3 Generative AI Use Cases in Information Services

AI is ubiquitous across virtually every industry. But what does this mean for your business specifically?

Training a generative AI model—whether from-scratch, fine-tuning, or prompt-tuning—requires AI-ready data: accurate, well-organized, and easily accessible. Information services companies, then, have a unique level of AI readiness which gives them a highly competitive edge.

But the more you wait to seize transformational AI opportunities, the more that edge will evaporate—and open your business to disruption. Read on to learn why now is the time to invest in generative AI, and three use cases that will add value to your user base and business.

How should information services companies think about AI?

The potential for generative AI is nothing short of transformational. It will change the design of products and experiences, and transform, or disrupt, business models. And while customer expectations for the technology will certainly change, the technology itself is here to stay.

Some companies are jumping in head first, while others are taking the “wait and see” approach. So which approach is correct?

Companies who adopt any technology for its own sake are rarely successful. That’s because users rarely use a product simply because of the technology, but the value it provides them. Until you deeply understand the problem you’re trying to solve, you risk building a bad product—millions of dollars invested and nothing to show for it.

But the “wait and see” approach is equally dangerous. The reality is that AI is already ubiquitous; there’s no going back. To succeed in the modern digital economy, information services companies must take AI seriously. Otherwise, you’ll fall behind.

So what’s the best path forward? Just like with any other technology, it starts with customer-centricity: understanding how the technology will drive user and business value.

If you can answer that question and leverage the right tools to build innovative and value-driving products, you could open up the door to numerous revenue opportunities that will help to retain, upsell, and close more business.

Here are three specific use cases that leverage generative AI capabilities to drive user and business value.

Use case #1: Transformational impact on product design

Generative AI will transform how we think about product design. It simplifies search and enables faster delivery of insights at the customer’s point of need.

As recently discussed with Bloomberg Industry Group CEO Josh Eastright on The Innovation Engine Podcast, the evolution of the customer experience requires companies to deliver insights and information at the customer’s point of need. Or, as Bob Moesta, President & CEO of The Rewired Group put it, providing information at the customer’s “point of struggle.”

Understanding the diverse ways in which data is consumed and distributed presents radical potential—and need for change—among data providers.

Information services leaders can take this opportunity to co-create with customers, discovering new and better ways to serve user needs, save time, and create value. Executives should empower cross-discipline teams to conduct user research, create design prototypes, and validate concepts with users, then build technical proof of concepts (POCs) for AI-enabled products. Of course, you will want to train models to ensure accuracy of data.

With this approach, leading companies can explore the “art of the possible” and go on the journey of discovery with customers, designing for simplicity, speed, and intuitiveness.

Challenge yourself and your organization to pursue “game changing” innovation—don’t simply improve on what you already have today. This approach will let you create new revenue streams and proactively lead, rather than react to disruption.

Use case #2: Accelerating data & insights generation

Think of a ChatGPT use case, but on a much larger and more sophisticated level. Users prompt the AI engine with a question, and the AI pulls from an existing data set to generate opportunities for cross-sell, deeper connections, enriched answers, and more comprehensive insights than before.

It should be obvious that this use case only drives value if the dataset used in generating the response is solid. What the AI offers, then, is the ability to surface relevant information from that dataset faster, more accurately, and in a user-friendly way:

• Automatically pulling data from multiple sources to provide enriched insights and more comprehensive outputs. This capability empowers sales to cross-sell data sets that provide more nuanced insights, deeper connections, and comprehensive answers.
• Analyze queries to determine user sentiment and intent—even if not explicitly stated
• Generate outputs to include not only requested information, but additional data that provides context and enhances interpretation
• Engage in clarifying questions if user queries are unclear
• Track user engagement to optimize content UX

These capabilities require you to build an “AI ready” technology stack with a data architecture in the cloud, model operations, a unified platform, data mesh, or data store to democratize access to information, APIs to make data consumable internally and externally – with appropriate data governance – and platform engineering strategy (the evolution of of DevOps) to better serve data scientists, AI engineers, and innovation teams.

Specifically, the unified data platform is a common theme emerging across leaders in the information services sector. By creating such a platform, organizations can take different, disparate data that once was siloed across the organization and make it available for building better products – and accelerate innovation.

Use case #3: Answering complex questions

Companies are charged with answering increasingly complex questions. For example, today’s volatile market results in the need for more sophisticated risk management.

Traditionally, companies have managed different categories of risk individually, knowing that they’re all interconnected but not having a good system for seeing how one impacts the other. New AI-powered risk models are changing the game by leveraging different types of data sets from multiple sources to offer unique benefits:

• Superior forecasting accuracy. Traditional regression models don’t adequately capture nonlinear relationships between macroeconomic trends and the company’s internal financials. AI models are able to capture these relationships, providing a more comprehensive view of risk.
• Optimized variable selection process. Feature & variable extraction processes often take up significant time. AI algorithms can process huge volumes of data and extract multiple variables, offering a wide coverage of risk factors. This leads to robust, data-driven risk models for stress testing.
• Richer data segmentation. Appropriate granularity and segmentation are critical to deal with changing portfolio composition. With AI, both distance- and density-based approaches for clustering lead to higher modeling accuracy and explanatory power.

By providing more accurate and sophisticated answers to complex questions, AI-powered products increase the value information services companies can offer to their customers. This, in turn, can be leveraged as premium content, opening up new revenue streams and materially contributing to the bottom line.

Final thoughts on generative AI in information services

Generative AI is here to stay, even though the specific application of the technology has yet to be solidified. Data and information services companies, because of unique access to proprietary data and expertise in your markets, have a competitive advantage in this space.

But if you wait too long, that competitive advantage will become a disadvantage. So don’t wait to start delivering transformational change to your user base.

The leaders in information services will reimagine their products and experiences—exploring the art of the possible—and achieve game-changing innovation that better serve customers, improve top-line results, and increase operational efficiency. Companies that pursue technology for technology’s sake without taking a customer centric approach will waste time and money, and risk disruption.

Make sure you’re working with a partner who can help you not only master the technology, but leverage it to provide the most value to your user at their point of need. Contact 3Pillar Global to get started today.