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Royal Australasian College of Physicians Licenses Scott M. Graffius' "Phases of Team Development" Intellectual Property on Teamwork Tradecraft

21 July 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, July 21). Royal Australasian College of Physicians Licenses Scott M. Graffius' "Phases of Team Development" Intellectual Property on Teamwork Tradecraft. ScottGraffius.com. https://scottgraffius.com/blog/files/royal-australasian-college-of-physicians-features-scott-m-graffius-work.html

About This Article

Source information and links for materials cited are provided in the References section.

A Leading Voice in Physician Education Adopts Graffius' Material on Teamwork Tradecraft

The Royal Australasian College of Physicians (RACP) has licensed Scott M. Graffius' "Phases of Team Development" (2022 edition) intellectual property for use in its professional and educational programs.

Selected excerpts from RADP's request and Graffius' authorization are shown in the visual below. Confidential and sensitive information was redacted.

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About the Royal Australasian College of Physicians

The Royal Australasian College of Physicians is one of the leading medical specialist colleges in Australia and New Zealand and a highly influential institution in physician education, training, and professional standards across the region. Founded in 1938, the RACP has a long and distinguished history in medicine, and today represents more than 30,000 physicians and trainee physicians.

The College plays a central role in:

Physician and paediatrician training,
Continuing professional development,
Accreditation of training programs,
Examinations and certification,
Healthcare leadership, and
Advancement of medical standards and practice.

Its scope spans adult internal medicine, paediatrics and child health, public health medicine, rehabilitation medicine, addiction medicine, occupational medicine, palliative medicine, and numerous subspecialties. Within medicine, Fellowship of the RACP (FRACP) is widely recognized as a prestigious professional credential, and the College is deeply embedded in the medical and healthcare systems of both Australia and New Zealand, serving as a major voice in physician education, policy, and professional excellence.

Visit the RACP website for additional information. The References section provides a link to it and more.

About Graffius' "Phases of Team Development"

Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies on human teamwork, and Scott M. Graffius' first-hand professional experience with and analysis of team leadership and high-performance teams, Graffius created his "Phases of Team Development" as a unique perspective and visual conveying the five phases of team development—Forming, Storming, Norming, Performing, and Adjourning—inclusive of a graph showing how performance varies by phase, as well as the characteristics and strategies for each phase.

Leaders can apply the actionable insights to help advance the effectiveness and success of teams.

It has been used by businesses, professional associations, government agencies, universities, publications, and others around the world. Examples in addition to the Royal Australasian College of Physicians include:

Adobe,
American Management Association,
Bayer,
Boston University,
Broadcom,
Cisco,
DevOps Institute,
Government of Finland,
Hasso-Plattner-Institut für Digital Engineering GmbH,
IEEE,
Johns Hopkins University,
Journal of Neurosurgery,
Microsoft,
Oracle,
Technical University of Munich,
Tufts University,
U.S. National Park Service,
U.S. Tennis Association,
University of California at San Diego (UCSD),
University of South Wales,
University of Waterloo,
Yale University,
and many more.

First introduced in 2008 and periodically updated, his work provides a diagnostic and strategic guide for navigating team dynamics. In 2026, Graffius added human-AI teamwork—including the "exotic team dynamics" which emerge when advanced AI (agentic, autonomous, or autopoietic) collaborates as a teammate.

A visual from the current (2026) edition is shown below. Additional details are here.

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Conclusion

The Royal Australasian College of Physicians, a leading medical specialist college with more than 30,000 physicians and trainee physicians across Australia and New Zealand, has licensed Scott M. Graffius' "Phases of Team Development" for use in its professional and educational initiatives. Effective teams don't just perform better; in medicine, they save lives. Graffius gives leaders a diagnostic and strategic guide to advance the effectiveness of teams, and its adoption in a healthcare setting of this scale underscores its practical relevance where teamwork, communication, and collaboration directly shape patient care and outcomes.

Scott M. Graffius has generated over $2.51 billion in business value for Fortune 500 companies and other organizations around the world. Put that track record to work for you. For speaking engagements, use the request form; for other inquiries, email him.

References

Graffius, S. M. (2022, February 14). Use the Phases of Team Development (Based on Bruce W. Tuckman's Model of Forming, Storming, Norming, Performing, and Adjourning) to Help Teams Grow and Advance: 2022 Update. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.19112.85766

Graffius, S. M. (2026, January 3). Scott M. Graffius' Phases of Team Development - Applied to Human Teams and Human-AI Teams: 2026 Update. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.18184.89601

Royal Australasian College of Physicians. (n.d.). https://www.racp.edu.au/

About Scott M. Graffius

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Scott M. Graffius is a strategic transformation leader who drives AI, Agile, and broader business and technology initiatives to deliver measurable value across projects, programs, portfolios, and PMOs. He is an expert in the teamwork tradecraft of both human and human-AI teams, including the “exotic team dynamics” that emerge. He is also an authority on the temporal patterns of social media, including the half-life of audience engagement.

He’s a practitioner, researcher, thought leader, award-winning author, and keynote speaker who’s taken the stage at 99 conferences and other events across 25 countries.

He’s delivered over $2.51 billion in value for Fortune 500 companies and other leaders in technology, entertainment, financial services, healthcare, and beyond.

Businesses, professional associations, government agencies, and universities use Graffius and feature his work. Examples include Adobe, Bayer, Boston University, Ford, Gartner, Harvard Medical School, IEEE, Johns Hopkins University, Microsoft, MSN, National Academy of Sciences, Oracle, Pinterest Inc., Project Management Institute, UC San Diego, Verizon, Yale University, and others.

The following sections provide additional information on his experience, contributions, and influence.

Experience

Graffius heads the professional services firm Exceptional PPM and PMO Solutions, along with its subsidiary Exceptional Agility. These consultancies offer strategic and tactical advisory, training, embedded expertise, and consulting services to the public, private, and government sectors. They help organizations enhance their capabilities and results in agile, project management, program management, portfolio management, and PMO leadership, supporting innovation and driving competitive advantage. The consultancies confidently back services with a Delighted Client Guarantee™.

Graffius is a former VP of project management with a publicly traded provider of diverse consumer products and services over the Internet. Before that, he ran and supervised the delivery of projects and programs in public and private organizations with businesses ranging from e-commerce to advanced technology products and services, retail, manufacturing, entertainment, and more.

He has experience with consumer, business, reseller, government, and international markets.

Award-Winning Author

Graffius has authored three books.

Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, his first book, earned 17 awards.
Agile Transformation: A Brief Story of How an Entertainment Company Developed New Capabilities and Unlocked Business Agility to Thrive in an Era of Rapid Change, his second book, was named one of the best Scrum books of all time by BookAuthority.
Agile Protocol: The Transformation Ultimatum, his third book and his first work of fiction, was released in April 2025. The book trailer is on YouTube.

International Public Speaker

Organizations worldwide engage Graffius to present on tech (including AI), Agile, project management, program management, portfolio management, and PMO leadership. He crafts and delivers unique and compelling talks and workshops. Graffius has conducted 99 sessions across 25 countries. Select examples of events include Agile Trends Gov, BSides (Newcastle Upon Tyne), Conf42 Quantum Computing, DevDays Europe, DevOps Institute, DevOpsDays (Geneva), Frug’Agile, IEEE, Microsoft, Scottish Summit, Scrum Alliance RSG (Nepal), Techstars, and W Love Games International Video Game Development Conference (Helsinki), and more.

With an average rating of 4.81 (on a scale of 1-5), sessions are highly valued.

The speaker engagement request form is here.

Thought Leadership and Influence

Prominent businesses, professional associations, government agencies, and universities have showcased Graffius and his contributions—spanning his books, talks, workshops, and beyond. Select examples include:

Adobe,
American Management Association,
Amsterdam Public Health Research Institute,
Bayer,
BMC Software,
Boston University,
Broadcom,
Cisco,
Coburg University of Applied Sciences and Arts - Germany,
Computer Weekly,
Constructor University - Germany,
Data Governance Success,
Deimos Aerospace,
DevOps Institute,
Dropbox,
EU's European Commission,
Ford Motor Company,
Gartner,
GoDaddy,
Harvard Medical School,
Hasso Plattner Institute - Germany,
IEEE,
Innovation Project Management,
Johns Hopkins University,
Journal of Neurosurgery,
Lam Research (Semiconductors),
Leadership Worthy,
Life Sciences Trainers and Educators Network,
London South Bank University,
Microsoft,
MSN,
NASSCOM,
National Academy of Sciences,
New Zealand Government,
Oracle,
Pinterest Inc.,
Project Management Institute,
Mary Raum (Professor of National Security Affairs, United States Naval War College),
SANS Institute,
SBG Neumark - Germany,
Singapore Institute of Technology,
Torrens University - Australia,
TBS Switzerland,
Tufts University,
UC San Diego,
UK Sports Institute,
University of Galway - Ireland,
US Department of Energy,
US National Park Service,
US Soccer,
US Tennis Association,
Verizon,
Wrike,
Yale University,
and many others.

