Businesses in a variety of industries are racing to develop and implement an artificial intelligence (AI) program. And why not? This pioneering technology is being used by businesses large and small to improve processes, catch errors quicker, predict customer behaviors and market conditions, and optimize their marketing (among other things). The expectations for success are high, and unfortunately, many budding programs are falling flat of expectations.
The truth is that most businesses hear the hype surrounding Artificial Intelligence, throw some money at it by hiring a data scientist or two (or more), and expect results to somehow magically appear. In reality, an effective Artificial Intelligence program doesn’t just materialize overnight, but instead needs to grow somewhat organically.
This is what’s known in the Artificial Intelligence world as the “AI Hierarchy of Needs.” Similar to Maslow’s Hierarchy of Needs, the Artificial Intelligence hierarchy requires certain basic needs to be met in order to advance and achieve greater levels of complexity and usefulness. Let’s examine what those are and how businesses can meet them.
All Artificial Intelligence, no matter what its purpose, requires data collection. This could be data from customer records, but it could also be a library of images, documentation, raw data from Internet of Things sensors, etc. Artificial Intelligence programs need a way to collect data reliably and consistently. Some of that data may already exist, but some of it may need to be generated, such as data generated by a user interface.
In order to make use of all this data, it first needs to be warehoused. It needs to be accessible not only to those who are managing the program but the Artificial Intelligence systems themselves. The data itself must be stored in a way that it is incorruptible, and in some cases, it also must be delivered between systems at extremely high speeds in order to facilitate real-time analysis.
In order for data to be useful, they must often be prepared in order for the Artificial Intelligence system to make use of them. That often means removing duplications, structuring or categorizing the data in a useful way, and rooting out any weird anomalies that might skew the results.
At this stage, you can start building your analytics model. That is, this is the stage where businesses can define what metrics to track, which can later be used to define the Artificial Intelligence system’s features. Depending on the type of Artificial Intelligence you are working towards, this is also the stage where you identify what data you will need to train the Artificial Intelligence.
Once all this is done, you can start testing your Artificial Intelligence system. This will likely involve some experimentation and validation to see if the Artificial Intelligence is working as expected.
Deep learning is an Artificial Intelligence term which essentially means an Artificial Intelligence system is able to emulate the way the human brain learns. With deep learning, computer systems can be put to work solving more complex problems. This really only happens when the previous stages are completed, and not just once, but for a variety of individual and often separate areas.
Just as a person doesn’t learn to talk or walk right after they’re born, so too do Artificial Intelligences need time to develop. That’s where we come in! We come to each project as an experienced partner, reducing risks and expenses by first developing prototypes (i.e. the lower levels of the Artificial Intelligence Hierarchy of Needs), and providing guidance as businesses build their long-term Artificial Intelligence program and infrastructure piece by piece.
If you’re ready to explore Artificial Intelligence for your business, contact us. We can assess your challenges and goals, and develop an Artificial Intelligence strategy that will be the backbone of your Artificial Intelligence program’s long-term success.