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Today’s AI use cases are an entirely new breed from those deployed just a few years ago. For one, they’re significantly more sophisticated. The rise of generative AI has unlocked a world of opportunities that were all but unimaginable until now. There are also more of them. Exponentially more. It’s easy to feel overwhelmed by the sheer number of AI use cases in various stages of development and deployment.
Navigating this ocean of AI use cases requires immense focus on behalf of enterprise leaders. It also requires considerable foresight. As the first step in an enterprise’s digital transformation, choosing which AI use cases make the most sense is critical. However, it’s not just about delivering immediate value (although that’s important too). In today’s evolving landscape, enterprises must also plan for future AI use cases, some of which may not even exist yet.
So, how can executives make sure they’re making the right decisions regarding current—and future—AI use cases? By looking at how other enterprises are deploying AI across their organizations. Let’s take a closer look at several real AI use cases that are delivering real ROI right now…
AI use cases: then and now
Before we mention any AI use case examples, we need to address the 800-pound gorilla in the room: generative AI. Since its mainstream debut two years ago, generative AI has stolen headlines and dominated boardroom meetings worldwide. That’s because generative AI isn’t just another capability or dimension of AI but a whole new way of thinking about AI itself. For the first time, AI can do more than simply analyze and automate—it can create.
Until now, most enterprises built their AI use cases solely around optimizing processes and analyzing data. The goal was simple: drive efficiency while reducing costs. Then came generative AI and the realization that AI could do more: it could enhance human capabilities. By generating rich, accurate insights gleaned from vast stores of data, generative AI can help enterprise employees work better. Instead of automating mundane tasks and crunching numbers, enterprises are now building AI use cases around the needs of their human workforce. And they’re seeing a spike in service quality, performance and value as a result.
Real-word examples of AI use cases
AI use cases—and generative AI use cases in particular—can be hard to define. That’s because different enterprises have different needs, both organizationally and by industry. However, for the sake of simplicity, most AI use cases can fit broadly into one of three buckets based on their intended usage:
- Customer-facing AI use cases
- Creative AI use cases
- Technical AI use cases
Customer-facing AI use cases
These AI use cases impact the customer experience (CX) either directly or indirectly. CX use cases are a popular entry point for enterprises beginning their AI journey. They’re an excellent source of high-quality customer data, which AI engines can convert into valuable—and actionable—insights. Examples of customer-facing AI use cases include:
Virtual customer service
Today’s chatbots and intelligent virtual assistants are nothing like those of yore. Combining natural language processing (NLP) and machine learning, these AI-powered self-service solutions not only offer more human-like interactions; they also provide data for other customer-facing AI applications.
Personalized customer journeys
The more data customers provide, the more AI can tailor future interactions to their unique preferences. For example, a streaming service might recommend a song or movie based on a user’s playlist or search history. Meanwhile, a retailer might remind a customer to reorder a frequently purchased product.
Cross-selling and up-selling
AI can also identify adjacent products a customer may be interested in based on previous interactions. For instance, a telecom subscriber may receive an automated text if a new generation of their mobile device is available or if they are eligible for a service upgrade.
In-call agent guidance
AI can assist customer service representatives during complex or high-effort calls. Acting as an in-call “copilot”, AI-powered agent assist software can auto-populate data entry forms and guide agents toward next-best actions using AI-generated on-screen prompts based on real-time conversation feedback.
Better promise management
In addition to guiding agents through live interactions, AI can help lessen the burden (and inaccuracy) of aftercall work (ACW). This includes capturing any promises made during a call and ensuring that the appropriate follow-up actions are taken, such as scheduling a future appointment.
Improved knowledge search
Advanced knowledge AI, which combines NLP, knowledge extraction and cognitive search, makes it easier for customers (and agents) to find information that’s relevant to their query. Knowledge AI has been used to improve website search capabilities, agent knowledge management systems and more.
Real-World AI Use Case: Self-Service Automation
Using AI, State Collection Service, a national leader in receivables management, was able to:
- Automate collection of $2M (within the first two quarters of operation)
- Achieve 25% call containment
- Reduce abandonment rates by 3-5%
Creative AI use cases
Some of the most exciting generative AI use cases are those involving content creation. Generative AI applications, like ChatGPT and Dall-E, can produce high-caliber text and visual content for a variety of purposes. In the enterprise space, multimodal AI platforms are enabling large organizations to build, test and deploy a whole new range of AI use cases around specific domains. Examples of these creative AI use cases include:
Marketing content generation
The same technology that’s enabling companies to create personalized customer journeys is also being used to create targeted marketing content. Online retailers, for example, are starting to use generative AI to create emails, ads and other marketing collateral that are tailored to microsegments and even individual shoppers.
Editorial content creation
Another creative AI use case that’s gaining popularity is generating editorial content, such as blogs and other topical articles. Content managers can train AI engines on a company’s voice, tone and target audience and then direct the program to produce on-brand editorial content on any number of subjects.
SEO optimization
In the same vein as editorial content creation, generative AI can be used to create SEO-optimized web content. AI engines can analyze top-ranking SEO pages for keyword usage and placement and generate optimized content in the company’s voice and tone.