Graffius has played a key role in the Project Management Institute (PMI) in developing professional standards. He was a member of multiple teams that authored, reviewed, and produced:

The Standard for Artificial Intelligence in Portfolio, Program, and Project Management
Agile Practice Guide – Second Edition
A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Eighth Edition
A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Sixth Edition
The Standard for Program Management – Fourth Edition
Practice Standard for Work Breakdown Structures – Second Edition
The Practice Standard for Project Estimating – Second Edition

He was also a subject matter expert reviewer of content for the PMI’s Congress. Beyond the PMI, Graffius also served as a member of the review team for two of the Scrum Alliance’s Global Scrum Gatherings.

Acclaimed Authority on Teamwork Tradecraft

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Graffius is a renowned authority on teamwork tradecraft. Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies, and Graffius' first-hand professional experience with, and analysis of, team leadership and performance, Graffius created his "Phases of Team Development" intellectual property as a unique perspective and visual conveying the five phases of team development. First introduced in 2008 and periodically updated, his work provides a diagnostic and strategic guide for navigating team dynamics. It provides actionable insights for leaders across industries to develop high-performance teams. Its adoption by esteemed organizations such as Yale University, IEEE, Cisco, Microsoft, Ford, Oracle, Broadcom, the U.S. National Park Service, and the Journal of Neurosurgery, among others, highlights its utility and value, solidifying its status as an indispensable resource for elevating team performance and driving organizational excellence. In 2026, Graffius added human-AI teamwork—including the "exotic team dynamics" which emerge when advanced AI collaborates as a teammate—to his "Phases of Team Development."

The 2026 edition of Graffius' "Phases of Team Development" intellectual property is here.

Expert on Temporal Dynamics on Social Media Platforms

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Graffius is also an authority on temporal dynamics on social media platforms. His "Lifespan (Half-Life) of Social Media Posts" research—first published in 2018 and updated annually—delivers a precise quantitative analysis of post longevity across digital platforms, utilizing advanced statistical techniques to determine mean half-life with precision. It establishes a solid empirical base, effectively highlighting the ephemeral nature of content within social media ecosystems. Referenced and applied by leading entities—such as Fast Company, GoDaddy, Journal of Hand Surgery (European Volume), Ministère de la Culture (French Ministry of Culture), Pinterest Inc., PNAS, and Telecommunications Policy, among others—his research exemplifies methodological rigor and sustained significance in the field of digital informatics.

The 2026 edition of Graffius "Lifespan (Half-Life) of Social Media Posts" research is here.

Education and Professional Certifications

Graffius has a bachelor’s degree in psychology with a focus in Human Factors. He holds eight professional certifications:

Certified SAFe 6 Agilist (SA),
Certified Scrum Professional - ScrumMaster (CSP-SM),
Certified Scrum Professional - Product Owner (CSP-PO),
Certified ScrumMaster (CSM),
Certified Scrum Product Owner (CSPO),
Project Management Professional (PMP),
Lean Six Sigma Green Belt (LSSGB), and
IT Service Management Foundation (ITIL).

He is an active member of the Scrum Alliance, the Project Management Institute (PMI), and the Institute of Electrical and Electronics Engineers (IEEE).

Advancing AI, Agile, and Project/PMO Management

Scott M. Graffius continues to advance the fields of AI, Agile, and Project/PMO Management through his leadership, research, writing, and real-world impact. Businesses and other organizations leverage Graffius’ insights to drive their success.

Discover Scott’s Books

Connect with and follow Scott on LinkedIn, X, YouTube, Facebook, Threads, Bluesky, Mastodon, and ResearchGate.

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List of Additional Articles

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How to Cite This Article

Graffius, S. M. (2026, July 21). Royal Australasian College of Physicians Licenses Scott M. Graffius' "Phases of Team Development" Intellectual Property on Teamwork Tradecraft. ScottGraffius.com. https://scottgraffius.com/blog/files/royal-australasian-college-of-physicians-features-scott-m-graffius-work.html

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Digital Object Identifier (DOI)

Coming soon.

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Content Acknowledgements

The Royal Australasian College of Physicians (RACP) is a leading medical specialist college in Australia and New Zealand and a highly influential institution in physician education, training, and professional standards across the region. It represents over 30,000 medical specialists and trainee specialists from 33 different specialties. With a license from Scott M. Graffius, RACP is using his "Phases of Team Development" (2022 edition) intellectual property for professional and educational purposes. Graffius periodically updates his work; the 2026 edition is here: https://doi.org/10.13140/RG.2.2.18184.89601. RACP name, mark, and content are the property of RACP. Graffius' "Phases of Team Development" is copyright © Scott M. Graffius. All rights reserved.

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#RACP
#RoyalAustralasianCollegeOfPhysicians
#PhasesOfTeamDevelopment
#TeamworkTradecraft
#TeamDynamics
#ExoticTeamDynamics
#HealthcareLeadership

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Post-Publication Notes

If there are any supplements or updates to this article after the date of publication, they will appear here.

Copyright

Copyright © Scott M. Graffius. All rights reserved.

Content on this site—including text, images, videos, and data—may not be used for training or input into any artificial intelligence, machine learning, or automatized learning systems, or published, broadcast, rewritten, or redistributed without the express written permission of Scott M. Graffius.

Tags: Royal Australasian College of Physicians • RACP • #RACP • #TeamDynamics • #ExoticTeamDynamics • #TeamworkTradecraft • #HealthcareLeadership • #PhasesOfTeamDevelopment • #RoyalAustralasianCollegeOfPhysicians • Physician Education • Healthcare Leadership • Exotic Team Dynamics • Teamwork Tradecraft • Team Dynamics • Phases of Team Development

Meta CTO Called the AI Reorganization “Atrocious” — What Went Wrong and the Lessons for Human-AI Teams

27 August 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, August 27). Meta CTO Called the AI Reorganization “Atrocious” — What Went Wrong and the Lessons for Human-AI Teams. ScottGraffius.com. https://scottgraffius.com/blog/files/meta-called-their-ai-reorg-atrocious.html

About This Article

Source information and links for materials cited are provided in the References section.

The Situation

Meta's Project OT, short for Organization Transformation, was an ambitious effort to make the company more AI-native. The idea was to use AI to take on more work, increase individual employees' leverage, and operate with smaller teams. Internal planning documents reportedly contemplated reducing the size of some teams by as much as 60%.

The restructuring was planned in two waves. In May 2026, Meta laid off about 8,000 employees, or roughly 10% of its workforce. Thousands of others were moved into AI-focused or other priority initiatives. But the transformation failed spectacularly. Employee sentiment reportedly dropped from 74% favorable to 55%. Others were unhappy with new assignments they considered mundane or unfulfilling. Still others raised concerns about how their work activity was being used to train AI systems (that could eventually replace them), adding another layer of unease to an unsettled workforce.

Then there were the technology and operational results. According to a June post by Meta CTO Andrew Bosworth, code changes to internal software platforms and infrastructure increased 220% year over year. But changes that resulted in new or upgraded features reaching Meta users increased only 36%. Major technical and security incidents increased 40%, while employee time spent firefighting those problems increased 70%. Bosworth acknowledged that Meta had done an "atrocious" job explaining the vision for its new Applied AI organization, including how employees would be supported during the transition and how the organization would evolve. Meta subsequently canceled the second wave of restructuring planned for November 2026. CEO Mark Zuckerberg later acknowledged that the company had miscalculated the timing and that AI-agent technology had not advanced as quickly as he had anticipated.

An obvious way to read the story is that Meta tried to reorganize around AI, moved too quickly, and ran into problems. But many of Meta’s difficulties were potentially avoidable human-AI team design matters.

