AI for Inventory Management: Optimize Stock Levels

AI for Inventory Management

• Predicts demand accurately
• Optimizes stock levels
• Automates replenishment
• Tracks inventory in real-time
• Reduces waste and overstock
• Improves supply chain coordination
• Saves costs

What is AI in Inventory Management?

Artificial Intelligence (AI) in inventory management refers to using advanced algorithms and machine learning techniques to optimize inventory tracking, ordering, and storage. AI enhances traditional inventory management systems by providing predictive analytics, real-time data processing, and automation capabilities.

Predictive Analytics

AI systems use historical data to predict future inventory needs. AI can accurately forecast demand by analyzing patterns in sales data, seasonal trends, and market conditions. This helps businesses avoid both overstocking and stockouts.

• Example: A retail company can use AI to analyze past holiday sales trends and predict the optimal inventory levels for the upcoming season. This ensures they have enough stock to meet customer demand without over-purchasing.

Real-Time Data Processing

AI integrates with various data sources, such as point-of-sale, warehouse management, and supply chain data, to provide a real-time view of inventory levels. This allows businesses to monitor stock in real time and make informed decisions quickly.

• Example: An e-commerce platform uses AI to sync inventory data across multiple sales channels in real time. If an item is sold in one channel, the system immediately updates the stock levels across all channels, preventing overselling and ensuring accurate stock information.

Automation of Routine Tasks

AI can automate many routine tasks in inventory management, such as reordering stock, setting reorder points, and managing safety stock levels. Automation reduces the need for manual intervention, minimizing human error and freeing up staff to focus on more strategic tasks.

• Example: A warehouse uses AI-driven robots to automate the picking and packing. The system can identify which items need restocked based on current inventory levels and automatically place orders with suppliers when stock falls below a certain threshold.

Optimization of Supply Chain

AI helps optimize the supply chain by identifying bottlenecks, predicting lead times, and suggesting the best transportation routes. This improves efficiency and reduces costs associated with warehousing and transportation.

• Example: A manufacturing company employs AI to analyze its supply chain data. The AI identifies a pattern of delays from a specific supplier and suggests alternative suppliers or adjustments to the lead time, ensuring the production line runs smoothly without interruptions.

Enhanced Decision Making

AI provides insights that help managers make better decisions regarding inventory policies, supplier selection, and demand forecasting. These insights are based on data-driven analysis, reducing the reliance on intuition and guesswork.

• Example: A supermarket chain uses AI to determine the most profitable layout for its stores by analyzing customer buying patterns and product placement. This results in better inventory turnover rates and increased sales.

Case Study: Amazon

Amazon is a prime example of a company that leverages AI in inventory management. They use AI to forecast demand, manage warehouse operations, and optimize their supply chain. AI-driven Kiva robots are used in Amazon warehouses to pick and move inventory efficiently. These robots use machine learning to navigate the warehouse, improving speed and accuracy in order fulfillment.

Benefits of AI in Inventory Management

Artificial Intelligence (AI) in inventory management provides numerous benefits, revolutionizing how businesses handle their stock and supply chain operations. These benefits include improved accuracy, cost savings, enhanced customer satisfaction, and more efficient operations.

Improved Accuracy

AI reduces human error in inventory management by automating data entry and analysis. This leads to more accurate tracking of inventory levels, demand forecasting, and order management.

• Example: A retail chain uses AI to automate inventory counts and updates. The system continuously scans barcodes and updates stock levels in real time, significantly reducing discrepancies and errors compared to manual counts.

Cost Savings

By optimizing inventory levels, AI helps businesses minimize costs associated with overstocking and stockouts. This ensures that capital is not tied up in excess inventory and reduces storage costs.

• Example: A grocery store implements AI to predict demand for perishable items accurately. By stocking the right amount, they reduce waste from expired goods and lower storage costs, leading to significant savings.

Enhanced Customer Satisfaction

AI-driven inventory management ensures that products are available when customers need them, improving customer satisfaction and loyalty. Real-time inventory tracking helps businesses meet customer expectations by preventing stockouts and delays.

• Example: An online retailer uses AI to monitor inventory levels and predict demand surges, such as during holiday seasons. This ensures that popular items are always in stock, leading to higher customer satisfaction and repeat business.

