Artificial intelligence introduces new concepts and controls along with a whole new vocabulary. This blog explains the key concepts in AI as they relate to small to medium (SMB) enterprise resource planning (ERP) software. Additionally, we define three categories of AI: Copilots, Automation, and Insight.
AI's Three Key Categories in Business Management Software
Artificial intelligence in enterprise resource planning (ERP) software comes in three categories:
- Copilots,
- Automation, and
- Insight.
You may hear terms like “coworkers,” “agents,” “developers,” or “orchestrators.” These terms often have similar meanings. For a user of ERP software:
- Copilot or coworkers answer questions and help with individual tasks.
- Automation agents can work without a prompt, importing documents into the system, alerting people of conditions, or taking action under a set of rules.
- Insight agents and tools help users turn data into information, identify trends and make suggestions.
AI Copilots & Chatbots
ERP providers have introduced AI-based assistants, often called “copilots”, which allow users to query the system or automate tasks using a simple chat interface. For example, with minimal jargon, a small business owner could ask the ERP, “How are sales this week versus last week?” and get a quick answer without navigating complex menus. These chatbots are built on large language models (such as GPT-based systems from technology providers) and integrated deeply into the ERP’s data and processes. They aim to improve accessibility and boost productivity by assisting with tasks ranging from report generation to workflow guidance.
Copilots are turning the user’s voice into the data entry tool. Instead of typing, users speak to their ERP systems.
“Hey copilot, please create a quote for Customer A for 12 widgets for delivery next Tuesday.”
“Copilot, how do you stop payment on a check? Will this put invoices back on the account?
Chatbots and copilots are used for help, ad-hoc reporting, data entry and so much more.
Document Ingestion & Automation
A high-value, low-hanging fruit for AI in ERP is automating the processing of documents like invoices, receipts, purchase orders, and expense reports. Using AI (specifically machine learning or computer vision), ERP vendors offer solutions that automatically read and interpret documents to extract data, classify transactions, and feed information into the ERP system. For SMBs, this automation reduces manual data entry, accelerates turnaround times, and minimizes human errors. For instance, intelligent AP invoice scanning can match invoice line items to purchase orders and flag discrepancies (a feature currently offered by solutions like Sage Intacct’s AP Automation with AI and Acumatica’s AI-based AP document recognition). Document ingestion agents can also handle tasks like intelligently processing expense receipts or extracting data from customer orders, with options to add human review for accuracy when needed.
Increasingly, we see third party solutions providing AI automation in their tools. Specifically, we see payment processing terms augmenting traditional optical character recognition (OCR) with AI reasoning. Another example is in warehouse management tools. Goods receipt processing can be streamlined by scanning the receiving document and the goods for automated entry. If you receive information electronically an AI agent can “interrogate” the document, enter it, and create a whole workflow around it.
Intelligent Insights & Forecasting Agents
Modern SMB ERPs increasingly embed AI to identify patterns and trends within data and to produce forward-looking forecasts. Examples include demand forecasting in inventory management (as seen in Microsoft Business Central, which leverages machine learning to predict stock needs) and predictive cash flow analysis. These AI insights aim to help business owners make more informed decisions by highlighting anomalies, predicting outcomes, and recommending actions. For instance, one vendor might offer a “Finance Intelligence Agent” or a “Cash Intelligence” feature (as we see in Sage Intacct R1 2026) that monitors financial metrics like cash flow and predicts shortfalls or opportunities. These insights can improve the quality of decision-making and lead to better
strategic outcomes.
Similar insight is often found in the sales functionality of modern systems. Systems like SAP Business One offer “sidecar” information on master data, depicting related information to the particular item or customer. For example, you can add analytics to your customer record to see top items, sales trends, aging or any other data from the system. Now with AI, modern systems are augmenting historical data, identifying trends and making suggestions to increase sales. Third party tools like FocusPoint for SAP have been providing eCommerce suggestions for years. Now their FocusPoint Nexus uses AI to drive strategic action and increase revenue.
Ensuring Accuracy, Governance, and Control in AI
Adopting AI in ERP raises concerns about accuracy and control. How do vendors ensure that AI suggestions or automated processes are correct and reliable? This is critical for small busineses, where errors in financial or operational data could have outsized negative impacts.
Human in the Loop
We all hear conflicting reports about what AI will do to the jobs of tomorrow. Will AI replace humans or will it raise the value of human work? Today, most software publishers and their implementation partners stress that AI features must involve human oversight at crucial points. For example, while an AI might automatically categorize an expense or predict demand, someone should review and approve or override these suggestions. This ensures that final decisions remain under human control, adding a safety net in case the AI’s recommendations are not fully accurate. This is especially true when implementing new software. Just like traditional integrations and automations, you do not want to speed up mistakes!
Validation and Audit Trails
Features are often built with validation workflows and auditability. ERPure.AI for SAP Business One and Dynamics 365 Business Central’s AI capabilities, for instance, include robust logging to track how an AI-driven suggestion was derived, and these systems often require human confirmation for critical tasks like financial postings. This not only helps in building trust in AI’s outputs, but supports compliance and governance.