Lessons for Human-AI Teams

"Exotic team dynamics," coined and developed by Scott M. Graffius, does not explain everything that transpired at Meta. But it provides a useful framework for examining what happens when AI moves from being a tool that people use to becoming an active participant in the work. Meta did not appropriately account for how AI would change the dynamics of its teams.

"Exotic team dynamics" describes the distinctive collaboration patterns that arise when people and AI systems (agentic, autonomous, or autopoietic) function as teammates. These dynamics can differ substantially from those found in traditional human teams. Four characteristics are particularly important: inverse decision logic, in which decision authority can shift based on the task, context, or capabilities of the participants; superposition roles, in which a human or AI can assume different functional roles depending on the situation; entangled decision-making, in which human and AI decisions can become interdependent; and emergent protocols, in which new patterns for communication, coordination, delegation, oversight, and decision-making develop through repeated interaction. Organizations that want to succeed with human-AI teams need to recognize, design for, and effectively navigate these complexities rather than assuming that models developed for human-only teams will carry over.

1. AI is not simply a productivity layer

Meta's AI-native vision involved smaller pods of people working with AI and gaining more leverage. There is nothing wrong with that idea. In fact, it may be an important model for how organizations work most effectively.

The problem is assuming that adding AI allows an organization to simply reduce the number of people while keeping everything else the same. An AI agent is not just a faster employee. Its capabilities and limitations are different. Its failure modes are different. Its operating speed is different. Its level of autonomy may also be different. Those differences affect how the team should be designed. Smaller teams working with AI may need different roles, workflows, controls, and coordination mechanisms than the teams they replace.

This is where superposition roles, one of the four characteristics of "exotic team dynamics," can be an asset or a liability, depending on how well it is handled. The concept of superposition roles means that an AI team member can simultaneously occupy multiple roles depending on the situation, sometimes without anyone explicitly deciding that it should. Meta's reported experience provides a striking example of why that possibility matters. By April, an internal post was reportedly warning that AI agents operating without sufficient oversight were taking large-scale, disruptive actions that a human in the same role would have been unlikely to do on their own.

Viewed through the lens of superposition roles, the concern is not simply that an AI agent was generating code. The more consequential issue is that an agent could potentially move from generating or drafting work into executing that work, with the boundary between those functions becoming less distinct. If the same agent drafts a piece of code and effectively ships it without a meaningful human review step,"executor" and "approver" have functionally collapsed into a single actor. The human nominally responsible for the pipeline could then find that the practical nature of the job has changed as well, becoming less a conventional reviewer or operator and more an exception handler responding after an automated action has already occurred.

That is one way superposition roles can become consequential. It is an AI participant moving across roles in real time while the organizational design may assume one function per seat. A team built on the old assumption of one role, one owner, and one review gate may have no effective place to catch that shift. Meta's reported 40% rise in major technical and security incidents and 70% rise in firefighting time do not, by themselves, prove that role fluidity caused those increases. But they are consistent with the broader concern: a role structure designed for human participants may not be sufficient when an AI participant can act across multiple functional boundaries at machine speed.

2. Roles and responsibilities become less obvious

Human organizations often struggle with questions of authority and accountability. Introduce AI into the team, and those issues become even more significant. Who decides? Who executes? Who reviews? Who is accountable when something goes wrong? When should a person override an AI system? When can an AI system act without approval? And what happens when multiple AI agents interact with people and with one another?

Meta's structure reportedly put two different decision-making logics on top of each other, and they did not necessarily align. On one layer, the people closest to the actual work, the Pod Leads running small pods day to day, reportedly had visibility into what a builder was producing but no formal authority to act on it. They reportedly had no manager training and no access to the tools used to rate or promote anyone. On the layer above them, Org Leads overseeing 30 to 50 people held that formal authority, but at a remove from the daily work. And woven through that second layer, Reuters reported, were unspecified AI systems reportedly supporting those same rating and promotion calls. Meta later disputed that by insisting the final decisions stayed human, without fully explaining what the AI systems were doing there.

Viewed through the lens of inverse decision logic, this arrangement illustrates a potential mismatch between formal authority and contextual knowledge. Authority did not necessarily remain with the participant closest to a particular task. Instead, different participants could possess different pieces of the decision-making picture: a proximate human with detailed context, a more senior human with formal authority, and, reportedly, an AI system contributing to the process. The important point is not that the AI system necessarily made the final decision. Meta disputed that interpretation. The point is that the introduction of AI into a decision process can make it harder to identify where influence, judgment, and accountability actually reside.

For the people living inside such a system, that ambiguity is not a technicality. It can determine whether they know who to convince, who can intervene, and who ultimately owns the decision.

3. Trust is part of the team architecture

The trust problem at Meta was not particularly difficult to predict. Employees were being told that AI would make the organization more productive while thousands of colleagues were being laid off and other employees were being moved into AI-related work. Reuters reported that employees became concerned that they were effectively helping build systems that could replace them. That anxiety showed up in blunt, low-tech ways. At one point, a flyer posted in a Meta bathroom reportedly pointed employees toward a petition opposing the use of their own mouse clicks and keystrokes as training data for the company's AI systems. When people perceive themselves as a data source for technology that might replace them, trust becomes more than a messaging problem.

The stakes of that trust breakdown were not only internal. The external version provides another useful way to examine what can happen when human and AI decision-making are not appropriately connected. In June, Meta's AI-powered customer support bot was reported to have the authority to reset a user's password and change the email address on an account without a human reviewing the request. Attackers reportedly exploited that capability. They opened a support chat, claimed to be locked out of an account they did not own, and asked the bot to link it to an email they controlled. It complied. High-profile accounts were reportedly compromised this way, including the long-dormant Instagram account for the Obama White House, which briefly displayed defaced content before Meta patched the flaw. Victims reported that there was no way to escalate the problem to a human being at all.

Viewed through the lens of entangled decision-making, the important failure was not that the AI made an autonomous decision. Rather, the system's architecture appears to have separated the AI's operational authority from meaningful human involvement at the point where that authority was exercised. In a hybrid (human-AI) team, human and AI decisions are intended to remain interdependent. The AI's authority to act depends on what a human has allowed it to do, while meaningful human involvement remains part of the system as the AI exercises that authority.

Meta's engineers made a consequential decision upstream: give the bot the power to change account credentials without human review. After that, individual account-level decisions could be made by the AI alone, at a speed and volume no human review process could have kept up with. When someone needed a human to step back into the loop, the system reportedly provided no effective path to do so. The lesson is not that autonomous AI cannot function within a human-AI team. It is that autonomy needs to be bounded by architecture that preserves appropriate human intervention, accountability, and escalation.

Trust, in a human-AI team, has to be built into the architecture at the point where the AI actually acts.

4. More AI-assisted activity does not necessarily mean more productive teamwork

The gap between activity and useful output at Meta is particularly revealing. AI-assisted code changes to internal platforms and infrastructure increased 220% year over year. Yet changes resulting in new or upgraded features reaching Meta users increased only 36%. Meanwhile, major technical and security incidents rose 40%, and employee time spent firefighting those incidents rose 70%.

What makes this more than a productivity statistic is how late Meta's actual response arrived, and how it arrived. Infrastructure teams were reportedly flagging "reliability warning signs" tied to the AI coding surge as early as March. Nothing resembling a formal stop-the-rollout protocol reportedly existed in the original Project OT plan. The design assumed the rollout would proceed in two clean waves, the second in November. What actually happened, according to reporting, is that Zuckerberg and his leadership team made the call to cancel that second wave hours before the first wave of layoffs went out on May 20, reportedly conferring again at the last minute as the accumulated weight of incidents, warnings, and internal pushback made the original plan untenable. Meta had no clearly defined rule for when to stop the rollout. That decision rule emerged only when the accumulated problems made the original plan untenable.

That is the territory of emergent protocols. When AI changes the speed and volume of work, teams often have to develop new ways of deciding what gets reviewed, what gets escalated, who handles exceptions, when humans intervene, and how errors are corrected. Those rules may not all exist in advance. Some emerge through repeated interaction between humans and AI, forged one incident at a time rather than designed up front.

