Column
Useful Articles
Practical articles on AI adoption, operational improvement, DX, and system development.Knowledge you can use in the field, explained clearly for beginners.
Lock It Down Before You Order: Heading Off Project Blowups Through Requirements and Estimates [Vendor Control, Part 1]
Whether a software project goes up in flames is mostly decided before the order is placed. This article walks through what the buyer should prepare: deciding whether to build at all, defining what 'done' means, turning vague requirements into numbers, non-functional requirements, agreeing on failure handling, and how to avoid a 'lump-sum' estimate. Part 1 of the series.
Don't Stop Mid-Project: How to Run Meetings, Reporting, and Risk [Vendor Control, Part 2]
Whether a project blows up is decided by how you run things after signing. This installment covers designing your meeting cadence and reporting frequency, making the ball and the deadline explicit, what to do when the vendor never reports, catching risk early and making calls in a crisis, and reporting up to leadership—the daily habits that keep a project moving. Part 2 of the series.
Bringing Trouble Under Control: Quality, Estimates, and Closing [Vendor Control, Part 3]
No matter how well you prepare, problems still happen. Poor-quality deliverables, a flood of bugs, scope creep, a critical issue right before launch, a project that won't close out—this article covers how to bring each of these under control and how to turn them into lessons for next time. The final installment, summed up in the three principles of project management.
How to Identify AI-Driven Improvement Opportunities and Bottlenecks in Your Organization
Deciding 'where to deploy AI' on gut instinct usually misses the mark. This guide walks through how to find real improvement opportunities, step by step: taking stock of your work (the 80/20 rule), pinpointing the true nature of a bottleneck (people, information, or approval), tracing root causes through process analysis, choosing the right analytical frameworks, selecting the right technique (rules / optimization / machine learning / LLM), and starting small.
Core Systems: Should You Adopt a Package or Build From Scratch?
"Build it around our own business" isn't always the right answer. We walk through how to choose without regret — from considering the option of not building at all, to the differences between packages/SaaS and full scratch builds, standard processes (best practices), judging by TCO and ROI, and an enterprise-wide optimization perspective — complete with a checklist.
What Is AI Adoption? Six Lenses for Embedding AI Across Your Teams
To avoid the trap of "we rolled out AI, but nobody uses it," you need a way to measure how deeply it has taken hold. This article breaks down six lenses—reach, depth, data use, training, governance, and a solid workflow foundation—together with maturity benchmarks, common pitfalls, and how to move forward.