What are some common AI/ML use cases? What use cases should business and IT leaders track as they shape a holistic process automation strategy? Let’s find out.
Why is AI/ML important?
Artificial intelligence (AI) refers to computer systems that mimic human thought and decision-making. Within AI, deep learning and machine learning play crucial roles. Deep learning uses neural networks to process large amounts of data and uncover patterns. Machine learning algorithms use mathematical formulas to learn from data and improve performance over time. All without human intervention.
Although generative AI tools like ChatGPT have gained significant attention, they were not the first widespread AI applications. Finance and investment trading organizations were early adopters of AI/ML and deep learning capabilities. They use these technologies for high-frequency trading—deciding when to buy or sell assets on stock markets and other exchanges. Other industries, such as life sciences, manufacturing, and utilities, have also embraced AI extensively. Adoption of artificial intelligence, machine learning, and deep learning technologies is expected to continue growing across sectors.
Business benefits of AI/ML
AI/ML offers businesses many advantages that go far beyond automating simple administrative tasks. It helps them save time, cut costs, and improve results. Here are some key benefits:
- Increased efficiency and productivity
AI/ML automates repetitive tasks. This frees up employees to focus on higher-value work. - Better decision-making
AI/ML analyzes vast amounts of data quickly. It provides actionable insights that help businesses make smarter decisions. - Enhanced customer experience
With AI/ML, companies can offer personalized experiences. This leads to higher customer satisfaction and more customer engagement. - Cost optimization
By automating repetitive tasks and improving business operations, AI/ML optimizes costs. Organizations can do more with the same resources. - Staying competitive
Using AI/ML helps businesses stay ahead of competitors. It enables innovation and keeps companies relevant in a fast-changing market.
Industries using AI/ML
Artificial intelligence and machine learning models are transforming industries, helping them innovate and outpace the competition. Here are the top industries leading the way:
Life sciences
AI/ML helps speed up processes around clinical trials and drug discovery. It also improves patient outcomes by supporting personalized treatments.
Finance
Banks use AI/ML for fraud detection and risk management. They also use it to analyze data to predict market trends and automate customer support.
Retail
AI/ML creates better shopping experiences with higher customer engagement. It powers personalized recommendations and optimizes supply chains. Retailers also use it to predict demand and manage inventory.
Manufacturing
In factories, AI/ML improves production efficiency. Predictive maintenance reduces downtime by providing an early warning system of worn-out machinery. Cognitive automation also helps speed up business processes.
Software technology
Tech companies rely on AI/ML for innovation. It powers virtual assistants to help with development tasks, improves cybersecurity with monitoring and detection, and drives advancements in software.
7 AI/ML use cases to watch.
Let’s examine a few use cases for AI/ML.
1. Document processing
Most organizations are drowning in documents. Whether it’s paper checks, electronic invoices, or bar code scans, companies often spend a lot of time processing documents. Intelligent document processing (IDP) allows companies to pull data from documents at scale without significant manual labor. AI tools can read documents using natural language processing, a technology that lets machines understand human language. Then, the AI can sort them into categories or generate responses. This saves organizations massive amounts of time and effort—and money. Plus, it reduces the potential for human error inherent in typing documents manually.
2. Financial fraud monitoring
One of the most widespread AI/ML concepts involves anomaly detection within data sets. When you train AI on a data set, the AI can develop a baseline of behaviors. Whenever something far outside the norm—an anomaly—occurs, the system can flag this anomaly for further analysis. Financial institutions use anomaly detection, during the crucial Know Your Customer (KYC) process to flag transactions for fraud or identity theft and monitor for cyber threats. For example, if someone’s credit card shows a large purchase made in a different country at the same time as a purchase in their hometown, the company could lock the card and run the purchase by the cardholder.
3. Credit and loan risk approval
Institutions in the finance sector can make extensive use of AI/ML capabilities to better understand creditworthiness and money lending risks. AI can analyze multiple data points like credit histories, credit utilization, and financial statements to understand whether it’s a safe bet to shell out credit to a potential borrower for a credit card, mortgage, or business loan. Plus, over time, AI can analyze data to spot potential patterns among default risks that human analysts might not see otherwise. This not only reduces risk but also makes processing applications far more efficient for loan officers and underwriters.
4. Medical imaging
One exciting area where we’ll see more use of AI/ML is the healthcare sector. For example, medical imaging professionals like radiographers and ultrasonographers will use more artificial intelligence to note potential issues in patients’ medical scans. Using machines to find potential issues and having a technician interpret the findings can be more accurate than relying on the erring human eye.
5. Customer service
One of the most interesting AI/ML use cases comes in the form of customer service. Chatbots powered by AI agents can use generative AI, predictive models, and natural language processing (NLP) to respond to customer requests and kick off workflows to resolve issues without human intervention. For example, agentic AI tools can process low-level refunds or craft email responses for human service agents to review. This saves service reps significant time on paperwork and customer interactions and improves the customer experience, allowing companies to improve long-term satisfaction.
6. Energy prediction
For decades, utilities have worked on smart grids. Devices like smart meters help utilities plan for peak usage times. AI and machine learning can analyze historical data like energy usage and weather patterns to better forecast demand. This helps utility operators predict energy usage and increase supply when demand grows without overloading the system (or helps them prepare for outages).
Plus, this new technology plays a major role in sustainability efforts, as AI can help optimize energy efficiency, usage, and distribution patterns and prevent waste. This also helps reduce costs for both utilities and industrial and residential customers.
7. Supply chain management.
The past few years have shown the fragility of global supply chains. Global events upturned typical supply/demand, leading to peaks in some areas and valleys in others. It’s hard to predict demand based on historical data alone—modern supply chain leaders need to use more sophisticated forecasting methods for supply chain management.
AI/ML capabilities let supply chain professionals better predict demand with real-time data across multiple data points to prevent shortfalls. They can also use AI to help with tasks like pricing, predicting weather patterns and routes for ships and transports, and building more responsive supply chain networks with their vendors and partners.
Challenges to implementing AI/ML
Security is one of the most significant challenges organizations face when adopting AI/ML technologies. These systems often require vast amounts of data to train algorithms effectively, including sensitive information that must be safeguarded against breaches. But with an AI process automation platform like Appian, you can mitigate these risks through advanced security measures like permissions and row-level security. These ensure employees and customers only access the data they are authorized to see. Additionally, Appian’s AI is designed to be stateless, meaning it processes data without retaining it, minimizing the risk of data leaks compared to systems that store user histories. This responsible AI approach strikes a balance between maintaining security and leveraging AI capabilities.
Another key aspect of security involves internal controls like user awareness training. By educating employees on avoiding the inclusion of sensitive data during AI interactions, organizations can significantly reduce the risk of breaches.
Beyond security, another challenge lies in integrating AI/ML into specific organizational processes. Many companies lack the platform necessary to effectively orchestrate AI across their operations. Appian’s data fabric technology lays the foundation by unifying and managing data, while process orchestration ensures AI integrates into workflows. Process is the frame for AI, providing the structure needed to harness AI’s potential responsibly and effectively.
AI/ML capabilities keep evolving
Want to learn more about how AI/ML fits into your overall automation strategy and what’s on the horizon for these technologies? To delve into emerging data and intelligent automation trends, hear from eight experts from AWS, KPMG, Deloitte, and more on the future of AI.