Increased Efficiency

AI automates routine tasks such as reordering, tracking, and reporting, allowing staff to focus on more strategic activities. This improves overall operational efficiency and productivity.

• Example: A distribution center employs AI-powered robots to manage stock placement and retrieval. These robots work faster and more accurately than humans, increasing the center’s throughput and efficiency.

Better Demand Forecasting

AI uses advanced algorithms to analyze historical data, market trends, and other factors to accurately predict future demand. This helps businesses plan their inventory needs more effectively.

• Example: A fashion retailer uses AI to analyze sales data from previous seasons and predict which items will be popular in the upcoming season. This enables them to stock the right products and avoid excess inventory.

Supply Chain Optimization

AI helps optimize the supply chain by identifying inefficiencies, predicting lead times, and suggesting the best transportation routes. This reduces delays and lowers transportation costs.

• Example: A manufacturing company uses AI to analyze its supply chain and identify bottlenecks. The system suggests alternative suppliers and optimal transportation routes, reducing lead times and costs.

Real-Time Insights

AI provides real-time insights into inventory levels, sales trends, and supply chain performance. This allows businesses to make informed decisions quickly and respond to changes in demand or supply conditions.

• Example: A consumer electronics retailer uses AI to monitor real-time sales data and inventory levels. When a new product launch increases demand, the system automatically adjusts stock levels and reorder points to ensure availability.

Enhanced Supplier Management

AI improves supplier management by analyzing performance metrics, predicting potential disruptions, and suggesting alternative suppliers. This ensures a more reliable supply chain and reduces the risk of stockouts.

• Example: A pharmaceutical company uses AI to evaluate supplier performance and predict potential delays. The system recommends backup suppliers to ensure continuous production and supply.

Inventory Optimization

AI optimizes inventory by balancing stock levels with demand, ensuring businesses maintain the right amount of inventory. This reduces the costs associated with holding excess stock while avoiding stockouts.

• Example: A home improvement store uses AI to optimize its inventory of seasonal items. The system analyzes weather patterns and sales data and ensures that items like lawnmowers and snow shovels are available in appropriate quantities during peak seasons.

Case Study: Walmart

Walmart utilizes AI to optimize its inventory management processes. The company uses machine learning algorithms to predict demand, optimize stock levels, and manage its supply chain efficiently. This has reduced stockouts, lower inventory costs, and improved customer satisfaction.

Conclusion

The benefits of AI in inventory management are substantial. It offers improved accuracy, cost savings, enhanced customer satisfaction, increased efficiency, better demand forecasting, supply chain optimization, real-time insights, enhanced supplier management, and overall inventory optimization.

Companies like Walmart demonstrate how AI can transform inventory management, setting a standard for businesses leveraging AI for better inventory control.

Key AI Technologies for Inventory Management

Machine Learning Algorithms

Explanation of Machine Learning in Inventory Management

Machine learning algorithms analyze vast amounts of data to identify patterns and trends. These insights help predict future demand, optimize stock levels, and make informed decisions about inventory management.

Examples of Machine Learning Applications

• Demand Forecasting: Machine learning models analyze historical sales data, market trends, and external factors to predict future demand.
• Stock Optimization: Algorithms recommend optimal stock levels based on predicted demand, reducing overstock and stockouts.
• Supplier Management: Machine learning evaluates supplier performance and suggests improvements.

Predictive Analytics

Role of Predictive Analytics in Forecasting Demand

Predictive analytics uses historical data and statistical algorithms to forecast future events. In inventory management, it predicts demand, identifies trends, and helps make proactive decisions.

Examples and Case Studies

• Walmart: Uses predictive analytics to forecast demand for various products, ensuring optimal stock levels.
• Zara: Utilizes predictive analytics to anticipate fashion trends and adjust inventory accordingly, minimizing excess stock and stockouts.

Internet of Things (IoT) Devices

Real-time Tracking with RFID Tags and Sensors

IoT devices like RFID tags and sensors enable real-time inventory tracking. These devices provide accurate data on stock levels, locations, and movement.

Benefits of IoT in Inventory Management

• Enhanced Visibility: Real-time tracking ensures up-to-date information on inventory levels and locations.
• Reduced Losses: IoT devices help prevent theft and loss by providing accurate tracking.
• Improved Efficiency: Automates inventory checks and reduces manual labor.

