Nearly one in three genAI projects will be scrapped

By 2026, 80% of enterprises will have used generative AI (genAI) APIs or large language models (LLMs) or deployed genAI-enabled applications in production environments, according to Gartner Research. That’s up from less than 5% in 2023, as companies embrace the technology to discover patterns and actionable insights and free up workers by automating tedious tasks.

Although 9% of companies are now leveraging genAI to transform business models and create new business opportunities, nearly a third of those projects will be abandoned by the end of next year — largely due to poor data quality, inadequate risk controls, escalating costs, or unclear business value, according to a new Gartner survey of 822 corporate leaders and board directors.

The survey results were released Monday.

   

“After last year’s hype, executives are impatient to see returns on genAI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, a Gartner distinguished vice president analyst. “As the scope of initiatives widen, the financial burden of developing and deploying genAI models is increasingly felt.”

AI deployments can be expensive, with costs ranging from $5 million to $20 million. By 2028, more than half the enterprises that have built LLMs from scratch will abandon them due to costs, complexity, and technical debt in their deployments, according to Gartner.

Even so, genAI tools are proving in some cases to be advantageous for earlier adopters. Across industries and business processes, companies are reporting a range of improvements that vary by use case, job type and skill level of the worker. Business leaders surveyed by Gartner reported a 15.8% revenue increase, 15.2% in cost savings and a 22.6% productivity improvement, on average.

“Unfortunately, there is no one-size-fits-all with genAI, and costs aren’t as predictable as other technologies. What you spend, the use cases you invest in, and the deployment approaches you take — all determine the costs,” Sallam said.

Last year was seen as the year of enterprise AI adoption, with 55% of organizations experimenting with genAI in workflows, according to an August 2023 report from consulting firm McKinsey & Co. At the time, however, fewer than a third of enterprises surveyed said they were using AI for more than one function, “suggesting that AI use remains limited in scope.” 

Lucidworks, which sells AI-powered search and discovery software, released the results of its second annual GenAI Global Study; it showed just 63% of global companies plan to increase AI spending in the next 12 months, down from 93% in 2023. LucidWorks also found that financial services organizations deployed only a quarter of the AI initiatives they had planned for 2024, even though nearly 50% of financial services leaders had a positive view of AI in 2023.

The biggest concerns around using genAI in financial services involves data security (45%), followed by accuracy (43%), and cost (40%), according to Lucidworks.

The global study, based on a survey of more than 2,500 business leaders involved in AI technology decision-making, made it clear genAI’s once explosive growth is cooling as businesses face cost and security hurdles. “Businesses are recognizing the potential, but also the risks and costs,” Mike Sinoway, CEO of Lucidworks, said in a statement.

US-based organizations remain among the more bullish among those planning to boost AI spend this year (69%), but even as investment remains high, more companies are looking to balance the potential of genAI with managing risks and costs.

Ironically, most companies deploy genAI tools out of competitive concerns; one-third of business leaders feel like they’re falling behind competitors despite almost everyone struggling to implement the technology, LucidWorks found.

Investment in AI continues, however, and by 2030, companies will spend $42 billion a year on genAI projects such as chatbots, research, writing, and summarization tools.

Though commercial LLMs dominate the marketplace at the moment, more companies are eyeing customized small models that use only internal data. Nearly eight in 10 companies use commercial LLMs, and 21% have opted for open source only, according to LucidWorks.

ROI remains hard to pin down

While the technology has been heralded by many as a boon to productivity, nailing down a return on investment (ROI) has proven elusive, according to Lucidworks and other studies.

Forty-two percent of companies reported they’d not yet seen a significant benefit from their genAI initiatives. Tech and retail sectors stand out with higher deployment and realized gains, but overall, most industries are slow to move beyond pilot programs, Lucidwork found.

Security remains a top concern for business leaders, but cost worries have surged 14-fold in the past year, according to Lucidworks.

Additionally, concerns around response accuracy have risen five-fold, likely due to issues with hallucinations, highlighting the need for careful LLM selection to balance cost and ensure accurate, secure results. “For [genAI], we are not saying that finding ROI may be difficult, but expressing ROI has been difficult because many benefits like productivity…have indirect or non-financial impacts that create financial outcomes in the future,” Sallam said in an earlier interview with Computerworld.

For example, using genAI to automate code generation could make a software developer more productive, giving them additional time to improve productivity and increase innovation. Down the line, that could mean faster time to market for new features — and happier customers.

“Measuring ROI is hard,” said Bret Greenstein, Data & AI leader at professional services firm PriceWaterhouseCoopers (PwC). But by adapting an LLM to perform a function or process, it’s easier to compare its performance — cost, accuracy and speed — against earlier processes.

In the simplest of terms, ROI is a financial ratio of an investment’s gain or loss relative to its cost; so when a company invests in genAI, the benefits of that spending should outweigh costs.

“Once you get [genAI] to consistently achieve this new level of performance, you deploy it in production with the proper governance and operational processes and track its usage,” Greenstein said. “When you have a use case that saves two hours in a six-hour process, and track its usage, you can aggregate the savings.”

What to look for when considering genAI

According to Gartner, executive leaders pursuing genAI projects should:

• Determine potential gains in business value derived from genAI business model innovation by exploring strategic alignment of business adjustments with the genAI deployment.

• Calculate the total costs of genAI business model innovation by considering both the expenses incurred in genAI deployment and the costs associated with necessary business adjustments.

• Make informed investment decisions by calculating and assessing the ROI of genAI business model innovation. This involves estimating the financial returns and comparing them to the total costs associated with the innovation, including those associated with needed business adjustments.

If the ROI meets or exceeds expectations, it presents an opportunity to expand investments by scaling genAI innovation and use across a broader user base or implementing it in additional business divisions, according to Gartner. If the ROI falls short, it might be necessary to reconsider investments and explore alternate scenarios for genAI.