The Evolution of Generative AI in 2025: From Novelty to Necessity
The year 2025 marks a pivotal moment in the journey of Generative AI (Gen AI). What started as a fascinating technological novelty has now become a crucial tool for companies across industries.
Generative AI: from finding a solution to a problem to a problem-solving powerhouse
The AI generation’s initial wave of enthusiasm was driven by the raw novelty of interacting with large language models (LLMs), trained on massive public data sets. Companies and individuals alike were rightly fascinated by the ability to type in prompts in natural language and receive detailed, coherent responses from the public boundary models. The human-like quality of LLMs’ results caused many industries to dive headlong into projects with this new technology, often without a clear business problem to solve or a real KPI to measure success. While there have been some major value unlocks in the early days of Gen AI, it’s a clear signal that we’re in an innovation (or hype) cycle when companies move away from the practice of first identifying a problem and then acting on it to look for a workable technological solution to solve it.
In 2025, we expect the pendulum to swing back. Organizations will look to Gen AI for business value by first identifying problems the technology can address. There will certainly be many more well-funded scientific projects to come, and the first wave of Gen AI use cases for summaries, chatbots, content, and code generation will continue to flourish, but executives will start holding AI projects accountable for ROI this year . The technology focus will also shift from general-purpose public language models that generate content to a set of smaller models that can be monitored and continuously trained in a company’s specific language to solve real-world problems in a measurable way. influence the operating result. way.
2025 will be the year that AI will become the core of the enterprise. Business data is the way to unlock real value with AI, but the training data needed to build a transformational strategy isn’t on Wikipedia, and never will be. It lives in contracts, customer and patient files, and in the messy unstructured interactions that often go through the back office or live in boxes of paper. Getting that data is complicated, and general purpose LLMs are a poor technology here, despite privacy, security and data management concerns. Enterprises will increasingly adopt RAG architectures and small language models (SLMs) in private cloud environments, allowing them to use internal organizational data sets to build proprietary AI solutions with a portfolio of trainable models. Targeted SLMs can understand a company’s specific language and the nuances of its data and deliver higher accuracy and transparency at a lower cost – while staying aligned with data privacy and security requirements.
The crucial role of data scrubbing in AI implementation
As AI initiatives increase, organizations must prioritize data quality. The first and most crucial step in implementing AI, whether LLMs or SLMs, is ensuring that internal data is free of errors and inaccuracies. This process, known as ‘data scrubbing’, is essential for managing a clean data domain, which is central to the success of AI projects.
Many organizations still rely on paper documents, which need to be digitized and cleaned for daily business operations. Ideally, this data would end up in labeled training sets for an organization’s own AI, but we’re still in the early stages of seeing that happen. A recent survey we conducted in partnership with the Harris Poll, interviewing more than 500 IT decision makers between August and September, found that 59% of organizations aren’t even using their entire data domain. The same report shows that 63% of organizations agree that they lack visibility into their own data and that this is hindering their ability to maximize the potential of GenAI and similar technologies. Privacy, security, and governance concerns are certainly obstacles, but accurate and clean data is critical; even small training errors can lead to compounding problems that are difficult to fix once an AI model gets it wrong. By 2025, data scrubbing and the pipelines to ensure data quality will become a critical area of investment, enabling a new breed of enterprise AI systems to function based on reliable and accurate information.
The growing impact of the CTO role
The role of the Chief Technology Officer (CTO) has always been crucial, but its impact will increase tenfold by 2025. In the coming years, parallels will be drawn with the ‘CMO era’, in which the customer experience was paramount under the Chief Marketing Officer. be the ‘CTO generation’.
While the CTO’s core responsibilities remain unchanged, the influence of their decisions will be greater than ever. Successful CTOs need a deep understanding of how emerging technologies can reshape their organizations. They also need to understand how AI and related modern technologies are driving business transformation, not just efficiency within the four walls of the company. The decisions CTOs make in 2025 will determine the future trajectory of their organizations, making their role more impactful than ever.
The 2025 predictions highlight a transformative year for Gen AI, data management and the role of the CTO. As Gen AI evolves from a solution in search of a problem to a problem-solving powerhouse, the importance of data scrubbing, the value of enterprise data domains, and the growing impact of the CTO will shape the future of enterprises. Organizations that embrace these changes will be well-positioned to thrive in the evolving technology landscape.
