Embracing Artificial Intelligence
As the pace of artificial intelligence (AI) adoption accelerates, it’s important to prepare your company for the future. Learn how AI and machine learning are defined and how to responsibly integrate these technologies into your business strategy.
The term artificial intelligence (AI) might conjure up images of chess-playing super robots, high-tech driverless cars and smart homes with personalities. In reality, you probably interact with some form of artificial intelligence every day. From negotiating an online return via chat bot to asking your voice assistant about the weather or scrolling through social media feeds with content curated based on your interests, AI has become a nearly seamless part of our daily lives.
The true power of AI, however, is not in the technology itself, but in the willingness to tap into and embrace its possibilities.
The Evolution of AI
Although its beginnings can be traced back to the 1950s, AI adoption has only really started to ramp up in the last decade. Similar to the sudden burst of the dot-com bubble in the late 90s, the use of AI and machine learning techniques has grown exponentially in recent years, in tandem with the rapid pace of other tech innovations.
To understand the evolution of AI let’s compare it to that of photography: The advent of the first pinhole camera in the early 1800s led to black and white photography, then color photography, then digital photography, then motion pictures, and now to the dynamic, digital-first experiences we have today.
To put it into perspective, it took 200 years for innovators to replicate the simple function of the human eye through photography and now, nearly 70 years after Alan Turing and others first introduced AI to the world, we are still trying to replicate the function of the human brain. The human brain, in the order of magnitude, is more complex than a human eye. So, despite the existence of robotic vacuums and virtual assistants, AI capabilities are still in their infancy.
Defining AI, Machine Learning and Deep Learning
Before you can effectively integrate AI into your business strategy, it’s important to understand the fundamental differences between AI, machine learning and deep learning.
Artificial intelligence is a broad field of study which, at its core, strives to understand, replicate and exceed human intelligence over time. Machine learning is a subset within artificial intelligence, focused on building software to learn decision patterns from data. This can be through supervised learning — where machine learning models are fed both data inputs and outputs and are taught to model a behavior. Or through unsupervised learning — where machine learning models create data outpoints by predicting patterns on their own. And deep learning is rooted in understanding how the human brain makes decisions and predictions.
How to Get Started With AI
Building a strong AI strategy begins with four key steps:
If you’re starting from the ground up, it’s important to find the right talent. Although that might vary depending on your business goals, you don’t necessarily need a team of data scientists, AI practitioners or AI researchers to make a real impact. Start small and find one to three people who are truly passionate about AI to explore what it can do for your business.
Whether your team is big or small, the right infrastructure needs to be in place for them to be successful. Empower your team by investing in the right tools, data and computer infrastructure.
AI probably won’t—and shouldn’t—solve all of your business problems. Think strategically about where you want to invest AI, the same way you would about choosing assets for your investment portfolio. You need to invest not only in ideas that have low technology and execution risk and incremental impact but also in ideas that are disruptive to your business in the short run but have the potential to yield high reward in long run.
Consider the areas where AI can truly help transform your business. Think back to the internet bubble: Companies that simply lifted their brick-and-mortar businesses and put them online often struggled because they didn’t adapt their business model to the changing technology landscape. Don’t be shy to re-think and re-define how your business works in the age of AI. Because this is where AI will drive the most disruption and opportunity.
Adopting AI Responsibly
A critical component of your AI strategy should be governance. Businesses need to be transparent about how they are using AI and, as business leaders, should expect to be held accountable. By establishing clear governance around your AI strategy, you may be able to use AI and machine learning in more strategic, impactful and, most importantly, responsible ways.
Here are three ways to think about AI governance:
1. Data governance – Do your clients know how you’re using their data? Ensure data privacy policies are clear, transparent and reflect your intentions.
2. Model governance – Do you understand the biases your models might have? Establish a governance structure around your AI models to identify good versus bad biases.
3. Use-case governance – Do you know where you should and shouldn’t apply AI in your business? Think strategically and choose use cases that will be low risk, yet impactful.