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

01 October 2026

BY SCOTT M. GRAFFIUS | ScottGraffius.com

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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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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.

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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.

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

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

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

Kindle ASIN: B0F2SJ83WT
Audible ASIN: B0DJG163R5

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

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International Public Speaker

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

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

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

The request form is here.

Thought Leadership and Influence

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

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

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

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

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

Authority on Teamwork Tradecraft

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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

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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, 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

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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

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

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Post-Publication Notes

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

Copyright

Copyright © Scott M. Graffius. All rights reserved.

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

Tags: Artificial Intelligence • 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