In an era of relentless technological advancement, AI integration isn’t merely an upgrade but a fundamental shift affecting every aspect of organizational operations. Unlike past tech shifts that required specific teams or experts to adapt, AI is a horizontal skill — demanding universal competency across entire organizations. For business leaders tasked with AI transformation, here’s the simple truth: every single employee needs to become an AI employee.
In the months and years to come, we will see the difference between companies that merely consider AI as a feature and those that fully integrate AI into their operations. This shift enhances productivity, security, and innovation, and it fundamentally alters competitive dynamics.
This transformation is evident when comparing the fluency of leading AI companies — primarily startups and tech giants — to more traditional companies. Top-tier AI organizations achieve Gen AI fluency with over 90% of their non-technical workforce proficient in AI; this is in stark contrast to the 28% average in firms outside of Tech. This widespread AI integration underscores that in cutting-edge organizations, understanding and utilizing AI is the norm, not an outlier. In these environments, AI fluency is not merely encouraged but is a fundamental expectation, fostering a culture of continuous adaptation and learning.
Setting a skills vision for your AI-ready workforce
To effectively integrate AI across their organization and ensure every employee is AI-ready, leaders should follow a clear, actionable playbook:
Begin by setting a skills vision and defining the AI competencies necessary for all employees. This vision should be dynamic, evolving with technological advancements and strategic business needs. It serves as the foundation for developing an AI-ready workforce.
There are different ways of structuring your skills vision. The easiest approach is AI Builders vs AI Users. The vast majority of employees will be AI users — using AI tools to augment and accelerate their existing workflows. Roughly 5% of employees will be responsible for building AI systems, platforms, products, language models, and evaluation tools — these are the experts that will equip your company with the tools it needs to succeed.
While the builders vs. users framework allows us to understand AI workforces in a broad sense, most organizations will need a more granular approach. An AI-ready workforce pyramid can be broken down into four levels: Center of excellence, “AI + X”, Fluency and Literacy.
– Center of excellence: Your center of excellence could be considered synonymous with “AI builders.” These are the data scientists, machine learning engineers, and software engineers you need to build an internal AI platform. They’re not applying AI tools to some other area of the company like sales or marketing — their entire role is to design, build, and refine AI tools for internal or external clients.
– “AI + X”: These are the subject matter experts whose roles can be transformed with the addition of AI. Employees at this level could come from every possible background — electrical engineers, mechanical engineers, financial experts. AI can help these employees become more well-rounded and build something truly meaningful in their specific area of expertise. While they don’t train foundational models or develop AI infrastructure, they need to use APIs, leverage retrieval augmented generation and prompt engineering methods, prototype solutions, call models, and sometimes even build end-to-end products. It’s worth noting that we’re already seeing talented individuals recognize the potential of becoming “AI + X” experts — in the deep learning class I co-created at Stanford, two-thirds of my students are not majoring in computer science.
– Fluency: At this level, you don’t necessarily need to know how to use AI tools or apply them to your workflows. Fluency is the required level for employees who are interacting with a technical counterpart. For example, a marketer selling a data product needs a certain level of understanding to be able to accurately and effectively market that product. A sales executive needs a degree of fluency to be able to answer questions from technical buyers, even if they’re not using the tools themselves.
– Literacy: This is the basic level of AI skills needed for front-line workers and individual contributors. For these employees, AI literacy could help them improve productivity depending on their role and responsibilities. But it’s equally important for these employees to be part of the broader cultural change and identify their own use cases: when every employee has achieved a standard level of AI literacy, that company is then in a much better position to innovate.
Executing your AI skills vision: challenges and strategies for leaders
Implementing your AI skills vision is often more complex than crafting it. Here’s how leaders can navigate these complexities effectively:
– Leveraging top talent: Your products will only be as good as your best contributors, and AI is no different. The experts that can come up with creative innovations will raise the bar for the rest of the organization. For this reason, at the Center of Excellence level, organizations must do everything they can to maximize the abilities of their strongest AI employees. These top performers set the standard for the entire organization. For example, I’ve seen a software company that transferred an expert in clean coding to a team struggling with maintaining clean code; within weeks, significant improvements were evident across the organization.
– Preventing a culture of “dangerous amateurs”: The behavior of the company’s leadership also makes a huge difference in the adoption of AI. CEOs and other executives must be able to set the tone for the rest of the organization — if they’re not proficient in AI today, they should acknowledge it and communicate how they plan on closing that skills gap. If executives only pretend they know about AI, their employees will do the same. Organizations with “dangerous amateurs” (as my friend and collaborator Fernando Lucini calls it) — those who overstate their abilities — will find it much more difficult to begin productizing AI, and they will run the risk of getting overtaken by competitors.
– Leading by example: As companies upskill their workforces, their CEOs should be at the forefront of developing their AI abilities. Executives should be willing to share their experiences — and their scores in benchmarked AI assessments — with their employees to foster a culture of learning.
No time to waste
The rapid evolution of AI highlights the need for companies to become skills-based organizations. Innovation relies on adapting to quickly changing skill demands. In 2016, I frequently used the programming language TensorFlow; less than a decade later, TensorFlow has changed so much that I can no longer use it effectively without updating my skills. This demonstrates how specific technical skills can become perishable.
Innovation requires employees to master cutting-edge skills, but these skills are impossible to learn quickly without strong foundations. The creation of ChatGPT in 2022, which was built on the transformer architecture first introduced in 2017, underscores the importance of durable skills. The development team’s solid foundation in mathematics, statistics, algorithms, data structures, coding, and English — skills that are durable — was critical. These durable skills, both technical and behavioral, are essential for long-term success. This illustrates why a T-shaped skills approach to employee development, blending a broad base of durable skills with narrow but deep perishable skills, is strategic for continuous growth.
The expiration date for perishable skills is coming faster than ever before, emphasizing the need for ongoing learning to stay competitive. If companies aren’t prepared to keep up with perishable skills, they’re going to get disrupted.
Innovation happens thanks to perishable skills but sustains thanks to durable skills. Organizations need to embrace the need for both or risk getting left behind.
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