AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures
07 August 2026
BY SCOTT M. GRAFFIUS | ScottGraffius.com
Recommended Citation
Graffius, S. M. (2026, August 7). AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures. ScottGraffius.com. https://scottgraffius.com/blog/files/ai-hallucinations-deception-and-unauthorized-agency.html
About This Article
Source information and links for materials cited are provided in the References section.
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 broader spectrum 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 broader range of behaviors that can undermine trust.
Recent reporting and research have highlighted this broader 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.
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:
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.
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.
Here's an overview of hallucinations, deception, and unauthorized agency:
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.
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.
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:
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.
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.
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.
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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
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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.
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:
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:
He was also a subject matter expert reviewer of content for the PMI’s Congress. Beyond the PMI, Graffius also served as a member of the review team for two of the Scrum Alliance’s Global Scrum Gatherings.
Acclaimed Authority on Teamwork Tradecraft
Graffius is a renowned authority on teamwork tradecraft. Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies, and Graffius' first-hand professional experience with, and analysis of, team leadership and performance, Graffius created his "Phases of Team Development" intellectual property as a unique perspective and visual conveying the five phases of team development. First introduced in 2008 and periodically updated, his work provides a diagnostic and strategic guide for navigating team dynamics. It provides actionable insights for leaders across industries to develop high-performance teams. Its adoption by esteemed organizations such as Yale University, IEEE, Cisco, Microsoft, Ford, Oracle, Broadcom, the U.S. National Park Service, and the Journal of Neurosurgery, among others, highlights its utility and value, solidifying its status as an indispensable resource for elevating team performance and driving organizational excellence. In 2026, Graffius added human-AI teamwork—including the "exotic team dynamics" which emerge when advanced AI collaborates as a teammate—to his "Phases of Team Development."
The 2026 edition of Graffius' "Phases of Team Development" intellectual property is here.
Expert on Temporal Dynamics on Social Media Platforms
Graffius is also an authority on temporal dynamics on social media platforms. His "Lifespan (Half-Life) of Social Media Posts" research—first published in 2018 and updated annually—delivers a precise quantitative analysis of post longevity across digital platforms, utilizing advanced statistical techniques to determine mean half-life with precision. It establishes a solid empirical base, effectively highlighting the ephemeral nature of content within social media ecosystems. Referenced and applied by leading entities—such as Fast Company, GoDaddy, Journal of Hand Surgery (European Volume), Ministère de la Culture (French Ministry of Culture), Pinterest Inc., PNAS, and Telecommunications Policy, among others—his research exemplifies methodological rigor and sustained significance in the field of digital informatics.
The 2026 edition of Graffius "Lifespan (Half-Life) of Social Media Posts" research is here.
Education and Professional Certifications
Graffius has a bachelor’s degree in psychology with a focus in Human Factors. He holds eight professional certifications:
He is an active member of the Scrum Alliance, the Project Management Institute (PMI), and the Institute of Electrical and Electronics Engineers (IEEE).
Advancing AI, Agile, and Project/PMO Management
Scott M. Graffius continues to advance the fields of AI, Agile, and Project/PMO Management through his leadership, research, writing, and real-world impact. Businesses and other organizations leverage Graffius’ insights to drive their success.
Discover Scott’s Books
Connect with and follow Scott on LinkedIn, X, YouTube, Facebook, Threads, Bluesky, Mastodon, and ResearchGate.
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Graffius, S. M. (2026, August 7). AI Hallucinations, Deception, and Unauthorized Agency: The Spectrum of AI Trust Failures. ScottGraffius.com. https://scottgraffius.com/blog/files/ai-hallucinations-deception-and-unauthorized-agency.html
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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://scottgraffius.com/blog/files/ai-hallucinations-deception-and-unauthorized-agency.html
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 broader spectrum 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 broader range of behaviors that can undermine trust.
Recent reporting and research have highlighted this broader 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.
Here's an overview of hallucinations, deception, and unauthorized agency:
| 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.
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About Scott M. Graffius
Scott M. Graffius is a strategic transformation leader who drives AI, Agile, and broader business and technology initiatives to deliver measurable value across projects, programs, portfolios, and PMOs. He is an expert in the teamwork tradecraft of both human and human-AI teams, including the “exotic team dynamics” that emerge. He is also an authority on the temporal patterns of social media, including the half-life of audience engagement.
