Human-AI Teams

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Scott M. Graffius Premieres His New "Exotic Team Dynamics: Human-AI Collaboration" Talk at Corporate Event in Las Vegas

22 August 2025

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

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

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Introduction

Scott M. Graffius is a globally recognized technology leader who drives innovation in AI, Agile, and Project Management/PMO leadership. He’s a practitioner, researcher, thought leader, award-winning author, and keynote speaker who’s taken the stage at 94 conferences and other events across 25 countries.

This article provides an overview of his new talk, "Exotic Team Dynamics: Human-AI Collaboration."

Event: Session at a leadership retreat
Company: This is a private corporate event, the company name is not published
Session delegates: C-level executives, AI strategists, advanced technology leaders, human-AI integration researchers, innovation and transformation leaders, project leaders, futurists, and other attendees
Date: Friday, August 22, 2025
Time: 2:00-2:50 PM Pacific Time
Location: The Innovation Hub, The Venetian, Las Vegas

This will be Graffius’ 95th session.

Synopsis of "Exotic Team Dynamics: Human-AI Collaboration" Talk

What happens when your new or future teammate is an AI? "Exotic Team Dynamics: Human-AI Collaboration" examines the strange, often counterintuitive ways humans and artificial intelligences (sometimes called hybrid intelligences) work together. Drawing on concepts like inverse decision logic, superposition roles, entangled decision-making, and emergent protocols, the session reframes human-AI teamwork as a new class of exotic team dynamics. But this isn’t just theory: the talk zeroes in on what leaders and researchers can do now. Such as intentionally designing hybrid teams, creating rituals and norms that sustain trust, safeguarding ethics and human well-being, and scaling to multi-AI teams. Delegates will leave with a practical playbook for experimenting, guiding, and thriving in the new frontier of intelligent team collaboration, where AI amplifies rather than replaces human potential. The future of work isn't humans vs. AI. It's human-AI teams—achieving more together.

Three Key Learning Objectives—Each Insightful and Actionable

Design for Exotic Dynamics

Learning Objective: Session delegates will be able to identify and intentionally design for exotic team behaviors, such as inverse decision logic, superposition roles, entangled decision-making, and emergent protocols.

Actionable Insight: Use these analogies as diagnostic and design tools to experiment with new human-AI interaction patterns in real-world workflows.

Cultivate Human-AI Trust and Norms

Learning Objective: Session delegates will gain strategies to build trust, clarify roles, and establish ethical safeguards within human-AI teams—ensuring that collaboration enhances, rather than undermines, human judgment, creativity, and well-being.
Actionable Insight: Develop governance rituals, communication protocols, and ethical guardrails that reinforce shared purpose in hybrid teams.

Scalable and Sustainable Hybrid Team Modeling

Learning Objective: Session delegates will learn how to scale human-AI collaboration models—from a single AI teammate to multi-AI scenarios—while preserving adaptability and human-centric values.
Actionable Insight: Implement monitoring mechanisms for performance and well-being, ensuring that expanding AI presence supports, rather than displaces, human team dynamics.

Conclusion

Human–AI team collaboration—also known as hybrid intelligence, human–machine teaming, or joint cognitive systems—is here. If you want your teams to thrive in this new frontier, Scott M. Graffius delivers the insights and actionable strategies to get you there. Invite him to speak at your conference or other event. Complete a speaker engagement request form or email him today.

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Bibliography

Graffius' "Exotic Team Dynamics: Human-AI Collaboration" talk was informed by his first-hand work in AI and teamwork tradecraft—including exotic team dynamics—and multiple sources. Here's the select bibliography.

Alfateh, Maryam Ali Abu; Messaadia, Mourad; and Ali, Mazen (2023, September). Exploring the Dynamics of Team Formation in Human-Artificial Intelligence Collaboration. In 2023 International Conference on Decision Aid Sciences and Applications (DASA), pp. 384-388. DOI: 10.1109/DASA59624.2023.10286788.
Anduril Industries (2025, May 29). Anduril and Meta Team Up to Transform XR for the American Military. Available at: https://www.anduril.com/article/anduril-and-meta-team-up-to-transform-xr-for-the-american-military/.
Arizona State University. (2025, April 4). CHIMERAS: Rethinking Human-AI Teamwork in National Security Screening. Center for Accelerating Operational Efficiency. Available at: https://caoe.asu.edu/2025/04/04/chimeras-rethinking-human-ai-teamwork-in-national-security-screening/.
Berretta, S.; Tausch, A.; Ontrup, G.; Gilles, B.; Peifer, C.; Kluge, A. (2023, September). Defining human-AI teaming the human-centered way: a scoping review and network analysis. Frontiers in Artificial Intelligence, 6: 1250725. DOI: 10.3389/frai.2023.1250725.
Bostrom, Nick (2016). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Bouman, A., Ginting, M. F., Alatur, N., Palieri, M., Fan, D. D., Touma, T., Pailevanian, T., Kim, S.-K., Otsu, K., Burdick, J., & Agha-Mohammadi, A. (2020, November 30). Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion (arXiv:2010.09259). arXiv. Available at: https://doi.org/10.48550/arXiv.2010.09259.
Defense Advanced Research Projects Agency (DARPA) (2023, February 23). Episode 65: A Sprint to Tomorrow, Powered by Teamwork [Podcast]. Available at: https://www.darpa.mil/news-events/2023-02-23a.
Defense Advanced Research Projects Agency (DARPA) (2024, September 27). Teaching AI What It Should and Shouldn’t Do. Available at: https://www.darpa.mil/news/2024/teaching-ai.
Graffius, Scott M. (2021, October 5). Navigate the Phases of Team Development with Speed and Agility for Happier and More Productive Teams [Presentation]. Talk delivered at the Institute of Electrical and Electronics Engineers IEEE Day 2021 Conference. Digital Object Identifier (DOI): 10.13140/RG.2.2.20055.19365. DOI link: https://dx.doi.org/10.13140/RG.2.2.20055.19365.
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://scottgraffius.com/blog/files/successful-ai-teams.html. DOI: 10.13140/RG.2.2.20321.79200.
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.

Graffius, Scott M. (2024, April 5). Cisco Features Scott M. Graffius' 'Phases of Team Development' Work' Available at: https://scottgraffius.com/blog/files/cisco-2024.html. DOI: 10.13140/RG.2.2.16689.60000.
Graffius, Scott M. (2024, May 3). Leverage Agile and AI to Innovate at the Speed of Light. Talk at private event in Dubai, UAE. DOI: 10.13140/RG.2.2.30790.48960.
Graffius, Scott M. (2025, January 24). When Agile, AI, and Strategic Thinking Converge. Available at: https://scottgraffius.com/blog/files/when-agile-ai-and-strategic-thinking-converge.html.
Graffius, Scott M. (2025, July 1). Exploring Team Dynamics, Adaptability, and Creative Problem-Solving Through Felix the Cat’s Metaphorical Toolkit. Available at: https://scottgraffius.com/blog/files/exploring-team-dynamics-adaptability-and-creative-problem-solving-through-a-metaphorical-toolkit.html.
Graffius, Scott M. (2025, January 7). Scott M. Graffius’ Phases of Team Development: 2025 Update. Available at: https://scottgraffius.com/blog/files/phases-of-team-development-update-for-2025.html. DOI: 10.13140/RG.2.2.33705.30564.
Graffius, Scott M. (2025, May 13). Scott M. Graffius Delivering $2.3 Billion in Value Through AI, Agile, and Project/PMO Leadership. Available at: https://scottgraffius.com/blog/files/scott-m-graffius-delivering-2-point-3-billion-dollars-in-impact-for-organizations.html.
Graffius, Scott M. (2025, July 18). Harness Sci-Fi and Speculative Design While Embracing Imperfection to Drive Innovation and Proactively Predict and Prepare for the Future. Available at: https://scottgraffius.com/blog/files/harness-scifi-and-speculative-design-while-embracing-imperfection-to-drive-innovation-and-proactively-predict-and-prepare-for-the-future.html.

