Key Takeaways
Organizations are seeking competitive differentiation through artificial intelligence transformation more than ever before, and Enterprise Resource Planning (ERP) is a core data backbone that enables organizations to drive AI transformation. With modern ERP at the core, organizations become future-ready through greater solution agility and the ability to transform internal operations and customer experience.
Understanding ERP Systems and AI Technologies
What is ERP?
ERP is about integrating core business processes using a centralized software platform. Specifically, ERP focuses on the flow of information between various business functions, including finance, supply chain management, manufacturing, procurement, and human resources, thereby connecting the organization with a single source of truth.
While some modules are considered core to an ERP, others vary by industry and are implemented based on company need. By streamlining core, often complex processes, providing the input for reporting and analytics, and enhancing decision-making, modern ERP solutions can significantly improve operational efficiency.
Defining AI Transformation
AI transformation is the process of embedding AI within the way a business operates, driving digital transformation across various functions. Beyond integrating AI into an organization’s end-to-end processes, AI transformation requires weaving AI into an organization’s DNA, from leadership to internal operations, all the way to the end customer. Through standardized processes and integrated data, ERP is a core enabler of AI transformation.
However, multiple dimensions revolving around people, processes, and technology must be considered, with each dimension working together as enablers for AI transformation.
Benefits of ERP in AI Transformation
ERP systems help optimize organizations through standardized processes and workflows. This creates opportunity to automate repetitive and manual tasks, which reduces human error and increases employee time for value-added work. Furthermore, an AI-enabled ERP system is capable of self-optimizing processes, going beyond just automating repetitive tasks, but learning and improving over time.
Implementations of AI-enhanced ERP systems experience efficiency gains, with AI features extending beyond basic monitoring to include autonomous decision-making in production scheduling, quality control, and supply chain optimization. Specific use cases and benefits can vary by industry.
Leveraging ERP Data for AI Success
AI use cases leverage ERP data to provide a more holistic view of operations, since ERP centers around streamlining data across business functions.
- Predictive maintenance uses ERP data such as:
- Equipment master data
- Production schedules
- Maintenance and work orders
- Quality inspection notes
- Historical repairs and service records
- Demand forecasting relies on:
- Historical sales and order data
- Inventory levels o Product and customer data
- Marketing and sales insights
- Supply chain data
The ability to leverage this data is essential for AI transformation, emphasizing the need for thorough AI and data strategy.
If an organization has a significant amount of siloed or outdated data, they often can make inaccurate and untimely decisions, having a rippling effect across the organization and the broader supply chain. By implementing modern ERP and aligning AI and data strategy, organizations position themselves for future AI success.
3 Implementation Considerations for AI Readiness
1. Data Quality Requirements for AI Readiness
For a long time, data readiness has been a focal point of ERP implementation projects, with data cleansing and migration often called “the long pole in the tent” when planning and executing an ERP implementation project. ERP implementations require high-quality data, which includes reviewing for accuracy, completeness, and consistency across key master data entities. With the evolution of AI, data readiness has taken on an expanded definition.
According to a global study by Precisely and Drexel University’s LeBow College of Business, only 12% of organizations report that their data is of sufficient quality and accessibility for effective AI implementation. AI-ready data quality must align to use case requirements to effectively forecast future trends. For example, a demand forecasting algorithm would use significantly different datasets from an organization implementing generative AI across enterprise data.
AI Use Cases Depend on the Right Data
Use cases may require data to include errors and outliers to be representative of patterns that machine learning algorithms will use for analysis. Finally, governance must align with evolving AI regulations and ethical considerations, and training data must be sufficient to avoid bias.
There is also an increasing emphasis for AI to leverage unstructured data like PDF documents, photos, unformatted sensor data, PowerPoint presentations, etc. This expansion of traditional data management practices requires a modern data infrastructure, and modern ERP provides an integrated platform that contributes to many of these requirements.
