Welcome back, classmates! Today is a big day because we are officially at graduation!! Caps are metaphorically in the air, and we are pausing to look both backward and forward at what we have learned together. Over the course of this school year, we have explored AI fundamentals, prompting, multimedia tools, reasoning models, deep research, ethics, workplace implementation, business strategy, philosophy, and hands-on tools like ChatGPT and Copilot.

That is no small accomplishment! You should feel proud of how far your understanding has come. Graduation is about stepping into what comes next with clarity and confidence. Today’s lesson is about the future of generative AI and how we, as workers’ compensation professionals, prepare ourselves to lead through it.

This entire series has been inspired by my professor friends Chris Snider and Christopher Porter, known as the Innovation Profs, whose Gen AI Summer School sparked my imagination and curiosity. Their ability to translate emerging technology into accessible, human-centered education is something I deeply admire. They asked big questions about where AI is headed, and I could not resist bringing those questions into the workers’ compensation world we live and breathe every day. Their work reminded me that the future does not belong to the most technical people in the room, but to the most thoughtful ones. This article is both a tribute to their vision and a bridge to how that vision shows up in our industry.

Autonomous and Agentic AI: From Assistant to Teammate

One of the biggest trends shaping the future of generative AI is the rise of autonomous, agentic systems that can take actions, versus those that simply generate responses. These AI agents are designed to plan, decide, and execute tasks with limited human input, which marks a meaningful shift from AI as a tool to AI as a teammate. In workers’ compensation, this could look like agents that automatically gather claim documentation, schedule follow-ups, or monitor compliance deadlines across jurisdictions. While that sounds futuristic, we are already seeing early versions through ChatGPT’s agent mode and workflow tools like Make and n8n. The key takeaway is that autonomy increases efficiency, and oversight remains essential. Two things can be true.

Multimodal Experiences: Beyond Text and Into Real Life

Generative AI is no longer confined to text. The future is undeniably multimodal. AI systems now see, hear, speak, and respond across text, image, audio, and video, often all at once. This matters deeply in workers’ compensation because communication is rarely just written; it is emotional, visual, and contextual. Imagine injured worker education delivered through short videos, voice-guided return-to-work explanations, or visual claim timelines that update in real time. When AI meets people in the way they naturally communicate, understanding improves and friction decreases.

AI in Everyday Life: When Technology Becomes Ambient

AI is steadily moving off our screens and into the physical world through wearables, smart devices, and augmented reality. Glasses that listen, interpret, and annotate the world around us are no longer science fiction as they are here! You can find this tech already in development by companies like Meta, Google, and Snap. For workers’ compensation, this could eventually mean hands-free injury documentation, real-time safety coaching, or instant policy guidance during on-site evaluations. When AI becomes ambient, it changes how we interact with information altogether. The challenge will be ensuring accuracy, privacy, and ethical boundaries as convenience increases.

AI Companions: Emotional Support & Human Connection

One of the most surprising trends in generative AI is the rise of AI companions used for emotional support, coaching, and connection. Studies now show that companionship and therapy-related interactions are among the top use cases for AI, surpassing productivity tasks. While workers’ compensation will never replace human care with digital companionship, this trend reveals something important about human needs. Injured workers often feel isolated, overwhelmed, and uncertain, and communication style matters deeply in those moments. AI can help us draft language that is calmer, clearer, and more validating, but the human relationship remains the foundation.

AI Browsers: A New Way to Navigate Information

AI-powered browsers represent a major shift in how we access and act on information online. Instead of searching, reading, and synthesizing across dozens of tabs, AI browsers collapse those steps into a single conversational experience. For workers’ compensation professionals who research regulations, medical guidelines, or legal trends, this could dramatically reduce cognitive load and research time. The browser becomes an active collaborator rather than a passive window. As this technology matures, information literacy will matter as much as information access.

Smarter and More Personal AI Assistants

The next generation of AI assistants will remember preferences, projects, and patterns over time, creating continuity and personalization. Persistent memory allows AI to function more like a trusted assistant than a one-off tool. In workers’ compensation, this could mean assistants that understand your claim philosophy, communication style, or compliance priorities. Personalization improves efficiency, but it also raises questions about boundaries and data governance. Knowing how to use memory responsibly will be part of future AI literacy.

What This Means for Workers’ Compensation

The future of AI points towards amplification of human expertise. Workers’ compensation relies on judgment, empathy, negotiation, and ethical decision-making, all areas where humans remain essential. AI will handle more of the structure, speed, and synthesis, allowing professionals to focus on people and outcomes. Organizations that succeed will be those that treat AI as a strategic partner rather than a novelty. Leadership in this space will require curiosity, courage, and continuous learning.

Class Takeaway

Graduation Day marks the beginning of intentional leadership in an AI-powered world. Generative AI is becoming more autonomous, more personal, and more embedded in daily life, and workers’ compensation professionals are uniquely positioned to shape how it is used responsibly. Your challenge moving forward is to stay engaged, ask thoughtful questions, and anchor technology in human values. The future belongs to leaders who understand both systems and people. You are now equipped to be one of them.

Class dismissed, graduates.

While this school year has officially ended, the conversation does not stop here. AI will keep evolving, and so will we. If you want to read all fifteen of our lessons, you can see them all here.

Welcome back to class, classmates! Today, we are heading into Computer Lab where your everyday Microsoft tools suddenly gain superpowers. These superpowers help you and your workflow become brighter, faster, and more dynamic than ever before. Microsoft Copilot has quietly woven itself into the apps you use every day, and understanding what it can really do is a game changer for anyone in workers’ compensation. While ChatGPT often gets the spotlight, Copilot is the tool that meets most of us right where we (and our organizations) live in Outlook, Word, Excel, Teams, and SharePoint. This week is about turning those tools from everyday software into intelligent collaborators. Computer Lab is officially open.

As always, this conversation is inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who explored the concept of AGI in their AI Summer School series. More recent, Professors Snider and Porter explored Copilot Agents in their newsletter. Their breakdown of prebuilt agents, custom agents, and productivity strategies showed how deeply Copilot integrates into the Microsoft ecosystem. Their work reminds us that innovation can exist in rediscovering the tools you already have. They gave us the spark, and now we are going to light it up in the world of workers’ compensation. Their wisdom helps us see how Copilot shifts from convenience to strategy when we learn its full potential. Plus, it is more than likely already on your computer. Time to take it for a test spin in this week’s Computer Lab.

What Microsoft Copilot Actually Is

Microsoft Copilot is an intelligence layer built directly into the applications you already use every single day. Think the ChatGPT of Microsoft. Copilot Chat gives you conversational support, while Microsoft 365 Copilot goes further by tapping into something called the Microsoft Graph. The Microsoft Graph connects and synthesizes the relationships between your emails, documents, meetings, and organizational information. Copilot understands your work context in a way ChatGPT or Gemini cannot unless you manually upload files. For workers’ compensation professionals, this means Copilot can summarize claims emails, extract details from medical documents, or analyze return-to-work spreadsheets with deeper insight. When a tool understands your environment, it becomes far more capable of supporting your success.

Why Copilot Matters for Workers’ Compensation

Workers’ compensation is full of documentation, deadlines, communication, and coordination. Copilot fits into this structure naturally with its information synthesis. Every claim involves emails, reports, notes, spreadsheets, and policy references, which are all housed inside the Microsoft ecosystem. Copilot can summarize inbox threads, draft first-pass communications, analyze spreadsheet trends, and prepare meeting recaps instantly. This reduces administrative friction and creates time to focus on empathy-based tasks that drive better outcomes. When administrative tasks shrink, humanity can expand. Imagine what you can do with all of that additional headspace!

Prebuilt Copilot Agents: Your New Digital Classmates

Prebuilt agents are one of Microsoft’s most exciting additions, because they give you specialized assistants designed for specific functions right out of the box. These agents appear inside the Copilot interface and can handle tasks ranging from research to analysis to survey creation. You do not have to set anything up, train anything, or upload a long list of instructions; they simply work. These prebuilt agents are particularly powerful for workers’ compensation because they reflect real tasks we manage every day. When you match a role-specific agent with your daily workflow, productivity becomes fluid and intuitive.

