In this exclusive interview, Mr. Dhananjay Goel, Fractional CTO and CEO of Enlight Lab, discusses how multi-agent AI systems are transforming industrial operations through intelligent automation, RFQ orchestration, and real-time supply chain visibility. He shares how “AI Employees” are enabling enterprises to evolve into adaptive, data-driven ecosystems powered by collaborative intelligence.
IT Voice- AI-powered digital workforces are rapidly transforming enterprises. How is Enlight Metals embracing this shift through its “AI Employees” initiative?
Dhananjay Goel- We believe enterprises are entering a transition where AI will no longer function as a software tool sitting beside the workforce. It will become part of the workforce itself.
At Enlight Metals, our “AI Employees” initiative is built around this idea. We are creating AI-driven operational systems that can independently coordinate repetitive workflows, process information in real time, and support faster decision-making across procurement, logistics, customer communication, and supply chain execution.
Industrial sectors have historically depended on manual coordination layers involving calls, spreadsheets, follow-ups, fragmented approvals, and delayed visibility. That operating model is no longer sustainable in a high-speed infrastructure economy.
Our objective is not simply automation. It is operational intelligence at scale.
By deploying AI Employees across core functions, we are reducing execution friction and enabling human teams to focus on strategic responsibilities such as ecosystem development, customer relationships, expansion planning, and complex problem-solving.
Over time, we believe every major enterprise will operate through a hybrid workforce where humans provide judgement, creativity, and leadership while AI systems manage coordination, execution speed, and data-driven operational flow.
That transition is already beginning.
IT Voice- Your multi-agent AI system is a key innovation. Can you explain how different AI agents collaborate to streamline core operations?
Dhananjay Goel- Most enterprises still use AI in isolated workflows. We are building interconnected AI systems that operate collaboratively across functions.
At Enlight Metals, different AI agents are responsible for specialised operational responsibilities such as RFQ management, procurement coordination, logistics visibility, pricing intelligence, customer interaction, inventory movement, and workflow monitoring.
The real value emerges from orchestration.
Instead of functioning independently, these agents continuously exchange contextual information and coordinate actions in real time. For example, a logistics agent can communicate shipment delays to a customer interaction agent, which proactively updates stakeholders while simultaneously alerting internal procurement systems to adjust timelines and inventory allocation.
This creates a unified operational intelligence layer across the organisation.
We see multi-agent systems as the future of enterprise infrastructure because businesses are becoming too dynamic and data-intensive for siloed execution models. In the future, organisations will increasingly operate through AI ecosystems where specialised agents continuously collaborate across departments, functions, and decision layers.
The enterprise itself will evolve into a real-time adaptive system.
IT Voice- RFQ automation is a major highlight. How has AI improved speed, accuracy, and response time in proposal generation?
Dhananjay Goel- RFQ workflows are traditionally one of the most time-intensive operational bottlenecks in industrial businesses. Multiple teams often coordinate manually across pricing, inventory checks, supplier validation, logistics calculations, and customer communication before a quotation can even be generated.
AI fundamentally changes that process.
Our AI-driven RFQ systems can analyse requirements, validate inventory availability, compare pricing structures, identify optimal sourcing pathways, and generate proposal drafts within minutes instead of hours or days.
The impact goes beyond speed. Accuracy and consistency improve significantly because AI reduces dependency on fragmented manual inputs and repetitive operational handling.
More importantly, faster RFQ response times directly influence business competitiveness. In modern supply chains, responsiveness itself has become a strategic advantage.
As industrial ecosystems become more digital and time-sensitive, enterprises that can compress decision cycles and execution timelines will outperform traditional operating models.
We believe AI-driven commercial coordination will become standard infrastructure across manufacturing and industrial sectors over the next few years.
IT Voice- Real-time logistics tracking is critical in your industry. How does your AI system ensure proactive updates and better supply chain visibility?
Dhananjay Goel- Supply chains are fundamentally information systems. Most delays, inefficiencies, and operational escalations happen because visibility breaks down across stakeholders.
Our AI systems continuously monitor logistics movement, delivery timelines, route conditions, inventory status, and coordination workflows in real time. Instead of reacting after disruptions occur, the system is designed to anticipate operational deviations early and trigger proactive communication across the network.
For customers, this creates greater transparency and predictability. For internal teams, it significantly improves planning accuracy and execution coordination.
We believe the future of industrial logistics will not be defined only by transportation infrastructure. It will be defined by intelligence infrastructure.
Companies that can create real-time visibility across procurement, warehousing, dispatch, transit, and delivery ecosystems will operate with dramatically higher efficiency and lower execution risk.
In large-scale industrial supply chains, information latency is often more damaging than physical latency. AI helps eliminate that gap.
IT Voice- What measurable improvements have you seen in efficiency, cost savings, or turnaround time after deploying AI-driven workflows?
Dhananjay Goel- The biggest measurable improvement has been operational compression.
Processes that previously required multiple manual touchpoints, coordination loops, and extended response timelines are now being executed significantly faster with greater consistency and lower dependency on repetitive human intervention.
