ai in erp Archives - Striven

AI in ERP: A Better Way of Staying Organized

With the advent of Artificial Intelligence (AI), the potential for optimizing accounting practices, document management, and operational procedures is being taken to a whole new level.

AI automates and optimizes complex tasks, ensuring accuracy and efficiency in financial operations. It streamlines document handling by intelligently organizing and analyzing content for better accessibility and consistency.

Additionally, AI boosts operational efficiency by predicting trends and optimizing supply chains, driving cost reductions and improving service delivery. This integration not only enhances daily operations but also provides strategic insights for sustained business growth.

Let’s dive in to some ways that your business can reap the benefits of AI in ERP software:

Simplifying Accounting Practices

AI technology offers a wide range of possibilities to transform and optimize accounting practices, especially for small and medium-sized businesses (SMBs) that might not have the same resources as larger corporations.

1. Smart Receipt Tracking and Expense Management

AI can simplify the tedious process of tracking receipts and managing expenses by automating data entry and categorization. By using smartphone apps integrated with AI, business owners can take photos of their receipts. The AI then automatically extracts relevant data (such as date, amount, and vendor) and categorizes the expense according to the business’s chart of accounts. This not only saves time but also reduces errors associated with manual entry, making expense reporting and tracking far more efficient and accurate.

2. AI-driven Cash Flow Forecasting

Understanding and predicting cash flow is essential for the survival and growth of any SMB. AI can analyze historical financial data, seasonal trends, and payment cycles to predict future cash flow accurately. This predictive analysis can help businesses anticipate cash shortages, plan for future investments, and make informed decisions about when to scale operations. Unlike complex financial models that require expert knowledge, these AI tools present their forecasts through user-friendly dashboards, making advanced financial planning accessible to non-experts.

3. Automated Accounts Receivable and Payable

AI can automate the process of managing accounts receivable and payable, improving efficiency and cash management. For receivables, AI systems can send automated invoice reminders to clients, track invoice payments, and even predict which clients are likely to pay late. On the payables side, AI can schedule payments to optimize cash flow, taking advantage of early payment discounts or avoiding late fees. This automation ensures businesses maintain healthy cash flow and spend less time on manual tracking and communication.

4. Enhanced Financial Decision Support

AI can serve as a financial advisor for SMBs by providing decision support based on real-time financial data analysis. For instance, AI can analyze spending patterns, identify areas where costs can be reduced, and suggest changes to pricing strategies based on market demand analysis. By offering insights that are easy to understand and act upon, AI helps business owners make smarter financial decisions without needing a background in finance or analytics.

5. Simplified Tax Compliance and Optimization

Tax compliance is often a complex and time-consuming process for SMBs. AI can simplify this by keeping track of tax regulations, identifying applicable tax deductions, and even preparing tax returns. By analyzing a company’s financial transactions in real-time, AI ensures that all financial activities are recorded in a tax-compliant manner, reducing the risk of errors or omissions that could lead to penalties. Additionally, AI can optimize tax strategies by analyzing the most tax-efficient ways to structure transactions and investments, ensuring businesses pay no more tax than necessary.

Simplifying Document Management & Organization

AI technology has the potential to significantly enhance document management and digital organization, particularly for small and medium-sized businesses (SMBs that may not have extensive resources to dedicate to these tasks.

1. Smart Document Categorization and Tagging

AI can automatically categorize and tag documents as they enter the system, using natural language processing (NLP) and machine learning algorithms to understand the content and context of each document. This process eliminates the need for manual sorting and filing, ensuring that documents are organized systematically and can be retrieved with ease. For example, an invoice can be instantly recognized as a financial document and tagged with relevant identifiers such as the vendor name, date, and amount, simplifying bookkeeping and financial management.

2. Intelligent Search and Retrieval

AI-enhanced search functionalities can transform how businesses access their digital documents. Instead of relying on exact file names or folder locations, users can search using natural language queries, such as “find the invoice from Vendor X in March,” and the AI system will retrieve the relevant documents. This capability significantly reduces the time spent searching for documents and improves productivity, as users can quickly find exactly what they need using intuitive search terms.

