Enterprises are moving beyond experimentation to full-scale adoption of AI agents in the coming year. This shift is reinforced by findings from the EY report, which shows that 91% of organisations cite speed of deployment as the biggest factor driving AI adoption decisions. This highlights the urgency to operationalise AI, including agentic systems, even as governance and coordination continue to lag. After rounds of pilots and prototypes, organisations will expect AI agents to start driving tangible business outcomes. Where scaling, governance, and cost control have been obstacles, connecting AI agents to real-time, governed data and integrating them across business workflows could propel full-scale AI adoption. Organisations are moving from using AI as a passive consultant to deploying it as an autonomous agent capable of executing “high-stakes” workflows in finance and engineering.
Recent advancements enabling organisations to deploy agent teams are a key step in operationalising AI agents at scale, and this could be particularly useful for finance and legal teams. However, fragmentation across the data estate hinders any organisation from maintaining consistency, governance, and control. Organisations risk having different departments choose their own tools, run their own POCs, and deploy solutions independently. Much like the early days of business intelligence, we are beginning to see AI silos forming within enterprises.
Human oversight is still essential to ensuring data quality and governance, providing a strong foundation for enterprises to deploy multiple AI tools and models to optimise workflows flexibly. In order to achieve this, regardless of where the data resides and without any vendor lock-in, organisations should look to a “Private AI” architecture. Leveraging platforms that are secure by design, enforce data residency and access controls. Through partnerships with the AI ecosystem, leading AI models are being directly embedded into enterprise lakehouse platforms and are key components in the organisation’s AI architecture. With this AI framework, organisations can deploy models on-premises and retain control over their data and AI models, ensuring compliance and security throughout the AI lifecycle.
In the current landscape, organisations must also:
- Ensure data sovereignty: Data remains within the organisation’s jurisdiction, aiding compliance with local and international regulations.
- Enhance security: By limiting data exposure to external entities, the risk of data breaches is significantly reduced.
- Maintain control: Organisations have full oversight of their AI models and data, allowing for better governance and accountability.
Also Read: AI at Scale – Shaping the Future of Intelligent Banking
As organisations become spoilt for choice with the emergence of new models and agents, it underscores the importance of integrating AI seamlessly into their data fabric. Underpinned by strong data foundations, standardised metrics, and sustainable governance, enterprises that keep pace with innovation in this manner will reap the most benefits.
Views expressed by: Mayank Baid, Regional Vice President – India and South Asia, Cloudera
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