Practical guides and evidence on turning agentic AI into measurable EBITDA

A step-by-step ERP data quality audit for mid-market companies: 10 fields, a 500-record sample, pass bands (above 80% proceed, 70 to 80% remediate, below 40% stop), remediation order and cost, and the re-audit rule. Takes 2 to 4 hours.

The measured gate that stops an AI pilot from scaling on faith: accuracy above 90%, cycle time down 25%, cost per transaction down 15%, exceptions under 8%, satisfaction above 3.5 of 5, plus five operational criteria, pass at seven of ten. Includes the week-24 production gate.

The clauses a mid-market AI acceptable use policy needs before a pilot goes live: sanctioned tools only, no shadow AI, no personal data through unsanctioned models, a five-minute kill switch, human-in-the-loop thresholds, audit trails, quarterly bias testing, and four cybersecurity guardrails, mapped to the NIST AI Risk Management Framework.

Four checks on your contract list that find margin leaking through legacy pricing, unmanaged discounts, missing escalation clauses and auto-renewals: the red flags, the math, and the 0.5% to 2% of margin a $150M company typically recovers.

The third FLOAT pocket: top-three supplier share of cost of goods sold, time since the last competitive bid, inventory turns against peers, and energy cost variance. Red flags, the margin at stake, and what a supply chain agent does with the answer.

How mid-market companies score UiPath, Microsoft Copilot Studio, Automation Anywhere and other agentic AI platforms: six weighted criteria, an 80-point pass band, current implementation and licensing ranges, the SLA addendum with refund triggers, and the four-week evaluation sequence.

From close to a gated pilot in 100 days: the seven-day EBITDA scan, the 72-hour opportunity ranking, the week-one investment committee memo with a 90-day cash figure, the four-week pre-launch sequence, and the pilot that reaches its gate at week 8.

The SEAS diagnostic applied to a publicly traded US manufacturer of protective clothing and safety apparel, using only its filings: a $6M to $10M annual EBITDA opportunity surface, $3M to $6M incremental net of management guidance, and $49M to $83M of implied enterprise value at 8.0x. Company anonymized.

The failure rate is now well documented, and the cause is not the technology. Most enterprise AI programs never reach the P&L because they are governed as technology projects rather than financial ones , with no baseline, no owner accountable for recovered dol

The defensible answer is a specific, bankable number , not a vague efficiency claim. In a representative mid-market company, recoverable EBITDA leakage runs to several margin points, concentrated in a small number of process pockets.

Leakage is not random. It concentrates in three structural pockets that standard financial reporting cannot see, because each is distributed across thousands of individually immaterial transactions.

RPA and agentic AI are different technologies with different ceilings. RPA automates predefined, rule-based steps; agentic AI reasons, handles exceptions, and operates across systems toward an outcome. For margin work, the difference is decisive.

The sequence that works is phased and gated, and it begins with diagnosis and a financial baseline , never with a broad rollout. It is the stage-gate discipline applied to AI.

Impact is defensible only when it is expressed as recovered dollars tied to a baseline, underwritten conservatively, and translated into margin points and exit-multiple effect , the language an investment committee actually underwrites.