We’re inundated with headlines about the power of artificial intelligence (AI) these days. AI is everywhere and most businesses know they will need to adopt it soon, if they haven’t already. A recent study from Gartner shows that 37% of organizations have implemented AI in some form. That’s a 270% increase in the past four years!
I suspect that there are many CIOs feeling the pressure from their boards or C-suite peers to implement AI-related technologies. But even with the increase in the number of companies implementing AI doesn’t mean that every organization should hop aboard the AI train right now.
AI isn’t something that you can just pop out of the box and have it work effectively. Like most technologies, it requires a little preparation. Trying to implement AI in an organization that isn’t ready is a disaster waiting to happen.
How do you know if your organization isn’t ready for AI? Look for these signs.
Your processes are undocumented or unclear
You can’t just “turn on” AI and expect it to magically – and instantly – solve problems or take on those tedious, repetitive manual tasks in your organization. The algorithms that power AI can only do what they’re told to do. This means that AI needs processes – and not just any processes. Your processes need to be clear, well-defined, and well-documented.
Organizations that are ready for AI have already identified and eliminated any convoluted parts of their processes. They’ve discovered and corrected gaps in process definitions. They’ve addressed process issues that caused human intervention and eliminated any waste or bottlenecks. They’ve already documented and polished their processes so that when they are ready to automate it, that automation can be implemented easily and quickly.
Your data is a mess
AI-related technologies rely on having data – lots and lots of data. And not just any data but accurate, reliable, relevant, and trustworthy data. One of the ways that the use of AI can be effective is that the algorithms that power AI have relevant and accurate data, in the proper context, on which to take action. If your company has taken a blasé approach to data capture and quality, this is a big red flag for AI adoption. Bad data is one of the main reasons that many AI projects fail.
It’s crucial that an organization has a robust approach to data capture, management, and quality before implementing AI. CIOs and IT leaders should investigate what data they already have, why and how the data is collected, and how that data is maintained.
Like any other technology-related initiative, bad data provided to AI only means bad data – and actions – out. Trying to adopt AI using unreliable data will only result in bad outcomes – only those bad outcomes will happen almost immediately.
Your team is resistant
Even though AI is all the rage, there are many IT professionals who are fearful that AI will automate them right out of a job. Implementing AI is an initiative that requires a purposeful approach to organizational change. If one member of the IT organization is resistant, the entire implementation could be at risk.
Leaders must help their teams understand that implementing AI does not indicate loss of jobs, but that some of the tedious, repetitive work done by people are better suited for AI – freeing up people to do the things that people do best – innovate, create, think, and plan. Associates should be provided with training to grow their skillsets for use in an AI-enabled world.
Communication and transparency across all levels are key for successful AI adoption. It’s important that those who will be working with AI are involved in the implementation process as early as possible. Team members will be more likely to engage and support the initiative when they have all the information upfront about how AI will be used.
There’s no business case for AI
The use of AI is trendy and exciting, but as I’ve pointed out already, AI is not a magic bullet.
It requires an investment of time and money. For an organization to realize the value in AI and for it to be implemented and managed correctly, AI implementation must solve problems that result in improved business outcomes. This is the only way AI is going to provide any ROI.
Yes, there are some eye-catching headlines around the use of AI out there. Don’t chase them. Look for the problems and opportunities in your company where AI use would help. Look for cases where the use of AI meets a need of your business or enables the achievement of a valuable business outcome. No, it may not be the most exciting use of AI – but it will be the most valuable.
You’re afraid to experiment
This is a real fear, especially among IT teams. You are too afraid of failing, so afraid of costing the business money and being unable to show any ROI, that you are paralyzed from experimenting with making AI work in your organization.
There are going to be stumbles and pitfalls along the way with AI adoption. They are unavoidable and inevitable, just like with any new or emerging technology. The key is to fail fast and learn so you can innovate, evolve and continue moving forward. You have to experiment to determine the right data infrastructure, the volume, and quality of the data, and getting the right people into the right roles. Adjustments will be necessary. AI will evolve and your business will evolve with it. Bottom line: be prepared to make those mistakes, find the learning opportunities and share those learnings across the rest of the business.
AI is not a passing fad. It’s only going to become more embedded in our world. So while there may be pressure to begin implementing AI right now, don’t make the mistake of getting in a race you’re not prepared for – it’s the fastest way to lose.
It’s not about being one of the first organizations to use AI. It’s about using AI correctly for your organization. Look for these signs to see if you are ready for AI and fix the foundation before you zoom off into an AI future. By starting from a strong foundation, you’ll be assured of success with AI.Share