10x AI and Automation

Intelligence on autopilot, the rise of Hyperautomation

Artificial intelligence (AI) and automation are two transformative technologies that have been rapidly advancing over the years. These technologies are significantly shaping up industries, businesses, processes, and even our daily lives. From leading machinery on factory floors to recommending movies on streaming services, AI and automation have become an integral part of modern society. This blog post will delve into these technologies, discussing their potential impact, challenges, and future prospects.

Artificial Intelligence involves traditional computational methods and machine learning algorithms to mimic human intelligence. It enables computers and machines to learn from new data, understand complex problems, and perform tasks that typically require human intelligence, such as recognizing speech, identifying images, making decisions, and translating languages.

Automation, on the other hand, refers to the use of machines, AI, robotics, and other technology platforms to carry out tasks that were previously done by humans. It streamlines processes and enhances productivity, accuracy, and efficiency while reducing human errors and operational costs.

The Emerging Trend: Hyperautomation

Hyperautomation is an emerging approach in automating as many processes and tasks as possible in an organization. It leverages AI, machine learning (ML), Robotic Process Automation (RPA), and other advanced technologies for process automation. Gartner, a global research and advisory firm, recently identified hyperautomation as one of the top 10 strategic technology trends. The pandemic-induced digital acceleration has made hyperautomation a critical part of workflow optimization. It's geared toward easing manual and repetitive tasks, consequently enabling organizations to operate in a more streamlined, cost-effective manner while enhancing their competitiveness.

While automation focuses on automating repetitive, manual tasks, often on a smaller scale, hyperautomation adopts a more holistic approach. Hyperautomation extends automation's capabilities with advanced technologies like AI and ML, allowing a more extensive and intelligent automation of processes. This approach empowers businesses to gain deeper insights, improve decision-making, and achieve significant improvements in efficiency and productivity. 

Consider an insurance company that needs to process insurance claims from policyholders. In a traditional automation scenario, the company might use a simple workflow tool to automate some of the steps involved in handling a claim. For instance, the tool might automatically send out an acknowledgement email when a claim is first submitted and organize the submission in the company's system based on basic parameters like the policyholder's ID. However, the claim would still need to be reviewed and processed by a human employee.

With hyperautomation, the process becomes much more advanced. The company might employ an AI-powered system which can not only automate the acknowledgement process, but also 'read' and comprehend the submitted documents, identify fraudulent claims, calculate claim amounts based on the policy's terms, and even make decisions on whether or not the claim should be approved, based on previous data. The system can learn from each claim it processes, getting better over time. It could also interact with other systems, such as the company's customer relationship management (CRM) platform, to provide a more enhanced and efficient customer experience.

Advancing to Hyperautomation

Implementing hyperautomation within an organization requires a strategic and systematic approach. It should start with gaining a comprehensive understanding of the existing processes and workflows, identifying gaps, bottlenecks, and potential opportunities for digital process automation. Next, the required data inputs need to be identified, and possible outcomes, such as - enhanced consistency, accuracy, and speed in task completion, reduced costs, and improved customer experience - should be predicted. Based on these insights, the appropriate automation platform and technologies can be chosen and adopted. It is also essential to plan for the automation of complex business and technology processes and tasks, leveraging AI tools like cognitive learning, computer vision (CV), and natural language processing (NLP). 

However, the transition to hyperautomation can also present challenges. For some companies, raw or poor-quality data and a lack of technical skills can impede their automation efforts. Moreover, the rapidly evolving marketplace of products and technologies can also make decision-making daunting. Finally, due to the stochastic nature of AI, this process needs to be repeated and tuned until desired goals are achieved - and in some cases, where the ROI remains unclear, the project may be halted. Despite these challenges, various sectors, including healthcare, supply chain, finance, banking, and retail, have successfully implemented hyperautomation, witnessing improved efficiency and accuracy in their operations.

Hyperautomation in Practice

A wide range of sectors have started to pick up on the trend of hyperautomation, achieving improved efficiency and accuracy in their operations. 

In healthcare, hyperautomation is being used to automate patient scheduling, prescription management, and claims processing. By employing AI to read and analyze health records, interpret diagnostic images, and even suggest treatment plans, hospitals are able to provide more efficient and personalized care. For example, PathAI, a Boston-based startup, has developed an AI platform that analyzes pathology slides, helping pathologists make more accurate diagnoses and providing patients with a better understanding of their health conditions.

The finance and banking sectors have also embraced hyperautomation. Banks are using AI to improve fraud detection, streamline customer service, and automate their back-office processes. For instance, JPMorgan Chase is using an AI program named COIN to review legal documents and transactions, reducing the time and errors associated with manual review.

In supply chain and logistics, companies are using AI and machine learning to predict demand, optimize routes, and manage inventory. Amazon, for instance, has successfully implemented hyperautomation in its warehouses to pick, pack, and ship orders. 

Retail companies, on the other hand, are using hyperautomation to enhance customer experience and other areas of operation. For instance, Walmart is employing AI and machine learning not only to streamline its in-store operations, such as restocking shelves and checking inventories, but also to analyze customer behavior and personalize shopping experiences.

Reshaping Employment Landscape

Without a doubt, AI and automation have revolutionized various industry sectors, sparking a concern over job displacement. However, one key aspect to consider is that while these technologies may indeed automate certain jobs, they also create new roles that didn't exist before. A classic example is the advent of AI specialists, data scientists, and Robotic Process Automation experts, among others. 

According to the World Economic Forum's Future of Jobs Report 2020, by 2025, the time spent on manual tasks will reduce by half, and humans and machines will share the workload equally, indicating that while some jobs may be displaced, new ones will emerge. 

This shift has been consistent in history like during the Industrial Revolution, where machines replaced many labor-intensive jobs, but also created new roles in sectors such as manufacturing and engineering. Similarly, while some roles may become automated in the future, the evolution of AI and automation will likely create new jobs that leverage these technologies.

Future of AI and Automation

The future of AI and automation looks promising. Investments in these technologies are rising, and they are expected to unlock unprecedented levels of productivity, efficiency, and innovation. For instance, AI and automation can provide viable solutions to challenges in healthcare diagnosis, climate change modeling, cybersecurity threats, and customer service management, among others. 

Moreover, the rise of hyperautomation indicates that these technologies are moving beyond simple task automation to orchestrating end-to-end processes, creating intelligent enterprises capable of superior decision-making and productivity.

However, the potential of AI and automation also carries challenges. Concerns around data privacy, ethical use of AI, digital divide, and workforce transition need to be addressed. Policymakers, businesses, and other stakeholders must work together to navigate these challenges, ensuring inclusive growth and benefiting all segments of society.

Conclusion

AI and automation have fundamentally transformed how we live and work, disrupting traditional business models and introducing new ways of doing things. From automating mundane tasks to revolutionizing critical operations, these technologies are reshaping the future. The prospects of hyperautomation further underscore the immense potential of AI and automation in enhancing business operations and creating new jobs, marking just the beginning of an era of accelerated digital evolution. We're going to see a rise of intelligent autonomous agents who not just automates repetitive tasks, but also undertakes high-level decision-making, problem-solving, and strategic planning, setting new benchmarks in efficiency, productivity, and innovation.

~10xManager