According to the World Economic Forum Future of Jobs Report, 85 million jobs will be replaced by AI by the year 2025. The same report states that 97 million new jobs will be created by 2025 due to AI. There is not an industry that may not be impacted. This profound impact of AI on various industries, jobs, and roles both positively and negatively, was a key topic at the recent AI x Future of Work Summit. During this event, a panel featuring Athena Karp, CEO & Founder at HiredScore, Obed Louissant, Chief People Officer & Senior Vice President at Aptiv, Bill Schaninger, Senior Partner at Modern Executive Partners, and Lauren Weber, Reporter at The Wall Street Journal, offered in-depth insights into how AI is shaping the global workforce. Their discussion provided a real-world perspective on the forecasts of the World Economic Forum's report, emphasizing the practical implications of AI's role in future employment trends. 

Here's a recap of their perspectives: 

1. AI is Bound to Influence Every Job, Role, and How Jobs are Planned

- Enhancing workforce efficiency at the C-suite level: 

“Where do we see AI particularly impacting jobs? Everywhere!” - Obed Louissant, Chief People Officer & Senior Vice President at Aptiv 

Starting as a Chief Talent Officer five years ago, Obed embarked on a journey to enhance workforce efficiency. It took a considerable 18 to 24 months just to organize the data effectively. This effort enabled his HR org to differentiate between various job roles, particularly in understanding the implications of talent attrition. For instance, losing an employee in a critical role, which is costly and challenging to fill, significantly differs from turnover in a role where there's an excess of capacity. 

Utilizing AI tools and sophisticated data analytics, they could discern these differences, leading to smarter workforce strategies. This approach has helped in retaining highly skilled, hard-to-find talent more effectively, altering his daily responsibilities and influencing the kind of talent they recruit. 

- Enhancing workforce efficiency at the Managerial level:

‍“If I’m a manager, one of the most challenging things that our managers go through is just doing performance evaluations or having a career conversation with somebody. Managers today spend an awful amount of time collecting data from multiple sources to then be able to have a conversation with somebody for 30 mins, but they may spend 6 hours to pull and aggregate the data together. What if the world pivoted and you could use tools to aggregate the data for the managers, then the time is actually spent on discussing

career progression for an individual to do better. So, I do think it is affecting every job, it is just a matter of time and magnitude.” - Obed Louissant 

- Automating parts of the job that are routinely performed, opening up bandwidth for employees to handle more challenging and critical tasks: 

“If every job gets transformed, what happens to the job that’s left?” - Lauren Weber, Reporter at The Wall Street Journal 

As AI and related technologies increasingly take over routine tasks, it's evident that most jobs will undergo significant transformation. 

Lauren mentioned a relevant example to consider is the case of call center employees. This sector is frequently discussed due to its quantifiable nature and the extensive academic research surrounding it. The adoption of AI in call centers can automate numerous tasks, leading to a reduction in the time spent on routine calls. However, this gives rise to a critical question: what becomes of the job that remains post-automation? 

In this scenario, it's likely that call center workers will handle more complex tasks. They might deal with emotionally charged, angry, or upset callers, which are challenges less suited to AI. This shift towards more demanding interactions raises several important considerations. Employers must then think about how to enhance the quality of the remaining job roles. As routine tasks are automated, the human element of the job could become more critical yet also more challenging. It is likely that the workers won’t get paid more for doing a more challenging job so it is the employer’s responsibility at the end of the day to ensure that these roles remain rewarding and manageable. 

AI's impact on jobs is widespread, affecting everything from strategic talent management to everyday managerial tasks. The magnitude of this change is just beginning to unfold. 

2. Don’t be Afraid of Generative AI! It has the Potential to Make Jobs More Manageable (If the quality of data inputs is good!) - Bill Schaninger 

Five years ago, pre-COVID, the focus was heavily on the impact of automation and the rise of robots on the workforce and workplace. Prior to that, attention was centered on machine learning and quantitative analysis, exploring their transformative potential. Going back 23 years, the Y2K scare dominated discussions, with widespread concern that the turn of the millennium would bring technological chaos. 

