Anyone can pick up a paintbrush and attempt to paint, but only in the skilled hands of an artist does it become an extension of their creativity and artistic vision. Similarly, artificial intelligence (AI), like a paintbrush, is just a tool. Without the skilled human touch, AI will never give more than average results.

AI is changing our world at breakneck speed, including the training and development space. But AI does not manage and lead itself; it needs human leadership and direction. The skills that set human professionals apart are becoming more and more important. These “power skills” —  often called “soft skills” — differentiate effective learning and development (L&D) leaders from those who just push buttons and hope for the best!

Why Power Skills Still Matter in an AI World

Power skills often get a bad rap from the start, but remember, these skills are the evolved forms of basic human traits — emotional intelligence (EQ), ethical judgment and strategic thinking — that make collaboration and innovation possible. AI should be seen as an augmentation tool. After all, without human guidance, AI risks producing irrelevant, inaccurate or even damaging outcomes.

This is where L&D leaders come into play. In most organizations, L&D leaders act as the natural bridge between people and technology, translating learning into measurable outcomes and knowledge into changed behaviors. They are champions of change management and drive growth. This means that these human skills remain the deciding factor whether or not AI becomes a lever of improvement or liability in our organizations.

3 Most Crucial Power Skills for AI in L&D

1. Prompting as a Creative Discipline

Prompting is quickly becoming a core workplace literacy skill. Strong prompts blend creativity with problem-solving. They are specific, contextual and goal oriented, much like giving clear directions to a team member. Just as a well-crafted story is engaging, a well-crafted prompt produces more compelling and effective results. At its core, prompting is structured problem-solving: building step-by-step scenarios with the desired outcome in mind.

A helpful framework to practice prompting is to use the Three P’s of Prompting:

• Premise — context and purpose (i.e., what do you want AI to do, specifically?)
• Persona — perception and expertise (i.e., who do you want AI to mimic?)
• Parameters — scope and constraints (i.e., what should the AI consider?)

What it looks like in practice:

Instead of typing “Create training objectives,” write: “Act as an instructional designer; create three learner-centered objectives for a 45-minute virtual session on giving feedback, aimed at mid-level managers in a manufacturing company, using Bloom’s taxonomy verbs.”

How to develop it:

Treat prompting as an iterative exercise. Start broad, review the output and refine your request until the desired outcome is reached. Document effective prompts for your team.

2. Discernment: Knowing When Not to Use AI

Not every task will benefit from the use of AI, and knowing when not to use it is just as important as knowing when and how to use it. It seems everyone is trying to put AI-powered features into absolutely everything. However, just because AI is front and center within many modern applications, doesn’t mean AI must (or should) be used for everything. Using AI only when it adds real value preserves L&D teams’ time and credibility.

Here are three questions every L&D leader should ask before clicking on the shiny new AI button:

• “Can AI do this faster or better than me?” If AI can’t complete a task faster or better than you, why waste time using it?
• “Does this task require a human touch?” If a task requires a human touch and/or EQ, why use AI to devoid the task of human perspective?
• “Are there risks in using AI here?” If there are potential risks in using AI for a given scenario, why risk your data?

AI is great at some things: like formatting, summarizing, brainstorming and prototyping. But for tasks like acting as a sole subject matter expert (SME), handling bias-sensitive content or processing proprietary data, human discretion is essential. High-stakes decisions, nuanced facilitation or culture-shaping communication should always be reserved for human discernment.

What it looks like in practice:

Asking AI to brainstorm 10 icebreaker activities for a workshop, then personally choosing the one that best matches your group’s dynamics and organizational tone.

How to develop it:

Experiment intentionally. Run side-by-side tests where you complete a task both manually and with AI, then compare the results for quality, speed and alignment.

3. Strategic Alignment

Strategic alignment is a common term used in the L&D industry. Training Industry defines it as “the process of prioritizing the training function’s goals and mission around key business priorities to ensure business results are achieved.” Another way of defining the practice of strategic alignment would be focusing on “doing the right things,” not just “doing things right.” Building high-impact training is critical in high-performing organizations, but it’s only high impact if the L&D team is supporting training that actually makes a difference.

When contemplating the use of AI, strategic alignment matters more than speed. For example, AI may make some work faster, but speed without strategy is simply wasted motion. Ensuring training initiatives are aligned to true business outcomes is far more important than novelty of AI use.

