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AI agent for vice president of strategies

Regulatory Horizon Scenario Agent

Turn pending rules into dated, costed scenarios that leadership can plan against.

PlanningDecisionsManagementExecutives and strategy
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Regulatory Horizon Scenario Agent: what goes in, what the agent does and what you get

What it does

Proposed rules are published long before they take effect, but strategy teams read them as news, not as inputs to planning. This agent reads proposed rules and timelines, links them to business lines, and models the impact under a few scenarios, such as the rule passing as drafted or with a delay. As the rule moves through stages, it updates the scenarios and the likelihood. It drafts options for leadership, such as changing product design or timing. The leader approves what goes to leadership. Edge case: a rule that affects a business line only in one region is modeled for that region only.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedYes, continueNoNo 1 STARTS WHEN A proposed rule is published 2 USES A TOOL Read the rule text and timeline 3 DOES Link it to the business lines and regions affected 4 USES A TOOL Pull business line revenue and cost data 5 DOES Model best, expected and worst scenarios 6 CHECKS THE RESULT Are the key assumptions backed by data or documents? If not: Ask the owner for figures and rerun the model.Back to step 4. 7 DOES Draft options for leadership with cost and timing 8 YOU APPROVE Leader approves what goes to leadership 9 USES A TOOL Track the rule through each stage 10 CHECKS THE RESULT Did the rule change stage or content? If not: Update the scenarios and likelihoods and notifythe leader. Back to step 5. 11 RESULT Scenario brief
Read the steps as a list
  1. A proposed rule is published
  2. Read the rule text and timeline
  3. Link it to the business lines and regions affected
  4. Pull business line revenue and cost data
  5. Model best, expected and worst scenarios
  6. Are the key assumptions backed by data or documents?If not: Ask the owner for figures and rerun the model. Back to step 4.
  7. Draft options for leadership with cost and timing
  8. Leader approves what goes to leadershipThe agent waits here for your OK.
  9. Track the rule through each stage
  10. Did the rule change stage or content?If not: Update the scenarios and likelihoods and notify the leader. Back to step 5.
  11. Scenario brief

How it decides

Models best, expected and worst cases using stage and past outcomes, and updates the likelihood each time the rule moves.

  • Model three scenarios for every rule above the exposure limit
  • Update within 5 days of a stage change
  • Likelihood uses stage and past outcomes
  • Regional rules are modeled by region

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Exposure limit for modeling
  • Regions covered
  • Scenario count (default 3)
  • Update speed (default 5 days)
  • Sources

What keeps you in control

It always asks you first

  • The brief to leadership
  • Any legal reading of the rule
  • Any plan change

Hard limits

  • Never give legal opinions
  • Never send the brief without approval

It stops when

  • Done: rule takes effect or is dropped
  • Stop: rule is outside the business
  • Stop: legal takes over

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensA proposed energy labeling rule affects two product lines in the EU. The agent models a 6 million cost in the expected case and 9 million in the worst. The check on assumptions fails because no one has packaging costs. After the data arrives, the expected cost drops to 4.5 million. When the rule moves to committee with a delay, the agent updates the likelihood and the leader sees the change.

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