Top 5 Roles on AI Teams

09 October 2023

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Effective Artificial Intelligence (AI) teams leverage critical hard skills and soft skills to develop and deliver successful AI products, including innovative solutions that address real-world challenges and have a positive impact on the world. (If you’d like to learn about the critical skills leveraged by effective AI teams, select here for our popular article on the topic.) This article focuses on the roles on AI teams.

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The composition (size and roles) of AI teams varies by organization based on the respective company’s needs, the nature of the AI endeavor including complexity, and other factors. However, AI teams commonly include (at least these) five positions. Here are the five along with brief descriptions, listed in alphabetical order:

AI Research Scientist: AI research scientists are responsible for conducting research and developing cutting-edge AI algorithms and models.
AI Software Engineer: AI software engineers are responsible for integrating AI components into software applications. They bridge the gap between AI research and practical implementation, making AI systems usable by end-users.
Data Engineer: Data engineers are responsible for building and managing the data pipelines that feed AI models. They ensure that data is collected, cleaned, and prepared in a format suitable for analysis and model training.
Data Scientist: Data scientists are experts in data analysis and interpretation. They preprocess, analyze, and extract valuable insights from data, which is vital for training and improving AI models.
Machine Learning Engineer: Machine learning engineers focus on implementing and deploying machine learning models into practical applications. They work to ensure that AI models are integrated into software systems effectively.

The above five roles collectively cover key aspects of AI development — from research and model creation to data management and software integration. Depending on requirements and characteristics (such as degree of complexity), additional positions may be needed.

An expanded list of 20 roles common to AI teams follow. Here are the aforementioned 5 and 15 additional positions, listed in alphabetical order:

AI DevOps Engineer: Combines development and operations skills to streamline AI model deployment. They automate tasks.
AI Ethicist: Ensures that AI systems are developed and used responsibly and ethically.
AI Infrastructure Engineer: Builds and maintains the infrastructure required for AI model deployment and scalability.
AI Legal and Compliance Specialist: Ensures AI projects comply with legal and regulatory requirements.
AI Marketing Specialist: Promotes and markets AI products and solutions to customers and stakeholders.
AI Operations (AIOps) Specialist: Focuses on the operational aspects of AI, including monitoring, maintenance, and troubleshooting.
AI Product Manager: Oversees the development and deployment of AI products.
AI Project Manager: Leverages general and specialized hard skills and soft skills, and applies their understanding of many aspects of artificial intelligence (such as machine learning, deep learning, automation, robotics, and natural language processing) to manage the planning, execution, and delivery of AI projects. Note: Titles vary by organization; the position may be called an AI Project Manager, AI Program Manager, Agile Project Manager, Technical Project Manager, Scrum Master, or another related designation.
AI QA Engineer: Ensures the quality and reliability of AI models through testing and validation.
AI Research Scientist: Conducts research to develop new AI algorithms and models.
AI Software Engineer: Builds and maintains software systems that incorporate AI components.
AI Trainer/Annotator: Prepares and labels data for training AI models, especially for supervised learning.
AI UX/UI Designer: Designs user interfaces and experiences for AI-powered applications.
Computer Vision Engineer: Specializes in developing AI models for image and video analysis.
Data Analyst: Examines data to uncover trends and insights that can guide AI projects.
Data Engineer: Manages data pipelines and prepares data for analysis and model training.
Data Scientist: Analyzes and interprets data to extract insights and inform AI models.
Deep Learning Engineer: Focuses on deep neural networks and architectures.
Machine Learning Engineer: Focuses on implementing and deploying machine learning models.
Natural Language Processing (NLP) Engineer: Specializes in working with text and language-related AI tasks.

The composition (size and roles) of AI teams varies, based on the complexity of the AI project and other factors. On smaller teams, individuals often ‘wear multiple hats.’ On larger teams, individuals typically have more specialized roles.

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This article covered the roles common to AI teams. Listings are not exhaustive of all potential positions. Additionally, as the field of AI continues to evolve and advance, new roles may materialize to address emerging challenges and opportunities.

(If you’d like to learn about the critical skills leveraged by successful AI teams, select here for our popular article on the topic.)

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References/Sources

All of the supplied links were functional when this article was published.

Apple (n.d.). Careers at Apple: Machine Learning and AI. Available at: https://www.apple.com/careers/us/machine-learning-and-ai.html.
C3.ai (n.d.). Best Practices in Prototyping: Cross-Functional Teams. Available at: https://c3.ai/introduction-what-is-machine-learning/cross-functional-teams/.
Deloitte AI Institute (2021). Building Successful AI Teams. Available at: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-ai-institute-building-successful-AI-teams.pdf.
Gartner (2020, December 15). Staff AI Teams with Various Roles and Skills for Success. Available at: https://www.gartner.com/smarterwithgartner/how-to-staff-your-ai-team.
General Services Administration (GSA) Centers of Excellence (CoE) (n.d.). AI Guide for Government. Available at: https://coe.gsa.gov/coe/ai-guide-for-government/understanding-ai-job-roles-career-path/.
Google Quantum AI (n.d.). Careers. Available at: https://quantumai.google/team/careers.
Graffius, Scott M. (2023, May 1). AI is a Team Sport: A Confluence of Diverse Technical and Soft Skills are Crucial for Success. Available at: https://www.scottgraffius.com/blog/files/successful-ai-teams.html.
Graffius, Scott M. (2023). What Successful AI Teams Have in Common [Video]. Graffius’ talk at the 2023 Conf42 Quantum Computer Conference. Available at: https://www.youtube.com/watch?v=u2qRKFbSKYE.
IBM (n.d.). Assemble the Team to Support a Data-Driven Project. Available at: https://www.ibm.com/garage/method/practices/culture/assemble-team-for-data-driven-project/.
Intel (n.d.). Artificial Intelligence Teams: Careers. Available at: https://jobs.intel.com/en/search-jobs/artificial+intelligence/599/1.
Intuit (2020, December 8). How to avoid conflicts and delays in the AI development process (Part I). Available at: https://blogs.intuit.com/2020/12/08/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-i/.

