A Human Industry in a World With Artificial Intelligence

June 2024

Most would agree that humans are the most intelligent beings to walk on this planet. The majority also likely recognizes that the human brain is the most powerful supercomputer in the world, but is that about to change? Will we become the second most intelligent species — behind artificial intelligence (AI)? If there is a modicum of credence to this possibility — be it now or a century from now — then how can we as a species survive and thrive in this age of AI?

Opportunities and Risks

There is no question that there are significant opportunities for us as humans and as an industry with AI. For example, consider some of the things we use in our daily lives: electricity, computers, cell phones, bottled water, etc. The raw materials that went into producing everything we have around us have always existed on this planet. It took humans thousands of years to figure out the right combination of raw materials to formulate the things we use every day. In contrast, Google’s DeepMind AI surfaced 2.2 million new materials in less than a year — and of those, 736 are in active development. The answer to some of the world's biggest problems could be in there. The first new antibiotic in over 60 years was discovered by AI in 2023.

AI is also accelerating at an exponential pace, and we are just scratching the surface when it comes to its potential — this is all good for us as individuals, as a society and especially as an industry.

Ultimately, we are a human-centered industry. It’s about people helping other people live sound financial lives. AI, like with any other technological advancement, is a tool. However, it is a very powerful tool that warrants focused study to ensure that it is being leveraged appropriately. There are risks and concerns that come with AI.

AI — specifically how AI systems arrive at the outputs they do — can be a “black box.” Therefore, it is imperative that the explosive growth of AI be balanced and tempered with core human values predicated on ethics and transparency. Transparency and explainability in AI outputs are crucial. If we cannot trust how an AI system got from point A to point B — in terms of output or prediction — it doesn’t really inspire us to trust in AI’s decision(s). We need to be able to understand how AI made its decision, and we need to ensure and be assured that fairness and transparency are incorporated in its decision-making process.

AI systems must be free of bias and embody our highest ethical standards on two levels — the AI model and the underlying data that this model leverages for training, and when operational. AI systems can sometimes draw unintended inferences or create causality with or without the underlying data itself being biased.

An example of this is when Amazon introduced an AI bot in its hiring process to vet incoming resumes for IT jobs; the AI bot then pushed select candidates in front of a human recruiter. The company used its own historical hiring data as part of the learning model. Since IT had unfortunately been a male-dominated field for a long time, the AI bot learned to screen out female applicants. Thus, applicants who included women’s sports or colleges (or other women’s groups) on their resumes were automatically excluded from consideration.

Fortunately, since humans were involved in this test pilot and recognized that only males were applying for these jobs, the company did not move forward with the trial program.

Data, Data, Data

AI is not a monolith — it is a sprawling field of study. Everything from facial recognition to robotics to generative AI — all of these are fields encompassed under AI. We’re surrounded by AI all day every day, from Google Maps to Netflix to social media. AI is predicated on one thing: data.

AI learns and is trained by a large volume of data: text and imagery. So, if you train an AI model to learn pictures of cats, at some point the AI is going to recognize images of cats and be able to discern these images from those of other animals. AI is only as good as the data it receives. It’s important for us to understand that AI cannot perform or function without data. AI is trained and it learns exactly like human toddlers learn; it needs information that it can clearly understand. If you don’t have good data, you’re going to have bad AI.

It is therefore important that humans and human judgment are at the center of AI activities.

Enabling AI Success

Whether we know it or not, we are in a pivotal moment with AI. Being ready with AI is a cultural transformation for our organizations, and that requires committed leadership. Things will change. Some functions of our jobs will advance to capitalize on the productivity boost we will see with generative AI. Any change — even change for the better — requires committed leadership to steward an organization through that change. We will evolve as an industry, and it is vital that we serve as champions for that change. Just as we learned to capitalize on technological advancements in our industry over the past 25 years — computers, the internet, email, mobile platforms — we have to commence the journey on how we can leverage AI and how we can educate our people to capitalize on AI. Automation resulting from AI might create concerns about job disruptions, but as I advise leaders — it’s unlikely that our jobs will be at risk due to AI because we are a human industry; rather, it is likely that our jobs will be at risk from a person who knows how to use AI. AI will become as ubiquitous as the internet is today, and we will be well served to learn how to successfully leverage it.

Conclusion

AI and robots are thinking machines that try to feel. Humans are feeling machines that try to think.

In our industry, we do an exemplary job teaching people tools of the trade and could do more of it using LOMA professional development. As leaders, we should also keep in focus developing soft skills within our firms: leadership, communication, collaboration, ethics, morals — the inherent traits and values that define us as humans and our industry — helping fellow human beings. These are things that AI will never be able to replicate, and they are characteristics that can’t be automated. Leadership can’t be automated. As leaders, by investing in our people and espousing soft skills development, we’re investing in the opportunities and downplaying mitigating risks associated with AI.