The promise of Agentic AI is undeniable. Autonomous systems that can reason, plan, and act to transform business outcomes are no longer the realm of science fiction; they are the next major inflection point in enterprise technology.
But for many organizations, the promise remains out of reach. Across industries, CIOs are surveying a landscape littered with stalled proofs of concept, ungoverned tool sprawl, and AI tool initiatives that never made it past the lab.
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Tiago Azevedo
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Legacy systems, siloed data, and long development cycles create friction that prevents AI from moving from pilot to production.
To unlock real value, enterprises need to stop experimenting with AI in isolation and start operationalizing it. That means integrating it into the very fabric of how they build, deploy, and manage software.
The next frontier: orchestration over experimentation
Unlike generative AI, which produces content or code based on prompts, agentic systems can take autonomous actions to complete tasks, from resolving customer support tickets to managing inventory delays. They can reason, learn, and collaborate with other agents and human systems.
However, autonomy without orchestration creates chaos. For AI to drive meaningful outcomes, it must interact seamlessly with existing enterprise applications, data, and human workflows. The next phase of AI is therefore not about more sophisticated agents, but about embedding those agents into governed, secure, and scalable operational environments.
Companies should deploy platforms optimized to unlock the full potential of this new technology that are compatible with the additional layers that agentic AI requires. That’s why platform choice is critical, as in order to act autonomously agentic AI introduces a new architectural foundation that integrates directly with applications, systems, and data.
A unified platform for agentic AI allows IT management to build, ground, orchestrate, and monitor multi-agent workflows with enterprise-grade control. It turns isolated innovation into repeatable impact and is the difference between AI running as an experiment on the edge and the technology becoming a strategic driver at the core.
Europe’s cautious climb toward operational AI
Tech leaders in enterprise business are already convinced that agentic AI can deliver significant benefits for their businesses. Yet research shows that Europe’s progress remains steady but cautious. Only 40% of European organizations have integrated agentic AI systems into applications and workflows, compared with 50% in North America and 60% in Asia.
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Several factors explain this gap. Europe’s complex regulatory landscape, which has been shaped by the forthcoming EU AI Act, is a key consideration. Equally, varying levels of technical expertise and a lack of unified development frameworks are slowing progress. There is undeniably an urgent need to secure the foundations for operationalizing AI responsibly via trusted platforms with built-in governance, monitoring, and security.
From silos to systems: making agentic AI part of the enterprise fabric
So, what does operationalizing AI look like in practice? It begins with integration. Agentic AI delivers its full potential when it can engage directly with existing enterprise systems, whether they are CRM, ERP, supply chain, HR, and beyond.
For example, here is an example from the supply chain. An AI agent could proactively identify a shipping delay, analyze the impact, and autonomously reroute inventory while updating the customer.
But to reach that level of sophistication, organizations need an underlying architecture that connects systems, data, and people. This is where low-code platforms play a decisive role.
A low-code platform provides the composable foundation needed to connect agents to workflows without requiring custom integrations for every use case. Instead of treating AI as a bolt-on and the many compromises that entails, enterprises can embed it directly into the lifecycle of how software is designed and deployed.
Developers can use prebuilt connectors, reusable components, and visual orchestration tools to assemble complex agentic workflows that span multiple systems. These can all be governed through a single, secure control layer.
In other words, low-code does not just make software development faster. It makes AI operationalization possible.
Build, buy, or orchestrate? The rise of agent-as-a-service
As with any innovative technology wave, many organisations face the classic “build or buy” dilemma. Around a third of companies plan to integrate prebuilt agentic AI tools within existing systems, while others are developing their own in-house using proprietary or open-source frameworks.
The future lies in a hybrid model where organizations combine custom-built agents tailored to specific business contexts with agent-as-a-service (AaaS) solutions that plug into standardized interfaces.
Here again, a low-code, platform-based approach provides the bridge. It allows companies to develop their own agents, orchestrate third-party ones, and ensure they all operate under a unified governance and security framework.
By treating AI agents as reusable components within a composable enterprise, businesses can finally move from fragmented innovation to scalable transformation.
Governance and the human element
Operationalising AI also means governing it. 64% of global executives cite governance, transparency, and compliance as their biggest AI concerns.
As new AI services proliferate, CIOs need to ensure that AI initiatives align with enterprise ethics, legal standards, and data integrity requirements.
Governance cannot be an afterthought. It must be embedded in the very architecture of how AI systems are built, deployed, and monitored. This is where platform-based approaches excel, offering a unified environment for auditability, version control, explainability, and compliance — essential to scaling AI responsibly.
Conclusion: from potential to performance
The story of agentic AI so far has been one of extraordinary potential and limited follow-through. Too many initiatives have remained confined to innovation labs, disconnected from business reality.
The next chapter must be about operationalization: bringing AI into the mainstream of enterprise architecture through unified, governed, and composable platforms.
Agentic AI is not a standalone project. It is the next logical step in the evolution of low-code and digital transformation strategies — where AI agents become reusable, secure, and auditable building blocks of enterprise workflows.
The organizations that succeed will not necessarily be those with the most advanced models, but those with the most robust operational frameworks. Only then will the enterprise truly realize the transformative potential of agentic AI.
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