Researcher: Your Instant Research Assistant

Researcher is the Copilot version of Deep Research from ChatGPT. Researcher functions like a dedicated AI analyst who reads information broadly and synthesizes quickly. When Researcher is prompted with a topic, it draws together internal files, web content, and data sources to produce a structured, citation-backed report. In workers’ compensation, this can summarize jurisdictional differences, compare medical guidelines, or explore case law trends across states. It does in minutes what used to take hours, making it easier to prepare presentations, educational materials, or employer consultations. Researcher becomes your new best friend when clarity and depth matter.

Analyst: Supercharged Excel for Claims & Compliance

Many people fear Excel, but Analyst changes that relationship by giving you a patient, analytical teammate built directly into your spreadsheet. We can all use a teammate like this! Analyst can identify key trends, calculate metrics, create charts, and run complex scenarios by writing Python code underneath the hood. This ability is powerful for workers’ comp teams dealing with loss runs, injury type trends, lag-time analysis, indemnity projections, or medical cost breakdowns. The best part is that Analyst makes the math accessible, understandable, and visual. When data becomes clearer, decisions become smarter.

Learning Coach: Your Personalized Training Partner

Learning Coach taps into the power of guided learning by supporting you with Socratic questioning, comprehension checks, and confidence-building steps. This agent is the Copilot equivalent of ChatGPT’s Study Mode and can help you understand complex topics, test your knowledge, and strengthen your skills. Workers’ compensation professionals can use this to interpret statutes, learn new communication strategies, or study best practices for high-risk industry claims. It turns learning from a task into an interactive dialogue. Growth becomes easier when learning feels like a supportive conversation.

Surveys: The Agent That Brings Voice & Insight Together

Surveys is a prebuilt agent designed to help you collect information, analyze responses, and streamline feedback cycles across your team or organization. It can generate survey questions, create real-time previews, and adjust the format based on your feedback. For workers’ compensation leaders, this can support employee training evaluations, safety climate assessments, or return-to-work satisfaction surveys. This can also be a powerful tool for employers to see how their injured employees have felt throughout the workers’ compensation process. Surveys takes away the complexity of survey design and replaces it with ease. When feedback flows more freely, culture grows stronger.

Custom Agents: Building Your Own Workers’ Compensation Tools

Copilot also allows you to build custom agents that speak your language and reflect your workflow. These custom agents can be created using Copilot Studio, which works like a conversational builder that transforms your instructions into a working tool. For workers’ compensation, this might include agents that analyze claim notes, support OSHA documentation, guide supervisor communication, or pull insights from internal policy documents. Custom agents help you scale consistency while creating your own digital extensions of expertise. This is where technology becomes personalized.

The Power of Integration: Outlook, Teams, Word & Beyond

The magic of Copilot lies in its ability to sit inside the tools you already use without changing your workflow. Outlook drafts emails with empathy and accuracy. Teams summarizes meetings with assigned action steps. Word rewrites documents with clarity and tone for your specific audience. Excel analyzes data. SharePoint organizes content. This integration makes AI adoption seamless and intuitive. When tools meet you where you are, progress becomes effortless.

The Human Factor: Where Copilot Enhances, Not Replaces

Even with all these capabilities, Copilot does not replace the adjuster, manager, safety personnel, or HR professional. It supports thinking, writing, and analysis, but Copilot does not feel, empathize, or build trust. Workers’ compensation will always require human judgment to balance compassion, dignity, and fairness. What Copilot does beautifully is reduce the burden of administrative work so humans have more space to do what humans do best. AI handles the structure and we handle the soul.

Class Takeaway

This session of Computer Lab shows us that Microsoft Copilot is an intelligent companion integrated into the tools we already use every day. It enhances claims processing, strengthens communication, and elevates compliance by giving us clarity, speed, and support. And, it does it with the tools we are already using daily! When we embrace these tools, we create more space for meaningful connections, thoughtful decisions, and compassionate leadership. Copilot helps free up your brain power to be used for purpose, not the administrative functions that tie up so much of our daily lives in workers’ compensation.

Your homework: Test one prebuilt agent and see how it shapes your workflow. This experiment may unlock a new level of efficiency you did not know you needed. Bonus, try a prebuilt agent with a Microsoft tool you may not use often and see how it changes the game.

Class dismissed.

Next week: Graduation Day – What’s Next for AI and How We Lead Through It.

Welcome back to class, classmates! Today we are stepping into Study Hall, which means we get to explore, experiment, and expand what we think is possible with AI in our daily work. This class is all about discovering the full range of ChatGPT’s capabilities because most people still use generative AI like it’s 2022 even though the tools have radically evolved. And if you have yet to explore these tools, welcome! These improvements shift AI from being a simple chat assistant into a deeply capable collaborator who helps us work smarter and communicate clearer. Study Hall is the time where we roll up our sleeves and unlock the magic that is already sitting at our fingertips. Get ready because this one is packed full of sparkles.

This project is inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who explored the concept of AGI in their AI Summer School series.  Their work showcases how far ChatGPT has come and how much power it holds when we use it intentionally and creatively. Their “10 Things You Might Not Know You Can Do in ChatGPT” list shows us that the biggest barriers to AI adoption are simply awareness and imagination. Their insights always spark my curiosity and push me to think bigger about how these tools serve the workers’ compensation community. Their influence continues to remind us that innovation becomes possible when we stay open, playful, and informed.

Reasoning Models: The “Think Before You Speak” Era

Great tools become fantastic when they help us think better, and that is what the latest reasoning models in ChatGPT are designed to do. These models give more than surface-level responses as they generate internal chains of thought before producing an answer. These internal chains of thought improve accuracy and clarity. The updates of ChatGPT help the workers’ compensation industry where complexity is constant and nuance influences outcomes. Whether reviewing medical narratives or breaking down legislative updates, reasoning models can help translate complicated information into actionable clarity in a consumable form. This shift allows us to work with more confidence and precision as these tools support our thinking instead of replacing it.

Deep Research: Your AI Research Assistant

Deep Research has quickly become one of the most powerful tools in the ChatGPT ecosystem because it works like a full research assistant. This tool is capable of conducting multi-step web reviews, synthesizing sources, and creating citation-supported reports tailored to your exact questions. In the workers’ compensation space, this capability can clarify state-by-state policy differences, rising cost drivers, emerging safety trends, or best practices for communication in high-risk industries. Deep Research gives us access to depth and quality of information that used to take hours or even days to gather. When time is one of the most precious resources in this field, tools that accelerate clarity become invaluable partners. Always make sure to double check the sources referenced!

Study Mode: Personalized Learning on Demand

Study Mode is built for learners who want to strengthen understanding through guided support, essential in the workers’ compensation industry where details shape outcomes. This mode functions like a personal tutor that uses questions, prompts, and explanations to help you learn deeply rather than memorize quickly. It adapts to your pace and needs by checking comprehension, offering examples, and guiding you through material step-by-step. For workers’ compensation professionals, this can support everything from preparing for CE credits to mastering new protocols or legislation. The tool turns learning into a personalized conversation rather than a lonely process. For information you want to stick to memory, give Study Mode a try!

Custom GPTs: Your Digital Extensions

Custom GPTs allow you to build tailored versions of ChatGPT that reflect your workflows, preferences, and communication styles. These tools allow you to embed knowledge, examples, templates, and instructions into one dedicated assistant that follows your voice. In workers’ compensation, this means you can design GPTs that draft initial claim letters, prepare supervisor checklists, review communication tone, or help refine return-to-work strategies based on your own organizational values. You can design return-to-work programs, safety initiatives, and customize communication surrounding safety and injury prevention. Custom GPTs give you the ability to scale your thinking, your clarity, and your consistency. This becomes a tremendous asset in a field defined by high volume and high stakes. Make sure to personalize your content!

