"How do we get real productivity from AI without losing control of the analysis?"
A governed AI workflow for analytics teams
I designed and piloted an AI-assisted analytics workflow that pairs natural-language prompts with Markdown SOPs and version-controlled Power BI templates. Human-in-the-loop validation is required throughout.
Repetition, fragile templates and knowledge trapped in chat history
Power BI template updates, report customization and recurring data reviews depended on repetitive manual steps. Each edit inside Power BI was slow and hard to govern, and English and French versions were maintained separately.
Troubleshooting lessons and business rules often stayed in individual project histories. The team kept re-explaining the same definitions, and AI prompts written ad hoc produced uneven results. Generated outputs could also look right without having been validated.
Designer of the target workflow
I designed the target workflow, created the natural-language instruction patterns, developed the Markdown SOP structures, supported the move toward version-controlled Power BI project files, and built validation into the automation design from the start.
How it was done
- Reverse-engineer proven work
Start from completed, validated projects and extract the ideal workflow, instead of inventing a new process.
- Generate the SOP before execution
The method is written in plain-language Markdown with clear inputs, checks and deliverables.
- Raise missing inputs up front
Dependencies and open questions go to stakeholders before anything runs.
- Build in prevention, not debug history
Lessons from earlier errors become safeguards in the SOP.
- Version-control the product
VS Code, Git-based practices and Power BI project files replace risky manual edits.
- Validate independently
Outputs are recreated in Excel or Power BI before the generated workflow is trusted more.
The workflow, running
Choose a repetitive task to see what the AI does and what stays with a person.
One model, two languages
A related implementation redesigned an internal Power BI product so that English and French behaviour comes from model-first changes, dynamic measures and language-aware bindings, instead of from duplicate templates. English stays as the baseline.
The work reached an ~85% visual-update checkpoint with six translated source fields. Manual and AI-supported workflows were compared side by side for a team demonstration.
Translated fields live in the model, not in visuals.
Titles and labels switch with a language selection.
Power Query logic protects against missing columns when sources change.
Less duplicate maintenance and fewer drift errors.
What the numbers say
Less time on selected repetitive tasks such as template updates, label maintenance, SOP drafting and data-review outputs.
Owner estimateOf visuals updated at the documented checkpoint of the bilingual redesign, using dynamic language logic.
VerifiedTranslated source fields supporting language switching, with English kept as the baseline.
VerifiedReusable operating model: request → SOP → clarification → execution → validation → documented output.
Owner-confirmedFigures use approved public wording: rounded, generalized or indexed so no client can be identified. Evidence standard
The value created
The result is a reusable model for governed AI assistance. The value is repeatability and controlled adoption, not automation for its own sake. Knowledge now lives in SOPs and prompts that the next analyst can pick up, and validation is a required step.
The 60–80% figure is an owner estimate for specific repetitive tasks, not total project time and not a formal time study. Automated location-level report production and Python-supported data review are still being explored and are not presented as results.
What this means for you
Want AI productivity your auditors and executives can trust?
I can map which of your analytics workflows suit AI assistance, write the SOP and prompt library, build the validation checks, and train your team to run it. Your knowledge stays with you after the engagement ends.
Confidentiality note: this case study is anonymized. The sector label is generalized and there are no client names, proprietary templates or source screenshots. All visuals are rebuilt with synthetic data that keeps the analytical concept but none of the original values. Contribution is described with specific verbs (owned, designed, developed) because this was delivered within a wider team.