Shortlist UK’s Best AI-Powered ERP Systems
Easily find AI-Powered ERP Software that integrates ML, NLP, Predictive Analytics, and LLMs to automate processes and predict outcomes, enabling adaptive decision-making.
What Do You Need An ERP Software For?
What Is AI-Powered ERP Software?
AI-powered ERP Software is an enterprise resource planning system that integrates with artificial intelligence capabilities like large language models (LLMs), chatbots, machine learning (ML), natural language processing (NLP), and predictive analytics.
While the goals of AI-powered ERPs remain the same as out-of-the-box ERP systems, the integration of AI-enabled capabilities transforms a business’s processes from reactive to predictive and self-optimising. Whereas traditional ERPs rely on manual inputs and static rules, AI-powered ERP Software continuously learns from operational data to automate its decision-making.
The use of AI in ERP systems encourages:
- Predictive forecasting
- Process automation
- Anomaly detection
- Dynamic decision support
- Continuous optimisation
In 2022, 65% of CIOs implemented some form of AI within their enterprise resource planning functions. As of 2024, one in six UK businesses has adopted artificial intelligence technology, with most citing ‘data management and analysis’ as the primary reasons.
What Are the Best AI-Powered & AI-Native ERP Systems In the UK?
IFS
IFS.ai is an industrial AI platform that integrates deeply with IFS systems to deliver contextual, real-time insights, automate operations, optimise asset performance, and reduce risk across industries like manufacturing, construction, utilities, and telecoms.
AI capabilities: Content generation, AI recommendations, Anomaly Detection, Forecasting & Simulation, Agentic AI, and Contextual Knowledge
Implementation Timeline: 3 months to 9 months
Oracle Netsuite
NetSuite AI embeds generative and analytic AI across its ERP suite to automate routine tasks, detect anomalies, generate content, and surface actionable insights using unified data for more informed decision-making.
AI capabilities: AI Anomaly Detection, Content Generation, Planning and Budgeting, Analytics Warehouse, and Narrative Reporting
Implementation Timeline: 24 hours to 4 weeks
Infor Cloudsuite
Infor’s Enterprise AI is a set of AI-powered tools embedded in its cloud platform that deliver predictive, prescriptive and generative capabilities, automate workflows via AI agents, support citizen data science, and tailor insights across industry-specific processes.
AI capabilities: GenAI Embedded Experiences, GenAI Assistant, Advanced Workspaces, Predictive AI, Prescriptive AI, and Decision Manager
Implementation Timeline: 3 months to 1 year
Rillet AI-native ERP
Rillet is an AI-native ERP focused on automating financial operations. It supports automated general ledger, multi-entity accounting, revenue recognition, AI-assisted reconciliations, and GAAP/investor reporting.
AI capabilities: AI-generated accruals, Bank reconciliation, Close management, Aura AI, and Multi-entity & multi-currency
Implementation Timeline: 7 days to 2 months
DualEntry AI-native ERP
DualEntry is an AI-native ERP focused on accounting and finance. It automates general ledger, bank matching, reconciliation, OCR data extraction, intercompany consolidation, outlier detection and generates AI-driven insights.
AI capabilities: Automatic bank matching, Bank fee automation, OCR reading, Automatic account reconciliation, Close and KPI tracking, Copilot, and Predictive insights
Implementation Timeline: 24 hours to 4 weeks
Why Businesses Are Integrating AI-Powered ERP Systems
Artificial intelligence has gone beyond just being a buzzword. It is considered an essential ERP capability, with 53% of UK businesses actively seeking more intelligent ERPs.
There are four key reasons why businesses seek out AI integration in ERP systems:
- AI-Powered Automation: Automation is the driving factor behind 42% of AI adoption among businesses. With ERP systems, AI provides the ability to automate workflows and functions based on data. This helps to reduce labour costs and increase the accuracy of decision-making.
- Predictive analysis: AI plays a vital role in predictive analysis by learning, understanding, and finding patterns to forecast outcomes. Alongside deep learning, predictive analysis is listed as the most critical ERP technology among CIOs.
- Competitive Pressure: Competitive pressure is the reason for 31% of AI adoption among IT professionals. Businesses want to ensure they’re not left behind in the race for AI-powered ERP capabilities.
- Future-Proof Operations: As well as future-proofing against competitors, AI learns how operations are carried out. It can then identify opportunities for greater success in future business functions.
Shortlist AI-Powered ERP Systems For Smarter Automation & Decision-Making
What Do You Need An ERP Software For?
Types of AI Integrations With ERP Systems
- Machine Learning (ML): Used for predictive analytics and detecting anomalies through algorithms that use data to predict future trends and help with overall decision-making
- Natural Language Processing (NLP): Used to understand unstructured text and extract in-depth analysis. Also used to improve customer experiences through Chatbots and customer interaction.
- Large Language Models (LLMs): Encourages generative and agentic AI capabilities to automate complex tasks, generate reports, and recommend decision-making through natural interactions. LLMs connect data across ERP modules to provide context-aware insights.
- Predictive Analytics: Uses historical and real-time data to forecast demand, inventory, financials and accounting, and maintenance schedules. This helps businesses to anticipate challenges and make proactive decisions.
- Chatbots: Used in conversational and interactive sessions with customers or employees to provide instant responses to queries. Queries include order updates, inventory levels, and HR requests.
ERP Capabilities That Benefit Most From AI-Powered Integrations
Human Resources Management
According to 69% of IT developers, ERP Human Resource (HR) modules are one key business process predicted to be entirely replaced by AI. The 3 key driving forces behind the adoption of AI for core HR tasks are improved efficiency, better employee experience, and reduced costs.
