How to Use Generative AI to Create Business Value
For those who haven’t been paying attention, generative AI has taken the world by storm. One of the more popular services is ChatGPT, an AI-powered chatbot created by OpenAI, which was released in November 2022. In just five days after launch, ChatGPT had garnered more than 1 million active users and over 100 million users two months after launch.[i],[ii] To compare, it took TikTok nine months, Instagram two and a half years, and Facebook four and a half years to amass that many users.[iii]
The revolutionary impact of artificial intelligence (AI) on various industries and lines of business is undeniable. From automated customer service to precision medicine, AI has fundamentally changed how we perceive and interact with technology. Traditionally, many AI applications were focused on predicting what was likely to happen in the future, detecting fraud or anomalous patterns, optimizing routes, segmenting customers, or recommending what to watch next on Netflix. Those applications still exist and will continue to remain important, but they were primarily based on numbers. That is, they often relied on historical transactional and demographic data (numbers-based) and would output another number–whether it be a prediction, forecast, likelihood to watch, a cluster (group) assignment or an optimization which would try to maximize or minimize some objective function (e.g. fastest route, shortest distance, etc.).
Now, with the increased sophistication of foundation models and large language models (LLMs), better training data and GPUs, generative AI has changed the game. In a nutshell, generative AI has catapulted us beyond the numbers. Generative AI can create entirely new content–be it a blog, headline, article, term paper, image, or even a piece of music. It’s certainly based on numbers but how you interact with it has changed significantly and the output is not a number. You can feed it a document and ask it via plain language to summarize the document, you can ask it to write a blog for you, create ad copy, an image or even generate music. AI is now more approachable than ever, it’s available to non-experts in statistics, mathematics, or programming. It’s available to creative professionals, accountants, engineering, programmers, and even students who use it for their tests and homework. Generative AI has essentially democratized artificial intelligence–making it readily available and easily accessible to a wide swath of the population. But how exactly did it come into existence and where is it heading?
The term ‘generative AI’ started gaining attention with the advent of advanced neural networks and deep learning techniques around the 2010s. Since then, it has been an active area of research and development. It’s important to understand that the development of generative AI didn’t occur overnight. It’s the result of decades of incremental advances in machine learning (ML), computer science, cheap data storage, and low-cost compute resources.[iv]
Generative AI holds the promise to shape the future of technology and redefine the limits of machine capabilities. From generating human-like text or software code, to creating realistic pictures, paintings and music, it has the potential to not only augment human creativity but also automate various tasks which were previously too challenging for rules-based systems. This automation will certainly make businesses more efficient and productive. But, as with any technology, generative AI also presents a series of challenges and ethical considerations that must be addressed before business leaders can add this game-changing technology to their portfolio.
At its core, generative AI encompasses a range of models capable of creating new content, whether it’s text, software code, images, voice, video, music, or even complex data structures and 3D models. These AI models learn the intrinsic patterns of their training data and then generate outputs that align with these patterns, effectively mimicking the style, tone, or structure of the original data. Let’s explore a few key types of generative AI and their business applications:
- Text & Code Generation Models (LLMs): The most well-known example of this is OpenAI’s GPT-4 models but others include Google’s PaLM 2 and Meta’s LLaMA. On the code side, GitHub Copilot and Tabnine are a couple of examples. These AI models are trained on vast datasets of text and can generate human-like, contextually relevant text and/or code. The business applications are wide-ranging, from drafting emails, writing product descriptions, creating software code, generating personalized marketing content, to automating customer service interactions through AI chatbots. They can also assist in content creation for news articles, blog posts, or social media updates, greatly reducing the time and cost associated with these tasks.
- Image Generation Models: Diffusion models are a prime example of AI models used for creating realistic images. Popular services include OpenAI’s DALLE-2, Midjourney and Stable Diffusion. Businesses can use these models in various ways, such as creating virtual models for fashion brands, generating realistic images of products for e-commerce, or creating visual content for marketing campaigns. They can also be used in fields like real estate, where AI-generated architectural designs or property images can be valuable.
- Music Generation Models: AI models like OpenAI’s MuseNet, Aviva and Soundful have the ability to compose music in a variety of styles and genres by learning the patterns in their training datasets. In business, these models can create unique soundtracks for video productions or background music for various settings such as restaurants, retail shops, or corporate events. They can also be instrumental in the creation of personalized music experiences for consumers, opening up innovative ways to engage customers.
By understanding the types of content generative AI can create, businesses can identify numerous ways to harness the power of generative AI. It can help automate and enhance various aspects of a business, from customer interaction to content creation, design, and beyond.