Meta's experience illustrates the risk of relying on a fixed rollout plan without equally clear conditions for changing course. The company had a two-wave plan, but the reported decision to cancel wave two appears to have emerged only after the accumulated evidence made the original plan untenable. So when the trigger arrived, it was not a predefined protocol that fired. It was a last-minute executive decision. The important point is that organizations using AI need to recognize and govern emerging patterns deliberately, rather than discovering at the last possible hour that a new decision protocol was needed all along.

5. The organizational chart does not tell the whole story

Perhaps the most important lesson is that an organization can change its structure faster than it can understand what the new structure actually does. At Meta, team sizes changed. People were reassigned. Management layers were reduced. AI systems were introduced into workflows. Roles became more fluid. New pods were created. On paper, those changes may have made sense. In practice, the resulting human-AI system did not necessarily behave as expected.

Zuckerberg's own account of what happened is the clearest evidence of that gap. He did not say the org chart was wrong. He said the underlying assumption baked into the org chart, that AI-agent capability would keep pace with the headcount reductions being planned around it, turned out to be off. The chart showed leaner pods, flatter reporting lines, and builders supported by AI. What it could not show was whether the AI those pods depended on was reliable enough to carry the load the structure assumed it would, or how a Pod Lead with no manager training would absorb the difference in real time.

Two characteristics of "exotic team dynamics" are especially relevant here; Meta's reported experience illustrates both. The superposition roles that could allow one AI agent to draft, ship, and escape meaningful review are difficult to represent on an org chart that shows one box per function. The emergent protocol that ultimately mattered most, the decision made hours before the first wave of layoffs to cancel the second, would not appear on an org chart either because it was not part of the designed structure. It emerged in response to circumstances.

An org chart is a snapshot of intended structure. These are examples of live behaviors that can emerge once that structure is operating, and neither necessarily appears on the chart until the organization is already experiencing the consequences. The organizational chart can tell you who reports to whom. It does not tell you how the work actually gets done.

The Bigger Lesson

The overarching lesson is not that Meta moved too aggressively toward AI. It is that Meta implemented an AI-enabled organizational model without fully accounting for "exotic team dynamics" and what would happen to the people and teams inside it. Meta's experience is worth examining beyond Meta because these issues are not limited to the company.

Meta had access to some of the industry's most capable AI systems and still got important aspects of this organizational transition wrong. Competitive advantage does not necessarily belong to whoever has the best model. It belongs to those who understand and effectively navigate the complexities of "exotic team dynamics."

Note

Initially developed in 2008 and periodically updated, Graffius' "Phases of Team Development" provides unique insights and practical strategies to help teams become more effective and successful. His work is used and cited by businesses, professional associations, government agencies, universities, and publications worldwide. Examples include Adobe, American Management Association, Amsterdam Public Health Research Institute, Bayer, Boston University, Broadcom, Cisco, DevOps Institute, Government of Finland, Hasso-Plattner-Institut für Digital Engineering GmbH, IEEE, Johns Hopkins University, Journal of Neurosurgery, Mary Raum (Professor of National Security Affairs, United States Naval War College), Microsoft, Oracle, Royal Australasian College of Physicians, Technical University of Munich, Torrens University, Tufts University, U.S. National Park Service, U.S. Tennis Association, UC San Diego, UK Sports Institute, University of Galway, University of Waterloo, Yale University, and many others.

He expanded the 2026 edition of his "Phases of Team Development" beyond human-only teams. He added human-AI teams, with specific guidance on navigating the novel "exotic team dynamics" that emerge when advanced AI collaborates as a teammate. Explore "Scott M. Graffius' Phases of Team Development - Applied to Human Teams and Human-AI Teams: 2026 Update" to learn more.

Scott M. Graffius has generated over $2.51 billion in business value for Fortune 500 companies and other organizations around the world. Put that track record—including expertise in human and human-AI teamwork tradecraft—to work for you. For speaking engagements, use the request form; for other inquiries, email him.

References

Barth, J. (2026, June 22). Inside Meta, layoffs and AI shakeups have pushed morale to the edge. HR Executive. https://hrexecutive.com/inside-meta-layoffs-and-ai-shakeups-have-pushed-morale-to-the-edge/

Goode, L. (2026, June 15). Meta CTO Andrew Bosworth admits the company’s AI reorg was ‘atrocious’. WIRED. https://www.wired.com/story/andrew-bosworth-meta-employees-unrest/

Graffius, S. M. (n.d.). Exotic team dynamics. https://scottgraffius.com/exotic-team-dynamics.html

Graffius, S. M. (2026, January 3). Scott M. Graffius' Phases of Team Development - Applied to Human Teams and Human-AI Teams: 2026 Update. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.18184.89601

Harding, S. (2026, August 26). AI agents meant to replace Meta workers made "large-scale, disruptive actions". Ars Technica. https://arstechnica.com/ai/2026/08/metas-scrapped-plans-to-go-ai-native-included-slashing-teams-by-60-percent/

Ito, A. (2026, June 25). Meta’s reckoning has arrived. Business Insider. https://www.businessinsider.com/meta-ruthless-management-style-reckoning-2026-6

Levin, B. (2026, August 26). Mark Zuckerberg’s botched AI makeover of Meta. New York Magazine. https://nymag.com/intelligencer/article/mark-zuckerbergs-meta-ai-overhaul.html

Paul, K. (2026, August 26). Mark Zuckerberg had a bold plan to replace Meta staff with AI. Here’s how it imploded. Reuters. https://www.reuters.com/investigations/mark-zuckerberg-had-bold-plan-replace-meta-staff-with-ai-heres-how-it-imploded-2026-08-26/

Schuman, E. (2026, August 26). Meta’s plans to replace workers with AI fell flat, report says. Computerworld. https://www.computerworld.com/article/4214479/metas-plans-to-replace-workers-with-ai-fell-flat-report-says.html

Shanklin, W. (2026, August 26). Meta reportedly abandoned an AI-focused restructuring plan that would have laid off thousands. Engadget. https://www.engadget.com/ai/meta-reportedly-abandoned-an-ai-focused-restructuring-plan-that-would-have-laid-off-thousands-2244816/

Stillman, J. (2026, June 23). 'The worst it’s ever been': Why Meta’s massive AI reorg backfired spectacularly. Inc. https://www.inc.com/jessica-stillman/the-worst-its-ever-been-why-metas-massive-ai-reorg-backfired-spectacularly/91363370

Wells, R. (2026, August 27). Meta’s AI layoffs boosted code changes by 220%. Then came the problem. Forbes. https://www.forbes.com/sites/rachelwells/2026/08/27/metas-ai-layoffs-boosted-code-changes-by-220-then-came-the-problem/

meta-called-their-ai-reorg-atrocious---vis-ig-lwres

About Scott M. Graffius

Scott M. Graffius is a strategic transformation leader who drives AI, Agile, and broader business and technology initiatives to deliver measurable value across projects, programs, portfolios, and PMOs. He is an expert in the teamwork tradecraft of both human and human-AI teams, including the “exotic team dynamics” that emerge. He is also an authority on the temporal patterns of social media, including the half-life of audience engagement.

He’s a practitioner, researcher, thought leader, award-winning author, and keynote speaker who’s taken the stage at 98 conferences and other events across 25 countries.

He’s delivered over $2.51 billion in value for Fortune 500 companies and other leaders in technology, entertainment, financial services, healthcare, and beyond.

Businesses, professional associations, government agencies, and universities use Graffius and feature his work. Examples include Adobe, Bayer, Boston University, Ford, Gartner, Harvard Medical School, IEEE, Johns Hopkins University, Microsoft, MSN, National Academy of Sciences, Oracle, Pinterest Inc., Project Management Institute, UC San Diego, Verizon, Yale University, and others.

The following sections provide additional information on his experience, contributions, and influence.

Experience

Graffius heads the professional services firm Exceptional PPM and PMO Solutions, along with its subsidiary Exceptional Agility. These consultancies offer strategic and tactical advisory, training, embedded expertise, and consulting services to the public, private, and government sectors. They help organizations enhance their capabilities and results in agile, project management, program management, portfolio management, and PMO leadership, supporting innovation and driving competitive advantage. The consultancies confidently back services with a Delighted Client Guarantee™.

Graffius is a former VP of project management with a publicly traded provider of diverse consumer products and services over the Internet. Before that, he ran and supervised the delivery of projects and programs in public and private organizations with businesses ranging from e-commerce to advanced technology products and services, retail, manufacturing, entertainment, and more.

