“ERP transformation through AI augmentation” is a mouthful, but every word is a building block that explains what happens when an enterprise resource planning system is transformed with a network of AI modules rather than refactoring, rearchitecting, or rebuilding it.
“AI ERP transformation” and “AI ERP augmentation” are accurate, shorter versions; but are not well defined. We hope to define them more clearly and also discuss the limitations of agentic AI approaches in an ideal implementation.
AI-ERP Augmentation vs. The 3 R’s of ERP Transformation
ERP transformation through AI augmentation can be compared to a traditional ERP transformation by understanding the main processes undertaken in most traditional ERP transformations.: refactoring, rearchitecting, or rebuilding.
- Refactor – Restructure and optimize the existing code (although not its external behavior) to improve nonfunctional attributes.
- Rearchitect – Fundamentally alter the structure of a system to shift it to a new application and data architecture and exploit new and better capabilities.
- Rebuilding – Redesign or rewrite the application components from scratch while preserving their scope and specifications.
Rearchitecting or rebuilding an ERP is an enormous undertaking and will almost certainly be a painful experience for everyone involved. The amount of planning, development, training and testing required to implement the rebuild or re-architecture of an enterprise system is immense.
There’s often no reason or not enough reason to go through that if you can transform your ERP without re-coding the entire system or restructuring your underlying data. Furthermore, these methods typically won’t fix all the issues the system can’t address because limited resources must be allocated beyond the 20% of effort that solves 80% of the problems. Our article on corner cases discusses this topic in depth. By augmenting your ERP with a sophisticated AI system or AI-ERP transformation you can rely on the flexibility of the AI system to be able to handle most of the remaining 80% of issues and allow the AI system to augment the underlying data capabilities without a complete revamp of the ERP application and data architecture.
Limitations to Agentic AI in AI Augmentation
Using an Agentic AI approach to AI-ERP augmentation has significant limitations. Agentic AI systems typically have difficulty with complex workflows and typically are not trustworthy enough to have write access to an enterprise database.
AI agents use LLMs to solve specific types of problems. AI Agents typically are artificial intelligence wrappers calling one to two instances of a single LLM and relying on the LLM for both logic and comprehension of the task. They rely on RAG from conversation history summaries and unstructured inputs as well as engineered prompts to guide the logic of the LLM and provide context for comprehension.
Agents sometimes send inputs and outputs to each other in a relatively unstructured manner forming a swarm. LLMs determine which functions or APIs to call when integrated with other systems, with little or no external control or oversight over the inputs which get passed to the functions or APIs. Improvement of AI agents is typically done through static prompt engineering and LLM fine tuning, and adjusting LLM parameters. These improvements are typically made manually by AI engineers.
A more sophisticated AI augmentation on the other hand offers a single, flexible interface that dynamically interacts with people, databases, and specialized tools in the software, without losing information or accumulating inaccuracies. It can quickly get information from multiple databases and create a temporary view of how the data should be presented across them, giving the right information to the right person at the right time, making further exploration easy, facilitating quicker, smarter decision making, and sparking innovation. In addition, the AI system can ensure that the new data can be connected to legacy data, that documentation is maintained, and that all of it follows business and security rules.
ERP transformation through AI augmentation is the transformation of new or legacy ERP platforms by augmenting them with networks of AI-powered modules that solve complex problems — or as we call it: AI-ERP augmentation.
Dodge the Dismal Performance of Most AI Business Initiatives
A July 2025 report from the MIT Media Lab’s Project NANDA found that 95% of AI business initiatives fail to generate a positive return on investment. The three primary reasons for this failure have a lot to teach us about integrating artificial intelligence with enterprise resource planning systems.
- Companies spent most of their money on initiatives with a lower return on investment.
- Agentic AI systems can only do one thing at a time.
- Companies primarily chose the least effective development teams.
A 95% failure rate doesn’t have to be a foregone conclusion. ERP transformation through AI augmentation, using a more sophisticated approach than agentics allows, addresses each of the reasons for failure: it was developed specifically for back office operations; its AI-powered modules can solve complex problems, incorporating learning as it acquires feedback, can adapt to problems it was not specifically trained on and at the same time, keep within the enterprise security and business rules.
| The GenAI Divide: State of AI in Business 2025 (MIT) | |
|---|---|
| MIT RESEARCH: REASONS FOR FAILURE | BETTER APPROACH: SUCCESS FACTORS |
| Most businesses in the study focused on sales and marketing initiatives, which have a lower ROI. | AI-ERP would be developed for back office operations such as logistics, supply chain management, and HR, which bring a higher ROI. |
| The AI pilots were usually torpedoed by the limitations of agentic AI which can’t retain feedback, adapt to context or improve over time. | AI-powered modules incorporate learning and solve complex problems. |
| Most companies preferred to develop their pilots in-house, which only had a 33% success rate. | Tapping companies like Atigro generated a 67% success rate over internal development teams. |
Understanding the Transformative Role of AI
ERP Transformation through AI augmentation can not only be much less expensive and much less painful than rearchitecting or rebuilding, it can deliver features and benefits that are truly disruptive. The flexibility of an augmented ERP solution allows it to integrate with a wide range of software products and applications. AI integration directly benefits business operations by streamlining and automating core business processes so people throughout the organization can do more with fewer resources.
Current State of Play
Out-of-the-box systems don’t meet the needs of the entire enterprise since almost by definition, enterprises must provide services which differ from their competitors and therefore have different requirements to support them with software. As a consequence, many business processes, procedures, and workflows happen outside of the ERP system; reducing transparency and inhibiting fast problem resolution.
When it can’t be managed in the system, information becomes scattered across multiple systems or maintained on spreadsheets. This fragmented approach makes it nearly impossible to access critical information when it is needed, leading to overburdened employees and poor productivity. In addition, data can be duplicated or is difficult to find when you need it.
A great example of this is in SCMs or supply change management systems. Supply chains have become more complex and global and instant operational visibility and responsiveness is expected. Legacy systems are too rigid to adapt and transforming them by rip-and-replace costs too much, takes too long, and still doesn’t fix all the issues.
AI-ERP Transformation
AI systems have the potential to improve and revitalize enterprise resource planning assets so that they meet the needs of the entire enterprise. Augmenting the system with intelligent AI systems beyond what the current Agentic AI systems allow reduces employees’ need to perform additional spreadsheet calculations or manage workflows outside the platform.
AI networks improve productivity around specific operations by reducing the number of ad hoc systems and broadening who can see and act on the additional, captured data. When all the data is in one place and can be accessed by everyone exactly when they need it; decision making happens in real time and opportunities can be seized.
Finally, AI augmentation solves the problem of rigid legacy systems once and for all. Their ability to adapt to new information and new workflows quickly and present information in a flexible user interface means that the words rearchitecting and rebuilding can be banished from your vocabulary forever.
About Atigro
Atigro is a proven ERP transformation firm that pairs its modular augmentation capabilities with AI-native frameworks. Atigro’s experience and expertise generate the rapid development and provisioning of new ERP functionality that meets dynamically changing business processes. You can learn more about implementing strategic AI capabilities to substantially improve business operations throughout your company by streamlining, automating, and optimizing workflows.