AI agents for strategy are software that do the recurring research work of a Chief Strategy Officer (CSO): watching competitors and markets, running scenario models, and tracking strategic initiatives against their milestones. In a 3-person strategy team, giving back 8 hours per person per week is worth about $62,000 a year at federal wage rates.
You keep every call. The agents gather the evidence, cite it and flag what changed since last week.
This page covers agents inside the strategy function at a $5M to $50M company. Where the company puts its AI budget is a CEO decision, covered in AI for the Chief Executive Officer.
AI agents can watch named competitors and markets, rerun scenario models when an assumption moves, and check strategic initiatives against their milestones, then deliver each result as a short document with sources. For a CSO at a $5M to $50M company, that covers the 3 recurring jobs that eat most of a small strategy team’s week.
An agent has 3 parts. A model (Claude or Microsoft Copilot, whichever your team already licenses), tools that connect it to your systems, and written instructions that say what to read, what to produce and when.
The tools are usually plain connections. Zapier, Make.com or a short Python script that reads HubSpot, a Google Drive folder and a list of public web pages, then posts the result to Slack.
What separates an agent from a chat window is the trigger. A chat assistant waits for a question, while an agent runs on a schedule or an event, fetches what it needs and hands you a finished document.
The pressure on CSOs is a bandwidth problem. Deloitte’s 2026 Chief Strategy Officer survey found 95% of CSOs expect intensified competition and AI-driven disruption to shape their priorities, more than half report too many priorities with too little time, and about half have 5 or fewer direct reports.
Their teams feel it too. Microsoft’s 2025 Work Trend Index, a survey of 31,000 workers, found 80% of employees globally say they lack the time or energy to do their work, and 82% of leaders expect to use agents as digital teammates within 12 to 18 months.
Small teams with big mandates are where agents earn their keep. Competitive intelligence teams already lean in: Crayon’s 2025 State of Competitive Intelligence report found AI adoption up 76% year over year, with 60% of teams using AI daily, mostly for summarizing content and analyzing large data sets.
Company size still matters. McKinsey’s 2026 State of AI survey found 40% of organizations above $1 billion in revenue scaling agents in at least one function, against 22% of smaller organizations, a figure that stayed flat from the year before.
So a $20M company is probably early. That’s fine, because the 3 jobs on this page can each run on one read-only agent.
A competitive intelligence agent checks a fixed list of sources on a schedule (competitor websites, pricing pages, job posts, press releases and public reviews) and writes a weekly brief of what changed, with a link to each source. The list is yours, so the brief stays short enough to read in 10 minutes.
I set up the signal list in 4 types:
The brief has a fixed shape. One line per change, the source link, the date found, and a “so what” sentence the analyst signs off on.
That last line belongs to a person. The agent can draft it, and your analyst checks it against the source and decides whether it matters.
Market research questions work the same way, with a research agent in place of a monitor. You ask a question (“How many distributors in our region carry a competing product line?”), and the agent searches, reads, and returns a draft answer with sources and a list of what it couldn’t find.
Multi-agent setups do this better than one agent working alone. Anthropic’s 2025 engineering report on its research system found a lead agent directing parallel subagents scored 90.2% higher than a single agent on its internal research evaluation.
The tradeoff is cost. The same report found agents use about 4 times the tokens of a chat session, and multi-agent systems about 15 times.
I cap a monitoring agent at 5 named competitors and the 4 signal types above, and I add a sixth competitor only when one of the 5 drops off. Vendor demos usually show one agent watching a whole market, and that setup produces more alerts than a small team can read.
The reason is reading time. Stanford’s 2025 AI Index found the cost of running a GPT-3.5-level model fell more than 280-fold in about 18 months, so compute is cheap, and an unbounded agent will happily produce 40 alerts a week.
Your analyst’s attention is the scarce input. A brief that takes 10 minutes gets read every Monday, and a 40-item feed gets skimmed and then ignored.
Here’s how the handoff works, using a real public announcement and a hypothetical company.
A $30M collaboration-software company runs a monitoring agent that checks competitors’ pricing pages, licensing announcements and release notes every business day. In April 2024, it flags Microsoft’s licensing change: new enterprise Microsoft 365 and Office 365 suites without Teams outside the EEA and Switzerland, plus a standalone Teams offering.
The alert links the original announcement and names the affected segment: new enterprise subscribers. It also drafts an implication. Some new enterprise buyers may now treat collaboration software as a separate purchase.
The strategy team checks the announcement and its limits before anyone acts. The biggest limit: existing Microsoft customers can keep, renew and add seats to their current suites with Teams, so the opening sits with new enterprise subscriptions.