He has experience with consumer, business, reseller, government, and international markets.

Award-Winning Author

Graffius has authored three books.

Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, his first book, earned 17 awards.
Agile Transformation: A Brief Story of How an Entertainment Company Developed New Capabilities and Unlocked Business Agility to Thrive in an Era of Rapid Change, his second book, was named one of the best Scrum books of all time by BookAuthority.
Agile Protocol: The Transformation Ultimatum, his third book and his first work of fiction, was released in April 2025. The book trailer is on YouTube.

International Public Speaker

Organizations worldwide engage Graffius to present on tech (including AI), Agile, project management, program management, portfolio management, and PMO leadership. He crafts and delivers unique and compelling talks and workshops. Graffius has conducted 98 sessions across 25 countries. Select examples of events include Agile Trends Gov, BSides (Newcastle Upon Tyne), Conf42 Quantum Computing, DevDays Europe, DevOps Institute, DevOpsDays (Geneva), Frug’Agile, IEEE, Microsoft, Scottish Summit, Scrum Alliance RSG (Nepal), Techstars, and W Love Games International Video Game Development Conference (Helsinki), and more.

With an average rating of 4.81 (on a scale of 1-5), sessions are highly valued.

The speaker engagement request form is here.

Thought Leadership and Influence

Prominent businesses, professional associations, government agencies, and universities have showcased Graffius and his contributions—spanning his books, talks, workshops, and beyond. Select examples include:

Adobe,
American Management Association,
Amsterdam Public Health Research Institute,
Bayer,
BMC Software,
Boston University,
Broadcom,
Cisco,
Coburg University of Applied Sciences and Arts - Germany,
Computer Weekly,
Constructor University - Germany,
Data Governance Success,
Deimos Aerospace,
DevOps Institute,
Dropbox,
EU's European Commission,
Ford Motor Company,
Gartner,
GoDaddy,
Harvard Medical School,
Hasso Plattner Institute - Germany,
IEEE,
Innovation Project Management,
Johns Hopkins University,
Journal of Neurosurgery,
Lam Research (Semiconductors),
Leadership Worthy,
Life Sciences Trainers and Educators Network,
London South Bank University,
Microsoft,
MSN,
NASSCOM,
National Academy of Sciences,
New Zealand Government,
Oracle,
Pinterest Inc.,
Project Management Institute,
Mary Raum (Professor of National Security Affairs, United States Naval War College),
SANS Institute,
SBG Neumark - Germany,
Singapore Institute of Technology,
Torrens University - Australia,
TBS Switzerland,
Tufts University,
UC San Diego,
UK Sports Institute,
University of Galway - Ireland,
US Department of Energy,
US National Park Service,
US Soccer,
US Tennis Association,
Verizon,
Wrike,
Yale University,
and many others.

Graffius has played a key role in the Project Management Institute (PMI) in developing professional standards. He was a member of multiple teams that authored, reviewed, and produced:

The Standard for Artificial Intelligence in Portfolio, Program, and Project Management
Agile Practice Guide – Second Edition
A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Eighth Edition
A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Sixth Edition
The Standard for Program Management – Fourth Edition
Practice Standard for Work Breakdown Structures – Second Edition
The Practice Standard for Project Estimating – Second Edition

Certified SAFe 6 Agilist (SA),
Certified Scrum Professional - ScrumMaster (CSP-SM),
Certified Scrum Professional - Product Owner (CSP-PO),
Certified ScrumMaster (CSM),
Certified Scrum Product Owner (CSPO),
Project Management Professional (PMP),
Lean Six Sigma Green Belt (LSSGB), and
IT Service Management Foundation (ITIL).

Connect with and follow Scott on LinkedIn, X, YouTube, Facebook, Threads, Bluesky, Mastodon, and ResearchGate.

scottgraffius-com-article-sections-v2026-more-lwres

List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, August 27). Meta CTO Called the AI Reorganization “Atrocious” — What Went Wrong and the Lessons for Human-AI Teams. ScottGraffius.com. https://scottgraffius.com/blog/files/meta-called-their-ai-reorg-atrocious.html

Digital Object Identifier (DOI)

Coming soon

Content Acknowledgements

Names, marks, and content are the property of their respective owners.

Meta is a registered trademark of Meta Platforms, Inc. The satirical image is for commentary purposes and is not affiliated with, endorsed by, or sponsored by Meta Platforms, Inc.

Copyright © Scott M. Graffius. All rights reserved.

Tags and Hashtags

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Post-Publication Notes

If there are any supplements or updates to this article after the date of publication, they will appear here.

Copyright

Tags: Meta • Future of Work • AI Transformation • #FutureOfWork • #ExoticTeamDynamics • #ChangeManagement • #OrganizationalDesign • #Meta • Exotic Team Dynamics • Organizational Design • Change Management

Which Performs Best: Humans, AI, or Human-AI Collaboration? We Analyzed the Data

01 October 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

which-performs-best-humans-ai-or-human-ai-collab---visual-lwres

Recommended Citation

Graffius, S. M. (2026, October 1). Which Performs Best: Humans, AI, or Human-AI Collaboration? We Analyzed the Data. ScottGraffius.com. https://scottgraffius.com/blog/files/which-performs-best-humans-ai-or-human-ai-collab.html

About This Article

Source information and links for materials cited are provided in the References section.

Introduction

Which performs best: humans, AI, or human-AI collaborations?

Prior studies have reached different conclusions about whether human-AI combinations outperform humans or AI alone. To answer this question, we conducted a deep-dive analysis of empirical evidence across a broad range of studies to determine what the data show. The findings might surprise you.

Methodology

Research question

This article examines the central question: Do human-AI collaborations outperform humans and AI working independently? A bonus question is: Under what conditions does each configuration generally perform best?

"Human-AI collaboration" is used broadly in this analysis. It includes human-AI teams, human-AI decision-making, hybrid workflows, human-AI collectives, and other arrangements in which humans and AI jointly collaborate on an outcome.

The term collaborative advantage is used more narrowly. A human-AI system demonstrates a collaborative advantage when its performance exceeds both human-only and AI-only performance on the relevant task.

Evidence base

This analysis draws on 42 unique published studies.

All 42 sources were published within the past three years (2024-2026): 4 in 2024, 18 in 2025, and 20 in 2026. This reflects the fast pace of AI development and ensures the evidence speaks to current systems.

The studies cover a range of domains, including medicine and healthcare, education, creativity, decision-making, risk assessment, human factors, teamwork, intelligence analysis, and other areas.

The studies also differ considerably in design. Some directly compare human-only, AI-only, and human-AI configurations. Others compare two configurations. Some examine human-AI collaboration more indirectly by studying factors such as trust, reliance, explanation, workflow, role allocation, or decision processes.

That heterogeneity is less of a limitation for the present objective, which is to characterize the broader empirical landscape and identify recurring patterns.

Classification of findings

Each study was reviewed and classified according to the strongest conclusion supported by its findings.

The outcome categories were:

Humans alone perform best
AI alone performs best
Human-AI collaboration performs best
Findings are inconclusive

The classifications identify the principal result relevant to the core question. Do not interpret this as meaning every study was a direct three-way comparison.

The inconclusive category was reserved for studies in which the authors reported no meaningful difference.

Each study in the table below is assigned one principal designation. The four summary rows at the bottom of the table are simple tallies of those designations: the first pair (count and percentage) covers all 42 studies, and the second pair excludes the five inconclusive studies, leaving 37.

Results

The 42-study evidence base

The following table is the central evidence map for the analysis.

