Largest Middle-East Jewellery Brand, Delivering Ecommerce Platform and Seamless Integrations of various front-end solutions
GSK partnered with us to create an end-to-end B2B Ecommerce Solution, empowering them to streamline their operations and drive online sales.
Staples entrusted us to develop a dynamic DIY Ecommerce Platform, allowing them to effortlessly showcase their products and engage customers with personalized shopping experiences
Delivered a comprehensive Food Delivery Website, featuring integrated chatbots, multiple payment gateways, and advanced delivery management functionalities
For Emaar, we delivered exceptional value through Staff Augmentation and upgraded their Dubai Mall Application with new features
We created a powerful E-commerce store for Juicy Couture, integrating a robust Product Information Management system to enhance their product showcasing and information management
15 Generative AI Use Cases for Enterprise Businesses
In today’s tech-driven era, enterprise leaders find themselves at a crossroads where they have to deal with high-interest rates, inflation, and geopolitical concerns. They look for ways to accelerate product development and enhance service quality. One such ‘way’ is through a technological revelation you have already heard of - Generative AI.
Generative AI isn’t just the most buzz-worthy tech trend of 2023. Between 50% to 60% of all organizations have already adopted the disruptive technology.
Whether you consider customer relationship management (CRM), enterprise resource planning (ERP), product engineering, or service delivery, the applications of generative AI for enterprises just keep growing.
Generative AI Enterprise Use Cases
Generative AI is a subset of AI that goes beyond data analysis to create new and unique content. Based on patterns learned from the data they are trained with, generative AI tools like ChatGPT, DALL-E, and StyleGAN have demonstrated groundbreaking results.
In general, the enterprise use cases for generative AI revolve around automating, simplifying, and enhancing the quality of operational workflows.
Quick Automation of Project Workflows
When it comes to workflow automation, generative AI helps enterprises, especially marketing and sales-driven organizations with content creation. With automated content generation, businesses can speed up their entire digital marketing workflow. Generative AI can create a variety of engaging blog posts and ad copies, as well as personalized social media posts and product descriptions to target the right audience.
Simplify Repetitive Tasks for Employees
Regardless of the industry you pick, there are always going to be repetitive tasks that become a hurdle for innovation in enterprises. In this scenario, generative AI not only streamlines repetitive tasks for employees but also provides consistent and high-quality outputs. This means you have better customer engagement and a higher number of satisfied customers across the board.
Maintain High-Volume Production Standards
As generative AI continuously learns from the data it processes, it can interpret complex data to refine production standards. Whether you are on the factory floor or in any other part of the organization, generative AI equips you with the right tools to address quality standards in production.
How Enterprises Are Using Generative AI Today
Although the technology itself is the same for all, not all enterprises are able to use generative AI to its full potential. In some enterprises, it may be used as part of a conventional subscription-based tool to improve operations to a certain extent. In other cases, organizations are even building their own AI models to add to their tech stack.
Industries Embracing Generative AI
Let’s look at the ways different industries are utilizing the power of generative AI:
E-commerce and Retail
To understand the use of generative AI in retail, we take the example of Shopify. The eCommerce platform has now introduced a feature called Shopify Magic, which allows retailers to generate product descriptions using generative AI.
Users can input their desired tone and keyphrases for the product-related content they want to generate. Then, Shopify’s magic tool automatically generates a description based on the given parameters. In the background, of course, its generative AI working its magic.
Shopify makes the list of a handful of retail enterprises that have adopted generative AI, and even though Shopify Magic is still in its infancy, it is being well-received.
Software Development and FinTech
Where there is online shopping, there ought to be transactions. That brings us to two of the notable industries that have a high AI adoption rate - FinTech and software development.
Stripe is a renowned financial services SaaS provider that uses OpenAI’s GPT-4 to enhance its operations.
The generative AI tool allows Stripe to improve query management, summarization, documentation, and more. An internal feature called Stripe Docs is also available for users to enter their natural language queries related to documentation.
Upon submitting their query, users receive a response where Stripe’s AI summarizes and extracts important information in a human-like format. Moreover, the SaaS provider is also working with OpenAI and other generative AI companies to create smoother subscription and checkout processes for customers.
