What is AI-Powered Business Intelligence?
AI-Powered Business Intelligence combines governed enterprise data, trusted KPI and semantic definitions, business intelligence, analytical methods and selected AI capabilities to help users move from data to insight, explanation and action. AI may support natural-language questions, summarisation, anomaly detection, forecasting or analytical assistance, but the solution still depends on sound data models, controls and decision context.
How is AI-powered BI different from a normal dashboard programme?
A dashboard programme can focus mainly on visual reporting. AI-powered BI is broader: it defines the decisions and KPIs that matter, creates governed semantic meaning, integrates source data, enables role-based exploration, introduces AI only where useful, connects insight to actions and establishes monitoring, ownership and change controls for ongoing operation.
Can the solution work without generative AI?
Yes. Many organisations can create substantial value through trusted data, semantic models, governed metrics, dashboards, alerts, statistical analysis and forecasting without generative AI. Natural-language or generative features should be added only where the use case, data readiness, platform capability, risk and operating controls justify them.
What data is normally required?
The required data depends on the decisions in scope. Common sources include finance and ERP data, CRM and customer interactions, sales and ecommerce data, operations and supply-chain records, service data, workforce information, product data, targets, budgets, reference data and selected external data. Data quality, history, granularity, latency, ownership and access requirements are assessed during discovery.
Do we need to replace our existing BI platform?
Not necessarily. DataConsultant can assess the current environment first. Existing data warehouses, lakehouses, semantic models and BI tools may be retained, rationalised, integrated or extended when they can support the required decisions, controls, performance and user experience. Platform change is recommended only when the requirement supports it.
How do you make KPIs consistent across teams?
The work can define metric owners, business definitions, calculation logic, dimensions, filters, source mappings, quality rules, approval points and change processes. These definitions are then implemented in an appropriate semantic or metrics layer so reports and AI-assisted analytical experiences use the same governed meaning wherever practical.
How are AI-generated analytical answers controlled?
Controls can include approved semantic models, scoped data access, tested priority questions, clear instructions, grounding in governed business definitions, output review, usage boundaries, logging, evaluation, escalation and human review for consequential decisions. The exact control set depends on the AI capability, platform and risk profile.
Can AI-powered BI support forecasting and anomaly detection?
Yes, when the business question and data support those methods. Forecasting can help estimate future measures and anomaly detection can highlight unusual movements, but both require suitable history, evaluation, thresholds or review logic, monitoring and clear communication of limitations. They are not included automatically in every implementation.
How do you address security, privacy and access?
The design can incorporate data classification, least-privilege access, role or attribute-based controls, row or object-level restrictions where supported, environment separation, sensitive-data handling, auditability, retention, secure integration and review of AI-specific data exposure. Final controls are aligned to the client environment and applicable requirements.
What deliverables can we expect?
Depending on scope, deliverables can include a decision and KPI catalogue, current-state assessment, target architecture, data-source mapping, semantic-model design, data transformations, dashboards, analytical logic, AI evaluation assets, access and governance controls, test evidence, operating procedures, monitoring measures, training materials and an implementation backlog.
How long does an AI-powered BI implementation take?
A reliable duration is confirmed during scoping. Timing depends on the number of decisions and users, data-source readiness, KPI complexity, historical data, semantic modelling, platform landscape, integration effort, security and governance reviews, AI evaluation, testing, rollout scope and adoption requirements.
How is AI-powered BI pricing calculated?
DataConsultant does not publish a fixed price for this solution. Consulting and implementation fees are scope-led and confirmed through a Request a Quote process. Cost drivers can include the number of use cases, data sources, reports, semantic models, integrations, user groups, AI features, testing depth, governance requirements, rollout scope and ongoing support. Third-party platform or cloud charges are treated separately unless explicitly included in the agreed scope.
Can DataConsultant support an MVP or pilot before enterprise rollout?
Yes, where a bounded decision area can provide meaningful evidence. A pilot should still define production-relevant data, KPI meaning, security, quality, evaluation and ownership criteria so success is measured against the future operating model rather than only a demonstration.
Can DataConsultant support the solution after go-live?
Ongoing support can be scoped for data and BI operations, monitoring, incident handling, quality improvement, semantic-model changes, report lifecycle management, adoption, AI evaluation, cost optimisation, enhancement prioritisation and knowledge transfer. Service levels and responsibilities are agreed separately.
