Artificial intelligence (AI) has the potential to revolutionize various aspects of business, including customer service, talent management, and application modernization. According to IBM’s Institute of Business Value (IBV), AI can enhance customer experience in contact centers by 70%, increase productivity in HR by 40%, and improve application modernization by 30%. However, organizations have historically struggled to scale and operationalize AI. One key factor in successful AI implementation is the availability of high-quality data. The need for a robust data foundation is becoming increasingly important as stored data is expected to grow by up to 250% over the next five years. Organizations are also faced with the challenge of managing data stored across multiple environments and dealing with complex or poor-quality data.
Data scientists, in particular, spend 80% of their time cleaning, integrating, and preparing data, which underscores the need for a trusted and integrated data platform for AI. Trust and the potential risks associated with AI models are also significant concerns for stakeholders. Many executives worry about the liabilities of AI, especially given the potential for generative models to produce toxic language or leak sensitive information. Therefore, organizations must address data challenges and ensure the responsible and transparent deployment of AI at scale.
One promising application of generative AI is in knowledge management. With AI-powered knowledge management systems, enterprises can effectively collect, create, access, and share relevant data for organizational insights. This is particularly beneficial in HR, talent management, customer service, and application modernization.
1. What is the potential of AI in business?
AI has the potential to enhance customer experience, increase productivity, and improve application modernization in various areas of business.
2. What are the challenges of scaling and operationalizing AI?
Organizations often struggle with accessing and managing data stored across different environments, as well as dealing with data cleaning and preparation processes.
3. How can organizations ensure the trustworthiness of AI models?
Establishing a trusted and integrated data platform for AI, and addressing data challenges, are crucial for ensuring the transparency and responsible deployment of AI models.
4. What is the role of generative AI in knowledge management?
Generative AI can help enterprises collect, create, access, and share relevant data for organizational insights in areas such as HR, talent management, customer service, and application modernization.
5. What is the significance of data quality in AI models?
Data quality strongly impacts the quality and usefulness of content produced by AI models, highlighting the importance of addressing data challenges and ensuring accurate, reliable data.
AI has the potential to transform various aspects of business, and the availability of high-quality data is crucial for successful AI implementation. Organizations face challenges in scaling and operationalizing AI, but generative AI offers promising solutions in knowledge management. By leveraging AI-powered knowledge management systems, enterprises can effectively collect, create, access, and share relevant data for organizational insights in areas such as HR, talent management, customer service, and application modernization. Addressing data challenges and ensuring the responsible and transparent deployment of AI at scale are key to harnessing the full potential of generative AI in knowledge management.