AI is only as good as the data that informs it, and the need for the right data foundation has never been greater. According to IDC, stored data is expected to grow up to 250% over the next 5 years. With data stored across clouds and on-premises environments, it becomes difficult to access it while managing governance and controlling costs. Further complicating matters, the uses of data have become more varied, and companies are faced with managing complex or poor-quality data.Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data, dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.
Trust and AI
With access to the right data, it is easier to democratize AI for all users by using the power of foundation models to support a wide range of tasks. However, it’s important to factor in the opportunities and risks of foundation models—in particular, the trustworthiness of models to deploying AI at scale.Trust is a leading factor in preventing stakeholders from implementing AI. In fact, IBV found that 67% of executives are concerned about potential liabilities of AI. Existing responsible AI tooling lacks technical ability and is restricted to specific environments, meaning customers are unable to use the tools to govern models on other platforms. This is alarming, considering how generative models often produce output containing toxic language—including hate, abuse, and profanity (HAP)—or leak personal identifiable information (PII). Companies are increasingly receiving negative press for AI usage, damaging their reputation. Data quality strongly impacts the quality and usefulness of content produced by an AI model, underscoring the significance of addressing data challenges.
Increasing user productivity with knowledge management
An emerging generative AI application is knowledge management. With the power of AI, enterprises can precisely collect, create, access, and share relevant data for organizational insights. Knowledge management applications are often implemented into a centralized system to support business domains and tasks—including talent, customer service, and application modernization.
HR, talent, and AI
HR departments can put AI to work through tasks like content generation, retrieval augmented generation, and classification. Content generation can be utilized to quickly create the description for a role. Retrieval augmented generation can help with identifying the skills needed for a role based on internal HR documents. Classification can help with determining whether the applicant is a good fit for the enterprise given their application. These tasksreduce the processing time from when a person appliesto receiving a decision on their application.
Customer service and AI
Customer service divisions can take advantage of AI by using retrieval augmented generation, summarization, and classification. For example, enterprises can incorporate a customer service chatbot on their website that would use generative AI to be more conversational and context specific. Retrieval augmented generation can be used to search through internal documents to answer the customer’s inquiry and generate a tailored output. Summarization can help employees by providing them a brief of the customer’s problem and previous interactions with the company. Text classification can be utilized to classify the customer’s sentiment. These tasks reduce manual labor while improving customer care and retention.