I Used a Monte Carlo Simulation to Decide Which AI Tasks to Automate First — Here’s What Won

The Problem Every Agency Owner Knows

You’ve read the announcements. You’ve seen the demos. You know AI can automate half your workflow — but which half do you start with? When every new tool promises to “transform your business,” the hardest decision isn’t whether to adopt AI. It’s figuring out what to do first.

I run Tygart Media, where we manage SEO, content, and optimization across 18 WordPress sites for clients in restoration, luxury lending, healthcare, comedy, and more. Claude Cowork — Anthropic’s agentic AI for knowledge work — sits at the center of our operation. But last week I found myself staring at a list of 20 different Cowork capabilities I could implement, from scheduled site-wide SEO refreshes to building a private plugin marketplace. All of them sounded great. None of them told me where to start.

So I did what any data-driven agency owner should do: I stopped guessing and ran a Monte Carlo simulation.

Step 1: Research What Everyone Else Is Doing

Before building any model, I needed raw material. I spent a full session having Claude research how people across the internet are actually using Cowork — not the marketing copy, but the real workflows. We searched Twitter/X, Reddit threads, Substack power-user guides, developer communities, enterprise case studies, and Anthropic’s own documentation.

What emerged was a taxonomy of use cases that most people never see compiled in one place. The obvious ones — content production, sales outreach, meeting prep — were there. But the edge cases were more interesting: a user running a Tuesday scheduled task that scrapes newsletter ranking data, analyzes trends, and produces a weekly report showing the ten biggest gainers and losers. Another automating flight price tracking. Someone else using Computer Use to record a workflow in an image generation tool, then having Claude process an entire queue of prompts unattended.

The full research produced 20 implementation opportunities mapped to my specific workflow. Everything from scheduling site-wide SEO/AEO/GEO refresh cycles (which we already had the skills for) to building a GCP Fortress Architecture for regulated healthcare clients (which we didn’t). The question wasn’t whether these were good ideas. It was which ones would move the needle fastest for our clients.

Step 2: Score Every Opportunity on Five Dimensions

I needed a framework that could handle uncertainty honestly. Not a gut-feel ranking, but something that accounts for the fact that some estimates are more reliable than others. A Monte Carlo simulation does exactly that — it runs thousands of randomized scenarios to show you not just which option scores highest, but how confident you should be in that ranking.

Each of the 20 opportunities was scored on five dimensions, rated 1 to 10:

• Client Delivery Impact — Does this improve what clients actually see and receive? This was weighted at 40% because, for an agency, client outcomes are the business.
• Time Savings — How many hours per week does this free up from repetitive work? Weighted at 20%.
• Revenue Impact — Does this directly generate or save money? Weighted at 15%.
• Ease of Implementation — How hard is this to set up? Scored inversely (lower effort = higher score). Weighted at 15%.
• Risk Safety — What’s the probability of failure or unintended complications? Also inverted. Weighted at 10%.

The weighting matters. If you’re a solopreneur optimizing for personal productivity, you might weight time savings at 40%. If you’re a venture-backed startup, revenue impact might dominate. For an agency where client retention drives everything, client delivery had to lead.

Step 3: Add Uncertainty and Run 10,000 Simulations

Here’s where Monte Carlo earns its keep. A simple weighted score would give you a single ranking, but it would lie to you about confidence. When I score “Private Plugin Marketplace” as a 9/10 on revenue impact, that’s a guess. When I score “Scheduled SEO Refresh” as a 10/10 on client delivery, that’s based on direct experience running these refreshes manually for months.

Each opportunity was assigned an uncertainty band — a standard deviation reflecting how confident I was in the base scores. Opportunities built on existing, proven skills got tight uncertainty (σ = 0.7–1.0). New builds requiring infrastructure I hadn’t tested got wider bands (σ = 1.5–2.0). The GCP Fortress Architecture, which involves standing up an isolated cloud environment, got the widest band at σ = 2.0.

Then we ran 10,000 iterations. In each iteration, every score for every opportunity was randomly perturbed within its uncertainty band using a normal distribution. The composite weighted score was recalculated each time. After 10,000 runs, each opportunity had a distribution of outcomes — a mean score, a median, and critically, a 90% confidence interval showing the range from pessimistic (5th percentile) to optimistic (95th percentile).

What the Data Said

The results organized themselves into four clean tiers. The top five — the “implement immediately” tier — shared three characteristics that I didn’t predict going in.

