AI automation is changing the game in business operations. For many companies, global competition is heating up fast on an increasingly crowded playing field. In the past, business leaders knew their competitors and how they operated. But now, executives across industries have to look over their shoulders for new challengers that arrive with surprising speed from virtually any corner of the globe.
To thrive in this environment, business leaders must embrace automation and AI to transform their business models, accelerate productivity, redefine customer experiences, and navigate inflation and supply chain disruptions.
However, harnessing the full potential of AI to scale automation goes beyond simple integration; it demands a strategic approach and a fundamental shift in how enterprises operate, including everything from customer onboarding and servicing to security practices and events management. Beyond cost savings and efficiency gains, AI automation is essential to orchestrating core processes and operationalizing AI’s insights across the enterprise. The most successful companies seamlessly integrate business process automation and human-AI interaction, where AI proposes solutions and humans make decisions.
In simple terms, this blog highlights eight examples of how to strategically use AI automation to drive next-level productivity, growth, and competitive advantage.
Today’s customers expect quick and hassle-free access to insurance products and services. If the claims process is slow and complicated, they’ll switch to another insurer that offers a better customer experience. That’s where AI-powered automation comes into play to streamline routine tasks in the claims submission process. Machine learning (ML) models can intelligently delegate claims based on damage. Computer vision tools can analyze claim images precisely (a tool like Google Cloud Vision can add sophistication with label detection on images). And virtual AI assistants enhance customer interactions throughout the claims journey, providing real-time support and guidance.
Appian’s Connected Claims solution streamlines claims process from end to end.
[ Discover the Connected Claims experience with low-code and intelligent automation. Read the Report from industry analyst Celent. ]
Managing a global customer support center means interacting with people who speak many different languages. AI translation can address this challenge, ensuring submitted issues are instantly translated into the support engineer's native language. Virtual assistants complement this process by immediately responding to common customer queries in the appropriate language.
Translation and natural language processing technologies working together
In supply chain automation, technologies such as Internet of Things (IoT) devices automate workflows on the shop floor and robotic process automation (RPA) and intelligent document processing (IDP) automate critical processes in back-office operations. A comprehensive approach to implementing AI in supply chain automation enables businesses to operate at scale with increased efficiency. Digital workers, powered by AI, play a pivotal role in streamlining supply chain operations, ensuring accuracy and efficiency from procurement to delivery.
Explore how AI and automation are shaping the future of supply chain management. Download: Supply Chain Automation: The Complete Guide.
In the retail sector, AI automation eases last-mile delivery pressures, allowing quicker responses to keep up with consumer expectations for fast delivery. Additionally, AI automation enhances supply chain agility by proactively monitoring customer demand and facilitating faster procurement to prevent shortages. Furthermore, AI boosts supply chain resilience by automating alerts for potential supplier delays or capacity issues, allowing for timely interventions such as seeking alternative suppliers or opting for premium freight.
Studies show that by leveraging generative AI, the world’s top 14 investment banks could potentially boost their front-office productivity by 27% to 35%. However, banking organizations have long relied on traditional, rule-based transaction monitoring and name-screening systems for anomaly detection, fraud prevention, and compliance. But today, financial crime is rising, and fraud patterns are continuously changing, adding new layers of complexity to fraud prevention.
AI for fraud prevention.
Data-agnostic AI solutions allow financial institutions to enhance existing systems to improve fraud detection and identify previously missed transactional patterns, data anomalies, and suspicious relationships between individuals and entities. AI automation can look for fraud patterns at a massive scale and learn to detect new patterns over time, driving game-changing improvements in the accuracy and efficiency of the fraud detection process.
AI for banking customer onboarding.
First impressions matter, and a seamless onboarding experience builds trust with customers. Traditional solutions and manual processes can hinder agility and responsiveness, adding friction to customer interactions. In contrast, Appian Connected Onboarding and Servicing, built on a low-code platform, offers adaptability and speed. With customizable process templates, you can automate tasks, ensuring a personalized onboarding journey, and benefit from AI-powered automated efficiency through features like document classification and data management.
[ Watch the video and learn how Appian helps leading financial institutions accelerate their onboarding processes. ]
Connected Onboarding tracks institutional onboardings at a high level and identifies issues.
In customer service, AI automation improves customer relationship management by helping companies keep pace with consumers’ fast-rising expectations. McKinsey reports that about 67% of millennials “expect real-time client service” and 75% expect a “consistent cross-channel service experience.” AI automation is fundamental in optimizing customer interactions while alleviating the workload on service representatives and call center staff.
Conversational AI tools, including chatbots and voice assistants, enhance customer interactions, providing instant responses and support. Virtual assistants, powered by speech recognition and image generation, provide seamless, personalized experiences throughout the customer journey.
Maintaining uptime and website reliability can make or break companies that rely on web services and eCommerce. AI automation helps organizations optimize reliability by constantly scanning systems, networks, and processes for potential disruptions and identifying any looming threats in a way humans alone could never accomplish. Nearly all major organizations supplement human oversight with AI to support their uptime and reliability needs.
Many more are also implementing AI for business process management—to monitor, maintain, and keep their services up and running and available for customers. For companies offering round-the-clock digital services, AI can help identify problems before they start while reducing crashes, hacking, and human error. Predictive maintenance, powered by machine learning algorithms, ensures the proactive identification and resolution of potential issues, minimizing downtime and optimizing business operations.
Businesses are increasingly leveraging the power of machine learning for predictive decision-making to gain a competitive edge in today's dynamic landscape. By harnessing advanced algorithms and vast datasets, organizations can analyze historical patterns and trends to make informed forecasts about future events.
For example, machine learning models excel at recognizing intricate relationships within data, enabling businesses to predict customer behavior, market trends, and operational outcomes. Whether optimizing supply chain logistics, enhancing customer engagement strategies, or fine-tuning marketing campaigns, machine learning algorithms play a pivotal role in transforming raw data into actionable insights.
By harnessing AI/ML capabilities, supply chain professionals better predict demand with real-time data across multiple data points. They also use AI/ML to help predict weather patterns and routes for ships and transports. That kind of predictive capability allows businesses to proactively address challenges, identify opportunities, and make strategic decisions that are not only data-driven but also forward-looking, fostering a more adaptive and resilient operational environment.
Integrating AI and automation is indispensable for advanced threat detection and response in the ever-evolving cybersecurity landscape. AI-powered systems can analyze massive datasets in real time, identifying anomalous patterns and potential security threats to swiftly detect and respond to cyber threats and mitigate potential damage.
AI-driven threat detection systems continually learn from evolving threats, providing a dynamic defense mechanism against sophisticated cyber criminals. Advances in machine learning algorithms are critical for proactively identifying and preventing cyber threats. By leveraging machine learning algorithms, organizations can quickly analyze historical and real-time data to recognize patterns associated with cyber attacks. ML also propels the development of predictive models that anticipate potential threats, allowing security teams to implement preemptive measures that can evolve with emerging threats.
Surviving and thriving in the next decade requires organizations to do more than just adopt AI automation at a basic level. Organizations must unlock their full potential to streamline insurance claims, provide multilingual customer support, optimize supply chains, transform finance with advanced onboarding and servicing, elevate customer experiences, ensure business resilience, deploy predictive maintenance, make data-driven decisions by leveraging machine learning, and fortify cybersecurity against fast-evolving threats.
[ For an independent analysis of leading digital process automation platforms, check out The Forrester Wave™: Digital Process Automation Software, Q4 2023. ]