We’re entering a new era of business operations — one where traditional workflows are being reimagined through the lens of AI agents in strategy. These aren't just tools or chatbots — they are intelligent, semi-autonomous collaborators that can analyze information, make recommendations, and even take action across systems.
In this 10-part series, "AI Agents in Business: The Enterprise Transformation Series," we’re breaking down how every major department — from Strategy to Finance, Marketing to IT — can harness AI agents to improve AI risk management, productivity, decision-making, and speed.
Each part will serve as a practical guide, showing:
The core standard processes in the department
Where inefficiencies exist
How AI agents can augment or automate the work
What tools, prompts, and context are needed
Before/after workflow designs using an agentic operating model
The risks, trade-offs, and implementation tips, including Agent KPIs
In Part 1, we begin with the Strategy function — the brain of the enterprise. It's where long-term goals are set, competitive positions are analyzed, and major bets are made. Yet ironically, strategy work is often the least automated and most slide-driven in modern companies.
This issue explores how AI agents can fundamentally transform the way strategic planning, market research, competitive analysis, and initiative tracking are done — with real use cases, agent recipes, and implementation steps you can use right away.
If you're a strategist, chief of staff, product leader, or founder, this is where to start.
1. Introduction
Strategy is the compass of the company. It defines where to go and why. But in most organizations, strategy work remains overly manual, slide-heavy, and slow to react to change. By the time a strategic plan is finalized, market conditions may have already shifted.
AI agents can radically change this dynamic. These aren't just chatbots — they are autonomous, context-aware assistants that continuously gather data, analyze scenarios, and generate strategic output on command.
This guide explores:
The core processes of the strategy function
How they can be transformed with AI agents
Specific tools, agent designs, and implementation recipes
A build-it-yourself guide for each process
2. Overview of Core Strategic Processes
Start by mapping your current strategic workflows. These are usually tied to planning cycles, research, goal alignment, and initiative tracking.
✅ Practical Tip: Document which of these are done quarterly vs. continuously. This will shape your agent design cadence.
3. Before & After: Strategy with AI Agents
Let’s visualize what’s actually changing when AI enters the strategy room.
4. AI Agent Opportunities & Recipes
You don’t need to “boil the ocean.” Start by embedding one agent per process. Below are low-code or no-code recipes for each strategic function.
1. Planning Co-Pilot
Use Case: Drafts annual/quarterly strategic plans from goal documents
Tool Stack: Notion AI + GPT-4 + Slack
Prompt Template:
“Generate a 2-page strategy draft for [Dept] based on our FY24 goals, key risks, and market outlook. Summarize into exec talking points.”
Practical Setup:
Load company goals into Notion
Connect GPT-4 to pull data from Notion via API or Zapier
Push draft to Slack for review threads
2. Competitive Research Agent
Use Case: Benchmarks competitors’ new moves
Tool Stack: Web scraper (Browse AI) + Feedly + Claude 3
Automation Flow:
Scrape competitor sites/blogs
Feed articles into Claude
Summarize changes, pricing, product launches
Prompt:
“Compare the top 5 pricing and GTM changes from competitors in the last 90 days.”
3. Trend Scanner Bot
Use Case: Weekly report of emerging market signals
Tool Stack: Feedly + Zapier + GPT-4
Delivery: Slack post + PDF
Prompt:
“Summarize 5 new emerging trends in [industry]. Rank them by potential impact and novelty.”
4. OKR Generator Agent
Use Case: Proposes draft OKRs from strategic themes
Tool Stack: GPT-4 + Notion + Google Sheets
Prompt:
“Based on this Q1 strategy document, propose OKRs for Product, Marketing, and Operations. Include target values.”
Optional Add-On: Use Loom to record short executive summaries per department.
5. Scenario Risk Planner
Use Case: Runs simulations on market changes
Tool Stack: Excel + ChatGPT Plugins OR CoPilot for Excel
Prompt:
“If raw material costs increase by 15% and revenue drops by 10%, what’s the impact on EBITDA?”
Bonus: Use GPT to generate “stress-test decks” in slide format.
5. Context Engineering: Making Agents Useful
An AI agent is only as good as the context it’s given. Here’s a quick template for shaping high-quality context that AI agents can reason with:
✅ Practical Tip: Store this context in a Google Doc or Notion page and feed it dynamically into prompts. Create a "Strategy Context Base" for agents to reference.
6. Tools & Stack: What You’ll Need
You don’t need a dev team to get started. Below is a low-lift AI agent stack to test strategy agents.
7. Outcomes & Metrics
Here’s what to track to prove ROI quickly:
✅ Practical Tip: Use GPT to summarize monthly strategy reviews. This reduces prep time and forces clarity.
8. Challenges & Mitigations
No AI implementation is frictionless. Expect early tension and plan accordingly.
9. Final Thoughts & Action Plan
Artificial Intelligence in strategy isn’t about replacing thinkers — it’s about expanding the strategist’s capacity to sense, simulate, and steer. With AI agents, strategic work can move at the speed of business, not the speed of meetings.
Your 5-Step Starter Plan:
Choose one process (e.g., trend monitoring)
Build a lightweight AI agent using GPT-4 + Zapier + Feedly
Define and document key context elements
Share outputs weekly in Slack or email
Iterate based on team feedback
Start small. Think big. Scale fast.
Coming Next:
Part 2 – Finance: Automating Financial Intelligence with AI Agents
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Let’s stack it up.
A. Pawlowski | The Strategy Stack