Within 5 working days, the team reviews 20 recently lost opportunities and picks 8 buyers to interview about how bundling shaped their decision. Then it approves a 6-week, $15,000 market test aimed at buyers evaluating new enterprise subscriptions, with a revised total-cost comparison and targeted sales outreach.
The agent’s contribution is an earlier signal with a source attached. The team’s contribution is turning that signal into a bounded experiment before committing to a broader change in positioning or investment.
Yes, for the mechanical part. An agent can build 3 to 5 scenarios from a driver-based model, rerun the numbers when an input moves (a 10% tariff, losing your largest customer, a competitor’s price cut), and write up what changes; you still pick the drivers, set the ranges and decide what the scenarios mean.
Here’s how I wire it. The base model stays in a spreadsheet in Google Drive, owned by a named analyst.
The agent works on a copy. It changes the inputs for each scenario, lets the spreadsheet or a Python script do the math, and writes a 1-page memo: revenue, gross margin and cash under each scenario, plus the 2 drivers that moved the result most.
I keep the arithmetic in the spreadsheet or the script on purpose. Language models write good narrative and make occasional math errors, so the model explains numbers the spreadsheet already calculated.
The useful trick is linking scenarios to the monitoring agent. When the competitor brief flags a price cut, the scenario agent reruns the “competitor price pressure” case that week, so the model reflects the market as it stands.
A scenario agent keeps the model current between planning cycles. Deciding which scenario the company bets on, and what to fund, sits with the CEO and the board, and AI for the Chief Executive Officer covers that call.
An execution-tracking agent reads your project and CRM systems each week, compares every strategic initiative to its milestones and target metric, and lists the ones that slipped, with the owner and the evidence. That covers the status-chasing a strategy team does before every quarterly review.
It needs an initiative register first. One row per initiative: name, owner, next milestone and date, target metric, the system that holds the metric, and the threshold that counts as slipping.
Most $5M to $50M companies already have the data. Milestones live in a project tool or a spreadsheet, and the metric lives in HubSpot, GoHighLevel or the accounting system.
The agent posts a short Slack summary each Monday: on track, at risk and slipped, with a link to the record behind each status. When it can’t find a number, it writes “missing” and names the system it checked.
That “missing” line does real work. It shows you which initiatives have no measurable metric, and that’s a strategy problem worth fixing before the next review.
The load is real for CSOs. Deloitte’s 2026 survey found nearly two-thirds of CSOs lead cross-functional transformation efforts, which means chasing status across departments that each report differently.
The CEO’s own weekly briefing and company KPI alerts are a separate agent, covered in AI Agents for the Chief Executive Officer.
Accurate enough to gather and summarize evidence, and wrong often enough that every claim needs a source a person can check. The control is mechanical: the agent cites a URL or file for each claim, writes “no source found” when it has none, and a named analyst spot-checks 5 claims in every brief.
3 rules cover most of the risk.
Read-only access. A strategy agent reads web pages, files and system records. It gets no permission to send, edit or publish anything outside the strategy team’s folder.
A source on every line. A claim without a link gets deleted before the brief goes out. That’s the rule I’d enforce hardest, because a confident wrong number repeated in a leadership meeting does more damage than a missing one.
A named owner. One person can change the agent’s instructions, read its logs and switch it off in under 5 minutes. I write this on a one-page Agent Card, the same format eMediaAI uses for every agent.
Start small. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, and it names rising costs, unclear business value and weak risk controls as the causes.
Vendor claims need the same scrutiny. Gartner also estimates only about 130 of the thousands of vendors selling “agentic AI” are genuine, and calls the rest agent washing: chatbots and RPA tools rebranded.
Test any tool on your own 5 competitors for 2 weeks before you sign. Count the claims with a working source and the claims your analyst had to correct.
The recommendation section of any strategy document stays with a person. Agents assemble the evidence, and your team writes the conclusion.
In my worked example, a 3-person strategy team gets 24 hours a week back, 8 per person, worth about $62,208 a year at $54 per loaded hour. Most of it comes from 2 jobs: weekly competitor and market monitoring, and assembling the strategy pack for leadership reviews.
These are planning estimates built on the assumptions in each row. Replace them with your own team’s 2-week time log.
| Task (3-person team) | Hours/week now | With agents | Hours given back | Mechanism |
|---|---|---|---|---|
| Competitor and market monitoring | 12 | 4 | 8 | Scheduled checks of 5 competitors across 4 signal types; analyst reads and annotates one weekly brief |
| Ad hoc market research questions | 6 | 2 | 4 | Research agent drafts each answer with sources; analyst checks 5 claims per answer |
| Strategy pack for leadership reviews | 6 | 1.5 | 4.5 | Agent assembles the pack from the brief, scenario memo and initiative status, then builds slides in Gamma |
| Chasing initiative status | 6 | 1.5 | 4.5 | Weekly comparison of each initiative against its register row; owners confirm only the slipped items |
| Scenario reruns | 4 | 1 | 3 | Agent reruns a copy of the driver model when an input moves; memo flags the 2 biggest drivers |
| Total | 34 | 10 | 24 |
24 hours × 48 weeks × $54 = $62,208 a year in strategy capacity given back, or 1,152 hours.
