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Find your real workflow constraints before buying AI tools.
AI tools appear across the product lifecycle, from research and requirements to design, coding, testing, and release. The promise is a productivity multiplier. The evidence is less even.
Our research report, The Constraint Moves: A Critical Look at AI-Enabled Product Workflows, separates measured improvements from marketing claims and asks whether faster tasks produce a faster product organisation.
The report finds strong evidence that AI can accelerate defined coding tasks. But results become mixed when developers work inside familiar codebases with complex context. Across discovery, design, testing, and release, the evidence is thinner, anecdotal, or produced by the vendors selling the tools.
The central problem is structural. If AI helps a team generate code four times faster while review, testing, integration, and decision-making remain unchanged, delivery does not become four times faster. Work accumulates in front of the next constraint.
Inside the report, readers will learn how to distinguish local productivity from system throughput, why self-reported speed can differ from measured performance, and how AI-generated volume can increase review queues, code churn, and integration pressure.
It also introduces the Constraint Map, a framework for identifying the slowest stage in your workflow before buying a tool. Leaders can use it to sequence AI adoption, strengthen downstream capacity, and measure outcomes like cycle time and customer delivery rather than tool usage.
The question is not where AI can produce more. It is where the workflow can absorb more.
About the author:
Ahana Roy
Content Marketing Manager
A writer at heart and a marketer by choice, Ahana heads content and social media at SDTC Digital, bringing an instinct for language and a sharp eye for what moves people. Working across the blog and social channels every day, she sees firsthand which stories earn attention and which get lost in the feed.
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