Power BI + AI/ML

Reusable analytics delivery IP for bringing predictions and risk scores into your Power BI reporting.

PRE-BUILT AND TAILORED

Predictive insight in Power BI

AI/ML in Power BI

Adapt model-output integration to the agreed data contract and reporting environment.

Reuse scope - analytics integration IP
Configure - model source, measures and refresh
Output - predictions beside business measures

Predictive dashboards

Build a forecast view that gives users the context needed for planning.

Reuse scope - predictive reporting IP
Configure - horizon, actuals and forecast context
Output - actual and forecast comparison

Risk scoring visuals

Build a risk-review view that lets authorised users compare and prioritise cases.

Reuse scope - risk reporting IP
Configure - score context and review criteria
Output - prioritised risk-review view

Scope at assessment

Agree the reusable asset set and supported integration approach. Model development, hosting and licences are not implied by the reporting accelerator.

ARTEFACTS AND ACCEPTANCE

Start with the decision, then the dashboard

Reuse avoids starting each reporting integration from a blank design; the data contract, report configuration and acceptance tests remain client-specific.

01

User and decision brief

Identify report users, their decisions and the actions a forecast or score should support.

Reuse - applicable reporting IP
Accept - user and decision brief

02

Model and data contract

Check output meaning, data access, model limitations and refresh needs.

Tailor - source and measure definitions
Accept - validated model/data contract

03

Configured report

Configure views and test the reporting environment before user review.

Tailor - report, refresh and permissions
Accept - refresh and security test results

04

User acceptance and handover

Validate interpretation with users and assign model/report monitoring owners.

Tailor - training and operating guidance
Accept - user sign-off and handover

Illustrative forecast review

Historical demand and a model forecast → actual and forecast comparison for an agreed horizon → a planner reviews assumptions and model limitations → a stock or capacity decision is recorded. This is an engagement example, not an actual dashboard screenshot or measured client result.

TIME TO VALUE AND FIT

What your Power BI environment needs

First value is a report that users can interpret and accept. Model availability, data contracts, licensing, refresh and access controls determine the delivery plan.

Platform and data

Confirm that the reporting environment can support the intended use and data access.

Agree - Power BI licensing and capacity
Check - data permissions and source access
Define - refresh mode and reporting scope

Model source and hosting

Decide whether the model is already available or needs a separate development scope.

Identify - model owner and output contract
Agree - hosting and integration boundary
Record - limitations and validation evidence

Security and support

Keep access and ownership clear across reports, data and model outputs.

Agree - permissions and row-level security
Test - refresh and authorised access
Name - report, model and support owners

Buying boundary

Accelerator adoption is scoped separately from software licensing. Agree the [engagement model](/company/our-approach#engagement-models), reuse and handover rights, licence dependencies and ongoing support before delivery.

CONNECTED DELIVERY