The power of generative AI lies in its ability to learn patterns and structures from large sets of data and generate new, unique instances based on this knowledge. This capability is harnessed using advanced machine learning techniques such as neural networks and deep learning.
At a high level, generative models are trained on large datasets – the larger and more diverse the dataset, the better the model can learn and generalize. For instance, a text generation model may be trained on millions of books, articles, and websites to understand language and context. Similarly, an image generation model learns from vast datasets of images, and a music generation model is trained on numerous pieces of music across various genres and styles.
Training these models involves iterative processes of learning and fine-tuning. The model generates outputs, assesses the quality of these outputs based on a given standard, adjusts its parameters to improve, and then generates again – this cycle continues until the model achieves a desirable level of proficiency.
Let’s look at how a business may approach training these models:
- Text Generation Models: If a business wants to automate customer service responses, they could train a model on a dataset of previous customer interactions, including queries and corresponding responses. This allows the model to understand the context, tone, and appropriate responses for a wide variety of customer issues. The trained model can then generate responses to new customer queries based on what it has learned.
- Image Generation Models: For an e-commerce business aiming to generate realistic images of products, the model would be trained on a large dataset of existing product images. It would learn the nuances of the product’s features, textures, and colors, and then it could generate images of new or modified products without the need for physical prototypes or photoshoots.
- Music Generation Models: Suppose a business wants to create unique background music for its retail stores. In this case, a model could be trained on a diverse range of music that aligns with the brand’s image. Once trained, the model can generate new, unique music that maintains the brand’s style and ambiance.
While training generative models requires technical expertise and resources, there are pre-trained models and user-friendly platforms available that simplify the process significantly. These platforms allow businesses to leverage generative AI without needing extensive AI knowledge or capabilities in-house.
Remember that while the potential of generative AI is vast, its use should be coupled with a careful understanding of the ethical considerations, including issues related to data privacy, copyright, and potential misuse of the technology.
The innovative potential of generative AI is being realized across a multitude of industries. Let’s explore some of the ways in which it is being applied:
- Banking and Finance: In this sector, generative AI can automate the creation of financial reports or news updates. AI models can also be used to generate simulations of various financial scenarios, aiding in risk assessment and decision-making. Goldman Sachs is using generative AI to write software code and Morgan Stanley Wealth Management is using it to help it’s financial advisors.
- Energy and Utilities: Generative AI can generate predictive models for energy usage and optimize energy distribution. For instance, it can help simulate different scenarios of energy consumption based on past data and inform strategies for load balancing. Another example is Octopus Energy who is using generative AI for customer service.
- Insurance: AI can assist in automating claim assessments by generating predictive models based on historical claim data. It can also be used in underwriting, generating risk profiles for potential clients based on a range of factors.
- Government and Public Sector: Governments can use generative AI to automate responses to public queries, create informative content, or predict potential public policy outcomes based on past data.
- Healthcare: In healthcare, generative AI can help in generating realistic 3D models for surgical planning or training. It can also be used to simulate patient responses to various treatments based on past patient data. Start-ups like Abridge AI Inc. make technology to write doctors notes and is currently being adopted by the University of Pittsburgh Medical Center (UPMC).
- Life Sciences: Generative AI can aid in drug discovery, simulating the likely outcomes of various chemical compounds. It can also help in genomic research by generating predictions about gene functions. Cognizant is using it for a variety of use cases including drug development.
- Manufacturing: AI can aid in product design, generating prototypes based on desired features. It can also optimize production planning by generating predictive models of the manufacturing process. Automotive manufacturers are using it to help design cars which costs up to $3 billion.
- Retail: In retail, generative AI can be used to create realistic images of products for online stores. It can also generate personalized marketing content for customers, enhancing customer engagement. Coca Cola has launched a contest and invited “digital creatives” to create artwork and ads for the company.
- Telecommunications: Telecom companies can use generative AI to predict network issues and optimize network performance. AI can also automate customer service responses, enhancing efficiency.
- Transportation and Logistics: Generative AI can be used to optimize routes and logistics by generating predictive models based on traffic data, weather conditions, and other factors. It can also assist in vehicle design, creating virtual models based on desired features.
Across these industries, the ability of generative AI to learn from data and create new, high-quality content or predictive models has proven to be a valuable tool. As AI technology continues to evolve, its applications across various industries are bound to expand even further.
Generative AI holds great promise for the future, with vast potential to impact industries and transform business operations. However, like any transformative technology, it also brings with it a set of risks and challenges that must be navigated.