Agent Mode and Apps: The Toolbelt Behind the Curtain

Agent Mode elevates what ChatGPT can do because it moves from responding to acting. Action is how we get things done. Agent mode helps ChatGPT browse, click, analyze, summarize, extract, and execute multi-step tasks with accuracy and oversight. Apps extend this further by integrating tools like Canva, Zillow, Coursera, or Spotify directly inside ChatGPT, creating a single hub for work and creativity. In workers’ compensation, this can help create safety visuals, organize caseloads, plan travel for conferences, or research industry trends with ease. This layer of capability shows us that ChatGPT is becoming more than a prompt box. Agent mode streamlines tasks we once juggled across multiple systems. Do what you say you are going to do…and now ChatGPT does!

Atlas Browser: AI Across Every Tab

The Atlas browser represents a leap forward because it weaves generative AI into everyday web use. It can summarize articles, compare sources, extract information, and support your thinking while you browse the internet. This brings efficiency and clarity into everyday workflows like reviewing medical guidelines, tracking market trends, or preparing educational content. The browser also allows for safe, controlled AI actions inside your tabs, helping automate repetitive tasks without disrupting core work. For workers’ compensation teams, this could dramatically shorten research time and free capacity for human connection and decision-making.

Hidden Features That Surprise and Delight

ChatGPT also includes smaller capabilities that feel like little superpowers in your pocket. Voice Mode allows hands-free conversation, perfect for communicating between meetings or reflecting on a challenge. Image creation offers a fast way to create educational or motivational visuals to support communication. Meeting recording and transcription convert conversations into action steps that save time and strengthen clarity. These small tools add up, helping reduce the friction of everyday work so people can stay focused on the heart of workers’ compensation: serving injured humans well.

The Real Power: The Human + AI Partnership

The true power of ChatGPT is how we use it as thinking partners. These tools help us communicate better, analyze faster, and solve problems with more clarity. Please remember, these models still rely on our judgment, empathy, and leadership. In workers’ compensation, your compassion, presence, and connection remain the differentiators that AI cannot replicate. AI supports the logistics so you can support the people, creating a partnership that elevates the work on both sides. This balance is where the real magic of generative AI lives. ChatGPT is a tool, and it is up to you how you use it.

Class Takeaway

Study Hall shows us that ChatGPT is a learning system, creative studio, research assistant, presentation coach, and strategic partner. Each feature is designed to enhance your thinking, your communication, and your leadership. When you interact with these tools intentionally, you gain back time, energy, and clarity that you can reinvest into the moments that matter most.

Your homework: Pick one new feature and explore how it supports your professional goals. This is how you build mastery and confidence in an AI-forward world.

Class dismissed.

Next week we head to the Computer Lab: What Copilot Can Really Do.

Welcome back to class, classmates! This semester, we made our way through tools, strategy, and systems, and now we arrived at Philosophy Class. This class is where we ask the big questions: not just what AI can do, but what it means. Artificial Intelligence has grown from an experiment to an ecosystem, and the next frontier is what many are calling Artificial General Intelligence (AGI). AGI is a world where machines think, plan, and create at or beyond our level. If that sounds futuristic, it is! The future is getting closer every day. This lesson today is about preparing our minds, workplaces, and ethics for what comes next.

Inspired by the Innovation Profs

As always, this conversation is inspired by my professor friends Chris Snider and Christopher Porter, aka the Innovation Profs, who explored the concept of AGI in their AI Summer School series. They broke down what it might look like for AI to evolve from simple chatbots into systems capable of reasoning, problem-solving, and independent innovation. Their thoughtful approach reminded me that progress without purpose can lead to chaos, but progress with intention can change the world. These questions matter to those of us in workers’ compensation because we are an industry built on humanity, recovery, and trust. How will AI reshape our systems, and more importantly, how will we stay human through it all?

Understanding the Levels of Intelligence

To understand where AI is headed, it helps to picture its evolution in levels similar to how we think about automation in vehicles. The first level, Chatbots, is where most of us live right now. These models, like ChatGPT or Gemini, can write, summarize, and communicate fluidly. They are task-specific and depend on human direction. Next comes Reasoners, systems that can solve complex problems with minimal prompting. Think of these models as AI PhDs that can analyze, synthesize, and explain. From there, we move toward Agents, AI that does more than provide answers. These models perform tasks such as scheduling meetings, managing files, or analyzing workflows on your behalf. Innovators will create new solutions and designs, helping us invent faster than ever before. Organizations represent AI systems capable of running end-to-end operations autonomously. We are currently sitting somewhere between Reasoners and early Agents. The future is beta testing and we are on the verge of something new, right now!

Where Workers’ Compensation Fits on the Map

What does this mean for our world of workers’ compensation? We are entering an era where AI will do more than summarize claim files. Our future AI will start identifying patterns, predicting outcomes, and suggesting strategies for return-to-work. Reasoning-level models could support decision-making by analyzing medical histories, injury trends, and jurisdictional nuances. Agentic models could coordinate communication between adjusters, providers, and employers, ensuring no detail falls through the cracks. As exciting as that sounds, it also raises big questions. How do we balance efficiency with empathy? How do we preserve the human connection when the machine can draft the message, schedule the meeting, and analyze the outcome? The key will be in using AI to enhance human judgment, not override it. The adjuster of the future will be empowered by technology. Future adjusters will be able to focus on care, context, and compassion, the elements no algorithm can.

Ethics and Agency: Who Holds the Steering Wheel?

As AI moves closer to autonomy, one of the most important discussions we will face is around agency. Who makes the final decision? Who bears the responsibility? These are important questions to ponder. In workers’ compensation, this has enormous implications. AI may recommend medical providers or settlement options, but the ethical weight still rests with people. We must set clear boundaries between AI input and human accountability. Governance becomes critical. Companies need to develop internal “preparedness frameworks” that define how AI is used, reviewed, and audited. This is very similar to a safety program where policies are proactive, transparent, and always evolving through learning. The ethical use of AI will define reputational trust and guardrails are essential.

Reasoners, Agents, and Innovators: The New Classmates in the Room

As Reasoners become more advanced, they will start solving complex problems. This can include calculating reserve projections, analyzing risk exposure, or identifying causation patterns across years of data. They will think “longer,” process deeper, and deliver insights faster. Early Agents will then take these insights and put them into motion by sending notifications, managing workflows, or pulling the right reports in seconds. The next level, Innovators, will help us reimagine how we operate. Imagine an AI helping to design new safety initiatives, draft legislation summaries, or predict which communication styles lead to faster recoveries. These tools will reshape what is possible. Remember, no matter how advanced these tools become, they will always need the human element. There will always be a need for people who can translate data into dignity, and predictions into progress.

Preparing for the Road Ahead

The path to AGI is not happening overnight. Timelines vary wildly from late 2020s to 2060s. How we get there is going to matter. Organizations should start preparing now by building cultures that value adaptability, transparency, and lifelong learning. Encourage curiosity. Train your teams to ask smarter questions, not just faster ones! AI will evolve beyond today’s limitations, and, so can we! As tools get smarter, humans must get wiser. We must become more reflective, more ethical, more intentional. The best professionals will complement AI, knowing when to trust data and when to trust instinct. In a world of Reasoners and Agents, wisdom becomes the ultimate skill.

The Philosophical Question

Philosophy Class would not be complete without a little reflection: what happens when the tools we build begin to think like us? The rise of AGI challenges our definitions of intelligence, creativity, and identity. In the workplace, it forces us to ask what makes human work meaningful. If AI can write a policy, analyze a claim, and forecast outcomes, what is left for us? The beautiful answer is everything that makes us human. Our empathy, adaptability, humor, and resilience cannot be coded. These elements are what breathe life into every system we build. The role of the human mind is shifting from information processing to interpretation and influence. It is time for philosophy to focus on the feeling elements, not just thinking about thinking!

