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5 AI Business Use Cases to Inspire You

Roland Alston, Appian
March 28, 2024

With the explosion of artificial intelligence (AI), innovative companies are harnessing enterprise AI at scale for a variety of business use cases, including repetitive task automation, product and service development, supply chain management, customer service operations, decision-making systems, and more. Today, a remarkable 73% of US companies have already adopted AI in at least some areas of their business, according to a recent survey by PwC, and more than half of these companies have implemented GenAI use cases.

In this blog, we’ll look at AI business use cases, along with some real-world examples that showcase practical applications of AI.

1. Enhancing automation for repetitive tasks.

AI can make routine tasks in a workflow more efficient. By enhancing and combining digital resources like intelligent document processing, APIs, and robotic process automation, AI can make automation make workflows more effective with sophisticated cognitive functions. Different from conventional automation that only performs set tasks, intelligent automation enables machines to independently think, learn, and decide, all under human supervision.

AI Use Case Example #1

As the insurance industry grapples with the inefficiencies plaguing traditional underwriting processes, the integration of AI-powered intelligent automation is revolutionizing the underwriting playbook. Consider this: underwriters spend as much as 40% of their time on manual tasks (such as gathering and entering data for submissions, renewals, etc.), which is on track to represent an industry-wide efficiency loss of up to $160 billion by 2027. Contrast that with AI-powered intelligent automation, which reduces manual workflows and streamlines the policy-issuing process from weeks to days or even minutes.

Just look at the success story of a leading insurer, CNA, which leveraged the power of the Appian Platform to achieve faster and more effective underwriting processes and system modernization. CNA developed ComPass™, a game-changing application built on Appian, that connects hundreds of strategic business partners in 164 countries.

The application enables underwriters to track the status of policies, potential issues, timing, claims, and payment status in real time from any location. It also provides a 60% time savings, allowing CNA to manage their end-to-end processes more efficiently and connect with their partners worldwide.

Now that we've highlighted the transformative impact of AI-powered automation in insurance underwriting, let’s explore other noteworthy ways AI is transforming insurance operations.

2. Ushering in a new era of product and service development.

AI is reshaping how companies develop products and services by streamlining the development process, speeding up how quickly and efficiently products get to market. AI also plays a crucial role in customizing products and services. It uses algorithms to adjust offerings to fit individual customer preferences, helping businesses connect better with their audience and increasing customer loyalty and value over time. Finally, AI can help companies align their digital strategies with evolving consumer expectations through its data analysis capabilities. AI tools can sift through vast amounts of data, identifying important trends, uncovering new opportunities, and predicting customer needs.

AI Use Case Example #2

Now that we've explored the benefits of using AI in product and service development, let's focus on how real-world companies harness this technology to innovate and compete.

Leverages AI algorithms to analyze user viewing habits, preferences, and interactions to recommend personalized content. This use case enhances customer satisfaction and retention while driving engagement and revenue.

Uses natural language processing to categorize songs and curate personalized playlists and recommendations based on user listening history and preferences. This enhances the user experience, keeping users engaged and loyal to the platform.

Employs AI in its autonomous driving technology, using machine learning algorithms to improve vehicle performance, safety, and navigation. Tesla continuously analyzes real-world driving data to enhance its vehicles' capabilities and user experience.

These AI examples are delivering next-level products and services, but the use cases don't stop there. Let's explore how companies are harnessing AI to drive efficiency, resilience, and agility in another critical area of business operations: supply chain management.

3. Increasing supply chain resilience.

In the fast-paced world of today's digital economy, AI-powered tools are transforming supply chain management. These platforms offer a range of capabilities from track and trace functions to claims investigation and compliance, harmonizing old systems and orchestrating workflows to boost both efficiency and cost-effectiveness.

For tracking and tracing, companies in manufacturing use AI as a sort of digital control tower. It helps them monitor the exact locations of shipments and ensures the quality, authenticity, and safety of products while they're on the move. This use of AI automation helps maintain product standards and protects against fraud and counterfeiting.