Travel and Hospitality
Here’s yet another industry that is using generative AI to improve customer experience. The travel and hospitality sector is all about ‘looking after’ the customers, and Expedia understands that.
Expedia uses the beta version of the popular generative AI tool ChatGPT for its travel planner. The AI integration allows Expedia users to ask questions and receive recommendations for travel, lodging, and other recreational activities. Not only that, but it also saves the suggested hotels and destinations using an AI-based feature. This means that customers can see what was recommended earlier and easily book their next destination.
In the context of enterprises, Expedia’s AI use case comes in handy for frequently-travelling employees. The solution streamlines the travel process and gives the employees more time for their high-priority tasks.
Generative AI Use Cases: Ethics and Compliance
Before we get into generative AI use cases, here’s a quick heads-up. With all the applications and developments in the field of generative AI, we often ignore the fact that the technology is still in its infancy. This means that there are still many unknown factors. For instance, the end-users are not aware of the way the AI models are trained, or what data is used to train them.
The fact is that generative AI models have a wide range of capabilities that can be very useful, but also very harmful when it comes to cybersecurity. Not to mention, the quickly growing skillset of generative AI models may result in them replacing skilled professionals.
Considerations for Ethical Use of Generative AI
With that said, what should enterprises do to ensure that they are ethically using generative AI? Here are a few tips to get started:
- Do not use sensitive data to train an AI model. The inputs must be depersonalized and non-sensitive
- Stay updated with current generative AI trends so that you know which tools are credible
- Set up an ethics policy for AI usage in your enterprise. It should guide internal users to use the right AI tools
- Provide training to all employees so that they stay one step ahead of AI and use it to advance in their careers.
15 Implementable Generative AI Use Cases
Code Generation, Documentation, and QA
Generative AI assists software developers and programmers by writing and completing code faster than ever before. Software teams use AI tools to write code in multiple languages and speed up their development process, hence increasing their productivity and efficiency.
Once the code is written, the most useful and quickly emerging use case of generative AI comes into play. Quality assurance is one of the most crucial phases in software development. With generative AI, it becomes as easy as implementing the right tools and entering the right prompts. AI models are capable of fixing bugs, analyze test cases, and also create various types of documentation.
Furthermore, generative AI-based coding tools are now advancing beyond the basics to help non-developers write code by using simple queries. By entering scenario-based queries in natural language, they can leverage generative AI to write code that works. This is a giant leap in the world of enterprises that are aiming at tech democratization.
Product and App Development
While writing code speeds up the process for software developers, generative AI is also being used to develop entire apps and products. From analyzing material options to proposing new designs, it has given a futuristic angle to product development. With generative AI, iterative product development teams can make design refinements in real and perform more efficiently.
Apart from assisting the development of digital products and apps, generative AI also finds use cases in semiconductor chip manufacturing and design. Not to mention, it has significantly improved customer support services by allowing companies to build chatbots based on OpenAI foundation models. Examples of such solutions include GPT-4, A121 Labs, Stability AI, and MOSTLY AI.
Blog and Social Media Content Writing
As we have seen earlier in this blog, generative AI is empowering marketing enterprises with automated content creation. Every marketing team aims to save time and resources and have a favorable time-to-market.
Generative AI is good news for marketers, as its large language models (LLMs) allow you to create unique content for blog posts and social media platforms. It also helps create engaging content for business websites and prevents you from missing out on details when writing product descriptions.
Most of these AI models allow users to provide instructions and specify the tone, voice, and other aspects of the content they want to generate. Enterprise users can even input past content written by their company and prompt the AI tool to follow the same tone. Examples of these AI tools include HubSpot Content Assistant, Phrasee, Notion AI, and Jasper.
Inbound and Outbound Marketing Communication
Apart from blog and social posts, marketing teams also need to enhance their inbound and outbound marketing campaigns. The traditional process requires employees to manually create contextualized emails and chats. These are then sent to current and prospective customers. Generative AI solutions are now automating this process by creating and sending emails.
