The ‘AI Activation Gap’
AI promises transformation, but for most companies, that promise is still out of reach. The reason? Data. Or rather, the lack of AI-ready data.
Qlik CEO Mike Capone calls it the “AI activation gap” — the chasm between AI hype and actual ROI.
Organizations racing to deploy advanced AI quickly hit a wall when their data infrastructure can’t keep up.
Missing links in the big data stack — from ETL tools and real-time pipelines to governance, quality management, and even vector databases— leave models starving for clean, integrated, and accessible data.
In a recent survey, 42% of data leaders admitted more than half of their AI projects were delayed, underperformed, or outright failed because the data simply wasn’t ready.
Senior executives may be pushing for generative AI, predictive models, and autonomous agents, but without robust platforms, governance, and secure access, most large-scale AI ambitions stall before they start.
The message from the experts is clear: there is no AI without data.
Scramble to Fix the Foundations
Tech giants and service providers are racing to close this gap. Dell’s AI Data Platform blends high-powered GPUs with advanced storage, governance, and transformation engines to keep AI fed with quality data.
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Databricks has launched Lakebase to serve AI models directly from a lakehouse architecture. Salesforce’s $8 billion acquisition of Informatica is all about marrying agentic AI with trusted, integrated data at scale.
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Meanwhile, players like IBM, Qlik, and Confluent are expanding portfolios and partnerships to provide the pipelines, streaming platforms, and observability tools needed to deliver fresh, trusted data in real time.
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Service providers such as Insight Enterprises, EY, and Indicium are building specialized AI data practices, often devoting 80–90% of AI project budgets to getting the data layer right before a single model is trained.
The opportunity is massive — and urgent. With AI evolving at breakneck speed, the companies that solve their data readiness problem today will be tomorrow’s AI leaders.
Those that don’t will remain stuck in the hype cycle, watching competitors turn their data into business-shaping intelligence.