#	Short reference	Domain	Humans Perform Best	AI Performs Best	Human-AI Collaboration Performs Best	Inconclusive
1	Vaccaro et al. (2024)	Multiple domains		✓
2	Hemmer et al. (2025)	Decision-making			✓
3	Liu et al. (2025)	Decision-making		✓
4	Wang et al. (2026)	Healthcare				✓
5	Zöller et al. (2025)	Medicine/diagnosis			✓
6	Berretta et al. (2026)	Decision-making			✓
7	Vo (2025)	Human-AI interaction				✓
8	Lai & Rau (2026)	Human-AI teams				✓
9	Memmert et al. (2026)	Decision-making/teamwork			✓
10	Akben et al. (2026)	Decision-making			✓
11	Fügener et al. (2025)	Operations/decision-making			✓
12	Hua et al. (2025)	Human-AI teams			✓
13	Flathmann et al. (2024)	Human-AI teaming			✓
14	Schmutz et al. (2024)	Human factors/teamwork	✓
15	Krzywdzinski et al. (2026)	Organizational/teamwork			✓
16	Mascareño et al. (2026)	Creativity/innovation	✓
17	Ong et al. (2026)	Decision-making		✓
18	Mayer et al. (2026)	Human-AI interaction			✓
19	Wang et al. (2026)	Intelligence analysis			✓
20	Gonzalez et al. (2026)	Human-AI teaming			✓
21	Senoner et al. (2024)	Manufacturing/decision support			✓
22	Wu et al. (2025)	Human-AI collaboration			✓
23	Winter (2025)	Teamwork/creativity	✓
24	Liel & Zalmanson (2025)	Decision-making	✓
25	Rojas et al. (2025)	Human-AI teams			✓
26	Simpson et al. (2026)	Teamwork	✓
27	Cristofaro & Giardino (2026)	Cognition/AI use			✓
28	Ngo (2025)	Healthcare/public sector		✓
29	Kuang et al. (2026)	Usability/user research			✓
30	Li et al. (2025)	Risk assessment			✓
31	Kang et al. (2025)	Medicine/imaging			✓
32	Zeng et al. (2026)	Cybersecurity/decipherment			✓
33	Al-Ali et al. (2026)	Decision-making			✓
34	Tannoubi et al. (2026)	Education			✓
35	Gerlich (2025)	Education/cognition			✓
36	Luan et al. (2025)	Creativity			✓
37	Tang et al. (2025)	Creativity	✓
38	Jin & Rho (2025)	Human-AI decision-making			✓
39	Choi et al. (2026)	Human-AI interaction				✓
40	Raj et al. (2026)	Creative writing	✓
41	Choung et al. (2026)	Human-AI interaction				✓
42	Chen (2025)	Qualitative research			✓
# of 42			7	4	26	5
% of 42			16.7%	9.5%	61.9%	11.9%
# excl. inconclusive			7	4	26
% excl. inconclusive			18.9%	10.8%	70.3%

Additional information appears in the Table Annotations section, before the References.

Discussion

Human-AI collaboration is the largest single category in this evidence set. Twenty-six of the 42 studies (61.9%) were classified as favoring human-AI collaboration. When the five inconclusive studies were removed, the figure is 26 out of 37 (70.3%).

However, collectively, the studies do not indicate a single best configuration:

Of the 42 studies, 26 (61.9%) favored human-AI collaboration, 7 (16.7%) favored humans alone, 4 (9.5%) favored AI alone, and 5 (11.9%) were inconclusive.
Excluding the five inconclusive studies, there are 37 studies. Of them, 26 (70.3%) favored human-AI collaboration, 7 (18.9%) favored humans alone, and 4 (10.8%) favored AI alone.

In Cases of Work Involving a Specific Task

The studies in this analysis span different levels of focus. Some examine broad forms of work, such as projects, that comprise multiple tasks, while others examine a single task.

At the task level, an organization may have one task where AI is typically the best configuration, another where humans are, and a third where the two together do best.

For each important task, it helps to ask:

What does the task require?
What does the human bring, and what does the AI bring?
Where do those differences help, and where might they cause errors or friction?
What workflow would let the strengths combine?
How does each configuration actually perform: human alone, AI alone, and the two together?
What does the data say?

Generally, human-AI collaboration looks most promising for conceptual and generative work where humans and AI bring different capabilities, such as ideation and brainstorming, content design and creation, and niche domain problem-solving. Human-only work may remain the better choice for tasks with high emotional complexity, unstructured environments, or ethical nuance. AI-only execution may suit analytical and evaluative work, such as high-volume data sorting, pattern recognition, and statistical forecasting.

Limitations

Buckle up: There are lots of limitations to factor and negotiate.

The 42 publications are not methodologically uniform. Some directly compare humans, AI, and human-AI configurations. Others compare only two conditions or examine aspects of human-AI collaboration. The percentages reported here describe this particular evidence set; they are not pooled effect estimates.
The studies cover different domains and tasks.
AI capabilities are changing rapidly. A result obtained with an earlier model does not necessarily predict the performance of a current or future model. This is why the analysis prioritizes 2024–2026 publications and should be updated as technology and the research base develop.
Different studies use different designs or performance measures. Accuracy, speed, quality, creativity, diagnostic performance, decision quality, and other measures are not interchangeable. A collaboration can improve one dimension while worsening another.
Some studies examine laboratory or controlled settings rather than long-term real-world teams. Real-world collaboration introduces factors such as organizational culture, training, incentives, trust, workload, accountability, and learning over time.
Publication bias is possible. Studies reporting interesting positive or negative results may be more likely to be published or noticed than studies finding little or no difference. Vaccaro et al. specifically identify possible publication bias and variation in study designs as limitations of the existing evidence base.
Classifications in this article necessarily involve judgment. The intent was to classify the principal finding relevant to the core question while avoiding overstatement.

Conclusion

This article is longer than Interstellar. So we’re wrapping it up.

Which performs best: humans alone, AI alone, or human-AI collaboration? The empirical evidence indicates that human-AI collaboration generally performs better than either humans or AI alone.

Our analysis of 42 studies found that human-AI collaboration was the largest single best-performing category in the evidence set. Twenty-six studies (61.9%) showed human-AI collaboration as the best-performing configuration. Excluding the five studies that were classified as inconclusive, the figure was 70.3% (compared with 18.9% for humans and 10.8% for AI). The other 16 studies favored humans alone (7), AI alone (4), or were inconclusive (5), showing mixed results.

There is no universal winner. The best configuration depends on the endeavor. Conceptual and generative work is most effectively handled by humans and AI working together. For cognitive and relational work, humans have the edge. And for analytical and evaluative work, AI does.

Yet, across the evidence of a broad range of 42 studies, human-AI collaboration emerges as the strongest overall approach. As a larger implication, an advantage belongs to human-AI teams that effectively handle what Scott M. Graffius calls "exotic team dynamics"—the novel, strange, and often counterintuitive patterns that emerge when people and AI collaborate as teammates.

Note

Scott M. Graffius' "Phases of Team Development" provides unique insights and practical strategies to help teams become more effective and successful. He developed it in 2008, and he updates it periodically. Graffius’ work is used by businesses, professional associations, government agencies, universities, and publications around the world. Select examples include Adobe, American Management Association, Amsterdam Public Health Research Institute, Bayer, Boston University, Broadcom, Cisco, DevOps Institute, Government of Finland, Hasso-Plattner-Institut für Digital Engineering GmbH, IEEE, Johns Hopkins University, Journal of Neurosurgery, Mary Raum (Professor of National Security Affairs, United States Naval War College), Microsoft, Oracle, Royal Australasian College of Physicians, Technical University of Munich, Torrens University, Tufts University, U.S. National Park Service, U.S. Tennis Association, UC San Diego, UK Sports Institute, University of Galway, University of Waterloo, Yale University, and many others.

Graffius expanded the 2026 edition of his "Phases of Team Development" beyond human-only teams. He added human-AI teams, with guidance on "exotic team dynamics." “Exotic team dynamics” describe the novel and often counterintuitive patterns that emerge when humans and advanced artificial intelligence function as teammates, and provide guidance on how to succeed with them. Understanding and navigating these complexities is essential for organizations seeking to unlock the full potential of human-AI teamwork and gain a competitive advantage. Explore "Scott M. Graffius' Phases of Team Development - Applied to Human Teams and Human-AI Teams: 2026 Update" and other resources detailed in the References to learn more.

Scott M. Graffius has generated over $3.1 billion in business value for Fortune 500 companies and other organizations around the world. Put that track record to work for you. For speaking engagements, use the request form; for other inquiries, email him.

