"As generative AI races from experimentation to enterprise-grade deployment, organizations are confronting a new set of challenges: data fragmentation, infrastructure choice, governance, ROI clarity, and the shift from POCs to fully productionized systems. In a keynote at ODSC AI West 2025, Helen O'Sullivan, AI Solutions Specialist Manager at Dell Technologies, broke down the emerging patterns she sees across startups and large enterprises building real-world AI applications."
"That first question is where many organizations stumble. "It can't just be a simple problem like improve sales," she explained. Instead, the problem must be actionable, measurable, and tied to business outcomes - such as improving sales productivity through automated readiness tools or deploying a domain-specific chatbot. This applies to Dell internally as well. O'Sullivan shared that the company identified 800 potential AI use cases before narrowing down to strategic priorities. The takeaway: focusing early prevents waste later."
Enterprise AI adoption begins with clarity about the specific, measurable business problem and the implementation environment—on‑prem, cloud, hybrid, or edge. Problems must be actionable and tied to outcomes, such as boosting sales productivity or deploying domain-specific chatbots. Early prioritization of use cases prevents wasted effort. Data location, governance, cleanliness, and access patterns determine suitable infrastructure and workload placement. Organizations face challenges including data fragmentation, infrastructure selection, governance, unclear ROI, and scaling from proofs-of-concept to production. A disciplined approach that aligns measurable use cases with data readiness and infrastructure choice accelerates successful, production-grade AI deployment.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]