Unleashing the Power of Generative AI in Business
In a world where AI is becoming increasingly prevalent, the use of generative AI in business is on the rise. Enterprises are projected to invest heavily in generative AI solutions, with spending expected to reach $143 billion by 2027. The potential economic impact of generative AI is staggering, with estimates suggesting it could add trillions of dollars to the global economy annually.
To fully harness the capabilities of generative AI, businesses must focus on four key pillars:
- Building a Strong Data Foundation
Establishing a robust data foundation is crucial for the success of generative AI initiatives. Whether fine-tuning existing large language models or creating new ones, a solid data strategy is essential. Data architecture, governance, and security measures play a vital role in ensuring the accuracy and reliability of generative AI outputs.
- Leveraging Large Language Model Operations (LLMOps)
LLMOps encompass the policies and procedures governing the lifecycle of generative AI models. From compliance and security to bias mitigation and prompt engineering, LLMOps ensure that AI is deployed effectively and responsibly.
- Managing for Potential Shadow AI
Shadow AI refers to the unauthorized use of AI tools and platforms within an organization. Implementing responsible AI policies can help mitigate security risks and ensure compliance while fostering innovation.
- Choosing the Right Use Cases
Selecting use cases that align with business objectives and values is critical. By focusing on areas where generative AI can provide tangible benefits, such as cost optimization, task automation, and customer support enhancement, organizations can maximize the value of AI investments.
Real-World Applications of Generative AI
Data Foundation and SQL Lookups
Generative AI can streamline customer service operations by automating SQL queries and providing quick access to information stored in databases. By training AI assistants on business databases, organizations can improve response times and enhance customer satisfaction.
LLMOps, Context, and Customer Feedback
Implementing LLMOps frameworks can help AI systems better understand context and improve customer interactions. For instance, an AI-powered social media monitoring tool can classify customer feedback and generate appropriate responses, saving time and improving service quality.
Transcription and Summarization in Contact Centers
Generative AI can revolutionize contact center operations by summarizing customer interactions and providing valuable insights to support agents. Studies have shown significant productivity gains and market growth potential in customer service applications of generative AI.
Looking Ahead
As generative AI continues to evolve, organizations must adapt to harness its full potential. By addressing data challenges, implementing effective operational frameworks, and selecting use cases wisely, businesses can unlock new opportunities for growth and innovation.
Stay tuned for more updates on the transformative power of generative AI in business.
Authors: Naveen Kamat, Vice President and CTO of Data and AI Services at Kyndryl, and Dennis Perpetua, Vice President and CTO of Digital Workplace Services at Kyndryl.
