GenAI continues to create quite a buzz. Although some skeptics point to an impending GenAI winter, conversations with enterprises indicate otherwise. CIOs and IT managers note that they are under tremendous pressure from their boards of directors, C-level executives, and lines of business leaders to deploy the new technology.
However, organisations have been experimenting with GenAI with varying degrees of success. IT managers note that despite moving several projects to proof of concept or limited deployment, many initiatives never make it to production. Projects take at least twice as long, and cost at least twice as much, as originally anticipated.
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That being said, most enterprises haven’t been systematically quantifying the impact of their GenAI projects. Very, very few organisations have concrete KPIs in place, and even fewer have conducted an ROI analysis. In 2025, expect organisations to take a more mature approach to implementing GenAI, with companies taking a step back, reflecting on what they’ve accomplished with early projects, reconsidering the costs and resources required, and creating more realistic mid- to long-term roadmaps. Essentially, they will be taking what last year was a shiny new toy and fun to play with, and getting more strategic about where it really can make the biggest impact.
Looking ahead, also expect AI Agents to make their way onto enterprise roadmaps and accelerate productivity through more complex automation. AI Agents will be empowered to take actions based on findings – an interesting development because when GenAI first appeared in the market, best practices dictated the need for a human in the loop. But times have changed, technology has matured, and guardrails have been implemented to the point where organisations can now deploy GenAI to not only make employees more efficient, but in many cases to bypass humans completely.
Throughout the next year the GenAI ecosystem will continue to be flooded with new language models. Specialised models, whether for a specific industry, a specific task, or even a specific language, will bring greater utility to organisations. Similarly, small language models, which don’t require as much processing power, are less costly to run and easier to customize, will help democratise GenAI. These alternative models will inevitably be part of the conversation when enterprises start thinking more strategically about their GenAI roadmaps.
Furthermore, small language models not only lower the cost and complexity of deploying GenAI, they also enable more organisations to consider running GenAI on-premises or at the edge. The transition to AI at the edge has significant implications for real-time applications that require low latency connectivity, since they perform better when processing is done closer to the point of data collection. Furthermore, AI processing at the edge can assuage data privacy and regulatory concerns, since potentially sensitive information does not need to be transported across large distances or regional boundaries.
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By GlobalDataGenAI has brought data management back into the limelight. To realise the benefits of AI at scale, organisations need to get their data-related ducks in a row, which means they need to obtain quality data, clean data, update data management and governance strategies, and ensure data security. And while not a particularly sexy topic, effective data management and governance are foundational to a successful AI strategy. C-level executives now have little choice but to acknowledge the connection between data and AI, and to make sizeable investments in data management and governance strategies.