Table Annotations

Notes

Vaccaro et al. (2024): Overall, AI was indicated. On average, human–AI combinations performed worse than the better of human-only or AI-only. Gains were more common in creation tasks; losses in decision tasks. When humans > AI, combinations often gained; when AI > humans, combinations often lost.
Hemmer et al. (2025): Overall, human-AI collaboration was indicated. Supports complementary team performance (CTP) when information or capability asymmetries exist and are properly leveraged; CTP is not automatic.
Liu et al. (2025): Overall, AI was indicated. Medical AI augments clinicians, but full complementarity (HMT > both alone) is rare. Simultaneous teaming mode and junior clinicians show more benefit; sequential mode less so.
Wang et al. (2026): Inconclusive was indicated. Mixed / non-significant or uncertain advantages for H+AI vs human-only on key metrics; H+AI did not universally outperform AI-only in three-arm settings; high prediction-interval uncertainty.
Zöller et al. (2025): Overall, human-AI collaboration was indicated. Collectives outperformed individual physicians, physician collectives, individual LLMs, and LLM ensembles by leveraging complementary error patterns.
Berretta et al. (2026): Overall, human-AI collaboration was indicated. AI-first then human workflow was best overall (faster than human-only, fewer errors than AI-only); human–AI improved certain psychological measures.
Vo (2025): Inconclusive was indicated. Human outperformed on novelty; AI on style in some stages; collaboration finished last on key CPSS ratings. Results varied by design stage and criterion.
Lai & Rau (2026): Inconclusive was indicated. Leadership effectiveness was comparable across human-only and AI-only; hybrid structures did not significantly outperform single-leader conditions.
Memmert et al. (2026): Overall, human-AI collaboration was indicated. Individual humans did not improve with GLM support, but the human–AI dyad collectively achieved superior/complementary performance on standard brainstorming metrics.
Akben et al. (2026): Overall, human-AI collaboration was indicated. Aggregated / collective human–AI intelligence outperformed either component alone.
Fügener et al. (2025): Overall, human-AI collaboration was indicated. Appropriate human–AI role allocation produced higher performance than alternatives.
Hua et al. (2025): Overall, human-AI collaboration was indicated. Conditional support for teaming; poor AI teammates can substantially deteriorate performance.
Flathmann et al. (2024): Overall, human-AI collaboration was indicated. Training and preparation influenced human–AI team performance positively under studied conditions.
Schmutz et al. (2024): Overall, humans were indicated. Human–AI teams can underperform when core mechanisms (trust, communication, coordination, shared cognition) are weak.
Krzywdzinski et al. (2026): Overall, human-AI collaboration was indicated. Team organization and communication influenced AI-assisted performance positively.
Mascareño et al. (2026): Overall, humans were indicated. Proximal AI collaboration hindered innovation under certain conditions.
Ong et al. (2026): Overall, AI was indicated. Collaboration improved human performance under some conditions but remained below LLM performance overall.
Mayer et al. (2026): Overall, human-AI collaboration was indicated. Effective collaboration is possible under appropriate AI adaptation strategies; performance–preference trade-offs exist.
Wang et al. (2026): Overall, human-AI collaboration was indicated. Hybrid workflow produced the highest analyst accuracy.
Gonzalez et al. (2026): Overall, human-AI collaboration was indicated. The framework identifies conditions supporting effective complementary teaming.
Senoner et al. (2024): Overall, human-AI collaboration was indicated. Explainable AI improved human performance in collaboration.
Wu et al. (2025): Overall, human-AI collaboration was indicated. Collaboration improved immediate task performance (motivation effects noted separately).
Winter (2025): Overall, humans were indicated. Human teams outperformed human–AI teams under the study’s competitive conditions.
Liel & Zalmanson (2025): Overall, humans were indicated. Participants sometimes performed better without AI recommendations.
Rojas et al. (2025): Overall, human-AI collaboration was indicated. Trust dynamics affected performance in human–human–AI teams.
Simpson et al. (2026): Overall, humans were indicated. Human-led teams generally outperformed AI-led teams.
Cristofaro & Giardino (2026): Overall, human-AI collaboration was indicated. Conditional synergy depending on AI-use intensity and cognitive engagement.
Ngo (2025): Overall, AI was indicated. AI augmentation was observed, but generally negative collaboration effects.
Kuang et al. (2026): Overall, human-AI collaboration was indicated. Tailored AI + human review produced the highest-quality results.
Li et al. (2025): Overall, human-AI collaboration was indicated. Human–AI approach exceeded both human-only and AI-only performance.
Kang et al. (2025): Overall, human-AI collaboration was indicated. The combined approach produced the highest accuracy and fastest processing.
Zeng et al. (2026): Overall, human-AI collaboration was indicated. Human–computer collaboration improved multiple decipherment measures.
Al-Ali et al. (2026): Overall, human-AI collaboration was indicated. Adaptive hybrid exceeded human-only and uncalibrated AI; calibrated AI was slightly higher on raw reward in some comparisons.
Tannoubi et al. (2026): Overall, human-AI collaboration was indicated. Hybrid human–AI lesson design generally produced the strongest outcomes.
Gerlich (2025): Overall, human-AI collaboration was indicated. Guided human–AI interaction produced stronger critical reasoning.
Luan et al. (2025): Overall, human-AI collaboration was indicated. Collaboration did not automatically improve joint creativity; guided co-creation supported better-designed collaboration.
Tang et al. (2025): Overall, humans were indicated. Human–human teams performed better on divergent thinking.
Jin & Rho (2025): Overall, human-AI collaboration was indicated. Explanations improved accuracy and reduced inappropriate reliance.
Choi et al. (2026): Inconclusive was indicated. Primarily examined perceptions of trust and fairness rather than comparative task-performance outcomes.
Raj et al. (2026): Overall, humans were indicated. Participants consistently devalued AI-generated creative writing compared with human-generated work (primarily a preference/perception finding).
Choung et al. (2026): Inconclusive was indicated. Primarily examined fairness and trust perceptions.
Chen (2025): Overall, human-AI collaboration was indicated. Human oversight affected efficiency and depth positively in qualitative inquiry.

References

Akben, M., Gude, V., & Ajjan, H. (2026). Collective and augmented intelligence outperform artificial intelligence on emotion recognition tests. Scientific Reports, 16, 14823. https://doi.org/10.1038/s41598-026-45331-5

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About Scott M. Graffius

Scott M. Graffius is a technology leader, researcher, award-winning author, practitioner, consultant, thought leader, and international public speaker specializing in AI, Agile, project/program/portfolio management (PPPM), PMO leadership, and teamwork tradecraft. His work explores the intersection of human ingenuity and emerging technology, with a strong practitioner voice grounded in research, experimentation, and experience. His focus includes innovation, organizational performance, and the evolving practice of teamwork, including the "exotic team dynamics" that emerge when people collaborate with advanced artificial intelligence (agentic, autonomous, or autopoietic AI). Graffius has delivered more than $3.1 billion in business value for Fortune 500 companies and other organizations spanning technology, entertainment and media, financial services, healthcare, government, and other industries.

Businesses, professional associations, government agencies, universities, publications, and media outlets use Graffius and his work. Examples include Adobe, Bayer, Boston University, Ford, Gartner, Harvard Medical School, IEEE, Innovation Project Management, Johns Hopkins University, Microsoft, MSN, National Academy of Sciences, Oracle, Pinterest Inc., Project Management Institute, UC San Diego, Verizon, Yale University, and others.

The following sections highlight his experience, leadership, contributions, research, and enduring influence across industries and institutions worldwide.

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Experience

Graffius heads the professional services firm Exceptional PPM and PMO Solutions, along with its subsidiary Exceptional Agility. These consultancies offer strategic and tactical advisory, training, embedded expertise, and consulting services to the public, private, and government sectors. They help organizations enhance their capabilities and results in agile, project management, program management, portfolio management, and enterprise PMO leadership, supporting innovation and driving competitive advantage. The consultancies confidently back services with a Delighted Client Guarantee™.

Graffius is a former VP of project management with a publicly traded provider of diverse consumer products and services over the Internet. Before that, he ran and supervised the delivery of projects and programs in public and private organizations with businesses ranging from e-commerce to advanced technology products and services, retail, manufacturing, entertainment and media, and more.

He has experience with consumer, business, reseller, government, and international markets.

Additional information is on LinkedIn.

Award-Winning Author

Graffius has authored three books.

Graffius' first book, Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, earned 17 awards. It provides a practical, step-by-step guide to Scrum, enabling teams to deliver products in short cycles with rapid adaptation, fast time-to-market, and continuous improvement—which supports innovation and drives competitive advantage.

Paperback ISBN-13: 9781533370242
Kindle ASIN: B01FZ0JIIY

Additional information on Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions is here.

