AI (Artificial Intelligence) Projects: Where To Start?
Artificial Intelligence (AI) is clearly a must-have when it comes to being competitive in today’s markets. But implementing this technology has been challenging, even for some of the world’s top companies. There are issues with data, finding the right talent and creating models that generate sufficient ROI.
As a result, many AI projects fail. According to IDC, only abut 35% of organizations succeed in getting models into production successfully.
“While we see AI technologies performing a swath of incredible feats such as Google Translate, AlphaGo, and solving a rubik’s cube, it can be hard to tell which business problems AI is apt to solve,” said Ankur Goyal, who is the CEO at Impira. “This has led to a lot of confusion—and a vendor community that has taken advantage of it by labeling things as AI when they aren’t. It’s very reminiscent of early last decade when cloud technologies took off and we had a lot of cloud washing going on. We had vendors marketing themselves as cloud players when their offerings were vaporware. Similarly, we are going through a period of AI washing now.”
So then, if your company is thinking of implementing AI, what is the best way to start? How can you help boost the odds of success and avoid the pitfalls?
Here’s a look at some strategies:
AI is not magic. It will not solve all your company’s problems. Rather, you need to take a realistic approach to the technology.
“Unlike traditional data analytics, machine learning (ML) models that power AI are not always going to offer clear-cut answers,” said Santiago Giraldo, who is the Senior Product Marketing Manager of Data Engineering at Cloudera. “Implementing AI into the business requires experimentation and an understanding that not every experiment is going to drive ROI. When an AI project is successful, it is often built on top of many failed data science experiments. Taking a portfolio approach to ML and AI enables greater longevity in projects and the ability to build on the successes more effectively in the future.”
Interestingly enough, there are often situations when the technology is really just overkill!
“Often times businesses take on AI projects not realizing that it might have been cheaper to continue a process manually instead of investing large amounts of time and money into building a system that doesn’t save the company time or money,” said Gus Walker, who is the Senior Director of Product Management at Veritone.
You don’t want to spend time and money on a project and then realize there are legal or compliance restrictions. This could easily mean having to abandon the effort.
“First, customer data should not be used without permission,” said Debu Chatterjee, who is the senior director of platform AI engineering at ServiceNow. “Secondly, bias from data should be mitigated. Any model which is a black box and cannot be tested through APIs for bias should be avoided. The risk of bias is present in nearly any AI model, even in an algorithmic decision, regardless of whether the algorithm was learned from data or written by humans.
