Every employee is now an AI employee. Here’s how organizations should prepare
In an era of relentless technological advancement, AI integration is not just an upgrade, but a fundamental shift that impacts every aspect of business. Unlike previous technology shifts that required specific teams or experts to adapt, AI is a horizontal capability that requires universal competence across entire organizations. For business leaders charged with AI transformation, here’s the simple truth: every individual employee must become an AI employee.
In the coming months and years, we will see the difference between companies that view AI as just a feature and those that fully integrate AI into their operations. This shift improves productivity, safety and innovation, and fundamentally changes competitive dynamics.
This transformation becomes clear when you compare the proficiency of leading AI companies – mainly startups and tech giants – with more traditional companies. Top-tier AI organizations are achieving fluent Gen AI, with more than 90% of their non-technical workforce proficient in AI; this is in stark contrast to the average of 28% among companies outside the technology sector. This widespread AI integration underlines that in advanced organizations, understanding and using AI is the norm, not an outlier. In these environments, mastery of AI is not only encouraged, but a fundamental expectation, fostering a culture of continuous adaptation and learning.
CEO and founder of Workera.
Create a skills vision for your AI-ready workforce
To effectively integrate AI across the organization and ensure every employee is AI-ready, leaders must follow a clear, actionable playbook:
Start by creating a skills vision and defining the AI competencies needed for all employees. This vision must be dynamic and evolve with technological advances and strategic business needs. It serves as the foundation for developing an AI-enabled workforce.
There are several ways to structure your skills vision. The simplest approach is AI builders versus AI users. The vast majority of employees will be AI users and use AI tools to extend and accelerate their existing workflows. About 5% of employees will be responsible for building AI systems, platforms, products, language models and evaluation tools. These are the experts who will equip your business with the tools it needs to succeed.
While the builders versus users framework allows us to understand AI workforces broadly, most organizations will need a more granular approach. An AI-ready workforce pyramid can be divided into four levels: Center of Excellence, “AI + X”, Fluency and Literacy.
– Center of Excellence: Your center of excellence can 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 don’t apply AI tools to another part of the business, such as sales or marketing; their entire role is to design, build and refine AI tools for internal or external customers.
– “AI + X”: These are the subject matter experts whose roles can be transformed with the addition of AI. Employees at this level can come from any background: electrical engineers, mechanical engineers, financial experts. AI can help these workers become more well-rounded and build something truly meaningful in their specific field. While they don’t train fundamental models or develop AI infrastructure, they do need to leverage APIs, leverage enhanced 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 who see the potential to become “AI +
– 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 communicating with a technical counterpart. For example, a marketer selling a data product needs a certain level of insight to accurately and effectively market that product. A sales manager needs a certain level of fluency to answer questions from technical buyers, even if they don’t use the tools themselves.
– Literacy: This is the basic level of AI skills needed for frontline workers and individual contributors. For these workers, AI literacy can help them improve productivity, depending on their role and responsibilities. But it’s just as important that these employees are part of the broader culture change and identify their own use cases: when every employee has achieved a standard level of AI literacy, that company is in a much better position to innovate.
Executing your AI skills vision: Challenges and strategies for leaders
Implementing your vision for AI skills is often more complex than creating them. Here’s how leaders can effectively navigate these complexities:
– Utilizing top talent: Your products will only be as good as your best contributors, and AI is no different. The experts who can come up with creative innovations raise the bar for the rest of the organization. For this reason, Center of Excellence level organizations must do everything they can to maximize the capabilities of their strongest AI employees. These top performers set the standard for the entire organization. For example, I’ve seen a software company transfer a clean code expert to a team that was struggling with maintaining clean code; Within weeks, significant improvements were visible across the organization.
– Preventing a culture of ‘dangerous amateurs’: The behavior of company leadership also makes a big difference in AI adoption. CEOs and other executives must be able to set the tone for the rest of the organization. If they are not proficient in AI today, they need to acknowledge that and communicate how they plan to close that skills gap. If executives only pretend to understand AI, their employees will do the same. Organizations with “dangerous amateurs” (as my friend and collaborator Fernando Lucini calls them) – those who exaggerate their capabilities – will find it much harder to start producing AI, and they will risk being overtaken by competitors.
– Setting a good example: As companies upskill their workforces, their CEOs must be at the forefront of developing their AI skills. Executives must 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 lose
The rapid evolution of AI underlines the need for companies to become skills-based organizations. Innovation depends on adapting to rapidly changing skills demands. In 2016 I made extensive use of the TensorFlow programming language; less than a decade later, TensorFlow has changed so much that I can no longer use it effectively without updating my skills. This shows how specific technical skills can become perishable.
Innovation requires employees to master cutting-edge skills, but these skills are impossible to learn quickly without a strong foundation. The establishment of ChatGPT in 2022, built on the transformer architecture first introduced in 2017, underlines the importance of sustainable skills. The development team’s solid foundation in math, statistics, algorithms, data structures, coding and English – skills that last – was critical. These sustainable skills, both technical and behavioral, are essential for long-term success. This illustrates why a T-shaped skills approach to employee development, combining a broad base of sustainable skills with limited but deep, perishable skills, is strategically important for continued growth.
The expiration date for perishable skills is coming faster than ever before, highlighting the need for continuous learning to stay competitive. If companies are not prepared to keep up with perishable skills, they will be disrupted.
Innovation takes place thanks to ephemeral skills, but endures thanks to sustainable skills. Organizations must embrace the need for both or risk falling behind.
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