Class Takeaway

The road to Artificial General Intelligence is challenging us to rise higher! AGI will help us define purpose. In workers’ compensation, that means we should be using both artificial and emotional intelligence to create systems that are faster, fairer, and more human than ever before. The goal is to make us more aware of what truly matters in the work we do. We can focus on what all parties NEED to be successful from the injured worker to the claims adjuster, the human resource professional to the safety manager. We can all find ways to work smarter.

Your homework: Ask yourself how you want to use intelligence to make your work more meaningful. Think about both kinds of intelligence: human or artificial. Identify one area in your process where technology can create time for deeper connection or reflection. Start with one step. Progress starts with presence.

Class dismissed.

Next week: Study Hall – What ChatGPT Can Really Do.

Welcome back to class, classmates! We explored how AI supports individuals, strengthens organizations, and shapes culture. Now, time to talk about its most powerful influence yet: the workforce itself. In this week’s Business Strategy Class, we are stepping into the boardroom to examine how generative AI is reshaping careers, skill sets, and the future of work. Change is here, and while that can feel uncertain, it is also deeply exciting! The question now is how will transform ourselves alongside AI?

This week’s lesson is once again inspired by my brilliant professor friends, Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series, highlighting how AI adoption impacts both individuals and systems. The Innovation Profs brought my attention to a recent Stanford University study on AI’s workforce. The research, titled Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence, examines how AI adoption is affecting entry-level workers, particularly those aged 22 to 25. The results? A 13% decline in employment for early-career professionals in roles most exposed to automation. Sobering, yes, but not hopeless. For leaders, educators, and employers, we are building a strategic roadmap for how to respond.

Automation vs. Augmentation: Two Paths, Two Outcomes

The Stanford study highlights an essential distinction between automation and augmentation. In simple terms, automation replaces; augmentation enhances. Jobs that rely on process repetition or predictable workflows we see in the workers’ compensation industry, like data entry, transcription, or entry-level coding, are most vulnerable to automation. Roles that depend on communication, creativity, or critical thinking remain steady. YAY workers’ compensation! In fact, in environments where AI is used to augment human work rather than replace it, employment growth has remained strong.

Hello? Are you with me? This is how we enhance the workers’ compensation experience including adjusters, human resources, and risk management! AI tools can automate claims summaries, generate initial reports, or analyze data trends. What they can’t do? Replicate empathy, advocacy, or relationship-building with social astuteness or apparent sincerity – think AUTHENTICITY. That is why the future of our field lies not in replacing people, but in elevating them. The smartest organizations will deploy AI to remove administrative friction so professionals can focus on the human heart of the claim enhancing listening, guiding, and connecting. Automation handles process; augmentation powers purpose.

Codified Knowledge vs. Tacit Knowledge

Another powerful insight from the Stanford study lies in the difference between codified knowledge and tacit knowledge. Codified knowledge is what you can learn from a textbook, manual, or course such as the “what” and “how.” Tacit knowledge is what you learn by doing, the unspoken intuition built through experience, relationships, and repetition. AI is excellent at absorbing codified knowledge. It can summarize laws, interpret trends, or produce technical explanations instantly. But tacit knowledge, the “feel” for a conversation, the instinct for timing, the judgment in a complex case, this remains uniquely human. This explains why entry-level workers (who primarily possess codified knowledge) are more affected by automation than experienced professionals. Over time, as people develop tacit understanding, their work becomes harder to replace.

This insight is gold for workers’ compensation. We must build programs that help employees move from knowledge-based work to wisdom-based work faster. This is where the human element and emotional intelligence gain significant importance. How? By creating mentorship pipelines, experiential learning opportunities, and reflection practices that develop judgment. We need to focus on training these skills plus empathy, and adaptability, skills AI cannot currently replicate. We have been needing to be more human for a long time, now it is become this or just let AI do it. And frankly, maybe this is the shakeup we have needed for reality to set in on what it truly means to be human and care about people.

The Human Advantage: Experience, Empathy, and Adaptability

While AI may disrupt entry-level roles, it simultaneously increases the value of higher-level human capabilities. The most sought-after professionals will be those who can think critically, communicate clearly, and adapt quickly. In other words, soft skills have become strategic skills. For the workers’ compensation ecosystem, this means adjusters who can interpret both data and emotion, HR professionals who can balance compliance with compassion, and safety leaders who can translate analytics into cultural action. These are the skills we have been talking about for decades! As AI handles technical work, people must handle relational work. The claim process of the future will be more personal, guided by professionals who understand how to use technology and how to talk to humans. The ultimate human advantage lies in reimagining how we lead through it.

How Leaders Can Respond Strategically

The Stanford study highlights workforce disruption, and it also offers a blueprint for leaders who want to build resilience into their organizations. Here are a few strategic moves to consider:

1. Redesign entry-level roles as learning labs.
Make early-career positions less about rote execution and more about guided learning. Pair employees with mentors, assign them to AI-assisted projects, and rotate them across departments to build well-rounded, cross-functional experience.

2. Invest in experiential learning.
Encourage internships, simulation-based training, and shadowing programs that expose employees to real-world decision-making. The more experience they gain early, the faster they move from codified to tacit knowledge.

3. Reframe AI as a collaborator, not a competitor.
Train teams to use AI tools as co-pilots to brainstorm, organize, and validate ideas versus silent judges. This mindset shift transforms anxiety into agency.

4. Audit your job ladders.
Review career progression paths to ensure employees have opportunities to grow into roles that rely on strategic, human-centered skills. Build bridges upward, not barriers.

Implications for Workers’ Compensation

In our industry, the future of AI-driven transformation is less about job loss and more about job evolution. Yes, repetitive administrative roles may shrink, but new ones will emerge such as data translators, compliance technologists, and empathy-driven communicators who bridge the gap between system and soul. We need professionals who can both interpret AI output and contextualize it through the lens of humanity.

Employers and carriers can start by reframing their workforce development strategy around three principles: reskilling, reimagining, and retaining. Reskill employees to use AI responsibly and creatively. Reimagine how roles evolve as automation increases. Retain great talent by offering purpose-driven work that leverages both heart and intellect. View AI as an ally in workforce renewal rather than reduction so we can open the door to a more balanced, innovative, and fulfilling future.

The Bigger Picture: From Disruption to Design

Every technological leap in history has reshaped the labor landscape. This one is unique because it is cognitive. It challenges how we think, learn, and work. The Stanford researchers call young workers “canaries in the coal mine,” but I see them as pioneers at the frontier. Their experiences reveal what all of us must prepare for: a future where value is how we can interpret, connect, and create. AI will continue to change the nature of entry-level work, and it will challenge leaders to design better systems for learning and growth. The future workforce will thrive because we built organizations where people feel equipped to evolve with it.

Class Takeaway

AI is the evolution of work. The Stanford study reminds us that the key to resilience is leaning into augmentation by helping people grow in ways machines cannot. In workers’ compensation, that means nurturing communication, empathy, and critical thinking alongside technology. The organizations that thrive will be those that treat AI a capacity-creating tool to help their employees shine. It is far beyond time to remove the automative, redundant process so we can be more people-centric first.

Your homework: look at your organization’s entry-level roles. Ask how each could become a launchpad for learning instead of a checklist for tasks. Identify where mentorship, reflection, and human development could turn exposure to AI into empowerment. The future is here and it’s hiring!

Class dismissed.

Next week: Philosophy Class – The Road to AGI

Welcome back to class, classmates! Last week, we explored how AI can elevate your individual role. These are what we consider those “micro” improvements that free up time and sharpen focus. This week, we are taking the next big step into Macroeconomics Class, where we explore how to scale those wins across an entire organization. In workers’ compensation, this means designing systems that are more efficient, compliant, and compassionate. By operating in this way, we can collectively transform how we work together with technology and people skills!