Not only that, but generative AI can also qualify certain contacts for the next step in the enterprise’s customer relationship management (CRM) platform. Examples of AI solutions enhancing project management capabilities in enterprises include Salesforce Einstein GPT and Twain.
Graphic Design and Video Marketing
Graphic designing and video creation are also crucial to a successful digital marketing team. Generative AI helps creative designers by generating realistic animations, images, and audio that can improve graphic designing and video marketing projects.
In addition to videos and images, some generative AI service providers also offer avatars and voice synthesis features. These allow marketers to complete their video production without the need for actors, expertise, or equipment.
While the avatars and voices being offered today still require some manual input to replace real human speakers, it is still a step ahead in the right direction. Rephrase.ai, Diagram, Synthesia, and Lightricks are examples of AI solutions offering graphic design and video marketing services.
Entertainment Media Generation
Moving on, the entertainment media industry is probably the one to benefit most from high-quality and realistic AI-generated images. Generative AI finds applications in movie making, video games, virtual storytelling, VR experiences, and podcast generation.
The entertainment industry finds it convenient to use these AI tools instead of setting up cameras, editing footage, and recording audio. According to tech experts, generative AI is all set to revolutionize film content creation, script writing, and video production. As we speak, AI tools like Latitude Voyage, Plask, Charisma, and Stability AI’s Stable Diffusion are being used to create interactive non-player characters (NPCs).
Performance Management and Employee Training
Apart from enhancing operational efficiency and accelerating processes, generative AI is also being used to analyze business performance and train employees. For instance, generative AI can be used in contact centers of an enterprise to analyze call documentation.
By using sentiment analysis, generative AI can provide business owners with valuable insights. With this information, you can assess customer service quality and set up training sessions for employees. Conversational AI tools like Gong, CoachHub AIMY, and Anthropic Claude allow your employees to get performance feedback and understand areas for improvement.
Reporting and Data Analytics
One of the reasons why generative AI has quickly become a part of enterprise reporting is its ability to process massive amounts of data. When it comes to analysis of unstructured and qualitative data, nothing can match the power of generative AI. It is improving business reporting and intelligence by providing real-time data-driven insights.
Of the areas where generative AI is being used, data narratives are probably the most interesting. There are highly contextualized explanations of datasets provided by AI that are far more advanced than typical visualizations and data dashboards. Examples of such AI solutions include Narrative BI, Dremio, and SparkBeyond Discovery.
Customer Support and Customer Experience
Generative AI chatbots aren’t really a new thing. They handle customer service queries along with virtual assistants all day long. That being said, the chatbots that have been used for straightforward customer service engagements are not as advanced as generative AI.
Now, with advanced AI models, you get additional resources to provide comprehensive human-like answers without manual inputs. Enterprises that have adopted this technology in its early days are building their own customer service solutions using OpenAI’s ChatGPT and API. Forethought SupportGPT, Gridspace, and IBM Watson Assistant are some of the examples of chatbots that are enhancing customer experience in enterprises.
Pharmaceutical Drug Discovery
Generative AI isn’t just the future of the pharmaceutical industry. It is already being used to enhance the efficiency of drug discovery and design operations. Using the valuable insights provided by generative AI, scientists are creating novel molecules and discovering disordered proteins. They are also designing better drugs and predicting clinical trial results using the groundbreaking technology.
This particular enterprise use case of generative AI is expected to grow by a significant margin in the years to come. Entos, Aqemia, New Equilibrium Biosciences, and Insilico Medicine are a few examples of AI tools being used in the pharmaceutical industry.
Medical Diagnostics and Imaging
When you consider the field of medical diagnosis and imaging, generative AI is still a relatively new concept. However, its potential is growing exponentially. As AI-based image processing and generation tools become more capable of zooming into medical images, professionals can now get a better understanding of areas they haven’t seen so clearly before.
Even in its infancy, generative AI is performing medical image analysis and offering basic diagnostics. Well-known tools like ChatGPT and GPT-4 are being tested in the pathology space, with ChatGPT even passing the U.S. Medical Licensing Exam. That said, the results still need to be cross checked with medical professionals and analyzed for anomalies. Other examples of AI solutions in medical are Paige.ai and Google Med-PaLM 2.
