Ronnie Sheth, CEO, SENEN Group: Why Now Is the Time for Enterprise AI to ‘Get Practical’

Why It Matters

Without clean, governed data, AI projects fail to generate measurable ROI, jeopardizing enterprise digital transformation budgets. Practical data‑first strategies turn AI from a hype exercise into a reliable revenue engine.

Key Takeaways

• Poor data quality costs firms average $12.9 million annually
• SENEN Group boasts 99.99% client repeat rate
• Enterprises often adopt AI without data readiness roadmap
• Data foundation enables unlimited, accurate AI model deployment
• Practical AI focus shifts from pilots to measurable value

Pulse Analysis

Enterprises are waking up to the hidden expense of bad data. Gartner’s research shows that poor data quality drains roughly $12.9 million per organization each year, eroding margins and stalling growth. This financial pain point has pushed senior leaders to prioritize data hygiene, governance, and literacy before committing to AI initiatives. By treating data as a strategic asset, companies can protect investments and lay the groundwork for scalable analytics.

SENEN Group’s methodology exemplifies the data‑first approach. The firm begins with a comprehensive data‑strategy audit, identifying gaps in quality, lineage, and governance. Once the data foundation is solid, SENEN helps clients design AI roadmaps that align with business outcomes, moving from descriptive to predictive analytics and finally to enterprise‑wide AI deployment. This staged progression ensures that each AI model is fed reliable inputs, delivering trustworthy insights and reducing the risk of costly rework.

The broader market is shifting from experimental pilots to pragmatic AI execution. Executives now demand clear ROI, measurable KPIs, and rapid time‑to‑value, forcing vendors and consultancies to offer end‑to‑end solutions that start with data readiness. As AI matures, organizations that embed data quality into their core strategy will capture competitive advantage, while those that chase hype risk sunk costs and stalled digital initiatives. The current climate makes 2024 the pivotal year for enterprises to operationalize AI with a practical, data‑centric mindset.