This week’s lesson draws inspiration, as always, my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their approach breaks implementation into three layers: Foundation, Framework, and Future. This approach is strategic yet accessible, a balance of logic and humanity that feels tailor-made for our industry. Translating this into workers’ compensation means learning how to scale innovation responsibly by using AI to strengthen the systems that protect people, not replace them.

Foundation: Understanding Tools, Limits, and Opportunities

Before scaling anything, we must understand what we are building with from systems to people. The foundation of an AI strategy begins with awareness such as knowing what tools exist, what they can do well, and where they fall short. In workers’ compensation, this can include everything from predictive analytics and automated reporting systems to large language models that draft communication templates or summarize claim notes. The key is identifying where inefficiency lives and where automation can create relief without eroding trust.

Understanding limitations is equally important. AI is not a magic wand and can have its challenges. AI can misunderstand nuance, struggle with tone, or fail to interpret emotion, much like people do. We must pay attention to these language gaps because it is critical in an industry built on empathy and regulation. There is also the question of cost: which tools fit your organization’s scale, and which require enterprise-level support? This foundational layer is like mapping your terrain before the climb, looking to see the obstacles before you start the ascent.  Once the tools, limits, and costs are clear, list your target processes for AI integration. Simply observe where you stand and record where friction slows your flow. This is how you effectively set the table for transformation at a pace your organization can be on pace.

Framework: Defining Values Before Technology

Too often, organizations ask, “How much AI can we use?” when they should be asking, “How much human involvement should remain?” This is the heart of the framework stage. Workers’ compensation is a people business, and any successful AI adoption must be human-first. Please read that line again. Any successful AI adoption must be human-first. We are in the business of people! Begin by clarifying your organization’s motivating values. Internally, these might include efficiency, consistency, innovation, and professional development. Externally, they may focus on transparency, compliance, and employee well-being. Every AI decision should reflect those values otherwise, technology risks drifting from purpose.

Once values are clear, evaluate each task with two guiding questions: 1. How much human involvement should this task require? 2. Can AI perform this task responsibly and effectively? Tasks that rely on compassion, judgment, or cultural understanding require a strong human hand. For standardized, repeatable tasks such as like compliance summaries or loss run analyses, AI can safely take the lead. When values define structure, technology supports humanity instead of replacing it.

Quick Wins, Definite Don’ts, and In-Between Cases

Now comes the part that sparks momentum, identifying your quick wins, definite don’ts, and in-between cases. Quick wins are tasks that AI can help with immediately. Think report generation, training document drafts, or meeting summaries. These small victories build confidence and demonstrate ROI without requiring major process changes. Definite don’ts are tasks that demand confidentiality, emotional sensitivity, or regulatory discretion. This is no different than handling private claim data or making policy interpretations. These require human oversight, secure systems, and ethical boundaries. In-between cases are where innovation grows. Gray areas exist that deserve experimentation: triaging claims, drafting educational materials, or analyzing safety data trends. Run pilot programs. Compare AI results with human performance. Refine your process until the blend of human and machine feels seamless. This is how organizational learning evolves by trial and error, testing through courage, curiosity, and careful control.

The Future: Innovating with Purpose

Once the foundation and framework are secure, the time has come to lift your gaze toward the horizon. The Future layer is about exploration in terms of asking bold questions about what is now possible. For workers’ compensation, AI opens doors we could not have imagined a decade ago. Imagine tools that analyze linguistic tone to identify compassion fatigue before it affects service quality. Or systems that cross-reference injury patterns with environmental data to prevent incidents before they occur. What once took months of manual analysis now happens in minutes. The most powerful question is “What can we do now that AI gives us time back?” Use that space to enhance leadership development, improve communication, and invest in people. The future of work is screaming for more humanity. Look around!

Measuring Impact and Adapting with Agility

Every macro strategy needs metrics. Before launching organization-wide AI initiatives, define what success looks like. Are you aiming to shorten claim cycles, increase reporting accuracy, or improve employee satisfaction? Choose a few key metrics that reflect both efficiency and empathy. Once implementation begins, monitor outcomes, iterate, and celebrate! Technology changes fast and your process should too. As AI improves, revisit your values, update policies, and realign your goals. Staying flexible keeps your strategy future-ready and your culture grounded. Adaptation encourages sustainability and celebration continues the innovative advancements around working smarter.

Leading with Courage and Clarity

Organizational AI transformation requires leadership in tone, not just title. Employees need to see that AI is not a threat, but a tool for empowerment. Leaders set that example through transparency, empathy, and education. Explain the why behind adoption. Celebrate small wins. Create spaces for questions and feedback. When people feel informed and involved, adoption shifts from compliance to collaboration. Build an AI-enabled culture that lasts, one rooted in trust, guided by values, and sustained by shared purpose. Tone and perception help (or hurt) change management, period. Lead by example with clarity and as much transparency as possible.

Class Takeaway

Scaling AI across an organization is about empowering humans. The Foundation–Framework–Future model ensures that innovation happens responsibly, strategically, and with heart. AI can automate process, but only humans can drive purpose. When technology and empathy work together, the results are meaningful!

Your homework: Gather your leadership team and map your Foundation, Framework, and Future. Identify one quick win to implement within 30 days, one definite don’t to safeguard, and one in-between case to pilot. Keep curiosity as your compass, and you’ll find the balance between progress and purpose.

Class dismissed.

Next week: Business Strategy Class – Generative AI & Workforce Impact.

Welcome back to class, classmates! We studied ethics, reasoning, and deep research. Now I welcome you to Economics Class, where we talk about what everyone secretly loves to measure: time and money. In workers’ compensation, both are precious resources. We are always looking for ways to streamline processes, reduce costs, and improve outcomes. We don’t do this just for efficiency’s sake, we do this so we can spend more time where it matters most: helping people heal and thrive. This week, we are learning how to implement generative AI directly into our daily work for smarter spend, higher productivity, and more peace of mind.

This project continues to be inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their lesson on implementing AI at work made me think about how perfectly this applies to workers’ compensation. Our world runs on repeatable processes from claims documentation, to compliance, from safety training, to reporting. What is great about these processes is that they are all ripe for automation when used thoughtfully. Chris and Chris focused on a tool called JobsGPT, which analyzes job roles and shows how AI can streamline daily tasks. I thought: what if we had the same concept for claims professionals, HR leaders, and risk managers? The result could change not only how we work, but how we feel at work.

Where to Begin: Breaking Down the Job

Start by brainstorming the tasks that fill your day. Claims professionals spend hours writing letters, summarizing medical notes, documenting conversations, and preparing reports. HR leaders write policies, update training manuals, and handle employee communication. Safety managers collect data, review incident reports, and conduct investigations. These are all areas where generative AI can help when used with structure and care. The first step is identifying which tasks are repetitive, time-consuming, or require the same pattern of logic each time. Those are your entry points. Once you list them, think about whether AI could assist with drafting, summarizing, analyzing, or communicating. Start small. Identify one task that drains time every week. Maybe summarizing telephone conversations, reviewing provider notes or incident reports, or writing return-to-work letter. Begin there. That is your opening door to efficiency.

Quick Wins: Do These Now

Quick wins are tasks AI can help with right away. These are low-risk, high-reward activities that do not touch private data or sensitive information. For example, you can use AI to draft first-pass content such as employee newsletters, safety bulletins, or training outlines. It can also be used to summarize lengthy meeting notes into clear action steps or help you reframe meetings that could have been an email. J AI can also help you brainstorm ideas for conference presentations, program names, or claims communication campaigns. These quick wins free up time immediately. They are your “instant ROI” projects that typically take thirty minutes but now take five. That time savings improves your calendar and your clarity. Instead of rushing from task to task, you can slow down long enough to think strategically about your team, your claims, or your client relationships. Efficiency creates space for humanity.

