AI has touched millions of lives since Apple’s Siri, Amazon’s Alexa, and Google Assistant exploded onto the market with the singular goal of making interactions, and lives, easier.
But AI has come a long way since 2011, moving toward functioning as infrastructure and fundamentally changing the landscape of the workplace.
A Brief Synopsis of AI as Infrastructure
To answer what that is, we need to define AI first. AI, in my opinion, is not artificial intelligence, but augmented intelligence. And augmented intelligence helps individuals become more efficient in their day-to-day lives, work, and every aspect of their existence by streamlining mundane tasks and saving people time to focus on more important matters.
Everybody should have access to AI generation tools and the flexibility to build AI that accommodates an defines their workflows. AI should function as infrastructure, providing the framework to enable humans to accomplish more, quicker.
Why Does This Matter for Companies?
Many data scientists spend up to 80% of their daily jobs doing data crawling, data cleaning, understanding the format of the data, feature extraction, running it through some sort of data analysis tool, and workflow integration.
Automating eliminates this kind of repetitive work, and the benefits are obvious: Increased efficiency from workers and empowering humans to take on more mission-critical tasks.
I recently learned that an airline hired hundreds of individuals whose sole responsibilities are to listen to phone calls and classify them into various complaint categories. That’s such a powerful message: The lack of automation requires this company to need hundreds more employees to get insight into CX. And what if this company’s customers suddenly doubled? It’d need hundreds more employees to listen to and categorize the influx of calls. How is that a 21st century solution?
Automating these kinds of menial jobs means that humans can transition into jobs on the reinforcement side of things. They can help increase accuracy by using human intellect to correct mistakes computers make.
What Do People Fear About AI as Infrastructure?
The fear about AI is that it will eliminate jobs, but automation is not going to replace people in the workplace. Humans are irreplaceable when it comes to correlation and decision-making.
Decision-making is a combination of art and science. If we let AI handle the science aspect of figuring out the fundamentals and doing the leg work, it makes room for more creative, decision-driven, mission-critical jobs, letting humans artfully craft solutions to pressing issues. Human creativity is the key to success – we’re just not efficient when it comes to collecting, storing, and processing data.
AI will simply augment people’s abilities, giving them all the information to make sound decisions.
What Does the Future Look Like?
I think the future is that everybody will have a personal AI assistant for their workflows, based on the nature of their work and work preferences. This frees up the majority of your time to focus on more important things. And I don’t even think that’s 10, or even five, years away. This could become commonplace this year or next.
This individualized AI creates the perfect harmony of humans and computers for better, more creative decision-making.
Why Should Stratifyd Be Your Choice for AI as Infrastructure?
Data + training algorithms = AI automation, so Stratifyd should be your choice for AI as infrastructure because this notion is embedded in our DNA. We have a massive pool of data and both supervised and unsupervised training algorithms which allow anyone to easily use our platform and generate AI models.
We’ve democratized both data and AI because, at the end of the day, AI as infrastructure needs to be for everyone – not just business units, or data scientists, or any other small, specific group of workers.
About the Author
Derek Wang is the co-founder and CEO of Stratifyd. He grew up in Beijing and came to Charlotte in 2006 to attain his Ph.D. in Computer Science from UNCC. After achieving his doctorate, he – along with the company’s two other co-founders – began conducting government-funded research on the ways AI could be used to ingest, analyze, and visualize unstructured data. This post-doctorate work was the foundation on which Stratifyd was built.
Wang's currently focusing on Stratifyd’s vision to take data and AI democratization to next level. The company’s goal is to define the market with unique capabilities that can understand people better than they understand themselves.