AI has quickly gone from novelty to normal. Most SMEs have tested it in some way – drafting documents, summarising meetings, experimenting with small automations.
But the next shift isn’t about generating content faster. It’s about how decisions are made with AI in SAP Business One.
That’s where agentic AI comes in.
There’s a growing tendency to label any automation as “agentic AI.” But there’s a clear distinction.
Robotic process automation (RPA) follows predefined rules. It automates repetitive, structured tasks that don’t change. Tasks such as keying an accounts payable (AP) invoice or extracting data from a standard form.
Artificial intelligence (AI) adds cognitive capabilities like data analysis. It can identify patterns, interpret information and support decision-making.
Agentic AI goes a step further. It works from a defined business objective. Rather than simply triggering an action when a condition is met, an agent references structured data from your ERP, extracts the relevant subset of information, and uses a large language model (LLM) to interpret the intentions behind the goal and to evaluate what actions would best support that objective.
Instead of asking, “Has this rule been triggered?” it asks, “What are we trying to achieve?” For example, the goal might be:
That’s the difference between automation and reasoning.
To understand the difference, let’s have a look at how common decisions change when you introduce a goal inside SAP Business One.
In a traditional setup, the ERP generates a purchase order as soon as stock falls below the minimum level. That’s rule-based automation.
With an agentic approach, the goal might be to reduce inventory holdings. Instead of automatically reordering, the agent could:
Review sales velocity
Assess whether the item is critical
Determine whether holding less stock aligns better with the capital strategy
It may decide not to reorder, even though the rule says it should, because it has established that, at this time, in this cycle, with this goal, placing that order is not advisable, even though it’s below the stock level.
Automating invoice entry saves time. But the bigger opportunity is not data entry. It’s what happens next. But what if the AP invoice exceeds your budget for that expense? Or if the bank account on the invoice is different from the other invoices?
An agentic system could ask:
Does this invoice align with the approved budget?
How are we tracking year-to-date?
Is the bank account consistent with historical patterns?
That’s not just speeding up admin, it’s strengthening financial oversight.
Another example concerns sales reps and territory performance. How do you increase your sales?
If a product begins lifting in one region, an agent could detect the pattern and prompt action – be it marketing, pricing, or supply adjustments aligned with a growth goal.
LLMs can hallucinate. That’s why guardrails and human oversight are critical.
The real opportunity isn’t fully autonomous systems running unchecked. You can specifically instruct agents on what to do and not do, building guardrails and adding the human in the loop at the end.
Agentic AI is an extra set of eyes that flags anomalies, highlights patterns, and surfaces questions you may not have thought to ask.
Decision authority still sits with leadership.
What we’re starting to see is businesses exploring how an ERP can effectively audit itself using AI.
That only works because ERP systems are built that way. An ERP enforces rules, structures data, and controls relationships among transactions, inventory, finance, and operations. That structure raises the quality and reliability of the data.
In a spreadsheet or a basic accounting tool, you can build a great model, only for a formula to break, someone to change a tab, or a link to stop updating. You often don’t see the mistake until it costs you money, stock availability, or service levels.
ERP systems reduce that risk because they force structure. They make you follow workflows. They capture the same data points consistently, every time. That’s why you can trust what comes out of it.
ERPs also give you better access to the data through APIs. You can pull what you need, when you need it – not just totals and balances, but the operational detail behind them.
In SAP Business One, that structure includes:
Quotation visibility against specific items, showing demand sitting in the pipeline
Tracking of quotation interactions and engagement history
Multiple sales order date fields (Required By, Not Before, Not After) that improve service-level context
Workflow-enforced data capture across order-to-cash and procure-to-pay
Deep API access that allows structured endpoints to be called reliably
The ability to introduce Model Context Protocol (MCP) as an interpreter layer between the ERP API and the LLM
That’s why agentic AI layers well on top of SAP Business One. You’re not asking a model to guess. You’re giving it structured, reliable information to work from.
For SMEs, the opportunity of agentic AI isn’t simply to save time. It’s to improve how decisions are made using the data already captured in SAP Business One.
If your workflows are structured and your data discipline is strong, you already have the foundation required for goal-driven automation. The next step is configuring and extending your ERP so automation and agentic AI work within the structure you already trust.
At Key Business Solutions, we help you get more from SAP Business One by strengthening workflows and identifying practical opportunities to layer automation and agentic AI into existing processes. Ready to explore? Start a conversation with our team today.