We have seen meaningful improvements in RFQ turnaround times, workflow visibility, response accuracy, customer coordination speed, and internal execution efficiency. Operational teams can now focus more on strategic oversight rather than repetitive administrative processing.
However, the larger impact is not only about cost savings. It is about organisational scalability.
AI allows businesses to handle increasing operational complexity without proportionally increasing coordination overhead. That becomes extremely important as industries scale across multiple geographies, vendors, customers, and infrastructure projects simultaneously.
The real value of AI is not simply reducing labour. It is increasing enterprise capability.
IT Voice- With reduced manual intervention, how are roles within your organization evolving toward more strategic and high-value functions?
Dhananjay Goel- The role of human talent is shifting from operational execution to strategic orchestration.
Historically, industrial businesses relied heavily on people managing repetitive coordination workflows. As AI increasingly handles those execution layers, human teams can focus on higher-value responsibilities such as relationship management, strategic sourcing, expansion planning, systems thinking, and complex decision-making.
This transition is important because AI does not eliminate the need for human capability. It changes where human capability creates the most value.
In our view, the future workforce will be defined less by routine task execution and more by adaptability, judgement, creativity, and the ability to work effectively alongside intelligent systems.
Companies that invest early in workforce evolution, AI literacy, and organisational redesign will build significant long-term advantages.
The future enterprise will not be human-only or AI-only. It will be collaborative intelligence at scale.
IT Voice- The integration of a voice-enabled AI agent is interesting. How will this enhance user interaction and operational productivity?
Dhananjay Goel- Voice interfaces will play a major role in the next phase of industrial digitisation.
A large portion of India’s industrial ecosystem still operates through phone-based coordination across suppliers, warehouses, transporters, plant operators, and field teams. Traditional enterprise software often creates friction because it requires technical interfaces and structured workflows.
Voice-enabled AI changes that dynamic completely.
Instead of navigating multiple systems manually, users can interact conversationally with operational infrastructure in real time. A plant manager can request dispatch updates, a procurement executive can check inventory movement, or a customer can verify delivery status through natural language interactions.
This dramatically improves accessibility, response speed, and workflow adoption.
More importantly, voice AI has the potential to democratise industrial technology adoption across regional and multilingual ecosystems where conventional software interfaces often create participation barriers.
We believe conversational AI will become one of the defining interfaces of the next industrial era.
IT Voice- What challenges did you face while implementing a multi-agent AI system, and how did you overcome them?
Dhananjay Goel- The biggest challenge was not technology. It was operational integration.
Industrial businesses operate through deeply interconnected workflows where even small inconsistencies in data flow, communication timing, or process logic can create cascading execution issues. Building multi-agent systems therefore requires far more than deploying AI models. It requires redesigning operational architecture itself.
Another challenge was ensuring coordination reliability between agents operating across different business functions. AI systems are only as effective as the quality of the operational context and governance frameworks surrounding them.
We approached this by building strong workflow standardisation, real-time monitoring systems, structured escalation layers, and continuous human oversight mechanisms during deployment phases.
Equally important was organisational adaptation. Teams needed to understand that AI was not replacing operational ownership. It was enhancing execution capability.
The companies that will succeed with AI are not necessarily the ones adopting the most tools. They are the ones redesigning how the organisation itself functions.
IT Voice- How do you ensure data accuracy, security, and reliability when multiple AI agents are handling critical business processes?
Dhananjay Goel- As AI becomes more integrated into enterprise infrastructure, trust and governance become non-negotiable.
At Enlight Metals, we approach AI systems with the same seriousness as financial or operational infrastructure. Accuracy, traceability, access control, and process validation are built into the system architecture itself.
Multiple verification layers, structured permissions, workflow checkpoints, and human escalation frameworks help ensure reliability across critical operations. AI systems are highly effective at execution speed, but enterprise resilience still requires governance, accountability, and oversight.
We also believe businesses must move toward responsible AI frameworks where transparency and operational auditability are embedded into decision-making systems from the beginning.
Over time, trust will become the defining differentiator in enterprise AI adoption.
Companies that combine automation with governance will scale sustainably. Companies that pursue speed without control will create long-term operational risk.
IT Voice- Looking ahead, how do you see multi-agent AI systems reshaping the metals industry and enterprise operations at large?
Dhananjay Goel- We believe multi-agent AI systems will fundamentally redefine how industrial enterprises operate over the next decade.
Industries such as metals, manufacturing, logistics, and infrastructure are still heavily dependent on fragmented coordination models built around manual communication and disconnected systems. Multi-agent AI introduces the possibility of real-time adaptive enterprises where procurement, inventory, logistics, pricing, manufacturing, and customer coordination continuously interact through intelligent operational networks.
The long-term implication is profound.
Industrial businesses will evolve from reactive organisations into predictive systems capable of anticipating disruptions, reallocating resources dynamically, and optimising execution continuously in real time.
This will significantly improve efficiency, reduce waste, compress execution timelines, and create far greater supply chain resilience.
More broadly, AI will become the operating infrastructure behind industrial economies. Just as electricity powered industrial expansion in the last century, intelligence infrastructure will power the next one.
The companies that recognise this shift early will not simply improve productivity. They will redefine industry leadership itself.