The math on strategy agents
Your strategy staff probably earn more than the analyst median, so the real figure is likely higher. Run your own numbers in the AI ROI Calculator.
Those hours go somewhere specific. Ask each person on the team to name the work they’d do with 8 more hours a week (customer interviews, partner calls, a pricing study that keeps slipping) and write it down before the build.
I run every task through one test before any agent gets built. If an agent would give back less than 20% of a task’s time, the task gets cut or merged into another report, because build and upkeep rarely pay back below that line.
Every row in the table clears it; the lowest is scenario reruns at 75%. The test still earns its place, because it usually catches a report that goes unread.
So start with the strategy pack. Ask the leadership team which pages of the last 2 packs they used, delete the rest, and only then decide what the agent assembles.
Ordered people first, then process, then tech. Each step names what it produces.
Days 1 to 30: people
Days 31 to 60: process
Days 61 to 90: tech
I’m Lee Pomerantz, Certified Chief AI Officer, founder of eMediaAI, Fort Wayne, Indiana. Every engagement runs in one order: people first, then process, then tech.
For a strategy team, that means the AI Opportunity Blueprint™ comes first. It’s a 10-day, $5,000 audit: your team records the routines on video, I map where the hours and the data go, and you get a prioritized workflow map, an ROI projection, a roadmap and governance recommendations.
If a task fails the 20% rule, the report says cut it. When the numbers hold, I build the agents with error handling and a full handoff through eMediaAI’s AI agent development, and the work is done when your team runs them without me for 30 days.
AI agents for strategy cost a one-time build fee plus model and connector usage, often on licenses a company already pays for, such as Claude or Microsoft Copilot and a Zapier or Make.com plan. Usage scales with scope: Anthropic’s 2025 engineering report found agents use about 4 times the tokens of chat, and multi-agent setups about 15 times. eMediaAI prices a build only after the $5,000 AI Opportunity Blueprint™.
Setting up a first strategy agent takes about 90 days from start to a keep-or-kill decision. The team spends 2 weeks logging time, about 30 days writing the competitor list, signal types and initiative register, and 2 weeks running the agent in parallel with the manual process. Each analyst gives a few hours across that period, mostly during the time log and the parallel run.
A Chief Strategy Officer’s first move is naming one owner and logging how the strategy team spends its week for 2 weeks. That log shows which recurring job costs the most hours, usually competitor and market monitoring. Then cut unread report pages, apply the 20% rule, and build one read-only monitoring agent that posts a weekly brief with a source link on every claim.
The ROI of an AI agent is hours given back multiplied by loaded hourly cost and working weeks, minus build and usage cost. A strategy team getting 24 hours a week back at $54 per loaded hour over 48 weeks gains $62,208 a year. Measure against a 2-week time log taken before the build, and count corrected claims as a cost.
The main risks of AI agents in strategy work are confident wrong claims, too much system access, and vendors overselling what their product does. Gartner estimates only about 130 of thousands of agentic AI vendors are genuine. Read-only access, a source link on every claim, a named owner and a 5-claim spot-check on each brief cover most of the exposure.
AI agents give strategy analysts back hours spent on monitoring, status chasing and report assembly, and the judgment calls stay with the analysts. In a 3-person team, that can mean 8 hours a week per person. Deloitte’s 2026 survey found more than half of CSOs report too many priorities with too little time, so the hours usually go to work that was already overdue.
An AI agent runs on a schedule or a trigger, fetches data from connected systems, and returns a finished document or action, while a chatbot waits for a typed question. An agent combines a model, tool connections and written instructions. A competitor-monitoring agent, for example, checks 5 competitors every Monday and posts a sourced brief to Slack without anyone asking.
An AI research agent can answer most desk-research questions for a $5M to $50M company, such as competitor counts, pricing ranges or distributor lists, with a source on each claim. Anthropic’s 2025 engineering report found multi-agent research scored 90.2% higher than a single agent on its internal evaluation. Primary research, such as customer interviews, still needs a person.
An AI competitive intelligence agent should report once a week for most $5M to $50M companies, in a brief short enough to read in 10 minutes. A weekly cadence across 5 named competitors and 4 signal types catches pricing, hiring and product changes while keeping alerts low. Add same-day alerts only for 1 or 2 signals, such as a competitor price cut.
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