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How to Cite This Article

Graffius, Scott M. (2023, October 9). Top 5 Roles on AI Teams. Available at: https://scottgraffius.com/blog/files/ai-team-roles.html. DOI: 10.13140/RG.2.2.34857.57449.

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About Scott M. Graffius

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Scott M. Graffius, PMP, SA, CSP-SM, CSP-PO, CSM, CSPO, SFE, ITIL, LSSGB is an agile project management practitioner, consultant, multi-award-winning author, and international keynote speaker. He is the Founder of Exceptional PPM and PMO Solutions™ and subsidiary Exceptional Agility™. He has generated over $1.9 billion of business value in aggregate for Global Fortune 500 businesses and other organizations he has served. Graffius and content from his books, talks, workshops, and more have been featured and used by businesses, professional associations, governments, and universities. Examples include Microsoft, Oracle, Broadcom, Cisco, Gartner, Project Management Institute, IEEE, Qantas, National Academy of Sciences, United States Department of Energy, New Zealand Ministry of Education, Yale University, Tufts University, and others. He has delighted audiences with dynamic and engaging talks and workshops on agile, project management, and technology (including AI, GameDev, and more) leadership at 87 conferences and other events across 25 countries.

His full bio is available here.

Connect with Scott on:

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About Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions

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Shifting customer needs are common in today's marketplace. Businesses must be adaptive and responsive to change while delivering an exceptional customer experience to be competitive.

There are a variety of frameworks supporting the development of products and services, and most approaches fall into one of two broad categories: traditional or agile. Traditional practices such as waterfall engage sequential development, while agile involves iterative and incremental deliverables. Organizations are increasingly embracing agile to manage projects, and best meet their business needs of rapid response to change, fast delivery speed, and more.

With clear and easy to follow instructions, the multi award-winning Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions book by Scott M. Graffius (Chris Hare and Colin Giffen, Technical Editors) helps the reader:

Implement and use the most popular agile framework―Scrum;
Deliver products in short cycles with rapid adaptation to change, fast time-to-market, and continuous improvement; and
Support innovation and drive competitive advantage.

Hailed by Literary Titan as “the book highlights the versatility of Scrum beautifully.”

Winner of 17 first place awards.

Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions is available in paperback and ebook/Kindle in the United States and around the world. Some links by country follow.

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About 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

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Thriving in today's marketplace frequently depends on making a transformation to become more agile. Those successful in the transition enjoy faster delivery speed and ROI, higher satisfaction, continuous improvement, and additional benefits.

Based on actual events, 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 provides a quick (60-90 minute) read about a successful agile transformation at a multinational entertainment and media company, told from the author's perspective as an agile coach.

The award-winning book by Scott M. Graffius is available in paperback and ebook/Kindle in the United States and around the world. Some links by country follow.

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The short link for this article is: https://bit.ly/ai-roles.

© Copyright 2023 Scott M. Graffius. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without the express written permission of Scott M. Graffius.

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Perplexity Standalone Article on AI Hallucinations Cites Research by Scott M. Graffius

15 February 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, February 15). Perplexity Standalone Article on AI Hallucinations Cites Research by Scott M. Graffius. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.16840.99847

Perplexity Standalone Article on AI Cites Graffius' Work

Founded in 2022, Perplexity is an AI-powered search and conversational answer platform designed to streamline information discovery by delivering concise, up-to-date responses grounded in web data and supported with citations.

Perplexity also publishes standalone articles on specific topics. It included Scott M. Graffius' research—"Are AI Hallucinations Getting Better or Worse? We Analyzed the Data"—among the sources cited in its standalone article on the subject. Excerpts are shown below.

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Details including links are in the Bibliography section.

More About Perplexity

Perplexity consistently ranks among the top 10 most popular generative AI tools based on web traffic and usage, and within the top 5 among AI search/chat assistants (Business Insider, 2025; DataStudios, 2025; Dotdesh, 2025; Liu & Wang, 2024; Similarweb, 2025; TwinStrata, 2025).

