Veronica Buitron is co-founder of the Chicago-based ad automation platform, Chassis.
It’s understandable if companies are having a difficult time ironing out a roadmap with respect to AI developments—after all, this is a tectonic shift affecting every industry. Workflows are shifting, job responsibilities are changing, entire departments are being overhauled and some companies will even be put out of business. Consequently, deciding how to plan for the future has never been more elusive.
The bad news is that my crystal ball is currently on the fritz. But as someone who has worked closely alongside artificial intelligence advancements over the past 20 years, the good news is that I’ve still got a pretty solid idea as to the near future of business operations in the age of AI.
Before we get to that, though, it can help to contextualize some of these advancements that seemed to have cropped up overnight.
AI’s Past
Artificial intelligence (AI) is by no means a new technology. While it may have become a major flashpoint in the tech scene within the past decade or so, it’s been in development since the ’70s. So what’s changed? Of course, the capabilities have improved greatly, but the main reason we’ve seen an explosion in the past year is the level of accessibility.
For nearly the entirety of AI’s lifespan, if you wanted access, that meant millions if not billions of dollars and mountains of data that the average company just could not access. Now, these insanely powerful tools have been democratized and put within everyone’s reach.
AI’s Present
Right now, much of the buzz is around language and media tools, with writers and designers being the main demographics worrying about their future. But drafting a blog post or summarizing an article is only the first iteration.
The ones that we’re most familiar with are mostly using the same core algorithms and datasets—with the large majority simply plugging into OpenAI. They’re black boxes that even their developers don’t fully comprehend. They take shortcuts, they fabricate information and they don’t have any shame about it. But I am confident that they are going to get better.
AI’s Future
Because the inner workings of this tech are largely uniform, most of these applications lack any sort of specialization, but I expect that we will soon see the market splinter off into more and more specific use cases. And that’s when things will get really nutty.
Every job and every industry will be shaken. Finance, accounting, software development—jobs that have been bastions for stability may soon be disrupted along with the rest of us.
Creating An Internal AI Roadmap
There are two main ways in which AI can be leveraged to add value to your company. The first and easiest use case here is to incorporate AI as an assistant that helps increase output. In my company, we encourage every department to research, use and share AI tools to help make our jobs more efficient—whether that’s an exec, a writer, a designer or a developer.
While each of these areas requires direct human oversight to account for potential errors, across the board, the time it takes to complete a given task is drastically reduced through the use of these new language models.
The second opportunity, which is harder to execute but will be critical moving forward, is enabling your product or service with AI. We can (and should) use this incredible technology to improve the user experience, speed up software development, enhance customer support efforts, etc.
Here I strongly, strongly advise you to seek out a reputable firm that has done extensive research. This is for two reasons. One, you don’t want to get burned by someone trying to capitalize on this digital gold rush. And two, many people may not even realize the types of use cases that exist for AI and the ways it can help a company.
Some people may think language models don’t apply to their business, but as we’re finding more and more, that is rarely the case. I believe that those who don’t start to incorporate AI into their workflows soon will likely face a steeper learning curve and potentially a huge disadvantage compared to their AI-enabled counterparts.
Creating An External AI Roadmap
Some might recall that there was a much smaller AI boom back around 2017 and 2018. Every application that had an algorithm somewhere in its architecture was suddenly “AI-enabled” tech. We can expect a similar thing to happen this time around.
Many companies will likely rebrand and market themselves as AI companies in order to appear cutting-edge. This, combined with the general lack of understanding of what these language models truly are or how they operate, will require companies large and small to do a sizeable amount of due diligence when purchasing new software.
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