First, they were all automation of existing capabilities. Not a single new build made the top tier. The highest-scoring opportunity was scheduling monthly SEO/AEO/GEO refresh cycles across all 18 sites — something we already do manually. Automating it scored 8.4/10 with a tight confidence interval of 7.8 to 8.9. The infrastructure already existed. The skills were already built. The only missing piece was a cron expression.

Second, client delivery and time savings dominated together. The top five all scored 8+ on client delivery and 7+ on time savings. These weren’t either/or tradeoffs — the opportunities that produce better client deliverables also happen to be the ones that free up the most time. That’s not a coincidence. It’s the signature of mature automation: you’ve already figured out what good looks like, and now you’re removing yourself from the execution loop.

Third, new builds with high revenue potential ranked lower because of uncertainty. The Private Plugin Marketplace scored 9/10 on revenue impact — the highest of any opportunity. But it also carried an effort score of 8/10, a risk score of 5/10, and the widest confidence interval in the dataset (4.5 to 7.3). Monte Carlo correctly identified that high-reward/high-uncertainty bets should come after you’ve secured the reliable wins.

The Final Tier 1 Lineup

Here’s what we’re implementing immediately, in order:

1. Scheduled Site-Wide SEO/AEO/GEO Refresh Cycles (Score: 8.4) — Monthly full-stack optimization passes across all 18 client sites. Every post that needs a meta description update, FAQ block, entity enrichment, or schema injection gets it automatically on the first of the month.
2. Scheduled Cross-Pollination Batch Runs (Score: 8.2) — Every Tuesday, Claude identifies the highest-ranking pages across site families (luxury lending, restoration, business services) and creates locally-relevant variant articles on sister sites with natural backlinks to the authority page.
3. Weekly Content Intelligence Audits (Score: 8.1) — Every Monday morning, Claude audits all 18 sites for content gaps, thin posts, missing metadata, and persona-based opportunities. By the time I sit down at 9 AM, a prioritized report is waiting in Notion.
4. Auto Friday Client Reports (Score: 7.9) — Every Friday at 1 PM, Claude pulls the week’s data from SpyFu, WordPress, and Notion, then generates a professional PowerPoint deck and Excel spreadsheet for each client group.
5. Client Onboarding Automation Package (Score: 7.6) — A single-trigger pipeline that takes a new WordPress site from zero to fully audited, with knowledge files built, taxonomy designed, and an optimization roadmap produced. Triggered manually whenever we sign a new client.

Sixteen of the twenty opportunities run on our existing stack. The infrastructure is already built. The biggest wins come from scheduling and automating what already works.

Why This Approach Matters for Any Business

You don’t need to be running 18 WordPress sites to use this framework. The Monte Carlo approach works for any business facing a prioritization problem with uncertain inputs. The methodology is transferable:

• Define your dimensions. What matters to your business? Client outcomes? Revenue? Speed to market? Cost reduction? Pick 3–5 and weight them honestly.
• Score with uncertainty in mind. Don’t pretend you know exactly how hard something will be. Assign confidence bands. A proven workflow gets a tight band. An untested idea gets a wide one.
• Let the math handle the rest. Ten thousand iterations will surface patterns your intuition misses. You’ll find that your “exciting new thing” ranks below your “boring automation of what works” — and that’s the right answer.
• Tier your implementation. Don’t try to do everything at once. Tier 1 goes this week. Tier 2 goes next sprint. Tier 3 gets planned. Tier 4 stays in the backlog until the foundation is solid.

The biggest insight from this exercise wasn’t any single opportunity. It was the meta-pattern: the highest-impact moves are almost always automating what you already know how to do well. The new, shiny, high-risk bets have their place — but they belong in month two, after the reliable wins are running on autopilot.

The Tools Behind This

For anyone curious about the technical stack: the research was conducted in Claude Cowork using WebSearch across multiple source types. The Monte Carlo simulation was built in Python (numpy, pandas) with 10,000 iterations per opportunity. The scoring model used weighted composite scores with normal distribution randomization and clamped bounds. Results were visualized in an interactive HTML dashboard and the implementation was deployed as Cowork scheduled tasks — actual cron jobs that run autonomously on a weekly and monthly cadence.

The entire process — research, simulation, analysis, task creation, and this blog post — was completed in a single Cowork session. That’s the point. When the infrastructure is right, the question isn’t “can AI do this?” It’s “what should AI do first?” And now we have a data-driven answer.