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Generative AI isn't just for making tasks easier; it's about enhancing what we can do and finding new ways to solve problems. We'll look at some key examples of Generative AI in action across different industries and offer advice for companies ready to start making their own Gen AI models.
Generative AI opens doors to innovation for forward-thinking organisations seeking to optimise their operations. Everyone has access to ChatGPT and Generative AI tools, but the real value can be gained by combining your teams unique expertise and your proprietary data together with this powerful new technology.
These are some common use cases we are seeing today:
01. Data-Driven Decision-Making
Challenge: Organisations want to leverage their data to help inform better decision-making, but some common obstacles stand in the way:
How Gen AI Helps: Generative AI can be used to create an AI empowered agent (think Siri and Alexa but for your internal systems), offering staff direct access to insights, documents, and data. Gen AI comprehensively reviews information, providing a single source of truth that bridges the gap between decision makers and actionable insights, empowering teams and facilitating informed decision-making.
This democratises insights across the business, while maintaining centralised data governance, and frees up capacity of internal teams to focus on more advanced, value-added insights.
To overcome data fragmentation, interpretation issues, and analytics bandwidth constraints, Siemens and Microsoft integrated Generative AI into Teamcenter and MS Teams, creating an AI agent for easy access to unified insights. This enhances decision-making and frees teams for higher-value tasks, improving productivity across industrial operations
02. Data Quality and Governance
Challenge: Organisations grapple with trust issues in data quality and governance due to the large volumes of data being created and challenges maintaining its quality.
How Gen AI Helps: Generative AI can automate data quality and governance workflows, improving classification, labelling, and filling in gaps. This results in quality outputs, ensuring consistency and boosting data usage, reliability, and trust across the organisation.
HSBC adopted a Generative AI solution to automate the labeling of customer financial documents, significantly reducing manual effort and improving data accuracy. This enhanced data governance and boosted trust in data-driven insights.
03. Intelligent Chatbots
Challenge: Frequently Asked Questions (FAQs) are static, requiring manual updates. Off-the-shelf chatbots fail to address this issue effectively, leading to customers unnecessarily contacting the call centre. Call centres are expensive and require large amounts of management and process to work effectively.
How Gen AI Helps: A custom-built Generative AI chatbot can be trained on a company’s unique policies, documents, CRM, and call histories. This chatbot can then respond to a broader set of customer questions, enhancing resolution, customer satisfaction, and incorporating localised nuances.
Bank of America's Erica chatbot leverages Generative AI to understand and respond to complex customer queries in a personalized and efficient manner. Erica handles a majority of inquiries, reducing call centre workload and improving customer satisfaction.
Allianz is utilizing Generative AI to analyze insurance claims data and identify patterns indicative of fraudulent activity. This proactive approach enables early intervention and reduces financial losses due to fraud.
04. Dynamic Content Generation for E-Commerce
Challenge: E-commerce platforms often struggle to keep product listings fresh and engaging, leading to decreased user interest.
How Gen AI Helps: Generative AI can dynamically generate product descriptions, images, and promotional content based on current trends, user preferences, and market demand. This ensures that the content remains appealing and relevant, ultimately driving user engagement and sales.
eBay implemented a Generative AI system that generates personalized product recommendations for individual users based on their browsing history and purchase patterns. This dynamic approach led to a significant increase in user engagement and sales conversion.
05. Fraud Detection and Prevention
Challenge: Organisations face ongoing threats from fraudulent activities, with traditional methods sometimes falling short in identifying sophisticated schemes.
How Gen AI Helps: Generative AI models can analyse large datasets to detect unusual patterns and anomalies indicative of fraudulent behavior. By continuously learning and adapting, these models enhance fraud detection capabilities, providing a more robust defense against evolving threats.
Westpac New Zealand's collaboration with ACI Worldwide to integrate Generative AI into their fraud detection systems marks a significant step in combating sophisticated fraud schemes. By analyzing vast datasets for unusual patterns, the Generative AI models provide a dynamic and adaptive defense, enhancing the bank's ability to preemptively address evolving threats and safeguard against fraudulent activities more effectively. This proactive approach demonstrates the potential of Generative AI in strengthening fraud detection capabilities within the financial sector.
Building your own Generative AI models might sound complex, but the payoff is worth it—leading to smarter, more efficient operations. Starting with focused projects and improving step by step, any organisation can master the use of Generative AI.
This isn't just about technology—it's about setting the stage for a future where innovation and creativity are amplified by AI.