Consumer-Friendly Synthetic Data Generation
Considering the potential security concerns that can be caused by generative AI, you may assume that this is just one of its negative aspects. But hold on - what if we could use the same factor to improve the data being generated and make it more consumer-friendly?
When you get your prompts right, generative AI will provide you exactly what you’re looking for. It can be used to create synthetic copies of data that is actually sensitive, hence allowing analysts to get what they need without being non-compliant. With these copies, data analysts and other enterprise members can create their own AI models. Examples of synthetic data generation tools include Infinity AI, Syntho Engine, Synthesis AI, and MOSTLY AI.
Smart Manufacturing and Predictive Maintenance
The manufacturing industry sees another use case of generative AI for enterprises. As it continues to transform modern manufacturing, generative AI is helping workers innovate and achieve their production goals. With predictive maintenance, generative AI models can create to-do lists and timelines to assists enterprises in improving their workflow. These models also suggest areas for improvement and simplify your process of collecting data from different parts of your production line.
Moreover, another process called inverse design also uses generative AI to analyze what’s missing in a process. Tools like Clarifai, C3 Generative AI Product Suite, and Biomatter can identify missing materials from a process and generate their replacement to suit the requirements.
Fraud Detection and Risk Management
Another area of enterprises that must not be missed is fraud detection and risk management. Although your business might get lucky without such measures, it’s always better to have AI by your side. In this use case, generative AI analyzes transaction data and identifies anomalies in the patterns. It helps in fraud detection to ensure smooth operations in finance and insurance sectors.
As of now, enterprises must invest in anomaly and fraud detection solutions that use generative AI to catch even the slightest deviation in transactional patterns. Simplifai InsuranceGPT and Docugami are the best known examples of such solutions.
Optimized Enterprise Search and Knowledge Base
Where there are enterprise tools, there needs to be documentation and a knowledge base. Generative AI is helping enterprises enhance both internal and external search capabilities. AI tools like Elasticsearch Relevance Engine, Glean, and Coveo Relevance Generative Answering not only identify enterprise resources for users but also company applications, web properties, and messaging tools.
Visitors can get a self-service by using generative AI tools incorporated into business websites and customer-facing displays. They can get answers to their brand-related questions and also get the right resources using AI chatbots.
Considering the rapid evolution of AI, it's not hard to guess that there will be even more enterprise use cases for generative AI in the days to come. As AI technology develops new capabilities, enterprises will have more opportunities to improve their operations. If you are an enterprise looking to adopt generative AI, you need to find the right model that works in your specific use case and aligns with your objectives as well as your customers.
How Generative AI is Redefining Product Development Strategies
Are you a product owner or a business analyst?
Then you know that keeping up with the time requires a combination of modern strategies and ground-breaking technology.
It’s no surprise that 60% of businesses are already implementing generative AI to accelerate and enhance every step of their product engineering process. Overnight, transformative AI technology has broadened the scope of opportunities and opened up new avenues for product development.
This post explores how businesses are using generative AI for product development and how it helps them create lasting value.
How Generative AI is Fueling Product Development
Whether you consider improving your time-to-market, making data-driven decisions, or improving overall efficiency, generative AI has got it all covered. Traditional methods of product development were time-consuming and resource-intensive, which meant that businesses had to deal with a higher upfront investment.
Assisting the Development of Market-fit Products
Since the traditional product development approach does not involve a deeper analysis of user feedback, you are unable to develop products according to their needs. As a result, there is a higher risk of developing a product that is not a market fit.
On the other hand, training generative AI for product development allows you to not only accelerate but also improve the accuracy of complex tasks. Instead of relying on guesswork, design teams can leverage user feedback and approach their projects with data-driven insights. Generative AI replaces assumptions with real information, which is why 92% of product managers see a lasting impact of AI on product management.
Broadening the Scope of Product Design
When it comes to product development, time-to-market is key to success. This is where the capabilities of generative AI really shine through. Businesses are using it to develop product ideas by analyzing what competitors are doing and what customers are expecting. Compared to manual brainstorming, the ideation process involving generative AI is much more comprehensive and creative.
