Robby the Robot, HAL 9000, androids, replicants, Cylons, the hosts in Westworld: through the 20th century and into the 21st, popular culture has been fascinated by the prospect of artificial intelligence and its implications for us as humans.
Slowly, we’ve seen artificial intelligence enter our daily lives: Siri will schedule your appointments and tell you a joke if you ask. Alexa turns on the moment you say her name, ready to add an item to your shopping list. But for every Siri, there’s a Tay, or some other story of AI not quite doing what it’s supposed to do.
When it comes to AI, we’re not quite at Asimovian levels yet; machines are capable of learning and are very good at the tasks we set them — predicting our buying behavior, for example — but we haven’t yet created an Artificial General Intelligence that’s smarter than we are. (Nor should we be in a rush to do so, a recent New Yorker article warns.)
AI in Real Life
That said, technology is finally catching up to science fiction’s flights of fancy. We’ve got voice-activated personal assistants in our homes and on our phones, and thanks to decades of research, self-driving cars are finally a reality. Chatbots greet us when we visit websites. AI looks at our buying patterns online and shows us ads tailored to our needs. Facebook tags our photos for us by identifying our friends’ faces.
Those are some of the more visible uses of AI; the use cases for artificial intelligence and machine learning in business are subtle, but in many cases, much more important.
AI enables precision medicine by using information about an individual — including genetic data and medical records — to prescribe health care for that specific person. AI can also fix machines before they break by using data to engage in predictive maintenance. AI can help predict and prevent customer churn by notifying a sales rep to step in when it looks like a customer is about to bail.
In the future, bias-less AI can help HR award pay raises to those who deserve them, or help warehouse managers keep workers’ productivity by monitoring hand movements via smart wristbands. The business implications of artificial intelligence are huge, so of course organizations are eager to get started with AI and machine learning.
Before a business can implement AI, however, they have to overcome some misunderstandings about what it is and how it can be applied to their organization’s needs. Below are some problems we see with customers who are starting their AI journey and how to address them.
Common Barriers to AI Adoption
- A lack of understanding of what AI and Machine Learning can do — AI, Machine Learning (ML), Robotic Process Automation, Augmented Intelligence… there are many flavors of artificial intelligence. Each is tailored to a specific kind of task — Machine Learning, for example, excels at detecting patterns and adapting to the changes in those patterns. Businesses that want to implement artificial intelligence must do their homework and learn about the different kinds of AI available and the use cases of each.
- Identifying business problems that AI/ML can solve is a challenge — Sometimes an organization may want to jump into AI with both feet simply because AI is the way of the future. But if the business wants to get into AI for the sake of AI alone, and doesn’t know what problems they need the technology to solve, that can be a problem. In these cases, organizations may choose a type of AI that isn’t compatible with their needs because they aren’t aware of what their needs are. In this case businesses should do an evaluation — on their own or with an outside group — to see what process are being done manually, and what inefficiencies can be addressed by AI.
- Confusion about data — Maybe a business doesn’t know if it has the supporting data for AI or ML efforts. Maybe that data isn’t in great shape. Maybe there isn’t enough data. AI is only as good as the data it has to work with, so before a business implements AI, the first step is an audit of a company’s data. Is your data clean and well-maintained? If not, the best AI in the world won’t be helpful to you.
- A lack of in-house skills to apply these techniques — AI can benefit all businesses, including organizations that don’t have an IT staff in place to maintain data and explain the different use case cases of AI. In fact, even companies with IT departments don’t have AI specialists onboard who can help them understand and implement AI and ML. That should not be a deterrent; those organizations should look externally for consultants who can help them implement best AI practices. That’s where we can help. Our experts can help you leverage AI for your business. Contact Omni to start learning what AI can do for your organization.