The current phase of enterprise artificial intelligence is defined not by a breakthrough algorithm but by a jarring disconnect between ambition and execution, prompting leaders across the sector to call for a fundamental pivot toward practicality. Ronnie Sheth, CEO of the international data and AI strategy firm SENEN Group, is at the forefront of this movement, arguing that for too long, organizations have chased the prestige of AI without establishing the necessary data foundations, leading to billions in wasted investment. Her central thesis is clear: now is the time for enterprise AI to "get practical" by prioritizing data quality and measurable business value over technological hype.

The financial imperative for this strategic shift is stark, beginning with the foundational element of data. Gartner estimates that poor data quality costs organizations an average of $12.9 million each year in lost opportunities and wasted resources, a figure that is magnified when faulty data feeds complex, high-cost AI models.[1] Sheth points out that many companies, driven by executive mandates to adopt AI, jump into complex projects without a proper blueprint or roadmap, resulting in impressive user numbers that lack measurable outcomes. This premature leap often bypasses the critical first step: checking the health of the enterprise data.

This strategic misstep has led to a widespread industry phenomenon that consultants now call "pilot purgatory," where AI proof-of-concepts (POCs) become perpetual experiments that never transition into revenue-generating, production-ready systems. The statistics paint a sobering picture of this reality. Recent market intelligence reveals a dramatic spike in abandoned AI initiatives, with 42% of companies scrapping most of their projects in 2025, a significant jump from just 17% the year prior.[2] The average organization reportedly scraps nearly half, or 46%, of its AI proof-of-concepts before they ever make it to the production stage.[2] Other studies offer even more pessimistic projections, suggesting that as many as 88% of AI pilots never transition to scale, and one prominent report estimates that 95% of enterprise AI projects fail to deliver meaningful business value.[3][4] This collective failure is not rooted in the technology itself, but in an organizational inability to align AI strategy with data readiness and clear business objectives, a process Sheth insists must be a deliberate, 'data-first' course of order.

The core of the practical AI movement lies in inverting the traditional technology-centric approach. Instead of a focus on procuring the latest large language model, the new mandate is for enterprises to dedicate the majority of their resources to building a strong data foundation and an effective deployment strategy. Industry analysis on successful AI rollouts suggests a resource allocation model that drastically reweights investment away from algorithms and hardware. While a common approach might see significant budget poured into the AI models and infrastructure, successful organizations consistently invert this, dedicating only a minor percentage to the algorithms themselves, and focusing the bulk of the investment on data cleaning, governance, and organizational change management.[5] The failure to do this—to ensure data quality, clear objectives, and seamless integration—is cited as the primary reason why companies are reporting that only between 18% and 36% of their AI initiatives are delivering the business value they expected.[6] For this reason, a growing number of enterprises are now, like SENEN's clients, coming to advisory firms first for help fixing their data, rather than for immediate AI adoption.[1]

The shift toward practicality also requires a move from an "AI-first" mindset to one that is "people-centric and value-driven."[7] Sheth's view emphasizes that AI should support human decision-making and business outcomes, not merely exist as a standalone technological showcase. This approach dictates that every AI use case must be tied to a specific, measurable objective that directly impacts the organization's profit and loss statement, whether through cost reduction, revenue increase, or risk mitigation. The most practical applications of AI demonstrate a clear, quantifiable return on investment. For example, the integration of AI capabilities into core business systems like Customer Experience (CX) platforms and Enterprise Resource Planning (ERP) can deliver substantial value, with economic validation reports showing a conservative return on investment of 214% over five years.[8] In the supply chain, a practical approach to AI has resulted in companies achieving an 18% reduction in demand forecasting errors and an increase of up to 15% in on-time deliveries.[9] Across the board, enterprises that manage to successfully transition from pilot to production are seeing tangible returns, with one major study indicating an average return of $3.5 for every $1 invested in AI.[10]

This tangible evidence of success underscores the current turning point in the industry. The era of exploratory, abstract AI projects driven purely by technological curiosity or competitive pressure is giving way to a more mature, disciplined phase of deployment. The focus on practicality means choosing the right use cases—those that are simple, high-impact, and directly linked to the corrected data foundation—to achieve what Sheth refers to as the "aha" moment of measurable business success. By grounding AI in rigorous data governance, clear strategic roadmaps, and a relentless pursuit of demonstrable ROI, the enterprise sector is finally moving beyond the hype cycle to unlock the transformative potential that has been promised for so long, making the 'practical' deployment of AI the new competitive standard.