Business Intelligence Buyer's Guide

Making Generative AI Work for Your Business

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Qlik‘s Chief Strategy Officer James Fisher offers commentary on making generative AI work for your business via these four key practices.

As we explored in my first article on generative AI, there is an incredible push and pull going on in business today related to the hype of this new technology. On the one hand, organizations are being bombarded with new information about generative AI and many understand that it could provide a stark, first-mover competitive advantage. On the other hand, adoption of new technology is difficult and leaders are cautious about going too fast due to risk, governance and trust concerns.

While we are definitely in a hype cycle with generative AI, it is not the only kind of AI that is beneficial and worth deploying. In fact, other forms of AI have been around much longer and are already an integral part of many data and analytics solutions. As McKinsey notes, “other applications of AI…continue to account for the majority of the overall potential value of AI.” My company, Qlik, first introduced AI into our products five years ago with natural language processing and generation. Today, with our holistic set of AI solutions, Qlik Staige, customers can innovate and move faster by making secure and governed AI part of everything they can do with our technology. This includes experimenting with and implementing generative AI models to develop AI-powered predictions. So, while generative AI is today’s hot topic, we shouldn’t lose sight of the fact that other forms of AI will continue to be very valuable, and if anything, new generative AI tools can actually complement traditional forms of the technology that you’re likely already using in some form.

This brings me back to the ultimate question I hear from business leaders all the time: With the influx of AI-powered solutions on the market, is it even possible to cut through all the noise and drive value for your business with AI? My answer is always a resounding yes. To start, below, I’ll outline three great applications where you can benefit from combining generative AI with current data and analytics strategies.

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Making Generative AI Work for Your Business

Ask Real-Time Questions, Get Real-Time Answers

The ability to ask questions and get answers in real-time is a very notable use case for integrating generative AI into your data and analytics efforts. The ideal scenario would be business users asking any question they want and getting contextually relevant content, with the most up-to-date responses available, from AI. Even better is combining this with small subsets of data from an analytics platform in real-time to greatly enrich the context and value of a business’ internal analytics. For example, a customer service representative could generate relevant information about the selections they make based on their real-time interaction with the customer. This arms a business with additional and more effective ways to support its customers, driving home a “customer-first” mindset, which is proven to drive more business value. PwC underlined the importance of this in one of its customer-intelligence deep dives, noting that a shocking 46 percent of all consumers will abandon a brand if its employees do not seem knowledgeable.

Implement Sentiment Analysis to Supercharge Customer Service

Another strong generative AI integration use case is sentiment analysis – a process to determine if a piece of text, like a sentence or a social media post, expresses positive, negative or neutral feelings. In best-case scenarios, businesses would be able to enrich text-based data sets, like product reviews, surveys or service tickets, by using AI to generate sentiment analysis. For example, once a service ticket is identified as “positive” or “negative,” the customer service representative would be able to formulate an appropriate response. Organizations that take it one step further could also implement automation to formulate generative AI-suggested responses to the negative tickets, then feed those suggested responses directly back into a CRM, service database or an analytics application for the customer service representative or other end-user to utilize. This equips organizations with an easier way to diffuse and resolve customer issues more seamlessly, always good for the bottom line.

Use Natural Language to Drive High-Value Insights

Pending the capabilities of a businesses’ analytics platform, one of the best ways I’ve seen to integrate generative AI is through augmenting analytics with natural language and incorporating third-party data into existing data models. In this case, business users would be able to ask specific questions in natural language of their data and receive answers back from their platform. A step further would allow enterprises to also augment existing data and KPIs with a narrative summary. For example, audio electronics company Harman uses ChatGPT on top of its analytics platform to use natural language to drive high value insights with the Qlik analytics engine. This enables analysts to run queries completely on chat searches and receive high-value answers and new prompts for additional analysis.

 The Sky is the Limit

Generative AI is already enabling businesses by bolstering analytics use across the enterprise and helping data experts be more efficient in their work. It spreads data literacy more seamlessly and encourages even novices to dig in. Ultimately, at this stage, new use cases for generative AI in data and analytics are discovered every day. AI has long held the potential to transform business: and generative AI is certainly helping to move the needle forward.

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