Artificial intelligence (AI) is coming to all sorts of places in our lives and businesses. Right now, it may seem more hype than reality, but many focused products are being developed, tested, and sold with AI as an integral element.
In industries such as medicine, there’s still a human in the decision-making and activity process before actions are taken. Systems such as automatic reordering programs seem to like older technology, but when you take the person out of the process and simply let the system manage inventory, it’s a real-live form of AI, especially when it uses predictive analytics to determine project needs.
The second case is an example of how AI uses enterprise resource planning (ERP) data to improve business, and it’s is easy for people to understand. Using Internet of Things data in analytics seems cool, but for businesspeople to really embrace AI in the enterprise, those intelligent systems have to work with the massive amount of historical transaction data in ERP databases.
Companies cannot simply dive into AI projects the same way they can use a new reporting tool—with just a bit of training. AI requires a lot of expertise to work properly, and a lot of thought goes into how to deploy AI within the company.
The incorporation of AI systems doesn’t happen by accident. It takes the right people with the right talent. Large companies can afford to hire teams of new people with specialized skills, but medium-sized companies might be doing well if they hire a couple of project managers to coordinate outside services.
Indeed, with the help of internal IT people, internal subject matter experts can make something good happen with AI quickly by using a cloud-based service. Here are three advantages integrating cloud-based AI has over hosting everything on premises, even while keeping the ERP database on premise.
It might be self-evident that going with a cloud-based service is going to be faster than setting up the servers and software on premises. Still, let me emphasize that it is a particular advantage.
In addition to the obvious are soft reasons for a speedier start to AI projects. Taking weeks (more likely, months) out of the schedule brings the tangible functions closer, which better keeps management’s attention through the inevitable stumbles. In addition, senior management takes more interest in short discussions of the math and statistics in use than in the usual old IT problems of server connectivity. Connecting from the cloud to the on-premises ERP system by using known methodologies is typically easier than trying to install and connect new technologies internally, as well.
Companies that provide AI services don’t just exist as websites. Some providers are huge and don’t provide services directly to small or even midsized companies, but there are hundreds of consultative firms that work exclusively with large vendors’ hosted ecosystems.
The consultants of the smaller cloud AI provider are a crucial resource. At the very least, they guide projects with useful templates or how-to videos. More likely, they can be hired to fill the technical gaps when implementers encounter roadblocks. It’s often easy to find consulting assistance from people who have already worked with both the cloud AI software and the database of the on-premises ERP software at another customer.
A major pitfall of AI for companies is tracking the ongoing improvements in operations. Like all internal teams and projects, AI project teams tend to inflate the impact and benefits of their work.
Cloud-based systems provide a clear break between the on-premises ERP system and the AI models. The cloud provider also has the inherent need to prove itself and its service value so that companies keep paying. All this leads to some good reporting available for analyzing the true use of the system.
The real impact is likely more difficult to measure and subject to the same fudging every other project and product is subject to. But, at least the cloud service has a few hard numbers.