of GenAI pilots produce zero measurable ROI
MIT, 2025
AI readiness diagnostics
Diagnose why AI initiatives fail before they start
Most AI initiatives do not fail because of the model. They fail because internal knowledge, systems, and workflows are not ready for AI in the first place.
AI Precursor is the flagship system we are building at AIKvn to evaluate real AI readiness before organizations invest in expensive pilots and deployments.
overall AI project failure rate
RANDglobal AI spending forecast by 2026
GartnerMost teams invest in AI before checking whether their environment is usable by AI.
Companies are investing heavily in AI, but many still do not know whether their internal documents, data, APIs, and workflows are actually accessible, current, and automatable enough to support reliable use.
AI projects fail early when knowledge is fragmented, documentation diverges from operational reality, and automation assumptions are treated as facts. AI readiness has to be measured before implementation becomes an engineering commitment.
Expectation vs reality
First-round web placeholder derived from the pitch deck structure. Final diagram refinement comes next.
Stub visual for review. Final SVG diagram to be redrawn in the next stage.
Knowledge readiness
Can relevant information actually be found, trusted, and kept current enough for AI use?
Execution readiness
Which workflows can really be automated today, and where do manual or hidden steps block progress?
Safety and control
Are automated actions bounded, auditable, and safe enough for controlled enterprise environments?
Reality over declarations.
We do not rely on questionnaires, architecture diagrams, or declarations alone. The method is intended to be grounded in observable system behavior and evidence-backed scoring.
- Whether your data can answer real questions
- Whether critical knowledge is actually visible to AI
- Whether workflows can be automated safely
- Whether automated actions can be executed with control
Inputs, diagnostics, output
Placeholder visual for the diagnostic flow. The reviewed production diagram will replace this asset.
Technical writing is the main public content channel.
We publish working notes, articles, and technical observations on enterprise AI readiness, retrieval quality, automation feasibility, and diagnostic methodology.
Insights is the primary publishing section. News remains reserved for company updates only.
Three evidence pillars
Placeholder measurement block for first review; final diagram comes in the next stage.
Primary call to action
Discuss your AI readiness or explore a pilot.
We are currently working with a limited number of organizations to validate AI Precursor in real environments. Typical discussions include whether your existing data is usable by AI, where documentation and reality diverge, what workflows can actually be automated, and whether a diagnostic pilot makes sense for your environment.
- AI readiness of current data and documentation
- Gaps between documented and observed processes
- Feasibility of a pilot evaluation