Until the recent advent of Generative AI, most AI systems were used for classification or regression. With Generative AI, models can be trained to generate content or images that do not resemble the training data directly. Such a creative possibility is a game changer.
Enterprises can immensely benefit from Generative AI in domains that require expert summarization or nuanced responses and also where natural language context needs to be encapsulated in creatives or even videos. With pre-trained models becoming available for general purposes, these eventually can be fine-tuned on domain-specific data.
In the Generative Text arena, ChatGPT needs no introduction. Behind the scenes ChatGPT model does two things: Next-word prediction eventually leading to a coherent response and tracking of conversation topic. However, for enterprises, such models must be fine-tuned for specific industries and use cases. For example, summarizing contractual documents or generating FAQs that are domain specific.
Secondly, we have Generative Image capabilities with models like DALL-E, that can generate digital images given an appropriate description in natural language. Enterprises may use it for synthetic image generation that is domain specific. For example, in car insurance, generating images of dented cars based on customer’s description, or in AdTech context, generating creatives based on brand and campaign objectives. Such data can lead to actual predictive models with crisp business objectives.
Generative models have been confined to large tech companies because training them requires a vast number of resources and data. Nonetheless, such models can be fine-tuned for enterprise use cases with much lesser data and resources.
Domain Specific Use Cases
- Fraud Detection: Synthetic fraudulent transactions generated through Generative AI can be leveraged to enhance classification models and detect fraudulent activities more accurately.
- Customer Service: Chat Bots trained using historical customer interactions and call centre logs can provide personalized customer service and improve customer experience.
- Anti-Money Laundering: Coupled with historical data on money laundering events, Generative AI can be used to generate synthetic money laundering data. Anti-Money Laundering models can then be trained on such data.
- Underwriting: By analyzing vast amounts of data from various sources such as social media, weather reports, and health records, Generative AI can accurately assess risks and determine premiums for insurance policies.
- Claims Processing: Generative AI can automate claims processing by analyzing data and providing prompt and accurate responses to customers. This can help insurance companies reduce costs and improve customer satisfaction.
- Risk Modeling: By generating scenarios that simulate different potential outcomes, Generative AI can help insurance companies identify and mitigate risks more effectively.
- Medical Record Analysis: Generative AI can analyze medical records to identify patterns and trends that can assist with the diagnosis and treatment of patients.
- Medical chatbots: Generative AI-powered chatbots can provide patients with medical advice and information, as well as help them navigate the healthcare system.
- Medical image analysis: Generative AI can interpret medical images like X-rays and CT scans, assisting in the detection of diseases and conditions, and in developing treatment plans.
Oil and Gas
- Predictive Maintenance: Generative AI-generated models can predict equipment failures by analyzing real-time data from sensors and other sources. This can help companies prevent costly equipment breakdowns and improve maintenance efficiency.
- Risk Assessment: Generative AI models can assess risks associated with oil and gas operations by analyzing data from various sources such as seismic surveys and production data. This can help companies identify potential hazards and implement mitigation measures.
- Exploration and Production: Generative AI can analyze geological data to optimize drilling operations, reduce exploration and drilling costs, and increase production efficiency.
Media and AdTech
- Automated Content Creation: Generative AI can help media companies create content at scale by automating the content creation process. For instance, AI can generate scripts for TV shows, movies, and advertisements by analyzing large datasets of previous works in the same genre or category. AI-generated content can be used for various purposes, such as personalization, localization, and testing different scenarios.
- Dynamic Creative Optimization: Generative AI can optimize ad creatives in real-time based on user behaviour and preferences. As an example, AI can generate personalized ad copies and visuals based on user demographics, browsing history, and social media activity. This can help increase engagement, click-through rates, and conversions by tailoring ad creatives to individual users’ interests and needs.
- Virtual Influencers: Creating virtual influencers that can be used for marketing campaigns and brand promotions is also something that Generative AI can help with. Virtual influencers are computer-generated characters that can interact with users on social media platforms and provide product recommendations, lifestyle advice, and entertainment. AI can generate realistic facial expressions, body movements, and voiceovers for virtual influencers, making them indistinguishable from real-life influencers.
Risks involved with Generative AI
Generative AI models are inherently probabilistic, and these are a function of the training data provided. Hence for enterprises, they carry some risks:
- Misuse of Data: Generative AI can be used to create realistic but fake data, which could be misused for malicious purposes such as fraud or deception. Enterprises should be cautious about the ethical use of generative AI and ensure that the data generated is used for legitimate purposes only.
- Bias in Data: Generative AI can amplify existing biases in the data used to train the models. This could result in unintended or discriminatory outcomes in the decision making process, especially in areas such as hiring, lending, and criminal justice.
- Intellectual Property Infringement: Generative AI can be used to create content that infringes on intellectual property rights, such as copyrighted works, trademarks, or patents. Enterprises need to be mindful of the legal implications of using generative AI to create content.
- Reputation Damage: If generative AI-generated content is not properly monitored, it can damage an enterprise’s reputation. For example, if an AI-generated news article is published with false information, it could cause significant harm to the enterprise’s credibility.
- Regulatory Compliance: Generative AI may fall under regulatory frameworks such as GDPR and CCPA, which could require enterprises to obtain explicit consent from individuals before using their data for generative AI purposes.