3. Automated Compliance and Record Keeping

AI can help SMBs navigate the complexities of regulatory compliance and record-keeping requirements. By understanding the specific compliance standards relevant to the business, AI can automatically classify, store, and manage documents in compliance with legal and industry-specific guidelines. This includes ensuring that sensitive information is stored securely, managing retention schedules to ensure documents are kept for the required periods, and automating the deletion of records that are no longer needed, thereby reducing legal risks and maintaining organizational efficiency.

4. Enhanced Collaboration and Version Control

AI can streamline collaboration on documents by managing versions and changes in real-time. It can track edits, suggest changes, and even predict potential conflicts between different versions of a document, ensuring that all team members are working on the most current version of a document, reducing confusion and improving the efficiency of collaborative projects. 

5. Predictive Document Organization and Prioritization

By leveraging AI’s predictive analytics capabilities, businesses can organize and prioritize documents and tasks based on urgency, relevance, and personal work habits. AI can analyze how users interact with different documents and tasks over time to predict which items will be most important on any given day. This personalized organization helps users focus on the most critical tasks, improving efficiency and reducing the cognitive load of managing a vast array of documents and information.

Streamlining and Automating Operations

From optimizing supply chains and streamlining HR processes to personalizing customer interactions, AI’s integration offers a multifaceted enhancement to how you can improve your daily business operations beyond the scope of accounting and document management: 

1. Supply Chain Optimization

AI within ERP systems can dramatically enhance supply chain management by predicting demand patterns and optimizing inventory levels. These tools analyze historical sales data and external factors like market trends and seasonal fluctuations to forecast future product demands, ensuring that businesses maintain optimal inventory levels. This not only reduces the risk of overstocking or stockouts but also helps in planning for procurement and production schedules more efficiently.

2. Human Resources Management

AI can streamline several HR processes from recruitment to employee retention. In recruitment, AI tools can parse through vast numbers of applications to identify the most suitable candidates based on predefined criteria. For employee management, AI can analyze employee performance data to help HR teams identify training needs and career development opportunities. This proactive approach in managing workforce needs helps in maintaining a well-organized and satisfied workforce.

3. Customer Relationship Management (CRM)

AI-enhanced ERP systems can transform CRM by personalizing customer interactions and improving service quality. AI tools can analyze customer data collected through various touchpoints to gain insights into customer preferences and behaviors. This information enables businesses to tailor their marketing strategies and customer service approaches, leading to higher customer satisfaction and loyalty. (You can read more about this topic on our next blog!)

4. Production Scheduling

In manufacturing, AI tools can optimize production schedules by analyzing machine availability, material supply, and workforce capacity. AI algorithms can predict the best production processes to minimize downtime and maximize output. This helps businesses ensure that production runs smoothly, meets deadlines, and maintains high levels of efficiency, all of which are crucial for staying competitive in fast-paced markets.

5. Maintenance Management

AI within ERP systems can also revolutionize maintenance management by predicting when machines or equipment are likely to fail or need servicing. Using predictive maintenance techniques, AI tools monitor equipment performance in real-time and alert managers to potential issues before they cause disruptions. This proactive approach minimizes downtime, extends the lifespan of machinery, and keeps operations running without unexpected interruptions.

Wrapping Up

AI’s role in modern business operations cannot be overstated. As we’ve seen, AI not only refines accounting and document management processes but also extends its capabilities to various facets of business operations, including supply chain management, HR, customer relations, production, and maintenance. 

By adopting AI-enhanced ERP solutions, businesses can not only stay organized but also gain a competitive edge through improved decision-making and operational efficiency. 

Embracing AI is not just about keeping up with technology trends; it’s about setting a foundation for sustainable growth and success in an increasingly complex business landscape.

AI in ERP: Reporting and Analytics

Effective reporting within a growing business is indispensable for maintaining a clear overview of its operations, financial health, and strategic direction. It provides the foundation for informed decision-making, enabling leaders to identify trends, measure performance against objectives, and pinpoint areas requiring intervention or opportunity for expansion.

