By definition, automation refers to any technologies or operations that are automatically controlled, rather than controlled by human labor. Increasingly, advancements in artificial intelligence and language processing models have improved automation at a rapid pace.
This flurry of activity presents an onslaught of new potential opportunities for those businesses in the private data repository space, as well as for the investors who put their money where their mouth is.
John Bunting, CEO and founder of Omaha-based Beeso Studio, said the organization helps startups locally as well as from across the country — and around the globe, in some cases — to build and scale their technology.
“The biggest automation investments we have seen over the last five years have been in AI/machine learning and natural language processing (NLP),” Bunting said. “By shifting manual entry and other menial tasks over to automation using AI and NLP, it has enabled users to increase efficiency, achieve higher quality and reduce overall costs.”
As technology has advanced, Bunting continued, the cost of automation that harnesses AI and NLP has been reduced, while capabilities on this front have only multiplied. He described how they build out a real-time coaching software that uses AI and NLP for one of their startups, Abstrakt.
“Abstrakt’s real-time call coaching software has reduced the time to onboard agents and has given managers better insight into call performance,” Bunting said.
Phoenix-based Abstrakt is described in a summary on Beeso Studio’s website as a “conversational AI platform for sales reps that has AI and natural language processing call coaching and conversation insights …” It was noted that these capabilities “lead to winning more deals.”
Bunting said Abstrakt’s call coaching software is currently handling 10,000 calls a day and, at the time of this writing, the startup was on track to triple usage by the end of the year.
Contemporary Analysis (CAN) President Nathan Watson spoke of investments in innovative automation in terms of his team’s transition to all-remote work. CAN is characterized as a leader in the data science community in the Midwest.
Reportedly, CAN has provided support on the likes of data-driven decision-making, predictive analytics, AI, data science and machine learning with application development, business intelligence, data visualization and warehousing, and data engineering services since 2008.
“We have invested in tools that are collaborative, meaning they track and show all changes that everyone else has made,” Watson explained. “This has helped us co-work on projects, even when they aren’t even in the same time zone — without worry that we will undo or spend time on frivolous parts of a project.
“Nothing is worse than working on a part of a project only to find out your partner has also been working on the exact same thing.”
Big Breakthroughs, Big Opportunities
Keith Fix noted how his firm, Retail Aware, is now focused on helping brands and retailers to capture first data from product displays in stores. With offices in Omaha and Chicago, the firm aids the aforementioned stakeholders in measuring data around in-store displays to drive brands’ return on investment.
CEO Fix referred to a presentation by Chamath Palihapitiya, whereby the founder of Social Capital spoke at greater length about the importance of first-party data and its evolution.
In a Twitter video posted in March, Palihapitiya discusses how, if one takes 1,000 of the same inputs and gives them to all of the “giants” — the Facebooks and Googles and so forth — all of these companies will come up with the same machine learning model.
“But, if you add one little ingredient that all of those other companies don’t have, your output can be markedly different,” he stated. “It’s like giving two great chefs three ingredients. But you give the third chef one extra one. That person has the ability to do something very special.”
Formerly, Fix highlighted automation and applied and generative AI when asked about general growth for Retail Aware, as well as broad trends in technology, speculating partly that “generative AI … may have its breakout year in 2023.”
“I’m finally starting to see it become commonplace in tools being built today,” he had remarked. “[Software as a service] tools without AI will feel like the pen and paper of yesteryear.”
In fact, Fix went so far as to classify applied and generative AI as among those major disruptive tech themes as we moved from 2022 into 2023. He described the former as the application of artificial intelligence to solve specific problems across the industry (for instance, powering the robots that bus tables in restaurants).
The latter generative AI was defined as the ability to use code to automatically generate content such as text, images and videos. Readers may be familiar with Jasper.AI, for instance, which Fix indicated has the potential to change the world of work by transforming today’s graphic artist into tomorrow’s “linguist typing into the system what image to generate.”
Fix remarked that his “predictions seem to be playing out.”
“A few other themes that have emerged since my prediction last year: The pace these generalist tools have been advancing has been at breakneck speed with the release of ChatGPT versions and Google Bard,” he noted. “We also saw corporates start to prohibit employees from using these tools out of fears of proprietary data ending up being used to train models used by competitors.”
By way of Google, Bard is reportedly an “AI experiment” by the tech force. As Google puts it: “We’ve long seen the potential to make information and computing much more accessible through conversational AI. Two years ago, we unveiled LaMDA (Language Model for Dialogue Applications), a conversational AI model capable of fluid, multi-turn dialogue and, last year, we launched the AI Test Kitchen, a new space where people could learn about, get hands-on experience with, and provide feedback on LaMDA.”
Furthermore, it was noted that Bard “enables you to collaborate with generative AI.” The Bard was described as a “creative and helpful collaborator … whether you want to help planning the perfect birthday party and drafting the invitation, creating a pro & con list for a big decision, or understanding really complex topics simply.”
The machine learning model powering Bard reportedly reads trillions of words, picking up on the patterns that make up human language. So, it was characterized as “good at predicting what might be reasonable responses.”
“What I’ve been hearing,” Fix added, “is that many of these open-sourced LLMs (language learning models) are now just as good as ChatGPT/Bard and many companies are looking to build internally to prevent private data from being public.”
Future-Forward Solutions
When surmising what’s ahead for small businesses, Bunting said AI will “continue to deliver the most benefit for businesses.”
He noted that as AI gets more user-friendly, more businesses will adopt the technology.
Bunting also predicts that the next big platform will be virtual reality.
“Currently, VR is mostly used by innovators however in the next two to five years VR will emerge as a viable technology platform,” he said. “Our team is currently working on solutions that use AI and VR.”
For those who may be considering new investments in technologies and/or updates to legacy systems, Bunting stressed that AI technology is not “one size fits all.”
“There is a spectrum of [artificial intelligence] that exists,” he said. “I think it’s best to analyze the problem and identify the right AI to deliver a benefit.”
“Sometimes,” he added, “the initial application of AI can be simple, then over time built upon to achieve higher intelligence and automation.”
Watson of CAN also provided his insights into best practices when exploring tech-driven investments, either as updates or as completely new solutions.
“Beware the cost of implementation — even of small changes to legacy systems,” he stated. “It may seem like it’s just a piece of software, just a small change of process. But, many times, people’s livelihood — their quotas, pay and company value — are tied to these processes and how they are defined.”
Watson noted the importance of thinking about the implications of automating processes that change even the most seemingly small details. For instance, changing the way a business defines a customer could be detrimental.
“ … their bonuses could be hit, their job may become in question,” he explained. “Nothing is worse than a VP who didn’t hit their bonuses or a manager whose job is now half automated, because you changed the definition of a customer or automated a critical, human touch-needed process.”
“Communication to all who use the system or process,” he emphasized, “is very much needed.”
