Algorithmic readiness is entering due diligence the way digital maturity did a decade ago. When AI agents mediate discovery, evaluation, and purchase, a target's visibility to machine buyers becomes part of what the business is worth. This page describes how we assess it.
AI agents increasingly find, evaluate, and select offers on behalf of human principals. A business that is invisible to machine buyers is losing demand it never sees.
Readiness lives in product data, discoverability, decision clarity, and delivery reliability. None of it appears in a data room by default, and it sits outside the scope of a conventional commercial workup.
A readiness gap found in diligence is negotiating information and a value-creation plan. The same gap found after close is an unbudgeted remediation program.
A focused read of a single target. The Algorithmic Readiness instrument is run against the target's commercial estate and scored against the Four Ds Framework. The deal team receives a clear readiness assessment, the material gaps, and a view of the demand at risk, delivered inside the diligence timetable.
The full diagnostic, compressed into the deal window. Everything in the Screen, plus working sessions with the target's management, a remediation map sized so it can be priced into the deal model, and a 100-day post-close plan the operating team can execute.
Portfolio programs, with quarterly readiness cycles across multiple holdings, are available on application.
The instrument is the same one used in our advisory practice: grounded in a published body of academic research on agentic commerce, scored against the Four Ds Framework, and derived from the forthcoming The Algorithmic Shopper (St. Martin's Press, 2027). The work is led personally by Paul F. Accornero, whose delivery record includes guest lectures at Harvard University and executive work across Europe and Brazil.
Engagements are confirmed in writing, with scope and terms set out in the engagement correspondence. To discuss a target or a portfolio, write to paul.accornero@aipraxis.ai.
The analysis and deliverables in both engagements are derivative works of Paul F. Accornero's copyrighted foundational research (U.S. Copyright Reg. No. TXu 2-507-027 and related registrations), provided under the Master IP & Legal Terms.