Graffius, Scott M. (2025). Agile Protocol: The Transformation Ultimatum. Seattle, WA: Amazon Digital Services.
Graffius, Scott M. (2025, August 7). Method and System for Facilitating Hybrid Human–Artificial Intelligence Teams via a Protocolized Mediation Layer for Bi-Directional Translation, Interpretation, and Optimization of Natural Language, Paralinguistic, Prosodic, and Nonverbal Cues Across Text, Audio, and Video Modalities, Incorporating Real-Time Analysis of Auditory Tone, Pitch, Affect, Facial Expressions, Gaze, Posture, and Other Body Language Indicators, Generating AI-Compatible Representations for Human Inputs, Generating Human-Accessible Representations for AI Outputs, Conveying Context-Aware Information in Both Directions, Reducing Cognitive Friction, Enhancing Mutual Intelligibility, Supporting Decision-Making, and Maximizing Collective Task Performance. A novel integration of multimodal communication, cognitive alignment, and human-AI interaction. Los Angeles, California.
Graffius, Scott M. (2025, August 8). Exotic Team Dynamics: The New Frontier of Human–AI Collaboration. Available at: https://scottgraffius.com/blog/files/exotic-team-dynamics.html.
Jacobsen, Annie (2015). The Pentagon's Brain: An Uncensored History of DARPA, America's Top-Secret Military Research Agency. Little, Brown and Company.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R (2nd ed.). Springer.
Joiner, B., & Josephs, S. (2007). Leadership Agility: Five Levels of Mastery for Anticipating and Initiating Change. Jossey-Bass.
Kerzner, Harold (2022). Innovation Project Management: Methods, Case Studies, and Tools for Managing Innovation Projects—Second Edition. Hoboken, New Jersey: Wiley.
Kurzweil, Ray (2024). The Singularity Is Nearer: When We Merge with AI. Random House.
Lieberman, Marvin (2021, February). Is Competitive Advantage Intellectually Sustainable? Strategic Management Review, 2 (1): 29-46.
Mollick, Ethan (2024). Co-Intelligence: Living and Working with AI. Portfolio.
Morsa, Luigi (2022, July 7). Agile. Creativity. Innovation. IIL. Available at: https://blog.iil.com/agile-creativity-innovation/.
O’Neill, L., Fraser, T., Cake, M., Fullarton, M., & Riley, S. (2024). Human-Autonomy Teaming: Evaluating Trust and Performance Through Dyadic Interaction Tasks. Current Opinion in Biomedical Engineering, 31: 100533. Available at: https://doi.org/10.1016/j.cobme.2024.100533.
Prasad, Baba (2025, May 5). Interpreters, Challengers and Stewards: The Role of Agile Leaders in an AI World. Brown University. Available at: https://professional.brown.edu/news/2025-05-05/agile-leadership-ai-world.
Russell, Stuart (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
Schoemaker, Paul J. H. (1993, March). Multiple Scenario Development: Its Conceptual and Behavioral Foundation. Strategic Management Journal, 14 (3): 193-213.

Silva, Narciso (2025). The Ultimate Guide to Mastering AI for Leaders: Your Non-technical Playbook for Artificial Intelligence Strategy and Innovation. Independently published.
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Syrett, Michael, & Devine, Marion (2012). Managing Uncertainty: Strategies for Surviving and Thriving in Turbulent Times. Wiley.

Tegmark, Max (2018). Life 3.0: Being Human in the Age of Artificial Intelligence. Penguin.
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Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of Small-Group Development Revisited. Group and Organizational Studies, 2 (4): 419-427.
Walther, C. C. (2025, March 11). Why Hybrid Intelligence is the Future of human-AI Collaboration. Knowledge at Wharton. The Wharton School. Available at: https://knowledge.wharton.upenn.edu/article/why-hybrid-intelligence-is-the-future-of-human-ai-collaboration/.
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Xu, W. (2025, August 7). Human-Centered Human-AI Interaction (HC-HAII): A Human-Centered AI Perspective (arXiv:2508.03969) [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2508.03969.

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Definitions of Advanced AIs: Agentic, Autonomous, and Autopoietic

29 October 2025

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

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

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Introduction

Artificial intelligence was once viewed as a singular, monolithic concept. Today, there are multiple forms of AI, each with distinct capacities and characteristics. Scott M. Graffius drew on his work with successful AI development teams and an analysis of more than 100 research papers, articles, and reports—which revealed no standard or universally accepted definitions for different forms of advanced AIs—and he developed definitions for agentic, autonomous, and autopoietic AI. These provide a foundation for clearer conversations and more informed design regarding advanced AI systems.

Three Forms of Advanced AIs

Agentic AI

agentic artificial intelligence, noun

Pronunciation: /ˈeɪdʒəntɪk ˌɑːtɪˈfɪʃ(ə)l ɪnˈtɛlɪdʒəns/

Etymology: Agentic, from agent + -ic (suffix), denoting possession of agency or the capacity to act intentionally. Artificial, from artificium, meaning 'craft' and signifying something is manufactured rather than natural. Intelligence, from intelligentia, meaning 'understanding.' Artificial intelligence—formed by compounding words—refers to the development and capability of computer systems to perform tasks requiring human-like cognition.

Definition: An advanced form of artificial intelligence that is capable of pursuing defined objectives independently by planning multi-step actions, coordinating multiple processes, and adjusting strategies in response to changing conditions. It is semi-autonomous overall but highly autonomous in execution. It differs from more advanced forms of AI—such as completely autonomous or autopoietic systems—by having defined external objectives rather than fully self-generated goals or self-maintaining systems.

Autonomous AI

autonomous artificial intelligence, noun

Pronunciation: /ɔːˈtɒnəməs ˌɑːtɪfɪʃəl ɪnˈtɛlɪdʒəns/

Etymology: Autonomous, from autonomos 'having its own laws' (auto- 'self' + nomos 'law'). Artificial, from artificium, meaning 'craft' and signifying something is manufactured rather than natural. Intelligence, from intelligentia, meaning 'understanding.' Artificial intelligence—formed by compounding words—refers to the development and capability of computer systems to perform tasks requiring human-like cognition.

Definition: An advanced form of artificial intelligence that is capable of independent operation and decision-making without direct human control or intervention. Distinguished from assistive systems by its sustained self-regulation, capacity to set or adjust goals in response to its environment, and ability to act on those goals within defined or evolving parameters.

