The use of generative AI is just beginning. IT leaders need to understand what this technology can and cannot do.
The growing interest in developing and deploying generative AI is changing the way people interact with software and the way companies interact with their customers. As companies adopt this technology for business purposes, there are many lessons to be learned along the way.
With a flurry of product launches and demonstrations showcasing the capabilities of generative AI and the emergence of new startups, pause to see where the most widely adopted and early insights reside as organizations deploy this technology into their enterprise environments. It is essential to consider
4 Use Cases for Generative AI in the Enterprise
The number of use cases for generative AI continues to grow. Here are four that IT leaders should understand.
- Improved customer engagement. For companies using call centers to address customer issues, generative AI (including GPT-3+ and other types of large-scale language models (LLMs)) will be more accurate than typical conversational chatbots. There is a possibility. Generative AI is adept at understanding tones and reactions, making customers feel naturally engaged. Conversational AI chatbots are one of the first use cases adopted by enterprises. These chatbots can interact with customers like humans by accessing internal information, performing queries, responding to inquiries and addressing common issues. Generative AI improves the quality, availability, and responsiveness of customer engagements for businesses using conversational AI, offering a compelling alternative to manually operated call centers.
- Better decision making. Generative AI has the potential to enable business users to quickly and easily extract useful insights from large data sets. This technology converts natural language questions into SQL queries that run on the company’s database to produce answers that users can understand. Organizations may benefit from improved efficiency by making better real-time decisions or asking additional questions to find more complete answers.
- Code faster. LLM is accurate in a variety of languages, including programming languages. A programmer who wants to create new software code and accompanying documentation can benefit from generative AI by reducing her time writing code by as much as 50%. For example, Microsoft Power Automate, a tool used for robotic process automation, can be programmed via natural language to automate tasks and workflows. This tool reduces the need for large teams of programmers and testers and minimizes the time and effort required to get an automated system up and running.
- optimized content and products. One of the primary use cases for generative AI in the enterprise is content and product creation. This technology helps businesses save time and resources by automatically creating new content such as product descriptions, marketing campaigns, and social media posts. Generative AI can also analyze existing product designs and generate new designs based on data. Companies may be able to use technology to create optimized products based on customer needs and preferences.
Big promises come with challenges
The emergence of generative AI poses several challenges for enterprises.
The technology is relatively immature, making widespread adoption difficult. Experimenting with new technologies requires patience as it integrates with existing business processes and workflows and as business and IT leaders identify more and more optimal use cases. As generative AI becomes more popular, the number of best practices and lessons learned will grow. Many of these existing problems will be simplified as the technology matures and application providers integrate generative AI more deeply into their core products.
One of the key issues with generative AI is presenting information accurately, even if it isn’t. The inaccuracies produced by generative AI are not suitable for permanent applications in industries requiring high accuracy, such as pharmaceutical, healthcare, and financial services. Organizations must carefully select the most appropriate application areas and establish governance and oversight to mitigate risk.
Generative AI requires corporate guidelines for data privacy and access to sensitive information, including personal and sensitive data. Training a publicly accessible LLM with your own data increases the risk of unintended loss of intellectual property, as your training results are accessible to competitors. Organizations must balance the need for innovation with the risks associated with generative AI by implementing strong policies and carefully designed frameworks.
Strategies for growth and innovation
As more companies share best practices and implement governance policies, generative AI will bring new advances that harness the potential of technology. Companies interested in experimenting can create small test groups to explore generative AI, including how it can help reconfigure key business processes. Business and IT leaders must work together to evaluate feedback, understand lessons learned, and develop strategies that incorporate generative AI.
Generative AI is an evolving technology, and as more use cases become available, all industries will need to determine the safety nets to comply with specific industry regulations. Integrating generative AI into everyday enterprise applications requires aligning business processes and workflows.
Businesses may need to consider exploring generative AI now to harness its potential. Organizations should establish and communicate clear rules and guidelines for the proper use and protection of confidential information. This process fosters innovation while protecting the broader interests of the company, facilitating a smooth and stable transition of generative AI models within the company.
Sanjay Srivastava leads Genpact’s digital and technology business. He oversees the company’s offerings in artificial intelligence, analytics, automation, and digital technology services. Prior to joining Genpact, Sanjay worked as a tech entrepreneur, launching four startups from founding stage to sustainable product businesses. These companies were eventually acquired by Akamai, BMC, FIS, and Genpact individually. Sanjay has also held executive roles in other large companies such as Hewlett Packard, Akamai and SunGard (now he is FIS), overseeing product management, global sales, engineering and services for his business.
