How to Upskill in the Era of Generative AI | Extreme Networks

Technological change has been central to some of the most significant upheavals in human history. From the industrial revolution to the digital age, each wave of innovation has transformed the way we work. As discussed in Justin Hurst's recent blog, it's crucial to plan for adapting to this future. A hypothetical network engineer like Samantha from that blog will have lived through the seismic threat to the workplace that generative AI brought about. As we can see repeatedly in the media today, there are fears that GenAI will replace all jobs, and we have reports of an exaggerated change curve with an emphasis on the shock, denial, anger, and depression phases.

However, generative AI, or GenAI, should be seen as a powerful tool for empowerment. By upskilling and educating our workforce, we can leverage these new technologies to enhance existing roles and boost productivity, ensuring that human contributions remain at the forefront of this evolution.

AI and ML today

The current resurgence of AI and machine learning tools is a relatively recent revolution. Most advances have only been made available to the mainstream workforce in the last decade and have primarily focused on simple automation. Tasks such as selecting a button on a web page, providing a chatbot response, or recommending an item to buy or place to go were the first wave of AI/ML that many interacted with. These basic applications streamlined roles by automating monotonous, repetitive tasks, and enhancing efficiency.

Contrary to initial fears, companies did not use early AI to replace workforces in roles like data entry and call handling, but instead up-skilled them to take on higher-value tasks. They now provide human feedback when system limits are reached and train these systems to reduce errors. Despite concerns about mass unemployment, the global workforce has actually grown by nearly half a billion people since the onset of the pandemic, illustrating the positive impact of AI and ML on employment.

All these changes were a result of machine learning improving our way of working. However, in the "After Transformer" (AT) Era, the focus has shifted to generative AI. Generative AI is a type of artificial intelligence that specializes in creating "new" content-such as text, images, videos, and audio- by learning from massive data sets. These AI models take vast datasets, including most of the text, images, and code from the public internet, and identify patterns and structures. When given prompts, they generate new data that resembles the input they were trained on. While this might sound straightforward, like a supercharged search engine that understands your intent, it's actually a completely different technology that even researchers are still working to understand fully. It's crucial to emphasize that GenAI is currently best suited to augment what workers do and why it is a tool that should be embraced, not feared.

The impact on the workforce

As Generative AI begins to automate routine tasks, there will be a growing need for workers to upskill or reskill in order to stay competitive. McKinsey projections suggest that by 2030, up to 30% of current work hours could be automated by generative AI, necessitating a shift in skills. As we become more adept at using and understanding GenAI, it will drive the creation of new roles, capitalizing on the surge in efficiency gained from fast access to knowledge and its transformation into language, code, or media. During this transition, it is crucial to acquaint the workforce with GenAI solutions that bolster productivity and efficiency, akin to how we've adopted other technological advancements and automation.

The evolution of workplace roles will continue, grounded in the inherent nature of humanity to improve, but it will require a structured approach. This approach should be tailored to each workplace and embrace the Augment, Replace, and Creation (ARC) framework to bring the individual and team along through the transition.

Step 1 - Preparation: Begin by educating the workforce on the current AI landscape, encompassing both machine learning and artificial intelligence techniques, including generative AI, with an emphasis on upskilling. The goal is to cultivate a workforce adept at discerning the distinct outcomes produced by GenAI and ML, enabling them to strategically integrate GenAI into their roles and objectives.

Step 2-Adaptation: After overcoming any initial skepticism, continuous support is essential to help the workforce customize the system to enhance both individual and collective outcomes. This involves tailoring solutions to precise requirements and refining the context and prompts to elevate the output's quality and consistency and pinpoint anomalies and errors. Through adaptation, we can build trust in the systems and begin to extract value by critically assessing and refining the results.

Step 3 - Integration: The culmination of this process aligns with the acceptance phase of the change cycle. Analogous to a Victorian-era individual marvelling at the advent of clean water or a Generation X member embracing infinite global communications, the assimilation of these tools will become second nature.

Employing these tools intuitively, akin to searching for a restaurant or navigating the quickest route to a destination, the workforce will seamlessly integrate GenAI into the workplace, thereby amplifying creativity, efficiency, and productivity across all domains.

Preparing for the future

Integrating generative AI processes is crucial for organizations to stay competitive in today's environment. This involves strategically reviewing existing systems to incorporate GenAI's innovative capabilities. By embracing these benefits early on, companies can improve decision-making, automate routine tasks, and foster a culture of innovation, setting the stage for reaping the rewards of increased value.

Start with pilot projects that can demonstrate the value of GenAI, then gradually expand its application across different departments. This will ensure a smooth transition that aligns with organizational goals while empowering the workforce to know they are driving AI and not the other way around. Once your organization is AI-ready, it is time to embrace a comprehensive upskilling initiative. Employees must be trained not only in the technical aspects of AI but also in context and prompt engineering to interact with AI systems effectively. This training should empower them to discern when and where there is value to input and extract improvement from GenAI systems. Upskilling programs must be tailored to various organizational roles, ensuring that every team member, from the front-line staff to the executive team, can leverage GenAI for improved performance and innovation.

However, a word of caution is necessary. Generative AI is a powerful tool, and everyone in the organization must be aware of the potential dangers and risks associated with using, creating, or deploying these solutions. The rapid introduction of regulations and safeguards surrounding the use of AI, such as the European AI Act or Executive Order 14110, will start to put constraints on the applications of AI. Initially designed for the pre-transformer era, these safeguards emphasized managing the risks of the outcome of using an AI system on the provider and user to ensure that they do not impact society adversely. Enterprises need to know and record the sources used to train their systems. If a solution uses Retrieval-Augmented Generation(RAG) to enhance a publicly available LMM, enterprises need to show that they have thought about the human in the loop - and the impact on broader society. As these regulations proliferate and take hold, training personnel on responsible AI will be essential, just as we do for other key corporate initiatives such as Diversity, Equality, and Inclusion (DEI) or Environmental, Social, and Governance (ESG).

Generative AI holds tremendous transformative potential, rapidly advancing even as it may be reaching limits in creativity due to restrictions on training data and the models' capacity to improve. The importance of upskilling the workforce to leverage this technology effectively cannot be overstated. Like every technological revolution, GenAI should be viewed as a tool that augments human work rather than replacing it. By adopting a three-phase upskilling strategy - Preparation, Adaptation, and Integration - organizations can ensure their workforce is well-prepared to smoothly transition to this new era, maximizing the benefits and potential of AI.

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