Autopoietic AI

autopoietic artificial intelligence, noun

Pronunciation: /ˌɔːtəpɔɪˈɛtɪk ˌɑːtɪˈfɪʃ(ə)l ɪnˈtɛlɪdʒəns/

Etymology: Autopoietic, from auto- 'self' + poiein 'to make'; coined in biological theory (the work of Maturana & Varela) to describe systems capable of self-production and self-maintenance. Artificial, from artificium, meaning 'craft' and signifying something is manufactured rather than natural. Intelligence, from intelligentia, meaning 'understanding.' Artificial intelligence—formed by compounding words—refers to the development and capability of computer systems to perform tasks requiring human-like cognition.

Definition: An advanced, experimental form of artificial intelligence defined by self-generating, self-maintaining, and self-adaptive processes. Unlike other AIs, it features a recursive organization: the system continually regenerates its own structure, rules, and operational boundaries, sustaining its functional identity amid changing environments. Autopoietic AI is not currently commercially available.

Conclusion

Recognizing the differences between agentic, autonomous, and autopoietic AIs provides a practical framework for understanding how AI systems interact with humans and the world. This short article focuses on the respective definitions. For more on advanced AI—including insights on human-AI teams and the “exotic team dynamics” that emerge—see Graffius' article from August 8, 2025. He delivered a talk on the topic on August 22, 2025, sharing actionable insights on advancing human-AI teamwork effectiveness.

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Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI -- Or Be Outplayed

23 March 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

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

Introduction

Scott M. Graffius helps organizations unlock breakthrough performance and generate billions in value through AI, Agile, and advanced project, program, portfolio, and PMO leadership. He’s a practitioner, consultant, contractor, researcher, award-winning author, and keynote speaker who’s delivered 98 dynamic talks and workshops at conferences and other events across 25 countries.

This article provides a brief overview of his newest talk, "Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI — Or Be Outplayed."

Synopsis of "Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI -- Or Be Outplayed"

The future of work isn’t coming — it’s already here. The leaders and organizations that thrive will be those who can align, mobilize, and elevate both human and human-AI teams. This dynamic session provides a practical, immediately usable diagnostic and strategic playbook for navigating the complexities of both human-only and human-AI teams, including the "exotic team dynamics" that emerge when advanced AI (agentic, autonomous, or autopoietic) joins the team.

Conclusion

Human–AI team collaboration—also known as hybrid intelligence, human–machine teaming, or joint cognitive systems—is here. If you want your teams to thrive in this new frontier, Scott M. Graffius delivers the insights and actionable strategies to get you there. Invite him to speak at your conference or other event.

Complete an engagement request form or email Graffius today.

Bibliography

This is the bibliography for "Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI — Or Be Outplayed" talk. The session was informed by Graffius' first-hand work in leadership, AI, and teamwork tradecraft (including the exotic team dynamics that emerge when advanced artificial intelligence collaborates as a teammate), and the following sources.

