How Generative AI Will Change IT Skills and Training: A McKinsey/Skillbox Media Analysis

Contents:

• How and why the GII is changing skills in the IT industry
• How the GII will ensure the transition from roles to skills
• How HR and L&D approaches will change

How and why the GII is changing skills in the IT industry

The authors of the article emphasize that the entire software development life cycle in the IT industry is currently undergoing a transformation. Changes are affecting every stage, from idea generation to product implementation. At each of these stages, generative artificial intelligence (GAI) tools are already being implemented, effectively solving content creation tasks and significantly accelerating the development process. In the future, GAI's capabilities will expand: it will be able to not only generate new product features but also conduct testing and develop prototypes. This opens new horizons for optimizing and improving the quality of software development. Despite the numerous benefits of generative artificial intelligence (GAI), businesses still face challenges in understanding how to adapt to change, what skills to develop, and what training to provide to employees, according to McKinsey experts. Clearly, training will be required for virtually all employees. However, the consulting company recommends focusing on two fundamental roles: software engineers and product managers. These skills will be especially important as GAI technologies rapidly adopt new technologies, making their development and training a key factor in successful business transformation.

According to McKinsey experts, software engineers must master three key skill categories to effectively work with generative artificial intelligence (GAI).

The first category is evaluating code generated by generative AI. At first glance, this may seem like a simple task. However, experts argue that code evaluation can be challenging for both novice and experienced developers. Being able to write code does not always imply the ability to evaluate it. Developers must not only identify errors but also ensure high code quality, identify potential problems, and train generative AI as they work. Effective code review requires a deep understanding of its structure, logic, and features, making this process a crucial aspect of software development in the era of artificial intelligence.

Engineers need a deep understanding of how to provide feedback for generative artificial intelligence (GAI) and what types of tasks are most effective in training specific tools. Experts emphasize the importance of this understanding for achieving optimal results in the development and implementation of GAI technologies.

The second category of skills involves the ability to combine various AI tools and models. This can be seen, for example, when applied AI is used to analyze the results of generative AI. Successfully completing such tasks requires skills in selecting appropriate tools and integrating them. A powerful combination of technologies enables more accurate and insightful results, making this skill particularly valuable in AI development and application.

The third category of skills involves design, which implies a deeper understanding of the context of work tasks. This allows not only to create aesthetically pleasing solutions but also to consider functional requirements and user needs. Deep immersion in design helps optimize workflows and improves overall project efficiency.

With the development of artificial intelligence technologies, coding tasks are becoming increasingly automated. This opens up new opportunities for engineers, allowing them to focus on developing important skills such as writing user stories, creating code frameworks, including code libraries and support programs, as well as understanding business outcomes and anticipating user intent. Communication skills are also becoming key, as engineers must effectively interact with teams, managers, colleagues, and clients. McKinsey analysts emphasize the importance of these changes in the context of today's digital economy, where the ability to adapt and develop new skills will become a key factor for success.

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Areas with the most pronounced skills shortages have been identified in the IT industry. In the current labor market, there is a significant shortage of qualified specialists in certain fields. This creates challenges for companies seeking to grow and implement innovative technologies. The main areas experiencing talent shortages include artificial intelligence, cybersecurity, software development, and data analytics. The increasing demand for professionals in these fields highlights the need to upskill and train new specialists, which could help fill gaps in the IT labor market.

McKinsey analysts argue that key skills for product managers will evolve in two main directions. These changes are related to adaptation to new technologies and market demands, making it essential to constantly update their knowledge and skills. Product managers must be prepared to master new tools and methods to effectively manage products and ensure their competitiveness. Understanding user needs and analytical thinking will be crucial factors for a successful career in this field.

The first area of ​​application of generative artificial intelligence (GAI) is related to the need to master new technical skills. While this may traditionally seem like a task not for technical specialists, it is important to note that in today's market conditions, product managers must also adapt. As generative artificial intelligence (GAI) becomes increasingly capable of facilitating the development of prototypes and product pilots, product managers are required not only to be proactive but also to actively participate in improving results. This requires basic programming knowledge to effectively interact with GAI tools. Furthermore, understanding how large language models work is becoming critical for product managers, as it allows them to integrate various technologies to create innovative solutions.

A second important area is risk management and trust building. To effectively engage with stakeholders, teams, and users, product managers need to develop empathy. This will help them recognize potential trust issues with generative artificial intelligence (GAI). Some users readily accept AI results, while others remain skeptical. In such circumstances, standard training on how to use new GAI-based tools may prove ineffective. Product managers must take responsibility for overcoming trust barriers. It is important that they learn to manage risks by regularly consulting with experts in the field and ensuring that the entire product development lifecycle runs smoothly.

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Non-technical professionals are increasingly recognizing the need to master IT skills. In today's world, where technology permeates all areas of life, having basic knowledge of information technology is becoming an important competitive advantage. The ability to work with digital tools, understand the basics of programming, and use various software solutions opens up new opportunities for career advancement and increased efficiency. Mastering IT skills helps not only in professional activities but also in everyday life, which makes this process especially relevant for specialists in various fields.