- Increased Efficiency: By automating tasks like content creation, customer service, and predictive modeling, businesses can drastically increase their operational efficiency.
- Cost Savings: Automation through Generative AI reduces the need for manual effort, leading to significant cost savings in the long run.
- Improved Decision Making: Generative AI can simulate various scenarios based on past data, enabling better strategic planning and decision-making.
- Enhanced Customer Experience: Personalized content and interactions powered by AI can provide a unique and improved customer experience, leading to higher engagement and customer retention.
- Innovation: The capability of AI to generate new content or ideas can drive innovation across various sectors like product design, marketing, entertainment, and more.
- Data Privacy: Generative AI requires large volumes of data for training. Ensuring that this data is collected and used in a manner that respects privacy laws and norms is a significant challenge.
- Ethical Considerations: The ability of AI to generate realistic content raises concerns about misuse, such as the creation of deepfakes or misleading information. It’s crucial to use Generative AI responsibly and implement measures to prevent misuse.
- Quality Control: While AI can generate content, the quality of that content can vary. Ensuring the generated content meets the required standards is a challenge that needs constant monitoring and feedback.
- Intellectual Property Rights: As AI starts creating content, questions arise around copyright and ownership. Regulators and businesses need to navigate these complex issues.
- Bias: If the data used to train AI models contains biases, the output generated by these models will likely perpetuate the same biases. Efforts must be made to ensure the data used for training is unbiased and representative.
As we look forward to the future, it is important for business leaders to consider both the potential benefits and risks of using Generative AI. By making informed decisions and using the technology responsibly, businesses can harness the power of generative AI to drive growth, innovation, and efficiency.
Navigating the world of Generative AI may seem daunting initially, but breaking it down into manageable steps can simplify the process. Here are some top recommendations for business leaders to get started with generative AI:
- Improve Your AI Literacy: Understanding the basics of AI and its potential applications in your industry is the first step. There are plenty of resources available online – from courses on platforms like Coursera or edX, to webinars, podcasts, and articles on the subject.
- Identify Use Cases: Think about the tasks that generative AI could automate or enhance in your business. This could be anything from content creation, customer service, product design, to predictive modeling. Start with a specific, manageable project where generative AI can make a tangible impact.
- Assemble an AI Tiger Team: Building or implementing AI solutions requires a certain level of expertise. If you don’t have the necessary skills in-house, consider hiring AI experts or partnering with an AI solutions provider.
- Choose the Right Tools: There are numerous AI tools and platforms available today that simplify the process of building and deploying AI models. These include cloud-based AI services from providers like Google, Microsoft, and Amazon, as well as a plethora of start-ups who have entered the market.
- Train Your Model: Depending on the complexity of your project and the resources available, you could either use pre-trained models or train a new model with your own data. Remember to consider the ethical implications of data collection and usage.
- Monitor and Improve: Deploying the model isn’t the end. Be prepared to continually monitor the performance of your AI system, provide feedback, and make improvements as needed.
- Consider the Ethical Implications: As you implement Generative AI, ensure you’re considering the ethical implications. This includes being transparent about your use of AI, ensuring data privacy, and taking steps to prevent misuse.
Getting started with Generative AI is an investment not only in technology, but also in time and resources. However, the potential benefits — from cost savings, improved efficiency, to new innovative capabilities — make it an investment worth considering or you risk becoming irrelevant.
If you’d like to learn more about AI, pick up a copy of Artificial Intelligence: An Executive Guide to Make AI Work for Your Business.
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[i] Milmo, Dan. 2023. “ChatGPT Reaches 100 Million Users Two Months after Launch.” The Guardian, February 2, 2023, sec. Technology. https://www.theguardian.com/technology/2023/feb/02/chatgpt-100-million-users-open-ai-fastest-growing-app.
[ii] Cerullo, Megan. 2023. “ChatGPT User Base Is Growing Faster than TikTok.” www.cbsnews.com. February 1, 2023. https://www.cbsnews.com/news/chatgpt-chatbot-tiktok-ai-artificial-intelligence/.
[iii] Walters, Natalie. 2019. “The Social Media Platforms That Hit 100 Million Users Fastest.” The Motley Fool. January 20, 2019. https://www.fool.com/investing/2019/01/20/the-social-media-platforms-that-hit-100-million-us.aspx.
[iv] Friedland, Alex. 2023. “What Are Generative AI, Large Language Models, and Foundation Models?” Center for Security and Emerging Technology. May 12, 2023. https://cset.georgetown.edu/article/what-are-generative-ai-large-language-models-and-foundation-models/.