Bibliography

Business Insider. (2025, October 8). Sam Altman touts ChatGPT’s 800 million weekly users, double all its main competitors combined. BusinessInsider.com. https://www.businessinsider.com/chatgpt-users-openai-sam-altman-devday-llm-artificial-intelligence-2025-10

DataStudios. (2025). Global ranking of the most popular AI chatbots by usage. DataStudios.org. https://www.datastudios.org/post/global-ranking-of-the-most-popular-ai-chatbots-by-usage

Dotdesh. (2025). 65 most popular AI tools ranked. Dotdesh.com. https://www.dotdesh.com/65-most-popular-ai-tools-ranked-october-2025/

Graffius, S. M. (2026, January 7). Are AI Hallucinations Getting Better or Worse? We Analyzed the Data. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.33179.53285

Liu, Y., & Wang, H. (2024). Ranked: The most popular generative AI tools in 2024. Visual Capitalist. https://www.visualcapitalist.com/ranked-the-most-popular-generative-ai-tools-in-2024/

Perplexity (2026, February 12). Study Finds Celebrated AI Model Guesses Answers, Lacks Cognition. Perplexity AI stand-alone article. https://www.perplexity.ai/page/experts-say-ai-lacks-true-huma-JfKEeQvcRhCddq0tgTAiMw

Similarweb. (2025). Top AI tools: Most used Gen‑AI in August 2025. https://www.similarweb.com/blog/marketing/seo/most-used-ai/

TwinStrata. (2025). Perplexity AI statistics 2026: Key data and trends. TwinStrata.com. https://www.twinstrata.com/perplexity-ai-statistics/

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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 97 conferences and other events across 25 countries.

He’s delivered over $2.3 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 96 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:

Practice Standard for Work Breakdown Structures—Second Edition.
A Guide to the Project Management Body of Knowledge—Sixth Edition.
The Standard for Program Management—Fourth Edition.
The Practice Standard for Project Estimating—Second Edition.

Additional details are here.

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, February 15). Perplexity Standalone Article on AI Hallucinations Cites Research by Scott M. Graffius. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.16840.99847

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Digital Object Identifier (DOI)

https://doi.org/10.13140/RG.2.2.16840.99847

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Content Acknowledgements

Perplexity is a generative AI search tool that produces conversational answers with citations. It also publishes standalone articles on specific topics. Perplexity cited Scott M. Graffius' "Are AI Hallucinations Getting Better or Worse? We Analyzed the Data" research among its sources for its "Study Finds Celebrated AI Model Guesses Answers, Lacks Cognition" publication.

This piece uses limited excerpts from the respective Perplexity content, attributed to Perplexity and used under fair use for news reporting and analysis.

Perplexity name, mark, and content are the property of Perplexity AI, Inc. or its affiliates or licensors.

Graffius' content is copyright © Scott M. Graffius. All rights reserved.

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#AI
#AIHallucinations
#PerplexityAI
#ArtificialIntelligence
#AIEthics
#GenerativeAI
#AIResearch
#TechTrends

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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: Artificial Intelligence (AI) • AI Hallucinations • Perplexity AI • Reasoning Models • AI Limitations • Scott M. Graffius • Generative AI Trends • AI Research

Quantum Computing, Advanced AI Acceleration, and the Rise of Exotic Team Dynamics in Human-AI Teams

02 July 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, July 2). Quantum Computing, Advanced AI Acceleration, and the Rise of Exotic Team Dynamics in Human-AI Teams. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.14506.99525

About This Article

This article includes a references section (with links to sources) and a glossary.

Introduction

Imagine a young oncologist and an advanced AI teammate collaborating on a complex case involving a patient with aggressive, treatment-resistant cancer. Drawing on quantum-accelerated molecular simulations, the advanced AI proposes a counterintuitive therapeutic strategy that departs from established clinical protocols yet demonstrates exceptionally strong predicted efficacy and low toxicity. Within minutes, the human-AI team evaluates data that would have previously required months—or even years—of conventional modeling, laboratory experimentation, and clinical testing. The physician contributes clinical judgment, ethical oversight, and contextual interpretation; the AI contributes unprecedented analytical speed, pattern recognition, and probabilistic reasoning. This scenario exemplifies the convergence and future potential of quantum computing, advanced AI, and the "exotic team dynamics" that arise when humans and advanced AI systems collaborate. Solutions once out of reach—in medicine and other fields—can be realized, and the results can be life-changing and life-saving.

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Quantum Computing

Quantum computing harnesses principles of quantum mechanics—such as superposition and entanglement—to perform calculations using qubits that can represent multiple states simultaneously, offering potential exponential advantages over classical bits for specific problems. It is being aggressively pursued by governments, national laboratories, big tech companies, and specialized startups. The shared bet is that certain intractable problems—molecular and materials simulation, large-scale optimization, cryptography, and other complex calculations—scale so poorly on classical computers that a new computational paradigm is essential. Hybrid quantum-classical systems are particularly promising: they leverage quantum processors for specific subtasks (e.g., optimization or simulation) while relying on classical hardware for the rest.

Research on quantum state distinguishability further underscores this trajectory. Researchers at the Massachusetts Institute of Technology have demonstrated new approaches for creating and identifying quantum states with higher reliability, addressing one of the central challenges in quantum measurement and readout fidelity. Improvements in state discrimination directly strengthen error correction protocols and enhance the practicality of hybrid quantum-classical architectures by reducing ambiguity in quantum outputs (MIT, 2026).

Early applications already show potential to reduce training time and energy consumption for machine learning models, especially in data-scarce or highly complex scenarios (Quandela, 2026).

