By, Anthony Kelly, Excendio Advisors
ERP integrators are acquiring Artificial Intelligence (AI) capabilities at an increasing rate to fulfill client requirements in the implementation arena. AI is now becoming standard requirement for many ERP systems. Gaining this ability to enhance ERP systems is an increasing skill requirement for integrators.
AI has the potential to significantly impact ERP systems in various ways. ERP systems include all functional and managerial areas of a company. Overall, AI is having a positive impact on ERP systems by helping businesses to automate tasks, improve decision-making, and streamline operations. This can lead to improved efficiency, reduced costs, and increased profitability.
Moving forward it is my opinion that AI skills are a must have for ERP integrators. To have these skills the integrator can acquire an AI company, train current employees, or hire the skills to remain relevant. Understand there is a severe shortage of AI skilled talent.
According to a 2023 survey by McKinsey & Company, 50% of companies have adopted AI in at least one function. This is up from 20% in 2017. Some of the industries with the highest rates of AI adoption include:
- Financial services
Listed are some examples of Large ERP integrators that have acquired AI capabilities:
- Accenture acquired AI startup Evrythng in 2019 to help its customers implement AI-powered solutions for product traceability and supply chain management.
- IBM acquired AI startup Watsons in 2015 to add AI capabilities to its ERP software Watson Supply Chain Intelligence.
- KPMG acquired AI startup CognitiveScale in 2018 to help its customers implement AI-powered solutions for financial forecasting and risk management.
- Deloitte acquired AI startup Rubikloud in 2019 to help its customers implement AI-powered solutions for customer experience management and fraud detection.
- Microsoft’s acquisition of Nuance Communications for $19.7 billion in 2021
- Salesforce’s acquisition of Slack for $27.7 billion in 2020
- Google’s acquisition of Fitbit for $2.1 billion in 2019
- Nvidia’s acquisition of Mellanox Technologies for $6.9 billion in 2020
- Intel’s acquisition of Habana Labs for $2 billion in 2019
ERP integrators of all sizes are increasingly acquiring AI capabilities to provide their customers with more comprehensive and value-added services. AI can be used to improve ERP systems for all industries and sizes in several ways, including:
AI is making employee training more personalized, adaptive, engaging, and effective. This can lead to several benefits for businesses, including improved employee performance, productivity, and satisfaction.
AI can be used to create VR and AR training classes that are more enveloping and interactive than traditional training approaches. This can be especially effective for training employees on intricate tasks or procedures.
Some specific examples of how AI is being used in employee training today:
- Salesforce uses AI to create personalized learning paths for its sales reps, based on their individual goals and the products they sell.
- Walmart uses AI to train its employees on new safety procedures and store policies.
- Delta Air Lines uses VR to train its pilots on new aircraft and flight procedures.
- Amazon: Amazon uses AI to gamify its training experiences. For example, Amazon has a leaderboard that tracks employees’ progress on training modules. Employees who complete training modules and achieve certain learning goals are awarded badges.
- Google: Google uses AI to create microlearning modules for its employees. The microlearning modules are short, focused, and engaging, and they cover a wide range of topics, such as coding, marketing, and project management.
- Netflix: Netflix uses AI to create personalized learning paths for its employees. AI adapts the learning paths based on each employee’s progress and performance.
Improved Data Analysis:
AI can enhance data analysis capabilities within ERP systems. Machine learning algorithms can process and analyze vast amounts of data, providing deeper insights into an organization’s operations, performance, and trends. This can help in making data-driven decisions. Big data and AI have a synergistic relationship. Big data analytics leverages AI for better data analysis. In turn, AI requires a massive scale of data to learn and improve decision-making processes.
AI-driven predictive analytics can help ERP systems forecast future trends, demand, and potential issues. This can be particularly valuable in supply chain management, inventory optimization, and demand forecasting.
Improvements in technology have dramatically changed what enterprise analytics can do, but predictive and descriptive analytics still require time, expertise, and lots of data. They often produce only narrow insights. However, AI is making it possible for analytics to automatically incorporate and process important context from a broad array of sources — many of which would have previously required analysts to navigate silos and poorly maintained data bases. Google applications can tell you based on your location, calendar, and traffic information that it’s time to leave for the airport if you want to catch your flight. Companies can increasingly take advantage of contextual information in their enterprise systems.
AI can automate routine and repetitive tasks within ERP systems. For example, AI-powered chatbots can handle customer inquiries, and robotic process automation (RPA) can streamline data entry and invoice processing.
Artificial intelligence holds revolutionizing potential across all industries. AI can be divided into two categories: automation and augmentation. By automating, we can eliminate the need for human labor, and by augmenting, we can use AI to enhance the intelligence and performance of human beings.
Natural Language Processing (NLP):
NLP capabilities can make it easier for users to interact with ERP systems using natural language queries. This can simplify data retrieval and reporting processes. Natural Language Processing (NLP) makes it possible for computers to understand the human language. Behind the scenes, NLP analyzes the structure of sentences and the meaning of words, then uses algorithms to extract meaning and deliver outputs. In other words, it makes sense of human language so that it can automatically perform tasks.
