AI (broadly defined to include data science, machine learning, and cognitive computing) can be confusing for decision makers. It promises great rewards but also great risks. AI is seemingly everywhere but it is also difficult to understand. It is touted as the way of the future but there is little experienced talent available. How does a business navigate this labyrinth? How does a business minimize risk and avoid mistakes?
Through our experience of applying AI within diverse businesses, we find the biggest mistake businesses make about AI to be quite basic: they start thinking about AI too late. This means businesses are often ill-prepared and not set up for success for when the time comes to invest in AI.
“By failing to prepare, you are preparing to fail.” ― Benjamin Franklin
I say this because most businesses know that AI already impacts or will impact their customers, industry, and themselves. It is well recognized that AI will be incredibly important to the future of the business.
Yet, it’s difficult to identify the exact implications, the details, of AI’s impact and how to respond and innovate. Lack of knowledge, resource constraints, organizational inertia, and lack of experienced talent compounds this problem where it becomes natural to simply delay “thinking about AI.” Fear of the unknown and of change don’t help! One of the most common ways we work with our customers is to help them work out how AI impacts their business - is it a threat or an opportunity? Typically, it’s both. Then, the next step is to figure out a roadmap to capitalize on the opportunity and address the threats.
I am not advocating that all businesses must invest in AI right away. Don't need to start hiring new employees or buying new software or services. Instead, I am proposing that companies take the time now, with some urgency, to think about how AI impacts their customers, business, and industry. And then, if necessary, take action to ensure that the business is setup for long-term success in an AI-enabled world.
The biggest danger of delay is that eventually when the business is ready to invest in AI, the necessary prerequisites and key success factors are missing - talent, culture, and especially, data. This further delays “AI development” and poses a significant competitive risk.
By preparing, the business can ensure it’s in control of its future. While this has always been true in business, complacency is especially dangerous when is comes to AI. AI requires data and that takes time.
Thinking about AI early on can provide a valuable strategic lead when it comes to collecting data for the future.
We live in the "age of data".
Data has always been useful in making critical decisions and driving successful business outcomes. Now, data has become the most valuable commodity in the world because by leveraging AI, data can be used to improve and automate decisions and processes.
This makes data collection critical. But, it can be challenging and can take time to get right: what data should be collected, how should it be stored securely, and what competencies and processes are needed to leverage the data.
Collecting just any data is not enough. Data needs to be differentiated, high quality, labeled, and meaningful enough to be useful. It can be easy to dismiss ethics of data collection but businesses worthy of our trust will take great care of their customers’ data.
There are other challenges too. Data collection often runs into a chicken-and-an-egg problem where you need some data (or AI) to gather other data that you need. This requires developing and launching a “basic” or less-than-ideal solution early on. This is an approach Facebook and Google use quite effectively and we often recommend as well.
There is added urgency because data can become exponentially valuable. Value of data compounds over time. Data often has its own gravity as well: data attracts even more data. For example, collecting data allowed Google to build a better search engine, leading to higher usage. This led to even greater data collection, and so on. A virtuous cycle of data collection, better product, and higher usage developed. This led to a significant competitive advantage and accumulation of value.
This exponential economics of data (compounding, gravity, and virtuous cycle) also has a downside: the later you start, the farther behind you fall. If a competitor started six months before you, just as an example, they may effectively have a nine month headstart.
It takes time for a business to learn how to think about AI, build the right culture, gather necessary data, earn trust of customers, and then actually build and launch the AI. Building a data-driven, AI-powered virtuous cycle takes a long time. However, average lifespan of businesses is declining. Businesses have less and less time to adapt to changes that impact their industries. Time, gained through preparation becomes a key competitive advantage.
This is why I believe there is no time like the present for businesses to start thinking about AI. Even if businesses don’t invest in developing or implementing this technology right now, it is critical that they learn to think about this, start painting a vision, identify a roadmap, and at a bare minimum, start collecting the necessary data.
Two years from now, when you are ready to start your AI journey, you don’t want to find out that you should have spent a few hours collecting some data two years ago.
"A business doesn’t want to run out of time. "
Don’t make the biggest, easiest, and most common mistake businesses make with AI. Start preparing, now.