He’s a practitioner, researcher, thought leader, award-winning author, and keynote speaker who’s taken the stage at 98 conferences and other events across 25 countries.
He’s delivered over $2.51 billion in value for Fortune 500 companies and other leaders in technology, entertainment, financial services, healthcare, and beyond.
Businesses, professional associations, government agencies, and universities use Graffius and feature his work. Examples include Adobe, Bayer, Boston University, Ford, Gartner, Harvard Medical School, IEEE, Johns Hopkins University, Microsoft, MSN, National Academy of Sciences, Oracle, Pinterest Inc., Project Management Institute, UC San Diego, Verizon, Yale University, and others.
The following sections provide additional information on his experience, contributions, and influence.
Experience
Graffius heads the professional services firm Exceptional PPM and PMO Solutions, along with its subsidiary Exceptional Agility. These consultancies offer strategic and tactical advisory, training, embedded expertise, and consulting services to the public, private, and government sectors. They help organizations enhance their capabilities and results in agile, project management, program management, portfolio management, and PMO leadership, supporting innovation and driving competitive advantage. The consultancies confidently back services with a Delighted Client Guarantee™.
Graffius is a former VP of project management with a publicly traded provider of diverse consumer products and services over the Internet. Before that, he ran and supervised the delivery of projects and programs in public and private organizations with businesses ranging from e-commerce to advanced technology products and services, retail, manufacturing, entertainment, and more.
He has experience with consumer, business, reseller, government, and international markets.
Award-Winning Author
Graffius has authored three books.
- Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, his first book, earned 17 awards.
- Agile Transformation: A Brief Story of How an Entertainment Company Developed New Capabilities and Unlocked Business Agility to Thrive in an Era of Rapid Change, his second book, was named one of the best Scrum books of all time by BookAuthority.
- Agile Protocol: The Transformation Ultimatum, his third book and his first work of fiction, was released in April 2025. The book trailer is on YouTube.
International Public Speaker
Organizations worldwide engage Graffius to present on tech (including AI), Agile, project management, program management, portfolio management, and PMO leadership. He crafts and delivers unique and compelling talks and workshops. Graffius has conducted 98 sessions across 25 countries. Select examples of events include Agile Trends Gov, BSides (Newcastle Upon Tyne), Conf42 Quantum Computing, DevDays Europe, DevOps Institute, DevOpsDays (Geneva), Frug’Agile, IEEE, Microsoft, Scottish Summit, Scrum Alliance RSG (Nepal), Techstars, and W Love Games International Video Game Development Conference (Helsinki), and more.
With an average rating of 4.81 (on a scale of 1-5), sessions are highly valued.
The speaker engagement request form is here.
Thought Leadership and Influence
Prominent businesses, professional associations, government agencies, and universities have showcased Graffius and his contributions—spanning his books, talks, workshops, and beyond. Select examples include:
- Adobe,
- American Management Association,
- Amsterdam Public Health Research Institute,
- Bayer,
- BMC Software,
- Boston University,
- Broadcom,
- Cisco,
- Coburg University of Applied Sciences and Arts - Germany,
- Computer Weekly,
- Constructor University - Germany,
- Data Governance Success,
- Deimos Aerospace,
- DevOps Institute,
- Dropbox,
- EU's European Commission,
- Ford Motor Company,
- Gartner,
- GoDaddy,
- Harvard Medical School,
- Hasso Plattner Institute - Germany,
- IEEE,
- Innovation Project Management,
- Johns Hopkins University,
- Journal of Neurosurgery,
- Lam Research (Semiconductors),
- Leadership Worthy,
- Life Sciences Trainers and Educators Network,
- London South Bank University,
- Microsoft,
- MSN,
- NASSCOM,
- National Academy of Sciences,
- New Zealand Government,
- Oracle,
- Pinterest Inc.,
- Project Management Institute,
- Mary Raum (Professor of National Security Affairs, United States Naval War College),
- SANS Institute,
- SBG Neumark - Germany,
- Singapore Institute of Technology,
- Torrens University - Australia,
- TBS Switzerland,
- Tufts University,
- UC San Diego,
- UK Sports Institute,
- University of Galway - Ireland,
- US Department of Energy,
- US National Park Service,
- US Soccer,
- US Tennis Association,
- Verizon,
- Wrike,
- Yale University,
- and many others.
