How Not to Get Started Implementing AI
We know. Your current way of doing things is just fine. It’s making money, and all this talk about AI is hype, or so you’ve heard. You aren’t even sure what people mean when they say AI, and you don’t have time to learn what’s going on. Lots of people will go under with the Fourth Industrial Revolution, but your business is AI proof. Famous last words. If Blockbuster, Kodak, and Gillette aren’t safe from disruption, there’s a good chance you won’t be either. However, if you’re sure that you want to dig in and ride out the AI hype, here’s how to start implementing AI.
[Related Article: Why Uncertainty in AI is Good for Business]
Do Not Learn About It
You’ve already stated that you don’t have time to find out what’s going on in AI, so you definitely shouldn’t take courses like Microsoft’s Professional Certification with edX.org or anything from Coursera’s catalog.
Once you go down that road, you might find out all the ways that AI can help transform your customer service model to something more consistent and less draining for your agents. You could discover how AI is revolutionizing the contract process, making them more efficient and faster. You may even find out that AI is revolutionizing the way you get insights from big data.
Do Not Assess Your Current Systems
Legacy systems are there for a reason. They are efficient and don’t allow new ideas to derail the process. Your staff spends most of their time maintaining your legacy systems and trying to communicate across the data silos that keep your business “agile.” Your legacy system is making money, and that’s all that matters.
If you do try to assess the current system, you might find out just how much money you’re spending maintaining systems that don’t communicate and that can’t handle the type of data processing you’ll need to tackle. You could find out the true cost of your data silos. You might even find out how AI could disrupt your legacy system for good, freeing up your data science teams for better projects and allowing true communication to reign supreme across departments.
Don’t Hire Good Talent or Invest in Training
Implementing AI projects will take expertise. Your current IT team may not be trained in AI engineering or in the type of computer programming that writes those complex deep learning algorithms. They’re too busy maintaining your legacy system to train, and you aren’t going to bring in a new person just to handle that.
If you took a look at your department, you might find out that your existing data science team is sick of troubleshooting. Morale is low, and the team is tired of hearing no at every turn. Their skills are going to waste, and there is no pathway for growth. On the other hand, you may also find out that they’re missing a critical piece or a position on the team — data engineering, for example — that streamlines the process and create faster rollouts. Training existing teams and hiring top talent for missing positions could improve your bottom line.
Definitely Do Not Measure
If your higher-ups have given the go-ahead for implementing AI projects (even small ones), it’s not the end of the world. The biggest thing to do right now is to avoid building measurable benchmarks that would allow you to measure the efficacy of your project. Just let that little seed die.
If you did measure, you might find out that AI helps your team hit business value benchmarks faster and with more consistency. You may also find out just how easy it is to begin implementing AI with your human teams to inform and enhance your team’s talents. If you’ve identified a realistic problem that AI might solve with implementation, measuring could let you know just how painful that problem was before.
Getting Started Is The Right Thing to Do
All kidding aside, many of the excuses you have for not starting a small AI project won’t hold up under scrutiny. AI isn’t magic. It’s a clear system that can help support your human team by offering data analysis not possible to do by hand and automation for repetitive tasks taking up so much of your team’s time. You don’t have to start perfectly or launch a full-scale project, but you do need to get started.
[Related Article: Is Your Business Ready for AI Adoption? How to Know for Sure]
AI could be worth $150 trillion by 2025, so getting started helps ensure you won’t be behind when the dust settles. Make sure your organization is ready for all the ways AI will improve your company’s position and begin considering how you can start implementing your own AI projects.
Read more data science articles on OpenDataScience.com, including tutorials and guides from beginner to advanced levels! Subscribe to our weekly newsletter here and receive the latest news every Thursday.