Incorporating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems presents an extraordinary opportunity for savvy business owners to gain a competitive edge in their industry. 

Two transformative AI-driven enhancements in reporting—Predictive Analytics and Natural Language Processing (NLP)—stand out for their potential to drastically improve strategic decision-making and operational efficiency for business owners and their staff.

Predictive Analytics for Strategic Decision-Making

At the heart of AI-enhanced ERP systems lies the power of predictive analytics. This technology goes beyond traditional reporting—it sifts through mountains of historical data to uncover patterns, predict future trends, and provide actionable insights.

For small and growing businesses, this means the ability to forecast market demands, identify potential supply chain disruptions before they occur, and anticipate consumer behavior changes with a degree of accuracy previously unattainable. 

Predictive analytics transforms raw data into a strategic asset, enabling these businesses to make informed decisions that are proactive rather than reactive. By leveraging AI to analyze reports and historical data, companies can plan with foresight, adjusting their strategies to seize opportunities and mitigate risks ahead of time. 

This level of strategic planning and insight can be a game-changer for small businesses looking to compete and grow in fast-paced markets.

The transformation is not just limited to those with deep technical expertise either—it extends to business owners and other stakeholders who are experts in their craft, not necessarily complex technical analysis. Making advanced data-driven insights accessible and actionable for all businesses is one of the significant benefits that AI has to offer.

Let’s delve deeper into how predictive analytics can be applied, with examples and insights that underscore its value for companies of any size.

Enhanced Demand Forecasting

Predictive analytics can significantly improve demand forecasting by analyzing historical sales data, market trends, customer behavior, and even external factors like economic indicators or long-range weather patterns. For instance, a small retail business can use predictive analytics to anticipate seasonal fluctuations in demand for certain products, allowing for optimized stock levels that prevent both overstocking and stockouts. This level of precision in inventory management directly translates to cost savings and improved customer satisfaction.

Let’s take a look at a hypothetical example scenario:

A mid-sized retail clothing store chain aims to optimize its inventory for the upcoming spring season. Historically, demand prediction has been challenging due to changing fashion trends, unpredictable weather patterns, and varying customer preferences across locations.

The store integrates predictive analytics into its ERP system, leveraging historical sales data, seasonal trends, regional preferences from its various locations, and external factors such as economic indicators and weather forecasts.


  • Pre-Implementation: Prior to using predictive analytics, the chain experienced a 20% overstock of unsold seasonal items and a 15% understock of high-demand items, leading to lost sales and increased storage costs.
  • Post-Implementation: With predictive analytics, the store achieves a more accurate demand forecast, reducing overstock by 10% and understock by 12%. This optimization leads to an estimated 8% increase in sales due to better availability of high-demand items and a 5% reduction in costs associated with inventory storage and markdowns.

Customer Behavior Prediction

Understanding and anticipating customer behavior can give businesses a competitive edge. Predictive analytics allows companies to analyze purchase histories, online behavior, and customer feedback to identify trends and predict future buying behaviors. This enables businesses to tailor marketing strategies, personalize customer experiences, and develop products or services that meet evolving needs. For a non-technical business owners, this means being able to strategically focus resources on high-value opportunities and customer engagement initiatives.

Let’s take a look at a hypothetical example scenario:

An emerging e-commerce platform, focused on eco-friendly home products, aims to enhance its marketing strategies and product offerings by better understanding and anticipating its customers’ purchasing behaviors. Despite a growing customer base, the company struggles to personalize marketing efforts and optimize its product mix to meet diverse customer preferences.

The company integrates AI-driven predictive analytics into its ERP system to analyze detailed customer data, including past purchase history, browsing patterns on the website, responses to previous marketing campaigns, and social media engagement metrics. The system is trained to identify patterns and predict future purchasing behaviors, segment customers into distinct groups based on their preferences and likelihood to purchase certain types of products.


