Agentic AI Planning Blueprint
Info-Tech Research Group Warns Agentic AI Projects Fail Without Proper Prioritization
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Info-Tech Research Group warns that agentic AI projects in data management will fail without a disciplined selection process. Info-Tech Research Group found that companies often pick projects based on excitement and flashy demos, not on solid evaluation. The firm released a new blueprint that uses three gates—agentic fit, readiness, and complexity—to score and prioritize which projects actually deserve funding.
The Problem: Hype Over Rigor in Project Selection
Data leaders face four common pitfalls when choosing agentic AI projects. Info-Tech Research Group reports that teams pick use cases based on marketing buzz instead of testing fit, readiness, and complexity. This "agent-washing" masks real problems. Companies don't discover data quality gaps and governance issues until a pilot is already funded and underway—making failure expensive and hard to reverse.
A Three-Gate Framework to Filter Bad Ideas
The new blueprint "Create Your Agentic AI Roadmap for Data Management" introduces a structured screening process. Info-Tech Research Group recommends using three gates: agentic fit (can this use case truly benefit from AI agents?), readiness (do we have the people, tools, and data quality?), and complexity (how hard will it be to execute?). Each use case gets scored and ranked, creating a sequenced, fundable roadmap instead of a wish list.
New Tools to Build Your AI Roadmap
Info-Tech Research Group includes two practical tools in the blueprint. The AgenticAI Scoring Kit lets teams rate each potential project against the three gates. The AgentIC AI Candidate Definition Workbook helps teams define and vet ideas before spending money. Together, these tools replace gut-feel decision-making with repeatable, measurable evaluation—reducing the risk that funding flows to the wrong projects.
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