The practical implications are becoming tangible. Quantum computing is transitioning from lab curiosity to strategic infrastructure. In April 2026, IBM and MIT launched a joint research lab focused on the convergence of AI and quantum computing, aiming to surpass classical limits (IBM, 2026). This momentum accelerated in June 2026 with executive orders aimed at advancing quantum innovation and securing the nation against advanced cryptographic threats, followed by a major announcement by the U.S. Department of Energy (DOE). On June 23, 2026, the DOE announced Quantum Genesis, a major initiative to deliver the world’s first fault-tolerant, scientifically relevant quantum computing capability by 2028. This includes a DOE Q Competition, a National Quantum Supercomputing User Facility (integrating quantum computing with high-performance computing, AI, and networking), and targeted R&D. The target domains—chemistry, materials science, plasma physics, and high-energy physics—directly support progress in AI (U.S. Department of Energy, 2026).

Advanced AI

The Quantum Genesis project provides an example of how quantum infrastructure can accelerate AI. Hybrid quantum-classical architectures are already demonstrating faster AI training and reduced data requirements for certain models (Quandela, 2026).

The relationship between the two technologies is bidirectional and self-reinforcing: AI helps overcome quantum challenges in error correction, qubit control, and algorithm design (Time, 2026), while quantum systems promise breakthroughs in the very domains that fuel next-generation AI—materials for better chips, molecular simulations for drug discovery AI, and complex optimization for model efficiency. This feedback loop shortens R&D cycles.

Meaningful quantum advantages for general AI training likely require further advances in scale and error correction, with hybrid systems providing early wins in specialized tasks rather than replacing classical GPUs in the near term. The call to extend the frontier of problems solvable with quantum computing and advanced AI (Nature Machine Intelligence, 2023) remains as relevant today as when it was first issued.

Exotic Team Dynamics

As advanced AI proliferates—accelerated in part by quantum-enabled breakthroughs—"exotic team dynamics" become more common.

Coined and developed by Scott M. Graffius, "exotic team dynamics" describe the novel patterns that emerge when humans and advanced AI collaborate as teammates, rather than operating within a traditional user–tool relationship (Graffius, n.d.). It positions human–AI teaming as a frontier domain in which the integration of human judgment and AI-driven computation produces new and distinct rhythms of collaboration and decision-making.

Graffius introduced the term in an August 8, 2025, article (Graffius, 2025a) and has since expanded it through a sequence of presentations and publications. He presented it at a corporate leadership event in Las Vegas later that same month (Graffius, 2025b) and again at an event in Paris that November (Graffius, 2025c). In 2026, he incorporated "exotic team dynamics" into the updated version of his popular and highly-cited "Phases of Team Development" work, extending its applicability from human-only teams to both human-only and human-AI teams (Graffius, 2026a). In March 2026, a companion practitioner-oriented talk and article further positioned mastery of these dynamics as a leadership necessity (Graffius, 2026b).

"Exotic team dynamics" are delineated further through four physics-inspired analogies that characterize its novel patterns.

Inverse decision logic, analogous to negative mass in physics, occurs when an AI teammate generates recommendations that run counter to human intuition or established practice yet prove superior upon careful evaluation. Such situations are especially likely in complex, data-rich environments where human decision-making is influenced by cognitive biases such as confirmation bias, status quo bias, and availability heuristics. Rather than encouraging blind trust or automatic skepticism, effective organizations develop trust-calibration protocols that evaluate counterintuitive AI outputs on their merits, document the rationale behind key decisions, and refine confidence through accumulated evidence. These practices help teams capitalize on AI-driven innovation while maintaining sound governance and accountability.

Superposition roles, inspired by quantum superposition, describe an advanced AI's ability to support multiple team functions simultaneously—for example, serving as analyst, strategist, researcher, risk assessor, and communications partner without the handoff friction or context-switching costs typical of human specialization. This capability can dramatically compress project timelines and improve organizational agility. At the same time, it introduces governance challenges because a single AI may contribute across multiple domains at once. Organizations therefore benefit from defining clear human ownership and accountability for AI-supported decisions, even when AI performs many functions in parallel.

Entangled decision-making, inspired by quantum entanglement, refers to the deeply interdependent relationship between human judgment and AI reasoning. Human insights continuously reshape AI outputs, while AI-generated analyses refine human understanding, creating decisions that emerge from ongoing collaboration rather than a simple sequence of independent contributions. This interaction can produce superior outcomes in complex, high-stakes environments, but it also complicates questions of accountability and auditability. Maintaining structured records of significant human-AI interactions and decision pathways helps preserve transparency while supporting effective governance.

Emergent protocols, analogous to emergent phenomena in complex systems, are the collaboration norms, workflows, communication patterns, and operating practices that develop organically through repeated interaction between humans and advanced AI. Over time, these evolving behaviors can become a significant source of organizational efficiency and competitive advantage by improving coordination and accelerating decision-making. However, they can also reinforce unintended biases or ineffective practices if left unexamined. Periodic reviews of human-AI collaboration patterns help organizations identify valuable innovations worth formalizing while correcting undesirable drift.

"Exotic team dynamics" serves as a diagnostic tool and a practical guide for designing and managing hybrid human-AI teams, where the AI is advanced. It is particularly relevant in research and development, crisis response, strategic planning, defense and national security, healthcare, financial services, and other domains with mission-critical operations (Graffius, 2026b). More broadly, the framework reflects a shift from treating AI as a tool to collaborating with it as a teammate. That transition signals a broader reconfiguration of organizational reality itself, where teamwork—including trust, decision-making, and other functions—is no longer solely a human construct but an emergent outcome of the interplay between people and machine intelligence (Graffius, n.d.).

