Last year Large Language Models (LLMs) took center stage, signaling a new era in technology and innovation. This transformative wave wasn’t just a blip on the radar for tech aficionados; it became a pivotal focus for Chief Information Officers and Chief Technology Officers across industries.
The initial enthusiasm led many organizations to start building their own LLMs based solutions, driven by the promise of revolutionizing how we interact with data and digital systems. However, the journey from excitement to execution unveiled a complex reality: while the first 80% of development seemed straightforward, the remaining 20%—involving data ingestion at scale, indexing, entitlements, and integration into existing enterprise systems—is a formidable challenge, consuming 80% of the effort. And while a must to make it work in an enterprise setup it’s not “exciting”, hence not much in focus with consequences down the line, when trying to go full “production”.
As we stepped into 2024, organizations had already earmarked significant budgets for AI, ready to invest heavily in what was perceived as the next frontier of competitive advantage. Nvidia’s stock prices soared with each new ChatGPT announcement, a testament to the market’s bullish outlook on AI’s potential.
Early pilots showcase the innovative capabilities of LLMs, yet the chasm between a successful pilot and operational deployment will become increasingly apparent. Contrary to – my view – popular belief, chat functionalities, while impressive, are not the ultimate application of LLMs. The real value is in automating internal processes, a more challenging endeavor but one with potentially transformative outcomes. By year end the initial euphoria will be coupled with scrutiny over the tangible returns on these hefty AI investments.
Looking ahead to 2025, a reality check will be on the horizon. The initial surge in budgets is likely to face cuts due to underwhelming results in some areas. This recalibration will see some organizations doubling down. The “it takes a tad longer” argument will be used to invest even more in hopes of unlocking the true potential of LLMs. Others will begin to question the initial hype surrounding chat functionalities. Market corrections are to be expected, with significant players like Nvidia and OpenAI likely to experience downturns as a consequence of changed market sentiment.
However, this period of disillusionment is not the end but rather a critical juncture. It mirrors the Gartner Hype Cycle’s trajectory, from inflated expectations to a trough of disillusionment, before climbing towards a plateau of productivity.
The true value of LLMs, as we will begin to see more clearly by mid-2025 and beyond, lies not in the flashy chat interfaces but in the less visible, yet far more impactful, automation of complex processes. This phase will usher in real productivity leaps and unveil new areas of progress, marking the transition from overhyped short-term expectations to meaningful, long-term outcomes.
In essence, the journey of LLMs from 2023 to 2025 is a microcosm of the broader tech innovation lifecycle. It underscores the importance of tempering initial excitement with a measured understanding of the challenges and complexities involved. For businesses, the key to leveraging LLMs effectively lies in realistic goal-setting, patience, and a strategic approach to integrating these technologies into their operational fabric. As we navigate this evolving landscape, the lessons learned will not only refine our approach to LLMs but also shape how we adopt and adapt to new technologies in the future.
