AI vs Traditional ERP: What’s the Difference?

The first appearance of Enterprise resource planning (ERP) software was in the 1990s. It automates most core business processes like inventory management, order fulfillment, accounts, HR and so forth. However, in recent years, artificial intelligence has been discovered to be useful in making ERP systems even smarter and more efficient.

Second, what is the distinction between legacy ERP platforms and the new AI-enriched ones? Why is it used to enhance existing functionality? How do businesses that have dated on-premise ERP systems go about considering upgrading them to cloud-based systems with built-in AI/ML out of the box?

In this article, we will compare traditional software with ERP AI software using several key parameters so that technology decision-makers can understand AI’s advantages in ERP software.

Automation Levels

Since its inception, legacy ERP platforms have automated processes to digitize manual tasks like ledger bookkeeping. However, automation was rules-based and had a limited scope. Machine learning algorithms enhance automation further by making the processes continue to optimize themselves without further programming.

For instance, an AI-based supply chain management system from ERP development company can analyze previous forecast accuracy for demand, product delivery time, inventory span, etc. It can then dynamically adjust algorithms to enhance future forecasting, transportation load efficiency and optimal stock levels throughout the distribution centers. Normal ERP software cannot achieve this level of continuous self-learning automation.

Another key difference is that artificial intelligence in ERP has computer vision capabilities through image recognition, object detection and other intelligent features. This allows an AI ERP system to automate visual inspection of products, equipment, facilities and more to detect quality issues, maintenance requirements, and other important information. Traditional ERP is blind to data.

In summary, legacy ERP brings rigid visual and rules-based automation, while AI enables flexible ERP and self-optimizing automation across a wider range of processes.

Analytics and Reporting

Traditional ERP platforms have reporting features that visualize data in charts, graphs and tables to provide business insights. However, the analysis options are typically basic summaries that are not very deep.

AI in ERP systems utilizes machine learning to discover higher data patterns, correlations and insight that humans couldn’t unroll manually over all that voluminous data. It includes predictive capabilities based on historical customer, product, operational and other data to predict future outcomes.

To illustrate, an AI and ERP supply chain management system could crunch billions of data points across the entire supply network in order to spot conditions that could well become inventory or transportation bottlenecks ahead of time. The AI algorithms continuously analyze data streams in real-time to refine predictions and mitigate emerging risks.

In addition, AI has natural language generation capabilities to create written analytic reports tailored to different managerial roles across the organization. Rather than sifting through basic data visualizations, executives can simply read a customized report that provides predictive insights explained in plain language and prescriptive recommendations for capitalizing on opportunities or addressing issues.

Human-Like Capabilities

The way AI systems work is completely different from the traditional rigid rule-based software. They are more human-like in their capability to do things like natural language processing, computer vision, speech recognition, and many other such things. In this way, human interactions with ERP software become more natural and easier.

For example, users are freed from going through the menu hierarchies, filling forms, and shit.org system responses and can rather just ask an AI assistant a question either in freeform voice or freeform text. Conversational commands not only enable the assistant to answer questions but also to execute actions, increasing productivity for employees.

Computer vision allows AI systems to interpret real-world visual data just like a human. This might include reading barcodes, recognizing products, detecting defects, identifying inventory issues, inspecting equipment and facilities, tracking the movement of people assets, and more. An AI-powered system has a set of virtual eyes to observe, analyze and optimize the physical world.

In essence, AI infuses more human-like intelligence into ERP software, closing the gap between automated systems and real employees. This brings simplicity and efficiency.

Continuous Learning

Traditional ERP systems have static rules and logic that require software upgrades or custom programming to improve functionality over time. However, AI-powered ERP platforms continuously evolve through machine learning algorithms that refine decision-making based on new data. It’s a form of autonomous self-improvement.

For example, consider an AI supply chain application that predicts customer demand for certain products. At first, its predictions may be off by a considerable margin. However, as it ingests more sales data over subsequent months, it continuously fine-tunes predictive modeling to become more accurate.

The ability to learn “on the job without human intervention” is critical to staying ahead in a fast-changing business environment. An AI system evolves over time and ultimately becomes operationally expert to the level of an approximation or sometimes even better than human domain experts. This enables data-driven, split-second decision-making.

Augmentation Instead of Automation

A common misconception is that AI aims to automate jobs, leading to unemployment completely. The reality is that AI-powered ERP platforms are designed to augment human capabilities rather than replace jobs entirely.

The goal is to free employees from tedious, repetitive tasks so they can focus on higher-value business activities. AI takes over routine data processing, alert monitoring, report generation, predictive modeling and other complex tasks that humans don’t enjoy and may not perform well. This leaves humans free to apply their judgment, emotions, critical thinking skills and real-world experience to exceptions and higher-order decisions.

So, an inventory manager augmented by AI for ERP could focus less on spreadsheet analysis and inventory monitoring. Instead, they have more capacity for supplier relationship management, pricing negotiations, new product evaluation, staff mentoring, and long-term strategy. AI amplifies what humans already do well rather than replacing them fully.

Implementation Challenges

Transitioning from a legacy on-premise ERP system to a new cloud-based AI platform brings formidable challenges, including data migration, user training, customization, integration and change management. Organizations must budget more time and resources for the upgrade.

That said, technological advances are making implementations progressively smoother over time. SaaS architecture eliminates the need for organizations to purchase and maintain their own hardware and data servers. Cloud storage handles the migration of high volumes of legacy data to the new system. Low code configuration speeds customization. API integration links the intelligent ERP with other applications. Better change management ensures high user adoption across the organization.

Although implementing AI in ERP software still presents greater initial hurdles than legacy systems, the long-term benefits typically outweigh the temporary disruptions of digital transformation. The uphill battle becomes easier over time while delivering exponential value.

AI ERP Adoption Trends

According to recent research by Market.us, the global market for AI ERP solutions will grow from $4.5 billion in 2023 to $45 billion by 2033, representing a CAGR of 26.30%. So, in less than a decade, artificial intelligence ERP spending will grow over 4X, indicating strong demand.

Driving this adoption is the pressure on organizations to leverage- next-gen technologies like AI, machine learning, cloud platforms, Big Data and analytics. Companies that fail to make this transition risk competitive disruption by falling behind peers who will achieve new levels of speed, quality, efficiency and innovation.

Gartner predicts that over 50% of large enterprises will rely on AI-powered ERP platforms by 2027 as part of their digital transformation initiatives and cloud-first strategies. Although market penetration remains low currently, AI ERP growth is starting to accelerate.

AI ERP Benefits Summary

Upgrading legacy on-premise ERP solutions to AI-powered SaaS platforms brings a multitude of benefits:

• Autonomous process optimization through machine learning instead of rigid rules-based software.
• Predictive insights from deep statistical analysis of big data across business units.
• Natural language and computer vision capabilities for simpler human-system interactions.
• Continuous self-learning for more agility in fast-changing market environments.
• Augmented intelligence to amplify human skills rather than full automation.

During the next ten years, artificial intelligence will create exponential improvements in the functionality of ERP software.

Implying the next evolution of business process automation and analytics, ERP with AI is the term. Those organizations that spread machine learning throughout their enterprise systems will win operations over their slow-to-adopt legacy peers. It is now time for technology leaders to begin planning to migrate to AI-powered ERP and beyond.