  • Pre-Implementation: The company’s marketing campaigns were generic, leading to a modest conversion rate of 2% and a customer retention rate of 30%.
  • Post-Implementation: Leveraging predictive analytics, the company crafts personalized marketing messages and recommends products that align with individual customer preferences, significantly enhancing customer engagement. This strategic approach results in a conversion rate increase to 4% and boosts the customer retention rate to 45%.

Leveraging NLP for Enhanced Reporting

Complementing predictive analytics, Natural Language Processing (NLP) revolutionizes how businesses interact with their ERP systems. NLP allows users to generate reports and extract insights using natural language queries, making advanced data analysis accessible to non-technical stakeholders. 

For example, this means that a marketing manager can simply ask the ERP system, “What were our best-selling products last quarter?” and receive an immediate, comprehensible answer, not another torrent of raw data that needs to be dissected and parsed by your company’s staff mathematicians. NLP helps data be accessible to anyone. 

This capability not only democratizes data access within the organization but also significantly speeds up the decision-making process. For small and growing businesses, where agility and swift responses to market changes are crucial, the ability to quickly generate and understand complex reports without needing specialized data analysis skills can dramatically enhance operational efficiency and strategic planning.

Accessibility and Usability

One of the greatest advantages of predictive analytics in today’s ERP systems is its increasing accessibility to non-technical users. Advanced analytics tools are now designed with user-friendly interfaces, dashboards, and visualization tools, making it easier to interpret complex data and apply insights to strategic decisions. Moreover, with the advent of AI-driven assistants and natural language processing capabilities, users can query their ERP systems in plain language to obtain forecasts and insights, further democratizing access to powerful predictive analytics.

Let’s take a look at a hypothetical example scenario:

A non-technical business owner of a small but growing online specialty food store wants to understand customer buying patterns to stock inventory more effectively and plan marketing campaigns.

The business owner uses an ERP system enhanced with AI and natural language processing (NLP) capabilities. This system allows the non-tech savvy operator to ask complex analytical questions in plain language, such as, “What were the top-selling products last quarter?” or “Predict the demand for product X in the next three months.”


  • Pre-Implementation: The owner had to rely on manual analyses or wait for insights from data analysts, often leading to delayed decisions and missed opportunities.
  • Post-Implementation: The owner can now directly interact with the ERP system, receiving instant analytics and forecasts. This immediate access to insights enables quicker strategic decisions, such as adjusting inventory levels in real-time or launching targeted marketing campaigns ahead of predicted demand spikes.

Wrapping Up

In the digital age where data is king, the integration of AI into ERP systems marks a paradigm shift for businesses of all sizes. Predictive analytics and NLP are not just buzzwords but powerful tools that transform data into a strategic ally.

As we’ve seen through various scenarios, these technologies empower business owners—regardless of their technical prowess—to harness complex data for informed decision-making, ultimately fostering a culture of proactive strategy and refined operational acumen. This technological empowerment is the cornerstone of modern business intelligence, leveling the playing field and enabling smaller enterprises to compete with industry giants.

As businesses continue to evolve in this dynamic data-driven landscape, those who embrace AI’s transformative potential within their ERP systems will undoubtedly lead the way in innovation, customer satisfaction, and market growth.

(Fun fact: you guessed it, the cover photo for this blog post is also AI generated!)

AI in ERP: How AI Can Transform The Way Businesses Use ERP Systems


Welcome to the frontier of business transformation, where Artificial Intelligence (AI) within Enterprise Resource Planning (ERP) systems is not just an innovation—it’s a revolution.

Before we dive deeper, let’s define some key terms:

  • Artificial Intelligence (AI): Technology that enables a machine to simulate human behavior at much faster speeds.
  • Enterprise Resource Planning (ERP): Software systems that manage and integrate the core processes of a business.

We invite you on a journey to explore how AI technologies are reshaping the landscape of ERP software, turning traditional business management processes into engines of advanced efficiency and decision-making prowess.

What if your business systems could not only react to the past but predict and plan for the future? Automating tasks, anticipating needs, making operations smoother and more intuitive— this is all possible thanks to AI’s ability to analyze vast amounts of data with unparalleled precision. 

