Upskilling for AI-Centricity

The integration of artificial intelligence (AI) into business operations is not just an idea anymore, its happing now, already reshaping industries and driving significant investments. The global AI market is projected to reach $190 billion by 2025 and an astounding 1.8 trillion by 2030, with sectors such as healthcare, finance, manufacturing, and retail leading the charge. ?Needless to say that AI is rapidly becoming integral to business operations, and becoming a key driver for efficiency, innovation, and competitive advantage

However, leveraging AI's full potential requires an engineering workforce equipped with the necessary skills and expertise to deliver on the organizational goals. This article delves into the importance of upskilling engineers for AI, a practical framework for strategizing this transformation and challenges.

The Need for Upskilling

Upskilling your engineering organizations is crucial for several reasons if you are setting a journey towards becoming AI-centric in the near future.

According to a McKenzie report, 30% of work currently done by computer engineers will be automated by 2030, out of that 18% will be due to Gen AI adoption. Generative AI will automate many routine tasks and coding needs enabling employees to focus on higher-value activities. There will be a natural push for engineers to focus on creativity, critical thinking, and quality of design.? IT teams including non-engineers will be expected to spend more time on strategic planning, innovation and data-driven decision making.

AI technologies are rapidly evolving, and engineering teams need to stay updated with the latest advancements to remain competitive. Additionally, integration of AI will create new roles or responsibilities in teams focused on AI development and governance such as AI ethicists and ML engineers. 68% of fastest growing jobs in US did not exist 20 years ago! Upskilling ensures that engineers are proficient in the most current AI tools and techniques, enabling them to fulfill those new responsibilities and meet newformed expectations.

Now, purely from a career perspective, while some engineers are worried about their roles getting eliminated, reality is AI will not remove but transform majority of these roles (approx. 68%). ?In fact, many leaders are concerned about finding enough good talent in coming years. ?Upskilling in AI will open the door to more opportunities and responsibilities for engineers.

Upskilling Approaches

Upskilling an engineering organization for AI involves several key considerations to ensure teams are adequately prepared to leverage AI technologies effectively to deliver on organization’s strategic goals. In fact, AI skills are going to be crucial for every role in the broader organization, just that the level of readiness and type of skills will vary depending on the role. So a comprehensive upskilling plan tailored to each role and aligned to the long term AI strategy of the organization is essential to enable the workforce.

LinkedIn Learning recently published a New Framework for upskilling across your organization . While this is a great start, it focuses primarily on general categories of roles in an organization. For an IT organization, especially engineering organization, deciding who should get what training can become a very tricky exercise. With the varieties of AI skills starting from basics to deep learning to specialized skills, it can be an exhaustive list of topics and offering those trainings to incorrect roles can result in a not-so-effective effort for the organization and intimidating experience for employees.

To address this issue, I propose an approach that expands this framework to make it more tailored to an engineering organization based on the types of teams. ?Given that IT teams are typically organized by their work nature and whom they serve, and considering that AI skill demands are going to be largely driven by these factors, it makes sense to base our strategy on the type and mission of each team. For instance, a product team focuses on delivering product features to customers, a support team handles customer issues and feedback to ensure a positive experience, and a CloudOps team deals with cloud administration and operations primarily serving other teams. The scope and impact of AI for these teams will vary significantly, making a one-size-fits-all upskilling approach largely ineffective. Instead, developing a strategy that considers the specific needs and impacts on these teams will ensure they acquire the necessary skills to prepare for future work aligned with the established AI strategy and business objectives.

While the needs and plans can vary significantly by organization, an upskilling initiative should always start with a thorough assessment of AI skill needed for each type of teams, as well as an evaluation of ?current skills & gaps within those teams. This process enables the planning of an upskilling program that is tailored to the unique needs of various teams.

?Now, how do you determine the AI skill needs for each type of teams? That’s where I would apply Googles upskilling framework, but driven primarily by the nature of teams. To summarize, this framework provides five distinct levels of upskilling based on the role of an employee.

Level 1: Understanding –AI basics and what AI can help with and cannot help with.

Level 2: Applying - AI to daily work.

Level 3: Building - Integrating AI to business capabilities

Level 4: Creating - Training & maintaining AI models and implementing AI solution.

Level 5: Specializing: Specialized AI tools for Security, DevOps, CloudOps etc.

Please refer above diagram on Types of IT Teams with color-coded team names. (Please note that this is not a complete list. Names & definitions can also vary depending on the organization) Each color code represents an upskilling category as described below:

• Teams in white background such as Tech Support teams, Content Teams etc., generally need to be upskilled to level 2, as most professionals in the organization. With proper training, these teams will have AI basics knowledge and will have the competency to identify areas for potential AI use in their daily work & choose appropriate tools.
• Teams in light blue background such as Product teams typically need to be upskilled to level 3. With an effective training, teams will have the competency to identify potential use of AI in business capabilities and integrate with appropriate AI models and tools to deliver AI-powered solutions to customers.
• Teams in dark blue background such as Foundational teams and Data Science teams typically need to be upskilled to level 4. With proper training, these teams will have competency in advanced topics like deep learning, neural networks etc. They will have the skills to develop ML applications, and train & fine-tune AI models as needed.
• Teams in purple background such as DevOps teams will typically be upskilled to level 5. These teams will get continuous, in-depth and specialized training on AI applications to support rest of the organization with unique admin & operational needs related to security AIOps, MLOps, Cloud solutions etc. ?

