Cloud & AI for the Enterprise
Enterprise intelligence must adapt to ambiguity, not collapse under it. Enterprise IT teams are spending too much time cleaning data and not enough time deploying intelligence. The assumption that AI systems need pristine inputs has become a costly bottleneck—especially in environments where data is inherently messy, fragmented, or incomplete. Across industries, the most effective decision-makers … Read more
AI systems must learn to reason through real-world data noise—not wait for cleansing. Enterprise AI initiatives continue to stall under the weight of a flawed assumption: that intelligence depends on data perfection. “Garbage in, garbage out” has become a default excuse for why models fail to ship, scale, or deliver measurable value. But the reality … Read more
Enterprise AI success depends on judgment, not perfection. Stop waiting for clean data—start designing for real-world mess. Enterprise AI projects stall for many reasons, but one excuse dominates: “Garbage in, garbage out.” It’s become a catch-all rationale for why systems fail to deliver. The implication is clear—until the data is pristine, the intelligence can’t be … Read more
Accelerate enterprise decision-making by removing silos, aligning data, and embedding clarity into workflows. Enterprise decision-making is under pressure. The volume of data has grown, but clarity hasn’t. Teams are flooded with dashboards, reports, and alerts—yet decisions still stall. The root issue isn’t lack of information. It’s fragmentation: of systems, incentives, and context. When decisions are … Read more
Enterprise IT leaders are tackling fragmented data integration to reduce risk, improve decision velocity, and unlock scalable value. Fragmented data integration is one of the most persistent blockers to enterprise agility. As systems multiply and business units digitize independently, data becomes scattered across incompatible platforms, formats, and governance models. The result is not just inefficiency—it’s … Read more
How to structure, govern, and activate your data for real ROI from GenAI and agentic systems. AI adoption is outpacing data readiness across most enterprise environments. GenAI and agentic systems promise productivity gains, but they rely on data that’s not just available—it must be structured, contextual, and governed. Without that foundation, AI outputs become unreliable, … Read more
How to align your data architecture, governance, and workflows to unlock real ROI from GenAI and agentic systems. AI adoption is accelerating, but most enterprise data environments aren’t ready for it. GenAI and agentic systems promise transformative productivity, but they require data that’s not just available—it must be structured, governed, and context-rich. Without that foundation, … Read more
As AI adoption accelerates, organizations are elevating the CDO role to align data strategy with business outcomes. In 2024, it seemed inevitable that AI transformation would deepen the alignment between data and infrastructure. Many expected the percentage of chief data officers reporting to CIOs to rise sharply. Instead, Forrester’s 2025 data revealed the opposite: a … Read more
Unlock AI’s full potential by eliminating data silos and building a unified, governed data foundation. AI is only as good as the data it learns from. Most enterprises already sit on vast volumes of valuable data—but it’s scattered across systems, teams, and formats. That fragmentation limits AI’s ability to deliver meaningful outcomes, especially when models … Read more
Unifying enterprise data is the fastest path to meaningful AI ROI—if you solve the right problems first. AI promises transformation, but most enterprise deployments stall before delivering measurable value. The reason isn’t the model—it’s the data. Fragmented, siloed, and inconsistently governed data systems make it nearly impossible for AI to deliver reliable insights at scale. … Read more