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Trying to do more with less is never easy, yet it’s the modus operandi for many organizations right now. The seemingly fragile economic climate — characterized by rounds of layoffs, inflation and overall uncertainty — is leading organizations small and large to tighten their purse strings.
According to Reworked’s forthcoming 2024 State of the Digital Workplace Report, nearly half (46%) of respondents cited budget constraints as their current biggest challenge, and only 32% expect these constraints to let up in the next year.
In this environment, when the general sentiment appears to be if you fail to experiment with artificial intelligence, you'll be left behind, how can companies move forward? Finding the resources (and taking on the risk) necessary to explore new AI tools and their use cases will be tricky.
The AI Gold Rush
Companies across every sector are trying to integrate AI and all its potential within their organization, and they feel they have a long way to go. Reworked’s report saw the number of organizations identifying that they’re just starting out with AI rise by 8% since last year.
There are obvious costs associated with starting these endeavors, as certain generative AI tools can get expensive. However, some experts argue that it’s the mad dash itself that could cost the most.
Christopher Lind, chief learning officer at ChenMed and workplace transformation expert, feels that many companies are scrambling in order to avoid missing out.
“A lot of folks are just trying to find a way to stick AI into something instead of actually figuring out where this makes the most sense,” Lind told Reworked. “Technically, you could implement or apply it anywhere, but it doesn't make sense to apply it everywhere.”
According to Tim Kulp, chief innovation officer at Mind Over Machines, this sense of urgency has also led leaders to take a “backwards approach” to AI implementation, one that prioritizes the technology itself over the problems it's meant to solve.
“If you implement a multi-million dollar AI system for a $10,000 business problem, you're not making good business choices,” Kulp said.
Related Article: Microsoft's Copilot in Viva Engage and the Trouble With AI Overreliance
Lowering the Cost of AI Entry
The risks of experimentation may be leading some companies to rethink AI investments altogether. Kulp said that some organizations have thrown their hands up, feeling that they lack the people, resources and safeguards available for a broad AI implementation.
This doesn’t have to be the case, according to Lind: “Implementing AI does not have to be super expensive. I think that's actually one of the biggest myths that people have, that if we're going to do this AI thing, we've got to spend millions of dollars. And so as a result, people are kicking it down the road.”
A better idea? Taking a more surgical approach to AI implementation, which could prevent companies from having to clean up any expensive messes that come with less focused exploration. Kulp said that by focusing first on what business problems your company has, you can better pinpoint where AI can provide a cost-effective fix. “If you don't try to implement the biggest solution that handles everything up front, you can pretty cost-effectively get some good AI solutions, if your use case justifies it.”
However, herein lies the problem, Lind explained.
“The first step is how well do you know your business right now? And a lot of times it's not nearly as well as organizations think,” he said. “[The solution] might be something as simple as automating a workflow, or it might be something much bigger, like implementing totally autonomous AI agents. Until you really know the pain points of your organization and what your constraints are, you just won't have an answer to that.”
To BYOAI or Not?
While implementing custom-built or more advanced AI solutions requires considerable costs, there are plenty of free or low-cost options available, such as OpenAI’s ChatGPT or built-in tools from Microsoft and Google. But are they worth the risk?
According to Kulp, it depends.
“You have to do what's right for your culture,” Kulp said. “If you're in a highly risk-averse culture or highly regulated culture, then start with what the risks are for this use case or that use case and go forward from there.”
Kulp did note that allowing unfettered use of free tools does mean sacrificing a standardized work environment, as well as quality control and data protection, which can all be costly in the long run.
“It’s cheaper to use the free version of chat GPT to do stuff, but it's not cheaper when you account for the HIPAA violation that's going to happen,” he said. “You need to recognize there's more costs than just the upfront subscription costs that you need to think through, or the system implementation costs.”
Lind agreed on the need for standardization and oversight of AI use in the workplace, mostly because employees will be using those kinds of tools whether companies know about it or not.
Putting together a team that can both develop safe use standards and optimal use cases is the best way to begin.
“Make sure you form an AI team that's cross-functional,” Kulp said. “Don't just hand it off to IT. I mean, I love my IT peers, but AI is a cross-disciplinary function. You really need to have people from different corners of the business to come in and talk about it.”
Related Article: It's Too Late to Stop Bring Your Own AI. So Get Your BYOAI Strategy in Place
Tips to Avoid Buyer's Remorse
While experimentation will still be instrumental in achieving the benefits of AI within the workplace, current constraints mean that this experimentation has to be given a great deal of thoughtful planning and oversight.
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Lind said the crowded landscape of point solutions and low-cost tools may result in companies finding themselves looking to trim down to get the most value.
“If you get yourself embedded with all these little random tools, they're going to start collapsing in on each other. And that's where I think in the next few years, you're not necessarily going to go looking for an AI tool. You're going to be saying, ‘Out of the products we use, which ones have the greatest AI capabilities?’”
Kulp said that red teaming AI solutions and making investments by use case, in addition to upskilling employees so they can actually use these tools, will prevent buyer’s remorse and lead to more value for companies.
