Is 2025 the year that the AI advances of recent years finally deliver significant results to corporate bottom lines worldwide? I believe the answer is yes. The technology has been ready for this shift for at least 18 months, but most users have yet to fully grasp the details needed to harness its full potential. Here are our predictions for how the AI space will evolve over the course of the year.
MLOps and LLMOps combine into AIOps: Machine learning and prompt engineering are very distinct techniques with different tools and use cases. Prompt engineering has become the “hot” item because its time to ROI is quicker than traditional machine learning, but it isn’t a magic bullet for all problems. As practitioners get a better handle on the strengths and weaknesses of both – and how they can be used together – expect to see a new wave of frameworks and platforms for leveraging both technologies as one.
The year of the automated call center agent: As consumers, we understandably worry about automation getting in the way of solving our problems. Anybody lost in a complex phone tree can attest to the limitations of technology in understanding your individual needs. However, AI-based call center agents or even AI-aided human agents have a financial ROI that is so massive that this is sure to be one of the areas that will change the most – and most quickly – over the coming year. The rising quality of voice-to-voice models means that businesses and consumers will actually benefit from this technical migration. Expect this trend to accelerate quickly in the marketplace.
Unlocking the true value of data lakes with AI: Data lakes have long held the promise of breaking down the silos in an organization, but their potential has been underrealized due to the sheer complexity of getting actionable insights from such massive, unstructured repositories. The use of large language models (LLMs) across corporate data lakes will enable organizations to mine them more effectively, uncover patterns and trends, and identify anomalies or mismatches across departments. Leveraging AI to analyze corporate data lakes represents one of the most significant and promising opportunities in enterprise technology today.
LLM quality keeps up its frenetic pace of improvement: There’s a lot of chatter that the process of simply scaling LLMs to ever-larger data centers may be on its last legs. Are we finally hitting the peak of what can be achieved with this technology? My prediction is no. I don’t doubt that there are a great deal of brilliant research ideas sitting on the backburner as scaling laws drive the field forward. Expect to see more variation in output as LLM providers try myriad different ways of continuing to advance AI, some of which will prove to be enough to see major progress continue.
As we look ahead, it’s clear that AI is on the cusp of driving transformative change across industries. From the integration of MLOps and LLMOps into AIOps frameworks, to the rise of automated call center agents and the unlocking of data lake value through LLMs, the potential for AI to impact the bottom line has never been greater. With rapid advancements in model quality and more targeted applications, this is the year when organizations that embrace AI strategically will begin to reap massive rewards, leaving those who hesitate scrambling to catch up. The time to harness the full power of AI is now.