Generative artificial intelligence is more than a buzzword for industries such as healthcare, financial and professional services, media, retail and gaming; these industries have a lot to teach us about generative AI’s capabilities. But despite the promising horizon, the implementation of GenAI poses certain challenges and risks. Organizations must take a systematic, cautious approach to harness the full power of AI.
Generative artificial intelligence is poised to revolutionize a wide range of industries by driving innovation, improving productivity, and fostering creativity. While each industry will vary in its speed and appetite for adoption, all indicators point to a systematic implementation across the board. This helps other leaders get a better idea of how they can apply GenAI to their business and what risks to consider when trying to implement it in their own organizations. And according to McKinsey, applications of this technology are poised to deliver a total economic value of as much as $4.4 trillion annually.
Let’s Look at Some Potential GenAI Use Cases Within Leading Sectors
The healthcare sector produces substantial volumes of data: research, patient data, hospital records, etc. It’s a complex and sometimes fragmented ecosystem. GenAI can collect, aggregate, and analyze this data to generate valuable insights for medical professionals. For instance, it can identify patterns, predict outcomes, and recommend treatments. Amazingly, Gartner estimates that more than 30% of new drugs and materials will be discovered using GenAI by 2025.
With all this data available, GenAI is an ideal vehicle to revolutionize healthcare businesses. Call centers, for example, handle millions of patient requests. With a GenAI framework, patients can interact with the AI to answer questions about benefits coverage, view price transparency, and get customized plans, saving call center operators valuable time and increasing patient satisfaction. This technology can also extend to clinical call documentation, where GenAI can accurately simplify the documentation and provide actionable insights to doctors and nurses.
Retail and Consumer Packaged Goods
GenAI is set to revolutionize customer experiences and personalization in the retail sector, potentially bringing anywhere from $400 to $660 billion into the industry each year.
This technology enables retailers to create targeted marketing campaigns, enhance customer engagement, and deliver customized experiences tailored to customers’ unique needs and preferences by analyzing customer data, predicting individual preferences, and offering personalized recommendations. Leading retailers are already using GenAI for a range of functions in this vein, including improving the natural language processing tools they use to make it easier for customers to find what they’re looking for through online searches.
Professional services, such as legal and contract document generation, staff training, and knowledge mining, all benefit from further advancements in AI. This sector has much to gain as it can use GenAI to increase its competitive edge, efficiency, and accuracy. It empowers professionals in these sectors by improving automation (and overall) efficiency by unlocking new levels of productivity and speed.
In the legal industry, AI-powered algorithms can analyze vast amounts of data to draft accurate and customized case documents and legal records, saving time and reducing human error. GenAI can create interactive and personalized learning experiences in staff training, tailoring content to individual needs.
With its sophisticated algorithms and machine learning techniques, GenAI enables media professionals to create engaging and personalized content at scale. Through automated video editing and production and AI-powered image and audio generation, AI streamlines processes, reduces costs, and enhances the overall viewer experience. Virtual reality, augmented reality, and interactive storytelling technologies also open new possibilities for immersive media experiences.
4 Important Factors to Consider When Implementing AI
There’s no doubt that GenAI will be a game changer. However, organizations must be cautious of the risks involved in implementation. Here are a few factors that leaders need to consider:
- Set up domain guide rails for GenAI.
General-purpose models are trained on enormous bodies of information across almost every discipline of human knowledge. This makes it possible, intentionally or unintentionally, to elicit responses from the AI that are irrelevant to business purposes and potentially harmful. Exposing customers or employees to such responses can negatively impact business outcomes, credibility, and goodwill. Legal liability may also be a consequence.
Establishing strong guide rails, curated training data, and access controls on the large language models to mitigate these risks is very important.
- Secure AI models.
To safeguard confidential information, give employees access to purpose-built business applications that utilize GenAI models within a robust information security framework rather than use generic options for enterprise-sensitive info.
This can be achieved by leveraging enterprise-grade frameworks offered by cloud service providers or implementing in-house solutions within a secure enterprise network. To ensure the highest level of data protection throughout the organization, companies must enforce encryption, access control, and data retention measures.
- Extend large language models.
Custom training or fine-tuning provides a higher level of control over the data used to train your models, resulting in increased transparency. Custom training also enables the creation of a transparency framework, making it possible to trace back each response from an AI to its source training documents.
For example, Nvidia collaborated with the University of Florida to develop a specialized LLM aimed at assessing healthcare records. Additionally, Nvidia is honing LLMs to interpret and produce unique protein sequences, paving the way for accelerated AI-assisted drug discovery.
- Moderate AI reponses.
To detect and remove harmful elements in generated responses, including irrelevant, inappropriate, plagiarized, or copyrighted content, you need to invest in moderation. The likelihood of harmful content increases in use cases relying heavily on pretrained knowledge from outside the enterprise. Moderation can be achieved using services that detect different types of harmful content combined with an ensemble of custom-trained classification models.
Although we’ve explored numerous applications of GenAI across various industries, this merely scratches the surface of its complete potential. Wipro understands this; it is currently investing a billion dollars into AI with the goal of integrating it into every platform, tool, and solution used internally and for clients. Leaders can keep up to speed with AI by considering the risks associated with GenAI and focusing its power on specific use cases. In doing so, leaders can reimagine current business and explore new ways to do business with superior customer experience and innovative business applications that drive revenue.
Written by Srini Pallia.
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