Robotic Process Automation (RPA)

Automation of Routine Tasks

RPA automates repetitive and time-consuming tasks, such as data entry, order processing, and inventory updates, allowing employees to focus on more strategic activities.

Examples of RPA in Inventory Management

• Order Processing: Automates the processing of purchase orders and updates inventory records.
• Inventory Audits: Conduct regular inventory audits, ensuring accuracy and compliance.
• Returns Management: Manages the returns process, updating inventory and processing refunds.

How AI Tools Improve Inventory Management

Artificial Intelligence (AI) leverages several key technologies to optimize inventory management. These technologies include machine learning, predictive analytics, computer vision, robotics, and natural language processing. Each plays a vital role in enhancing various aspects of inventory management.

Machine Learning

Machine learning algorithms analyze vast amounts of data to identify patterns and predict future inventory needs. These algorithms improve their forecasting capabilities by continuously learning from new data.

• Example: An e-commerce company uses machine learning to analyze customer purchase history and seasonal trends. The system predicts which products will be in high demand and adjusts inventory levels accordingly, reducing overstock and stockouts.

Predictive Analytics

Predictive analytics combines historical data with advanced statistical techniques to forecast future inventory requirements. This helps businesses make informed decisions about ordering and stocking products.

• Example: A beverage company uses predictive analytics to forecast demand for different drinks during various seasons and events. This ensures they stock the right quantities, minimize waste, and maximize sales.

Computer Vision

Computer vision technology uses cameras and AI to monitor inventory levels visually. It can identify and count items on shelves, detect stock discrepancies, and alert managers to potential issues.

• Example: A supermarket chain uses computer vision to automatically scan shelves and track product quantities. The system alerts staff when stock is low, or products are misplaced, ensuring shelves are always well-stocked.

Robotics

AI-powered robots automate repetitive tasks such as picking, packing, and sorting inventory, enhancing warehouse operations’ efficiency, accuracy, and speed.

• Example: A fulfillment center uses AI-driven robots to pick and pack items for shipment. These robots navigate the warehouse, locate products, and bring them to packing stations, significantly reducing processing time and errors.

Natural Language Processing (NLP)

NLP enables systems to understand and interpret human language, facilitating better communication between inventory management systems and users. This technology can be used for voice-activated commands, chatbots, and automated customer service.

• Example: A retail warehouse employs an NLP-based chatbot to assist staff with inventory queries. Employees can ask questions about stock levels, reorder points, and shipment statuses and receive accurate responses in real-time.

Internet of Things (IoT)

IoT devices collect real-time data from various sources, including sensors and RFID tags, to monitor inventory levels and environmental conditions. This data is integrated into AI systems for better inventory control.

• Example: A pharmaceutical company uses IoT sensors to monitor the temperature and humidity of their storage facilities. The data is fed into an AI system that adjusts storage conditions to maintain optimal levels and prevent spoilage.

Blockchain

Blockchain technology ensures the transparency and security of supply chain transactions. It provides a decentralized ledger of all inventory movements, enhancing traceability and reducing fraud.

• Example: A luxury goods retailer uses blockchain to track the authenticity and movement of high-value items from the manufacturer to the customer. This guarantees product authenticity and prevents counterfeiting.

Case Study: Alibaba

Alibaba uses AI technologies, including machine learning, computer vision, and robotics, to optimize its inventory management. The company’s smart warehouses have AI-driven robots that handle picking and packing while computer vision systems monitor inventory levels. Predictive analytics helps forecast demand and manage stock, ensuring efficient and accurate operations.

Implementing AI in Inventory Management

Implementing AI in inventory management involves several key steps: assessing current systems, selecting appropriate AI technologies, integrating existing processes, training staff, and continuously monitoring and optimizing the system. These steps ensure a smooth transition and effective use of AI in managing inventory.

Assessing Current Systems

The first step is to evaluate the existing inventory management system to identify areas that could benefit from AI. This involves analyzing current processes, identifying pain points, and setting clear goals for the AI implementation.

• Example: A retail company assesses its inventory system and identifies frequent stockouts and overstocking issues. They set goals to reduce these issues by 20% through AI implementation.

Selecting Appropriate AI Technologies

Choose the AI technologies that best address the identified needs. This could include machine learning for demand forecasting, computer vision for real-time inventory tracking, or robotics for automating warehouse operations.

