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Corporate Training · New for 2026

AI-Enabled Commercial Analytics & Automation: Build It Yourself, with AI

Your analysts do not need another BI license. They need to build: pricing analytics, commercial insights, and the automations that eliminate the manual work between them. This program teaches your team to do exactly that with Claude and OpenAI, using the same AI tools and workflows Revology runs its own practice on.

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What Your Team Leaves With

Working AI workflows

Recurring analyses that run in minutes, not days

A versioned pricing analytics build

Waterfall, margin bridge, and scenario builds that rerun on new data

A company second brain

An AI-readable knowledge base your future workflows draw on

A promo ROI starter engine

Baseline, lift, mechanic-level ROI, and a reallocation view

Why This Program Exists

Mid-market analytics teams are drowning in requests they answer by hand: pricing reports assembled in Excel, promo recaps rebuilt every Monday, customer analyses that take three weeks because the data lives in five systems.

 

Frontier AI models changed what a small commercial team can build for itself, but only for teams taught to use them as builders rather than chatbot users. Over three days, your team learns to stand up AI-assisted analytics, automate recurring commercial workflows, and build the company “second brain” that makes every future analysis faster than the last.

Who This Is For

Commercial teams who want to build, not buy

Pricing, RGM, finance, marketing, and commercial analytics teams (analyst to director), plus the data and BI professionals who support them. No data science degree required; participants should be comfortable in Excel and curious about code. Teams leave with working assets they keep using.

Pricing, RGM & Finance

Analysts and managers who own pricing decisions and margin reporting, and who rebuild the same analyses every cycle.

Marketing & Commercial Analytics

Teams accountable for promo recaps, campaign readouts, and customer analyses that currently take weeks per request.

Data & BI Professionals

The people who support the commercial teams, and who want AI to multiply their output instead of adding to their queue.

Learning Outcomes

What your team will build

Working AI workflows

Recurring commercial analyses (pricing performance, promo recaps, margin bridges) that run in minutes instead of days.

A trade promo effectiveness starter engine

Baseline and lift decomposition, mechanic-level ROI after funding, and a reallocation view your team keeps extending after the cohort.

A trade promo effectiveness starter engine

Baseline and lift decomposition, mechanic-level ROI after funding, and a reallocation view your team keeps extending after the cohort.

A company second brain

A structured, AI-readable knowledge base of your pricing decisions, definitions, playbooks, and institutional context that every future AI workflow draws on, so the intelligence compounds instead of evaporating with turnover.

Quality gates and governance guardrails

Automated checks on every build, adversarial AI review, reconciliation tie-outs against source systems, what to automate versus what a human signs off, and how to handle data confidentiality.

 

Program Modules

Three days, from raw data to a shipped build

One discipline throughout: reproducible builds, quality gates, and a knowledge base that compounds. Cohorts run in person or online; hands-on sessions use your data under mutual NDA where feasible.

Day 1

AI Foundations & the Pricing Data Backbone

  • How an AI-forward pricing team operates: builds, gates, and a shared knowledge base
  • Directing AI for analytical work: prompting, context, verification habits
  • Where AI fails and how to catch it
  • The data foundation: source-of-record rules, column dictionaries, reconciliation gates
  • Lab: raw sales and promo extracts to a validated pricing dataset

Day 2

Building Pricing & RGM Engines with AI

  • The versioned pipeline pattern: inputs change, the build reruns, nothing is hand-edited
  • Pricing builds: waterfall and margin-bridge views, price-performance workbooks, tier logic
  • Elasticity and promo-lift modeling feeding scenario models business users run
  • Trade promo effectiveness and optimization: baseline, lift, mechanic-level ROI, reallocation
  • Lab: ship a working pricing or promo ROI build from your own data, tests first

Day 3

Quality Gates, Automation & the Second Brain

  • The quality gate: test suites on every build, adversarial AI review, human sign-off
  • The meeting-to-model workflow: calls and emails become encoded, traceable decisions
  • The second brain: context files, structured handoffs, a start-and-close session ritual
  • A methodology library that turns one-off analyses into reusable internal IP
  • Governance and confidentiality, configured for your data policies
  • Capstone: one working build with its quality gate, plus a 90-day roadmap

Taught the Way We Work

Not a vendor demo. Not a prompt-engineering seminar.

Revology runs its own research, analytics, deliverable production, and knowledge management on these exact workflows, and the instructors are the partners who built them. You learn the operating model of an AI-forward analytics firm, adapted to your stack, your data policies, and your team.

Partner-led instruction
Taught by the senior practitioners who run Revology’s engagements
Built on your stack
Adapted to the AI tools your company has approved, your data policies, and your workflows
Working assets, not notes
Every team leaves with a shipped automation and a 90-day build roadmap

Frequently Asked Questions

Program details, answered

Do participants need programming experience?

No. AI-assisted building is the point: participants who have never written Python leave with working analyses because the AI writes the first draft of the code and they learn to direct, verify, and improve it. Experienced analysts go deeper, faster.

Which AI tools does the program cover?

Claude and OpenAI models, plus the workflow patterns that generalize across vendors: structured prompting, reusable skills and instructions, retrieval from your knowledge base, and agentic automation. We adapt to the tools your company has approved.

How is company data handled during training?

Hands-on sessions run on your data under mutual NDA where feasible, or on realistic synthetic datasets where not. A governance module covers confidentiality, tool configuration, and review policies your team takes home.

How does this program relate to Revology's advisory work?

It is the Adoption & Execution layer of our end-to-end practice, available standalone. Teams that have been through a Revology engagement use it to extend what was built; teams that have not use it to build their first AI-enabled analytics capabilities in-house.

Ready to build in-house?

Tell us about your team and stack; we’ll shape the cohort around both.

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