This 7-part blog series embarks on a comprehensive journey into the symbiotic relationship between AI and ERP, illuminating how AI technologies are not just augmenting ERP systems but are becoming integral to their evolution.

This series will dive into how AI can help your business:

  • Enhance reporting and analytics capabilities
  • Use financial data to better understand insights, trends, and risks
  • Stay organized and efficient while employees work across the globe
  • Create better and more personalized messaging aimed at prospects and customers
  • Analyze your competitors’ strategies to stay ahead of the competition
  • Explore how augmented reality systems will be able to compliment ERP systems

AI and ERP In The World Today

Right now, small business leaders are starting to find success incorporating AI technologies into their business management strategies. According to a study conducted by Forbes, 91% of businesses that have implemented AI have seen an increase in their success.

Furthermore, According to IDC, by leveraging AI in ERP systems, businesses can expect to reduce operational costs by up to 30% by the end of 2024. This is not just improvement—it’s transformation.

While many businesses that have incorporated AI tools have seen success, it’s not a given that small and medium sized business owners are always going to incorporate cutting edge technologies.

In a 2022 BPC and Public First poll, 74% of small business owners believed digital skills were important to their business. However, only 21% of that group were early adopters of new technology, and only 52% planned on making investments in digital tools over the next year.

And while business owners commonly welcome ERP solutions into their organization—the global ERP market is expected to top $78 billion by 2026—few have yet to use AI to harness the full potential of the data that these systems wield.

The integration of various forms of AI into ERP systems marks a pivotal shift from static, historical data analysis to dynamic, predictive insights. AI empowers ERP systems to learn from past trends, enabling businesses to forecast future outcomes with remarkable accuracy.

Here are a few real-world success stories of how businesses can benefit from an ERP system that harnesses AI to get the most out of their data:

Mitsubishi Electric

One compelling example of AI’s impact in ERP comes from Mitsubishi Electric. They created a new artificial intelligence technology that can assess the effectiveness of manual tasks on production floors in a matter of minutes. This is achieved without the need for operators to set up training data for the AI in advance, by utilizing a probabilistic generative model, which estimates the tasks’ outcomes based on probable scenarios. Multiple tests have shown that this new generative AI technology can reduce Mitsubishi’s work analysis training time by 99%. 99%!


This US pharmaceutical distributor leveraged an AI-powered ERP system to automate production cost analysis, utilizing historical transaction data, external datasets like weather conditions, and predictive algorithms.

The outcome was a significant reduction in the time spent on pricing tasks (from 5 hours to 1 hour) and an increase in time allocated to value-added activities. This strategic pricing optimization led to over $500 million in incremental revenue annually​.


An early adopter of AI-enhanced ERP technology, Walmart has been utilizing in-memory computing alongside AI for handling over 1 million transactions per hour. The retailer has achieved a 10-15% improvement in in-stock availability, a 15-20% reduction in supply chain costs, and a 20-25% increase in inventory turnover. The use of AI provides Walmart with near real-time data insights, enabling better visibility across its extensive retail operations​

While these case studies showcase the impact AI in ERP has had on larger companies, these examples underscore a broader principle: AI’s capacity to revolutionize processes isn’t confined to multinational conglomerates. Businesses of any size can harness similar AI tools because ERP software isn’t something exclusively reserved for Fortune 500 companies anymore.

Looking Ahead

Imagine a world where your ERP system doesn’t just respond to your commands but anticipates your needs, where predictive analytics and intelligent automation turn complex data into actionable insights, and strategic decisions are not just informed but foresighted. From optimizing supply chains in real-time to personalizing customer experiences and automating financial forecasts, the AI-enhanced ERP system is not just a tool but a game-changer.

Whether you’re a C-level executive striving to lead your organization into a new era, an IT professional tasked with implementing cutting-edge solutions, or a business analyst keen on decoding the future of business efficiency, these guides are your gateway to the next frontier in enterprise technology.

This is the dawn of a new era in business technology, where AI and ERP together pave the way for unprecedented efficiency and strategic advantage.

(Fun fact: the cover photo for this blog post is AI generated!)