• Example: A manufacturing company implements machine learning for better demand forecasting and robotics to automate the picking process in their warehouse.

Integrating with Existing Processes

Seamlessly integrate the chosen AI technologies with existing inventory management systems. This may involve updating software, installing new hardware, and ensuring compatibility with current processes.

• Example: An e-commerce business integrates AI-driven predictive analytics with its existing ERP system to improve inventory accuracy and streamline reordering processes.

Training Staff

Training employees on the new AI systems is crucial for successful implementation. Staff should understand how to use the new tools, interpret AI-generated insights, and respond to system alerts.

• Example: A logistics company conducts training sessions for warehouse staff to familiarize them with AI-powered robots and how to interact with these machines during the picking and packing process.

Continuously Monitoring and Optimizing

Monitor the AI system’s performance regularly to ensure it meets its goals. Collect user feedback, analyze system data, and make necessary adjustments to optimize the system’s effectiveness.

• Example: A grocery store continuously monitors the AI system’s performance, predicting demand for perishable items. They adjust the algorithms based on feedback and changing market conditions to improve accuracy.

Case Study: Zara

Zara, a leading fashion retailer, successfully implemented AI in its inventory management. It uses machine learning to analyze sales data and predict product demand.

AI-driven systems also optimize their supply chain by automating inventory replenishment and distribution. This implementation has helped Zara maintain optimal stock levels, reduce costs, and respond quickly to changing fashion trends.

Leading AI Tools and Platforms for Inventory Management

SImplementing AI in inventory management can greatly enhance efficiency and accuracy. Here is an overview of some popular AI inventory management tools businesses use to optimize their inventory processes.

1. IBM Watson Supply Chain

IBM Watson Supply Chain leverages AI to provide real-time insights into supply chain operations. It uses machine learning to predict demand, optimize inventory levels, and manage supply chain disruptions.

• Features: Predictive analytics, real-time monitoring, anomaly detection, automated decision-making.
• Example: A global electronics manufacturer uses IBM Watson to predict component demand, reduce excess inventory, and ensure timely part availability.

2. Oracle AI for Inventory Management

Oracle’s AI-driven inventory management solutions help businesses optimize their supply chains. The tool uses machine learning to forecast demand, manage inventory, and automate procurement processes.

• Features: Demand forecasting, automated replenishment, inventory optimization, integration with ERP systems.
• Example: A large retail chain uses Oracle AI to automate inventory replenishment, ensuring shelves are stocked with high-demand items while minimizing overstock.

3. Microsoft Dynamics 365

Microsoft Dynamics 365 integrates AI capabilities to enhance inventory management. It uses predictive analytics and machine learning to optimize inventory levels and improve supply chain efficiency.

• Features: Predictive analytics, real-time inventory tracking, automated order management, integration with other Microsoft tools.
• Example: A pharmaceutical company uses Microsoft Dynamics 365 to track inventory levels in real-time, ensuring critical drugs are always available and reducing stockouts.

4. SAP Leonardo

SAP Leonardo is an AI and machine learning suite that integrates with SAP’s ERP systems to enhance inventory management and provide predictive analytics and automation to optimize supply chains.

• Features: Machine learning, IoT integration, predictive maintenance, real-time analytics.
• Example: An automotive manufacturer uses SAP Leonardo to predict parts demand and optimize inventory, reducing lead times and improving production efficiency.

5. Llamasoft AI

Llamasoft offers AI-driven supply chain and inventory optimization solutions. The platform uses machine learning to forecast demand, optimize inventory levels, and manage supply chain risks.

• Features: Demand forecasting, supply chain modeling, risk management, inventory optimization.
• Example: A food and beverage company uses Llamasoft to forecast demand for seasonal products, ensuring optimal stock levels and minimizing waste.

6. ClearSpider

ClearSpider is an AI-enabled inventory management tool that provides visibility and control over inventory. It offers real-time tracking, automated replenishment, and demand forecasting.

• Features: Real-time tracking, automated replenishment, demand forecasting, integration with multiple systems.
• Example: A healthcare provider uses ClearSpider to track medical supplies across multiple locations, ensuring critical items are always available and reducing manual inventory checks.

7. E2open

E2open uses AI to provide end-to-end supply chain management solutions. It offers inventory optimization, demand sensing, and real-time visibility across the supply chain.

