The promise of AI – where is AI taking us?
So, where is this era of AI implementation headed and what does it mean for all industries? At this point, we’re looking at an industry of geopolitical influence, a technologically obvious decision that comes with a lot of responsibility, cost, and opportunity. As long as companies and product managers are aware of the risks, costs, and level of investment needed to properly care for an AI program, use it as a source of curiosity, and apply AI/ML to projects that create success early on and build from that knowledge, those that invest in AI will find themselves experiencing AI’s promise. This promise is rooted in quantifying prediction and optimization. For example, Highmark Inc. saved more than $260M in 2019 by using ML for fraud detection, GE helped its customers save over $1.6B with their predictive maintenance, and 35% of Amazon’s sales come from their recommendation engine.
When a third of your revenues are coming from an AI algorithm, there’s virtually no argument. Whatever investment you make in AI/ML, make sure you’re leveraging it to its maximum capacity by properly planning and strategizing, finding capable talent that’s aware of the space and potential dangers, and choosing the right scalable infrastructure to limit your refactoring.
As long as your AI/ML projects are directly married to outcomes that impact cost savings or revenue, you’ll likely experience success within your own career if you’re overseeing these projects. The recommendation of starting small, applying it to a clear business goal, tracking that goal, and showing off its effectiveness is a smart strategy because this chapter details the many areas of maintaining an AI program, as well as potential areas where it might experience hurdles. Justifying the time, investment in headcount, and infrastructure expenses will be challenging if you’re not able to communicate the strength and capabilities of AI to even your most hesitant executive.
This will also be important for your technical resources (data scientists, data engineers, and ML engineers) as well as for your business stakeholders. It’s one thing to know more about the ML algorithms you’ll be using or to get a few recommendations about how to best store your data, but you really won’t have the intimacy and fluency needed to truly be an agent of change within your organization if you don’t iterate with your own projects and grow your knowledge and intuition about what works best from there. We learn through iteration and we build confidence the more we complete a task successfully. This will be the case for you as a product manager as well.
In the previous example, GE offered cost savings to its customers, Highmark prevented future bottlenecks by predicting fraud, and Amazon grew its revenues through ML. When we think about the promise of AI and where it’s taking us, these examples drive the idea that this is the home of the latest industrial revolution. It’s not just something that will offer benefits to companies but to everyone all at once. The distribution of the benefits may not be completely equal because, ultimately, it’s the companies that are investing in this tech and they will look to experience the highest return on this investment first, but the point stands that consumers, as well as businesses, will experience benefits from AI.