For instance, you can create more design variations while staying true to a given criteria. When you feed AI with your design preferences, target audience, and product specifications, it can help you validate your ideas and refine your product. That’s not all - generative AI also helps you achieve digital shelf success by helping you understand the shifting demands of customers.
Finesse - An AI-driven Fashion Retailer
Finesse takes the on-demand fashion trend to the next level with the help of AI. Also known as the first “AI-driven fashion house”, it uses a combination of an AI-based voting system and its proprietary AI algorithms to determine the quantity of each item to be manufactured.
Since the whole process is based on customer demand, it helps the brand in preventing overproduction to optimize costs. In the meantime, the customers can get their hands on the products that they love, and the brand does not have to go through the traditional post-launch user acceptance testing process.
Benefits of Incorporating Generative AI in Product Development
The power of generative AI is not limited to gathering customer feedback and utilizing it for product ideation. It also allows businesses to get the latest insights and base their decisions on market research and trend analysis. Here are some of the benefits of using generative AI for product development:
Enhanced Creativity and Innovation
Normally, product development involves bringing together a group of individuals for brainstorming and ideation. The goal here is to get different perspectives and make room for innovation. But what if we replace the individuals with AI?
With generative AI, you get a significant boost in data processing power, which means you can analyze a wide range of data points related to emerging trends and new product ideas.
AI enhances your team’s creativity by shedding light on areas that you may be ignoring. It factors in data points from different channels, product ratings, focus groups, and social platforms to put you on the right track.
Speeding up Iterations and Prototyping
In the early days, this kind of processing power wasn’t associated with quick results. However, that is not the case anymore. Generative AI analyzes vast amounts of data to identify patterns and delivers the output within seconds. Unlike traditional product prototyping where you need a lot of time and resources, AI-based prototyping essentially automates the whole process.
It speeds up your prototyping process by leveraging machine learning algorithms to create new features and designs. Basically what you are getting is better quality and diversity of ideas in less time than it would take a team of individuals. You can innovate and create differentiated products while reducing operational costs.
Since every other brand is using personalization to engage customers, you can’t skip this part either. According to a 2022 Salsify research, 70% of U.S. shoppers are more likely to buy an item if there are “personally relevant” images, text, and reviews for it.
Thankfully, you can leverage generative AI to tailor your products to the needs of your customers. As seen in the Finesse example earlier on, you can allow your customers to choose how the product should look like. This agile approach to product design allows your team to try out multiple concepts, curate their ideas, and have better chances of engaging the right audience. Numerous eCommerce businesses are now using the customer-first approach as a way to get positive reviews and make their way to the top.
Cost-efficiency and Resource Optimization
While AI is doing its magic and accelerating your operations, it also helps diagnose problems and eliminate risks. No matter how well you plan your product development process, there are always going to be some hurdles. Generative AI automates repetitive, time-consuming, and resource-intensive tasks to help you save time and manage costs efficiently.
Generative AI can enhance your designs and provide the most suitable options based on manufacturing costs. This involves analysis of the costs associated with materials, assembly, and manufacture. Then, it helps you minimize material waste, optimize the supply chain, and opt for cost-effective options. Generative AI is also capable of suggesting efficient assembly flows and procurement timelines.
Challenges and Considerations
With so many benefits, you might think that generative AI also has some drawbacks. And you’re right. Here are some of the challenges involved in implementing generative AI for product development:
- Complexity of Training Generative AI: In order to implement AI for product engineering, you need specialized knowledge and expertise. Apart from ensuring data security, you need to deal with the complexity of training and maintaining the AI models.
- Ethical Concerns: This is where the difference between humans and machines shines through. Generative AI can raise ethical concerns when it is used to collect and process personal information or intellectual property. The outputs can also be subject to copyright issues and malicious uses.
- Data Quality and Bias: Based on the quality and diversity of the data they are trained with, generative AI models can sometimes deliver biased outputs. As this mainly depends on the datasets used to train the AI, businesses need to be careful about data quality and bias.
- Lack of Control: While the scope of data processing is to be appreciated, you cannot always maintain control of content generated using generative AI. These AI models can even produce undesirable and unexpected outputs that need to be verified and amended manually.
