Hello, fellow humans! In Tuesday’s newsletter, we covered the International Monetary Fund’s study of AI impact in European economies and Denmark’s AI transition in particular. There is a lot in these studies to consider from a public policy perspective, but for now, I’d like to focus on what we can do individually to capture as much upside as possible in this AI transition.

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Understanding your position in the AI career matrix is valuable. Acting on that understanding is essential.

To recap from Tuesday, the IMF identified three factors that will determine how your role will be impacted by AI:

The research on occupational transitions reveals both encouraging news and sobering realities: college-educated workers show substantially higher success rates in transitioning from high-exposure, low-complementarity (HELC) roles to high-exposure, high-complementarity (HEHC) positions, and these transitions correlate with meaningful wage premiums. But these successful transitions don't happen by accident—they require deliberate strategy and consistent execution across three time horizons.

Here are some key points:

Women, highly educated workers, high-income earners, and natives are expected to significantly benefit from AI.

Higher income brackets have higher complementarity.

This data from the IMF represents the current state of these occupation categories. The study anticipates that HEHC roles will grow their salaries and employment rates, while HELC will shrink both in the number of workers and wages.

Timeline to Successful Transition:

Low complementary roles not only pay less, but have less room for income growth, and less room for complementary productivity gains. High complementary roles not only have more upside in the upper salary ranges for the category, they also have the opportunity to transition to high complementary plus high productivity roles, and that’s where the real compensation opportunities are.

Sarah spent three years as a data entry specialist at a mid-size logistics company, earning a modest salary. Recognizing that AI-powered OCR and data extraction tools were automating her core tasks, she invested 18 months in evening courses on data visualization, SQL, and business analytics. She volunteered for a project using AI tools to clean and process data, then built dashboards visualizing operational insights. This proof point helped her transition to a business intelligence analyst role that pays substantially more. The key factor was moving from executing data entry tasks that AI can automate to interpreting data patterns for strategic recommendations that AI cannot own.

What AI is Changing About Data Careers | Dice

Marcus worked in phone-based technical support, a classic HELC position facing displacement from AI-powered chatbots and automated troubleshooting. Rather than defending his existing role, Marcus identified that complex customer accounts need strategic relationship management that understands business context, renewal negotiations, and identifies growth opportunities. He pursued certification in customer success methodology, positioned himself for high-touch accounts, and documented retention improvements. Within two years, he transitioned to a Customer Success Manager position with a substantial pay increase while working with fewer but more complex accounts.

Leveraging AI as a CS Leader | Gainsight

Jennifer performed document review and legal research as a paralegal—tasks increasingly handled by AI legal research platforms. By embracing the technology, she became the firm's expert in AI legal tools. She learned to design effective AI prompts and queries, validate AI-generated research, and train other staff. She combined her legal knowledge with technology expertise to become a Legal Technology Specialist, bridging the gap between attorneys and AI systems. Her salary increased, and her role became central to the firm's modernization strategy.

How AI is Creating New Opportunities for Paralegals in 2026 | NexLaw

Each of these cases illustrates the transition from execution-focused HELC roles to judgment-and-strategy-focused HEHC positions. Rather than fight AI adoption, these workers position themselves as essential complements to AI systems. Research shows that college-educated workers under 40 have the best opportunity for transition success, and the core skills that all of these transitions depend on are:

Let’s look at a hypothetical roadmap for acquiring the skills to get to this new AI-powered productivity space.

Your first horizon focuses on understanding and experimentation. Begin by rigorously assessing your current position against the three-zone framework. Don't rely on job titles—analyze the actual tasks you perform. What percentage of your work involves routine cognitive tasks versus complex judgment? Where do you add value that AI cannot easily replicate?

Build AI literacy by experimenting with current tools in your domain. If you're in financial analysis, use AI for initial data processing and pattern identification. If you're in legal services, test AI research tools. If you're in customer service, explore AI-assisted response systems. The goal isn't to accelerate your own displacement—it's to understand the enhancement-versus-replacement boundary in your specific work context.

Document your judgment processes. When you make decisions, note what contextual information, tacit knowledge, or stakeholder considerations you're weighing. This documentation serves two purposes: it clarifies where your complementarity lies, and it creates proof points for future career transitions.

Start building your transition network now. Identify people currently in target HEHC roles related to your field. A data entry specialist should connect with business intelligence analysts. An administrative coordinator should network with operations strategists. A legal assistant should build relationships with litigation specialists. These connections provide both role models and potential advocates.

The second horizon is where real transformation happens. This period demands investment in developing skills that create complementarity with AI systems.

The Complementarity Skill Stack:

Complex Problem-Solving: Move beyond executing defined processes to structuring ambiguous problems. This means learning to define what questions need answering before you answer them—a skill AI systems struggle with in novel contexts.

Ethical Reasoning and Stakeholder Management: Develop your ability to navigate competing interests, understand regulatory constraints, and make judgment calls that balance multiple considerations. AI can provide information, but someone must take responsibility for decisions affecting people.

Cross-Functional Systems Thinking: Cultivate the ability to see connections across domains, understand second-order effects, and recognize patterns that span organizational silos. AI excels at narrow optimization; humans excel at broad integration.

Human-Centered Design: Focus on understanding unstated needs, reading emotional cues, and building trust-based relationships. These capabilities remain stubbornly difficult for AI systems to replicate.

Pursue transitional credentials strategically. Micro-credentials, industry certifications, and specialized training programs provide faster re-entry points than traditional degrees. A clerical worker pursuing data analytics certification can transition to analyst roles. An administrative professional earning project management credentials can move to coordination and strategic roles.

Create proof points demonstrating your productivity gains from AI augmentation. Document projects where you've used AI tools to deliver better outcomes faster. These concrete examples become the stories you tell in interviews for HEHC positions.

The third horizon looks toward roles that don't fully exist yet. As AI adoption matures, new occupation categories emerge at the human-AI interface.

Emerging HEHC Opportunities:

Consider entrepreneurship leveraging AI to scale services in underserved niches. A former paralegal might use AI research tools to offer legal services to small businesses. A former financial analyst might use AI to democratize investment advisory services. The key is positioning yourself as the human expert who ensures AI-generated outputs serve client needs appropriately.

Complementarity Skills Development:

AI Augmentation Training:

Transitional Credentials:

Community and Networking:

The defining question of AI-era careers isn't "what can AI do?" but "where do I create value that AI cannot replicate?" The data shows clearly that college-educated workers succeed at substantially higher rates in navigating these transitions, and those who successfully move from HELC to HEHC roles can capture real wage premiums. But education alone isn't sufficient—it must combine with strategic positioning, complementarity skill development, and demonstrated AI augmentation capability.

Your career exposure to AI is likely already determined by your occupation. What remains in your control is building complementarity. The workers who thrive won't be those who compete with AI on its terms—processing information, recognizing patterns, executing algorithms. The winners will be those who focus relentlessly on judgment, context, relationships, and responsibility—the irreducibly human elements of work.

The career transitions documented in the research didn't happen to people. They happened because of people—individuals who recognized their position, developed complementary capabilities, and positioned themselves at the human-AI interface where value creation is highest.

The window for strategic action is now. AI adoption is accelerating, but we're still in early stages. Career transitions can take 18-24 months of deliberate effort. The question isn't whether AI will reshape your occupation. The question is whether you'll reshape yourself to thrive in that transformation.