Lokad’s vendor reviews expose the evidence gap in AI-first planning
The recurring issue is not the lack of AI vocabulary, but the lack of public evidence connecting architecture to operating results.
AI-first planning vendors increasingly use similar language while offering very different architectures, implementation models and evidence.
Observation
Lokad has published detailed reviews of established and emerging planning vendors, repeatedly distinguishing credible product scope from weak public evidence.
Operational implication
Buyers can mistake polished terminology for demonstrated capability, especially around AI, probabilistic forecasting and autonomous decisions.
Decision architecture
Vendor evaluation should begin with named decisions, data requirements, model ownership, integration boundaries, approval controls and measurable outcomes.
Data requirements
Customer evidence should identify the baseline, planning scope, data used, implementation effort and operational result.
What should remain lightweight
Early vendor exploration and sandbox tests can remain outside the production architecture.
What should be integrated
Only validated logic, interfaces and controls should be industrialized in APS, ERP or BI.
The evidence standard must rise as the AI claim becomes broader.