Meet our Advisors: Tom Patterson
Tom Patterson is Senior Vice President of Corporate Development and Strategy at BetterUp. He leads growth, partnerships, and M&A to advance the Human Transformation Platform (HTP). He helps global enterprises use AI to enhance leadership, workforce resilience, and organizational performance.
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If organizations ignore the human element in AI, vulnerabilities worsen, causing employee disengagement, anxiety, and performance declines.
To frame this important discussion, we speak with Tom about the risks AI poses to organizations and employees when not properly applied or adopted.
Human Readiness as the Constraint on AI Adoption
What are the risks for enterprises adopting AI if they fail to strengthen the human connectivity of their organizations?
Enterprises adopting AI must strengthen human connectivity to avoid amplifying disengagement, anxiety, and performance decline. Fear of job irrelevance and workplace fragmentation are growing risks. Poorly managed AI adoption may increase healthcare costs and workforce instability. Successfully adopting AI requires a fundamental shift in how we maintain human connection.
Rapid AI integration increases the risk of employee fear of automation and workplace fragmentation. Without strong psychological and structural support, these risks can lead to higher healthcare costs, attrition, lower morale, and challenges in talent retention. Human connectivity should be central to AI adoption strategies for organizational stability.
Risk in AI-Driven Self-Development
What necessary safeguards must AI coaching systems incorporate to mitigate the risks associated with unsupervised LLM interaction for personal development?
Unsupervised interaction with large language models (LLMs) for personal development poses risks for many people. The takeaway is that AI coaching systems must include mental health detection, escalation pathways, HR-aware safeguards, and features that reinforce user agency, all grounded in evidence-based approaches. Without these, enterprises face unnecessary risk. This infrastructure is essential and requires:
Mental Health Detection and Escalation Pathways
The system must continuously train to detect acute distress cues—anxiety, depression, self-harm, or crisis. On detection, AI follows a clear escalation protocol, not just suggesting help. This means prompting access to clinical support, sharing verified crisis hotlines, and, with user consent, alerting a designated HR contact or mental health professional.
HR-Aware Safeguards and Privacy Mechanisms
In corporate settings, AI coaching must follow all labor laws, privacy regulations, and internal HR policies. Safeguards prevent the AI from storing sensitive data that could be used for discrimination. Mechanisms must protect user anonymity and data segmentation, ensuring privacy unless there is a safety threat.
Mechanisms that Reinforce User Agency
The coaching system must empower users without manipulating or creating dependency. It should clearly distinguish AI-generated advice from guidance provided by human professionals. It should give users clear options to manage shared data, set interaction frequency, and end coaching sessions easily. And it should reinforce the need for users to find and internalize their own solutions, rather than just follow prescribed actions, to build self-efficacy and critical thinking.
Grounding in Evidence-Based Approaches
What are the necessary grounding principles and ethical commitments required for the safe and effective adoption of AI coaching methodologies within enterprises?
AI-generated coaching methods and feedback must be grounded in established, peer-reviewed evidence from fields such as cognitive-behavioral therapy (CBT), motivational interviewing, positive psychology, and organizational development. Main takeaway: Auditable systems ensure outcomes align with proven strategies, not just anecdotal success.
Without these safeguards and ethical standards, enterprises risk legal liability, brand damage, and ethical failure, including worsening mental health issues. Takeaway: Ethical design and risk management are nonnegotiable for successful adoption of AI coaching.
Human-in-the-Loop Healthcare and Development
AI models require extensive data for accuracy, especially in unusual cases. Human judgment—pattern recognition, empathy, and experience—remains critical in healthcare and enterprises. The future lies in augmenting, not replacing, humans through ongoing training, feedback, and recognizing AI limitations.
Human judgment offers nuanced recognition, empathy, and contextual experience. In healthcare and enterprise environments, it serves as the essential safety net and final decision-maker.
AI should enhance human performance with data tasks and predictive insights. This requires ongoing training for both models and users, feedback loops for algorithms, and continuous practice.
Finding the right balance between AI and human oversight is crucial. Using AI as a tool to enhance human ability—rather than replace it—drives better results in healthcare, operations, and enterprise success.
More About our Advisor
Tom Patterson is a three-time founder and operator who built and scaled venture-backed companies acquired by LinkedIn, Nextag, and Even Financial. He advises CEOs, boards, and investors on market creation, AI transformation, and enterprise growth. Tom serves on the board of Dale Carnegie & Associates and advises multiple organizations.
Tom is a father and active youth sports coach in football, basketball, baseball, and softball. He is deeply committed to human development in both his professional and personal life and has practiced martial arts for many years.
At the intersection of human potential and AI, Tom champions a future where AI augments people, enabling organizations to thrive in rapid technological change.