Definite Don’ts: Keep These Human

Not everything belongs in AI’s hands. Some tasks are too sensitive, nuanced, or personal to delegate. Anything involving confidential information including but not limited to claim details, medical records, or personal employee data should stay within your secure systems. Check your organizations policies on AI use. The same goes for decision-making around ethics, performance management, or return-to-work accommodations. Why? Tone and judgment are still human art forms. AI can draft, but it cannot discern emotion. AI does not sense when a worker is scared about surgery or when a supervisor feels frustrated but misunderstood. That is where your leadership and empathy shine. Think of AI as your assistant, not your replacement. You lead the conversation, and let AI take notes.

In-Between Cases: Test and See

The in-between category is where things get interesting. These are tasks that might benefit from AI, only after experimentation. This is your testing ground for growth. You might try using AI to generate first drafts of policy updates, training quizzes, or standard letters, then compare those to your current process. You could test how AI analyzes large amounts of data, like loss run summaries or survey responses. Run side-by-side trials. Compare quality, accuracy, and time savings. Some outputs will amaze you while others will need major revision. Experimentation is where the magic happens. Create your own AI playbook, the set of tools and prompts that make your work better, faster, and more enjoyable. Again, check your organization’s policies first!

The 70/20/10 Rule for Implementation

Once you start experimenting, use the 70/20/10 rule. Let AI handle 70% of the work such as the drafting, summarizing, or analysis. You take 20% to refine, edit, and ensure accuracy. Then use the final 10% to add your personal touch: empathy, tone, or strategy. This balance ensures that your communication stays human while your workload stays light. Imagine AI drafting a complex return-to-work plan. It structures the timeline, references modified duty tasks and writes the base letter. You then review, add empathy-driven phrasing, and ensure it aligns with your company’s voice. The end product? Professional, efficient, and heartfelt in less than half the time.

Why This Matters for Workers’ Compensation

Generative AI helps with precision, consistency, and better human outcomes. In our industry, speed is important and also, accuracy and tone are everything. Using AI strategically can reduce administrative friction and increase emotional bandwidth. Adjusters can spend more time talking to injured workers instead of typing. HR leaders can focus on culture instead of clerical work. Safety managers can prioritize prevention instead of paperwork. There is also a financial upside. Of course there is, this is an effective business strategy! When communication improves, litigation drops. When training is consistent, injury frequency declines. When return-to-work is personalized, claims close faster. Each of these results translates to measurable savings. Implementing AI thoughtfully can generate compassion-based ROI with a return on integrity as well as investment.

Building Your Implementation Plan

Where to begin? Start with small steps to implementing AI in your job:

• Step 1: Map your tasks. List everything you do in a typical week, from claims documentation to internal communication.

• Step 2: Label each as a Quick Win, Don’t, or Test & See. Be honest about what drains your time and what requires your expertise.

• Step 3: Pilot one or two quick wins. Track time saved, quality of results, and your overall workload balance.

• Step 4: Establish guardrails. Protect data privacy, maintain ethical standards, and clearly define what AI can and cannot do in your role. Double check your organizational specifications around AI.

• Step 5: Share your results. Bring what you learn to your team or leadership. Show how AI can enhance the human side of work by working smarter, not harder.

Leadership Lessons from Implementation

Leaders set the tone for how technology is received. When you model responsible experimentation, you empower your team to do the same. Encourage questions, celebrate small wins, and share best prompts in your team’s prompt library. Remember that change fatigue is real, and we are changing at a rapid pace in our present environment. For some people, AI feels intimidating or even threatening. Approach those feelings with empathy and reassurance. Make it clear that the goal is enhancement, not replacement. When people feel safe to learn, they innovate faster. This is how we future proof the workforce with one conversation, one tool, one confident experiment at a time. Teach yourself something new, and then go share it with someone else.

Class Takeaway

AI implementation is a human project involving reclaiming time, amplifying expertise, and creating space for what truly matters. The tools are ready. The guardrails are clear. The opportunity is enormous. When we align AI with our values of empathy, ethics, and excellence, everyone benefits.

Your homework: Pick one “quick win” from your job this week and let AI help. Time yourself. Review the result. Then share what you learned with a colleague or your team. Innovation spreads faster when it is shared, and in workers’ compensation, collaboration is the best currency we have.

Class dismissed.

Next week: Economics, Part II – Implementing AI at the Organizational Level.

Welcome back to class, friends! We have been sharpening our pencils, building our libraries, and experimenting in the lab. Now, it is time to head into History Class. History is where we dig deeper, uncover sources, and learn from the wisdom already written. In the world of AI, this lesson is about Deep Research, one of the newest and most powerful capabilities that turns curiosity into actionable insight. And just like in workers’ compensation, where we must often trace the story behind the injury or the evolution of a claim, Deep Research gives us the ability to go beyond the surface and understand the full context.

This project continues to be inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their exploration of Deep Research reminded me so much of our industry. In workers’ compensation, we need to be asking better questions, following the threads, and seeing the bigger picture. Chris and Chris used the Deep Research tool to study how to grow their newsletter. This got me thinking this same approach could change how we research claims trends, evaluate medical data, or shape injury-prevention strategies. Their curiosity fueled innovation in communication, and now we are bringing that same spirit into the heart of workers’ compensation!

What Is Deep Research?

Deep Research is an agentic AI capability. This is a fancy way of saying it is a model that can take your question, search the web in multiple steps, read credible sources, and compile a comprehensive, citation-backed report. Instead of giving you a quick summary like a standard model, Deep Research goes out, reads what is actually out there, and then comes back with a full explanation complete with sources, links, and context. Think of this as the difference between asking a coworker a question and hiring a full research assistant who brings back the data, the references, and a neat executive summary.

In workers’ compensation, this can be a powerful tool. Imagine being able to ask, “What are the latest treatment success rates for lumbar strain in manufacturing employees?” or “What are the best practices for preventing delayed recovery?” and getting a six-page report summarizing the top medical journals, regulatory updates, and evidence-based approaches. You could have your own in-house research department without waiting three months for a white paper! (This gets exciting for some of us research nerds… yes, that’s me! J)

Why Deep Research Matters in Workers’ Comp

Our industry thrives on patterns, precedent, and proof. Whether analyzing loss runs, evaluating treatment guidelines, or developing safety protocols, we rely on data to make informed choices. Deep Research helps us find that data faster and interpret it more effectively. Instead of scrolling through outdated PDFs or trying to recall which state revised its telehealth reimbursement last quarter, you can ask Deep Research to find, verify, and summarize the information for you.

For example, an adjuster could use Deep Research to examine trends in delayed reporting or psychosocial barriers to recovery. A risk manager could use it to identify the latest workplace safety innovations in similar industries. A medical management professional could use it to compare surgical outcomes versus physical therapy recovery rates across patient populations. These insights allow us to make data-driven decisions, not gut guesses, and that precision strengthens both claims outcomes and communication.

Real-World Applications

Time to bring this into everyday practice. Picture a claims team preparing for an employer’s quarterly review. Instead of manually pulling reports, they ask Deep Research to explore “emerging musculoskeletal injury trends in light industrial workforces in 2024–2025.” The tool reads current studies, regulatory updates, and industry publications, then delivers a structured, sourced summary with recommendations. The team walks into the meeting prepared to discuss prevention strategies, predictive analytics, and training recommendations with confidence and authority.

Or think about a nurse case manager managing a high-cost chronic pain claim. By using Deep Research, they can pull evidence-based approaches for multidisciplinary pain management and identify which treatments have the strongest recovery outcomes. This helps guide provider conversations and supports justification for utilization review. When done well, it enhances both quality of care and cost control. Win:Win!

For employers, Deep Research can identify the top trends in employee engagement around safety and wellness programs. Want to know which communication strategies reduce incident rates or which ergonomic investments yield the best ROI? Deep Research can find the data and compile it into an actionable plan. When knowledge becomes accessible, strategy becomes intentional.

