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Embracing hyper-automation for speed, efficiency, and accuracy
Digital transformation across companies and industries has exponentially accelerated and so has the rate of automation. The typical business as we know it could be 40% to 60% automated within five years. That’s a massive jump from where we are today, but it’s a realistic estimate given the speed of what we call hyper-automation.
Hyper-automation is when automation of business processes across the enterprise increases to the extent that entire processes—not just pieces of them—are fully automated, with no human intervention. It’s the next logical step from automating specific, routine, repetitive activities with robotic process automation. Here, the focus is on completing tasks more quickly, efficiently, and accurately, and freeing up people’s time for higher-value pursuits.
Hyper-automation will increase exponentially across all industries. Enterprises must embrace it for speed, efficiency, and accuracy.
With hyper-automation, companies are taking aim at other parts of processes where judgement and higher-level decision making are needed. Coming to their aid are increasingly sophisticated artificial intelligence (AI) and machine learning (ML) solutions to automate processes from start to finish.
Hyper-automation use cases are rising across industries, boosting productivity and business resilience.
With advanced AI and ML now easily within reach for most organizations via the cloud, the number of use cases for automation across industries is multiplying quickly. This points to the future in which a large part of all business processes will be fully automated, leading to greater productivity and efficiency, better customer experience, and stronger operational resiliency.
Think about the consumer goods and retail industry. There’s a tremendous opportunity for companies to use AI and sensors in distribution to continually monitor and tweak temperatures in trucks to ensure products don’t get spoiled. In asset-heavy industries such as manufacturing, energy, utilities, and transportation, AI-based computer vision—coupled with drones and machine learning—is enabling companies to visually inspect equipment, power lines, and other assets in remote or potentially dangerous terrains without having to send technicians for manual inspections. And in the upstream supply chain, AI can anticipate an impending disruption, model potential impacts on a company’s ability to purchase specific products or materials, recommend the right response to expedite supply chain decisions—or, in some cases, even act on its own by creating a self-learning supply chain to minimize the effects on business.
Boost from the cloud
Cloud makes hyper-automation more accessible and affordable.
The cloud provides a strong foundation for hyper-automation, automating the process of storing, digitalizing, managing, and accessing data. It offers companies with access to state-of-the art automation tools and the ability to experiment with cloud-native features to rapidly create new capabilities. It ensures scalability, flexibility, security, and access to new and emerging technologies, such as artificial intelligence (AI), robotic processing automation (RPA), and machine learning (ML), to run automated processes and propel innovation.
With innovative offerings from leading cloud providers like Google Cloud and experienced partners like TCS, companies are deploying automation to multiple new use cases, enabling them to improve entire business processes in a cost-effective and quick manner.
A leading European airline, for instance, was facing a significant shift in how customers wanted to interact with the company. Instead of relying solely on interactive voice response (IVR) channels, customers were increasingly using social media channels such as Facebook, Twitter, and WhatsApp. The airline wanted to design and implement customer service functionalities within social messaging platforms to maximize its reach with customers and enhance customer experience.
For this, the company built a multi-lingual, multi-platform social media assistant, leveraging Google Cloud’s native contact center AI and natural language processing features. Chatbots were developed to respond to customer queries, collect missing data, and provide updates on flight and passenger status. The underlying eight AI engines handle nearly 150,000 queries per month—a 4x increase in message volume and 3x boost in the number of users—with no additional customer service resources.
Be future ready
Applying hyper-automation to critical use cases aligned to business priorities is the way forward.
Many organizations have adopted smart analytics tools, AI, and ML to make sense of data and transform the way they engage with customers, operate internally, and interact with their partners. And cloud is playing a key role in helping businesses adapt to this change. It has democratized access to cutting-edge AI and ML-powered tools that enable organizations to drive better business outcomes. In the process, cloud is also fueling the surge in AI adoption and hyper-automation.
Experts believe that hyper-automation will become key to survival for businesses. In fact, Gartner predicts that by 2022 businesses worldwide would have spent $596.6 billion on technologies that enable hyper-automation. Furthermore, by 2023, cloud-based AI services are expected to grow nearly five times greater than today.
Companies need to begin preparing for this future now and ensure they are not left behind. They need to prioritize where they should apply hyper-automation—the critical use cases—in line with their business priorities and strategies for growth. They need their leaders to champion the change and rally the organization to bring these use cases to life. And they need to acquire, develop, or borrow from external partners the skills needed to keep pace with technology changes that are sure to come.
Hyper-automation is coming. Are you ready to embrace it?