2. Building an AI-ready ERP Infrastructure
The decisions that an organization makes regarding technology infrastructure have a significant impact on the success of their artificial intelligence initiatives. Key infrastructure decisions to support AI readiness often revolve around cloud strategy, with emphasis on public versus private cloud.
Public Cloud
Public cloud refers to services offered by third-party providers that are shared across multiple organizations. Public cloud environments provide:
- High scalability
- Managed AI tools and services
- Flexible, pay-as-you-go pricing
Private Cloud
Private cloud offers a dedicated infrastructure for a single organization. This model delivers:
- Greater control over data
- Enhanced security
- Stronger support for regulatory compliance
However, private clouds typically involve higher upfront investment and ongoing maintenance costs.
Hybrid: Balance Flexibility and Cost
A hybrid approach achieves flexibility while managing cost and risk. To support this flexibility, composable architecture is becoming increasingly important.
Composable architecture refers to a modular approach to building enterprise AI applications, where critical components such as ERP, data integration and orchestration, AI models, and best-of-breed solutions are designed to be interchangeable and easily integrated. According to Gartner in the World Economic Forum, by 2026, organizations adopting composable architectures will outpace competitors by 80% in feature implementation speed.
Priorities for Modular, API-Driven Architecture
A few key priorities of a modular architecture include:
- having an API-first design
- avoiding vendor lock-in
- enabling flexibility
The critical role of an ERP platform in this modular architecture is to provide a central data hub that integrates with AI tools regardless of where they are implemented. Modern cloud-based ERP provides a single source of truth to ensure consistency and accuracy of critical business data and becomes an anchor that integrates with modular applications and AI tools through APIs and event-based frameworks.
ERP also standardizes processes and workflows that AI agents can augment, such as automating invoice matching or demand forecasting, enabling organizations to embed AI into core business workflows. While infrastructure is an enabler for AI, many of the biggest hurdles are organizational.
3. Organizational Readiness and Change Management
Many organizations have invested in talent and technology and have rolled out pilots but struggle to realize the full value.
Organizations must treat artificial intelligence initiatives as organizational transformations rather than IT projects, especially in the context of traditional ERP systems. Executive sponsorship should be extended beyond budget approval to active engagement. Employee concerns such as job displacement and mistrust about AI-driven decisions can hinder their adoption. These can be addressed through:
- Clear and transparent communication
- AI awareness and skills training
- Change-management support
Establishing Governance to Manage AI Risk
Organizations should also establish clear governance with risk and communication protocols to help mitigate risks such as bias, privacy, and security while helping to manage resistance to change and foster buy-in. By addressing these challenges, organizations position themselves to take advantage of emerging AI capabilities, ultimately leading to more effective supply chain management.
The Future of ERP in the Era of AI
The Evolution of AI Technologies in ERP
Artificial intelligence is shifting ERP from systems of record to systems of intelligence, or in other words, intelligent decision-making platforms. There are a few critical capabilities that have a significant role in this transformation.
Predictive analytics is widely used across ERP environments for:
- demand forecasting
- predictive maintenance
- inventory optimization
These capabilities already have high adoption rates. Generative AI is scaling rapidly. Natural language interfaces eliminate menu navigation and simplify tasks such as:
- requirements documentation
- dashboard creation
Agentic AI: The Next Step in ERP Intelligence
Emerging now, agentic AI can reason based on context, and act without frequent human intervention. AI agents expand on chatbots and virtual assistants with the ability to integrate with enterprise systems to execute workflows and trigger processes. ERP is often the central hub for AI agents due to the structured processes and workflows that agents can leverage to execute tasks consistently.
AI agents can enable faster workflow cycles in ERP and CRM platforms by orchestrating workflows such as auto-resolution of IT service tickets, rerouting supplies due to inventory shortages, and triggering procurement. Predictive analytics identifies patterns, generative AI interprets and communicates, and agentic AI acts autonomously. When you put all three AI capabilities together with ERP as a core data backbone, you get an intelligent organization that enhances its business operations.
