Gartner’s latest research reveals a sobering reality for companies “struggling” to extract value from their generative artificial intelligence (Gen AI) projects.
According to the report, a staggering one-third of these projects will be abandoned before they even reach the finish line.
The Hype VS. Reality
Generative AI was the buzzword last year, with businesses eager to tap into its transformative potential. However, the excitement is starting to wane.
Gartner’s analyst, Rita Sallam said, “After last year’s hype, executives are impatient to see returns on Gen AI investments, yet organizations are struggling to prove and realize value. As the scope of initiatives widen, the financial burden of developing and deploying Gen AI models is increasingly felt.”
Sallam notes that the costs associated with these projects—ranging from millions to tens of millions—are a significant burden, leading many companies to question the value they’re getting in return.
The Cost Factor
Gartner’s report shows a clear picture of the financial hurdles. It states at least 30% of Gen AI projects will be abandoned after the proof-of-concept stage by the end of 2025.
Sallam cites that the project costs range from $5 million to $20 million, putting pressure on deployment.
Even on the lower end, a company using a generative AI API for coding assistance could be looking at an initial cost of $100,000 to $200,000, plus ongoing expenses of up to $550 per user annually, Gartner estimates.
On the higher end, if you’re planning to spend on fine-tuning “foundation” AI models or developing custom models from scratch, get ready to fork out between $5 million to $20 million upfront, with costs of $8,000 and $21,000 per user annually.
Is There a Silver Lining?
While the challenges are significant, it’s not all doom and gloom. Some companies are already reaping the rewards of their Gen AI investments, reporting boosts in revenue, cost savings, and productivity.
But there’s a catch. Gartner warns that these benefits are often hard to quantify, making it tricky for companies to justify ongoing investment.”Gen AI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment,” Sallam pointed out. “Historically, many CFOs have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.”
Aside from costs, Gartner said factors that could doom AI projects include “inadequate risk controls” and “poor data”.
It’s not just the financial aspect that could derail these projects. Gartner also highlights issues like inadequate risk controls and poor data quality as potential factors that could lead to failure.
Despite these challenges, the report contrasts with other surveys, such as one from Bloomberg Intelligence, which found that companies “working on” deploying Gen AI “co-pilot” programs doubled between December last year and July 2024.
The Takeaway!
Now, the key takeaway is clear—while the technology holds immense potential, it isn’t without challenges.
Balancing the costs with the potential benefits, managing risks, and maintaining a long-term perspective will be crucial for companies looking to succeed in this space.
Co-founder and Chief growth officer