How Can Enterprise-Level Businesses Unleash the Power of Generative AI?

Today businesses are rapidly adopting digital transformation and focusing on automating their manual operations to upscale employee efficiency and effectiveness.

This change undeniably involves the integration of Artificial Intelligence. Many large enterprises have started to invest heavily in AI. A study from IBM states 35% of companies reported using AI in their business, and an additional 42% are exploring AI. AI adoption is growing steadily, up four points from 2021. (Source: https://www.ibm.com/downloads/cas/GVAGA3JP).

While there are many different types of AI, generative AI is currently in the spotlight, due to the emergence and rapid ascension of ChatGPT.

Let's briefly explore generative AI, its advantages, and how enterprise-level companies are integrating it into their daily operations.

What is Generative AI

Generative AI refers to a subset of machine learning techniques that aim to create new and original data based on existing data. These models can learn to generate new images, text, audio, or video that closely resemble real-world data. Generative AI algorithms analyze patterns in large datasets and use them to produce new outputs that are similar but not identical to the original data. 

How Generative AI Differs from Traditional AI

Even though traditional AI and generative AI involve machine learning, they have different goals, methods, and applications.

Traditional AI, also known as supervised learning, involves training a model to make predictions or decisions based on given data. It finds the best model that accurately predicts a specific output based on a given input.

Generative AI, on the other hand, focuses on generating new data that resembles the training data. Generative AI models are built using deep learning techniques like deep neural networks. These models are trained on large datasets of real-world examples to learn the patterns and relationships between the input and output data. 

Another difference between traditional AI and generative AI is the type of data used.

Traditional AI models typically require labeled data, which means the data is manually related to the correct output. In contrast, generative AI models use unlabeled data that is not correlated with a specific output.

Traditional AI models are used for purposes like classification, regression, and prediction. Generative AI models are used for tasks such as image and video generation, text synthesis, and content creation.

Generative AI Models: ChatGPT and DALL-E

ChatGPT and DALL-E are two examples of generative AI models that have recently gained a lot of attention. 

ChatGPT

ChatGPT, where GPT stands for Generative Pre-trained Transformer, is a language model trained to generate human-like responses to natural language prompts. Developed by OpenAI and released for testing to the public in November 2022, it is already regarded as the most advanced AI chatbot to date.

This technology has the potential to revolutionize the way that businesses interact with customers, employees, and other stakeholders.

Enterprise businesses are primarily using ChatGPT in customer service and support. By leveraging ChatGPT, companies are providing personalized and timely support to their customers without the need for human intervention. The application of generative AI in this context is significantly reducing costs and improving customer satisfaction. 

According to a recent report from Gartner, by 2022, 70% of all customer interactions will involve AI chatbots or similar technologies. Furthermore, the same report states that chatbots can save businesses up to 30% on customer support costs. (Source: https://www.gartner.com/smarterwithgartner/top-cx-trends-for-cios-to-watch#:~:text=Gartner%20predicts%20that%20by%202022,up%20from%2015%25%20in%202018.)

Furthermore, companies are using ChatGPT to create and curate creative content. Generative AI is capable of producing a variety of content such as blog posts, social media updates, and product descriptions. With ChatGPT and similar applications, businesses are saving time and resources while maintaining a high level of quality with their creative assets.

DALL-E

DALL-E is a powerful image generation model that creates unique and surreal images from textual descriptions. This technology has revolutionized the way businesses create visual content, especially for marketing and advertising purposes.

Enterprise-level businesses use DALL-E for product design and visualization.

By using DALL-E to generate realistic images of products, businesses are able to

accelerate the design process, improve collaboration between teams, and provide more engaging product visualizations for their customers.

For example, Adidas used AI-powered image generation tools to generate realistic images of shoes, which reduced the time and cost associated with traditional product photography.

According to a study by Demand Metric, visual content such as images and videos are more effective at driving engagement and conversions than text-based content. (Source: https://www.demandmetric.com/content/content-buyers-journey-benchmark-report).

DALL-E is also used for data visualization and exploration by enterprise-level businesses to gain insights from large and complex datasets. This helps data analysts better understand patterns and trends, which results in more informed decision-making.

