Agentic AI for enterprise
A single task often means gathering data from several systems, passing it between teams, and waiting for review. Agentic AI can find relevant information, prepare the next step, and route decisions to the right person at agreed checkpoints.
Connect permitted documents and systems, with references back to source material for human review.
Keep the task history, next step, and owner together so teams can handle handoffs and exceptions.
Define access, approval points, and activity records, particularly where accounting data, customers, or orders are involved.
Examples from global organisations
These are cases published by the organisations themselves. They are not Enersys projects or client results.
Manufacturing
In use at a Bosch plant since late 2025
Bosch uses its Shopfloor Agent at the Bamberg plant to draw on error records and suggest ways to resolve faults. Operators review the guidance and act on the equipment.
Read the case from BoschLogistics and warehousing
DHL reported use across several regions on 11 Nov 2025
DHL Supply Chain says HappyRobot agents are in use across several regions for appointment scheduling, driver follow-up, and high-priority warehouse coordination by phone and email.
Read the case from DHL GroupTrading and selling
Announced seller capabilities; availability varies by market and feature
Seller Assistant can monitor inventory, prices, and listings. Sellers set the conditions, choose recommendations or actions, and review and approve actions before they are carried out.
Read the case from AmazonWhat Enersys can design and deliver with your team
The scope depends on existing systems, data quality, and risk. We agree the desired result before choosing a deployment approach.
Inventory documents and access rights, design retrieval with source references, then assess on-premise models, private cloud, or external services against the actual constraints.
Set what an agent may read, propose, or do; add approval points, escalation when uncertain, and activity records for later review.
Design connectors while keeping systems of record authoritative. Test permissions, duplicate submissions, and what happens when a downstream system is unavailable.
Inventory datasets, map fields, clean records, rehearse migration, and reconcile important balances or records with data owners before cutover.
Select models for the task, test answers and tool calls, review cost, security, and data changes, and train the team that will own the workflow.
Choose one workflow with a clear owner, such as answering questions from internal manuals or preparing purchase-order information. Compare results with the current process, examine errors, and decide where the system must wait for approval before expanding.
A local LLM alone does not guarantee data security or PDPA compliance. Access rights, purpose, retention, and data ownership still need separate decisions.
A chatbot usually answers within a conversation. An agentic workflow can gather information across systems, prepare a next step, or call tools within defined permissions. Work that affects data or transactions should have human review points based on risk.
Not necessarily. Start with the data needed for a pilot, identify its source system and owner, then decide whether to connect through an API, read in place, or migrate only what is needed.
We design deployment and knowledge options around each organisation’s data, systems, and requirements. The right choice may be an on-premise model, private cloud, or external service rather than a single setup for every task.
Where is the information, who must review it, and which step consumes the most time? We can assess whether AI fits that task or whether the process needs attention first.