Subscribe To Newsletter AI/Automation The 3 Zeros: How to Reap Greater Business Value from AI and Automation Enterprises have clearly embraced Artificial Intelligence (AI) and automation. Manufacturers rely on embedded sensors and data analytics to predict when equipment is most likely to require maintenance, repair or replacement. Financial institutions use AI to flag suspicious activity and anticipate delinquent accounts, and many of today’s top retailers are replacing human agents with bots in order to boost customer service and sales. Yet it’s still early days for AI. We recently produced a research report titled “Leadership in the Age of AI,” which revealed that 79% of enterprises have experimented with AI technologies in order to identify potential benefits to their businesses. But many organizations are implementing technologies incrementally rather than fully, preventing AI and automation from becoming mainstream. You can’t inch toward disruption. For AI and automation solutions to be truly transformational, they must be part of a large-scale strategy that focuses on delivering business benefits. For example, a financial services client recently automated its loan origination processes. While the primary goal was to improve loan quality and decision-making speed, they used an Infosys-developed AI and automation platform that also enabled them to transform the customer experience and reduce fraud. However, to seize these bigger opportunities, enterprises need a new way of looking at how AI and automation can solve critical business problems. Enter the concept of the three zeros. Zero distance to information Zero disruption to business operations Zero latency in processes By viewing AI and automation through these three lenses, organizations can reap greater business value from their technology investments in the form of happier customers, a more productive digital workforce and new business models. A Litmus Test for Transformation Zero Distance to Information As organizations compete to meet customer demands faster than ever, the ability to retrieve information and insights in real time has become a critical competitive differentiator. An AI-powered predictive analytics platform can analyze past outcomes and project future ones by using sophisticated computer models and complex algorithms. There are many applications for this capability, from predicting customer churn and forecasting cash flow to flagging delinquent accounts and creating segmentation models. Below are some real examples of customer success stories. An automotive company analyzes 13 GB of customer data, including transactions, equipment holdings and disputes, to predict the propensity of customer churn A pharmaceutical manufacturing plant predicts the breakdown of equipment days in advance, with high accuracy and a reduction of false alarms A professional sports association analyzes the historical data of players’ performances, their strengths and weaknesses, and predicts their future behavior and the outcomes of matches But an AI platform is only as good as its algorithms. And it can take time to discover which algorithms are best suited to which tasks. One of our clients, a telecommunications company, adopted an interesting approach to this dilemma. It implemented a collaborative and continuous machine learning modeling process that allows the company’s data scientists, data engineers and business analysts to set up challenges, compete and collaborate across the organization. By pitting humans against robots, the telco creates the best model possible and discovers new algorithms quickly, enabling users to obtain better, faster predictions. Since introducing this approach, the company has cut the time to select an analytical model in half. Further, the machine learning tools ensure the algorithms are always improving. And the ability to share the models across the organization as part of its model catalog allows for benefits across the enterprise. Zero Disruption to Business Operations In today’s capital-intensive and tightly integrated supply chains, downtime can be costly. According to an ITIC survey, more than 98% of large enterprises say that on average, a single hour of downtime per year costs their company over $100,000, while 81% report that the cost exceeds $300,000. Disruptions to business operations can also lead to legal fines, regulatory violations, disgruntled customers and a damaged reputation. An AI platform delivering predictive and prescriptive analytics enables service technicians to administer preventive maintenance just when it’s needed. This not only prevents downtime and unnecessary checks and services, but also minimizes confusion when issues arise. One large U.S.-based fashion retailer had poorly monitored servers that required IT teams to continually respond to alerts and notifications. A single problem could take an hour to resolve, resulting in lost productivity, revenue and customer trust. The retailer worked with our experts to build a predictive model, and the AI platform enabled scale and accelerated impact. Today, the platform parses historical data to accurately assess anomalies and alert staff to exactly where they are occurring. By using regression and dynamic threshold techniques, the platform also suppresses false alarms, ensuring that only relevant, real-time information is delivered – via tools such as Splunk, Elasticsearch and Grafana – through a dashboard to the monitoring team. Since deploying AI as a platform, the retailer has reduced the time to fix server problems from an hour to just five minutes. This has saved $1.5 million each year in diminished downtime. Just as important, by eliminating interruptions, the company has increased consumer trust and strengthened its brand reputation. Zero Latency to Processes Companies from fast-food chains to Facebook are redefining business processes with chatbots – smartly designed AI-enabled computer programs that can simulate or mimic conversation with human beings over the internet. Customers can converse with these round-the-clock agents, getting answers to their most pressing questions, such as when a product will ship or whether an item is in stock. Many B2C companies are deploying chatbots to offer a more personalized customer experience. A leading analyst predicts that chatbots will power 85% of all customer service interactions by the year 2020. Retailers, for example, can integrate chatbots with sensors and message notifications that allow the retailer to know when a customer is entering its store and to reach out to the customer with targeted offers via a smartphone app. Other applications include generating qualified leads for sales teams and empowering users to take advantage of self-service tools. However, B2B businesses stand to gain the most from these digital workhorses. If properly deployed, bots can reshape the way these companies do business by recasting time-consuming manual activities on an enterprise scale. For example, a large telecommunications client recently deployed a wide array of bots across multiple functions, including: An HR payroll email bot that uses natural language processing techniques to automate email responses to mundane and repetitive tasks An order inconsistency management bot that automates the processing of orders that have got stuck in the system A bot that automates bi-monthly server security patching activities By handling critical operations automatically, the bots not only eliminated latencies inherent in manual task handling but also created a digital workforce for greater order-to-cash visibility. Together, these advantages reduced cost by $2 million a year and increased productivity by 85%. The Outlook for AI and automation AI and automation will be two of the most important technologies in the next decade. From manufacturing to retail, they will upend age-old business models, turbocharge productivity and enrich customer experiences. However, for AI and automation to truly transform an organization, they must extend beyond select applications and processes, and tackle opportunities on an enterprise scale. By looking for opportunities through the lens of the 3 zeros – zero distance to information, zero disruption to business operations and zero latency to processes – organizations can discover the full potential of AI and automation.