In a recent LinkedIn post, Melissajeanperri delves into the critical distinction between superficial tool training and genuine AI-powered operational improvement within product organizations. Melissajeanperri argues that many companies mistake basic software tutorials for effective AI adoption, leading to a significant gap in actual productivity gains.
The post highlights a common scenario where leadership introduces a coding assistant, followed by a brief demonstration video that is mislabeled as “AI training.” Melissajeanperri points out the ineffectiveness of this approach, stating:
“Leadership picks a coding assistant and roll it out to the team. A few weeks later a senior engineer puts together a 20-minute video walking through prompt examples. That gets called “AI training” in the all-hands. Then everyone goes back to work expecting different output.”
The Gap Between Adoption and Improvement
Melissajeanperri identifies a prevalent disconnect: while AI tool adoption rates may be high, the corresponding improvement in operating models is often lagging. This chasm, according to Melissajeanperri, is where the essential work of training employees to think more effectively with AI tools resides – a crucial area often overlooked in budgeting and strategic planning.
“That is the gap most product organizations are sitting in. AI tool adoption is high. Operating-model improvement is not yet a reality for most teams. Between those two numbers sits the work nobody has built a budget line for. Training people to think better with the tools,” Melissajeanperri writes.
Cultivating “Shared Judgment” for Compounding Returns
The core of Melissajeanperri’s argument centers on the need to develop what they term “shared judgment” across product, design, and engineering teams. This involves fostering a collective ability to discern when AI is beneficial, when it hinders progress, and how it impacts the decision-making process for future product development.
Melissajeanperri explains the foundational concept behind Product Institute:
“This is the gap Product Institute was built around long before AI made it urgent. Shared judgment across product, design, and engineering. When AI helps. When it gets in the way. What changes in how your team decides what to build next. That muscle is what compounds.”
Companies that are successfully leveraging AI, in Melissajeanperri’s view, are those that prioritize investment in this critical thinking muscle. Their training expenditures focus on enhancing decision-making capabilities rather than merely teaching tool functionalities.
Investing in Decision-Making, Not Just Tools
Melissajeanperri contrasts superficial “AI training” with the more impactful approach of cultivating strategic thinking. The former might involve learning keyboard shortcuts or basic prompt engineering, while the latter focuses on how teams collaborate, evaluate options, and make informed decisions in an AI-augmented environment.
As Melissajeanperri concludes:
“The companies pulling ahead with AI right now are the ones investing in that muscle. Their training spend changes how their teams make decisions, not which keyboard shortcuts they know.”
The post concludes by posing a vital question to readers: “What kind of training has actually changed how your team makes decisions in the last six months?” This prompts reflection on the true nature of effective AI integration and development within modern workplaces.
📝 About This Content
This article is based on insights shared by Melissajeanperri on LinkedIn.
📅 Originally posted on June 7, 2026 | View original post on LinkedIn →