The Benefits of Generative AI

Generative AI has the potential to bring numerous benefits to enterprise-level businesses, particularly in terms of improving their profitability, efficiency, and ROI. Here are some of the advantages that Generative AI offers businesses:

Customized Product and Service Offerings:

Generative AI helps businesses create personalized products and services for their customers. This leads to higher customer satisfaction and loyalty, as well as, increased sales and revenue.

Enhanced Supply Chain Management:

‍Generative AI helps businesses optimize their supply chain management by predicting demand, identifying inefficiencies, and improving logistics. According to a study by McKinsey & Company, AI-enabled supply chain management can result in a 20-50% reduction in forecasting errors, a 10-40% reduction in inventory levels, and a 5-10% improvement in on-time delivery rates. (Source: https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution).

Streamlined Business Operations:

‍Generative AI can automate repetitive tasks and improve decision-making processes, resulting in streamlined business operations and increased efficiency.
Marketing and media are already feeling the impacts of generative AI. As per Gartner, by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022. (Source: https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises)

Improved Fraud Detection and Risk Management:

‍Generative AI helps businesses identify and prevent fraudulent activity and manage risk more effectively.

Enhanced Customer Engagement:

Generative AI improves customer engagement by providing personalized recommendations, chatbots for customer support, and sentiment analysis to understand customer feedback.

Key Challenges while implementing Generative AI 

Here are some of the key challenges that businesses may face when implementing generative AI:

Data Quality and Bias:

‍Generative AI algorithms rely heavily on large, high-quality datasets to generate accurate and reliable results. However, if the data is biased or incomplete, then algorithms may produce low-quality results.

Lack of Expertise and Resources:

Developing and implementing generative AI solutions can be a complex and resource-intensive process. Businesses may struggle to find qualified AI talent, and may not have the necessary infrastructure and computing resources to support these initiatives. 

According to a report by O'Reilly, 37% of surveyed companies cited a lack of AI expertise as a major challenge.

Explainability and Interpretability:

‍Generative AI algorithms can be difficult to interpret and explain, making it difficult for businesses to understand how the algorithms arrived at a particular decision or output. This lack of transparency can make it challenging to identify and address errors or biases in the algorithm.

Use Cases for Generative AI 

Drug Designing

A huge amount of capital is invested in taking a drug from discovery to the market. The discovery process takes an average of 4 years and requires significant investment. To expedite the process, the pharma industry is already using generative AI to help design new drugs. By applying generative AI in this way, the industry has seen a significant decrease in the timeline and the costs associated with drug discovery.

Insilico Medicine, a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, successfully discovered a potent, selective, and orally bioavailable small-molecule inhibitor of CDK8 for cancer treatment using a structure-based generative chemistry approach enabled by the Chemistry42 multi-modal generative reinforcement learning platform. (Source: https://www.eurekalert.org/news-releases/985766).

Synthetic Data Creation

Generative AI offers a solution for creating synthetic data, which is a type of data generated through computer algorithms rather than being obtained from direct observation of the real world. By using generative AI to create synthetic data, the privacy of the sources of the data used to train the model can be protected. This is particularly useful in industries such as healthcare, where sensitive patient information is involved. By generating synthetic healthcare data, researchers and analysts can conduct their work without revealing the identities of the patients whose medical records were used.

Model/Part Designing

Industries such as manufacturing, automotive, aerospace, and defense also benefit from generative AI. These industries use generative AI to help design parts or models, such as performance, materials, and manufacturing methods, that need to be optimized to meet specific goals and constraints.

Generative design enables the creation of components that are specifically tailored for a given application, resulting in increased efficiency and minimized waste. For example, automakers can employ generative design to create lighter and more aerodynamic designs that are optimized for fuel efficiency.

The potential of generative AI is vast and far-reaching across various industries. While its benefits are substantial and undeniable, businesses still need to approach integrating generative AI into their businesses with caution. Before implementation, businesses need to make sure that they are “AI ready” and have the proper strategy in place.

Without a strong foundation and the right strategy, businesses will fail to fully capitalize on all that generative AI has to offer.

If you’re considering implementing AI in your business and need strategic guidance and a knowledgeable partner, then get in touch with us https://sparsolutions.com/contact-us. Our team of experts will meet you where you are so that you can take your business to the next level with AI.