Conclusion

Quantum Genesis is one of several catalysts driving advances in AI capabilities. Together with related initiatives, it is accelerating the shift from AI-as-tool to AI-as-teammate—precisely where "exotic team dynamics" emerge.

The future of work will be defined by people and advanced AI working as collaborators. Organizations that invest in understanding quantum-driven AI progress and mastering “exotic team dynamics” will gain decisive advantages in innovation and competitiveness.

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. (n.d.). Exotic team dynamics. ScottGraffius.com. https://scottgraffius.com/exotic-team-dynamics.html

Graffius, S. M. (2025a, August 8). Exotic team dynamics: The new frontier of human–AI collaboration. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.18048.49921

Graffius, S. M. (2025b, August 22). Scott M. Graffius premieres his new "Exotic Team Dynamics: Human-AI Collaboration" talk at corporate event in Las Vegas. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.34380.07047

Graffius, S. M. (2025c, November 21). This is what happens when advanced AI joins your team [Presentation]. Corporate event, Paris, France.

Graffius, S. M. (2026a, 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

Graffius, S. M. (2026b, March 23). Human-AI teamwork: Master the exotic team dynamics that emerge when collaborating with advanced AI — Or be outplayed. ScottGraffius.com. https://scottgraffius.com/blog/files/human-ai-teamwork-master-the-emergent-exotic-team-dynamics-or-be-outplayed.html

IBM. (2026, April 29). The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing. IBM Newsroom. https://newsroom.ibm.com/2026-04-29-the-mit-ibm-computing-research-lab-launches-to-shape-the-future-of-ai-and-quantum-computing

MIT. (2026, June 15). How to create distinguishable states for quantum systems. MIT News. https://news.mit.edu/2026/how-to-create-distinguishable-states-for-quantum-systems-0615

Nature Machine Intelligence. (2023, August). Seeking a quantum advantage for machine learning. Nature Machine Intelligence, 5, 813. https://doi.org/10.1038/s42256-023-00710-9

Quandela. (2026, January 15). Quandela identifies four quantum computing trends for 2026. The Quantum Insider. https://thequantuminsider.com/2026/01/15/quandela-quantum-computing-trends-2026/

Time. (2026, April 7). AI helped spark a quantum breakthrough. The world "is not prepared." https://time.com/article/2026/04/07/ai-quantum-computing-advance/

U.S. Department of Energy. (2026, June 23). Energy Department announces initiative to create and deploy the world's first scientifically relevant, fault-tolerant quantum computers. https://www.energy.gov/science/articles/energy-department-announces-initiative-create-and-deploy-worlds-first

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Glossary

Advanced AI refers to emerging systems that surpass traditional tools or monolithic concepts, acting as genuine teammates in human-AI collaboration and creating exotic team dynamics, with a progression across agentic, autonomous, and autopoietic forms. Also see agentic AI, autonomous AI, and autopoietic AI.

Agentic AI is an advanced form of artificial intelligence capable of independently pursuing defined objectives through planning multi-step actions, coordinating processes, and adapting strategies to changing conditions, operating with high autonomy in execution but guided by external goals. It is commercially available and capable of planning multi-step actions, coordinating processes and tools while remaining semi-autonomous overall. Also see advanced AI.

AI. See advanced AI.

Artificial intelligence. See advanced AI.

Autonomous AI is an advanced form of artificial intelligence that operates and makes decisions independently without direct human intervention, featuring sustained self-regulation, the ability to set or adjust goals based on its environment, and action within defined or evolving parameters. It does not yet exist in commercial products. Also see advanced AI.

Autopoietic AI is an advanced, experimental, and hypothetical form of artificial intelligence characterized by self-generating, self-maintaining, and self-adaptive processes, where the system recursively regenerates its own structure, rules, and boundaries to sustain its identity in changing environments. No commercial systems currently exist. Also see advanced AI.

DOE Q Competition is a milestone-driven national competition established by the U.S. Department of Energy (DOE) as part of its Quantum Genesis initiative to accelerate the development of fault-tolerant quantum computing. It challenges participants to build quantum systems capable of performing scientifically meaningful computations, with an emphasis on reliable logical qubits rather than simply increasing physical qubit counts. The competition focuses on solving high-impact problems in areas such as chemistry, materials science, plasma physics, and high-energy physics. By fostering collaboration among industry, national laboratories, and academia, the DOE Q Competition aims to advance practical quantum computing for scientific discovery and national competitiveness.

Emergent protocols are new communication patterns, norms, and interaction protocols that evolve organically in human-AI teams without explicit programming or predefined rules. Also see exotic team dynamics.

Entangled decision-making occurs when human and AI inputs and decisions become deeply interconnected and interdependent, influencing each other to produce outcomes neither could achieve alone, potentially creating synergies or conflicts. Also see exotic team dynamics.

Exotic team dynamics (shown here without quotation marks) was coined and developed by Agile, AI, and teamwork tradecraft expert Scott M. Graffius. It refers to the novel, often counterintuitive, emerging collaboration patterns that occur when humans work alongside advanced artificial intelligences—specifically agentic, autonomous, or autopoietic AI—as teammates. Exotic team dynamics feature new rhythms of interaction, decision-making, and role-sharing that differ from traditional human-only teamwork while still relying on core principles such as trust, communication, and adaptability. Also see advanced AI, agentic AI, autonomous AI, autopoietic AI, emergent protocols, entangled decision-making, inverse decision logic, and superposition roles.

