Open navigation menu

AI-Powered Reporting

Reports that explain themselves, built on numbers everyone trusts.

Analysts spend much of the reporting cycle collecting, reconciling and formatting numbers, leaving little time to explain them. AI can draft the narrative, answer follow-up questions and spot unusual movements, but only if it sits on governed metrics.

We modernise reporting in that order: trusted metrics first, automation second, AI narrative and natural-language questions third.

The problem

What usually gets in the way

  • Monthly and board reporting depends on manual spreadsheets and copy-paste.
  • Commentary is written late and varies by author.
  • Business users cannot ask a follow-up question without raising a request.
  • AI features in BI tools give wrong answers because definitions are inconsistent.

What we deliver

Capabilities

01

Report rationalisation

Retire duplicate reports and agree the core set that drives decisions.

02

Governed metric layer

Consistent definitions shared by every report and AI feature.

03

Automated production

Scheduled, reconciled and distributed reports without manual steps.

04

AI narrative

Drafted commentary that explains changes, reviewed by the owner before release.

05

Natural-language questions

Ask questions of governed data in English or Arabic, with answers that show the query.

06

Anomaly alerts

Unusual movements flagged with likely drivers.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. ConsumptionExecutive and board packsNatural-language Q&AAlerts
  2. AI servicesNarrative generationText-to-query on the semantic layerAnomaly detection
  3. Semantic layerGoverned metricsRow-level securityDefinitions and owners
  4. DataCurated reporting modelsReconciliation checksRefresh orchestration

How we engage

Start small, prove value, scale

  1. 01Assess

    2–4 weeks

    Inventory reports, effort and metric conflicts.

    • Report inventory
    • Metric conflicts
    • Modernisation plan

  2. 02Pilot

    6–10 weeks

    Automate one critical report with AI narrative and Q&A.

    • Automated report
    • Narrative workflow
    • Effort saved vs. baseline

  3. 03Scale

    Ongoing

    Extend across functions on the shared metric layer.

    • Rationalised portfolio
    • Self-service Q&A
    • Adoption tracking

Outcomes

What we measure

  • Hours spent producing reports
  • Days to close the reporting cycle
  • Number of reports retired
  • Questions answered by self-service
  • Accuracy of AI answers on test questions

Where it applies

Example use cases

  • Banking

    Monthly management pack with drafted variance commentary.

  • Government

    Programme performance reporting for leadership.

  • Retail

    Daily trading report with anomaly alerts.

  • Technology

    Product and revenue metrics with natural-language Q&A.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Board pack template

A reusable structure for metrics, narrative and approvals.

In development

Narrative prompt library

Tested prompts for variance commentary, reviewed for accuracy.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Power BI
  • Tableau
  • Looker
  • Microsoft Fabric
  • Snowflake
  • dbt
  • Azure OpenAI
  • Qlik
See the full technology ecosystem →

Insights

Further reading

From Dashboards to Decisions

Most dashboards fail at the hand-off, not the chart. Run a decision inventory instead of a report catalogue, and make every metric earn its place by triggering a named owner’s action at an agreed threshold.

Read →

From BI to Decision Intelligence

Business intelligence made the report its unit of design. Decision intelligence makes the decision the unit of design. Until that switch happens, more dashboards and AI chat over data will deliver faster answers to questions nobody acts on.

Read →

Measuring the Value of Enterprise AI

Most AI value claims are not wrong so much as unproven. The fix is to design the measurement before the build: a baseline, a credible comparison group and metrics finance has already agreed to accept.

Read →

Talk to us about AI-Powered Reporting

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

Next solution

Knowledge and Retrieval Systems

Make the organisation's knowledge findable, citable and usable by AI.

Continue