How Deep Research Works

Unlike regular AI models that respond instantly, Deep Research takes time. This model is actually thinking. When you activate the tool, it asks clarifying questions to refine the search, ensuring the results are relevant to your true intent. Once you answer those questions, it launches into multi-step analysis performing searches, reviewing articles, cross-checking facts, to build a structured report that references every source it uses. (Make sure to double check that the references are real as sometimes the models like to hallucinate sources!)

You could see updates like, “Researching peer-reviewed studies on return-to-work duration for shoulder injuries” or “Gathering data on state-level indemnity cost trends.” After several minutes, you receive a comprehensive, source-linked document that reads more like a professional briefing than an AI reply. In workers’ compensation this kind of accuracy is invaluable, especially in a space where every jurisdiction, regulation, and case type carries nuance.

Using Deep Research Wisely

With great power comes great responsibility (and yes, a little sparkle). Deep Research is only as strong as the instructions you give it. Prompting is important! The more context you include, the better your results. In workers’ compensation, this could include industry, claim type, audience, and intent. Start with clear prompts like: Research the impact of early intervention on workers’ compensation claim costs or summarize new state legislative trends affecting telehealth reimbursement in 2025. You could also ask for the AI Deep Research model to identify the top three evidence-based treatment strategies for repetitive strain injuries.

Once you receive the report, take time to review, verify, and synthesize. This tool provides information and this is where you provide wisdom. Use it to inform your expertise as a tool. Keep an eye on source credibility, especially when citing results for legal or medical decisions. Deep Research is your assistant. It is a tool to support.

How It Changes Learning and Leadership

The magic of Deep Research is that it teaches. The more you use it, the more you uncover credible publications, new best practices, and innovative programs across the country. It bridges the gap between theory and application, empowering claims leaders to stay ahead of change. For training, it can generate research packets for new adjusters or HR staff, accelerating their learning curve and reinforcing evidence-based thinking. For executives, it can summarize entire policy shifts or national trends in a single report.

When we combine our professional insight with AI’s ability to synthesize data, we are able to create a stronger, more informed workforce. Deep Research helps us shift from reactive to proactive, from catching up to leading forward. We need to be on the front end of claims, helping injured workers’ to take proactive claims management moving forward. In an industry that depends on trust, accuracy, and human care, this tool helps us make smarter decisions faster without losing the heart behind the work!

Class Takeaway

Deep Research is your all-access pass to smarter insights and stronger strategy. Deep Research curates information, connects it, and makes it useful. For workers’ compensation, it means fewer silos, better communication, and faster access to evidence that drives outcomes. The next time you are developing training, revising policy, or analyzing trends, let Deep Research be your partner in discovery.

Your homework: Ask Deep Research to explore a question you have always wanted better data for, something that would truly help your team. Read the report, highlight what stands out, and share one new insight at your next team meeting. Learning together is how we grow stronger. Learn and pass it on!

Class dismissed.

Next week: Economics Class: Implementing AI for Efficiency, Savings, and Smarter Spend

Welcome back to class, classmates! We are mid-way through the semester and at this point, we have handled math, lab work, revisited English class, and even a trip to the library. Now, time for Civics Class. Civics is where we talk about rules, responsibilities, and what it means to be a good digital citizen in this brave new world of AI. Just like in workers’ compensation, ethics is a foundational component into how we work. Ethics shapes trust. Privacy protects people. Guardrails ensure the work we do serves rather than harms. As AI becomes woven into the daily fabric of claims, communication, and care, it is our job to understand how to use it responsibly, transparently, and with integrity.

This project continues to be inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their session on AI Ethics reminded me that every great system, whether human or machine, depends on quality values and clear boundaries. The Innovation Profs talk about AI’s “ethical cast of characters,” and I smiled because it feels a lot like a video game. (For me, old school Nintendo of the 1980s.) Each character has a challenge to defeat before advancing to the next level of responsible use. The framework is clear, clever, and what we need in the world of workers’ compensation. Thus, today I present you with a guide for navigating both the risks and the responsibilities that come with innovation.

Meet the Ethical Cast of Characters

Every hero needs to know their villains. When it comes to AI, we face a lineup of ethical challenges with each one requiring awareness, reflection, and action. Think of these as the “boss levels” of the workers’ compensation industry’s AI journey. Time to meet the cast.

The Hallucination Ghost

Hallucination is when AI confidently makes something up. It is the ghost that slips false facts into reports, misquotes laws, or invents data that sound believable but are not true. In workers’ compensation, that can lead to real consequences such as a misrepresented statute, an inaccurate claim summary, or an incorrect medical guideline. Oh, and we have seen this play out in some of our legal groups. The fix? Verification. Always confirm AI-generated content with primary sources, policy manuals, or credible data. If AI writes a letter, check it like you would check a new adjuster’s first draft. Trust is earned, not automated. And always fact check your sources, especially statutes and compliance-related information. Not doing this can hurt you, your organization, and the people you serve.

The Bias Monster

Next up is Bias. Bias is a sneaky monster that feeds on the data AI was trained on. Because datasets come from human writing, they can reflect human biases around race, gender, or occupation. In workers’ compensation, that might look like stereotypes about certain job types or assumptions about injury severity. The solution is to train AI better and audit the results. Ask, “Who benefits from this outcome?” and “What voice might be missing here?” Ethical use of AI means seeing bias before it becomes behavior. We all have bias. Being aware of it and then doing something about it help regulate our work.

The Copyright Keeper

Enter the guardian of Intellectual Property. AI learns from enormous amounts of existing content, which sometimes includes copyrighted material. For our industry, this means being cautious about how we use and distribute AI-generated materials. This becomes especially important in training, marketing, or consulting work. Always make sure content is original or appropriately cited. Give people credit where credit is due. Think of it as respecting your colleagues’ work the same way you would want your own reports or playbooks respected. In short: cite your sources and stay classy.

The Privacy Protector and Security Sentinel

These two travel together like a superhero duo: Privacy and Security. They guard the sensitive data we handle daily including but not limited to medical information, payroll details, employer records, and claim notes. AI tools can make processing faster, but only if we protect what matters most. Never input personally identifiable information or claim-specific details into a public model. Use secure, approved systems for analysis, and anonymize examples whenever possible. Data privacy is a promise to the people we serve. Flip the script. Would you want your personal data input into public AI? Probably not.

The Green Guardian

Meet Environmental Impact. The Green Guardian reminds us that technology, while immaterial is not invisible. Training large AI models takes massive energy and water resources. While that may feel far removed from workers’ compensation, this is a nudge to think sustainably about digital practices. Do we really need to regenerate that prompt five times? Are we saving outputs for reuse to minimize resource waste? Ethics includes stewardship, meaning that we all have a responsibility to take care of the environment while we innovate. Even small choices can ripple into big change.

The Workforce Transformer

Workforce Displacement is less a villain and more a complex character in this story. AI changes how we work in workers’ compensation, not necessarily eliminating people. This is where we must reframe the work, the purpose of what we do, and highlight the significance of human connection. As we look at work in workers’ compensation, some of these work tasks include automation handling repetitive documentation while humans focus on empathy, negotiation, and connection. Our challenge is to upskill, reskill, and empower our teams to use AI as a collaborator, a resource, and a tool. The most successful professionals will be those who learn to lead AI versus trying to compete with it. This transformation is an invitation to grow. Work smarter, not harder. And do it well.

The Misinformation Magician

Now comes Misinformation. This is the trickster of the bunch of our video game-based characters. This is the risk of using AI-generated content without fact-checking or of seeing false information circulate in claims communication or policy documents. For example, an AI summary might misinterpret new case law or outdated medical guidance. The antidote? Transparency. When you share AI-assisted work, disclose it. When you publish, verify it. Misinformation only wins when we fail to slow down and think critically.

The Harmful Content Hydra

Last, and certainly not least, is Harmful Content. This character is the many-headed hydra of the internet. AI models are trained on massive datasets and as a result, they can accidentally reproduce harmful or offensive material. This is especially critical when communicating with injured workers, where empathy and psychological safety matter. Always review tone and phrasing, especially when dealing with sensitive topics like pain, disability, or termination. Our words can either heal or harm. Choose them with heart and remember, these word choices ultimately reflect your character, personal brand, and impact your organization.

