Even technical employees will need training in new skills to use AI

One executive recently described artificial intelligence (AI) as “like giving superpowers to everyone.” This is an exciting vision of an AI assistant that eliminates boring tasks, provides helpful support, and empowers people. But you can’t give someone superpowers and expect them to use them.

Business and HR leaders looking to master these superpowers are training their employees to leverage AI, hiring talent to build those AI tools, and deploying that talent to scale AI capabilities. Must be maintained. For companies that can do this, the benefits are significant in terms of productivity and new value.

Here’s the first reality check: Almost every job is equipped with AI tools that allow employees to do their jobs more efficiently, but they require extensive training. Effective training starts with understanding how AI is changing the way people work. Our own experiments show that generative AI tools can reduce the time it takes to refactor code by 20% to 30% and the time it takes to generate code by 35% to 45%, but the speed improvements depends on the complexity of the task and the experience of the developer. These tools are ideal for relatively repetitive tasks and provide a starting set of code that developers can work on and improve.

But as tasks become more sophisticated, AI tools become more like co-programmers helping developers create software. Using these AI tools requires new skills from developers, especially when it comes to generative AI. These new skills include how to better understand end-user intent, translate that intent into code, and test the results with subject matter experts. , and how to closely track and quickly tune models based on performance. We need to evaluate the solutions proposed by AI and understand which AI tools are best suited for which tasks (and we need to be able to combine different AI tools to unlock better capabilities) ).

The power of AI to change the way people work extends far beyond developers. Let’s take a retail store as an example. A merchant’s main goal is to build a strategy that drives growth and increases profits for the category. AI-powered analytics can help you with many fundamental tasks, including strategic and tactical pricing, SKU selection and assortment, purchase size and timing, inventory planning and management, promotion mix and timing, markdowns, and more. can be transformed. Generative AI will be able to do more. Generative AI can help sellers design different visual merchandising plans for each section of a store aisle and rank them by different criteria (cost, feasibility, sales potential, etc.) Please imagine. Or imagine if a negotiation tool could help sellers choose bargaining positions and support tactics (e.g., acceptable concessions, pricing guidelines) tailored to each key vendor.

In each case, the retailer is leveraging the superpowers provided by AI tools to extract insights from previously owned but underutilized datasets. And in each case they need to learn new skills. For sellers, this includes rapid engineering to obtain AI models to build optimal category plans, image enhancement to develop initial options offered for planogramming shelf space, and as a negotiation coach. It includes collaboration between AI and humans when using bots. At her vendor meeting. This authority allows her to step away from her day job and focus on the future of her category, how shopper needs and segments are changing, and how competitors within the category are performing. You will be able to work on other tasks, such as considering whether or not you have a child. This also requires skill improvement.

Amazon recognizes the importance of AI training in improving the skills of its merchants to incorporate new information (e.g., new sources, use cases, models) and enable them to incorporate vetted prescriptive recommendations. Did. Sellers worked with engineers to learn how to fine-tune the AI-based models developed by Amazon for pricing, SKU selection, promotions, and more. Amazon automates many of its tasks, so it’s the seller’s job to guide the development of these automations while freeing up time for other tasks, such as recruiting and negotiating with suppliers. became. The logical conclusion is how some jobs will move from managing the execution of AI recommendations to designing automated AI-based solutions that also do the execution.

To provide these AI tools to your employees, you need to find people who can build and maintain them. According to our research, the most in-demand AI talent includes software engineers, data engineers, AI data scientists, and machine learning engineers. Finding these people is a big challenge. Respondents to a recent McKinsey report highlighted the difficulty in recruiting AI talent. In China, for example, the demand for talent who can build AI products is expected to increase sixfold by 2030, but domestic and international universities and existing top AI practitioners can only meet that demand. It is estimated that only one in three

As we argue in the book, rewiring, Finding great talent starts with understanding the skills you need. We’ve found that too often companies hire AI engineers but aren’t given jobs that match their skills. Understanding the AI ​​solutions needed to achieve business goals and what skills are needed to develop those solutions is the granularity companies need to take to identify the right talent. It’s work. With this clarity in place, companies should incorporate AI experts into the interview process to reassure potential talent that they are working with great talent. Additionally, HR leaders need to develop recruitment programs that not only improve processes but also optimize the candidate experience.

Importantly, CIOs and technology leaders need to ensure they have complementary talent pools to effectively leverage AI talent and tools. For example, data engineers are needed to develop data architectures that allow AI tools to access high-quality data. Machine learning operations engineers need to be able to refactor and manage AI solutions over time and train and “fix” them over time. In fact, the companies benefitting the most from AI are hiring more data scientists and machine learning engineers than any other digital role.

Hiring the best AI talent is not enough. As specified in rewiring, The key to retaining top talent is creating an environment where AI talent can flourish. Some of the lessons that apply broadly to technology talent also apply when it comes to AI talent. This is especially true in the area of ​​career paths. While some digital colleagues aspire to be promoted to general management positions, more than two-thirds of developers do not want to become managers. They would rather keep their craft sharp and continue to hone their skills. Skills are the most important currency for AI talent, and it cannot be overstated that the ability to build skills is a key motivator for AI talent. Companies with the highest benefits from AI are nearly three times more likely than other respondents to say their organization has a development program to develop the AI ​​skills of technology personnel. It will be expensive.

That’s why it’s important to invest in creating long-term learning journeys that help technical employees develop the breadth and depth of their skills and the behavioral skills that the organization values. When designing learning journeys, it is important to differentiate between skill families. Resist the temptation to view all technical roles as interchangeable (“they’re all engineers”).

AI superpowers are real and potentially transformative. But its full potential value will not be realized until companies commit to training their employees how to use their AI tools and finding AI talent to create and manage their AI tools. It will not be done.

Eric Lamarre, Alex Singla, and Suman Thareja are senior partners at McKinsey and are the authors of: Rewired: McKinsey’s guide to winning the competition in the age of digital and AI.

The opinions expressed in commentary articles are solely those of the author and do not necessarily reflect the author’s opinions or beliefs. luck.

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