The problem with new tech is that adopting it is full of uncertainty. Even if you’ve moved into a leadership position from data science, knowing how and when to implement AI adoption from a business perspective is challenging. You need to move forward, but you also have a lot more at stake than when you build models during your college or interning years.
So how can you get the ball rolling without the blind risk? Here are three practical signs that your business is ready for machine learning and AI integration.
You Value Your Humans
There’s a lot of talk about business value, return on investments, and cost efficiency, but honestly, if you don’t understand the importance of your human team, you aren’t ready for AI.
AI isn’t a magic bullet. It’s still woefully bad at completing many simple tasks, and it can’t automate its way through innovation. It’s only when you realize the true talents of your team can you integrate AI initiatives.
For starters, AI won’t replace humans. Not completely anyway. Humans still need to organize higher-level thinking, implement creativity, and build relationships. We can do that much better when we don’t have to muddle through big data processing because 1.) we have the time and 2.) those insights can feed into our decision making.
Your team is probably worried about being replaced by AI, so reassuring them that their jobs are either safe or about to evolve to something more interesting can help you ease those adoptions with the right mindset and company culture. If you go into it thinking you’ll eliminate your human workforce, you’re going to be sorely disappointed.
You Know What You Want
I’ll say it a second time. AI is not a magic bullet. You must have a specific question or particular purpose you’re trying to take care of with your data. Going into it to “see what insights come out” isn’t going to get you anywhere but decision paralysis.
Not all use cases are suitable for your organization. Knowing precisely where you plan to deploy and how that deployment will provide ROI is the most critical component of your plan. You must know what you want to fix.
Data is inherently neutral, and all the best data in the world with no direction won’t help. “We want to find insights about something/everything” won’t help. Taking the fortune teller approach to your data initiative will not help. Narrow down to a specific area instead.
For example, one of the most significant areas of AI influence is in customer service. You want to build a chatbot that can answer the questions that take up the bulk of your customer service agents’ time. That one model allows your human team to get more done more efficiently with measurable results. You can increase customer satisfaction through ultra-fast responses and free up your humans for higher-order tasks.
You Have the Data or Know Where to Get It
Machine learning is data-heavy and deep learning even more so. We have the data we need to launch these initiatives, but not all of it is available in a form we can actually use. If you’re a small business planning a deep learning initiative, but you don’t know how you’re going to get access to your data set, your plan is going nowhere.
If you have data, but you have no one responsible for cleaning it, your plan won’t get very far either. Deploying AI is more than just building the coolest model. You need a plan for how to acquire and clean data, so it’s ready to use.
If you have a data problem, you must get to the bottom of it before deploying AI. If your data is in silos, break those down. If you struggle with grasping a unified vision of your data across the board, get it worked out. If you have no point of contact and no system for future collection, it’s time to make the hire. Whatever your struggle, taking the time to work it out first is well worth the effort in the long run.
Build Your Culture Before AI Adoption
You know you’re ready when you value the work your current employees provide and want to find ways to facilitate their talents and strengths. You have a clear idea of what you need from your AI initiative, and you have the data to back that up. The last thing you need is a company culture that embraces failure and learns from mistakes to leverage the full power of your AI initiatives. Will you get it right on the first try? Probably not. But with these signs in place, you can pivot quickly to find what works.