AI as a Multiplier, Not an Equalizer: Melissa Perri Analyzes Impact on Product Teams

In a recent LinkedIn post, Melissa Perri discusses the unexpected impact of Artificial Intelligence (AI) on product teams, revealing that it is not the great equalizer many had hoped for, but rather a powerful multiplier of existing organizational models.

Perri, host of the Product Thinking with Melissa Perri Podcast, shared insights derived from the State of AI in Product 2026 survey, which polled 309 product leaders. Contrary to the expectation that AI would level the playing field for product development, Perri’s analysis suggests it amplifies pre-existing conditions within organizations.

“The finding that stuck with me most is the one in this clip. AI is not leveling the field. It is acting like a multiplier.”

AI’s Amplifying Effect on Organizational Models

According to Melissa Perri, organizations that entered the AI era with a mature and healthy operating model are experiencing AI as a strengthening force. Conversely, those with pre-existing weaknesses are finding that AI exacerbates their existing problems.

Perri highlights a stark contrast in how different-sized organizations are perceiving AI’s impact. “Organizations that already had a mature, healthy operating model before AI are far more likely to say it is strengthening them, and far less likely to say it is making things worse,” she notes. This trend is particularly evident when examining the size of teams reporting on AI’s effects.

The Discrepancy in Small vs. Large Organizations

The survey data, as analyzed by Perri, shows a significant difference in AI’s perceived benefit based on company size. Small teams report AI strengthening their operating model at a rate of 48%. However, this figure drops considerably for larger organizations.

“Small teams report AI strengthening their operating model at 48%. Organizations of 500 or more drop to 20%, even though the big companies are spending more, training more, and hiring more for AI.”

This finding is particularly counterintuitive, given that larger companies are often investing more heavily in AI, including increased spending, training, and hiring. Despite these greater investments, they are reporting lower levels of positive impact from AI on their operating models.

The Uncomfortable Truth About Pre-existing Models

Perri delivers an uncomfortable but crucial message: AI does not inherently fix broken systems. Instead, it magnifies whatever was in place before its integration.

“Whatever your operating model was before AI, AI just gives you more of it,” Melissa Perri argues. “If it was already broken, AI does not fix it, it scales the cracks.” This perspective challenges the notion that AI can be a silver bullet for operational inefficiencies, emphasizing the foundational importance of a robust operating model.

Call to Reflection

Perri concludes by posing a reflective question to her audience, prompting them to consider the specific outcomes of AI integration within their own organizations:

“If you brought AI into a shaky operating model, what has it amplified, the strengths or the cracks?”

Her analysis, presented through her podcast and social media, encourages business leaders to critically assess their internal structures and how AI is interacting with them, suggesting that a solid foundation is key to harnessing AI’s potential effectively.