THE ENTERPRISE LLM REALITY CHECK
How organisations are evolving their LLM strategies from pilots to production-ready AI. he conversation around large
T language models( LLMs) has changed dramatically over the past year. The excitement surrounding generative AI has shifted from proof-of-concept demonstrations to practical deployment, with enterprises increasingly embedding LLMs into customer service, IT operations, software development and knowledge management. Yet as organisations race to scale AI across the business, a more mature conversation is emerging.
Success is proving to depend less on the sophistication of the model itself and more on the quality of data, governance and enterprise architecture supporting it.
Many organisations have now crossed the line from experimentation to production, particularly where AI is supporting structured business processes with clearly defined outcomes.
Antonio Rizzi, VP Solution Consulting, EMEA South at ServiceNow, said:“ Organisations have moved beyond experimentation where AI is applied to structured, measurable workflows supported by high-quality data. In these environments, AI has a clear‘ railroad’ to follow: it can understand the process, take defined actions and produce outcomes that can be measured.
“ Where organisations still overestimate the technology is in expecting AI to compensate for poor organisational foundations. If processes are not defined, data is fragmented and critical knowledge exists only in people’ s heads, AI cannot automate reliably or create consistent value. The real constraint is therefore not only the maturity of the models, but also the maturity of the organisation adopting them.”
Kevin Jury, AdvisoryX, Data, Engineering Services and Custom Apps Lead, APJ & MEA at DXC Technology, agrees that the biggest advances have come
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