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How to increase EBITDA with AI in a $50M to $500M company

You have been told to make AI pay, this year, with the team you have. This page sets out the method SEAS uses to turn that mandate into a measured EBITDA number: a diagnostic in days, a gated pilot in eight weeks, and a board-ready case at every step.

For CEOs, CFOs, COOs, owner-operators and transformation leads · Updated 25 September 2026

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Why the pressure is real, and why most AI programs still miss the profit line

The board wants AI to show up in EBITDA. The evidence says most programs never get there. MIT's research found 95% of enterprise AI pilots deliver no profit and loss impact, and the same body of work found organisations succeed about 67% of the time when they buy a system rather than build one, while internal builds succeed one third as often. In BCG's September 2025 survey of 1,250 executives, the small group of companies that had built AI into how they operate posted 1.7 times the revenue growth and 1.6 times the EBIT margin of the laggards.

The failures are not about the models. They come from running AI as a technology project: no financial baseline, no owner accountable for recovered dollars, and no gate that ties each phase to a measured result before more money is spent. SEAS is built around the inverse of that pattern.

What "improving profit with AI" means in a mid-market business

EBITDA leakage in a $50M to $500M company is not random. It concentrates in three pockets: administrative and SG&A process inefficiency, pricing and contract drift, and supply-chain and vendor concentration. Each is invisible on a standard profit and loss statement because it is spread across thousands of individually immaterial transactions. Agentic AI is the first tool that can examine every transaction rather than a sample, which is why it reaches leakage that finance reviews and RPA cannot.

In the SEAS reference model, a $150M-revenue company with $18M of EBITDA carries approximately $5.3M of recoverable, defensible leakage per year, about 3.6 margin points: roughly $2.5M in supply-chain and vendor spend, roughly $1.8M in administrative and SG&A processes, and the balance in energy and operational spend. Your number will differ. The point of the method is to find yours in days, on your own data, before you commit to anything.

The sequence SEAS runs: seven days, eight weeks, eighteen months

Seven days from download to a board-ready case. Eight weeks to a measured go or no-go decision. Eighteen months to full deployment. Every step names the file that runs it.

Day 1. Read the Read Me First and Sections 1 to 3 of RAPID SEAS, the daily execution manual. About three hours.
Day 2. Run the FLOAT diagnostic on your trailing twelve-month profit and loss statement. It sets the financial baseline every later claim is measured against.
Days 3 to 7. Prove that the leaks exist. Each of the 35 agents ships as a prompt you run on your own CSV export in about 15 minutes, so you confirm a leak before spending a dollar on a platform. Rank the opportunities and write the board case from the templates.
Weeks 1 to 8. One contained pilot on one or two high-value processes, behind a gate with pre-agreed thresholds: accuracy above 90%, cycle time down 25%, cost per transaction down 15%, exceptions under 8%, user satisfaction above 3.5 of 5, plus five operational criteria. Pass at seven of ten, or pause for a fixed remediation period.
Months 3 to 18. Scale only what passed the gate, with a second production-readiness gate at week 24 and the recovered dollars reported against the baseline.

What you download

The 309-page SEAS Master Playbook: the six-phase architecture and the core operating system.
RAPID SEAS: the daily execution manual with the 12-week toolkit.
35 build-ready agent specifications with production prompts: 9 cross-functional and 26 industry-specific agents across 13 sectors.
Nine live Excel workbooks (ROI, MOIC, EBITDA, budget, vendor matrix, Gantt, data fitness) holding 1,711 formulas.
EBITDA PathFinder Pro v3.0: a seven-module diagnostic that runs offline on Macintosh or PC.
The Templates Library: investment committee memo, LP update, CEO briefing, AI scorecard, and board and fund-level decks.
The Project Manager's Runbook with a 29-milestone Gantt, the 90-day pilot playbook and the agent deployment tracker.
Governance appendices mapped to NIST AI RMF, ISO 42001, SOC 2, SOX, GDPR, CCPA and the EU AI Act.
The FLOAT diagnostic system, the ERP data export guide, the vendor landscape matrix, sample datasets and a worked diagnostic.
90 days of direct email support from the author, with a reply within two business days.

No coding is required. The runbooks assume the tools you already run: your ERP, your CRM and Excel.

What it costs, against what the alternative costs

SEAS is $2,997, paid once. No subscription, no per-seat licence, no consulting retainer. A consulting engagement that produces the same system costs $100K to $500K, takes three to nine months, consumes internal bandwidth and ends when the project ends. The downside is bounded: a 100% refund within 90 days, processed by Polar rather than by me, matched to the 90-day deployment window and 90 days of direct email support.

If a $150M-revenue firm follows the FLOAT seven-day stress test and identifies up to $5.3M of EBITDA leakage, the $2,997 price is a rounding error against the recovery. Nothing on this page asks you to take that on faith: the diagnostic runs on your own numbers first.

Owner-operators and family businesses

Founder-led and family-owned companies in the $50M to $500M range have no private equity sponsor and usually no consulting budget. SEAS was built so that their own team captures the same EBITDA: the diagnostic, the agents, the workbooks and the gate are the same, and the only outside cost is the licence.

If you want proof before you pay

Two free steps exist for exactly that reason. The FLOAT diagnostic is a 90-minute review that quantifies your recoverable EBITDA from a trailing twelve-month profit and loss statement. The two-minute fit assessment tells you honestly whether SEAS fits your revenue scale and data readiness, including when the answer is no.

Frequently asked questions

How quickly can AI show a profit result in a mid-market company?

With SEAS, seven days from download to a board-ready case and eight weeks to a measured go or no-go decision on one pilot. The typical benchmarks are far slower: Deloitte found most organisations take two to four years to reach a satisfactory return on a use case, with only 6% seeing payback inside a year.

Do I need data scientists or engineers to use SEAS?

No. SEAS is designed for operating executives and advisors, not engineers. The agent library uses plain-language prompts, the runbooks assume standard business tools such as your ERP, CRM and Excel, and no coding is required.

How much EBITDA can AI realistically recover?

In the reference model, a $150M-revenue, $18M-EBITDA company carries approximately $5.3M of recoverable leakage per year, about 3.6 margin points, concentrated in supply-chain and vendor spend, administrative and SG&A processes, and energy and operational spend. The FLOAT diagnostic finds the figure for your own company.

We already use AI tools and see no profit from them. Does SEAS still apply?

Yes. The FLOAT diagnostic identifies whether your current tools are generating measurable EBITDA recovery or only operational activity. Most companies discover that significant leakage persists despite prior AI spend.

What if it does not work for us?

A 100% refund within 90 days of purchase, no questions asked, processed by Polar. The method is also built to fail fast: a 90-minute diagnostic, an eight-week pilot and a go or no-go gate with measured thresholds, so stopping is an evidence-based decision rather than a sunk cost.

Your EBITDA number is one diagnostic away. SEAS ships the diagnostic, the agents, the workbooks and the gate as one download.

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