That said, AI can absolutely move the fulcrum of efficiency, but like the other power skills above, strategic alignment is a critical competency for every L&D leader. AI can accelerate production, but it can’t decide what matters. That’s the job of a training manager.

What it looks like in practice:

Using AI to quickly prototype a new onboarding module, but first align with leadership on business goals (e.g., retention, ramp-up speed, sales readiness, etc.) so the end product drives measurable impact.

How to develop it:

Strengthen business acumen by regularly meeting with stakeholders outside L&D. Learn their key performance indicators (KPIs) and challenges, and always filter AI-generated work through that lens.

Power Skills in Action: Real L&D Examples

Consider these real L&D use cases that may benefit from leveraging power skills with AI:

• Instructional Design: AI can be used to draft training outlines in minutes — but apply human expertise to remove irrelevant content and ensure accuracy.
• Brainstorming: AI can be used as a “24/7 brainstorming buddy” to push ideas further, faster, with human judgment to spot AI “hallucinations” and inaccuracies.
• Interpersonal Leadership: Leading people still requires skills like EQ, gut instinct, personality understanding and privacy protection — areas where AI may fall short.

Understanding these power skills in action can help L&D leaders see how they can responsibly and effectively implement AI across the organization.

Becoming an AI Advocate in Your Organization

Being an AI advocate is less about expertise and more about modeling safe experimentation and sparking conversations. It can be as simple as raising potential AI use cases in meetings or as advanced as chairing an AI taskforce.

Successful AI advocates within organizations build bridges with their information technology (IT), legal and compliance teams. These groups share curiosity but may be removed from frontline operations; partnering with them strengthens responsible AI use.

Here are a few simple ways to build AI experience and become an AI Advocate:

• Learn by doing: AI is a language; fluency comes through immersion.
• Prompt engineering: Create and share prompt libraries and best practices with others.
• AI communication: Stay plugged into tech news and your organization’s messaging.

Guardrails for Responsible AI Use

Using AI is a skill, and like any other skill, in high-stakes environments, mistakes can be catastrophic to the business. Instead of banning AI entirely, L&D leaders can create guardrails so mistakes are learning opportunities, not disasters.

“Hallucinations,” another AI buzzword, refers to generative AI outputting inaccurate or outdated information. Fact checking has always mattered, but it’s now a non-negotiable — especially when AI confidently frames these false statements as fact, L&D leaders must train their teams to be more diligent than ever.

Here are a few concepts that any L&D leader can use to responsibly leverage AI with their teams:

• Consider implementing multi-layered review processes: Distribute review tasks across stages (i.e., ideation, development, SME review, final check) so no single person is the “AI goalkeeper.”

• Start a hallucination sharing board: Posting and sharing inaccurate AI responses helps teams identify these issues themselves and learn AI patterns.
• Avoid relying solely on AI for SME or personally identifiable information (PII)/legal-sensitive content in unsecured systems: Always confirm accuracy with human experts and trusted resources.

These guardrails reinforce the three most crucial power skills: discernment guides when to use AI, prompting ensures iterative refinement and strategic alignment keeps AI efforts focused on high-impact outcomes.

The AI Leadership Mindset Shift

Remember, AI is another tool in the L&D leader’s toolbox, not a teammate. AI should minimize blind spots, not amplify them. This is where, once again, the power skills of humans remain essential in the age of AI. “Leading with AI is an invaluable skill that requires modeling humility and continuous learning — not claiming “expert” status but being willing to experiment — and sharing mistakes openly to encourage safe experimentation and collaboration.

EQ has never been more important than it is today; AI may be able to mimic tone, but it doesn’t truly feel emotion. Human connection is still the heart of training and development. L&D leaders still need to be able to read the room, sense learner needs and foster trust with learners and stakeholders alike — something no algorithm can replace.

Think of AI as an apprentice: fast, tireless and eager, but dependent on direction. It can speed up prototype development and administrative tasks — but strategy, vision and final decision-making must stay human. The L&D leaders who will thrive in the future aren’t the ones who let AI lead, but the ones who lead with AI.

The Human Edge

Anyone can wield a tool, but mastery transforms it. AI, like a paintbrush, is only as impactful as the human guiding it. A training manager’s role is not to compete with AI, but to use their human skills so the technology works for them, not the other way around.

In an AI-powered workplace, the greatest competitive advantage will always be the power skills that make L&D leaders human.