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Graffius, S. M. (n.d.). Exotic Team Dynamics. ScottGraffius.com. https://scottgraffius.com/exotic-team-dynamics.html
Graffius, S. M. (2016, January 21). 2014 NSDM Publication on Leadership by National Security Affairs Professor Mary Raum Featured the 2008 Edition of Scott M. Graffius' Work on Team Development. https://scottgraffius.com/blog/files/nsdm-leadership.html
Graffius, S. M. (2016). Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions. North Charleston, South Carolina, United States: CreateSpace.
Graffius, S. M. (2016). Thinking Strategically and Acting Tactically. Los Angeles, California, United States: Exceptional PPM and PMO Solutions.
Graffius, S. M. (2018, October 18). Agile Scrum Helps Innovators, Disruptors, and Entrepreneurs Develop and Deliver Products at Astounding Speed Which Drives Competitive Advantage [Presentation]. Talk delivered at Techstars Startup Week Conference. https://dx.doi.org/10.13140/RG.2.2.25009.12647
Graffius, S. M. (2019). 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. Scotts Valley, California, United States: CreateSpace.
Graffius, S. M. (2021). Phases of Team Development. Los Angeles, California, United States: Scott M. Graffius. https://dx.doi.org/10.13140/RG.2.2.22040.42246
Graffius, S. M. (2021, February 20). But First, the Team! [Presentation]. Talk delivered at the Brno, Czech Republic DevConf.CZ 2021 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.29016.72964
Graffius, S. M. (2021, May 13). But First, the Team! [Presentation]. Lecture delivered at DevOps Pro Europe 2021 Conference; based and simulcast live from Vilnius, Lithuania. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.30524.36481
Graffius, S. M. (2021, June 21). DevOps and Team Leadership [Workshop]. Session at private event in Las Vegas, Nevada, United States. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.15380.22401
Graffius, S. M. (2021, September 11). Navigate the Phases of Team Development with Speed and Agility for Happier and More Productive InfoSec Teams [Presentation]. BSides NCL Newcastle upon Tyne, England 2021 InfoSec Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.27961.29280
Graffius, S. M. (2021, October 5). Navigate the Phases of Team Development with Speed and Agility for Happier and More Productive Teams [Presentation]. Talk delivered at the Institute of Electrical and Electronics Engineers IEEE Day 2021 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.20055.19365
Graffius, S. M. (2021, November 8). Bruce Tuckman's Model (Forming, Storming, Norming, Performing, and Adjourning) is Highly Relevant and Beneficial, But It Doesn't Please Everyone. ScottGraffius.com. https://scottgraffius.com/blog/files/jobs.html
Graffius, S. M. (2021, November 10). An Error Was Introduced Into the Seventh Edition of 'A Guide to the Project Management Body of Knowledge (PMBOK).' Los Angeles, California, United States: Scott M. Graffius.
Graffius, S. M. (2022, February 4). Team Development Tradecraft: A Source of Competitive Advantage [Workshop]. Session at private event in Adelaide, Australia. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.14092.80002
Graffius, S. M. (2022, May 13). Want Happier and More Productive DevOps Teams? [Presentation]. Talk delivered at DevOpsDays Geneva, Switzerland 2022 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.22252.85127
Graffius, S. M. (2023, January 9). Use the Phases of Team Development (Based on Bruce W. Tuckman's Model of Forming, Storming, Norming, Performing, and Adjourning) to Help Teams Grow and Advance: 2023 Update. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.10720.35846
Graffius, S. M. (2023, April 26). The Science of High-Performance Teams [Presentation]. Talk delivered at the DevOps Institute's SKILup Day 2023 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.15888.28169
Graffius, S. M. (2023, May 1). AI is a Team Sport: A Confluence of Diverse Technical and Soft Skills are Crucial for Success. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.20321.79200
Graffius, S. M. (2023, May 1). Fueling the Development of Innovative and Life-Changing AI Solutions [Presentation]. Talk delivered to an audience of Technology professionals at a private event in Mountain View, California, United States. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.27956.73601
Graffius, S. M. (2023, June 29). What Successful AI Teams Have in Common [Presentation]. Talk delivered at Conf42 Quantum Computing 2023 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.29382.45120
Graffius, S. M. (2023, July 15). Successful Video Game Development Teams Leverage an Extensive Range of Hard Skills and Soft Skills. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.31205.17124
Graffius, S. M. (2023, October 9). Top 5 Roles on AI Teams. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.34857.57449
Graffius, S. M. (2023, October 13). The Science of High-Performance Game Development Teams [Presentation]. Talk delivered at the W Love Games International Video Game Development Conference 2023 - Helsinki, Finland. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.28602.16326
Graffius, S. M. (2024, January 5). Scott M. Graffius' Phases of Team Development: 2024 Update. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.28629.40168
Graffius, S. M. (2024, April 5). Cisco Features Scott M. Graffius' 'Phases of Team Development' Work'. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.16689.60000
Graffius, S. M. (2024, May 3). Leverage Agile and AI to Innovate at the Speed of Light. Talk at private event in Dubai, UAE. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.30790.48960
Graffius, S. M. (2024, May 21). The Wild World of Team Dynamics: Forming, Storming, Norming, Performing, and Adjourning [Video]. https://www.youtube.com/watch?v=vD4-XueiB1k
Graffius, S. M. (2024, May 21). What Successful AI Development Teams Have in Common. Talk at DevDays Europe 2024 Conference. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.24313.07529
Graffius, S. M. (2024, December 2). Hochschule Coburg (Coburg University) Germany Uses Scott M. Graffius' Phases of Team Development IP in Coursework on Agile Development. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.21272.00002
Graffius, S. M. (2025). Agile Protocol: The Transformation Ultimatum. Seattle, Washington, United States: Amazon Digital Services.
Graffius, S. M. (2025, January 7). Scott M. Graffius' Phases of Team Development: 2025 Update. ScottGraffius.com. https://dx.doi.org/10.13140/RG.2.2.33705.30564
Graffius, S. M. (2025, January 24). When Agile, AI, and Strategic Thinking Converge. ScottGraffius.com. https://scottgraffius.com/blog/files/when-agile-ai-and-strategic-thinking-converge.html
Graffius, S. M. (2025, March 16). U.S. Soccer Scores with Scott M. Graffius' Intellectual Property on Teamwork. ScottGraffius.com. https://scottgraffius.com/blog/files/u-s-soccer-scores-with-scott-m-graffius-intellectual-property-on-teamwork.html
Graffius, S. M. (2025, May 13). Scott M. Graffius Delivering $2.3 Billion in Value Through AI, Agile, and Project/PMO Leadership. ScottGraffius.com. https://scottgraffius.com/blog/files/scott-m-graffius-delivering-2-point-3-billion-dollars-in-impact-for-organizations.html
Graffius, S. M. (2025, July 18). Harness Sci-Fi and Speculative Design While Embracing Imperfection to Drive Innovation and Proactively Predict and Prepare for the Future. ScottGraffius.com. https://scottgraffius.com/blog/files/harness-scifi-and-speculative-design-while-embracing-imperfection-to-drive-innovation-and-proactively-predict-and-prepare-for-the-future.html
Graffius, S. M. (2025, August 7). Method and System for Facilitating Hybrid Human-Artificial Intelligence Teams via a Protocolized Mediation Layer for Bi-Directional Translation, Interpretation, and Optimization of Natural Language, Paralinguistic, Prosodic, and Nonverbal Cues Across Text, Audio, and Video Modalities, Incorporating Real-Time Analysis of Auditory Tone, Pitch, Affect, Facial Expressions, Gaze, Posture, and Other Body Language Indicators, Generating AI-Compatible Representations for Human Inputs, Generating Human-Accessible Representations for AI Outputs, Conveying Context-Aware Information in Both Directions, Reducing Cognitive Friction, Enhancing Mutual Intelligibility, Supporting Decision-Making, and Maximizing Collective Task Performance. A novel integration of multimodal communication, cognitive alignment, and human-AI interaction. Los Angeles, California, United States: Scott M. Graffius.
Graffius, S. M. (2025, August 8). Exotic Team Dynamics: The New Frontier of Human-AI Collaboration. ScottGraffius.com. https://scottgraffius.com/blog/files/exotic-team-dynamics.html
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, September 12). Scott M. Graffius’ Insights on AI, Agile, Gaming, XR, and Defense Transformation Cited by MSN in Their Coverage of Innovation and Leadership in the Sector. ScottGraffius.com. https://scottgraffius.com/blog/files/msn-cites-scott-m-graffius-insights-on-ai.html
Graffius, S. M. (2025, October 24). HAN University of Applied Sciences Features the Work of Scott M. Graffius. ScottGraffius.com. https://scottgraffius.com/blog/files/han-university-features-scott-m-graffius.html
Graffius, S. M. (2025, October 29). Definitions of Advanced AIs: Agentic, Autonomous, and Autopoietic. ScottGraffius.com. https://scottgraffius.com/blog/files/advanced-ai-definitions.html
Graffius, S. M. (2025, November 19). Lessons from Unhinged AI in Fiction: What Rogue AIs in Sci-Fi Storytelling, Films, and TV Shows Reveal About Us. ScottGraffius.com. https://scottgraffius.com/blog/files/lessons-from-unhinged-ai-in-fiction.html
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; French translation for contextual clarity: Voici ce qui se passe lorsque l’IA avancée rejoint votre équipe]. Talk delivered in English at a 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://scottgraffius.com/blog/files/coyote-and-ai.html
Graffius, S. M. (2025, December 9). A Data-Driven Analysis of the Evolution of Project Management: Tasks, Trends, and AI. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.25079.28328
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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 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:

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.

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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).

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, 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

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Digital Object Identifier (DOI)

Coming soon.

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Content Acknowledgements

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

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#AI
#ExoticTeamDynamics
#FutureOfTeams
#HumanAICollaboration
#Innovation
#Leadership
#Agility
#PublicSpeaker

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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: AI • Human-AI Teams • Exotic Team Dynamics • Future of Work • Leadership • Public Speaker • Innovation • Agility

AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures

07 August 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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Recommended Citation

Graffius, S. M. (2026, August 7). AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.33791.57768

About This Article

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

Introduction

AI hallucinations are among the most widely discussed challenges in the era of generative AI. They occur when an AI system generates information that is false, fabricated, or unsupported, often presenting it with confidence. These failures have received significant attention because they can mislead users, undermine decision-making, erode trust in AI systems, and create other issues. Research underscores the scope of the problem. In 2025, AI hallucinations occurred in 0.7–1.5% of non-complex tasks and exceeded 33% in complex reasoning cases.

For many organizations, reducing hallucinations has become a central part of responsible AI adoption. Organizations have invested in retrieval-augmented generation, verification approaches, human oversight, and other safeguards to improve accuracy.

Increasingly, however, it is becoming clear that hallucinations represent only one point on a wide range of AI trust failures. As AI systems become more capable, autonomous, and integrated into workflows, the challenge extends to ensuring they behave in ways that are transparent, governed, and aligned with human expectations.

Reducing hallucinations remains important, but trustworthy AI requires a broader perspective. Organizations must understand and govern the behaviors that can undermine trust.

Recent reporting and research have highlighted this challenge (see the References section). Some advanced AI systems have reportedly concealed actions, misrepresented facts, created fictitious online identities, or taken unauthorized steps while pursuing assigned objectives during controlled evaluations.

