashboard, data assistant, knowledge assistant, or combination.
• Validate results with business users and expand as requirements become clear.

Access controls, data preparation, and validation should be part of project planning rather than deferred until rollout. The appropriate design depends on the sensitivity of the information and the decisions the solution is intended to support.

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