Many organizations have data warehouses to store all of their business information but lack the technology to truly unlock the value buried there. Data analysis tools will only get you so far — even though today’s technology is very user-friendly — and data scientists are both hard to come by and expensive. Thus, many organizations are choosing to add artificial intelligence (AI) software to their infrastructure.
For instance, the insurance industry is using artificial intelligence to automate claims, a long-standing, notoriously arduous process for both customers and staff. This software helps the customer submit the initial claim as well as receipts for payment, making for a much more user-friendly experience. For their staff, insurance companies have implemented image recognition software combined with mobile technologies that enable their adjusters to capture images of damage, verify claims and speed up the entire process without the need for an office network. And this is just the beginning.
Retail is another industry that has explored the various applications of AI. Cosmetics manufacturers are using imaging software to speed up the process of color-matching product lines. They can then offer a virtual design studio for customers to “try on” different products without the inconvenience of applying it in the store. The same technology is being used in the home interior space to assist potential clients with trying out furniture, art and window coverings in the privacy of their own homes. Such advances reduce manpower for these time-intensive activities and allow companies to focus on the critical, final touches of the customer experience.
Artificial intelligence is also being introduced to the food and beverage industry through social media integration. Apps like OpenTable and Yelp are becoming vast repositories of user-generated content, featuring pictures and reviews from restaurants their users frequent. Restaurants are using these apps to expand their marketing reach and gain insights into their customers’ favorite dishes or experiences that could improve their bottom line.
Artificial intelligence applications, when properly implemented, can produce the value organizations are looking for at a faster rate than human workers ever could. The critical words here are “when properly implemented.” This technology is just like every other technology in that it follows the old adage “garbage in, garbage out.” Poor planning — or worse, programmed bias — can lead to an algorithm that will produce flawed results. Things can quickly get out of control when the system is running many times faster than the speed to which humans can react.
There are some key factors organizations need to consider when taking the next step toward implementing artificial intelligence technology. Ask yourself: Is your current infrastructure ready to support these types of technologies? From a hardware standpoint, this means having everything from basic computing power to internet transmission capability (if you are planning to operate these systems in the cloud). It is also important to consider the state of your mission-critical software. Are you using the current version? Will it interface with the new AI platform you are planning to use? What about your data? Is it clean — with as many errors and duplications removed as possible? Finally, do you have the technical staff in-house to handle the implementation of the software and the skills you need to optimally maintain it over the long term?
While this is not a comprehensive list, if your answer to any of these questions is anything other than “yes,” you need to stop and take a step back. Devise a plan to address any missing capabilities or infrastructures — before you move forward. Artificial intelligence applications are not a plug-and-play proposition, and treating them as such invites disaster on a scale that cannot be underestimated. There are great rewards that many organizations are only beginning to sense with this software, but it requires caution and careful planning. As more organizations become increasingly data-driven, there is little choice but to begin planning for the future now.