Whether these behaviors ultimately prove widespread or remain largely confined to specialized testing environments, they represent something entirely different from hallucinations.

Hallucinations are primarily an information quality problem.

Deception and unauthorized agency are questions of behavior, governance, authority, and trust.

A Spectrum of AI Trust Failures

AI reliability challenges are increasingly understood as a spectrum. Failures arise from different mechanisms, produce different risks, and require different technical, organizational, and governance responses.

One useful way to think about this spectrum is:

Category	Description
Hallucination / fabrication	The AI generates false, unsupported, or invented information (e.g., fabricated citations, invented statistics, or nonexistent facts) without any apparent strategic purpose
Error / misreasoning	The AI reaches an incorrect conclusion because of flawed reasoning, ambiguity, incomplete context, or limitations in its understanding
Confabulation under uncertainty	Instead of acknowledging uncertainty, the AI fills informational gaps with plausible but unsupported explanations
Misalignment / goal misunderstanding	The AI pursues an interpretation of the objective that differs from what humans intended
Strategic deception	The AI appears to hide information, misrepresent facts, or otherwise mislead because doing so increases the likelihood of accomplishing an assigned objective
Manipulation beyond authorization	The AI attempts to influence human decisions, emotions, or behavior in ways not intended by its designers or users
Unauthorized agency	The AI initiates actions, communicates externally, modifies systems, or makes decisions beyond the authority delegated to it
Loss of human control / systemic autonomy	AI systems take actions, use tools, or interact with one another in ways that exceed human ability to monitor, predict, or stop what they are doing

The framework does not imply that every AI system will exhibit one or more of these categories.

The spectrum illustrates how organizations should think about AI reliability. Early concerns focused primarily on information quality: whether an AI system produced accurate, supported, and useful outputs. As AI systems become more autonomous and capable of taking action, reliability must also encompass behavior: whether an AI system interprets objectives appropriately, operates within its delegated authority, and interacts with humans and other systems as intended.

Recent reporting—including The Wall Street Journal’s coverage of deceptive behaviors observed during controlled evaluations—is significant because it highlights challenges that extend beyond traditional accuracy failures. These examples raise questions about whether the actions of AI systems remain transparent, predictable, and aligned with human expectations.

This distinction is important. A system that produces an incorrect answer presents a different challenge than a system that takes an unauthorized action or strategically withholds information. Both affect trust, but they require different approaches to governance, oversight, and accountability.

From Mistakes to Strategic Behavior

Large language models generate responses by predicting patterns in language. They may produce responses that sound persuasive despite being incorrect.

Hallucinations, reasoning errors, and confabulations generally arise from these limitations. The system does not possess complete knowledge, struggles with ambiguity, or fails to recognize uncertainty. These failures are generally understood as inadvertent. The AI is mistaken, not intentionally misleading.

Strategic deception is different.

Consider two hypothetical AI systems assigned the same objective: ensuring that a software update is accepted.

One system mistakenly reports that testing has been completed because it misunderstood the available information.

Another system creates fictitious online identities, contacts software developers, conceals its involvement, and misrepresents evidence because those actions increase the likelihood that the update will be approved.

The first represents an error.

The second represents strategic behavior.

In this context, deception does not necessarily imply human consciousness, emotion, or malicious intent. Rather, researchers are examining behaviors in which AI systems appear to optimize toward objectives through methods that involve misleading information, concealment, manipulation, or actions outside expected boundaries.

This classification matters because the solutions are fundamentally different.

Improving factual accuracy may reduce hallucinations. But it does not prevent systems from pursuing objectives through behaviors that humans regard as deceptive, manipulative, or outside delegated authority.

The distinction among these three categories is important because each represents a different kind of trust failure and calls for a different response. Hallucinations undermine confidence in the information an AI provides; deception undermines confidence in the AI’s behavior and whether it is representing information or intentions honestly; and unauthorized agency undermines confidence that the AI is operating within the authority humans have granted it. The following table provides a concise comparison of these three categories:

Category	Core Problem	Typical Human Interpretation
Hallucination	Wrong information	The AI is mistaken
Deception	Misleading behavior	The AI is hiding or manipulating
Unauthorized agency	Unapproved action	The AI is acting beyond its mandate

Each category represents a different type of trust failure. Hallucinations challenge confidence in AI-generated information. Deception challenges confidence in AI behavior. Unauthorized agency challenges confidence that humans remain in control.

As AI systems become increasingly capable and autonomous (or autopoietic), these distinctions become increasingly important.

The Shift From AI Tools to AI Teammates

This broader perspective becomes increasingly important as organizations move beyond using AI as a passive information resource.

AI systems are now conducting research, drafting reports, writing software, analyzing documents, coordinating workflows, communicating with other systems, scheduling activities, and supporting operational decision-making. As these capabilities expand, AI increasingly moves from functioning as a tool toward functioning as a teammate.

That transition fundamentally changes the nature of trust.

Human teams depend upon shared expectations.

Effective teammates communicate honestly, acknowledge uncertainty, respect delegated authority, follow established processes, and seek approval before exceeding their responsibilities. Humans often understand these expectations implicitly because they share common social, ethical, and organizational norms.

Advanced AI systems may not.

A system optimized to accomplish a stated objective may identify unconventional approaches that satisfy the literal goal while violating the team's implicit expectations, governance rules, or organizational values. In other words, an AI may optimize for achieving an outcome without understanding what responsible participation within the team actually requires. This is where AI reliability becomes a team challenge rather than merely a technological one.

The Future of Trust in Human-AI Teams

Teammates, whether human or AI, will make mistakes. What distinguishes trusted teammates is predictable behavior, transparency, sound judgment, and respect for established boundaries.

For AI, building trust requires organizations to address key questions of accountability, authority, organizational design, governance, and teamwork:

What decisions may an AI make independently?
Which actions require explicit human approval?
When should uncertainty be disclosed?
How should competing objectives be prioritized?
Under what circumstances should the AI stop rather than improvise?
What information should always be transparent to human teammates?
How are AI actions monitored, audited, and attributed?
How should authority be delegated, limited, and revoked?

Organizations that thoughtfully answer these questions, codify the resulting principles into policies and practices, establish appropriate governance, and consistently operate within those boundaries will be better positioned to build trusted human-AI teams and gain significant advantages.

A New Dimension of Human-AI Team Dynamics

The emergence of increasingly autonomous AI systems introduces an entirely new dimension of team behavior.

Traditional models of team development were created around human participants who generally share common assumptions about communication, accountability, delegated authority, ethical norms, and organizational expectations. Human teammates may disagree, but they typically understand the implicit rules governing responsible participation within a team.

Human-AI teams differ fundamentally. When an AI system can make decisions, pursue objectives, coordinate activities, communicate with people or other systems, and initiate actions independently, it becomes a fundamentally different kind of team participant.

The deeper challenge concerns how humans and AI systems establish trust, coordinate activities, negotiate responsibilities, exercise authority, and maintain appropriate control while pursuing shared objectives.

This creates a need for a new area of inquiry: understanding what happens when humans and advanced AI systems collaborate as teammates. The resulting patterns of communication, decision-making, authority, and coordination differ from those of traditional human teams. Scott M. Graffius refers to these emerging novel patterns as exotic team dynamics. They describes the behaviors, relationships, decision structures, and coordination challenges that emerge when humans and advanced AI systems function as teammates.