How to Overcome the Challenges of Generative AI
First and foremost, businesses need to ensure that they can train AI models effectively using their own data. This not only allows them to get verifiable outputs but also achieve maximum accuracy. You need to be sure that the generative AI model can identify and process the unique datasets provided by your business and produce the right outputs.
The best way to ensure that your AI models are properly trained is to have a check and balance. Team members should communicate errors and uncertainties in the outputs they get from generative AI. This may also require team members to cite the sources where the generative AI has picked the data, and then explain how it is justified.
While you have assigned tasks to AI experts for training a particular AI model, you need to conduct regular tests to mitigate potential risks. For instance, you need to ensure that there is no bias in the data being used to train the models and whether the outputs being generated contain any harmful content. These security checks also give your business the opportunity to identify vulnerabilities or the involvement of bad actors in the form of potential cyber-attacks.
So how do we deal with the issue of bad data quality? By ensuring transparency.
You need to have measures in place to ensure that the data collected to train AI models is evaluated. Your business must respect provenance and have the consent to use the data.
To ensure transparency, your business can use open-source or user-provided data to train AI models. When you are using AI outputs to carry out a task, you need to be transparent and clearly state that the content is generated using AI. Watermarks on the content or in-app messages can be used to differentiate between human-written and AI-generated content.
Future Trends and Implications
AI will Determine Whether or Not a Product is Market-fit
According to statistics, a shocking 35% of SMBs and startups fail because their products are simply not in demand. Soon, AI will help solve this problem. Generative AI-based data analysis will allow businesses to gather accurate data and establish a better understanding of what is required in the market. This includes targeting the right audience and developing the right products within a given category.
AI is already helping businesses understand their customers better with the help of extensive data analysis. It digs deeper into their preferences and searches to find hidden problems in existing products. Up until yesterday, we didn’t have any tools to analyze such information and provide suggestions for product designs and features.
AI will Enhance Human Capabilities for Product Development
Although machine learning has been around for quite some time, the capabilities of AI are now increasing at a drastic rate. While the previously used machine learning algorithm learned patterns and produced outputs using the data provided, the future will see the generation of unseen data. AI will soon be able to apply the ‘learned’ patterns to new and unique data and generate new outputs.
In fact, generative AI models are already advanced enough to take things further. They can not only use unseen data but also develop an understanding of the thought process and ideation behind the datasets provided.
From what we have seen in recent times, it is safe to say that generative AI is transforming product development and giving a new angle to innovation. Apart from streamlining processes, improving customer experience, and empowering startups, generative AI is assisting product owners with new design ideas and development processes. As we see the technology evolve, the use cases of generative AI in product engineering and management are set to expand further.
How Generative AI Enhances Inventory Management in Retail
The retail sector is accustomed to razor-thin margins and serious competition, but there is no bigger concern than inventory management. Overstocking, stockouts, and inventory shrinkage cost businesses around the world approximately $1.8 trillion. The impact of manual inventory tracking can be seen in the form of delayed orders and inconvenience for customers, which comes as a big blow to retailers.
In order to meet customer demands, you always need to have enough stock in your inventory, and this is only possible with a comprehensive inventory management system.
By providing additional capabilities to monitor and control stock levels, the buzzworthy generative AI technology is now allowing retailers to offer enhanced customer experiences.
This post covers everything you need to know about the application of generative AI to manage stock levels in retail.
Why Retailers Should Embrace Generative AI
Even though poor inventory management directly affects profitability, a shocking 43% of small businesses still do not manage their inventory even today. This means that when an order comes in, 34% of businesses end up selling products that aren’t available in their inventory. As a result, customers face significant delays in shipment, and the business loses its chances of getting any positive reviews.
Utilizing generative AI for supply chain optimization means you can solve longstanding inventory problems like stockouts, overstocking, and shrinkage. A typical retail supply chain comprises the following processes:
- Customer Service
The capabilities of AI, which extend beyond data processing, become particularly useful in retail as it can predict future trends and analyze large amounts of data in real-time. By incorporating generative AI in retail, you can track the quantity, location, and status of raw materials, products being manufactured, and finished goods.
