It’s clear that generative AI has become the favorite new toy of the modern enterprise. Since 2022, companies have been rushing to integrate chatbots and AI assistants into their workflows for productivity gains, while some are even building their own large language models (LLMs).
Last month, banking giant JPMorgan launched LLM Suite, a ChatGPT-like ‘research analyst’ that can help workers generate ideas, solve spreadsheet problems, and summarize documents.
But for every generative AI success story, there will likely be companies that regret jumping on the generative AI hype train so soon. A quarter (25%) of 1,255 IT leaders in the UK and US already regret investing in AI so quickly, per a survey [PDF] by Asana on the state of IT leadership released earlier in 2024.
There are a couple of reasons for this. One, many IT leaders are feeling the need to adopt generative AI as part of a cost-cutting drive and to improve the employee experience – 51% of those surveyed said they were under pressure from those higher up. Two, some IT leaders assume that generative AI’s impact is going to be immediate and inevitably end up being disappointed.
“The buzz around generative AI masks the complexities and limitations involved in implementing it. Companies dream of overnight transformations, but the truth is that seeing success takes time, ongoing refinement, and a sizable investment in both tech and talent,” Peter Wood, CTO at Spectrum Search, tells ITPro.
The rush to ride the generative AI train can lead to bad investments, which, in turn, can result in buyer’s remorse.
“The key is to manage your expectations and focus on the long-term gains rather than the immediate returns,” advises Jeff Watkins, CTO at CreateFuture. “I would urge people to think less in terms of return on investment and more in terms of return of reducing risk. Investing in generative AI is likely to become a cost of doing business.”
Getting your AI house in order
According to a recent Cognizant study of 2,200 executives worldwide, 76% intend to use generative AI to create new revenue streams, while 58% are using the goal of higher revenue to justify their business cases.
While you shouldn’t expect an immediate gain from generative AI, there are steps you can take to avoid making expensive and, potentially, misguided investments, Watkins tells ITPro.
For starters, you need to know exactly the problems you want to solve by implementing generative AI, he adds. Are you looking for internal efficiencies or do you want to improve customer or client engagement and experience?”
Wood agrees: “You need to step back and thoroughly evaluate your needs and capabilities. The starting point is to carry out an in-depth analysis”.
Both stress the value of performing due diligence on vendors and not taking their marketing claims at face value. AI model developers may push their products as being able to be seamlessly integrated into existing operations but these claims need to be checked prior to signing any contracts. Proof of concepts can be especially beneficial at this stage.
“Running a pilot test of an AI tool in a controlled environment can give you useful insights into how it performs in the real world and what sort of integration challenges you might face,” says Wood. Other things to consider include the long-term support and scalability options that a vendor provides.
Creating the right culture for generative AI
Once you’ve identified the right vendor and product, the next step is to get buy-in from other C-suite executives and stakeholders.
Asana’s report highlighted that investment in AI is being hindered by a lack of understanding of what the technology can do and the value it can bring. Only 14% of the IT leaders surveyed indicated their firm had set aside a dedicated budget for AI projects. However, 62% have seen their influence within their company grow since last year thanks to the emergence of generative AI.
Wood says that IT leaders need to leverage this growing influence to educate both those at the board level and employees on the potential risks and rewards of investing in AI.
“You need to make sure that those making the investment decisions are clued up about the ins and outs of AI, including the iterative nature of its deployment, as this can help manage expectations,” advises Wood. And bear in mind that investing in generative AI isn’t simply a case of ‘one and done’. You need to be willing to adapt as LLMs continue to advance.
“A culture of continuous learning and adaptability can turn any feelings of remorse into useful lessons and long-term success,” adds Wood.
There’s no getting away from the fact that generative AI will stick around as an important tool in enterprise toolkits. By 2026, more than 80% of enterprises will be using LLMs and APIs or have deployed generative AI applications for improving workforce productivity and enhancing customer experience according to Gartner.
But generative AI in its current form is very much like the early days of the internet. There will be plenty of peaks and troughs to come, says Watkins. By taking these steps, companies are giving themselves the opportunity to reduce the likelihood that they’ll regret investing in the technology.
“There is always a risk of some level of buyer’s remorse due to the current hype. But companies that take a strategic approach to AI investment are likely to see significant long-term benefits – or at the very least, mitigate the risk of being left behind,” concludes Watkins.
