FEATURE: TRANSFORMING THE ENTERPRISE WITH AI robust data governance framework, then define clear use cases and evaluate their ROI. Validate these use cases through pilot projects before scaling, ensuring close collaboration among IT, business groups, and data science teams.
“ Ongoing investment in skill development, supported by strategic partnerships with AI vendors and specialised service providers, is essential for successfully integrating AI into the organisation,” says Walid Gomaa, CEO Omnix.
commoditised and integrated into everything,” says SandboxAQ’ s Leichenauer.
An effective AI-readiness strategy starts with defining how AI agents, robots, and humans will work together within an agentic automation framework. Companies start by mapping critical processes and deciding where autonomous agents will streamline complexity or accelerate outcomes. Next, robust data governance and secure integration shield sensitive data and grant fair access to the best data assets.
There are two main components to make an enterprise AI-ready. The first is to assess and integrate AI tools into the enterprise infrastructure. Most enterprises will not be building their own tools, but will use third-party tools supplied by a vendor.
The second component of the strategy is workforce development. As AI tools become commoditised and highly integrated, those who are able to train their workforce to get maximum use out of these tools will come out ahead. Adoption rates at a person-by-person level lag behind without adequate upskilling.
“ Data security is paramount when it comes to using these tools, and enterprises will have to consider a trade-off between self-hosting open source models and trusting third parties with their data. Over time the AI models themselves will become more
“ At the same time, employee upskilling using low-code automation and AI tools facilitates smooth adoption. Pilot projects then test real-world viability, allowing teams to refine models and processes within contained environments. Continuous feedback mechanisms deliver ongoing performance, establishing trust and measurable ROI,” says UiPath’ s El Zarka.
“ To make an enterprise AI-ready, organisations should shift from a data-driven approach to an AI-driven one. CIOs need to revisit their IT stack, prioritising platforms over individual applications. This enables seamless integration and scalability for AI solutions,” says Zoho’ s Paramasivam.
Additionally, adopting low-code tools can empower non-technical users to create AI-driven applications, accelerating innovation and reducing time-to-market.
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