For many years, the terms artificial intelligence (AI) and machine learning (ML) were quickly dismissed as just marketing hype and buzz. It seemed like every new technology had AI working for it somewhere. Some used true AI. A lot, however, were just mechanical Turks, of which the marketplace was aware. Fact is, AI and ML existed, but they had not matured and organizations still had not yet figured exactly how they could benefit their businesses, much like how the marketplace is currently viewing the emerging 5G standard. They know the benefits, but how exactly they can put the rubber to the road for their business is still being determined.
AI and ML have matured and now it’s known how they can help companies, organizations and industries and interest it it is growing. AI and ML were made to crunch massive amounts of data, faster and with greater accuracy than any individual or team. The business world creates incredible amounts of data. The Internet of Things (IoT) alone is expected to create 847 zettabytes of data per year by 2021. That works out to roughly 2.3 zettabytes per day. To put that in a scale that humans use on a daily basis, 2.3 zettabytes is 2.3 trillion gigabytes. That is an astronomical amount. There is no point in collecting a single byte of data if it’s not going to be analyzed and immediately turned into actionable intelligence that can help a business to make impactful decisions. Again, this is the job AI and ML were made to do.
Before an organization can begin crunching through massive data lakes, they need to know two things: 1) What goal do we want AI to accomplish for the business, i.e., what does success look like? 2) Do we have the data to support it? If any organization has positive answers for those two questions, then AI/ML is right for them. The next step is finding a “venue.”
AI and ML require a lot of two things: computing and storage. Investing in these infrastructures and resources requires a large capex expenditure and can prove quite a gamble. But there is a flexible, scalable alternative – the cloud. CSPs can easily provide the computing and storage infrastructure needed to support any AI or ML initiative in a cost effective manner that doesn’t require huge investments up front. It can also scale up or down to meet the exact business needs of an organization at any point in time, keeping operations as lean as possible.
AI platforms like IBM’s Watson are great for helping an organization get their AI projects off the ground and start delivering ROI. However, with Watson, comes a learning curve. Not only for Watson itself, as it starts to learn and train itself from the data, but for the staff of people who have to operate it (yes, even with the rise of AI, people will always be needed). The necessary data science expertise needed to identify features and build successful machine learning models will prove hard to come by.
If you’re interested to learn how your organization can start leveraging the advancements of Industry 4.0 and the digital transformation in a cost effective and manageable manner, please contact us today!
Senior Marketing Communications Manager
Key Information Systems