81% of HR leaders have already experimented with artificial intelligence, with HR departments embracing AI in major functions:
- Employee records management (78%)
- Payroll (77%)
- Recruitment (73%)
- Performance management (72%)
- Onboarding (69%)
76% of CHROs believe their department will be lagging behind others if it does not adopt generative AI tools in the next 12-24 months.
Customer Support
It is estimated that 95% of customer interactions will be powered by AI by 2025. The use of generative AI – such as chatbots – for customer experience (CX) has proven to increase resolution times and improve CSAT scores.
Businesses embracing AI in ERP systems have the benefit of improving customer interactions and CX through the use of Natural Learning Processing (NLP). NLP has played a significant role in enabling the use of chatbots and text prediction, which has – and will continue to – heavily influenced customer-facing factors:
- Chat-based customer support (57%)
- Email communications with customers (53%)
- Search functionality (45%)
- Voice-based interactions (42%)
ERP Finance & Accounting
The driving force behind AI in ERP finance modules is automation. Essentially, all financial processes are manual: data entry, reporting, data collection, and verification. Making the entire process time-consuming, costly, and prone to errors.
An Oracle finance survey highlights, “incorporating AI into ERP systems can yield a 36% drop in errors and reduce the time it takes to close the books by 3.5 days.”
Integrated with data from other functions and departments – and collected in one ERP system, AI understands talent gaps, predicts salary costs, and identifies the most profitable areas of a business.
Supply Chain & Manufacturing
AI is heavily utilised in Manufacturing ERP tools, with an emphasis on optimising supply chain processes. AI functions allow for greater predictive planning, analysis, and even maintenance.
While striving for greater warehouse efficiency and better on-time delivery, AI technology is drastically improving the accuracy of inventory management and tracking. The use of AI-enabled cameras and sensors automates stock control, which has improved production planning and prevented overstocking.
3 Leading Examples of AI-Powered ERP Systems
1. Transformative Retail AI: Walmart & SAP
Mike Hanrahan, CEO of Intelligent Retail Lab by Walmart, claims “AI is going to be as transformative to retail as e-commerce was”.
Walmart was an early adopter of SAP’s cloud ERP system. This allowed the multinational retail corporation to integrate AI technology with its data-collection system. The goal was for “better inventory [management], having the products [customers] want on the shelves, and having cleaner, safer stores”.
As a test, Walmart first infused AI technology across its New York store. This included AI-enabled cameras and sensors that generated 1.6TB of data every second.
These cameras worked with an ERP system to understand what products were selling, what products needed replenishing, and how long food products had been sitting on the shelves.
2. Mitsubishi Electric and Oracle Cloud
Positioned at the top end of industrial automation, Mitsubishi Electric implemented the Oracle Cloud ERP system to help with its just-in-time manufacturing practice. With up to 10,000 work orders a day, they needed a system to help bridge AI and process automation.
Once deployed, Mitsubishi Electric saw several gains in its industrial processes:
- Equipment uptime increased by 60%
- Production increased by 30%
- Manual processes were reduced by 55%
- Floor space reduced by 85%
3. Award-Winning Mobile App: TD Bank Group (TD) & Microsoft
TD partnered with Microsoft and implemented Microsoft Azure ERP. The system’s AI capabilities allowed TD to harness the potential of their AI subsidiary, Layer 6.
The integration of AI has enabled TD to focus on improving CX through its banking app. Ultimately, this led TD to receive an award for innovation in artificial intelligence.
Potential Risks of an AI-Powered ERP System
Data Privacy & Security
Using AI in ERP tools will require a huge amount of processing data. This opens up an increased risk of data hacking from smart attackers or cybercriminals. Once all business data can be accessed on a single system, it will need to be properly secured and encrypted.
Quality of Data
It’s important for leaders not to be overawed and relinquish complete control. After all, the technology is only as good as the data that is put into the system. If the data is incomplete or contains errors, then the decision-making of an AI-powered ERP will produce incorrect results.
Expertise & Training
The use of AI in ERP systems is complex and requires user training or a dedicated team of experts. It’s not a case of feeding the system data and leaving it to run by itself. Instead, AI applications need to be routinely assessed and maintained. The large pool of processing data needed for an ERP system to function also needs to be regularly monitored by experts.
Hidden Costs
Beyond implementation and the integration of AI-powered applications, there are further costs:
- Hardware (such as sensors, cameras, and IoT devices)
- Additional modules and applications with AI functionality
- Hiring expert personnel
Small and mid-sized business leaders need to understand these extra costs, which could act as a deterrent when budgeting for an AI ERP solution.
AI-Powered ERP FAQs
What Are the Future Trends of AI and ERP Systems?
AI capabilities are already deeply ingrained in ERP products. But, there are areas – or trends – for further progression:
- Hyperautomation: ML or AI-powered ERPs are a significant driving force behind hyperautomation capabilities. They provide the function to automate nearly all business processes that help to reduce human involvement and eliminate human error.
- Augmented Reality and Virtual Reality: AI-constructed data sets are at the centre of how businesses will use AR and VR in the workplace. Through digital overlays, business leaders can see predictive analysis, real-time reporting, and in-depth data insights.
- Explainable Artificial Intelligence (XAI): XAI is a method used to understand how an AI model uses data and generates algorithms to come to a final decision. This is used to help humans see the processes the model took. Essentially, building trust and confidence for initiating AI models in business operations.