Future of work refers to the evolving nature of how work is structured, coordinated, and performed, as technologies, organizational models, and human–machine collaboration reshape roles, processes, and outcomes. It is a broad, cross-disciplinary concept that captures ongoing shifts in skills, workflows, governance, and team dynamics driven by advancements such as AI, automation, and emerging paradigms. As it relates to this article, the future of work is examined through the convergence of quantum computing, advanced AI, and exotic team dynamics, where humans and intelligent systems operate as integrated, adaptive teams, achieving capabilities that exceed what either humans or machines could accomplish independently.

GPU (graphics processing unit) refers to a specialized type of computer processor originally designed to accelerate graphics rendering, but now widely used in artificial intelligence due to its ability to perform large numbers of parallel calculations efficiently, making it well-suited for training and running deep learning models.

Human-AI teams are collaborative systems in which humans and advanced artificial intelligence—agentic, autonomous, or autopoietic AI—work as teammates. Also see advanced AI, agentic AI, autonomous AI, autopoietic AI, and exotic team dynamics.

Inverse decision logic is when AI challenges human assumptions by prioritizing counterintuitive yet optimal choices, causing decision-making to deviate from traditional logic and leading to unexpected and novel effects. Also see exotic team dynamics.

Phases of Team Development (shown here without quotation marks) is popular and highly-cited material on teamwork tradecraft developed by Scott M. Graffius. Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies on teamwork, and Graffius' 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. It includes a graph showing how performance varies by phase, as well as the characteristics and strategies for each phase. Leaders can apply these insights to advance the success of teams. Graffius initially developed his unique material in 2008, and he periodically updates it. The 2026 edition covers both human teams and human-AI teams, including the "exotic team dynamics" which emerge when collaborating with advanced AI. Businesses, professional associations, government agencies, universities, publications, media outlets, and others around the world have featured and used Graffius' Phases of Team Development.

Quantum bit. See qubit.

Quantum computing is a form of computation that uses principles of quantum mechanics to process information in ways fundamentally different from classical computing. Instead of classical bits that represent either 0 or 1, quantum computers use quantum bits (qubits), which can exist in superposition—meaning they can represent multiple states simultaneously. Qubits can also become entangled, allowing the state of one qubit to depend on another, even across distance, which enables highly correlated computations. These properties allow certain classes of problems—such as factoring large numbers, simulating quantum systems, and optimization tasks—to potentially be solved more efficiently than on classical computers. However, quantum systems are extremely sensitive to noise and decoherence, making practical, large-scale quantum computing an ongoing engineering and scientific challenge.

Quantum Genesis is a U.S. Department of Energy initiative to develop and deploy the world's first scientifically relevant, fault-tolerant quantum computing capability for research and development. As a component of the broader Genesis Mission, it integrates quantum computing with artificial intelligence, high-performance computing, and advanced scientific facilities to accelerate discovery across fields such as materials science, chemistry, physics, and energy. The initiative reflects a shift from viewing quantum computers as isolated research systems to treating them as part of a broader computational ecosystem designed to solve problems beyond the reach of classical computing alone.

Qubit (quantum bit) is the fundamental unit of information in quantum computing, analogous to the bit in classical computing. However, unlike a classical bit, which can exist only in a state of 0 or 1, a qubit can exist in a superposition of both states simultaneously, with probabilities associated with each outcome. Qubits can also become entangled with one another, meaning the state of one qubit can depend on the state of another, even when separated by distance. These properties enable quantum computers to process certain types of complex problems more efficiently than classical computers.

Superposition roles are about AI supporting or occupying multiple team roles and functions simultaneously in parallel, or dynamically shifting emphasis or sharing functions with humans based on contextual needs. Also see exotic team dynamics.

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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 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

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

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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Sign up for Miro—it's free!

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List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, July 2). Quantum Computing, Advanced AI Acceleration, and the Rise of Exotic Team Dynamics in Human-AI Teams. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.14506.99525

Digital Object Identifier (DOI)

DOI: 10.13140/RG.2.2.14506.99525

Content Acknowledgements

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

#AdvancedAI
#AdvancedTechnology
#AIResearch
#ArtificialIntelligence
#AutonomousAI
#AutopoieticAI
#EmergentProtocols
#EntangledDecisionMaking
#ExoticTeamDynamics
#FutureOfTechnology
#FutureOfWork
#GPU
#HumanAITeams
#InverseDecisionLogic
#QuantumComputing
#QuantumGenesis
#Qubit
#ResearchAndDevelopment
#SuperpositionRoles
#Technology

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: Advanced AI Acceleration • Advanced Technology • AI Research • GPU • Qubit • Human-AI teams • Inverse decision logic • Future of work • Technology • Superposition roles • Rise of Exotic Team Dynamics in Human-AI Teams • Research and Development • Quantum Genesis • Quantum Computing • Future of Technology • Exotic team dynamics • Entangled decision-making • Emergent protocols • DOE Q Competition • Autopoietic AI • Autonomous AI • Artificial intelligence