Data Quality & Governance Tools
SMB ERP solutions are improving data governance tools to enhance AI accuracy. Clean data yields better AI recommendations. Some vendors also use principles like outlier detection in their insights to flag data points or predictions that deviate significantly from norms, signaling that human review is needed.
Accuracy, Acceptance and Success
Planning is crucial for your AI project success.
- Greater process variance requires more agent training and/or human in the loop.
- Data cleanliness improves forecasting accuracy.
- Clear audit trails and validation increases AI acceptance.
Effective planning, training and testing increases both the effectiveness of the AI agents as well as acceptance by your staff.
Current vs Future AI in ERP
We are in the early stages of AI in the small to mid-sized business market. Although Gemini, ChatGPT, Claude and Microsoft Copilot have all become part of our daily lives, they just started entering our daily business activities. At present, most AI capabilities in SMB ERPs function as assistive technologies rather than fully autonomous systems. In other words, they serve as co-pilots.
- Current State: Today’s AI copilots can provide suggestions, like drafting an email to a customer, recommending an inventory restock level, or summarizing a financial report. Typically a human user needs to review and accept those suggestions before action is taken. Similarly, AI-driven invoice scanning might extract fields and match them to records, but a human may verify when the system is uncertain or when exceptions arise.
- Future Potential: Trasks transition from human in the loop, to partial automation, and when conditions are right to full automation. The transition occurs as AI systems prove their accuracy over time. With training on company-specific data and rigorous testing, routine data entry and certain kinds of predictions (like reordering stock when predicted to run low) become largely automated. However, even the most optimistic advocates emphasize that critical oversight will always remain.
The level of human involvement thus is expected to diminish for routine, high-volume tasks if AI models are sufficiently trained. But for strategic decisions or exceptions, human review remains essential to ensure trust and thoroughness. For example, financial closing processes can be largely automated. . Realistically, different tasks will see different degrees of automation; for instance, Sage’s “Close Automation” feature uses AI to pinpoint bottlenecks in the financial close. However, the actual decisions to address those bottlenecks still fall to human accountants and controllers.
Return on Investment in AI for SMB Customers
In our previous 3-part blog series: When is AI Worth the Conversation, we discussed the four “flags” to consider:
- Volume – Happens often enough to justify the consideration.
- Consistency – Structured, repeatable and consistent work.
- Error Tolerance – Mistakes are low stakes and easy to correct.
- Labor Savings – Labor savings compared to the cost of AI.
From an ROI perspective, all three AI categories notably bring in efficiency and improved outcomes which translate into business value for SMBs.
- Efficiency Gains: Eliminating routine manual tasks (like data entry, report generation, or document reconciliation) can significantly reduce the hours staff spend on administrative duties. For example, AP automation (via document ingestion agents) reduces the time needed to process vendor invoices, which allows finance teams to focus on higher-value work. These labor savings can either reduce the need to hire additional staff or allow current staff to engage in more strategic roles.
- Cost Reduction: AI can help minimize costly errors by ensuring data accuracy (through automated validation checks and better data extraction from documents). Reductions in error rates can lower costs associated with rework or compliance issues. Similarly, improved forecasting can prevent overstocking (reducing inventory carrying cost) or understocking (avoiding lost sales).
- Better Decision Quality: The insight/forecasting agents allow SMBs to base decisions on more accurate predictions and data-driven recommendations. This leads to better financial management (e.g., improved cash flow utilization), better resource planning (e.g., aligning inventory with predicted demand), and better customer service (e.g., anticipating customer needs). These improvements support revenue growth, profitability, and competitive advantage.
Plan Your AI Investments
In Part 3 – Know Before You Commit, we discussed the importance of understanding your current processes and their costs. The foundation of any honest AI evaluation is knowing what the process costs today. Not a manager’s estimate or a guess based on job titles and salary ranges.
Most importantly, know why you are using AI. Your AI project should:
- Support the business objectives.
- Have clearly defined goals and metrics.
- Be grounded by your business processes.
- Consider the human aspects.
Be certain to incorporate the team members who are involved in any process impacted by AI Agents. Train the staff in the AI concepts and tools being used.
The businesses that get the best long-term results from AI aren’t the ones that move fastest. They’re the ones that asked the right questions first; about volume, consistency, error tolerance, true costs, and realistic savings. Then they made decisions based on what the numbers said.
ERP Based Digital Transformation
SMB companies are not typically looking to revolutionize their industry or completely remake their organizations with AI. Business owners want to increase efficiency, grow sales, expand offerings and other similar incremental improvements. Those improvements come on top of a business platform. The core of the business platform is likely your ERP system. Accordingly, when thinking about AI you should have a solid core and leverage the philosophy and tools from that ERP publisher. This is what we call ERP based digital transformation.
The upcoming blogs will detail five vendor-specific approaches and elaborate on the above themes in more detail. They will map each vendor’s:
- AI capabilities to the three categories,
- Discuss built-in governance and accuracy control mechanisms,
- Evaluate human involvement needed, and
- Examine the ROI implications in a practical SMB context.