Businesses today are learning how best to deploy AI to help with productivity, while balancing it with human touch. We’ve all seen the headlines about OpenAI and ChatGPT and the potential they have, but in practice, how are those technologies actually going to help enterprises? How can we be sure that large language models (LLMs) are being developed effectively?
Quickbase recently held a webinar with David Linthicum, Chief Cloud Strategy Officer at Deloitte, and Dion Hinchliffe, VP and Principal Analyst at Constellation Research, to address these topics. Dion and David sat down Quickbase Chief Marketing Officer Eric Olson to share their views on productivity, mistakes made with AI and cloud adoption, and the ways in which we do work are changing.
The Silo Culture of Digital Transformation
The digital transformation explosion in the past decade saw businesses rapidly adopting new tools, applications, and systems to get their work done. In the short term, these tools provided lots of productivity benefits – steps within a workflow could be automated, with data automatically stored within these tools.
But as time went on and digital transformation got more complex, so did the data and the tools needed to analyze it. These linear solutions aren’t easy to integrate, and oftentimes they don’t share data or information with one another. Rather than figure out how to connect these systems, organizations were creating more and more silos on top of one another with system acquisition.
“The inability to get data in a real-time way is killing productivity,” says Linthicum. “Businesses didn’t spend the time optimizing data in these systems, and just created silos on top of silos.”
This type of data fragmentation leads to confusion as to where information is, which can directly impact employee productivity. In a recent survey, Quickbase found 70% of workers lose 20 hours a week just trying to untangle disconnected systems to find the data they need.
“It’s a self-inflicted wound,” Linthicum says. “Without spending the time to focus on the future, businesses focused on the newest flashiest technologies, and were unable to leverage IT resources strategically to give value back to the businesses.
Without the foresight on data normalization, we went from a complete drought of tools, to drowning in the volume of them. Today’s approach to Dynamic Work
means that data needs to be tightly woven together and easily accessible for those who need it.
“We’re in an environment of experimental change,” Hinchcliffe said. "The digital landscape has become more complex, and tools can help, but we’ve just been accumulating systems instead of normalizing data. AI can help knit all of this data together in a way that truly drives value.”
AI’s Role in Adapting to the Future
Businesses have more than enough tools and technology at their disposal to solve these problems. Now, it’s about orchestrating them in a way that’s going to drive the most value for the business.
“We have all the tools in place, and they work. It’s not a technology problem,” says Linthicum. “This comes down to culture and understanding how these tools can be optimized to work better. We have the weapons, lets go figure out how to fight the war.”
As businesses make this culture shift, it’s important to realize that now everyone in an organization plays a role in digital transformation – not just IT departments. With the advent of technologies like AI, you have to adopt and onboard new technologies in a native way much quicker than before.
“There’s an inflection point here around democratization of IT,” says Olson. “It’s no longer IT’s job – everybody plays a role.”
The organizations who have the people best positioned to make effective change will be the ones who get the most value out of their technology investments.
“Your ability to leverage technology is ultimately a force multiplier for any business trying to scale,” says Linthicum. “Whenever there’s a cash crunch in the budgets, we always take money off of IT. That’s the wrong approach. We need to be investing into the technology side to help solve business challenges”
With LLMs built and trained on existing data, they no longer need to track down and hunt the information they need to get their jobs done. Rather than losing productivity to mundane processes, AI models are able to grab the data that’s needed instantly, giving a much clearer picture of what next steps are.
“Generative AI will have nothing short of a transformative effect on low-code/no-code platforms by making it much easier for these platforms to provide useful functionality based on high-level requirements, even for citizen developers,” says Hinchcliffe.
An example of this action is the U.S. Food and Drug Administration. Prior to onboarding low-code and generative AI in their processes, their field inspectors were using upwards of 300 individual applications and systems to conduct site visits. Inspectors couldn’t keep up with the processing demands of these applications, and many food supply chains were shut down. The FDA implemented a low-code system powered by generative AI, slashing the amount of time needed to document each part of the inspection, and empowered their field workers to make the most out of their time.
As businesses like the FDA continue to find new, innovative ways to streamline processes and drive efficiency, it’s worth taking a look ahead at what 2024 and beyond can hold for AI.
2024 and Beyond
AI’s impact is going to be felt across industries. As a result, generalized LLMs and AI-driven processes are going to start bending to fit the mold of each unique environment.
“The future of generative AI will be verticalized,” says Linthicum. “LLMs built for specific industries can automate processes and share knowledge between people in those industries. It’s going to be a game-changer.”
The democratization aspects of AI means that everyone in the business now has a hand in IT processing. As a result, there’s going to be a push for people to learn new skills and abilities to help them adapt to the new way of doing things.
“Organizations must be able to upskill and invest in their workers,” says Hinchcliffe. “You can upskill people than ever before, and that’s going to be so important with disruptive technologies like AI.”
Additionally, Hinchcliffe and Linthicum believe that we’re only at the very tip of the iceberg when it comes to the deployment of AI, and that it isn’t taking over every single element of the business.
“It needs to be a balancing act,” says Linthicum. “The idea that legacy technology is gone is just wrong. It’s a legitimate service that needs to be integrated with new technology. Integration is essential.
As businesses continue adapting to the rise of generative AI, it’s imperative that they’re deployed carefully. There still needs to be a balance between human touch and AI deployments. People must be there to work on and train these models to achieve operational efficiency.
“Holistically, generative AI is very complex,” says Linthicum. “We’re not going to have huge LLMs that can answer all questions on Earth. It’s going to be localized to help get the job done.”
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