COVID-19 pandemic has accelerated the pace at which organizations are adopting disruptive technologies to cope with the new normal. By2021, the world will witness a rise of hyper-automation practices, an intersection of AI and ML with autonomy driven by robotic and cognitive process automation.
A strategic approach to implementing hyper-automation would aid in crunching up the investment made for redundant tasks while summoning a driving force to initiate innovation. Hyper-automation offers powerful analytical tools and capabilities that enable human-machine collaboration, enhance customer experience while relieving extra operational investments, and boosting productivity.
Automation can be rule-based or cognitive. When infused and democratized across the enterprise and reimagined leveraging automation and AI, we can achieve hyper-automation. This can augment human capabilities allowing them to complete processes faster, more efficiently, and with fewer errors. Hyper-automation will thus free humans from the routine, mundane tasks and enable them to focus on more creative value-added work for the organization.
Few examples of hyper-automation could be using Natural Language Processing (NLP) to interpret human speech or translate it into various languages or using Optical Character Recognition (OCR)to read images and extract relevant information from it or using ML to analyze patterns and detect discrepancies.
AI can help a customer support centre chatbot to analyze their customer’s data and input to suggest appropriate and quick resolutions based on past transactions and history. This can considerably reduce the workforce needed for customer support. Even if a call eventually gets transferred to a human, the executive will have enough information about the customer to ask the right questions.
During the pandemic, Swiggy used AI to check mask usage compliance by delivery partners and to stop delivery of non-essential items.
The lockdown in 2020 drove e-commerce retailers to rapidly segregate essential and non-essential items using natural language models and add neighbourhood grocery vendors and their items onto their platform. Restaurants had to update their menus as many of the items could not be delivered to home, and this was accomplished majorly by having computer vision models to read the physical menus and extract relevant information with the price.
Online pharmacies can benefit greatly by having AI read and validate prescriptions. Tele-consultations with doctors can be done faster and easier by having AI run through test/scan results and patient history. With continuous learning, bots will give more insights about the patient readily to a doctor. AI algorithms can also analyze large amounts of data through electronic health records for disease prevention and diagnosis, furthering their utility in early detection and outbreak prevention of communicable diseases.
Digitizing the KYC process to eliminate the need for physical document submission and verification is something traditional banks still don’t offer. AI-based computer vision technology can be used to verify documents. Optical/Intelligent Character Recognition (OCR/ICR) technologies can digitize scanned documents. Lastly, NLP can make sense of lengthy documents like contract documents. AI can make physical verification unnecessary.
By repeatedly having a machine learning from thousands of images (both valid and doctored), AI can easily detect a forgery in documents submitted online, e.g., bill submissions for a claim, or in the submission of documents to get a legal document (e.g., driver’s license or Aadhar Card)Processes can be seamless and efficient, providing much ease to customers. They need not queue up at an RTO or other centres to get the document. This is much needed in post-Covid life.
AI can have path-breaking innovations in the entertainment industry. What if while watching a movie on an OTT platform, you suddenly take a fancy to a particular attire or object which is in the movie and want to know the brand or where to buy it from? Image recognition and search features can help bring this feature to the customer.
The Bangalore Metro recently tested out a driverless train. How can AI enable the driverless train to identify an emergency and take appropriate action, or how can AI make the ride more comfortable for the consumer by auto-adjusting ambient temperature? Will this be the future of travel? It will be interesting to watch out.
While the criticality of AI to aid hyper-automation is imperative today, organizations must analyze the integration of hyper-automation into their process and areas that need to be prioritized for a successful outcome.
The base of a successful and effective hyper-automation project is founded on pre-requisites such as data acquisition, ingestion, cleaning, integration, storage, governance, protection, and consumption supported by future-ready AI technologies and superior design considerations. This requires having multiple domain experts working together to achieve the end goal, enabled by the advantages of hyper-automation.
Hyper-automation can bring about vast benefits to an organization in terms of flexibility, increased ROI, employee productivity and engagement, and benefits to the society at large in terms of convenience, ease, faster services, and safety.
Source: Economic Times
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