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Reskilling in a World of Generative AI Systems

By May 8, 2023June 7th, 2023No Comments

It is hard to get away from postulations about how the new, powerful AI systems will impact how work gets done. Just look at the most recent strike from the Writers Guild of America. There is a lot of talk about machine learning and artificial intelligence (AI) and how it will take over jobs shortly. I believe we have arrived at a time where we need to think about and consider how ChatGPT, OpenAI, MS Bing, Google, etc. are impacting how jobs get done and which tasks these systems are better, faster, or more efficient in completing than humans as the genie is out of the bottle and we can no longer ignore it. As we enter this accelerated AI world, there are many considerations about how these new systems are implemented.

Myths About Artificial Intelligence

Before we start devising strategies on how to utilize this technology in your organization, let’s dispel some AI myths.

Myth #1: AI will take over jobs.

The first myth is about jobs. The big question that I get the most is: Are AI systems going to take over all the jobs? The quick answer is, No! Will it impact many jobs? The answer is, Yes! Think about AI as taking over certain tasks within job roles, such as researching case law for precedence in a pending lawsuit, creating profit and loss statements, perform market research, or performing rote tasks that have established processes. All of these can be done better, faster, and more efficiently by technology than a human. In the example of researching case law, a human conducting research to prepare for a lawsuit will take an average of weeks, whereas AI can conduct the research in hours as it pulls from its vast database.

This advanced technology means we will be dealing with job transformation in many industries.

AI is impacting a cross-section of job categories and will touch all levels of organizations. It is too early to know the extent of job displacement and other impacts; but assuming a utopian mindset, we can expect new job categories in the future that haven’t been invented yet. If you think about the last 30 years, I will say that technologies already have had a major impact; but going forward, the changes will be expedited and much more noticeable. To avoid a dystopian society, our machine-learning creations will need to work synergistically with humans to create more meaningful work. It will mean a shift from mundane or highly manual tasks to those that create value for customers in new and exciting ways. It is what we do with these new technologies and data sources that is the most important, especially when we aim to delight customers and stakeholders.

In a Wall Street Journal article, they described the future impact of advanced technology: “It affects every industry, not just manufacturing, logistics or transportation, and is unique in the degree to which it is affecting white-collar as well as blue-collar workers. We’re either witnessing the end of work as we know it or ‘merely’ a profound transformation of what jobs humans do. Either way, the economic and political ramifications are likely to be on par with the impact of the past 50 years of outsourcing and globalization.”

The final takeaway from this myth is that machines will not take over all our jobs, at least not completely. But how we spend our time will most certainly be changing in this new environment.

Myth #2: AI can make rational decisions.

This brings me to the second myth in this evolution, which is the extent to which artificial intelligence can rationalize and make decisions. Let’s be clear, as fantastic as the algorithms are now and soon, AI is not cognizant in the same way as a human is. This means while AI can digest tons of data in a short time, the software is looking for patterns, which is something the software is better at. Even though AI programs may seem to be thinking, they are just processing algorithms. That is, the AI software cannot go beyond what the algorithm can do to perform additional tasks unless the algorithm and tasks have been connected somehow. As an example of this, AI software cannot make dinner. Think about how many steps there are and what could go wrong in the preparation. There are small but important adjustments in this task that cannot be easily programmed. These types of decisions and needed adjustments are still in the realm of human capacity.

In your organization, I am sure you can think of many tasks that cannot be solved easily with an algorithm. I worked with a client recently on this journey to implement intelligent software. We were creating a form that would capture customer data all in one place so that employees could more easily quote on the creation of specialized products. Previously, employees were capturing this information via their business email, and it wasn’t in a standardized format or stored anywhere collectively. The process was becoming unruly, and the time spent in gathering this data manually took a large percentage of their time away from value-added activities in working with their customers.  The department was not able to sustain its growth plans without hiring a lot more employees in keeping with the current process. Their customers valued the personal relationships they built with employees, and chasing down emails was taking away from key relationship building as conversations were much more transactional in nature. In other words, by creating an automated form, that time was given back to the employees. The automated data collection form streamlined the company’s process and reduced the need for so much email communication. Over time, the data collected can allow for more meaningful conversations around strategic opportunities. The data also can reveal competitive landscape trends that can help their customers grow their businesses in high-demand areas.

No system can build these relationships with customers. Systems can reveal areas of opportunity for our businesses, but we are the ones who need to act on those opportunities. Data collection is certainly key, but the real value in the future is what you do strategically with that knowledge. That is what differentiates humans from machines. The machines collect the data and can recognize patterns. In turn, humans will need to make sense of this information to decide which direction or strategies to put in place to be competitive.

I think there are opportunities to partner with departments like human resources, training, and organizational development to develop strategies for transitioning employees and preparing them for the skills of the future. In organizations I’ve researched that have started implementing AI solutions, their biggest lesson learned is that employee transition and reskilling need to happen earlier than you think—and before the actual transition happens. Employees need to be involved in creating this new reality and understand what makes sense for the machines to do and what roles will remain with them.

If you are a leader in Human Resources (HR) or other functions that support employees, you have an opportunity to be more strategic by moving away from processes and tasks yourselves. Our organizations are going to need HR support now more than ever to help pull through these transformations successfully. HR can help our leaders identify the skills and competencies needed ahead of time. This will allow us to be transparent with our employees and clear about the future needs of the organization.

Coexisting with Artificial Intelligence

We are already in the midst of a transformation to artificial intelligence and machine learning for creating better customer experiences. Think about your Netflix® or Amazon experiences. I often wonder how those applications know exactly what to offer up to me. By leveraging the Internet for shopping or searching for the right show to watch on a Sunday evening, you are creating a data trail that starts to tell a story about you and what you like or not. If you look at companies at the forefront of this transformation, you will find Facebook®, Amazon, Netflix, and Google™ (also referred to as FANG). Think about the amount of data they have collected from you over the last few years. They have refined their AI solutions (they have a network of AI solutions working together seamlessly in the background) to the point that they are quite sophisticated. But this did not happen overnight; they’ve been testing and learning what works overtime. They’ve been willing to take risks on a global scale to learn. I am not saying that we, as leaders, will be working with AI at that level of sophistication, but it is important to note that AI is here, and we need to figure out how to coexist with it.

I point this out because those organizations that embrace AI and start to strategically think about best uses will have a competitive advantage over those who lag or wait. So, the question is, where do we start? I would suggest starting small and testing specific processes that are ideal for automation or utilizing generative AI like ChatGPT. We all have opportunities within our organizations to create a better customer experience or to improve the backend processes that fall under operations, finance, and administration. The key is to focus on outcomes. How will the customer or stakeholder experience be enhanced by automating this process or procedure? Alternatively, think about areas that could be automated with AI or bots that could capture important customer data to better reveal insights about customers. Just like my earlier example about capturing customer requests automatically versus manually, automating data collection processes allows you and your employees to spend more of your valuable time and effort on building relationships with customers at a deeper level than before. Those leaders who have already started down this path are beginning to see employee engagement surge as they perform more meaningful work. This transition is powerful if done right.

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