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When it comes to claims investigation and compliance, top supermarket chains are adopting AI automation to make the process of investigating claims with distributors smoother. By automating compliance team tasks, including getting rid of error-prone methods like spreadsheet tracking, these companies are significantly cutting down the time it takes to process claims—taking the process from hours to just minutes. This not only saves countless hours each year but also boosts operational efficiency and customer satisfaction.

AI Use Case Example #3

Amadori, a leader in the Italian agriculture-food sector, uses Appian to simplify and modernize work in its supply chain. Using Appian's AI automation and data fabric capabilities for fleet management, Amadori reduced the lead time between maintenance and order generation by 466%. As a result, what once took two weeks now takes about three days. Amadori’s real-world success story underscores the immense potential of AI-powered solutions in streamlining supply chain operations.

4. Revolutionizing customer service.

AI-enabled customer service is leading the way in customer experience improvement. It's all about giving customers a tailored experience, making their interactions smoother, and cutting down on service costs. AI solves problems quickly by automating the handling and directing of customer inquiries, ensuring they get to the right department or person fast. 

Here are four more ways AI is changing the game in customer service that could really benefit your business:

  • AI can foresee issues by looking at past data, helping you spot and sort out problems early on.

  • AI lessens the load of manual tasks by sorting and categorizing info from documents and emails, freeing up agents to give a more personalized service.

  • AI provides crucial insights from analyzing data, which helps customer service agents tailor recommendations based on customer preferences and feelings, boosting satisfaction.

  • AI-driven chatbots and virtual assistants offer quick, round-the-clock answers, improving response times and making sure customers get help when they need it.

AI Use Case Example #4

When Leroy Merlin, the third largest home improvement retailer in the world, needed to manage a sudden increase in eCommerce, in-store orders, and refund and return requests, they turned to the Appian Platform to incorporate intelligent automation and intelligent document processing (IDP) powered by AI. With Appian's AI and automation capabilities, Leroy Merlin took their refund and return process from 15 days to 1.5–2 days.

5. Complex decision navigation.

How to prepare for disruption, enhance customer experiences, and achieve more with fewer resources are crucial questions in today's business landscape. Operational decision-making is a key, yet often challenging, aspect of leadership.

This is where AI comes into play. By delving into historical contexts and finding patterns in huge datasets, AI equips decision-makers with the ability to quickly extract insights and make well-informed decisions on the fly.

However, the common issue of data silos can block the smooth flow of information, impacting a company's capacity to make swift and informed decisions. This is where the concept of data fabric becomes vital. Data fabric is a technology that seamlessly connects data across various storage spaces, like databases, the cloud, and other enterprise systems. It allows AI to access all data centrally without needing to move it around, effectively breaking down data silos. With this, AI can fully harness historical data, providing executives with the tools to make strategic decisions with outstanding accuracy and depth of insight.

For example, in Appian AI Copilot, end users can harness data in a simple builder interface with aggregation, filtering, sorting, and formatting features. Once a report is set up, users can use AI Copilot to get deeper AI-generated insights from the data. This digital tool uses the power of generative AI to help users gain new insights from reports, even suggesting the next steps to address business needs based on report data.

AI Use Case Example #5

National Westminster Bank (NatWest), the UK’s largest business bank, found that before departments could even start a project or launch a product or application, they had to go through multiple layers of policy checks and approvals—taking up to 73 days. They built a new, AI-powered change-risk management solution, underpinned by a data fabric. They consolidated 14 separate processes into one automated and optimized end-to-end process that has reduced change-risk time down to just minutes.

Choosing an AI business use case.

As your enterprise looks for areas where adopting AI could make you more efficient, choosing a use case is the right place to start. Consider this guidance from George Casey, Principal, Data Scientist at RSM US LLP: “It’s not about the technology—all innovation starts with a problem to be solved. When you look at opportunity that way, it’s important to focus on the ‘why?’ Why would we do this? Why are we trying to solve a particular problem? What's in it for us? Where is there value? Answer those, and then we can start getting to the how.”

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