The Impact of AI Hyper Automation on Enterprises
"AI hyper automation? Great, another tech buzzword to add to my ever-growing list"
I thought to myself in 2022, rolling my eyes as I saw the term for the first time in an article. As someone who's spent years in the tech industry, I've had my fair share of numerous fancy terms used to describe mundane developments. However, something about this one made me pause.
You see, I've always been fascinated by automation and the idea of machines taking over mind-numbing and repetitive tasks, freeing me up to be more creative. But AI hyper-automation sounded like automation on steroids. AI Hyper automation is a more advanced form of hyper automation that leverages AI to automate complex processes, make intelligent decisions, and continuously improve.
From my understanding, AI makes a difference in the following ways
- Intelligence: AI enables systems to learn, adapt, and improve over time, making them more flexible and responsive to changing conditions.
- Decision-making: AI can automate decision-making processes, analyzing data and making informed choices.
- Process optimization: AI can continuously monitor and optimize automated processes, identifying bottlenecks and inefficiencies.
- Scalability: AI-powered hyper-automation can handle more complex and dynamic processes at a larger scale.
In this article,I will share my perspective on how this technology is reshaping enterprises.
Role of AI in Hyper Automation
It Makes Hyper Automation More Hyper
One of the most exciting aspects of AI in hyper automation is something I like to call cognitive automation. This means that your automated system can understand and process unstructured data like emails, documents, and images. For instance, a customer service AI can analyze customer emails, extract relevant information, and use automation tools like Activepieces to trigger data transfer. It was difficult to achieve this with traditional automation systems which could probably only generate and send generic emails based on a specific prompt.
AI hyper automation ensures continuous monitoring of workflows to identify inefficiencies and suggest improvements. For instance, a financial services firm could use AI to optimize its loan approval process. In this case, AI can automatically extract data from loan applications, credit reports, and other documents, eliminating manual data entry and reducing the risk of errors. These systems can also analyze vast datasets to assess applicants' creditworthiness in real time, providing instant feedback and reducing processing time.
Lesser Load on IT
AI hyper automation can handle tasks like software updates, system monitoring, and data backups. AI hyper automation can also automatically categorize and prioritize IT tickets. For instance, an AI system can analyze incoming support requests, identify patterns, and delegate them to the appropriate team members based on their expertise. This speeds up response times and also ensures that IT teams are not overwhelmed with low-priority issues. Another fascinating role of AI hyper automation is its ability to turn every department into an AI powerhouse. It enables non-technical teams to build and deploy tailored automation machines for their departments without depending so much on IT teams. AI hyper automated system can also analyze historical data and identify patterns, then alert IT staff to potential problems, enabling them to take corrective action before a failure impacts business operations.
Use Cases of AI Hyper Automation in Enterprises
Customer Service One of the use cases of AI hyper automation in customer service is handling calls. A hyper automation system can handle a large volume of incoming calls daily. It uses natural language processing (NLP) to understand the customer's query, even if it's expressed unclearly. Once the query is understood, the system can:
- Initiate claims: If the customer is reporting a claim, the AI can guide them through the process, collecting necessary information and updating the relevant systems.
- Route calls: Based on the customer's needs, the AI can determine the most appropriate department and route the call accordingly, ensuring that the customer is connected to the right person for their issue.
AI hyper automation can also be integrated into websites and mobile apps. This allows customers to interact with the system directly, receiving instant assistance. For example, the AI can analyze a customer's browsing history and purchase behavior to suggest relevant products or services.
Supply Chain Management
AI hyper automation enhances supply chain operations using predictive analytics. This means the company can forecast demand more accurately, for better inventory management. AI hyper automation enables your supply chain team to analyze historical sales data and market trends to optimize stock, reduce waste, and ensure product availability. AI hyper automation can also involve using machine learning algorithms to analyze traffic patterns, weather conditions, and delivery locations to determine the most efficient routes for delivery drivers. This not only speeds up delivery times but also reduces operational costs associated with fuel and labor.
Finance
A good example is American Express which uses AI hyper automation to automate fraud detection. American Express collects a massive amount of data from every transaction, including details like purchase amount, location, time, and customer behavior. AI algorithms are trained on this historical data to recognize normal spending patterns for each customer. These patterns can include factors like frequent purchase locations, typical spending amounts, and purchase timing.
The AI continuously monitors incoming transactions and compares them to these established patterns. If there is a significant difference from an expected behavior, it is flagged as potentially fraudulent. This analysis is performed in real time, allowing American Express to detect and prevent fraudulent transactions immediately.
When a transaction is flagged as suspicious, the AI can take various actions. It can block the transaction, preventing the fraudulent charge from going through. Additionally, the AI can alert the customer about suspicious activity and request verification. If necessary, the AI can initiate a more in-depth investigation.
Marketing
Coca-Cola utilizes AI hyper automation to personalize marketing campaigns. AI algorithms analyze consumer behavior and preferences, and then create targeted advertisements that resonate with specific audiences. This approach has led to increased engagement and higher conversion rates in their marketing efforts.
Amazon uses AI hyper automation to streamline its email marketing campaigns. By analyzing customer data, Amazon’s AI systems create personalized email content that includes product recommendations, special offers, and relevant updates. This targeted approach has resulted in higher open and click-through rates, driving more sales.
AI hyper automation is reshaping enterprises, and it might just be the most exciting (and slightly terrifying) development in business tech I've seen in years.