• Features: Demand sensing, inventory optimization, real-time visibility, supply chain collaboration.
• Example: A consumer electronics company uses E2open to synchronize its supply chain, improving forecast accuracy and reducing inventory holding costs.

Case Study: Amazon

Amazon leverages a combination of AI tools to manage its vast inventory efficiently. Amazon optimizes its warehousing, uses AI-powered robots, predictive analytics, and real-time data processing, reduces delivery times, and ensures product availability. These technologies enable Amazon to handle millions of products across numerous fulfillment centers, maintaining high customer satisfaction and operational efficiency.

AI in Inventory Management: Top 10 Real-Life Use Cases

1. Walmart’s Predictive Analytics for Demand Forecasting

Technology/AI Tool: Predictive Analytics, Machine Learning

Explanation: Walmart uses predictive analytics and machine learning to analyze historical sales data, market trends, and external factors to forecast demand. This helps maintain optimal stock levels and reduce overstock and stockouts.

Benefits:

• Accurate Demand Prediction: Ensures that the right amount of stock is available.
• Cost Reduction: Minimizes excess inventory and associated holding costs.
• Improved Customer Satisfaction: Products are available when customers need them.

2. Amazon’s Real-Time Inventory Tracking

Technology/AI Tool: Internet of Things (IoT), Machine Learning

Explanation: Amazon uses IoT devices like RFID tags and sensors for real-time inventory tracking. This data is processed using machine learning algorithms to monitor stock levels and predict when replenishment is needed.

Benefits:

• Enhanced Accuracy: Real-time tracking reduces human error.
• Operational Efficiency: Automated tracking and replenishment streamline operations.
• Reduced Stockouts: Ensures timely restocking and availability of products.

3. Zara’s AI-Driven Inventory Management

Technology/AI Tool: Predictive Analytics, Machine Learning

Explanation: Zara leverages AI to forecast fashion trends and manage inventory accordingly. The system analyzes sales data and market trends to align stock levels with consumer demand.

Benefits:

• Optimized Stock Levels: Aligns inventory with current fashion trends.
• Reduced Waste: Minimizes excess stock and markdowns.
• Increased Sales: Ensures timely availability of trending products.

4. Sephora’s Personalized Product Recommendations

Technology/AI Tool: Machine Learning, Recommendation Engines

Explanation: Sephora uses AI to analyze customer data and provide personalized product recommendations. The system learns from customer preferences and behavior to suggest relevant products.

Benefits:

• Personalized Shopping Experience: Tailored recommendations enhance customer satisfaction.
• Increased Sales: Personalized suggestions drive higher purchase rates.
• Customer Loyalty: Enhanced shopping experience fosters customer loyalty.

5. Target’s Automated Replenishment

Technology/AI Tool: Robotic Process Automation (RPA), Machine Learning

Explanation: Target uses RPA and machine learning to automate the replenishment process. The system automatically predicts when stock levels drop below a threshold and triggers reorder requests.

Benefits:

• Reduced Manual Effort: Automates the ordering process.
• Improved Efficiency: Ensures optimal stock levels with minimal manual intervention.
• Cost Savings: Reduces emergency order costs and stockouts.

6. Macy’s Real-Time Inventory Visibility

Technology/AI Tool: IoT, RFID Technology

Explanation: Macy’s employs RFID technology to provide real-time visibility into inventory levels across multiple locations. This helps in accurate tracking and efficient inventory management.

Benefits:

• Enhanced Visibility: Provides up-to-date information on stock levels.
• Reduced Losses: Minimizes theft and loss with accurate tracking.
• Improved Accuracy: Ensures precise inventory records.

7. Home Depot’s Predictive Maintenance

Technology/AI Tool: Predictive Analytics, IoT

Explanation: Home Depot maintains its fleet and equipment using predictive analytics and IoT. The system predicts maintenance needs to prevent breakdowns and ensure smooth operations.

Benefits:

• Preventive Maintenance: Reduces downtime by predicting maintenance needs.
• Operational Efficiency: Ensures continuous operation without unexpected interruptions.
• Cost Savings: Minimizes repair and replacement costs.

8. Best Buy’s Customer Segmentation for Inventory Planning

Technology/AI Tool: Machine Learning, Data Analytics

Explanation: Best Buy uses AI to segment customers based on purchasing behavior and preferences. This data helps plan inventory to meet the specific demands of different customer segments.