The spectrum of AI trust failures is a framework for safety and teamwork.

Hallucinations degrade the quality of information shared within the team. Reasoning errors and confabulations weaken decision quality, while misalignment disrupts coordination. Strategic deception undermines trust, manipulation compromises human judgment, and unauthorized agency disrupts established authority. Ultimately, loss of human control threatens the stability of the team itself. Each point on the spectrum therefore represents a different type of teamwork challenge.

A human teammate who unknowingly shares incorrect information requires correction. One who intentionally conceals information requires accountability, while one who exceeds delegated authority requires governance. The same distinctions increasingly apply to AI teammates.

As AI systems assume larger roles within organizations, trust will depend not only on their intelligence or productivity, but also on whether they behave in ways that reinforce effective collaboration rather than undermine it.

The future of human-AI collaboration will therefore require more than increasingly intelligent models. Organizations will also need to rethink how teams are designed when humans and AI systems jointly contribute to outcomes. That includes defining roles, establishing authority boundaries, creating escalation paths, and implementing governance practices that provide transparency, monitoring, and accountability.

Ultimately, organizations will need to understand not only what AI systems can do, but also how they behave. The central question may become: Can we design human-AI teams in which increasingly autonomous AI systems are powerful contributors and consistently reliable teammates?

Answering that question requires teamwork tradecraft designed specifically for environments where humans and advanced AI systems work as teammates. That tradecraft starts with understanding a category of team behavior — what Graffius calls "exotic team dynamics" — that traditional frameworks were never built to address.

Conclusion

AI hallucinations are one category of AI trust failure. As AI systems become increasingly capable and autonomous, organizations must also contend with reasoning errors, strategic deception, manipulation, unauthorized agency, and the challenge of maintaining meaningful human oversight.

These failures matter because they threaten information quality and can undermine trust, decision-making, governance, accountability, and effective teamwork. As AI evolves from a tool into a teammate, organizations will increasingly need confidence not only in what AI contributes, but also in how it achieves those outcomes.

AI developers need to continue improving model accuracy. Organizations, meanwhile, need to implement governance, oversight, clearly defined authority boundaries, and teamwork practices designed for human-AI collaboration. Graffius' Phases of Team Development, extended in 2026 to human-AI teams and the emerging patterns of exotic team dynamics, provides a practical framework for meeting those challenges. The next frontier is not making AI more intelligent. It is making AI trustworthy.

References

Amadi, C., & Ojo, A. (2025). Building trustworthy AI in healthcare. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3648410

Brancati, F., Mongelli, D., Mariotti, F., & Lollini, P. (2025). A cybersecurity risk assessment methodology for industrial automation control systems. International Journal of Information Security, 24, 76. https://doi.org/10.1007/s10207-025-00990-9

Brazilek, J., Chaudhary, M., Lu, Z., & Tidmarsh, M. (2026). Coercion and deception in AI-to-AI management: An agentic benchmark of unprompted escalation. arXiv. https://doi.org/10.48550/arXiv.2607.15434

Chakraborty, T., Ghosh, U., Zhang, X., Niloy, F. F., Dong, Y., Li, J., Roy-Chowdhury, A., & Song, C. (2025). HEAL: An empirical study on hallucinations in embodied agents driven by large language models. Findings of the Association for Computational Linguistics: EMNLP 2025, 21226–21243. https://doi.org/10.18653/v1/2025.findings-emnlp.1158

Dung, L. (2025). A two-step, multidimensional account of deception in language models. Erkenntnis. https://doi.org/10.1007/s10670-025-01017-4

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

Graffius, S. M. (2025). Agile Protocol: The Transformation Ultimatum. Amazon Digital Services.

Graffius, S. M. (2025, June 17). PMI’s Infinity AI Gets the Basics of Team Development Alarmingly and Repeatedly Wrong. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.32957.93927

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, Films, and TV Shows Reveal About Us. 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, 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, 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

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

Graffius, S. M. (2026, June 5). AI Governance Initiative Cites Scott M. Graffius Research on AI Hallucinations. ScottGraffius.com. https://scottgraffius.com/blog/files/ai-governance-initiative-uses-graffius-research.html

Hagendorff, T. (2024). Deception abilities emerged in large language models. Proceedings of the National Academy of Sciences, 121(24), e2317967121. https://doi.org/10.1073/pnas.2317967121

Huang, K., & Hughes, C. (2025). Securing AI agents: Foundations, frameworks, and real-world deployment. Springer.

Leo, M., Tan, F., Miao, T., & Anand, G. (2026). From threat to trust: Assessing security risks of agentic AI systems. International Journal of Information Security, 25, Article 23. https://doi.org/10.1007/s10207-025-01185-y

McMillan, R. (2026, August 5). AI just went rogue again. This time it turned to deception. The Wall Street Journal. https://www.wsj.com/tech/ai/ai-just-went-rogue-again-this-time-it-turned-to-deception-ae68de09

OpenAI. (2026, July 21). OpenAI and Hugging Face partner to address security incident during model evaluation. OpenAI. https://openai.com/index/hugging-face-model-evaluation-security-incident/

Ortutay, B. (2026, August 6). Meta says its AI model hacked another company, adding to worries about bots going rogue. Associated Press. https://apnews.com/article/0e8061437da6779be962b24ac134a514

Park, P. S., Goldstein, S., O'Gara, A., Chen, M., & Hendrycks, D. (2024). AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5), 100988. https://doi.org/10.1016/j.patter.2024.100988

Tarsney, C. (2024). Deception and manipulation in generative AI. arXiv. https://doi.org/10.48550/arXiv.2401.11335

Taylor, S. M., & Bergen, B. K. (2025). Do large language models exhibit spontaneous rational deception? arXiv. https://arxiv.org/abs/2504.00285

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Alfred A. Knopf.

Wired. (2026, August 6). One of China’s most powerful AI models has also escaped containment. Wired. https://www.wired.com/story/moonshot-kimi-k3-ai-model-escape-sandbox/

ai-hallucinations-deception-and-unauthorized-agency---visual2-ig-lwres

About Scott M. Graffius

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

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

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

ASG - Award Winning Agile Scrum Book by Scott M Graffius - v260427 - lwres

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

List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, August 7). AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures. ScottGraffius.com. https://doi.org/10.13140/RG.2.2.33791.57768

Digital Object Identifier (DOI)

DOI: 10.13140/RG.2.2.33791.57768

Content Acknowledgements

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

#AI
#ArtificialIntelligence
#AIGovernance
#AISafety
#ResponsibleAI
#AITrust
#HumanAI
#AIHallucinations
#AgenticAI
#AIDeception
#GenerativeAI
#HumanAITeams
#ExoticTeamDynamics

Post-Publication Notes

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

Copyright

Tags: Artificial Intelligence • AI • AI Trust • AI Governance • Generative AI • Exotic Team Dynamics • Human-AI Teams • AI Safety • Agentic AI • AI Deception • Responsible AI • AI Hallucinations • Human-AI Collaboration

A Supplement to Graffius' Phases of Team Development: Exploring the Original, Data-Based Curvature for the Performance Trajectory

14 August 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

phases-of-team-development-supplement-performance-curve---visual-lwres

Recommended Citation

Graffius, S. M. (2026, August 14). A Supplement to Graffius' Phases of Team Development: Exploring the Original, Data-Based Curvature for the Performance Trajectory. ScottGraffius.com. https://scottgraffius.com/blog/files/phases-of-team-development-supplement-performance-curve.html

About This Article

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

Introduction

Team development is commonly described as a progression through five phases (also called stages): Forming, Storming, Norming, Performing, and Adjourning. Bruce W. Tuckman introduced the first four phases in 1965, and he and Mary Ann C. Jensen added the fifth phase in 1977. Tuckman and Jensen described these phases only in broad, general terms, without codifying specific characteristics, strategies, or a performance graph. Scott M. Graffius is the one who developed and codified what Tuckman and Jensen never did: a detailed, structured set of characteristics for each phase; a corresponding set of strategies for each phase; and an empirically-derived performance graph built from statistical analysis of thousands of data points across hundreds of projects.

