Manoj Chaudharty, CTO, Jitterbit, says CIOs now have multiple pathways to tailor AI to their business without losing control of outcomes.
AI’s rapid advance from concept to capability is proving central to rethinking how work gets done.
In the manufacturing sector, AI is proving transformative, enabling businesses to unlock new efficiencies, improve quality and build resilience across supply chains and production environments.
But successful AI adoption hinges on more than potential; it demands strategy, accountability and integration into real-world operations.
Manufacturing has long embraced automation. What’s changing now is the nature of what’s being automated and how. Rigid, rules-based systems are making space for adaptive, intelligent solutions capable of learning, optimising and making decisions. This shift to agentic AI systems that can operate autonomously with accountability promises much greater benefits to manufacturing in particular than the rush to Gen AI ever did.
Leading platforms such as IBM and NVIDIA are already integrating AI agents into existing enterprise workflows that support both business and IT stakeholders. This more accessible and collaborative model empowers manufacturers to build intelligent agents that enhance operations while maintaining the transparency and control required in such a data-intensive industry.
Modern manufacturing environments are complex by nature. Multiple production lines, diverse machinery, fluctuating demand and sprawling supply chains generate immense volumes of data. Even traditional, industry-specific solutions can fail to maximise efficiency. Properly deployed AI, on the other hand, excels in finding patterns and making decisions in dynamic, data-rich contexts.
Layered AI architectures, built to encompass both automation and human oversight, are enabling manufacturers to bridge the gap between enterprise data and application workflows. These approaches allow for the creation of accountable AI agents that can drive decisions in areas like predictive maintenance, logistics optimisation and inventory forecasting – with built-in guardrails to ensure transparency and mitigate risks such as data bias or hallucinations.
Ninety-nine per cent of enterprises have integrated AI into their operations and 31% of enterprises are already planning for agentic AI. Despite this growing adoption, many manufacturers still face barriers such as high implementation costs, integration complexity and perhaps, most crucially, uncertainty around oversight. The trust just is not there to allow for wider deployment. This is why accountability is emerging as a critical success factor in agentic AI adoption.
Layered AI systems that include human-in-the-loop oversight provide a framework for scaling AI responsibly. Whether building agents in-house through natural language or low-code tools, leveraging pre-built options from trusted marketplaces or outsourcing agent development, CIOs now have multiple pathways to tailor AI to their business without losing control of outcomes – even allowing less technically adept job roles to contribute working and accountable agents.
Importantly, AI isn’t about replacing workers, it’s about elevating them. With AI handling repetitive or time-consuming tasks, teams can redirect their focus to innovation, process improvement and strategic problem-solving.
The rise of AI assistants in tools like low-code application builders and API managers illustrates how AI is becoming embedded not just in back-end systems, but in everyday workflows that include the creation of AI tools themselves. These assistants allow developers and business users alike to build and deploy complex functionality using natural language, lowering the barrier to entry and accelerating time to value. The maximum value of AI can only be achieved this way, by allowing everyone within an organisation to build with and benefit from it.
The future of manufacturing will be shaped not just by automation, but by intelligent systems that can adapt, optimise and act with accountability.
For CIOs, the imperative is clear: adopt AI not as a trend, but as a strategic enabler, one that integrates into your architecture, empowers your people, and delivers measurable outcomes.
With a thoughtful approach to deployment, the right tooling and a commitment to accountability, AI can help manufacturers drive sustainable transformation – from the factory floor to the boardroom.