Probably, the most popular examples of NLP in action are virtual assistants, like Google Assist, Siri, and Alexa. NLP understands written and spoken text like “Siri, where is the nearest gas station?” and transforms it into numbers, making it easy for machines to understand. In the hospitality industry, voice kiosks allow for ordering and payment based on voice command. Another well-known application of NLP is chatbots. They help support teams solve issues by understanding common language requests and responding automatically. Reducing the amount of time needed from human intervention.
AI can enhance fraud detection capabilities within ERP systems by identifying unusual patterns and anomalies in financial transactions and procurement processes.
Before AI, fraud prevention systems would rely on rules alone, which are great at analyzing past fraud patterns. By combining supervised learning algorithms trained on historical data with unsupervised learning, digital businesses gain a greater level of understanding and clarity about the risk of customers’ behaviors. Decisions to accept or reject payment, stop fraudulent activity to limit chargebacks and reduce risk are all possible.
Rule engines and predictive analytics can scale only so far in uncovering fraud. Businesses will often revert to tougher standards for transaction approvals if they have been burned by fraud. The result is a bad customer experience. By having an AI-based fraud prevention system do the work of evaluating historical data and anomalies, customer experiences can stay more positive, and the more sophisticated fraud attacks can be avoided.
Maintenance and Asset Management:
AI-powered predictive maintenance can be integrated into ERP systems to monitor the condition of machinery and equipment, helping organizations schedule maintenance tasks more efficiently and reduce downtime.
Enterprise asset management (EAM) is a combination of software, systems and services used to maintain and control operational assets and equipment. The aim is to optimize the quality and utilization of assets throughout their lifecycle, increase productive uptime and reduce operational costs. Enterprise asset management involves work management, asset maintenance, planning and scheduling, supply chain management and environmental, health and safety (EHS) initiatives.
Supply Chain Optimization:
AI can optimize supply chain management by analyzing data from various sources to improve inventory management, supplier selection, and logistics planning. Artificial intelligence can take over much of the difficult work of supply chain management and optimization, freeing employees for other things and improving efficiency.
AI systems are fast, efficient, and tireless, making it possible to improve efficiency in a supply chain, reduce the need for human work, improve safety, and cut costs.
Through automation and optimization, AI can help organizations reduce operational costs and improve overall efficiency, which is a significant benefit of ERP systems.
Another way in which AI can be implemented in your organization to cut costs is by reducing repetitive tasks. This can help improve worker productivity and reduce waste. What AI is best at are the tasks that humans find boring and repetitive. When you can implement AI to take on those tasks, you can help your workers get back to doing the work that only humans can do.
Organizations track procurement costs and losses. Yet, that data sits unused after the month’s or year’s end. The data can be fed into a machine learning algorithm which can look for patterns and make suggestions as to how your business can become more efficient. By using your data, you can start to predict changes you need to make before they become issues and get automated insights. Machine learning can help you get a better understanding and save money by category, supplier, and business unit.
Enhance Reporting and Insights:
AI can generate advanced reports and provide executives with real-time insights into business performance, helping them make informed decisions. AI-powered reporting tools are revolutionizing the way businesses analyze and interpret data. AI helps identify patterns, trends, and anomalies in data that might be impossible to detect for humans. AI reporting tools offer insights that employees can use to make data-driven decisions promptly.
AI can enable ERP systems to continuously learn and adapt to changing business conditions and requirements, ensuring that they remain relevant and effective over time. To cope with real-world dynamics, an intelligent agent needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively.
The important issues of continuous learning are:
- Improved accuracy and performance: Continuous learning allows AI systems to improve their accuracy and performance over time as they are exposed to more data and learn new things. This is important for AI systems that are used in critical applications such as self-driving cars and medical diagnosis.
- Adaptability to change: Continuous learning allows AI systems to adapt to changes in their environment and data distribution. This is important for AI systems that are used in real-world applications, where data is constantly changing and evolving.
- Reduced need for human intervention: Continuous learning can reduce the need for human intervention to keep AI systems up to date. This is important for AI systems that are deployed in large-scale or distributed environments.
Integration and Emerging Technologies:
AI can be integrated with other emerging technologies such as the Internet of Things (IoT) and blockchain to create more robust and intelligent ERP systems.
Smart technologies like AI and machine learning will be at the core of ERP. These technologies can complement ERP functions by managing and integrating diverse applications and then using the data to support decision making. This allows the ERP system to optimize workflows, shorten lead times, and reduce errors. AI-based tools can also use system-generated data to initiate more informed decision-making by helping to identify red flags before business is affected.
AI is also helping to make ERP systems more accessible and affordable for businesses of all sizes. While AI holds great promise for enhancing ERP systems, it’s important for organizations to carefully plan and implement these technologies to ensure they align with their specific business goals and processes. Additionally, considerations around data privacy, security, employee acceptance and ethical AI usage should be considered when integrating AI into ERP systems.
Overall, AI is expected to make ERP systems more intelligent, efficient, and user-friendly. Companies in all sectors are looking to invest in AI with their ERP system to stay ahead of the competition.