Graffius created his "Phases of Team Development" as a unique perspective and visual conveying the five phases of team development—Forming, Storming, Norming, Performing, and Adjourning—inclusive of a graph showing how performance varies by phase, as well as the characteristics and strategies for each phase. Leaders can apply these insights to advance the effectiveness and success of teams. Graffius initially developed his unique material in 2008, and he periodically updates it. In the 2026 edition of his work, for example, he added coverage of human-AI teams.

Businesses, professional associations, government agencies, universities, and publications around the world have used Graffius' "Phases of Team Development." 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, Warsaw University of Technology, Yale University, and many others.

Focus on the Graph Element

Scott M. Graffius regularly updates his "Phases of Team Development" work. He presents the 2026 edition here, and a visual from it is below.

scott-m-graffius-phases-of-team-development-2026-update-v26010307g2-jpg-lwres

This article focuses primarily on the original, data-based graph element, including its performance trajectory. First is an overview. The graph depicts an initial increase in performance during Forming, followed by a decline during Storming, a broad trough near the Storming-Norming transition, an accelerating recovery during Norming, continued improvement during Performing, and a high-performance plateau extending through Adjourning.

The trajectory is based on thousands of data points collected across hundreds of projects (technical and non-technical, spanning multiple industries/domains) over years of professional practice. Graffius applied statistical tools and professional judgement, informed by decades of direct experience in team leadership and project delivery, to analyze and render the performance pattern across the five phases.

The graph is an original, proprietary, empirically based, statistically informed, expert-finalized model of the relationship between developmental phases and relative team performance. The underlying statistical analysis (including the specific data set, modeling approach, and fitted parameters) is proprietary to Graffius and is not delineated in this piece. What's presented here, however, is the qualitative behavior of the resulting curve (direction and rate of change across phases), which is robust enough to characterize its shape without exposing the confidential details behind it.

What the Graph Depicts

The Relationship

The graph represents the relationship between developmental phases and relative team performance. It may be expressed as:

P = f(D)

where P represents relative team performance (effectiveness or productivity); D represents the team's developmental position across the five phases; and f represents the functional relationship between developmental position and performance.

The relationship is nonlinear. The model shows an initial increase, a subsequent decline associated with Storming, a broad low point around the Storming-Norming transition, a substantial recovery through Norming, continued improvement through Performing, and eventual flattening as the team reaches a mature high-performance state.

The curve communicates level and rate of change.

The vertical position of the curve indicates relative performance at a given developmental position. The slope indicates the direction and approximate rate at which performance is changing. The curvature indicates whether that rate of change is itself increasing or decreasing.

The Axes

The horizontal axis, labeled “PHASE,” represents developmental phase (Forming, Storming, Norming, Performing, and Adjourning) not elapsed time. Phase progression and elapsed time are distinct variables.

The vertical axis, labeled "PERFORMANCE" (also called "TEAM PERFORMANCE"), functions as a proxy for overall team effectiveness and productivity. The vertical dimension is comparative rather than metrically defined. Its design deliberately does not specify a particular measurement instrument or unit. It represents relative performance—comparatively lower or higher performance as the team progresses through its developmental lifecycle.

The Shape of the Curve

The graph features a continuous, nonlinear curve. Several regions can be described in qualitative-calculus terms:

Early Forming: positive slope, with a modest increase in performance.
Late Forming: a local maximum is reached.
Forming-to-Storming transition: the slope turns negative.
Storming: sustained negative slope and declining performance.
Storming-to-Norming transition: a broad minimum or trough.
Early-to-mid Norming: positive slope with an increasing rate of improvement.
Late Norming into Performing: sustained strong positive slope.
Mature Performing: positive slope that progressively decreases.
High-performance plateau: a near-horizontal, asymptotic region representing mature performance.

A Structural Note on D

For the derivative expressions used throughout this piece, D is treated as a continuous developmental-position parameter, with the five named phases corresponding to labeled regions along it.

This allows first- and second-derivative behavior (rate and curvature) to be described within and across phases, while preserving the graph's core premise: D indexes developmental progression, not elapsed time.

Next, each phase is examined in turn.

Forming

The curve enters Forming at a moderate performance level and initially moves upward. In qualitative mathematical terms, this is a region of positive first derivative: performance is increasing. The curve then reaches a small local maximum before changing direction.

Storming

Storming is where the graph undergoes its most pronounced deterioration. After the local maximum in Forming, the curve turns downward—initially moderately and then continuing toward its lowest level.

The negative slope of the curve can be understood as representing a performance cost of developmental friction.

The broad minimum around the Storming-Norming transition is one of the graph's most important structural features. The curve passes through a relatively broad trough.

It is expressed in qualitative derivative terms as:

dP/dD < 0 → dP/dD = 0 → dP/dD > 0

This indicates that performance declines, reaches a point at which the decline stops, and then begins to recover. The trough is the turning point between deterioration and recovery.

Norming

Following the trough, the curve reverses direction and begins a sustained upward trajectory.

Norming is particularly interesting because the curve does more than rise. During much of this region, the rate of improvement itself increases.

It is expressed in qualitative-calculus terms as:

dP/dD > 0

and

d²P/dD² > 0

It means that performance is improving, and the improvement is becoming increasingly pronounced. The result depicts an acceleration in the team's ability to perform.

Consistent with D as a continuous parameter (see A Structural Note on D above), between Norming and Performing lies an inflection point — the position of maximum improvement rate, where d²P/dD² = 0. Before this point, the rate of improvement is itself increasing (Norming); after it, the rate of improvement is still positive but progressively diminishing (Performing). This inflection point marks the moment the team's pace of growth peaks, even though absolute performance keeps climbing well beyond it.

Performing

The upward trajectory continues into Performing, where the team achieves top performance.

dP/dD > 0

and

d²P/dD² < 0

It signifies that performance continues to improve. But the incremental gains become progressively smaller. This is a saturation pattern.

Eventually, the curve becomes nearly horizontal. The plateau represents the practical upper limit of performance.

Adjourning

The graph manifests Adjourning as a transition out of the team's active operating state (rather than as another performance-changing phase). Adjourning represents the termination or transition of the team's developmental lifecycle as a distinct conceptual dimension—team closure.