Technical AI use cases
Unlike creative or customer-facing AI use cases, enterprises generally build technical AI use cases around processes or operations that are ripe for optimization or in need of improvement. These applications typically work “behind the scenes,” quietly performing critical functions and driving backend efficiencies. Examples of technical AI use cases include:
Compliance
Enterprises in highly regulated industries, like finance and healthcare, are increasingly turning to AI to shoulder their heavy compliance workload. For example, insurance providers are using AI to analyze consumer interactions to ensure that agents comply with rules regarding aggressive marketing, legal disclosures and more.
Fraud detection
Among the fastest growing technical AI use cases are those involving fraud detection. With so much data being stored and shared digitally, companies are under increased pressure to beef up their customer protection capabilities. AI can analyze scores of customer interactions for anomalies and other red flags that may indicate fraudulent activity.
Privacy and data security
Just as companies are using AI to detect potential signs of fraud, they’re also using it to strengthen their customer privacy and data security protocols. Popular examples include voice recognition and AI-generated authentication applications.
Automated coding
Performed manually, coding can be a time- and labor-intensive task. However, with generative AI, coders can now develop a repeatable coding command that transforms simple, plain-English input into executable code.
Real-World AI Use Case: Process Compliance
During Medicare and Medicaid open enrollment, this healthcare services company used AI to:
- Ensure its contact center agents complied with CMS standards
- Streamline member authentication and eligibility verification
- Reduce scripting time from months to weeks
Industry AI use cases
When it comes to enterprise AI use cases, there is no “one size fits all.” Different industries—and even different businesses within those industries—have different needs. The beauty of AI (and generative AI in particular) is its ability to tailor enterprise applications to a company’s unique needs. While most AI use cases will likely fit into one of the three “buckets” mentioned above, variations abound at the individual business level. Here are a few examples of industry AI use cases being used by real companies right now:
Finance
Leading credit union PSCU used AI to unify the multiple banking systems in use by its contact center into a simplified, single view, slashing customer hold times and average handle times by double digits.
Healthcare
MMM Healthcare, a sister company of InnovaCare, used AI to integrate 12 core patient care systems into a unified application that provides service representatives with a 360-degree patient view, including interaction history, related healthcare gaps and suggested next-best actions.
Insurance
Leading auto insurer, The General, used AI to create a unified desktop to help its customer service representatives navigate relevant job aids and workflows. Their AI use case also benefited new hires with a 35% reduction in time to proficiency.
Telecommunications
Using AI, DirecTV increased sales conversions with real-time sales team guidance and “just in time” recommendations pulled from rich campaign management software data.
Travel & Hospitality
Priceline.com used AI to create more empathetic and personalized traveler experiences. The online travel agency’s AI use case enabled non-IT staff to quickly and easily build call flows based on relevant customer data.
Technology
Founded by Microsoft Azure engineer, Venkat Thiruvengadam, software company DuploCloud uses AI in its virtual sales engagements to gauge buyer engagement and sentiment and to build sellers’ emotional intelligence skills through targeted coaching.
Retail
Shufersal, Israel’s largest supermarket chain, used AI to create a digital customer service concierge that connected their mobile app, website, IVR, print receipts (QR codes) and messaging (WhatsApp) channels.
Logistics
An international service provider for UPS used AI to allow customers to track package deliveries, make alternate arrangements, such as pick-up at store, route to different locations and make payments remotely.
Utilities
Puget Sound Energy built an AI use case around visual customer service, enabling utility customers to start, stop or transfer service, report/check the status of an outage and pay their bills remotely.
Education
The University of Derby uses AI to assist seasonal staff during Clearing, a service in the UK that helps place students at higher education institutions. By integrating AI in-call guidance with its Zendesk platform, the university is able to achieve 100% compliance with the defined Clearing procedure.
Gaming & Entertainment
One of the largest casino and hotel companies in the U.S. turned to AI to optimize its sportsbooking platform, increasing user adoption (36%) and satisfaction (87%) with AI-powered analytics and scalable, 24/7 player support.
Choosing the right AI use cases
For enterprises starting (or currently undergoing) their digital transformation, choosing which AI use cases to focus on is vital to ensuring the project’s success. While different enterprises will have different goals and priorities, the “right” AI use cases will be those that offer the biggest impact and the longest lifetime value. To achieve both—fast time-to-value and room for future growth—organizations should start with a trusted enterprise AI and data platform as a foundation for all use case development and deployment. This foundational approach offers multiple benefits, including:
- The ability to unify all enterprise knowledge, data and AI models under one umbrella
- Total data governance and sovereignty, for complete data access control
- The power to convert unstructured sources into AI-ready data and knowledge
- The freedom to create domain-specific generative AI models and agents
Uniphore’s multimodal AI and data platform offers enterprises the ultimate foundation to build, test, deploy and refine their AI use cases—including generative AI use cases. Our AI engine room transforms data from hundreds of enterprise sources into a central powerhouse of AI-ready knowledge. From here, enterprises can build limitless, domain-specific AI applications, AI agents and copilots leveraging our pretrained models or using their own. The result: an AI-native enterprise ecosystem that accelerates the AI journey, enabling businesses to realize the value of their AI use cases quickly while also allowing them to scale—and evolve—with the times.
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