How Agentic AI Can Impact Businesses

What Is Agentic AI? 

Agentic AI refers to AI systems that act with some degree of autonomy toward a goal. Unlike traditional models that merely respond to queries or predictions, agentic systems can:

• Set intermediate objectives
• Choose and sequence actions
• Monitor their performance
• Adapt based on feedback or changes in their environment

In non-technical terms, think of Agentic AI as a “digital project manager” — not just answering questions, but making decisions, planning, and interacting with tools and people to complete tasks on your behalf.

These agents can operate across time (e.g., working toward a multi-day objective), tools (e.g., using APIs, Excel, CRMs), and teams (e.g., coordinating via Slack or Teams). And they improve over time, either via feedback, new data, or goal refinement.

Applications Across Industries 

Each business brings complex operations, fragmented systems, and decision bottlenecks — a fertile environment for Agentic AI.

Here are some high-level examples of how Agentic AI could create impact across different industries:

• Smart Procurement Agents in trading and logistics that dynamically scout vendors, negotiate contracts, and adjust sourcing strategies based on price shifts, shipping disruptions, or environmental goals.

• Predictive Maintenance Schedulers for infrastructure or industrial businesses that proactively coordinate repairs by interpreting IoT signals and balancing costs, logistics, and technician availability.

• Sustainability Trackers that autonomously collect, verify, and report emissions or resource usage across supply chains for compliance and disclosure.

Rather than building separate AI workflows for each process, agentic systems generalise

by learning to handle variability, coordinate multiple tools, and even hand tasks back to humans when appropriate.

Smarter IT Operations in Industrial Settings 

Across manufacturing, automotive, and chemical sectors, day-to-day operations rely on interconnected IT and production systems. When one part of the system slows down or fails, it can delay shipments, disrupt production, or compromise compliance.

Agentic AI is enabling a shift from reactive troubleshooting to proactive, intelligent management. These systems don’t just alert people when something goes wrong — they can spot early warning signs, investigate the root cause, and, in many cases, take corrective action on their own.

For example, in an automotive supply chain, a digital agent could detect a delay in a parts inventory system, identify that it’s caused by a bottleneck in a cloud database, and adjust system configurations or escalate to the right support team before production is affected.

The result is less downtime, faster problem resolution, and better use of scarce IT resources — without needing to overhaul existing infrastructure. Recent reports show that 90% of consumer goods and retail executives plan to use to agentic AI in supply chain operations.

Streamlining Repetitive Workflows in Core Operations 

In industries like manufacturing and chemicals, many core business processes — from order management to compliance tracking — remain highly manual and repetitive. These activities often span multiple systems, teams, and spreadsheets, leading to delays and errors.

Agentic AI can take over many of these tasks. Rather than just providing information, it can act on behalf of a team — reviewing data, making decisions within set boundaries, and coordinating across departments or systems

Examples include:

• Agents that manage incoming orders, check stock availability, and coordinate with suppliers — helping fulfilment teams respond faster and more accurately.

• Agents that monitor production data to catch recurring quality issues, retrieve related documents, and propose improvements to avoid repeated faults.

• Agents that track environmental data and supplier emissions to ensure imports meet EU compliance standards like the Carbon Border Adjustment Mechanism (CBAM), helping avoid costly reporting errors or penalties

By automating routine but complex operations, agentic systems free up people to focus on solving problems, improving processes, and making strategic decisions. Studies show that workers using agentic AI are 72% more likely to feel “very productive”.

Accelerating Digital Engineering and Development 

In a world where industrial companies are increasingly also adopting software capabilities — building digital twins, automation systems, and embedded analytics — development speed and quality are becoming a competitive edge.

Agentic AI can support software teams behind the scenes, helping them move faster and with greater confidence. These agents aren’t writing entire systems from scratch, but they are helping with everything from testing and documentation to deployments. As a result, in the last year, 82% of developers have used agentic AI for writing code.

In practice, this might mean:

• Generating scenarios based on previous software issues — ensuring new updates don’t break anything.

• Managing software rollouts automatically checking for compatibility and logging deployment steps.

• Keeping documentation up to date, especially in regulated environments where traceability is critical.

When beginning to invest in digital infrastructure, agentic AI offers a way to keep development cycles tight and transparent — a quiet force multiplier behind innovation.