How GAI will enable the transition from roles to skills

Generative artificial intelligence (GAI) significantly increases the productivity and efficiency of teams, which can lead to the feeling that teams are becoming smaller. Experts predict that this could indeed happen in the future. However, there's also a countertrend—a potential increase in team sizes. This is because roles, positions, and functions within teams won't so much shrink as they will shift in unpredictable ways. Companies must be prepared to adapt to the new demands and dynamics brought by the GII, which will require rethinking traditional structures and implementing new approaches to collaboration.

Development engineers may face an increase in the volume of tasks, which will lead to the intersection of their functions with product managers. This creates a need for new roles, such as product developers—specialists capable of not only writing code but also developing product requirements, creating prototypes, and working with various AI tools. New roles will also emerge in security, code review and approval, and training large language models. These changes highlight the evolution of the IT sector and the need for specialists to adapt to new market demands. Experts believe that HR and senior management will need to adapt to new challenges and labor market demands. This includes the need to implement innovative approaches to HR management to effectively address changes in the business environment. It is important to focus on developing a corporate culture, supporting employee initiatives, and implementing modern technologies to optimize processes. In addition, attracting and retaining talented specialists will be a key aspect, which requires strategic planning and active management involvement in shaping the company's attractive image.

• a more flexible approach to understanding the skills that form a particular role;
• standardization - understanding what tools and, accordingly, skills the company will need;
• risk management - employees must have clear guidance and an understanding of the risks they face when working with AI and what exactly is expected of them.

How HR and L&D approaches will change

McKinsey experts emphasize that key changes in the field of human resource management will focus on strategic planning and the development of training programs. These aspects are becoming increasingly important for improving the effectiveness of HR departments and adapting to rapidly changing market conditions. Investments in strategic planning and training will help organizations not only optimize internal processes but also ensure sustainable growth and competitiveness.

A key challenge for HR leaders, as well as training departments, is that artificial intelligence is not simply replacing individual positions, but transforming the specific work tasks inherent to these roles. Therefore, strategic planning must be based on employee skills. To achieve this, it is necessary to create a comprehensive knowledge base of skills, classify them, and structure them into a unified hierarchy. The authors of the article emphasize that HR cannot cope with this task alone; it will require support from senior management and internal leaders, including developers. Effective management of employee skills will provide a company with a competitive advantage in a rapidly changing labor market.

McKinsey experts emphasize that for employee skills to be useful, companies should view them as data, not static documents. This means analyzing this data, regularly updating it, and making important business decisions based on the information obtained. This approach will optimize talent management and improve team efficiency.

Experts note that the company successfully uses artificial intelligence to create a database and hierarchy of employee skills. The AI-powered system analyzes job postings, job descriptions, HR data, and LinkedIn profiles to identify the necessary skills for various positions. Each employee can compare their experience with the required skills and confirm their proficiency. If confirmed, the information is updated in the employee's work profile and in the overall HR system. This approach ensures a more accurate match between employee qualifications and company requirements and promotes effective human resource management.

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When engaging with AI-powered corporate training tool providers, it's important to ask a few key questions. First, inquire about the technologies used in their solutions and how they help tailor training to employee needs. Inquire about how their tools collect and analyze data on learner progress, as well as how they personalize the learning experience.

Don't forget to inquire about support for various content formats and the ability to integrate with existing systems in your company. It's also important to find out what mechanisms are used to evaluate training effectiveness and how the provider plans to update their solutions in line with new trends in AI and training.

Additionally, inquire about technical support and training options for administrators, as well as the scalability of the solution. By asking these questions, you can gain a full understanding of the vendor's capabilities and how their tools can enhance the effectiveness of corporate training in your organization.

Learning is at the center of any company's talent retention and development program. In a rapidly changing market and advancing technology, HR and L&D departments are faced with the question of what skills to develop in employees. Experts argue that continuous professional development, or upskilling, is the only true answer. However, even among the new skills required for software engineers, it can be noted that they are not always directly related to their traditional responsibilities. This can lead to employee resistance and concerns about changes in their professional role. To successfully implement new training programs, it is important to consider these aspects and provide support throughout all stages of the learning process. To address this issue, McKinsey recommends considering the concept of apprenticeships. This allows less experienced professionals to learn from more experienced colleagues, promoting their professional growth and skill development. This approach helps create an effective system for transferring knowledge and experience within the company, which, in turn, improves the overall qualifications of employees and reduces turnover. Internships provide hands-on learning opportunities, promoting adaptation to change and demonstrating important skills such as problem-solving and common sense when evaluating code quality. To maximize the effectiveness of an internship, experts should be actively involved in the process, not just formally marking tasks. Their authority and institutional knowledge play a key role, allowing them to recognize risks specific to a particular company. Actively involving mentors in interns' training not only improves the quality of training but also promotes the development of the skills necessary for teamwork. Experts in their field will work alongside junior colleagues on tasks, monitoring their work and achievements. They can act as mentors and coaches, demonstrating by example how to effectively complete tasks that were previously unrelated to their experience. This includes the ability to connect their activities with the company's business goals and an understanding of the needs of product users. This approach promotes team development and improves the quality of work, which ultimately leads to the successful achievement of shared goals.

To ensure successful employee participation in internships, it is necessary to create a motivational system. The authors of the article recommend integrating internships into the employee performance appraisal process and allocating dedicated time for training within the workflow. This will not only increase interest in internships, but also improve the overall qualifications of the team.