There's a reason we don't guess. Everything in our diagnostic system is built on 10 years of 2811 supporting startups and established businesses — combined with the latest evidence from AI adoption research worldwide.

This isn't a template we downloaded. It's a methodology we built, tested, and refined across hundreds of real companies, real processes, and real results.

What “evidence-based” actually means

When we say evidence-based, we mean three things:

Empirical data from 2811's decade of work — patterns from hundreds of diagnostics, implementations, and follow-ups across industries and countries.
Latest research from MIT, McKinsey, and sector reports — we continuously integrate findings from the State of AI in Business, BCG's AI adoption studies, and academic research on SMB automation.
Feedback loops from every report we generate — each diagnostic result feeds back into our model, making every subsequent analysis more accurate.

How it shows up in your PDF report

When you receive your diagnostic report, every number has a source. Here's what we mean:

Automation Score (0–100) — calibrated against our database of similar businesses by size, industry, and region. When we say you're at 35, it's because we've scored hundreds of companies like yours and know what 35 looks like operationally.
Monthly Money Loss estimates — calculated using localized hourly rates (we have configs for US, Mexico, Colombia, Chile, Argentina, Brazil, and Spain), adjusted for your team size and the specific tasks you described. Not a generic formula — your formula.
Quick Wins with savings projections — ranked by impact and effort based on what we've seen actually work in companies at your stage. If we recommend automating your quote follow-up process, it's because we've seen that exact change save $200–$500/month for similar businesses.
30/60/90-Day Roadmap — sequenced based on implementation patterns that minimize disruption. We know from experience that changing more than two processes simultaneously causes adoption failure.
14-Day Action Plan — daily tasks designed around what a real business owner with limited time can actually accomplish. Not aspirational — achievable.

Why experience matters more than algorithms

Anyone can build an AI that generates recommendations. The hard part is building one that generates the right recommendations — the ones that a 15-person distribution company in Bogotá will actually implement, or that a dental practice in Austin can execute next week.

That precision comes from years of watching what works and what doesn't. From seeing a Zapier integration save a manufacturing firm 12 hours per week. From watching a WhatsApp Business automation recover 8 lost leads per month for a food distributor. From learning that the first automation should always be the one with the lowest effort and highest visibility — because early wins build momentum.

Our system doesn't just know what's possible. It knows what's probable — because it's seen it happen.

Continuously updated, never static

The AI landscape changes weekly. New tools launch. Prices drop. Capabilities expand. Our system integrates these changes continuously:

Tool recommendations are updated as new solutions emerge.
Cost estimates reflect current market pricing.
Industry benchmarks evolve as we process more diagnostics.

This is why a diagnostic from us today is more accurate than one from six months ago — and six months from now, it will be even sharper.

The research behind the numbers

Our system doesn't run on intuition. It's anchored in specific research that defines how we measure, evaluate, and recommend:

McKinsey Global Institute found that roughly 60% of all occupations have at least 30% of activities that are technically automatable. This defines our estimation ceiling: we never assume more than 40% automation per task, keeping our projections conservative and defensible.
BCG (2024) reported that only 26% of companies have moved beyond AI experimentation. This means 74% are still in early stages — and a diagnostic like ours positions you among those already taking action.
MIT Sloan found that companies partnering with external AI specialists are 2x more likely to successfully deploy automation versus purely internal attempts. This backs our approach of external guidance combined with internal execution.

How the scoring algorithm works

Your automation score isn't an arbitrary number. It's calibrated against hundreds of real assessments and adjusted across three dimensions:

By industry — a manufacturing company typically scores 25-45, while tech services range 50-70 and retail/services fall between 35-55. Your score reflects how automated you are relative to your industry, not in absolute terms.
By region — labor costs and tool availability vary by country. A business in Mexico has access to CONTPAQi and Aspel, while in Colombia it's Siigo and Alegra. Automation opportunities depend on your local ecosystem.
By size — a 5-person team has different automation potential than a 50-person one. Recommendations are adjusted to what you can realistically implement with your current resources.

In essence, your score represents the distance between your current automation level and the optimal level for your specific profile.

ROI patterns from 10 years of data

After hundreds of diagnostics and implementations, the patterns are clear:

Average savings found per diagnostic: $800 USD/month. This is consistent across industries and regions, adjusted for local costs.
Typical return timeline: first quick win implemented in 1-2 weeks, measurable ROI within 30 days.
Real case: a distribution company in Colombia automated order follow-ups and reduced manual work by $650/month with three changes that took 10 days to implement.
Real case: a dental practice in the US automated invoicing and reminders, recovering 12 hours per week of admin time.
Key pattern: the first automation always delivers disproportionate value — not because it's the biggest, but because it builds the confidence and momentum for the ones that follow.

We also observe that businesses following the 14-day action plan have a significantly higher implementation rate than those trying to do everything at once.

From academic research to your action plan

The gap between “AI can transform your business” and “here are the 3 steps you execute this week” is where we live. Every recommendation in your report:

Names a specific tool that has been tested in at least 10 real implementations.
Includes a concrete workflow: what triggers the automation, what it does, and what the result is.
References outcomes from businesses similar to yours.

The 30/60/90-day roadmap structure is based on change management research about adoption windows — we know there's a critical 14-day period where inertia determines whether a change sticks or gets abandoned. That's why the 14-day plan is so specific: it's designed to carry you through that window.

Trust the process, verify the numbers

We don't ask you to take our word for it. Every report comes with transparent methodology: you can see how your score was calculated, where the savings estimates come from, and why specific tools were recommended for your situation.

Evidence-based means you can check our work. And we want you to.

An evidence-based system: 10 years of data behind every recommendation