The Apprenticeship Conundrum, Part 2

In Mary Shelley's Frankenstein, the monster is not evil by design – it is abandoned, misunderstood, and untrained. In Part 1 of this series, I argued that we are at a similar inflection point in the professional services world. We've built the machine – GenAI – but we haven't yet built the systems, mindsets, or skills to wield it wisely. Now, in Part 2, I want to test a hypothesis: we must move services professionals from doers to builders.

This isn't just a semantic shift. It's a redefinition of value, identity, and career progression in consulting, legal, executive search, technology, and other high-cognitive-content fields. It demands a new apprenticeship model – one that doesn't just teach how to do the work, but how to design the work.

From Doers to Builders: Rethinking Apprenticeship in the GenAI Era

The core idea is that the "doing" in many service jobs is being automated or augmented, shifting the human edge upstream to designing and orchestrating work. Traditionally, junior professionals learned by doing – executing tasks, grinding through analyses, and making slide decks – gradually earning the right to shape the approach. But GenAI is collapsing that ladder. Routine execution is increasingly handled by AI, from drafting documents to analyzing data.

What's left – and growing in importance – is the architecture of the work: devising the right questions, shaping AI prompts and workflows, applying judgment to AI outputs, and integrating multiple tools and people into solutions. In other words, moving from task performer to solution builder.

As AI pioneer Kai-Fu Lee notes, "AI will increasingly replace repetitive jobs, not just blue-collar work but a lot of white-collar work. … That's a good thing because what humans are good at is being creative, being strategic, and asking questions that don't have answers." The value we bring is moving to those very human capabilities – creativity, strategy, complex problem-solving – which cannot be automated.

In my conversations with enterprise services clients, I generally lay out four transformational impacts of GenAI on these firms – from ways of working (how core tasks are executed) to domain solutions (how we deliver client impact), business models (how we capture value), and crucially, skills & capabilities (how the human edge moves upstream).

The Data Is Compelling

External data and industry sentiment strongly reinforce this concern. A recent survey noted that after companies raced to implement GenAI, employees were "expected to adapt almost overnight, learning new skills and a new way of working." Yet many firms haven't put in place the training or structures to support that.

Over 78% of organizations are now using GenAI in at least one business function, but more than 80% don't see a tangible impact on the bottom line. Why? Because people need new skills and models to harness the tech. There is a "massive visibility gap" between C-suite ambition and employee readiness: while 79% of executives are confident in meeting AI transformation goals, only 28% of employees feel adequately trained, and just 25% say they can use AI effectively in their work.

Meanwhile, clients and markets are starting to expect this evolution. In one anecdote, a consulting partner noted some clients question paying for junior staff if those juniors are just doing tasks an AI could handle: "We've had clients say, 'We're not prepared to pay for junior staff…can't you do this with GenAI?'"

A Framework for Skills in the GenAI Era

I categorize the evolving skill set into three groups: Reinforce, Accelerate, Build New. This framework emerged from my benchmarking work and has been validated by conversations with colleagues, clients, and industry research.

Reinforce: Timeless Human Skills

These are the classic capabilities that have always defined effective professionals – and will continue to differentiate us in an AI world. Because AI handles the rote parts, these human skills become even more prominent.

Structured Thinking & Problem Solving: The ability to break ambiguous problems into components, analyse them, and recombine insights into a solution. GenAI can generate answers, but it takes a human to formulate the right problem and interpret the answers in context.

Client Empathy: Understanding needs, motivations, and pain points at a deep level. This goes beyond what's written in a project brief. It's the knack for reading between the lines. AI cannot replicate genuine human empathy or build trust the way a person can.

Communication: The ability to explain, persuade, and tell a story with data and insights. Now AI can draft a slide or email, but humans must still ensure the narrative is coherent, contextual, and compelling.

Accelerate: We Are All Managers Now

These skills were always valuable, but GenAI has turned them from optional advantages to core requirements. From their first day, new colleagues are now managers—they will convene a small army of digital agents, tools, and other semi-autonomous digital operators.

Target Operating Model & Workflow Design: All professionals need to understand that they must change the Target Operating Model design of their client or their own firm. Workflow design—the skill of mapping out how work gets done—is urgent now because with AI tools in the mix, the optimal workflow is very different.

Knowledge Management: Enterprise services run on knowledge. Effective use of GenAI often depends on having good knowledge bases. The skill here is twofold: organizing and curating knowledge assets, and using AI tools to access knowledge quickly.

Cross-Functional Fluency: Being conversant across different domains—especially technology, data, and the "business" side. When an engagement involves developing an AI-driven solution, a consultant or lawyer might need to work with data scientists, evaluate an AI vendor, or understand how an algorithm works.

Build New: Frontier Skills for the AI Era

Finally, there are skills that are essentially new additions to the professional toolkit, born of the AI age.

Prompt Engineering: The art and science of crafting prompts to elicit the best results. For most professionals, this means learning how to interact with AI models in a sophisticated way—translating a nebulous task into a precise prompt or series of prompts that guide the AI effectively.

System Orchestration: The ability to design a stack or system where multiple tools (AI models, software platforms, data sources, and people) all work together to achieve an outcome. Every professional is now a bit of a product manager. We have to build solutions, not just slide decks.

AI Judgment & Oversight: The ability to exercise judgment in a human-AI collaborative context. Knowing when to trust the AI's output, when to double-check it, and when to override it completely. AI can be extremely convincing and still wrong. This skill prevents us from becoming rubber-stamp operators who just pass along whatever the machine says.

Making It Happen

So how do we make this shift from doers to builders happen? It comes down to intentional changes in how we train, manage, and reward our people:

1. Redesign Apprenticeship from Day 1: Incorporate builder tasks early. Teach new consultants how to use AI research assistants in their first week.
2. Give Junior Colleagues Room to Design: Actively allocate parts of projects where junior staff get to design the approach, not just execute someone else's plan.
3. Mentoring and Knowledge Sharing: Encourage "reverse mentoring" where a digitally native junior might help a senior use an AI tool, while the senior imparts domain wisdom.
4. Infrastructure and Sandboxes: Provide AI platforms, datasets, and low-code development tools. Remove friction from trying new things.
5. Leadership and Incentives: Leaders must actively champion this. Include technology adoption in performance reviews.
6. Hiring Strategy: Consider recruiting profiles with builder mindsets—those already oriented toward the intersection of AI and business.

Conclusion

Frankenstein's monster wasn't doomed by its strength; it was doomed by the lack of guidance and education provided by its creator. GenAI, the "monster" we've unleashed in professional work, is similarly powerful and neutral on its own. Whether it becomes a force for good depends on us.

Can we build it? Yes, we can. But only if we're deliberate. We need to redesign apprenticeship to teach building, not just doing. We need to give our younger professionals the tools, context, and confidence to architect solutions – not just execute tasks.

In an age of powerful machines, the professionals and organizations who thrive will be those who reimagine themselves as creators and orchestrators. Let's not make the mistake of leaving our "monsters" unguided. Instead, let's apprentice a new generation of builders – a generation equipped to shape the future hand in hand with AI.