Utilizing generative AI for inventory management, you can get real-time insights into your stock levels in different locations and manage them effectively. Unlike manual inventory management, AI can factor in numerous variables like lead times, demand patterns, seasonality, carrying costs, and the risk of obsolete inventory. It also allows you to optimize your inventory to make products available when they are needed, and avoid overstocking at the same time.
Boost Customer Satisfaction
When your customers are able to find the right product in your stock, you get a hundred percent satisfaction rate. But when they don’t, it means you have simply lost a valuable lead. With the help of generative AI, you can create exceptional shopping experiences for your customers and provide them with more than just what they are looking for.
By analyzing their searches and preferences along with supply and demand metrics, generative AI enables your business to exceed customer expectations. Not only that, but the availability of hot-selling products in your stock means you have a higher chance of gaining customer loyalty.
Cost Savings and Efficiency Gains
Before AI, inventory management was based on hit and trial. You had to rely on guesswork for ordering inventory items. Sometimes it worked, and sometimes you ended up having extra items on your shelves. However, generative AI has now enhanced the accuracy of inventory management by a significant margin. You can order just the right quantity of items at the right time to save both time and money.
Considering the high efficiency of AI, you can cut down on costs in terms of storage, wastage, and higher sales. All this is possible because generative AI allows you to forecast how much is needed, and sell faster so you do not have to store items in your warehouses for longer periods of time. Looking at the small margins retailers have to play with, this kind of cost-effectiveness is what everyone needs. Instead of wasting resources on overstocking, your business can spend more on innovation and growth.
Real-world Applications of Generative AI in Supply Chain Management
While AI offers so many benefits, it’s no surprise that brands around the world have incorporated generative AI into their supply chain. The following are some notable examples of real-world applications of generative AI:
The secret behind the popularity of the retail giant Walmart is its use of generative AI for selecting suppliers and managing risks. By employing generative AI, Walmart is able to analyze different factors like geographical locations, financial standing, and supplier performance to make the best business decisions. The aim is to partner with an enhanced supplier base and eliminate loopholes in the supply chain.
Procter & Gamble (P&G)
The case is quite similar at Procter & Gamble. The renowned brand utilized generative AI for demand forecasting, whereby historical data and variables such as market trends, weather, and more are considered to predict the demand for each product. With the help of AI, P&G is able to enhance demand forecasting, reduce costs, and improve its overall inventory performance.
When it comes to logistics, you need to have your delivery routes planned out to extract maximum business value. The global logistics service provider, DHL, understands this and utilizes generative AI for resource allocation and route optimization. With AI working its magic, DHL can adjust its delivery routes dynamically. In this case, generative AI factors in real-time data such as weather, traffic conditions, transit times, and fuel requirements.
Similarly, Flexport has also integrated generative AI to benefit from real-time supply chain visibility and optimization. Techniques like predictive analytics and demand forecasting allow the logistics platform to identify potential disruptions in freight routes and find the best possible solutions well before time.
Apart from the key players that have an active role in supply chains, there are also analytical entities that benefit from generative AI. One such example is Llamasoft, which has incorporated generative AI to dry-run different supply chain scenarios and allow companies to make better decisions. Using generative AI analytics, businesses are able to identify loopholes and optimize their supply chain effectively.
How Generative AI Transforms Planning in Supply Chains
With all its analytical power, it’s not hard to guess that AI helps improve the planning and scheduling processes of supply chains. This is made possible by considering variables like production capacity, resource availability, order priority, and the customers themselves.
Considering these factors, generative AI can lay out complete production plans that include schedules, sequences, and resources. The ability of AI to analyze multiple variables simultaneously means that production plans are free of loopholes and ensure maximum production efficiency.
One example of applying generative AI to improve demand forecasting can be seen in biotech companies, which use generative AI tools to get useful insights for production planning. For instance, the company can simulate scenarios where they use specific chemicals to develop their products and assess the impact it has on the global market. In fact, generative AI is now advanced enough to recommend courses of action in case the company’s own ideas go wrong. When it comes to risk management, AI is second to none.
