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  • Writer's pictureJennifer Bittinger

Simple Solutions

Simplicity is the key to success. This sounds like a great tagline, but is it actually true? I came across an editorial by Richard Koch of who shared about the "5 Bad Reasons Managers Don't Simplify". It struck me as fascinating that nine times out of ten people don't simplify or find simple solutions because they are too lazy to dig deeper to find the best solution or their vision is so clouded by their problems they can't step back from the situation and see things more clearly. Koch said, "The fact is, people don’t design complex products just for the heck of it -- they’re driven by a vision of how the products could be more useful and attractive. The danger is, they get locked into the assumption that it’s okay for the product or business system to become increasingly complex -- heavier, more expensive, more convoluted and harder for the uninitiated to use -- as long as performance continues to improve... Yet managers who are used to progressing only by adding complexity often see simpler products not as a step forwards but as a step backwards."

Personally, I have witnessed a wide variety of challenges in business, from budget overages and product failures to marketing fumbles and closed doors. But the most important thing I’ve learned in each situation is that the greatest solution is most likely the simplest. That’s why I knew that the innovative tool of artificial intelligence would revolutionize businesses bringing simple solutions to even the most complex challenges. Even more so, I was amazed to find that natural language generation was a “fine-tipped” tool that could be programmed to meet the specific needs of companies and corporations of all sizes and markets bring simplicity to their world.

For many people, the term "artificial intelligence" still sounds sterile and futuristic, like something you would hear in a Star Wars movie, but it’s actually quite simple. Artificial intelligence (AI) is a self-learning computer approach demonstrated by machines. Natural language generation (NLG) is a software process that transforms structured data into natural language. Yes, these mechanical functions have complex parts and programming within them, but their purpose is quite simple—interpreting large amounts of data to provide natural explanations of what the data is really saying, what changed in the data, and why it changed. This is what humans need to make better decisions.

I fell in love with natural language generation because it brought clarity to confusion and saved me time. When I discovered that on average only 20% of employees in a company understand data and data graphs correctly and that the other 80% of the employees have the completely wrong interpretation, then I knew that every workforce could greatly benefit from natural language generation. The simple task of inputting large amounts of data through our sophisticated natural language generation software, setting pre-requested algorithms (or questions for the software), and getting 1-2 sentence DSN’s (data story narratives) in a fraction of the time that it would take humans will be able to help companies of all sectors, including finance, construction, telecommunications, entertainment, healthcare, pharmaceutical, retail and more. The net result is humans can focus on high value tasks while NLG accomplishes the mundane tasks of explaining data. Additionally, NLG-driven tasks and content contain less errors than human work and can be produced at a much higher frequency and consistency.

The terms sound complex, but the solutions are simple. As the late Steve Jobs said, “When you first start off trying to solve a problem, the first solutions you come up with are very complex, and most people stop there. But if you keep going, and live with the problem and peel more layers of the onion off, you can often times arrive at some very elegant and simple solutions.”


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