A recurring theme is the tendency to fixate on catastrophic scenarios that threaten jobs. In a recent BBC interview, the question of whether companies should compensate employees displaced by robots was raised, reflecting a common but narrow perspective. This highlights how innovation and progress often lead to fear and predictions of doom. However, there's an essential aspect to consider, especially now: the quality of input and output in our systems. Poor knowledge management and routine information handling can result in flawed models and tests, including personalized onboarding processes. Previously, a strong company culture could mitigate operational weaknesses. But with the shift to remote work and reduced in-person interactions, the opportunity to organically absorb company culture and knowledge has diminished.

In this landscape, generative AI presents a compelling use case. It can offer contextual advice tailored to an individual's level of understanding, but its effectiveness depends on the underlying knowledge base. Thus, generative AI has the potential to make many jobs more manageable, efficient, and of higher quality, provided it is built upon a robust foundation of knowledge. 

3. Generational Divide, Worker Empowerment, Labor Movements Will Play a Key Role in the Adoption and Implementation of AI in the Workplace 

Historical responses to technological advancements have always been a mix of excitement and fear. When research on generative AI's impact on labor was in its infancy, there was a limited understanding of its potential effects. Now, with a surge in academic and research papers on the topic, the landscape has become dense and ever-evolving. 

- Generational divide: “Younger people will grow up playing with this technology and they won’t be afraid of it the same way older people will be. Their familiarity (with the tech) is just a totally different animal than mine.” - Lauren Weber 

As per a 4,000-person survey by Salesforce, 70% of Gen Z uses new generative AI technologies. Also found: 68% of those who haven’t tried generative AI are Gen X or boomers. The generative AI divide is largely a generational divide. Non-believers tend to be older and more cautious around new technology. 68% of non-users are Gen X or baby boomers, and almost nine out of 10 non-users don’t see how generative AI will impact their life, and 40% says they aren’t familiar with the technology. 

- Resistance to new technologies: A key insight in understanding technology adoption is recognizing its inherently conflict-ridden and negotiated nature. Contrary to the notion of swift and complete job market overhauls by new technologies, the reality is much slower, marked by human resistance, negotiation, and fear. 

- Worker empowerment and labor movements: In the current socio-economic climate, where workers' voices are increasingly prominent, as seen in various labor movements, this empowerment is likely to significantly influence how AI technologies are integrated into workplaces. The role of workforce empowerment in shaping the integration and transformation of job roles by AI is a critical aspect of this narrative. 

4. Buying Versus Building Your own AI Solutions

Things organizations should consider when making the decision to buy versus build their own AI solutions: 

- Balancing Open Source Utilization with Business Needs: Open source technologies can sometimes lead to a tendency to do ‘just enough’ because they are freely available. However, it's crucial for organizations to identify what they must excel in. If understanding the deep technical aspects isn't essential for your business, using free open-source tools makes sense. But if your unique business edge relies on proprietary elements, then relying too much on open source may not be wise. 

- Investment in High-Level Capabilities vs. Satisfactory Solutions: 

“What do you have to be great at? If navigating the algorithm, what’s underneath it and the access to it is not what you have to be great at, then use what’s free and make it work. The extent to which you need it to be proprietary because you have some special sauce, then I’d be cautious about it. You only have so many investment dollars.” - Bill Schaninger, Senior Partner at Modern Executive Partners 

Day in and day out, organizations make strategic decisions about investing in talent. Not every role in your company needs to be filled by a top expert. Some positions require world-class talent due to their strategic importance, while others can be managed with competent but not necessarily exceptional skills. 

In a similar vein, when it comes to building versus buying AI, the key is balancing where to invest in high-level capabilities and where to settle for satisfactory solutions. This decision should align with your organization's strategic priorities, considering the limited resources like budget and manpower. You can't excel in everything, so it's about choosing the right areas to focus on for your company's success. 

To delve further into AI’s evolving role in our global workforce dynamics, watch the full discussion here.