Autonomous shopping carts that follow food market customers and robots that pick ripe cucumbers faster than humans may grab headlines, but essentially the most compelling applications of AI and ML technology are behind the scenes. Increasingly, organizations are finding substantial efficiency gains by applying AI- and ML-powered tools to back-office procedures corresponding to document processing, data entry, worker onboarding, and workflow automation.

The facility of automation to reinforce productivity within the back office has been clear for many years, however the recent emergence of advanced AI and ML tools offers a step change in what automation can accomplish, including in highly regulated industries corresponding to health care.

“Previously, AI was seen as a fancy and expensive technology that was only accessible to large firms with deep pockets,” says Himadri Sarkar, executive vice chairman and global head of consulting at Teleperformance, a digital business services company. “Nonetheless, the event of easy-to-use generative AI tools has made it possible for businesses of all sizes to experiment with AI and see how it could actually profit their operations.”

Organizations are taking note with revolutionary use cases that not only promise to enhance back-office operations but in addition deliver bottom-line advantages, from cost savings to productivity gains.

AI in motion

In line with McKinsey’s 2022 Global Survey on AI, AI adoption has greater than doubled—from 20% of respondents having adopted AI in not less than one business area in 2017 to 50% today. It’s easy to know this technology’s growing popularity: as difficult economic times meet increasing customer expectations, organizations are being asked to do more with less.

“Firms are attempting to optimize their use of resources in an inflationary environment,” says Omer Minkara, vice chairman and principal analyst with Aberdeen Strategy and Research. “Adding to the pressure is the indisputable fact that many firms must defer their technology spend and headcount increases.”

Fortunately, AI and ML solutions might help bridge this gap for a wide selection of industries by automating and optimizing various back-office tasks and processes. A retailer, for instance, may use AI-powered chatbots to handle routine customer inquiries, track orders, and reply to refund requests, improving response times, enhancing customer experience, and freeing up contact center agents. At the identical time, financial institutions are discovering the facility of ML to discover anomalies inside large volumes of knowledge that will indicate fraud—an early warning system against financial loss. Organizations across industries can employ AI and ML tools to extract and analyze information from documents, corresponding to invoices, contracts, and reports, and to scale back the burden of manual data entry while speeding up processing times and minimizing human errors.