Graffius has played a key role in the Project Management Institute (PMI) in developing professional standards. He was a member of multiple teams that authored, reviewed, and produced:
- The Standard for Artificial Intelligence in Portfolio, Program, and Project Management
- Agile Practice Guide – Second Edition
- A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Eighth Edition
- A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Sixth Edition
- The Standard for Program Management – Fourth Edition
- Practice Standard for Work Breakdown Structures – Second Edition
- The Practice Standard for Project Estimating – Second Edition
He was also a subject matter expert reviewer of content for the PMI’s Congress. Beyond the PMI, Graffius also served as a member of the review team for two of the Scrum Alliance’s Global Scrum Gatherings.
Acclaimed Authority on Teamwork Tradecraft
Graffius is a renowned authority on teamwork tradecraft. Informed by the research of Bruce W. Tuckman and Mary Ann C. Jensen, over 150 subsequent studies, and Graffius' first-hand professional experience with, and analysis of, team leadership and performance, Graffius created his "Phases of Team Development" intellectual property as a unique perspective and visual conveying the five phases of team development. First introduced in 2008 and periodically updated, his work provides a diagnostic and strategic guide for navigating team dynamics. It provides actionable insights for leaders across industries to develop high-performance teams. Its adoption by esteemed organizations such as Yale University, IEEE, Cisco, Microsoft, Ford, Oracle, Broadcom, the U.S. National Park Service, and the Journal of Neurosurgery, among others, highlights its utility and value, solidifying its status as an indispensable resource for elevating team performance and driving organizational excellence. In 2026, Graffius added human-AI teamwork—including the "exotic team dynamics" which emerge when advanced AI collaborates as a teammate—to his "Phases of Team Development."
The 2026 edition of Graffius' "Phases of Team Development" intellectual property is here.
Expert on Temporal Dynamics on Social Media Platforms
Graffius is also an authority on temporal dynamics on social media platforms. His "Lifespan (Half-Life) of Social Media Posts" research—first published in 2018 and updated annually—delivers a precise quantitative analysis of post longevity across digital platforms, utilizing advanced statistical techniques to determine mean half-life with precision. It establishes a solid empirical base, effectively highlighting the ephemeral nature of content within social media ecosystems. Referenced and applied by leading entities—such as Fast Company, GoDaddy, Journal of Hand Surgery (European Volume), Ministère de la Culture (French Ministry of Culture), Pinterest Inc., PNAS, and Telecommunications Policy, among others—his research exemplifies methodological rigor and sustained significance in the field of digital informatics.
The 2026 edition of Graffius "Lifespan (Half-Life) of Social Media Posts" research is here.
Education and Professional Certifications
Graffius has a bachelor’s degree in psychology with a focus in Human Factors. He holds eight professional certifications:
- Certified SAFe 6 Agilist (SA),
- Certified Scrum Professional - ScrumMaster (CSP-SM),
- Certified Scrum Professional - Product Owner (CSP-PO),
- Certified ScrumMaster (CSM),
- Certified Scrum Product Owner (CSPO),
- Project Management Professional (PMP),
- Lean Six Sigma Green Belt (LSSGB), and
- IT Service Management Foundation (ITIL).
He is an active member of the Scrum Alliance, the Project Management Institute (PMI), and the Institute of Electrical and Electronics Engineers (IEEE).
Advancing AI, Agile, and Project/PMO Management
Scott M. Graffius continues to advance the fields of AI, Agile, and Project/PMO Management through his leadership, research, writing, and real-world impact. Businesses and other organizations leverage Graffius’ insights to drive their success.
Discover Scott’s Books
- Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions — Deliver Products in Short Cycles with Rapid Adaptation to Change, Fast Time-to-Market, and Continuous Improvement
- 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
- Agile Protocol: The Transformation Ultimatum
Connect with and follow Scott on LinkedIn, X, YouTube, Facebook, Threads, Bluesky, Mastodon, and ResearchGate.
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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://scottgraffius.com/blog/files/ai-hallucinations-deception-and-unauthorized-agency.html
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