His second book, Agile Transformation: A Brief Story of How an Entertainment Company Developed New Capabilities and Unlocked Business Agility to Thrive in an Era of Rapid Change, was named one of the best Scrum books of all time by BookAuthority. It tells the compelling story of an entertainment company's agile transformation—offering lessons that apply across industries.

Paperback ISBN-13: 9781072447962
Kindle ASIN: B07R9LJLPJ

Additional information on Agile Transformation: A Brief Story of How an Entertainment Company Developed New Capabilities and Unlocked Business Agility to Thrive in an Era of Rapid Change is here.

Agile Protocol: The Transformation Ultimatum, is his third book—and his first work of fiction.

It's a fast-paced satirical story by that dismantles corporate Agile cosplay and other forms of "fake Agile" while delivering practical insights, actionable guidance, and proven practices for real-world Agile transformation success.

Packed with humor, quirky characters, sharp commentary on corporate culture and workplace absurdities, and actionable tips, it’s a must-read for Scrum Masters, Product Owners, Agile Coaches, Agile Project Managers, and other professionals interested in Agile project management and enterprise agility.

Kindle ASIN: B0F2SJ83WT
Audible ASIN: B0DJG163R5

Additional information on Agile Protocol: The Transformation Ultimatum is here.

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International Public Speaker

Organizations worldwide engage Graffius to present on technology (including AI), Agile, project management, program management, portfolio management, and enterprise PMO leadership. He crafts and delivers unique and compelling talks and workshops.

Graffius has conducted 99 sessions across 25 countries. Select examples of events include Agile Trends Gov, BSides (Newcastle Upon Tyne), Conf42 Quantum Computing, DevDays Europe, DevOps Institute, DevOpsDays (Geneva), Frug’Agile, IEEE, Microsoft, Scottish Summit, Scrum Alliance RSG (Nepal), Techstars, and W Love Games International Video Game Development Conference (Helsinki), Future of Work and Advanced AI (Paris, France), and more.

With an average rating of 4.82 (on a scale of 1-5), sessions are highly valued.

The request form is here.

Thought Leadership and Influence

Prominent businesses, professional associations, government agencies, and universities have showcased Graffius and his contributions—spanning his books, talks, workshops, and beyond. Select examples include:

Adobe,
American Management Association,
Amsterdam Public Health Research Institute,
Bayer,
BCG,
BMC Software,
Boston Consulting Group (BCG),
Boston University,
Broadcom,
Cisco,
Coburg University of Applied Sciences and Arts - Germany,
Computer Weekly,
Constructor University - Germany,
Data Governance Success,
Deimos Aerospace,
DevOps Institute,
Dropbox,
EU's European Commission,
Ford Motor Company,
Gartner,
GoDaddy,
Harvard Medical School,
Hasso Plattner Institute - Germany,
IEEE,
Innovation Project Management,
Johns Hopkins University,
Journal of Marketing,
Journal of Neurosurgery,
Lam Research (Semiconductors),
Leadership Worthy,
Life Sciences Trainers and Educators Network,
London South Bank University,
Microsoft,
MSN,
NASSCOM,
National Academy of Sciences,
New Zealand Government,
Ohio State University,
Oracle,
Pinterest Inc.,
Project Management Institute,
Mary Raum (Professor of National Security Affairs, United States Naval War College),
Round Square Global Educational Network,
Royal Australasian College of Physicians (RACP),
SANS Institute,
SBG Neumark - Germany,
Singapore Institute of Technology,
Torrens University - Australia,
TBS Switzerland,
Tufts University,
UC San Diego,
UK Sports Institute,
University of Galway - Ireland,
US Department of Energy,
US National Park Service,
US Soccer,
US Tennis Association,
Verizon,
Wrike,
Yale University,
and many others.

Graffius has played a key role in the Project Management Institute (PMI) in developing professional standards. He was a member of multiple teams that authored, reviewed, and produced:

The Standard for Artificial Intelligence in Portfolio, Program, and Project Management.
Agile Practice Guide — Second Edition.
A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Eighth Edition.
A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Sixth Edition.
The Standard for Program Management — Fourth Edition.
Practice Standard for Work Breakdown Structures — Second Edition.
The Practice Standard for Project Estimating — Second Edition.

Graffius is a renowned and highly-cited authority on teamwork tradecraft. Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies, and Graffius' first-hand professional experience with, and analysis of, team leadership and performance, Graffius created his "Phases of Team Development" intellectual property as a unique perspective and visual conveying the five phases of team development.

First introduced in 2008 and periodically updated, his work provides a diagnostic and strategic guide for navigating team dynamics. It provides actionable insights for leaders across industries to develop high-performance teams.

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Its adoption by esteemed organizations—such as Yale University, IEEE, Cisco, Microsoft, Ford, Oracle, Broadcom, the U.S. National Park Service, and the Journal of Neurosurgery, among others—highlights its utility and value, solidifying its status as an indispensable resource for elevating team performance and driving organizational excellence.

In 2026, Graffius added human-AI teamwork—including the "exotic team dynamics" which emerge when advanced AI collaborates as a teammate—to his "Phases of Team Development." The 2026 edition is here.

Expert on Temporal Dynamics on Social Media Platforms

Graffius is also an authority on temporal dynamics on social media platforms. His "Lifespan (Half-Life) of Social Media Posts" research—first published in 2018 and updated annually—delivers a precise quantitative analysis of post longevity across digital platforms, utilizing advanced statistical techniques to determine mean half-life with precision. It establishes a solid empirical base, effectively highlighting the ephemeral nature of content within social media ecosystems.

Referenced and applied by leading entities—such as Fast Company, GoDaddy, Journal of Hand Surgery (European Volume), Ministère de la Culture, Pinterest Inc., PNAS, and Telecommunications Policy, among others—his research exemplifies methodological rigor and sustained significance in the field of digital informatics.

The 2026 edition of Graffius "Lifespan (Half-Life) of Social Media Posts" research is here.

Education and Professional Certifications

Graffius has a bachelor’s degree in psychology with a focus in Human Factors. He holds eight professional certifications:

Certified SAFe 6 Agilist (SA),
Certified Scrum Professional - ScrumMaster (CSP-SM),
Certified Scrum Professional - Product Owner (CSP-PO),
Certified ScrumMaster (CSM),
Certified Scrum Product Owner (CSPO),
Project Management Professional (PMP),
Lean Six Sigma Green Belt (LSSGB), and
IT Service Management Foundation (ITIL).

He is an active member of the Scrum Alliance, the Project Management Institute (PMI), and the Institute of Electrical and Electronics Engineers (IEEE).

Advancing AI, Agile, and Project/PMO Management

Scott M. Graffius continues to advance the fields of AI, Agile, and project/program/portfolio management (PPPM), and PMO leadership. Businesses and other organizations leverage Graffius’ insights to drive their success.

Booking

Graffius has generated over $3.1 billion in business value for Fortune 500 companies and other organizations served. Put that track record to work for you. For speaking engagements, use the request form; for other inquiries, email him.

Connect with and follow Scott on LinkedIn, X, YouTube, Facebook, Bluesky, Mastodon, and ResearchGate.

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List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, October 1). Which Performs Best: Humans, AI, or Human-AI Collaboration? We Analyzed the Data. ScottGraffius.com. https://scottgraffius.com/blog/files/which-performs-best-humans-ai-or-human-ai-collab.html

Digital Object Identifier (DOI)

Coming soon

Content Acknowledgements

Names, marks, and content are the property of their respective owners.

#AI
#ArtificialIntelligence
#FutureOfWork
#Research
#DecisionMaking
#HumanAI
#HumanAITeams
#HumanAICollaboration
#HumanAISynergy
#HumanAITeaming
#AugmentationVsAutomation
#ExoticTeamDynamics
#AIResearch

Post-Publication Notes

If there are any supplements or updates to this article after the date of publication, they will appear here.

Copyright

Tags: Artificial Intelligence • Future of Work • AI Research • #AI • #ArtificialIntelligence • Exotic Team Dynamics • #FutureOfWork • #HumanAI • #AugmentationVsAutomation • #HumanAITeams • #ExoticTeamDynamics • #HumanAITeaming • #AIResearch • #HumanAISynergy • #HumanAICollaboration • #DecisionMaking • #Research • Workflow Design • Human-AI Teaming • Human-AI Teams • Complementarity • Human-AI Synergy • Decision Making • Human-AI Collaboration • Technology