A Critical Analysis of "AI and Quantum Computers Will Be Frenemies"

25 August 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, August 25). A Critical Analysis of "AI and Quantum Computers Will Be Frenemies". ScottGraffius.com. https://scottgraffius.com/blog/files/critical-analysis-of-ai-and-quantum-computers-will-be-frenemies.html

About This Article

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

Introduction

AI and quantum computing are sometimes considered competing technologies. AI is transforming computing now, while quantum computing is being developed to tackle problems that conventional computers struggle to solve. But that framing misses something more interesting. In "AI and quantum computers will be frenemies," published on July 29, 2026, The Economist examines a more complicated relationship. It involves competition but also complementarity and mutual enablement. This critical analysis examines the article’s main arguments, strengths, and limitations and open questions. A separate Note at the end considers the article through the perspective of "exotic team dynamics."

Overview of The Economist Article

The Economist looks at the relationship between AI and quantum computing in three areas: the problems they may address, how the technologies could support each other, and their use of resources.

Problems they may address. Quantum computers may eventually enable high-fidelity simulation of quantum mechanics for applications in materials science, chemistry, and biology. At the same time, AI can use pattern recognition to address some of the same challenges. The article argues that these approaches could be complementary, with quantum simulations producing better data for AI models and AI helping identify promising problems for quantum systems to investigate.

How the technologies could support each other. AI may help with quantum computing challenges such as error correction and software development, while quantum systems may eventually enhance selected AI workloads, including quantum reservoir computing. The article ultimately presents a two-way relationship rather than the more familiar idea of AI simply accelerating progress in another technology.

Resources. AI depends heavily on energy-intensive, GPU-based data centers, while quantum systems require their own specialized infrastructure. The article argues that these differences could create both competition and opportunities for cooperation, particularly as the energy demands of AI continue to grow.

Strengths

The article has several strengths. The top three follow.

1. Rejecting a Zero-Sum Framing

The article’s principal strength is that it rejects a simplistic, zero-sum (one side’s gain is the other’s loss) account of AI and quantum computing. Rather than suggesting that one technology will make the other irrelevant, The Economist recognizes that their capabilities differ and may be complementary. That is an important distinction. Discussions of emerging technologies often become contests over which technology will win, when the more interesting possibility is that both may have important roles.

2. A Compelling Feedback Loop

The distinction between AI-based prediction and quantum mechanical simulation is another strong point. AI can infer patterns from large bodies of data, while sufficiently capable quantum computers may eventually provide more faithful simulations of quantum phenomena. The most interesting possibility is not necessarily choosing between the two. Quantum systems could generate higher-quality data for AI models, while AI could help identify the most promising scientific questions or candidate materials for quantum investigation. The result could be a feedback loop in which each technology increases the usefulness of the other. The Economist presents this as a potentially self-reinforcing cycle rather than a simple division of labor.

3. Reciprocal Technological Support

Another strength is the two-way relationship the article describes. AI may help with quantum error correction and software development, but the relationship could run in the other direction as well. The article points to quantum reservoir computing as one possible way quantum systems could contribute to AI. That is more interesting than the familiar narrative in which AI accelerates the progress of other technologies. The article also reports Telstra’s 90% reduction in AI model training times using quantum reservoir chips.

Limitations and Open Questions

The article has limitations and raises additional questions. Here are three that stand out.

1. Cooperation Is Not Guaranteed

The article’s conclusion that cooperation is likely the winning strategy is plausible, but it is more asserted than demonstrated. Technical complementarity does not automatically produce organizational or commercial cooperation. AI and quantum computing will still compete for capital, specialized talent, research attention, infrastructure, and strategic priority. The article identifies resource competition as one of its three areas of overlap. However, it does not fully explore the institutional conditions that would make cooperation more likely than substitution or rivalry.

2. From Demonstration to Deployment

Technical promise is not the same as demonstrated value. For example, it reports that Telstra reduced AI model training times by 90% using quantum reservoir chips. That is an intriguing result, but important questions remain. What model and workload were involved? What was the baseline? How large was the experiment? What happened to performance quality, energy use, and total cost? And has the result been independently replicated? An impressive early result can be meaningful without demonstrating a broad commercial advantage.

3. The Full Energy Equation

The energy argument also warrants more qualification. Comparing a quantum system with GPU-intensive AI infrastructure on power consumption alone does not establish an end-to-end energy advantage. A fuller assessment would include cooling, control systems, calibration, error correction, classical computing, pre-processing, post-processing, utilization, and the amount of useful computational work actually accomplished. The article poses an important question, but does not fully answer it. Its comparison of quantum systems operating at the megawatt level with much larger GPU data center infrastructure is compelling. But the relevant measure needs to be useful work accomplished per unit of total energy and cost.

Conclusion

The Economist’s "AI and quantum computers will be frenemies" makes a good case that AI and quantum computing should not be viewed simply as rivals. The more interesting possibility is that they could help advance each other. Quantum computing could eventually provide specialized capabilities and higher fidelity simulations, while AI could help with quantum hardware, software, error correction, and identifying high-value problems for quantum systems to tackle.

That vision remains conditional, however. Technical demonstrations do not by themselves establish scalable, economical, or trustworthy real-world value. Much will depend on progress in fault tolerance, rigorous validation, end-to-end evaluation, and responsible governance. The Economist article succeeds as an overview of an important technological relationship. The more difficult questions involve what happens when these technologies move from promising demonstrations to practical, sustained use.

