FEATURE
Almost anyone can now spin up a working prototype from a natural language prompt, and for rapid experimentation or early-stage MVPs, that’ s genuinely powerful. The overestimation creeps in when that same loosely guided approach gets pushed straight into production. Enterprises aren’ t just shipping apps, they’ re building systems that need resilience, security, explainability and long-term maintainability, and prompting alone won’ t get you there.
“ That gap between‘ it works in a demo’ and‘ it’ s safe to run at scale’ is exactly the market gap we saw, and why we built Dava. Flow. This is our structured, governed methodology that keeps AI-driven delivery without losing the guardrails enterprise-grade software demands.”
Choosing the right model strategy
As organisations mature their AI programmes, selecting the appropriate model architecture has become just as important as selecting the right use case.
Elie Al Chini, Director of Sales – ServiceNow Business Group at DXC Technology, said the decision should always begin with the business objective rather than the technology itself.
“ The starting point shouldn’ t be the model. It should be the business problem you’ re trying to solve.
“ For many organisations, public foundation models provide the fastest route to value for use cases such as knowledge management, content generation and employee productivity. Where data sensitivity, regulatory requirements or specialised expertise are involved, smaller domain-specific models or hybrid architectures often deliver better accuracy, greater control and lower operating costs.
“ Increasingly, we’ re seeing enterprises adopt a multi-model strategy rather than standardising on a single platform. Different models have different strengths, and the most effective AI environments combine multiple models with enterprise data, governance and business-specific AI agents.
“ Ultimately, the right choice is the one that delivers the required business outcome while meeting security, compliance and cost objectives.”
Trust starts with governance
As enterprise AI becomes embedded in business operations, governance and explainability have become board-level concerns rather than purely technical discussions.
According to Rizzi,“ Trust requires end-toend observability across AI models, agents, workflows, tools and data. Organisations need to understand which models are being used, what information they are accessing, what decisions they are making and what actions they are taking.
“ An AI Control Tower can provide this oversight by acting as a common governance layer across
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INTELLIGENT CIO MIDDLE EAST www. intelligentcio. com