Data readiness is critical for AI adoption — in 2025, organizations will finally acknowledge this fact.

Data quality and readiness have always been foundational to business success in the broadest of senses. But with AI and other advanced systems now entering the equation, the ramifications of poor-quality data are steeper than ever. Bad data contributes significantly to the fact that AI projects fail about 80% of the time. It all goes back to the “garbage in, garbage out” principle — in essence, AI systems amplify and replicate the inconsistencies and inaccuracies found in low-quality data sets. And that can lead to millions of dollars lost in poor decision-making and misguided investments.

However, data readiness is about more than just quality — it’s also about having systems and infrastructure capable of supporting AI initiatives. Without proper governance and robust processes, organizations may find their efforts falling short.

Luckily, a change is coming. I predict that leaders will finally embrace the importance of data readiness in 2025, working to address the foundational elements of their data strategy before investing in AI (or, at the very least, alongside AI investments). By building a solid foundation, businesses will unlock the potential of AI while minimizing risks associated with low-quality or biased data.

Data engineers will be in high demand next year

As more organizations accept and appreciate the importance of data readiness for AI and analytics success, the demand for skilled data engineers will grow. More than half of the work required to support AI is rooted in integrating, cleaning, and preparing data for use in models. So, individuals who excel in executing these tasks at scale will become very, very popular in 2025.

Companies like Databricks, Snowflake, and Microsoft are already leading the charge here, investing heavily in experts, as well as tools and platforms, that enable data management at scale.

AI success rates will improve next year as organizations test smaller pilot programs

The AI hype cycle is unlikely to ever truly die. However, we’re seeing a shift in AI perception as more leaders acknowledge the inherent limitations of AI-driven technologies. In this new era, businesses will shift their focus from chasing the latest AI buzzword or trend to solving tangible problems.

Too often, companies jump into AI without a clear strategy, asking, “How can we use AI?” rather than, “What business problems actually need solving, and can AI be a part of the solution?” In 2025, organizations that thrive will prioritize aligning AI with specific goals, such as automating repetitive processes, improving customer service, or optimizing resource allocation. This shift requires identifying high-value, low-effort projects to generate early wins and build organizational confidence. For example, automating customer call routing can deliver measurable ROI quickly, setting the stage for larger initiatives.

By treating AI as a tool for solving business challenges rather than as a magic solution, industry leaders will see more successful AI programs.

Generative AI will play a supportive, but not dominant, role in the AI landscape of 2025

While tools like ChatGPT have captured public attention, their utility remains niche compared to the broader potential of AI in process automation and predictive modeling. Generative AI excels at specific tasks, like creating text-based outputs or brainstorming, but its real value lies in augmenting traditional AI use cases. For instance, generative tools can streamline workflows by assisting with customer service scripts or internal documentation. However, these applications are secondary to core business solutions like preventative maintenance or resource optimization.

Businesses must avoid treating generative AI as a one-size-fits-all solution and instead focus on how it can complement existing processes. In this context, its role will evolve as part of a broader strategy centered on practical, measurable outcomes.