Note: Exotic Team Dynamics Perspective

The Economist touches on ideas relevant to "exotic team dynamics," the novel patterns that emerge when people and advanced artificial intelligence (agentic, autonomous, or autopoietic) collaborate as teammates. The connection becomes more apparent when considering how AI and quantum computing could eventually operate within larger human-led workflows. Humans could establish objectives and constraints, AI could analyze information and generate or prioritize possibilities, quantum systems could perform specialized computations or simulations, and classical computing infrastructure could support data processing, orchestration, control, and verification. The more interesting question is how these capabilities interact - with the output of one participant potentially becoming the input for another and creating feedback loops and new forms of interdependence.

The issue may not ultimately be whether AI and quantum computing become friends, enemies, or "frenemies." Instead, it may be whether organizations can effectively design, lead, and govern increasingly complex teams in which humans, AI, quantum systems, and classical infrastructure work together. Organizations that can effectively navigate the complexities of "exotic team dynamics" will be better positioned to turn these emerging capabilities into competitive advantage.

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

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

Graffius, S. M. (2025, August 8). Exotic Team Dynamics: The New Frontier of Human–AI Collaboration. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.18048.49921

Graffius, S. M. (2025, August 22). Scott M. Graffius Premieres His New "Exotic Team Dynamics: Human-AI Collaboration" Talk at Corporate Event in Las Vegas. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.34380.07047

Graffius, S. M. (2025, October 29). Definitions of Advanced AIs: Agentic, Autonomous, and Autopoietic. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.10025.66402

Graffius, S. M. (2025, November 19). Lessons from Unhinged AI in Fiction: What Rogue AIs in Sci-Fi Storytelling Reveal. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.29673.35687

Graffius, S. M. (2025, November 21). Navigating the Spectrum of Advanced AI – Agentic, Autonomous, and Autopoietic. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.21284.74882

Graffius, S. M. (2025, November 21). This is What Happens When Advanced AI Joins Your Team [Presentation]. Corporate event, Paris, France.

Graffius, S. M. (2025, December 1). Beep Beep! Why Wile E. Coyote Is the Patron Saint of AI Failure. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.35578.15048

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

Graffius, S. M. (2026, March 23). Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI — Or Be Outplayed. ScottGraffius.com. https://scottgraffius.com/blog/files/human-ai-teamwork-master-the-emergent-exotic-team-dynamics-or-be-outplayed.html

Graffius, S. M. (2026, June 22). L'avenir du Travail et de l'IA Avancée / Future of Work and Advanced AI. ScottGraffius.com. https://scottgraffius.com/blog/files/lavenir-du-travail-et-de-lia-avancee.html

Graffius, S. M. (2026, July 2). Quantum Computing, Advanced AI Acceleration, and the Rise of Exotic Team Dynamics in Human-AI Teams. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.14506.99525

The Economist. (2026, July 29). AI and quantum computers will be frenemies. https://www.economist.com/science-and-technology/2026/07/29/ai-and-quantum-computers-will-be-frenemies

critical-analysis-of-ai-and-quantum-computers-will-be-frenemies---visual-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

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

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

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.

Sign up for Miro—it's free!

(Want more features? You can always upgrade to a paid plan.)

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

List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, August 25). A Critical Analysis of "AI and Quantum Computers Will Be Frenemies". ScottGraffius.com. https://scottgraffius.com/blog/files/critical-analysis-of-ai-and-quantum-computers-will-be-frenemies.html

Digital Object Identifier (DOI)

Coming soon

Content Acknowledgements

Names, marks, and content are the property of their respective owners. "AI and Quantum Computers Will Be Frenemies" is the property of The Economist. The Economist name, mark, and content are the property of The Economist Newspaper Limited. This critical analysis by Graffius includes limited excerpts of "AI and Quantum Computers Will Be Frenemies," attributed and used under the fair use doctrine for purposes of news reporting, commentary, and analysis.

#AI
#QuantumComputing
#ArtificialIntelligence
#HumanAI
#ExoticTeamDynamics
#AIResearch
#QuantumTechnology
#EmergingTechnology
#AIInnovation
#FutureOfWork
#TechStrategy
#TheEconomist

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: Artificial Intelligence • Quantum Computing • AI Strategy • The Economist • Future of Work • Technology Innovation • Emerging Technology • Quantum Technology • Human-AI Collaboration • Exotic Team Dynamics • AI Research • AI and Quantum Computing

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.

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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.

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.

Authority on Teamwork Tradecraft

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.

nsdm-publication-by-nsa-raum-referenced-work-by-graffius-on-team-development---2026-refresh-IG-lwres

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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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(Want more features? You can always upgrade to a paid plan.)

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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

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Top 5 Roles on AI Teams

Perplexity Standalone Article on AI Hallucinations Cites Research by Scott M. Graffius

Perplexity Standalone Article on AI Cites Graffius' Work

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Quantum Computing, Advanced AI Acceleration, and the Rise of Exotic Team Dynamics in Human-AI Teams

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Quantum Computing

Advanced AI

Exotic Team Dynamics

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A Critical Analysis of "AI and Quantum Computers Will Be Frenemies"

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Overview of The Economist Article

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

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