You have to crawl before you walk. It's an overused cliche, but a particularly relevant way to think about artificial intelligence (AI). AI algorithms start out like babies. They're capable of learning, but they need to be taught. How fast they learn and how well they perform depends in large part on their teachers, environment, and the quality of the data they take in.
Using AI in business is a hot topic right now, and machine learning is disrupting the way organizations think about process automation. But according to a 2017 report from the Boston Consulting Group, only about one in five companies has incorporated AI into some offerings or processes, and only one in 20 organizations uses AI extensively.
So far, the movement in AI is mostly what everyone's planning to do. But there are already plenty of high-profile examples of innovative companies using AI in business to improve data analytics, customize the customer experience, predict customer behavior, enhance cybersecurity, and boost productivity throughout almost every business function.
So, how do you know if your company is actually ready to make the leap?
1. You've Already Automated Your Processes
AI in business is about delegating decision-making to machines, which are capable of analyzing far more data much faster than the human brain. But if you're relying on humans to go collect data and feed it into the machine, you're not getting the productivity boost and real-time insights that make AI a good investment.
For example, say you run a hotel chain and want to price rooms more competitively. You don't want to have the cheapest rates in town (or on the online aggregator sites), but you don't want your competitors wooing away potential customers by charging $10 less. An AI solution could check all of your competitors' rates and automatically adjust pricing accordingly, but it's only useful if you've already automated the process of checking competitor rates. Otherwise, you would need people looking up the various rates for every hotel in each city where you operate one. Doing this process manually could take several days, by which time the data would be outdated, causing you to change rates based on what competitors were previously charging, not what they are charging. Automating this process would ensure rates are based on current data.
2. Your Data is Connected and Structured
Successfully training AI requires feeding it a lot of data from a variety of sources. But not just any data. It needs to be good, clean data. If you're collecting data in multiple, unconnected systems, your AI solution can receive incomplete or conflicting information.
Let's say you're an insurer using AI to predict when customers are about to jump ship. You have data about these customers in your CRM, marketing automation platform, claims and billing platforms, and your unified communications solution. If your AI only pulls data from one of these sources, it's making uninformed decisions. And if it's pulling from all of them but the information isn't standardized between them, it's likely getting conflicting data.
For example, a customer calls several times in one week. The speech analytics technology in your unified communications platform detects frustration in her voice and assumes she's a flight risk. But if AI could access the CRM, marketing automation platform, and claims software, it would know she's been a customer for 15 years, she regularly engages with your content marketing, and she recently had a tree fall on her house and is dealing with repairs—which is frustrating no matter how quickly you settle the claim. The AI might then determine that she isn't such a great flight risk after all.
The bottom line? Incomplete data yields incorrect assumptions.
While over 60 percent of those surveyed believe their company needs an AI strategy, only half of those companies actually had one.
3. You Have a Need and a Strategy for Using AI in Business
Using AI in business is exciting and potentially transformative. It's also a big investment of time and money, so you want it to focus on your biggest problems — and the problems it's most likely to actually solve. That means determining:
Which processes are most important to achieving your strategic business goals
Which processes could benefit most from more insightful decision-making
Which processes are ready for AI — meaning they're already automated and their relevant data has been standardized
There's a lot of strategy involved. Yet, while over 60 percent of those surveyed in the Boston Consulting Group study believe their company needs an AI strategy, only half of those companies actually had one.
4. Your Team is Ready to Train and Collaborate with AI
Adopting AI often requires new hires, organizational shifts, and cultural changes.
You need skilled data scientists who can build good algorithms, collect and structure the relevant data needed to train algorithms, and properly supervise that training. You also need employees who understand how to work with AI and build off one another's strengths. Managers need to know how to address employee concerns about losing their jobs to machines. In cases where that might be a genuine concern, employees need to be trained in order to keep their professional skills relevant.
Finally, you need technology partners who understand the data needs of AI and have the cloud-based systems and industry expertise to help you build information infrastructures for AI, whether you're ready to use it now or you're still getting up to speed.
If your organization falls into the second category, you have time to catch up. Take comfort in knowing that most of your competitors aren't running with the technology yet, either.
Need partners who can help your business learn to walk with AI? Speak with Vonage Business about how to prepare your telecommunications for those first steps.