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, inside operational systems.
That’s where agentic AI comes in.
What’s the difference between RPA, AI and agentic AI?
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:
-
Reduce stock
-
Increase sales in a specific segment
-
Protect margin
-
Stay within budget
That’s the difference between automation and reasoning.
What this looks like in practice
To understand the difference, let’s have a look at how common decisions change when you introduce a goal.
Inventory decisions
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.
Accounts payable beyond ingestion
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.
Sales pattern detection
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.
Guardrails in agentic AI
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.
Why ERP matters
What we’re starting to see is businesses exploring how an ERP like SAP Business One 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.
That’s the real reason agentic AI layers well on top of ERP. You’re not asking a model to guess. You’re giving it structured, reliable information to work from.
The shift for SMEs
For SMEs, the opportunity of agentic AI isn’t simply to save time. It’s to improve how decisions are made.
For most businesses, outgrowing your tools becomes apparent when you can’t deliver on time. When vendors start stretching you because payments slip. When customer expectations outpace internal capacity. When the number of moving parts exceeds the number of people managing them.
If that sounds familiar, it’s time to look at the structure underneath it. A connected ERP platform like SAP Business One brings order to finance, inventory and sales. From there, you can layer automation and agentic AI in a way that supports service levels and working capital, not just efficiency. Ready to explore? Start a conversation with the Key Business Solutions team today. .