Empower Sourcing Strategies with Generative AI
Once you have your supply chain planned out, the next step is to select the right suppliers. Fortunately for modern retailers, generative AI can assist and improve the supplier selection process with its analytical power. It can analyze supplier performance, pricing, associated risks, capabilities, and rankings to help you make informed decisions.
That’s not all, a leading US retailer even developed their own generative AI-based bots to negotiate purchasing terms and costs with vendors. Not only does it allow them to get the job done in a shorter amount of time, but it also helps reduce costs. If you think about it, generative AI has taken all the complexity out of the supplier selection process. It’s no surprise that 75% of the vendors prefer to negotiate with the bots instead of humans at the retail company.
Accelerate Production with Generative AI
The good news does not stop there. Generative AI can also assist retailers in speeding up their production process. Whether you are considering designing new parts for your production line or making your products more durable, generative AI can analyze numerous designs to suggest the ideal way forward.
Accelerating and improving production has obvious advantages in the retail environment. For this reason, businesses are training generative AI models on their datasets to achieve higher levels of productivity and efficiency. Preventive maintenance, another technique of generative AI, helps identify areas that are likely to fail or malfunction in your production line, hence allowing you to improve overall equipment effectiveness (OEE).
A leading manufacturer in Europe has incorporated generative AI to automate its production line. With the help of AI-based factory automation and product lifecycle management, the company is able to achieve more in less time. It has even improved its production cycle by employing automated inspection with AI.
Logistics Revolution: How Generative AI Optimizes Movement
After manufacturing your products, the next question is how to move the stocks to different warehouses and outlets. But you can rest assured that generative AI can help you in this step as well. Generative AI can optimize your logistics network by processing data points like transport links, demand patterns, and warehouse locations. This allows AI to create the most efficient logistics configuration with lower delivery times and costs.
Speaking of logistics and route selection, it's worth mentioning that one of the biggest logistics service providers in the US utilizes a proprietary AI platform for optimizing its supply chain. This includes optimizing delivery routes, warehouse selection, and improving productivity by 30%. Moreover, generative AI also allows you to cut down on operational costs while allowing you to customize your logistics process like never before.
Strategies for Implementing Generative AI in Supply Chains
With all its powerful applications and benefits, generative AI is still not tailor-made for your specific business needs. As the implementation varies from one use case to another, you need to ensure that your AI solution integrates seamlessly with your existing applications. In some cases, companies have to re-engineer some of their existing processes to enable the AI solution to fit in.
Here’s how you can implement generative AI in your supply chain:
- Define what you want to achieve by implementing generative AI in your supply chain. This will allow you to select the right AI models to work with.
- Ensure that your data collection process is automated so that the AI algorithms can access the latest information
- Process the data to be fed to the AI and machine learning algorithms
- Identify high-impact use cases to create a synchronized ecosystem of traditional and modern business processes
- Implement generative AI to improve internal collaboration and coordination in your organization
- Identify the functionality and skill required for specific tasks and improve productivity
- Incorporate pilot initiatives to reduce risks and develop scalable processes
Overcome Challenges and Maximize Generative AI’s Potential
Now that we have discussed the benefits of generative AI for optimizing supply chains, let’s address some of the implementation challenges.
- Generative AI models work best with high-quality data inputs. So the most obvious challenge is data availability and quality.
- Training generative AI models is a time-consuming and costly process as it requires expertise and computational power.
- Supply chain decisions require transparency. It’s often difficult to interpret and explain to stakeholders how generative AI has reached the said results.
- Generating synthetic data based on assumptions to simulate supply chain scenarios can lead to legal and ethical challenges.
With that being said, the retail industry continuously faces challenges in one form or another. While the implementation of generative AI inventory management may cost you more than manual inventory management, the results are also that much better. At the end of the day, your best bet is to have AI and human intelligence working together in perfect harmony.
With technological advancements transforming inventory management, the competitive retail industry is looking for the best AI solutions. Leveraging generative AI for supply chain optimization allows retailers to achieve higher efficiency and also makes room for innovation. With accelerated operations and enhanced decision-making, business owners are able to identify and work on hidden weaknesses that have a significant impact on their customer base.