At a time when the pace of innovation outstrips adoption, Generative Artificial Intelligence stands at the crossroads of skepticism and potential, offering enterprises not just a glimpse into the future of technology but a tangible toolkit to redefine the present.
A few days back, Adam Selipsky created some ripples. The CEO of Amazon Web Services said generative AI is valuable but “dramatically overhyped.”
While the hype is created for several reasons, some of it has served the purpose of bringing AI to the forefront of CXO discussions. It is fair to say that true enterprise use cases are still being discovered. The potential value of Generative AI is not merely a standalone or downstream application with a fluent user interface. Gen AI can serve as a powerful and complementary tool that works with other machine learning models and applications.
The widely adopted enterprise Generative AI use cases can be grouped into the following areas:
- Knowledge Management
- Productivity improvement
- Customer service and engagement
Streamlining Knowledge Management:
Leveraging Gen AI to enhance content integration and distribution, making it more accessible and useful for frontline staff.
The process of gathering, storing and distributing knowledge has been traditionally dominated by large content management system (CMS) vendors. However, such solutions have been bogged by two key issues which has limited the scope of usage for frontline staff:
- Outdated content
- Integration of the system with user’s workflow
Hence, one would frequently see information being sought separately via emails, chat etc.
With Generative AI technologies the entire process of integrating new / modified content and distributing the same in a personalized manner to the end user has undergone a dramatic turnaround. Large Language Models have brought in the ability to:
- Quickly process new content.
- Extract relevant information and summarize.
- Provide the information to the business user via custom summaries and searches that are integrated with the user’s work process.
An example of such a system that we have recently implemented is for market analysts to quickly integrate information like reports and analysts calls, that is published by listed companies.
Read more: The Ultimate Enterprise Playbook (guide) for Generative AI Implementation and Adoption
Boosting Productivity Across Tasks:
The role of Gen AI in automating and improving tasks involving creativity, judgment, and non-rule-based actions, aiming at significant productivity enhancements.
Generative AI has the potential to dramatically improve human productivity in tasks that involve creativity, judgment and action which is not always rule based.
There are several examples of content creation in marketing like image creation, automated product reviews, blog posts, and so on. However, there are several pitfalls in using Gen AI based content directly in such use cases. The HBR article on marketing uses cases talks about some of those like inaccuracy (confabulation), user dissatisfaction, copyright infringement, etc.
The most compelling and meaningful uses cases that we have seen which avoid such pitfalls are those which enhance human productivity by creating a tool that helps in quickly navigating through large volumes of content. One of the uses cases that we have implemented is an assessment assistant for schoolteachers to grade student answer scripts.
The tool is a huge productivity improvement for teachers and via steps 1 (Anchoring) and 3 (creating an output that can be further refined by humans) we avoided the typical pitfalls of using Generative AI for creative use cases.
Another simple but effective productivity solution is summarizing a meeting recording and generating call to action automatically using a Large Language Model.
Enhancing Customer Service and Engagement:
Gen AI can improve the quality of customer interactions by providing service agents with deeper insights and faster resolution capabilities.
Augmenting service experience delivered by the customer service agent requires two key ingredients:
- Understanding the customer context
- Quickly resolving the issue
By adopting Generative AI based issue classification, sentiment analysis across various customers, agent productivity and effectiveness can be dramatically improved.
Generating Issue and Product 360 views involves social media listening of X feeds, Google reviews tracking the product handles to identify and classify issues. These are used to build the knowledge base around the issues and customer sentiment for the agent to effectively engage with the customer and resolve the issue.
As we navigate the evolving landscape of technology, Generative AI offers practical solutions for improving efficiency, enhancing customer service, and driving innovation within enterprises. It’s clear that adopting Gen AI isn’t just about keeping up with technology trends — it’s about setting your business apart and addressing real-world challenges effectively.
Our experience shows starting small by testing and validating a few use cases with quick proof of concepts (PoC) often works well before committing large-scale investments, time, and resources.
Adopting Gen AI is a step towards leveraging technology to create tangible business value, ensuring your enterprise remains at the forefront of innovation.
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Shivakumar is a keen follower of scientific trends and an Asimov fan. He believes solid execution is the key to the success of any strategy and is focused on building a world-class data science team at Prescience. He has a B.Tech from IIT Delhi and an MBA from IIM Lucknow, with 20+ years of experience in the technology space.
