Unlock AI Value: Start With Overlooked Data, Says BCG
▼ Summary
– VB Transform is a long-trusted event for enterprise leaders focusing on AI strategy, helping companies move beyond experimentation to productizing and scaling AI applications.
– Companies face challenges in AI adoption, including data quality, governance, and balancing tech with people, processes, and design, per Boston Consulting Group’s Braden Holstege.
– AI-ready data is critical for enterprise AI success, with Gartner predicting 60% of projects lacking it will fail by 2026, yet many organizations still lack proper data management practices.
– Enterprises must navigate AI model drift and user sophistication, with Bank of America’s Awais Sher Bajwa emphasizing collaborative implementation over rushed deployments.
– Cloud-based, on-prem, and hybrid AI deployments present trade-offs in cost, security, and optimization, with open-source models like Llama and Mistral increasing compute demands.
Enterprise AI adoption hinges on unlocking hidden value in overlooked data sources, according to industry experts at recent tech conferences. As organizations move beyond pilot projects, they’re discovering that success often lies in rethinking how they leverage existing information assets rather than chasing flashy new solutions.
During recent discussions at major industry events, leaders emphasized that data quality and governance form the foundation for scalable AI implementations. Braden Holstege from Boston Consulting Group highlighted how forward-thinking companies are extracting insights from previously untapped data streams, analyzing customer churn patterns, product complaints, and unstructured feedback using advanced language models. “Data isn’t monolithic,” he noted. “Transaction records, development logs, and even casual user comments all contain potential goldmines when processed with modern techniques.”
Microsoft’s Susan Etlinger reinforced this perspective, explaining how AI-ready data infrastructure transforms theoretical possibilities into practical solutions. “The real magic happens when teams balance clear business objectives with the flexibility to discover unexpected insights,” she said. This approach aligns with Gartner research predicting that over half of midsize enterprises will prioritize AI-optimized data systems to accelerate deployment timelines.
However, challenges persist. Bank of America’s Awais Sher Bajwa pointed out that model drift and user sophistication create unique scaling hurdles. “Today’s employees already understand conversational AI from consumer apps,” he observed. “The challenge shifts from basic training to refining implementation strategies that align with existing workflows.”
Infrastructure decisions further complicate adoption. While cloud platforms offer testing flexibility, experts warn that compute costs and migration complexities require careful evaluation, especially with open-source models demanding significant resources. Holstege noted, “The cost-benefit analysis between proprietary systems and alternatives like Llama involves more variables than ever before.”
As enterprises navigate these considerations, one theme emerges clearly: Sustainable AI value starts with reimagining data assets already within reach, not just acquiring new technologies. Those who master this paradigm will likely pull ahead in the race to operationalize artificial intelligence effectively.
(Source: VentureBeat)