Benefits:

• Targeted Inventory: Aligns stock levels with customer demand.
• Improved Sales: Ensures product availability for targeted segments.
• Customer Satisfaction: Meets specific customer needs effectively.

9. Nike’s AI-Optimized Supply Chain

Technology/AI Tool: Predictive Analytics, Machine Learning

Explanation: Nike uses AI to optimize its supply chain operations. The system forecasts demand, manages inventory levels, and coordinates with suppliers to ensure timely delivery of products.

Benefits:

• Optimized Supply Chain: Ensures smooth and efficient operations.
• Reduced Lead Times: Minimizes delays in product delivery.
• Cost Efficiency: Reduces costs associated with supply chain disruptions.

10. H&M’s Heat Mapping for Store Layout Optimization

Technology/AI Tool: Heat Mapping, Data Analytics

Explanation: H&M uses AI-powered heat mapping to analyze customer movements and optimize store layouts. This helps the company place high-demand products in prominent locations.

Benefits:

• Improved Customer Flow: Optimizes store layout for better navigation.
• Increased Sales: Strategically placed products drive higher sales.
• Enhanced Shopping Experience: Creates an engaging and convenient shopping environment.

These real-life use cases demonstrate AI’s transformative impact in inventory management, providing significant benefits such as improved accuracy, reduced costs, and enhanced customer satisfaction.

AI in Inventory Management: Frequently Asked Questions

How does AI help in inventory management?

AI helps by predicting demand, optimizing stock levels, automating replenishment processes, and providing real-time tracking. It ensures accurate inventory records and reduces waste.

What technologies are used in AI for inventory management?

Technologies include machine learning, predictive analytics, IoT devices, and robotic process automation (RPA). These tools analyze data, forecast trends, and automate tasks.

How does predictive analytics improve demand forecasting?

Predictive analytics uses historical data, market trends, and external factors to forecast future demand. This allows retailers to plan inventory more effectively and avoid overstock or stockouts.

Can AI automate the inventory replenishment process?

AI can automate replenishment by predicting when stock levels will drop below a certain threshold and triggering reorder requests. This ensures optimal stock levels without manual intervention.

What are the benefits of real-time inventory tracking with IoT?

Real-time tracking provides up-to-date information on stock levels, locations, and movements. It reduces human error, prevents theft, and improves accuracy in inventory management.

How does machine learning help in inventory optimization?

Machine learning analyzes sales data and customer behavior to predict demand and recommend optimal stock levels. By aligning inventory with actual demand, it helps reduce overstock and stockouts.

What is the role of RPA in inventory management?

RPA automates repetitive tasks like data entry, order processing, and inventory updates. It frees employees to focus on more strategic activities and improves accuracy in routine tasks.

How can AI improve supply chain coordination?

AI provides real-time insights into inventory levels and demand patterns, helping better plan and coordinate with suppliers. This ensures timely replenishment and reduces lead times.

What are some real-life examples of AI in inventory management?

Examples include Walmart’s predictive analytics for demand forecasting, Amazon’s real-time inventory tracking, and Zara’s AI-driven inventory management for fashion trends.

How does AI help reduce inventory costs?

AI minimizes excess inventory and associated holding costs by accurately predicting demand and optimizing stock levels. Automated processes also reduce the cost of manual labor and errors.

Can AI improve customer satisfaction in retail?

By ensuring product availability through accurate demand forecasting and optimal stock levels, AI helps meet customer needs more effectively, leading to higher satisfaction.

How do AI recommendation engines work in retail?

AI recommendation engines analyze customer data to suggest products based on preferences and behavior. Techniques like collaborative filtering and content-based filtering are used to provide personalized recommendations.

What challenges might retailers face when implementing AI in inventory management?

Challenges include data quality issues, integration complexity with legacy systems, and managing employee resistance. Proper planning and training can help address these challenges.

What are the best practices for implementing AI in inventory management?

Best practices include assessing current inventory practices, identifying suitable AI tools, ensuring seamless integration, starting with pilot projects, and providing comprehensive training to employees.

How do retailers choose the right AI tool for inventory management?

Retailers should assess their specific needs, ensure compatibility with existing systems, consider scalability, evaluate support and training provided by the vendor, and conduct a cost-benefit analysis to choose the right AI tool.