Even companies eager to jump on the GenAI bandwagon have struggled to organize their data and get past deployment hurdles, and nobody likes to spend all that time, effort, and money to build technology that can't be shipped because it can't be trusted.
Happy New Year! Today: after years of hype and investment, enterprise generative AI applications are still awaiting their breakout moment, the data-center investment boom continues, and the latest funding rounds in enterprise tech.
This year marked a turning point for enterprise tech as spending recovered and the economy stabilized following years of rising interest rates and supply-chain disruption. While no one knows what lies ahead, here are five things we thought summed up a pivotal year.
Even companies eager to jump on the GenAI bandwagon have struggled to organize their data and get past deployment hurdles, and nobody likes to spend all that time, effort, and money to build technology that can't be shipped because it can't be trusted.
If you stick around through a certain number of enterprise technology cycles, you'll start to see a pattern: Promising new technologies struggle to find momentum inside conservative IT departments wary of fixing systems that aren't broken, until they start losing business and employees to the early adopters. We saw this with the internet, mobile apps and cloud computing, and billions of dollars have been spent over the last two years to convince companies that generative AI is the next platform shift.
The cycle has unfolded with a little more urgency this time around thanks to executives and directors who remember companies that failed to move quickly enough to adapt and suffered the consequences. But entering 2025, it's hard to find companies that are breaking away from the pack thanks to their investments in generative AI.
Those past cycles were driven by demand: regular people wanted to use the internet and smartphones to consume content or do business, and regular developers wanted to use cloud services to build powerful and more resilient applications.
But outside of a few specific areas such as software development, it's not clear how much demand there is for generative AI technology at the moment. Microsoft is even "forcing" users of Microsoft 365 in certain countries throughout Asia to use its Copilot tools while charging them extra for the privilege, according to the Wall Street Journal.
Platform shifts don't happen overnight, of course, but the hype cycle behind this would-be shift has arguably hurt adoption more than it has helped. Even companies eager to jump on the GenAI bandwagon have struggled to organize their data and get past deployment hurdles, and nobody likes to spend all that time, effort, and money to build technology that can't be shipped because it can't be trusted.
The primary issue right now is that what Gen AI did was give you early prototype success that was honestly beyond anybody's wildest dreams. [But the] final 10% ends up being very challenging, and where we see a lot of folks within these projects really struggling is to cross that line on accuracy.
"The primary issue right now is that what Gen AI did was give you early prototype success that was honestly beyond anybody's wildest dreams," according to Ed Anuff, chief product officer at DataStax. But the "final 10% ends up being very challenging, and where we see a lot of folks within these projects really struggling is to cross that line on accuracy," he said in an interview last month at AWS re:Invent.
AWS's Swami Sivasubramanian echoed that sentiment: "They actually have something almost 99% there, and then they used to tell me the last 1% turns out to be the longest, because I can't afford to get this wrong," he said at re:Invent.
In response to those struggles, vendors have pitched agents as the way to deliver the promise of generative AI technology, but "I think it's huge misconception to come in and think that you're just going to plug in the agents and it is just magically going to figure it out," said Alex Rinke, co-founder and co-CEO of Celonis, in an interview last month.
This is a critical year for enterprise generative AI. Even though tech spending has rebounded after the economy proved more resilient than people had anticipated back in 2022, businesses can't afford to throw money away on prototype after prototype without seeing a return.
One reason cloud computing was such a huge platform shift was that the operational and deployment burden of enterprise software moved from the customer to the vendor.
In ye olden days, enterprise software companies sold licenses for big, complex software packages and customers were more or less on their own to get that software up and running in their data centers, but cloud vendors didn't get paid until companies were actively using their services and were therefore incentivized to get customers into production.
Right now, cloud companies are making lots of money from selling the picks and shovels needed to make AI prototypes while customers struggle to get across the finish line. If enterprise generative AI is really going to be a platform shift, that probably needs to change.
(This post originally appeared in the Runtime newsletter on Jan. 7h, sign up here to get more enterprise tech news three times a week.)
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