Preparing Your Organization for AI-Powered ERP
Implementing ERP and AI successfully requires a strategy that focuses on multiple dimensions centered around people, processes, and technology, and prioritizes business value over technology experimentation. Use cases should be strategically prioritized to focus on delivering high value to the organization.
Building Readiness Across an Organization
Building readiness in an organization requires multiple organizational priorities, including:
- establishing governance
- investing in continuous training
- ensuring data readiness
- adopting a composable infrastructure that enables flexibility
Organizations who invest in modern cloud-based ERP systems are better positioned for success as ERP provides a platform for integrated data, standardized processes, and infrastructure backbone that makes enterprise-wide AI transformation possible. The convergence of ERP modernization and AI transformation is happening now, and by treating this as an organizational transformation, not just a technology project, organizations become future-ready and will capture significant value in the era of artificial intelligence.
Key Benefits of AI in ERP Systems by Industry
Manufacturing
Use cases with high adoption in
manufacturing are often centered around predictive maintenance, quality control, dynamic production scheduling and resource optimization, and demand forecasting improvements. Per SmartDev, unplanned downtime costs manufacturing organizations an average of $260,000 per hour.
Predictive maintenance incorporates sensor and ERP data, helping to inform how the asset is running. By consolidating and interpreting with AI to make maintenance recommendations, companies not only avoid machine failures, but also improve procurement, safety, and quality.
Retail and E-commerce
Accuracy improvements to demand forecasts, inventory optimization, and dynamic pricing are relevant in today’s retail and e-commerce companies. Retail and e-commerce businesses are constantly challenged with balancing inventory levels to avoid overstock and stockout situations. Leveraging machine learning algorithms to analyze massive amounts of data can significantly improve forecast accuracy.
Healthcare
The World Economic Forum reported in January of 2024 that by 2030, there is a projected shortage of nearly 10 million physicians, nurses, and midwives. They outlined practical AI use cases to help organizations overcome these challenges. For example, helping radiologists to more quickly and accurately analyze X-ray and MRI images. Additional AI use cases include automation of repetitive administrative tasks like data entry for electronic health records and predictive analytics to help with patient demand and staffing in the
healthcare industry.
Financial Services
The financial services industry is seeing AI opportunities for fraud detection and prevention, risk management and compliance automation, as well as credit risk assessment improvements. A specific example of this was announced by the U.S. Department of the Treasury, stating that machine learning prevented and recovered over $4B in fraud in the fiscal year 2024.
Logistics/Supply Chain
According to Supply & Demand Chain Executive, 65% of logistics companies plan to implement AI, but only 23% have a formalized AI strategy (Gartner). Some common AI use cases include route optimization, autonomous decision-making through agentic AI, and like other industries, demand forecasting and inventory optimization.
Professional Services
In the professional services industry, key themes for AI use cases revolve around optimizing resource allocation, predicting project performance, automating repetitive tasks, forecasting financials, and ensuring billing accuracy. Organizations can improve resource utilization by leveraging AI-driven scheduling tools to analyze historical project information, such as timelines and performance, to predict future resource needs, reducing project delays and increasing productivity.
Working with RubinBrown
ERP, AI, and data implementations require careful planning to assess needs, challenges, and opportunities. ERP, AI, and data consultants can draw from a wide range of clients across various industries to provide these insights and recommendations, particularly in the realm of digital transformation. An advisory firm like RubinBrown and our team of consultants can work with you to optimize and select ERP software, define and implement your AI and data strategy, and dramatically improve processes, technology, and performance.
Schedule a call with one of our partners today.
Sources
####
Published: 01/30/2026
Readers should not act upon information presented without individual professional consultation.
Any federal tax advice contained in this communication (including any attachments): (i) is intended for your use only; (ii) is based on the accuracy and completeness of the facts you have provided us; and (iii) may not be relied upon to avoid penalties.