AI Implementation: Shifting from Build to Discover
Developing an AI agent has become remarkably straightforward, often taking only a few hours. However, this ease frequently misleads businesses regarding where AI truly offers value. Many organizations typically initiate the “Build” phase: selecting a model, integrating it with platforms like Jira, GitHub, and MCP, and then constructing a workflow. Only after these steps do they attempt to ascertain the specific problem their solution addresses.
Understanding Context Audit and Contextual Debt
PROSTO24 explores an alternative methodology that prioritizes identifying pain points before development begins. This approach focuses on pinpointing areas where teams expend substantial time on context switching, manual coordination, and navigating between disparate systems. Central to this method are the concepts of contextual debt and conducting a Context Audit.
The audit aims not only to uncover these inefficiencies within tools such as Jira, Confluence, and GitHub but also to critically evaluate which issues genuinely warrant automation. Not every identified problem guarantees the viability of an AI-driven solution. By performing an audit and pinpointing critical areas, companies can proceed with more informed implementation, adhering to a Discover → Build → Measure cycle.
This article really highlights a crucial point about AI implementation. The idea of “contextual debt” is fascinating – I hadn’t thought about it in those terms before. I’m curious, how do you differentiate between a ‘pain point’ that genuinely warrants an AI solution versus one that might be better addressed with process improvements or better human training? Also, what are some of the most common pitfalls companies encounter when trying to conduct a Context Audit effectively?