Small and midsize businesses (SMBs) are adopting artificial intelligence faster than ever according to a recent survey we conducted at Vistage. Our report, based on input from 1,467 leaders of SMBs, revealed that nearly one-third (29.5percent) of CEOs felt that AI is among the technologies that will have the greatest impact on their business in the next year.
That might even be an understatement. The use cases of AI are quickly multiplying as more companies are turning to the technology to automate rote tasks, manage supply chains, personalize customer experiences, improve go-to-market strategies, increase operational efficiencies and more. In some cases, businesses are already using AI that is embedded in their business systems without even knowing it.
As a leader, you might feel compelled to rush into AI so your company can start reaping these benefits. But that approach poses both financial risks and technical risks. Instead, begin with brass tacks. Focus on building a strong knowledge base about how AI works, and how you can realistically integrate it into your business. If you're just getting started, here are the top five things you should know before making the investment.
1. The definition of AI
What, exactly, is artificial intelligence? It's the theory and development of computers to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. AI makes it possible for machines to process massive amounts of data in order to identify patterns, glean insights and take action based on those insights. A simple example is the Nest home-monitoring device, which basically gathers data about the conditions in your home and takes action based on that data.
2. The difference between AI and BI
AI is sometimes confused with business intelligence (BI), but they're not the same. BI looks at data and tells you what happened in the past and what the future may be based on those patterns. One company I spoke with used AI to identify patterns in questions their customer service agents received. Based on historical questions they received, they can predict the next question their customers will have, and can proactively address those questions. AI looks at data -- like question history -- and tells you why things happened the way they did, what will happen in the future and recommends what you can do about it.
With BI, you can determine which customers you want to look at. With AI, you can begin to understand how you want to interact with those customers. AI plus BI equals the customer experience.
3. The continuum of descriptive, predictive, and prescriptive analytics
To draw a clearer distinction between AI and BI, it can be helpful to think of them in the context of descriptive analytics, predictive analytics, and prescriptive analytics.
Descriptive analytics looks at Key Performance Indicators and answers the question, "What happened?" For example, if your company had great sales results last quarter, descriptive analytics could tell you what happened. Predictive analytics uses business intelligence to answer the question, "What will happen going forward?" For example, if your company lost a lot of customers last quarter, predictive analytics could help you identify other customers that you're at risk to lose so you can focus on retaining them. Prescriptive analytics uses artificial intelligence to answer the question, "What would happen if I took a particular action?" For example, if your company wanted to find out what would happen if you offered a different pricing model to at-risk customers, prescriptive analytics could tell you whether you would actually retain those customers, and how many.
4. The right questions to ask.
Before you jump into AI, ask yourself: Why do you want to bring AI into your company? When and where do you want to implement it? How will you implement it? What does success look like for you? What do you think AI will do for your business? Who knows your system internally? How will AI help you respond better than your competitors? It's wise to address these questions before you take action with AI.
5. The expected return on your investment.
Most small businesses have limited IT budgets and limited technical expertise. If that's true for your company, don't make AI-related purchases until you can make a direct connection between your investment and return. For example, consider sales and marketing applications that can bring in more revenue by improving how you engage prospects or work with customers. Or look at Internet of Things solutions that can help you save money on heating, cooling or lighting in your property. In other words, be sensible and strategic about what you spend your money on.