generative AI

Generative AI, in simple words, are AI platforms like GPT3, OpenAI Playground, Google Bard, and Amazon Codewhisperer, which use machine learning algorithms to generate new data based on patterns and structures found in existing data. Its models are based on deep learning tools like Generative Adversarial Networks, Variational Autoenconcers (VAEs) or transformers. 

Interestingly, while we may think that Generative AI is more of a recent phenomenon, research shows that in unique ways, this tool has existed since the mid-1900s. For example, in the 1950s, American mathematician and computer scientist, Claude Shannon, developed the ‘Ultimate Machine’, a mechanical device that could turn itself off when it was turned on. In later periods, scientists developed computer programs which could generate simple music and images using rules and algorithms. 

But, one can safely say that Generative AI took off in a big way only in the 2010s when larger datasets became available to train AI models. In fact, the GAN deep learning algorithm, which is one of the widely used approaches in Generative AI, was developed only in 2014 by Ian Goodfellow, an American computer scientist. 

Today, the tool is being creatively and innovatively used for various purposes across sectors. For example, the tool is being used in the gaming industry to create realistic game environments, characters, animations and visual effects. It is being used in healthcare to analyse medical images and develop new drugs for treatment. In the retail sector, it is being used to analyse customer data and create personalised shopping experiences for customers, and so on. 

Generative AI in B2B Businesses 

A Merit expert says, “One of the biggest advantages of Generative AI, is that it can analyse large volumes of data quickly and accurately, which can help businesses identify patterns and trends that may not show up through traditional data analysis methods. Given that data collation, structuring and management continues to be one of the key priorities for businesses today, Generative AI can go a long way in easing data management and application for them.” 

With that said, let’s look at potential applications this tool has in the B2B business space. 

Product Design and Development 

Generative AI can help B2B businesses create new products and services that are customised to their clients’ needs. AI models can analyse customer data and generate new product ideas, designs, and features that meet their specific requirements.  

For example, Siemens developed a software tool called the Siemens Innovation Platform, which uses generative AI and other advanced technologies to support product design and development.  

In one instance, it used the innovation platform to develop gas turbine blades for power generation applications. Gas turbine blades are usually complex and highly specialised components that require precise design and manufacturing processes to ensure optimal performance and efficiency.  

Using the Siemens Innovation Platform, its engineers were able to generate and evaluate thousands of design options for gas turbine blades, based on a range of parameters such as weight, durability, and aerodynamic performance. The system also incorporated data from manufacturing processes to ensure that the design could be manufactured efficiently and cost-effectively.  

The result? Using generative AI and other advanced technologies allowed Siemens to reduce design time by up to 75%, while also improving the performance and efficiency of the gas turbine blades.  

Supply Chain Optimisation 

It can help B2B businesses optimise their supply chain by analysing data on inventory levels, demand, and production capacity. AI models can generate insights and recommendations that can help businesses reduce costs, improve efficiency, and minimise waste. 

For example, DHL, the global logistics company, developed the Resilience360 platform which optimises supply chain and mitigates risk by providing real-time insights and recommendations based on a range of parameters like weather conditions, geopolitical events, and supplier performance.  

In one instance, DHL used this platform in its partnership with an automotive parts supplier in Mexico. The supplier was experiencing frequent supply chain disruptions due to weather-related events and transport delays. Using the Resilience360 platform, DHL identified potential disruptions and recommended strategies for mitigating the impact. For example, it used the system to identify alternate transportation routes and supplies to ensure that the supplier had the necessary components to meet its production targets. 

Predictive Maintenance 

It can help B2B businesses predict and prevent equipment failures by analysing data from sensors and other sources. AI models can generate alerts and recommendations that can help businesses perform maintenance before a failure occurs, reducing downtime and improving reliability. 

For example, GE (General Electric) developed an AI-powered tool called the Predix platform to help businesses predict and prevent equipment failures. In one instance, GE’s partner, a global oil and gas company, was experiencing frequent equipment failures in its offshore drilling operations.  

Using its Predix platform, GE analysed data from its operations and identified patterns that were indicative of impending equipment failures. The system could then use this information to recommend maintenance action to prevent the failure from occurring in future. 

Customer Service 

It can help B2B businesses improve their customer service by providing personalised recommendations and solutions to customer queries. AI models can analyse customer data, such as purchase history and preferences, to provide customised recommendations and solutions to their specific needs. 

For example, Zendesk has developed an AI-powered tool called Answer Bot, to help businesses automate their customer support operations. The tool can be integrated with various messaging channels, such as email, chat, and social media, to provide customers with quick and accurate responses to their inquiries. 

In one instance, its customer, a financial services company, was experiencing a high volume of customer inquiries related to account balances, transaction history, and other account-related information. Using the Answer Bot, Zendesk automated the handling of these inquiries, providing customers with quick and accurate responses. The system also learned from previous interactions and adjusted its responses accordingly, improving the accuracy and relevance of its responses over time. 

Risk Management 

It can help B2B businesses manage risks by analysing data on market trends, economic indicators, and other factors that can affect their business. AI models can generate insights and recommendations that can help businesses minimise risks and maximise opportunities. 

For example, Allstate, an insurance company, developed an AI-powered tool called the Arity platform, to help insurance companies assess risk and develop more accurate pricing models. The platform collects data from various sources, such as mobile devices and connected vehicles, to analyse driver behaviour and predict the likelihood of accidents. The platform also identifies patterns in driver behaviour that may be indicative of risky driving habits. 

In one instance, its partner, a ridesharing company, was experiencing a high rate of accidents among its drivers, which was resulting in significant insurance claims and costs. Using the Arity platform, Allstate was able to analyse data from the ridesharing company’s drivers and identify patterns that were indicative of risky driving habits. The platform could then recommend training programs and other interventions to help drivers improve their driving habits and reduce the risk of accidents. 

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