The AI Divide: Why AI Won’t Replace You. (Someone using it will.)
AI is not coming for your job. But someone who knows how to effectively use it might be.
As artificial intelligence continues to reshape every industry, the real divide is not between humans and machines, but between professionals who learn to leverage AI and those who don’t. For leaders of large teams and ambitious individuals alike, this is both a challenge and an unprecedented opportunity. Those who embrace AI as a co-pilot will make better decisions, move faster, and unlock new creativity. Those who ignore it will increasingly struggle to keep pace.
We stand at the edge of a profound transformation in how knowledge workers achieve their goals. Infusing AI into daily workflows is not a minor upgrade — it’s as fundamental as when humanity first reimagined mechanical tools in the age of electricity. The scale of change ahead is nothing less than historic.
From my own experience leading large teams, I know you can’t rely solely on a few forward-thinkers in leadership or just a couple of members from an operations team charged with process improvement. You need the entire team engaged — identifying new tools, experimenting, and sharing what works. That requires both skills and process.
If I were to lead a large team again, here are the core capabilities I would develop to ensure they not only stay relevant and competitive but thrive in the AI era.
1. Overview of AI Tools & Techniques
Before anyone can apply AI effectively, they need to know what’s out there — from large language models like ChatGPT and Claude, to generative image tools, workflow automation platforms, and domain-specific AI solutions.
Deliver some initial training on what these technologies can do, such as: Large Language Models (LLMs), Natural Language Processing (NLP), Machine Learning (ML) for pattern recognition and prediction, Robotic Process Automation (RPA), Generative AI, Agentic AI, etc. Each has different abilities and is key for different use cases.
Host quarterly “AI landscape” sessions to review new and evolving tools.
Assign “tech scouts” to share discoveries each week.
Maintain an internal wiki of vetted AI platforms and examples.
2. Most Common AI Use Cases in Business
Understanding how AI is already creating value helps employees spot opportunities.
Study case studies in your industry and adjacent sectors.
Share examples of AI solving real problems in customer service, market analysis, R&D, or operational efficiency.
Keep a running “Could AI Do This?” list for repetitive or time-consuming tasks.
3. AI Tool Familiarity & Workflow Integration
The productivity boost comes when AI is woven into daily processes — not treated as an occasional experiment.
Integrate tools like Microsoft Copilot, Google Workspace AI, or Notion AI into live projects.
Rotate tool usage across teams to cross-pollinate ideas.
Capture proven AI workflows in your standard operating procedures.
4. Critical Thinking & Judgment
AI can be confidently wrong — human discernment remains essential.
Run exercises to intentionally find flaws in AI output – e.g., ask it questions like: What data supports this conclusion? What alternate interpretations exists?, etc. Run different models against each other.
Study examples of AI errors in high-stakes environments.
Reward validation, not blind acceptance.
5. Data Literacy
AI is only as good as the data it works with.
Offer foundational training in data basics.
Encourage use of analytics tools like Power BI, Tableau, or Excel AI.
Host “data days” where teams work hands-on with available datasets.
Train teams on how to apply AI models to your proprietary data and market insights.
6. Business Acumen – Identifying AI Benefits
The ultimate test of any AI initiative is its measurable business impact.
Train employees to link AI projects to specific business goals such as revenue growth, cost savings, or risk mitigation.
Use business case templates that require quantifiable benefits.
Regularly review AI projects against strategic goals.
7. Digital Curiosity & Experimentation
The people who explore, tinker, and try new tools gain a permanent edge.
Want to make some real progress? Dedicate one hour per week for exploration. Yes, that means across the entire organization!
Use discovery platforms like Product Hunt or Futurepedia.
Share learnings in short, informal updates.
8. Change Agility
AI adoption is not a one-and-done event — it’s a continuous adaptation process.
Conduct retrospectives after new tool rollouts.
Normalize trial-and-error, celebrating lessons from failures.
Offer training in change management to build resilience.
9. Ethical AI Use & Governance Awareness
Responsible AI adoption safeguards your brand and customer trust.
Provide ethics training from credible sources like Mozilla or Harvard.
Create and socialize a clear AI usage policy.
Make bias and fairness part of your ongoing team discussions.
10. Collaboration in AI-Augmented Teams
Tomorrow’s teams will blend human and AI contributions.
Co-create documents, designs, and campaigns with AI.
Use AI meeting assistants for notetaking and summarization.
Debrief on AI-assisted work to refine the human-machine workflow.
11. Use Case Design & AI-Augmented Problem Solving
The ability to design and test high-value use cases is the next competitive advantage.
Run short “AI sprints” to explore potential solutions.
Use structured templates like the AI Use Case Canvas.
Focus first on pain points that drain time or resources.
Scaling What Works
Skills are only half the equation — leaders must build a repeatable process to take individual wins and make them company-wide. That means piloting promising AI ideas in small teams, measuring impact, documenting the workflow, and then embedding it into the standard operating model. Without this step, your best AI innovations risk staying siloed, rather than driving enterprise-wide gains.
We can draw a useful comparison between today’s shift in knowledge work and a well-documented transformation from the past. Frederick Taylor revolutionized operations at Bethlehem Steel — and later influenced countless other organizations — by developing standard operating procedures that turned the company into an industry leader. Rather than relying on one worker’s method, he carefully studied all the techniques used across the workforce, identified the best elements, and combined them into a single, fully optimized process. That approach to standardization produced a 4X increase in pig-iron output — and the same potential lies before us today with AI.
Just as Taylor’s work never stopped evolving, our journey with AI is iterative: each new adoption improves operations and opens the door to the next advancement.
Building a Growth Mindset Culture
AI will keep changing — faster than most other technologies in history — and that’s a good thing. Leaders must create a culture where employees view these shifts as opportunities, not threats. A commitment to continuous learning, where experimentation is encouraged and education is ongoing, will keep your organization adaptive and competitive in the face of constant evolution.
How to get started
Before you get going on some of the more advanced suggestions here, start by ensuring that you have a baseline of foundation knowledge in your entire organization. I would recommend 3 mandatory, 2-hour training courses on the first three suggestions above to ensure that everyone on the team is coming to the table with the same understanding of what’s possible and what’s practical:
1. Overview of AI Tools & Techniques
2. Most Common AI Use Cases in Business
3. AI Tool Familiarity & Workflow Integration
End each with a quick test and some time for group discussions on what was learned and, again, have the entire team take this training as it will serve as a foundation for all the other steps outlined above.
AI is a Force Multiplier, Not a Magic Wand
For leaders, the job is to create an environment where experimentation thrives, proven ideas scale, and learning never stops. For individuals, the opportunity is to actively seek out knowledge, try new tools, and find measurable ways to make an impact.
The divide isn’t between AI and humans. It’s between those who wield it well — and those who stand still. The choice is yours.
Duane Kotsen, Partner