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Become More Efficient with These 6 Applications of AI

Roland Alston, Appian
January 5, 2024

Businesses are in a persistent productivity slump that could last through 2030, according to a 2023 World Bank study. The tech boom that powered innovation and growth over the last three decades is fading. Many companies are counting on artificial intelligence (AI) to boost operational efficiency and counteract these alarming trends.

This article explores seven noteworthy AI applications that open up new possibilities for organizations to streamline processes, reduce costs, and continue unlocking next-level efficiency instead of slowing down.

1. Predictive analytics: anticipating efficiency.

Predictive analytics, powered by AI, equips businesses with the ability to anticipate future trends, customer behavior, and market dynamics with remarkable accuracy. This capability is a game-changer when it comes to taking operational efficiency cost savings to the next level:

  • Foresight in planning: Predictive analytics forecasts future trends, enabling businesses to prepare for shifts in demand, changes in customer preferences, and evolving market conditions. This proactive approach enhances operational efficiency.

  • Optimized resource allocation: With the power of prediction, businesses can allocate resources efficiently, reducing waste and optimizing their operations. This results in cost savings and better resource management.

  • Proactive problem resolution: Predictive analytics can be used to not only forecast positive outcomes but also to identify potential issues before they become critical. This proactive approach allows businesses to take preventative measures, minimizing disruptions and maximizing efficiency.

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2. Process automation: elevating efficiency to new heights.

Routine tasks, though essential, can be incredibly time-consuming and monotonous. Processes such as data entry, invoice processing, and email sorting are prime examples. They are essential for day-to-day operations but can eat up precious human resources. Many companies are experimenting with artificial intelligence applications, using them as standalone technology for specialized use cases or embedding them in applications that handle core business processes with human intervention.

Consider these three benefits of applying AI automation:

  • Cost savings: One of the most immediate and tangible benefits of AI-driven automation is cost savings. AI-driven automation reduces labor costs, minimizing errors and resulting in substantial cost savings over time. 

  • Error reduction: Another crucial aspect of routine task automation is the remarkable reduction in errors in yields. Eliminating human error from processes like data entry or invoice processing leads to smoother operations and enhanced efficiency. 

  • Enhanced productivity: As routine tasks are automated, employees are liberated from mundane and repetitive work. The overall effect is a boost in productivity and efficiency across business functions.

[ Deploying AI doesn't have to come at the cost of data privacy. Learn why: Implementing Private AI: A Practical Guide. ]

3. Data analysis and insights: precision for informed decision-making.

Data analysis is the bedrock of operational efficiency and informed decision-making in the modern business landscape. It provides the necessary insights to streamline critical processes, understand customer behavior, identify market trends, and make strategic choices. AI plays a pivotal role in enhancing the data analysis process, making it more efficient and effective:

  • Real-time insights: One of the most significant advantages of AI-driven data analysis is its ability to provide real-time insights. This means businesses can access critical information about customer behavior, market dynamics, and operational trends as they happen, enabling quick and informed decision-making. 

  • Accurate predictions: Predictive analytics, powered by AI, furthers data analysis by forecasting future trends and events. Accurate predictions allow businesses to respond proactively to changes in the market, ultimately enhancing operational efficiency.

4. Personalized customer experience: efficiency through tailored engagement.

Data analytics and AI empower businesses to analyze customer feedback and behavior, enabling personalized experiences, tailored recommendations, and predictive insights that drive loyalty and long-term customer value. But personalization isn't just about improving customer experiences; it's also a potent driver of efficiency in customer satisfaction:

  • Improved engagement: Personalized experiences lead to improved customer engagement and satisfaction. Efficient, friction-free interactions lead to satisfied customers, contributing to loyalty and repeat business.

  • Automated personalization: AI achieves personalization on a massive scale by analyzing extensive datasets and customizing offerings for each customer. This automation ensures that personalization remains efficient and scalable, even when dealing with a large customer base.

  • Efficient marketing: Personalized marketing campaigns generated by AI allow businesses to deliver precisely targeted messages to the right audience at the right time. This precision minimizes ad spend waste and enhances the efficiency of marketing efforts.

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5. Process mining: efficiency at the heart of business operations.

Process mining is the cornerstone of operational efficiency and profoundly impacts a business's ability to deliver products and services effectively. It's designed to help organizations discover, monitor, and improve business processes by streamlining workflows, enhancing resource allocation, and making sure each process contributes to overall operational efficiency and effectiveness.

How Process Mining Works

By pinpointing bottlenecks and inefficiencies, process mining empowers organizations to channel their optimization efforts into areas where they can have the most significant impact, resulting in streamlined processes and heightened efficiency. Embracing AI-driven process mining is no longer a choice; it's a strategic necessity for organizations seeking to remain agile and efficient in the age of AI. Process mining will give you tools for:

  • Identifying bottlenecks: Traditional methods of bottleneck identification involve manual observation and data analysis, which can be time-consuming and inaccurate. AI, on the other hand, excels at this task. Machine learning algorithms can analyze vast amounts of process data, identifying patterns and anomalies that may not be apparent to human observers.

  • Automating tasks: Many processes involve repetitive, error-prone tasks like data entry and document processing. AI excels at automating these tasks, allowing employees to focus on more creative work. 

Continuous monitoring: Efficient processes demand continuous monitoring and optimization. AI systems continuously monitor processes, providing real-time insights and enabling immediate adjustments for maximum efficiency. For example, AI-driven sensors can detect anomalies in manufacturing, enabling proactive maintenance to reduce downtime and boost efficiency.

Process mining overview.

6. Managing risk and compliance in financial services.

In today's ever-changing regulatory environment, financial institutions face complex compliance, customer service, and fraud detection requirements, and they’re at risk of significant penalties for non-compliance. AI applications are particularly well-suited for optimizing compliance in financial services, including credit assessment, KYC, anti-money laundering, fraud detection, and risk management. That said, let's delve into specific applications that highlight where AI is making a significant impact:

  • Fraud detection: protect against financial losses. AI-powered fraud filters use advanced algorithms to detect unusual patterns or behaviors that may indicate fraudulent activities. These systems can swiftly identify and respond to threats, protecting organizations from financial losses and reputational damage.

  • Cybersecurity: safeguard against threats. In an increasingly digital world, AI plays a crucial role in cybersecurity. AI-driven security systems can detect and respond to cyber threats in real-time, protecting sensitive data and minimizing the impact of security breaches.

  • Regulatory compliance: meet complex requirements. AI, assisted by human intervention, helps organizations comply with ever-evolving regulations. Businesses can reduce the risk of regulatory violations and associated penalties by automating compliance checks and monitoring workflows.

Insurance underwriting: optimize risk assessment. AI automates and enhances the insurance industry's underwriting process by analyzing diverse data sources to assess risks accurately. For instance, Appian Connected Underwriting Life Workbench auto-assigns workloads and provides a 360-degree view of case data so insurers can make complex risk decisions efficiently and monitor and process incoming applications in real time.

Connected Underwriting Dashboard.

Overall, these six AI applications transform the business landscape by supercharging efficiency across many functions. From automating routine tasks to optimizing processes, ensuring quality control, and managing compliance and risk effectively, AI drives operational efficiency. Embracing these AI applications is not just a choice; it's necessary for businesses striving to stay competitive in the digital age.

Transform your organization with Appian.

In the near-future competitive landscape, companies will either be good at AI or bad at business. Appian provides the capabilities you need to operationalize AI, secure your data, and optimize the efficiency of your organization. 

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