How to Stay Ethical in an AI World

How do we defeat this cast of characters? With knowledge, clarity, and accountability. Some challenges can be managed through vigilance and good human editing to ward off hallucination and bias. Others, like privacy, intellectual property, and misinformation, require organizational policies and sometimes even legal oversight. Ethics is a value and impacts a collective organizational culture. The key is to integrate ethics into every step of the process. Build privacy policies that cover AI use. Train employees on proper input boundaries. Establish internal review procedures for AI-generated communication. And most importantly, lead by example! If you are transparent, intentional, and kind in your use of these tools, your team will follow suit. Ethics begins with everyday choices, not policy manuals. Your employees and peers are watching. What is not addressed and brought to light affirms behavior.

Why This Matters in Workers’ Compensation

Workers’ compensation runs on trust between employers and employees, between claims professionals and injured workers, and between companies and their partners. Trust is built when our words match our values. Using AI responsibly ensures that we maintain that trust while embracing innovation. We can use these tools to improve efficiency and access, but the human element must always lead. We can approach AI through the lens of care by protecting privacy, honoring fairness, and maintaining transparency. When we do so, we strengthen the entire ecosystem. We prove that progress and ethics can coexist beautifully. In a space that touches people’s lives so deeply, this combination is essential.

Class Takeaway

AI ethics builds a world where innovation supports integrity and efficiency amplifies empathy. As we move forward, remember the ethical cast of characters, and keep your own guardrails strong. Lead with curiosity, protect with conscience, and communicate with compassion.

Your homework: Review one area of your work where you are already using or planning to use AI. Identify which ethical “character” might appear there: hallucination, bias, privacy, misinformation. Create a plan to address it. Responsibility starts with awareness and ends with action.

Class dismissed.

Next week: History Class: Deep Research in Claims & Medical Management

Welcome back to class, friends! Sharpen those pencils and grab your calculators. Today we are heading into Math Class. In workers’ compensation, so much of our world is built on logic, planning, and pattern recognition. From setting reserves to structuring return-to-work plans, success comes down to how we connect data and human insight. This week, we are talking about reasoning models, the next evolution in AI tools designed to “think” before they speak. These models do more than identify patterns. They pause, process, and provide thoughtful, step-by-step conclusions, the kind of disciplined thinking we love in our industry!

This project continues to be inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their exploration of reasoning models immediately caught my attention because it speaks to how we make decisions in workers’ compensation every day. Our industry depends on critical thinking, accuracy, and accountability. Translating the Innovation Profs work into our world gives us an opportunity to explore how reasoning models can help us strengthen both the analytical and the human sides of our work. This is where logic meets empathy.

Plus, a little sparkle never hurts!

What Are Reasoning Models?

Reasoning models are a newer generation of large language models that go beyond predicting the next word. Traditional models focus on the speed of getting to the “answer” as fast as possible. Reasoning models, on the other hand, slow down and plan before they respond. They generate an internal chain of thought, check their logic, and then deliver a concise, accurate response. Reasoning models act like a thoughtful colleague who sketches ideas on the whiteboard, talks through the logic, and then summarizes the best answer for everyone to follow.

Reasoning models such as OpenAI’s o-series, Anthropic’s Claude 4, and Google’s Gemini Pro represent the most advanced versions available right now. These models are designed to handle tasks that require structured reasoning such as math, law, financial forecasting, and complex decision-making. For workers’ compensation, this means reasoning models can analyze claims data, forecast cost exposure, or help structure return-to-work timelines in ways that are both detailed and digestible. Think of it like having an analytical partner who does not get overwhelmed by the paperwork.

Why Reasoning Models Matter in Workers’ Compensation

Our work in workers’ compensation is rarely simple. Decisions require layers of logic as this space operates with policy language, jurisdictional nuances, medical details, human factors, and budget implications. Reasoning models thrive in these multi-step environments like the world of workers’ compensation. These models can follow the full path from data to decision, considering each factor instead of jumping to conclusions by organizing the chaos. By offloading the mental heavy lifting, reasoning models give us more space to focus on empathy, strategy, and the human connections that drive recovery.

How Reasoning Models Work

When we ask reasoning models a question, an internal “chain of thought” is created. You might see a note like “thinking for 40 seconds” before the answer appears. This is the model mapping out its logic. The result is an explanation of how the model got there in addition to the answer. That transparency helps us check the reasoning, spot assumptions, and refine prompts for even better results.

For example, if you would ask, “Based on these restrictions and wage details, estimate temporary total disability exposure for the next six months,” a reasoning model will not guess. The model will average recovery durations, modified-duty options, wage replacement calculations, and potential medical interventions. The reasoning model will then summarize the logic, showing how each factor contributed to the estimate. This step-by-step approach mirrors the way seasoned claims adjusters think, only faster.

Real-World Applications in Workers’ Comp

Think about reserves. Setting reserves is where reasoning models can make a real difference. If you input injury details, jurisdiction, and historical claim data, the model can produce projected medical, indemnity, and expense reserves with justification for each category. It can compare optimistic, expected, and conservative outcomes and flag the conditions that might trigger a change. This helps new adjusters learn faster and ensures consistency across teams.

In return-to-work planning, reasoning models can develop phased schedules that align medical restrictions with safe job tasks. How many times have you heard an employer or leader say they have no modified duty options available? Turn to the reasoning model. These models can generate scripts for supervisors, communication plans for employees, and checklists to track progress. These models can also integrate ADA considerations and highlight milestones for reevaluation. When someone is juggling multiple cases, having a structured, logic-based assistant keeps everyone aligned.

Even in policy interpretation or compliance work, reasoning models shine. You can paste statute excerpts or policy language into the model and ask for a plain-language summary of what is required versus what is recommended. You can then ask for a version tailored to supervisors, employees, or executives, and even ask the model to break it down like you are a fifth grader. The model can even generate documentation checklists, turning abstract rules into actionable steps. Action is power!

Tips for Getting Better Results

Using reasoning models effectively requires intentional prompting. Start by asking the model to “walk through its thinking step by step before deciding.” This type of prompting encourages reasoning that separates strong results from generic ones. As the model to “compare two or three possible approaches” before recommending one, which can help uncover creative options that may have not been considered.  You can also ask the reasoning model to cite its sources, which if you are like me, I want to know where the information is coming from.

Ideally, you want to educate the reasoning models to respond in a manner that is accurate and manageable. If you are brainstorming solutions, a prompt such as “Give me three very different options” expands the conversation. When you need real-world context, specify it: “Use examples from workers’ compensation data” or “Frame this for a self-insured employer.” The more context you provide, the more usable the output becomes.

Keeping the Human in the Loop

As powerful as reasoning models are, remember that they are tools. These tools are not replacements for professional judgment. As the human behind the tool, we must still verify outputs, maintain data privacy, and use discernment before acting on recommendations. These models do not hold claims adjusting licenses or carry empathy. Reasoning models are her to enhance and support what humans already bring to the table. The goal is to amplify your expertise. These models handle the structure so we can handle the soul. When we combine AI’s logic with human empathy, programs become smarter, faster, and more compassionate.

Class Takeaway

The disciplined thinkers of the AI classroom? Meet our reasoning models. These models take time to plan, analyze, and explain. For workers’ compensation, they represent a natural evolution toward precision and trust. Whether you are using them for reserve analysis, return-to-work design, or compliance interpretation, reasoning models can help you make better decisions faster, with greater transparency and consistency.

Your homework: test a reasoning model this week on one claim scenario. Ask it to “show its work,” evaluate the steps, and then compare its reasoning to your own. The more you experiment, the better you will understand where these tools shine, and where your human insight still leads the way.

Class dismissed.

Next week: Civics Class: Ethics, Privacy, and Guardrails in AI for Workers’ Compensation