Research program
Zero Neo asks why a business should buy specialized software for operations when much of the functionality already exists in the platforms it owns, its systems of record, and increasingly capable AI. Specialized SaaS won because packaged configuration was cheaper than custom configuration; the research program tests whether AI reverses that, and looks for the cases where it does not. New software is the last resort.
When does AI make configuring the platforms a business already owns cheaper than buying specialized software, and when does it not?
Program
Zero Neo is a research program on one position: a small or midsize business should buy no new software for an operation when the platforms it already owns, its systems of record, and AI already do the job. New software for business operations should be the last resort if Microsoft 365 or Google platforms are already good enough. Zero Neo is the program that tests whether AI has made that position defensible, and looks for the places where it is wrong.
The note finds that a business's application collection is nobody's design or responsibility, and that it should be reconciled on a schedule the way clinicians reconcile medications. Applications accumulate the way medications do. Each addition was reasonable at the time; the collection is nobody's deliberate design and nobody's responsibility to review.
The published evidence that fragmented, poorly usable software has measurable costs in healthcare, what it supports, and what it does not. The evidence establishes a cost and locates the defect between applications. It does not show that consolidation fixes it, so the note serves as context for the Zero Neo hypothesis, not proof.
Treat the owned platform as a substrate of capabilities rather than a bundle of products, then map those capabilities against the jobs that category software is bought to perform.
New business-operations software should be the last resort. Start with the capabilities already owned and move outward only when evidence shows a real gap. The principle reverses the burden of proof: the proposer must show why the outcome cannot be achieved with what the business already owns.
Subscription price is the easiest number to compare and the least decisive. The hypothesis stands or falls on total operating cost, including the costs of assembling badly.
Systems of record at the bottom, platform capabilities above them, AI orchestration above that, then workflows, then the mixed workforce, with observable outcomes at the top and purchased software entering only from the side.
The larger effect may be in organizations that do have IT. Governing a few strategic platforms is a different job from administering hundreds of applications and the integrations between them.
Small businesses historically chose between buying SaaS and hiring consultants. If AI can configure owned platforms, a third option appears for organizations that never had one.