Machine Learning is changing functions such as customer service, marketing and even BI and the top C-suite has agreed on the inevitability of AI transforming the business landscape. Insights from a top research firm indicate that AI capabilities will be baked into every software product by 2020.
According to a recent survey by Teradata that polled 260 enterprises across the globe, organizations are seeing AI as a strategic priority that will help them outpace the competition in their respective industries. Atif Kureishy, Vice President, Emerging Practices at Think Big Analytics, a Teradata company mentioned that to leverage the full potential of this ground-breaking technology and gain maximum ROI, businesses will need to revamp their core strategies. “AI has an embedded role from the data center to the boardroom,” he said adding it is time for organizations to create a new position in the C-suite position —Chief AI Officer.
According to McKinsey’s Simon London, today every industry can make use of AI technology what with use cases right across the value chain and across the operations of most companies. But even though there are a lot of applications and portfolio-of-initiatives across every industry, it is important for business leaders to understand this technology, find the potential area of application and understand how it can be leveraged as a competitive weapon.
This will be the first step for the Tier I leadership team before diving into AI-enabled processes and AI-enabled business. The turnaround for a business case is fast and Mckinsey’s Peter Breuer believes starting off with simpler use cases can help tech leaders prepare for more advanced uses cases in the future.
The year 2017 has seen several conversations around this disruptive technology and we have heard from senior management from IT/TES, pharma, retail, CPG industries talking about the plunge in AI technology with specific use cases and POCs underway with vendor partners. Generally speaking, in India finance is considered the major sector with the most demand for AI solutions especially in areas related to fraud management, compliance and security.
According to Avi Patchava, VP – Machine Learning & Artificial Intelligence, InMobi, “At a macro-level, India will see more news stories of implementations of AI use cases in traditional industries such as manufacturing, healthcare, banking, pharmaceuticals and automotive.” For example, in addition to the incorporation of new forms of unstructured data – such as images and voice recording – companies will also start to consider how to apply reinforcement learning techniques to build more dynamic systems for more challenging use cases.
Let’s Outline A Few Use Cases That Matter The Most To CXOs
Chatbot as an interface agent: Many Indian banks (dubbed as innovators or early adopters) have integrated chatbot to handle customer queries and improve the customer experience and call centre management companies too are experimenting with chatbots for automating support enquiries. According to Raj Bhatt, CEO, Knowledgefoundry, “Chatbots will become truly smart by connecting with recommendation engines and forecasting algorithms and becoming able to answer our everyday questions.”
RPA Adoption will rise: RPA has become a critical part of the digitization process and consulting majors like Infosys that are homing their BPM capabilities alongside its RPA capabilities, so the two can pull each other into projects, depending on the requirements, noted 451 Research. For example, Infosys has brought RPA and AI into the mix, and has dubbed this approach as the ‘art of super glue.’ The company applies BPM and RPA and AI, so that the human agents in the process can focus on customer needs and exceptions.
Meanwhile, Wipro emphasized a scientific approach to RPA technology that can automate routine back office transactions such as data entry and validation, claims processing, policy admin, F&A, and report generation. Some of the benefits include upto 60% reduction in transaction handling time and 100% accuracy in transactions processed by robots. The key selling points for RPA is that the cost-efficiency factor and can automate labour intensive, high frequency tasks but the downside is identifying the right tasks to be automated.
Forecasting and reducing revenue churn: India’s telecom and retail industries are reeling from customer churn as newer models emerge. For example, brick-and-mortar stores and big-box stores are ambushed by the likes of Amazon (significantly deepened its presence in India), Flipkart and Paytm. While telecom operators like Jio and Airtel are fighting a pitched battle over customer acquisition. Given this scenario, the players are consolidating their tech backbone with AI and ML systems in place for better execution.
For a systematic approach to AI implementation across large organizations, one needs senior management buy-in and a clear strategy on how to utilize internal resources to attain business goals. The senior management should also devise the metrics to be achieved from piloting AI applications/services and measure the kind of ROI gained in the future.
Try deep learning using MATLAB