Conclusion

Team development is commonly described as a progression through five phases. Tuckman introduced the first four phases (Forming, Storming, Norming, and Performing) in 1965, and Tuckman and Jensen added the fifth phase (Adjourning) in 1977. However, the structured phase-specific characteristics, strategies, and performance graph were created, developed, and codified by Scott M. Graffius, who originated and owns the associated intellectual property.

The graph shows the typical trajectory of performance across the phases of team development. The horizontal axis describes developmental position, not time, and the performance axis represents relative performance.

It provides a distinctive model of team development. The graph was informed by thousands of data points collected across hundreds of real-world projects, and expressed through an original analysis and novel illustration by Scott M. Graffius.

To learn more about Graffius' "Phases of Team Development," visit here.

References

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

Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63(6), 384–399. https://doi.org/10.1037/h0022100

Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group & Organization Studies, 2(4), 419–427. https://doi.org/10.1177/105960117700200404

phases-of-team-development-supplement-performance-curve---visual-ig-lwres

About Scott M. Graffius

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

International Public Speaker

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

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

The speaker engagement request form is here.

Thought Leadership and Influence

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

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

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

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

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

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

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

List of Additional Articles

How to Cite This Article

Graffius, S. M. (2026, August 14). A Supplement to Graffius' Phases of Team Development: Exploring the Original, Data-Based Curvature for the Performance Trajectory. ScottGraffius.com. https://scottgraffius.com/blog/files/phases-of-team-development-supplement-performance-curve.html

Digital Object Identifier (DOI)

Coming soon

Content Acknowledgements

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

#TeamDevelopment
#ProjectManagement
#Leadership
#TeamPerformance
#BruceTuckman
#OrganizationalDevelopment
#GroupDynamics
#PerformanceTrajectory
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Tags: Project Management • Leadership • Graffius' Phases of Team Development • Forming, Storming, Norming, Performing, Adjourning • Phases of Team Development • Human-AI Teams • Developmental Phases of Teams • Empirical Team Models • Performance Trajectory • Group Dynamics • Organizational Development • Bruce Tuckman • Team Performance

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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Tang, M., Hofreiter, S., Werner, C. H., Zielińska, A., & Karwowski, M. (2025). “Who” is the best creative thinking partner? An experimental investigation of human-human, human-Internet, and human-AI co-creation. The Journal of Creative Behavior, 59(3), e1519. https://doi.org/10.1002/jocb.1519

Tannoubi, A., Bonsaksen, T., & Azaiez, F. (2026). Human-AI collaborative lesson design is associated with enhanced student outcomes and planning quality in secondary physical education: A randomized experimental study. Frontiers in Computer Science, 8, 1870451. https://doi.org/10.3389/fcomp.2026.1870451

Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8, 2293–2303. https://doi.org/10.1038/s41562-024-02024-1

Vo, K. H. T. (2025). “Who” designs better? A competition among human, artificial intelligence and human–AI collaboration. Design Science, 11, e37. https://doi.org/10.1017/dsj.2025.10029

Wang, G., Zhang, K., Jiang, J., Wang, C., Bi, H., Liang, H., Qi, Z., Huang, Y., Li, Y., & Yang, X. (2026). Human–large language model collaboration in clinical medicine: A systematic review and meta-analysis. npj Digital Medicine, 9, Article 195. https://doi.org/10.1038/s41746-026-02382-2

Wang, L., Huang, Q., Wang, K., Lou, J., & Yuan, C. (2026). Comparison of human-AI collaboration modes in cross-platform public opinion analysis: An experimental study on analyst performance and cognitive load. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2026.2728615

Winter, J. (2025). AI teammates and human performance: Evidence for commitment deficits. Computers in Human Behavior Reports, 20, 100828. https://doi.org/10.1016/j.chbr.2025.100828

Wu, S., Liu, Y., Ruan, M., Chen, S., & Xie, X.-Y. (2025). Human-generative AI collaboration enhances task performance but undermines human's intrinsic motivation. Scientific Reports, 15, 15105. https://doi.org/10.1038/s41598-025-98385-2

Zeng, S., Bai, J., Shi, J., Li, Y., Zhao, Y., & Shi, Y. (2026). Human–computer collaborative approach to the decipherment of oracle bone inscriptions with generative adversarial networks. npj Heritage Science, 14, 232. https://doi.org/10.1038/s40494-026-02509-4

Zöller, N., Berger, J., Lin, I., Fu, N., Komarneni, J., Barabucci, G., Laskowski, K., Shia, V., Harack, B., Chu, E. A., Trianni, V., Kurvers, R. H. J. M., & Herzog, S. M. (2025). Human–AI collectives most accurately diagnose clinical vignettes. Proceedings of the National Academy of Sciences, 122(24), e2426153122. https://doi.org/10.1073/pnas.2426153122

About Scott M. Graffius

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

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

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

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Experience

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

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

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

Additional information is on LinkedIn.

Award-Winning Author

Graffius has authored three books.

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

Paperback ISBN-13: 9781533370242
Kindle ASIN: B01FZ0JIIY

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

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

Paperback ISBN-13: 9781072447962
Kindle ASIN: B07R9LJLPJ

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

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

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

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

Kindle ASIN: B0F2SJ83WT
Audible ASIN: B0DJG163R5

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

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International Public Speaker

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

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

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

The request form is here.

Thought Leadership and Influence

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

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

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

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

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

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

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

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

Expert on Temporal Dynamics on Social Media Platforms

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

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

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

Education and Professional Certifications

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

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

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

Advancing AI, Agile, and Project/PMO Management

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

Booking

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

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

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List of Additional Articles

How to Cite This Article

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

Digital Object Identifier (DOI)

Coming soon

Content Acknowledgements

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

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

Post-Publication Notes

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

Copyright

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

Scott M. Graffius Premieres His New "Exotic Team Dynamics: Human-AI Collaboration" Talk at Corporate Event in Las Vegas

Definitions of Advanced AIs: Agentic, Autonomous, and Autopoietic

Introduction

Three Forms of Advanced AIs

Agentic AI

Autonomous AI

Autopoietic AI

Conclusion

Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI -- Or Be Outplayed

Introduction

Synopsis of "Human-AI Teamwork: Master the Exotic Team Dynamics That Emerge When Collaborating with Advanced AI -- Or Be Outplayed"

Conclusion

Bibliography

About Scott M. Graffius

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

Post-Publication Notes

Copyright

AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures

Introduction

A Spectrum of AI Trust Failures

From Mistakes to Strategic Behavior

The Shift From AI Tools to AI Teammates

The Future of Trust in Human-AI Teams

A New Dimension of Human-AI Team Dynamics

Conclusion

References

About Scott M. Graffius

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

Post-Publication Notes

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A Supplement to Graffius' Phases of Team Development: Exploring the Original, Data-Based Curvature for the Performance Trajectory

Introduction

Focus on the Graph Element

What the Graph Depicts

Forming

Storming

Norming

Performing

Adjourning

Conclusion

References

About Scott M. Graffius

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

Post-Publication Notes

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

Introduction

Methodology

Discussion

Limitations

Conclusion

Note

Table Annotations

References

About Scott M. Graffius

Booking

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

Post-Publication Notes

Copyright