Knowledge Hub:

Our Knowledge Hub brings together expert insights, real-world examples, and emerging research on human-centered AI adoption. Whether you're guiding a team or exploring your own transformation, these articles offer practical perspectives to help you build confidence, trust, and capability in a rapidly changing world.

Featured Post

Hidden Factor Makes AI Adoption Work

A controlled pilot in financial services reveals the missing ingredient Everyone's rolling out “transformative” AI tools. Most are wondering why adoption stays flat. We ran an experiment with 137 employees at a financial institution deploying Microsoft Copilot. Two matched groups. Same self-paced training materials. But one group got something extra: four team-coaching sessions over eight weeks. Topics focused on psychological safety, peer learning, and solving real work prob

Research

Peter Stefanyi

Nov 3, 20253 min read

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Colaborix Articles

The Canary in the Codebase

Peter Stefanyi, Ph.D., MCC, Colaborix GmbH March, 2026 What Junior Tech Jobs Reveal About the Real Future of Work In 2026, the economy does not look broken. Unemployment remains relatively low.GDP hasn’t collapsed.Wages, on average, have not imploded. And yet something fundamental is shifting. Not in the headlines. In the hiring pipeline. The first place we can see it clearly is in tech — and specifically in early-career coding and knowledge work roles. The Calm Surface: Macr

Peter Stefanyi

Mar 35 min read

AI Didn’t Burn Us Out.

We Burned Ourselves Out by Letting AI Run Ahead of Structure. Peter Stefanyi, Ph.D., MCC, Colaborix GmbH February, 2026 About a year ago, we started Colaborix GmbH with what felt like a solid and timely ambition:to help organizations adopt AI with humans — through facilitation, team coaching, and thoughtful consulting. At the time, that already felt like a differentiated stance.Most of the market was focused on tools. We wanted to focus on people and teams. What we didn’t r

Peter Stefanyi

Feb 264 min read

When AI Increases Work Load Instead of Reducing It

What new research reveals—and why “work-type design” is the missing fix Peter Stefanyi, Ph.D., MCC, Colaborix GmbH February, 2026 In February 2026, researchers at UC Berkeley Haas reported a finding that should make every CEO and transformation leader pause: generative AI didn’t free up time at work—it intensified work. ( Haas News | UC Berkeley Haas ) Their eight-month ethnographic study inside a ~200-person tech company observed a clear pattern: employees moved faster, ex

Peter Stefanyi

Feb 255 min read

AI Doesn’t Break Teamwork — Invisible AI Does

A structural explanation for why AI adoption and “collaboration pushes” fail — and the simplest fix. Peter Stefanyi, Ph.D., MCC, Colaborix GmbH February, 2026 1) The paradox Most leaders have the same experience: AI tools roll out quickly, usage looks high, and yet the business impact is uneven. In parallel, collaboration initiatives often produce more meetings than results. Post-mortems blame culture, readiness, or resistance. These explanations are comforting. They also mis

Peter Stefanyi

Feb 23 min read

Why Most AI and Collaboration Initiatives Fail

And the One Structural Mistake Leaders Keep Making Peter Stefanyi, Ph.D., MCC, Colaborix GmbH January 2026 Over the last few years, we’ve seen reports of the same pattern repeat itself across industries: A global rollout of AI tools that excites everyone—then quietly stalls. A major “collaboration push” that fills calendars but doesn’t move results. A Lean or Agile transformation that works brilliantly in one unit and collapses in another. The post-mortems usually sound famil

Peter Stefanyi

Jan 294 min read

Colaborix AI Adoption Research

Colaborix Flexi Team System Designing Human–AI Production Systems Across Work Types

Peter Stefanyi, Ph.D., MCC, Colaborix GmbH February, 2026 0. Executive Abstract This manual defines the Colaborix Flexi Team System : a structural operating model enabling small teams to continuously reconfigure roles, meeting formats, decision rights, and AI usage based on the dominant work type . The system integrates: 4 Work Types as “work physics” (independent, sequential, reciprocal, complex/mixed) Team performance architecture : structural design gates capability devel

Peter Stefanyi

Feb 36 min read

Task Interdependence as a Design Constraint: Restoring Thompson’s Typology to Match Organizational Interventions to Work Structure

Peter Stefanyi, Ph.D., MCC. Colaborix GmbH, January 2026 Abstract Organizations invest heavily in interventions intended to improve performance—training, teamwork programs, collaboration platforms, process improvement systems, and more. Yet results are highly variable, and the same intervention can succeed dramatically in one context and fail in another. We argue that a major source of this variance is conceptual: research and practice frequently treat “task interdependence”

Peter Stefanyi

Jan 298 min read

AI Adoption Without Cognitive Decline: A Practical Guide for Professionals

Peter Stefanyi, Ph.D., MCC, Colaborix GmbH January 2026 Executive Summary The Core Problem : AI makes you faster, but are you getting smarter or just more dependent? This guide translates decades of research on GPS, calculators, and automation into practical wisdom for AI adoption. We identify three key factors that determine whether AI amplifies your capabilities or quietly erodes them: How you use AI (augmentation vs. replacement) How much you use it (occasional vs. cons

Peter Stefanyi

Jan 1411 min read

From Augmentation to Dependence: A Unified Theory of Cognitive Delegation in the Age of Generative AI

V6.1 From Augmentation to Dependence: A Unified Theory of Cognitive Delegation in the Age of Generative AI Peter Stefanyi, Ph.D., MCC, Colaborix GmbH , January 2026 Abstract The rapid adoption of generative artificial intelligence (AI) has reignited concerns about cognitive decline and technological dependence. We present a unified theoretical framework grounded in validated research on GPS navigation, calculators, search engines, and human-automation interaction. The model i

Peter Stefanyi

Jan 1427 min read

Why “Research-Backed” Often Means Nothing — And How to Read Articles More Critically

In a world of AI hype, it's important to consider research quality. We increasingly see articles that sound convincing because they include a citation , graph, reference to a study, or make the claim “research shows that…” And yet, many of these pieces are marketing, product advocacy, or ideology wrapped in scientific language. The problem is not that research is quoted. The problem is that research quality is rarely questioned . Not all research is equal Science is not a s

Peter Stefanyi

Dec 29, 20252 min read

Knowledge Hub:

Featured Post

Hidden Factor Makes AI Adoption Work

Colaborix Articles

The Canary in the Codebase

AI Didn’t Burn Us Out.

When AI Increases Work Load Instead of Reducing It

AI Doesn’t Break Teamwork — Invisible AI Does

Why Most AI and Collaboration Initiatives Fail

Colaborix AI Adoption Research

Colaborix Flexi Team System Designing Human–AI Production Systems Across Work Types

Task Interdependence as a Design Constraint: Restoring Thompson’s Typology to Match Organizational Interventions to Work Structure

AI Adoption Without Cognitive Decline: A Practical Guide for Professionals

From Augmentation to Dependence: A Unified Theory of Cognitive Delegation in the Age of Generative AI

Why “Research-Backed” Often Means Nothing — And How to Read Articles More Critically

Colaborix Flexi Team System Designing Human–AI Production Systems Across Work Types