GenAI, from experimentation to adoption

Another realization enterprises had is just how important data is to AI initiatives, especially those composing their AI services.  Organizations are finding they have outdated data or incomplete data sets. Companies tend to invest heavily in the data plane — where data is stored, organized and managed. Now, they need to invest in data engineering to prepare data for grounding and fine-tuning their AI models. 

Predictions around future growth and concerns for AI 

There is a lot at stake for organizations. AI will reshape enterprises and industries. Within the enterprise, AI will act as an assistant, advisor, agent or all three, changing business processes, applications and daily work tasks. Industries will innovate, engage customers and deliver value in fundamentally new ways. But without a vision and enterprise AI strategy, backed with a use case roadmap and strong business cases, this cannot be recognized. We expect some organizations will make the AI pivot in 2025 out of the experimentation phase. In doing so, they will begin recognizing the exponential benefits of their collective AI use cases starting in 2027. For those organizations that do not pivot in 2025, their experimentation phase will slip into 2026 as they fall behind their competitors. The difference between these two paths will be significant, impacting productivity gains, speed of innovation, customer relationships and financials. It’s crucial to keep moving forward on this journey. 

The good news for CIOs is that you have an opportunity to take a leadership role with AI, especially as organizations mitigate risk by keeping AI model development centralized. Don’t forget to consider the support employees will need to adopt AI and develop a change management plan to bring everyone along.