Generative AI is every Organization's "Business"

By, Phane Mane and Brian Peet

In late November 2022, when OpenAI made ChatGPT publicly available it marked a significant milestone in the history of technological evolution rivaled only by the Internet, Smartphones, and Jet engines.

ChatGPT is a form of Artificial Intelligence (AI) that uses Natural Language Processing (NLP) using very powerful Machine Learning (ML) based systems called Large Language Models (LLMs) to create “human-like” content.??

Even though AI and ML technology have been around for over 50 years, the ability of a computer to create content that imitates human output is relatively new – at least in the non-academic world.? A notable part of ChatGPT’s release is that the technology that was used mostly by universities and enterprise-scale corporations has suddenly become accessible to ordinary people and small to mid-sized businesses.?

Generative AI refers to the use of AI/ML-based models to generate NEW content such as text, images, audio, video, and even code based solely on the data that the “model” was trained on.

Since the launch of ChatGPT, there have been more than 300K+ models that can perform a variety of things such as image-to-text and text-to-image conversion, document/visual question answering, text classification, translations, summarizations, conversational, automatic speech recognition, etc. ? Essentially for most content generation activities performed by humans today, it is likely that there is already a ‘model’ to assist or complement with underlying tasks all while their ability to tackle ‘human-like’ tasks continues to advance.?

Despite the rapid evolution of Generative AI technologies and their applicability to real-world use cases, not all industries stand to benefit from its prowess to the same extent.?

Per a recent report from McKinsey and Company “Despite gen AI’s commercial promise, most organizations aren’t using it yet” and “Some industries will gain more than others” - this is likely because industry specific use cases where Generative AI can make an effective impact is yet to be identified let alone those that may be specific to your organization.

In this blog, we will touch on a few use cases from Healthcare, Pharma, Life Science, MedTech, and Advanced Manufacturing industries where “Generative AI” can be leveraged without introducing risks especially if you are a mid-size organization who doesn’t want to be left behind.??

While there are many scenarios for advanced usage of Generative AI like treatment recommendations, Genomic data analysis, symptom checking, and predictive analytics, etc. here are a few scenarios that are relatively easy to implement:?

Image Synthesis -? elevating the resolution of medical images, providing clinicians with clearer and more nuanced visuals for diagnostic purposes, or generating synthetic medical image enhancements to aid segmentation, anomaly detection, classification, etc.?

Simulation -? create realistic simulated environments for thorough testing and validation of imaging devices which can allow manufacturers to assess device performance under diverse conditions, reducing development costs and accelerating the testing phase.

Anomaly Detection – a pre-trained classification that could detect anomalies in manufactured medical images. This precision in anomaly detection is crucial for quality control, allowing for the timely identification of imperfections or irregularities in the manufacturing process.

Demand Forecasting – using a combination of historical data, market trends, and other relevant factors Generative AI models can help forecast demand through dynamic modeling techniques useful in mitigating risks and avoiding disruptions, ensuring a resilient and agile supply chain.

Customer Segmentation - by analyzing customer data such as purchase history, preferences, demographics, and behavior, KNN models can help target customer groups such that your business can deploy tailored sales/marketing strategies to improve your product positioning.

Product Recommendations - if you have an eCommerce storefront then by identifying products that are often purchased together by customers with similar profiles, a Generative AI model can recommend personalized bundles increasing cross-sell/upsell opportunities.

Administrative Tasks - most Life Science/MedTech companies have to deal with extensive administrative tasks, including data entry, documentation, and regulatory compliance. Generative AI can help automate such tasks enhancing your employee efficiency and reducing the likelihood of errors allowing them to focus on more complex/strategic aspects of their work.

Synthetic Data - privacy concerns and data scarcity are common challenges in medical research. Generative AI can address these issues by generating synthetic medical data that closely resembles real-world data. This synthetic data can be used for research, training, and testing algorithms (that often require large data sets) without compromising patient privacy.?

So as you can see, “Generative AI” can transform your business with potential benefits in every part of your organization ranging from R&D teams to customer service, marketing/sales, finance, operations, and HR for overall employee productivity.

We believe that the AI revolution can be equally beneficial to companies of any size and that you don’t have to be a large enterprise that can afford to hire expensive research firms or management consultants to get started.?

While Generative AI continues to evolve rapidly per McKensey’s report “… Inaccuracy, cybersecurity, and intellectual property infringement are the most-cited risks of generative AI adoption.” so we suggest identifying the right type of use cases that are practical, easy to validate, and have a lower risk of exposing inaccurate generated content to your users.??

We recommend the following recipe for your organization to get started on Generative AI.?

In defining the solutions, using proven techniques like Test data preparation, Fine-tuning, Retrieval Augmented Generation (RAG), Reinforcement Learning from Human Feedback?(RLHF), Reward Modelling, etc. can ensure that generated content serves your company’s needs.

In conclusion, Generative AI is no longer an optional technology - it should be every organization’s business so it is imperative that you get started on the journey ASAP.?

I hope you find this blog useful in your understanding of Generative AI. Please comment with your thoughts and let us know other topics that you would like us to discuss in the future.