Many companies rush into adopting artificial intelligence without fully preparing their data or having a clear plan. This often leads to wasted resources and missed opportunities. Experts say that focusing on data quality and strategic planning is the smarter way to succeed with AI initiatives.

The Importance of Data Quality Before AI Adoption

Before jumping into AI projects, organizations should check the state of their data. Poor data quality can sink AI efforts and cost companies millions each year. Gartner estimates that bad data costs organizations an average of $12.9 million annually. Fortunately, more companies now understand the importance of clean, reliable data and are working to improve it.

Ronnie Sheth, CEO of SENEN Group, emphasizes that many businesses start with a desire to implement AI but lack a solid foundation. She notes that rushing into AI without good data often results in impressive numbers but no real results. Sheth has been in the data and AI field for years and has seen firsthand how vital good data is to success.

Shifting Focus from AI Pilots to Practical Strategies

Sheth points out that many organizations begin AI projects because of executive mandates or buzz, not because they are ready. They may have user adoption but lack measurable outcomes. As a result, Sheth says the conversation has shifted toward being more practical and strategic.

Now, companies are coming to SENEN Group for help with their data first. The initial step is always fixing data issues—cleaning, organizing, and establishing a strong foundation. Once data quality is assured, organizations can then build and deploy AI models with confidence. This approach ensures that AI solutions produce accurate and meaningful results.

Building a Roadmap for Sustainable AI Success

Sheth shares examples of clients who started with data governance but soon realized they needed a clear data strategy—the reasons behind data collection, the desired outcomes, and the ultimate goals. Developing this strategy helps organizations move from raw data to descriptive analytics, then to predictive analytics, and finally to AI-driven insights.

She emphasizes that setting up a solid data and AI strategy is essential for long-term success. It’s about creating a roadmap that guides the organization through each step, avoiding the pitfalls of rushing into AI without proper preparation. This methodical approach builds confidence and improves the chances of achieving real value from AI projects.

Sheth believes now is the perfect time to focus on practical AI initiatives. Instead of experimenting with pilots or chasing innovation for its own sake, companies should prioritize building a strong foundation. This way, they can scale AI solutions effectively and sustainably, ensuring measurable benefits and avoiding costly mistakes.

Inspired by

• https://www.artificialintelligence-news.com/news/ronnie-sheth-ceo-senen-group-why-now-is-the-time-for-enterprise-ai-to-get-practical/

Sources

• forbes.com
• senengroup.com