Core AI Skills That Actually Matter
When leaders ask me how to approach AI training, they're usually thinking about the technical stuff: prompt engineering, data security protocols, and which tools to use when. And yes, those matter. But if that's where your AI training stops, you're setting your team up to use a Ferrari like a golf cart. The real question isn't "How do we write better prompts?" It's "How do we fundamentally shift how our people work?"
I've spent 25 years helping organizations build learning ecosystems that drive actual transformation, not just check compliance boxes. And what I'm seeing now is a massive gap between what companies think they need to teach about AI and what will actually make their teams more effective.
The technical skills? These are worthwhile, but your team will acquire them as they use the AI tools. What they won't automatically develop—and what will determine whether AI becomes a genuine multiplier or just another underutilized tool—are 4 human-centric AI skills that have nothing to do with technology and everything to do with how we think.
The 4 Core AI Skills, At A Glance
- Delegation: Moving from transactional search to outcome-based management.
- Curiosity: Using iteration to turn a tool into a thinking partner.
- Contextual Intelligence: Making implicit knowledge explicit for better results.
- Discernment: The expertise required to evaluate AI outputs critically.
Delegation: The Art Of AI Management, Or Beyond The Souped-Up Search Engine
Most people use AI like an extremely fast research assistant. They ask it questions, get answers, and maybe copy-paste some results. This practice is like hiring a talented analyst and only asking them to file papers.
Effective AI delegation means handing off substantive work with clear outcomes in mind. It requires a shift in mindset to "Here's the problem I'm trying to solve, here's the context you need, now help me think through solutions," rather than "Give me three bullet points about X."
Why Delegation Is The Hardest AI Skill
Think about what makes delegation hard with actual humans. To do it well, you need to:
- Know what result you actually want, which forces you to clarify your own thinking.
- Understand what the person (or tool) you're delegating to can and can't do well.
- Provide enough context without micromanaging every detail.
- Review their work critically and iterate.
Every single one of those challenges shows up in AI use.
The people on your team who've never learned to delegate effectively—those who hold everything close and those who dump work without context and get frustrated with the results—will struggle with AI in exactly the same ways. Team leaders who have developed strong delegation skills will recognize this pattern immediately.
When your team delegates well to AI, you aren't just getting faster outputs. You are offloading cognitive work so your people can focus on higher-level thinking. You are using AI as a thought partner, not just a production tool.
Curiosity: The Fuel That Makes AI (And Everything Else) Work
Here's what happens when people lack curiosity: they accept the first output an AI tool gives them and move on. They use it transactionally—get an answer, done, next task.
But AI's real power emerges through iteration and exploration. The first response is rarely the best response. The obvious answer often isn't the most useful answer. And the question you started with might not be the question you actually need answered.
From Transaction To Exploration
Curious people ask "What if?" and "Why not?" They push back against the algorithm. They ask for alternative approaches and explore tangents that might lead somewhere interesting. They're comfortable with a certain amount of messiness in the process because they understand that's where insight emerges.
Think about how you'd work with a really smart colleague. You wouldn't just ask them a question, get an answer, and walk away. You'd have a conversation. You'd follow up. You'd push each other's thinking. You'd discover things together that neither of you would have reached alone.
Curiosity turns a tool into a thought partner. Organizations and teams that cultivate curiosity—creating space for exploration rather than just checking off tasks—will get exponentially more value from AI than those that treat it as a faster typewriter.
Contextual Intelligence: Making Implicit Knowledge Explicit
AI doesn't know what you know. It doesn't understand your organization's culture, your customers' unspoken needs, or the political landscape you are navigating. It cannot read between the lines.
This is actually one of the most valuable aspects of working with AI—it forces you to articulate what you usually keep implicit. But it only works if your team possesses contextual intelligence: the ability to recognize what background information matters and make it explicit.
Contextual intelligence is key to a process called context engineering, which acts as an intermediary layer between external LLMs like ChatGPT, Gemini, or Claude, and our people. This intermediary layer provides the contextual intelligence needed to make LLM output relevant, while also ensuring both the security of our organization's data and intellectual property.
The "New Team Member" Test
When someone with strong contextual intelligence delegates work to AI, they treat the AI like a brand-new employee in need of onboarding and continual coaching. They ask:
- What background info is essential here?
- What assumptions am I making that need to be stated?
- What constraints might not be obvious?
Without this skill, employees give AI the bare minimum—often just the surface-level task description—and wonder why the results feel generic.
Contextual intelligence is closely tied to systems thinking: the ability to see how pieces connect. These are the people who can explain not just what needs to happen, but why it matters to the business.
Discernment: A Critical Human Skill
Discernment, or knowing what's actually good, might be the most critical AI skill, and it's the one I worry about most.
AI generates confident-sounding output regardless of whether that output is insightful, accurate, or appropriate. Your team needs the discernment to tell the difference.
Discernment isn't about spotting obvious errors in AI output—that's relatively easy. It's about evaluating its:
- Soundness: Is the logic flawed?
- Depth: Does the analysis go deep enough?
- Relevance: Do the recommendations account for what actually matters?
- Framing: Is the problem even defined correctly?
Why Subject Matter Expertise Still Matters
You cannot discern quality in an area where you lack knowledge yourself. That's one reason I push back when people talk about AI replacing human experts. You need more expertise, not less, to evaluate AI-generated output.
Think about the L&D field. I can immediately spot when AI generates training objectives that sound good but don't align with actual business outcomes. Someone without an L&D background would think it looked and sounded professional…but a human L&D professional would know better.
Without discernment, teams end up drowning in plausible-sounding content that doesn't actually advance their work. The volume increases, but the quality stagnates or declines.
AI Skills For The Long Term
If you're building training for your team around AI, by all means, cover the technical basics. Teach prompt fundamentals. Address data security. Review which tools serve which purposes. But don't stop there.
Build in learning experiences that develop delegation skills through simulations where people practice scoping work, providing context, and evaluating results. Create space for curiosity by encouraging experimentation and iteration rather than rushing to finished products. Develop contextual intelligence through exercises that make implicit knowledge explicit. Strengthen discernment by having people evaluate AI outputs together and discuss what makes something truly good versus just good enough.
These aren't skills you can develop in a two-hour workshop. They require practice, reflection, and ongoing development. They're more like leadership development than technical training.
But here's the thing: these are also skills that make your people better at their jobs regardless of AI. Someone who delegates well, stays curious, provides good context, and exercises discernment is more effective in every aspect of their work.
AI just makes the lack of these skills more visible and more costly.
The organizations that invest in developing these capabilities alongside technical AI skills will find that AI can be a tool for transformation. Their teams will produce better thinking, faster. They'll solve problems more creatively. They'll make better decisions.
The organizations that focus only on technical skills will wonder why they're not seeing the productivity gains they expected, even though everyone knows how to write a prompt.
The bottom line: AI tools are evolving rapidly. The technical skills you teach today might be obsolete in six months. But delegation, curiosity, contextual intelligence, and discernment are foundational capabilities that apply to every AI tool your team is using, regardless of how those tools evolve. And these are the capabilities that merit our investment and focus.
Want to know more about transformative AI skills that future-prep your people and your organization? Reach out to our experts to discuss how we can help bring your vision to life.