This approach provides a tailored framework for creating an upskilling program that aligns with the unique needs of both the organization and its individual teams. It's important to recognize that the specific tools, languages, and technologies chosen for upskilling will depend on the organization’s goals and needs. For instance, if your business does not anticipate building and training an AI model in the near future, your program can place less emphasis on upskilling Level 4 teams. Thoughtful consideration of these elements is essential for ensuring the program's efficiency and effectiveness.

Soft Skills

While technical expertise is crucial, soft skills play a significant role in ensuring effective collaboration, innovation, and the successful implementation of AI initiatives.

Critical thinking and problem-solving abilities are essential for analyzing complex problems and developing innovative AI-driven solutions. Effective technical communication is crucial for explaining AI concepts to non-technical stakeholders, and collaboration skills are essential for teamwork and cross-functional projects. Understanding ethical implications and ensuring responsible AI practices are critical for developing trust and accountability. Upskilling plans should include coaching on important soft skills at appropriate levels.

Training Resources

Good news is, there are plenty of resources available for learning on AI topics. ?To help with the overall global needs around upskilling, industry leaders in the AI space have recently started offering training programs, many of them free or at nominal costs. For example, LinkedIn Learning now offers more than 250 courses on diverse AI topics, a substantial increase from previous years. ?Google offers a variety of AI training programs through its Google AI and Google Cloud platforms. Similarly, AWS provides comprehensive AI and machine learning training through its AWS Training and Certification programs.

While these offerings provide excellent resources for learning, they cannot replace a comprehensive upskilling plan tailored specifically to your organization. Without a focused approach, individuals might become overwhelmed by the sheer number of topics and miss out on trainings crucial to their team and role. Additionally, an organizational upskilling program is the most effective channel to socialize AI strategy, ethical standards, and AI guardrails. Missing this opportunity can have long-term consequences on the progress of your AI journey. A customized program that integrates these key organizational aspects and leverages available resources at the same time will be the most effective approach.

Practical Challenges in Upskilling

Balancing short-term and long-term goals is never a trivial task. Engineers often face tight deadlines, making it difficult to allocate time for training without affecting project timelines. Prioritizing urgent project demands over long-term upskilling efforts can hinder the overall development of AI capabilities. Resource allocation presents another challenge, with budget constraints limiting the ability to fund high-quality training programs and certifications.

The transition period where engineers acquire new skills can temporarily slow down project progress. Maintaining engagement is crucial, as continuous learning can lead to burnout if not managed properly, particularly if engineers feel overwhelmed by their regular duties and additional training needs. Ensuring that engineers see the value in upskilling and remain motivated to participate in training programs is essential.

Evaluating the impact of training programs and their effectiveness can be challenging. Justifying the return on investment from upskilling initiatives to stakeholders requires clear metrics and evidence of benefits.

Having clear and well-socialized organizational goals related to AI is key to addressing these challenges. Clear objectives and measures can help form guidelines to balance short-term and long-term priorities. It can also be a major motivational factor for engineers as teams start owning these long-term goals.

The Risk of Not Upskilling

Recent studies reveal that only 25% of organizations investing in or planning to invest in AI have a comprehensive upskilling plan for their employees. Additionally, only 39% of employees who actively use AI at work have received AI training from their companies. Without proper guidance and support from leadership, the majority (78%) of AI enthusiasts are resorting to using their own tools. This can potentially create a counter-productive environment that stems from a lack of consistency and strategic approach towards AI. It can also pose data and legal risks.

Without the necessary skills, engineering teams may struggle to innovate, leading to stagnation and missed opportunities. Failure to automate and optimize processes with AI can lead to higher operational costs, as manual processes are often slower and more error-prone. Additionally, a lack of investment in upskilling can lead to higher turnover rates, as skilled professionals often prefer employers who prioritize and invest in their development. Lastly, organizations that delay upskilling risk losing their competitive edge, as competitors with adequate talent can deliver superior products and services more efficiently, capturing a larger market share.

Conclusion

Upskilling for AI is not just a strategic choice but a crucial necessity for engineering organizations aiming to thrive in an AI-driven world. Implementing a comprehensive upskilling plan tailored to different team types and focusing on both hard and soft skills ensures that engineering teams are well-prepared to utilize AI technologies effectively. Delaying this investment can lead to several significant risks, including falling behind competitors, stifling innovation, incurring higher operational costs, and losing critical talent. Embracing the AI revolution through a carefully crafted upskilling program will pave the way for sustained growth and success.

oOo

If you would like to delve deeper into this topic or have a question regarding upskilling your organization, please feel free to connect with me on LinkedIn (Jai Thomas ) or reach out on X (formerly Twitter) @jaithomas . I look forward to engaging with you!

References

1. A New Framework for AI Upskilling Across Your Organization
2. Forbes: How Business Leaders Can Reskill Their Workforce For The AI Era
3. AI at Work Is Here. Now Comes the Hard Part
4. Harvard Business Review: How to Build an AI-First Company
5. McKinsey & Company: The Future of Work in America