How to Build an AI-Ready Workforce

Training and upskilling can prepare employees for the age of artificial intelligence.

Written by Sameer Maskey
Published on Sep. 12, 2023
How to Build an AI-Ready Workforce
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The surge in generative AI innovations is leading to a complete disruption of the workplace. These technologies are redefining tasks like content creation and report generation and are also aiding complex activities like drug discovery and even spaceship launches. 

5 Tips for Building an AI-Ready Workforce

  1. Understand what AI means for and can do for your business.
  2. Identify the skills gaps and training opportunities in your workforce.
  3. Remember that not everyone will need the same level of AI training and upskilling.
  4. Make sure training tools are relevant, topical and engaging. 
  5. Create ways for employees to use their new skills.

On one hand, AI’s introduction has reduced certain types of roles entirely. On the other, the job market is flooded with jobs that have witnessed a steady rise since the proliferation of generative AI tools like ChatGPT, Bard, DALL-E2 and Synthesia. 

With this growing workplace disruption, it is critical for organizations to understand how to build an AI-ready employee base. Below, I outline some steps that can be beneficial as organizations consider developing a workforce that is equipped to match the exponential rise in AI-related roles and tools alike.

Read More About AI and the WorkplaceHow Generative AI Will Revolutionize In-House Legal Work

 

Understand AI’s Value Proposition for Your Business

For most businesses, AI may immediately showcase the promise of automation-led efficiency and data-powered insights. However, AI’s impact on businesses and their bottom lines can vary significantly based on what stage a company is in, its overall size, its data capabilities and its industry’s regulatory obligations. Outside of these, there are considerations like what is their most pressing problem right now, for instance time to market, customer churn, demand prediction, etc. 

For example, a hospital may need to identify whether there is a more pressing need to better manage patient flow with a diagnostic chatbot or whether it can benefit from computer vision systems informing them about a patient’s vitals regularly. Similarly, a telecommunication provider needs to understand whether it needs an AI assistant to address common customer queries and frustrations, thus curbing customer churn or whether it needs a sales assistant powered by a prediction engine for improved leads mapping.

Once organizations have made a clear assessment of the AI strategies and solutions with the most return on investment based on their individual considerations, they must move to the next stage of mapping out teams equipped to make the most of AI. 

 

Identify and Assess AI Skills Gaps and Opportunities 

Because AI is a vast field, different areas of expertise can come in handy for organizations. Additionally, the AI strategy and solutions a business wants to adopt often dictate the makeup of the ideal teams.

From data analysts and annotators to data and AI engineers and machine language model experts and AI researchers, the needs may vary based entirely on the scope of the AI project. But the question remains around whether or not everyone who works with AI needs to be a technical expert. The answer is no.

Businesses mainly need teams that can either build efficient solutions or use existing solutions that are available in the market without much hand-holding. This means there is an opportunity to upskill employees based on their current domain and industry expertise, their willingness to learn and their flair for certain areas that can prove beneficial when picking up AI concepts. 

For instance, a strong background in linear algebra and coding can serve as a solid foundation for learning artificial intelligence. However, not everyone who wants to work with an AI system needs to necessarily become proficient in just these fields. 

A financial analyst, for example, can simply learn how to leverage the basics of modeling techniques to build and/or tweak sound machine learning-based financial models. A medical researcher can just learn how to use pre-trained vision models to scan through centuries of medical research. Similarly, HR teams can learn how to tap data analytics for improved employee insights, including in skills assessment.

Between hiring hybrid teams of in-house experts and consultants and mapping out opportunities to train the existing workforce, businesses have several routes to explore when trying to bridge the AI skills gap. But if they choose to employ training and upskilling, it’s important to ensure both efficiency and effectiveness of these training programs.

 

Use Effective Training and Upskilling Programs

Whether it’s building AI skills from the ground-up or training to bridge existing skills gaps, organizations will not be able to deliver valuable training in the absence of engaging, topical and relevant training content. 

 

Ensure Relevance and Timeliness of Training Content

With the rapidly evolving AI landscape, new AI skills are emerging. For example, LinkedIn job posts referring to “generative AI” have increased 36-fold in comparison to last year. This growth in generative AI roles has also subsequently given rise to a demand for related skills, such as prompt engineering, which the World Economic Forum deems the No. 1 job of the future. Some other prevalent in-demand AI skills include solving critical data and algorithm challenges and building AI models for automation.

When curating relevant training programs, organizations must keep track of in-demand roles and subsequent skills as well. For example, prompt engineering requires the ability to fine-tune language models through the use of effective prompts. For the budding workforce, ensuring that the training content doesn’t only focus on traditional AI skills but also a mix of linguistics, problem statement understanding and basic concepts of large language models can be beneficial. 

 

Manage Expectations

Other important elements to keep in mind when building reskilling and upskilling programs are course length, team and resource management as well as managing expectations of learners as well as their supervisors both so that neither work deliverables nor training output are impacted. 

It’s also imperative to understand that not all employee groups will want the same level or type of AI training either. For example, those in the executive leadership team need to make decisions based on the AI system’s outputs and will, therefore, focus on how the system compares to others in use; how to measure the success delivered by the AI solutions; and understanding what the implementation process would entail.

In contrast, those looking to learn to be an AI engineer need to master all the foundational skills needed in building and managing the development of AI solutions, complete with analytical critical thinking. Managers will focus on making the most of AI features that help optimize their day-to-day and enable growth.

 

Maintain Engagement

A training program becomes ineffective when participants lose interest at any point in their learning journey. Fortunately, today organizations have the opportunity to use AI itself as a tool when curating content, delivering engaging assignments, continually tracking participants’ progress and fine-tuning training efforts based on the evolving AI proficiency levels of different employee groups. 

Between delivering AR/VR-based training to showcasing how AI is able to detect a student’s level of engagement in class, trainers today have multiple ways to ensure learning isn’t a tedious process. 

However, engagement of the program holds no value if it doesn’t result in progress of the workforce – something that can only be measured through applied learning.

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Create Ways for Employees to Use Their New Skills

When organizations invest in AI training and upskilling, they should find ways to take it a step further and challenge their workforce to think of creative, out-of-box ways to implement the skills that they have learned to solve specific business problems, including the talent-gap problem. 

For example, instead of hiring large AI teams, organizations may find their teams keen on hiring one or two senior AI personnel or make use of low code no code platforms such as GitHub CoPilot and solutions like Robotic Process Automation, thus leveraging AI itself as a solution to bridging talent gap. 

In addition, through healthy internal competitions, knowledge sharing sessions, hackathons or incubator programs, organizations can help foster a culture of Innovation and simultaneously encourages a continuous learning mindset. Such avenues can also serve as valuable tools in assessing the success of the training programs.

Ultimately, organizations need to leverage a mix of in-house AI talent, upskilled talent with in-house training programs in AI, external AI consultants and new skill augmentation methods to build competent AI teams and make the most of AI. 

By adopting an experimental mindset, businesses can continually make the most of AI’s possibilities without shouldering the burden of time and cost to find AI talent. An integrated approach also enables them to equip the entire demographic of the workforce, irrespective of roles and years of experience, with comprehensive AI readiness.

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