Dr B K Mukhopadhyay
(The author is a Professor of Management and Economics, formerly at IIBM (RBI) Guwahati. He can be contacted at firstname.lastname@example.org)
Dr. Boidurjo Rick Mukhopadhyay
(The author, international award-winning development and management economist, formerly a Gold Medalist in Economics at Gauhati University)
"Artificial intelligence is kind of the second coming of software". Instead of serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Prior to exploring the many ways how Artificial Intelligence (AI, hereafter) can be defined or recognise potential opportunities and challenges in machine or deep learning, common debates seem to first point out some of the ethical concerns that AI brings in the contemporary society. Policy makers and scientists thinks that AI: a) with increased automation technology would give rise to job losses, B) embodying the sophistication and complexity of AI would call for redeployment or retrain employees to keep them in jobs, C) will trigger the effect of continual machine interaction on human behaviour and attention; D) ignites the need to address algorithmic bias originating from human bias in the data; E) will develop the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently. Finally, the very common and surface level risks of AI are established on the grounds of customer privacy, potential lack of transparency, technological complexity.
The benefits of AI, however, are many-faced and can be multi-dimensional. For businesses, AI can support both product and process-innovation. Starting from improving simple features like simple spam filters, smart email categorization, voice to text features or what our smart personal assistants like Siri, Cortana or 'Google Now' can do for us on a daily basis in addition to automated responders and online customer support. AI further helps in sales and business forecasting, improving security surveillance, smart devices that adjust according to behaviour.
At a quick glance, let us understand the 'day-to-day' merits of AI for businesses. A) AI improves customer services, thinking of virtual assistant programs that provide real-time support to users (e.g. billing); B) Efficient optimization of logistics and procurement assignments – e.g. use AI-powered image recognition tools to monitor and optimise your infrastructure, plan transport routes, etc; C) Improve and increase manufacturing output and efficiency – e.g. automate production line by integrating industrial robots into your workflow and teaching them to perform labour-intensive or mundane tasks. D) Predict performance – e.g., use AI applications to determine when you might reach performance goals, such as response time to help desk calls. E) Predict behaviour –e.g., use Machine Learning algorithms to analyse patterns of online behaviour to, for example, serve tailored product offers, detect credit card fraud or target appropriate adverts. This list is certainly not exclusive but surely in giving an idea of the scope of benefits that AI brings for businesses.
Machine learning is one of the most common types of artificial intelligence in development for business purposes, it is primarily used to process large amounts of data quickly. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the internet of things – into a digestible context for humans. For example, if you manage a manufacturing plant, almost all of your machinery is connected to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it's too much data for a human to ever sift through, and even if they could, they would likely miss most of the patterns. This is where machine learning comes in, it is also a broad category. The development of artificial neural networks, an interconnected web of artificial intelligence 'nodes', has given rise to what is known as 'deep learning'.
Deep learning is a more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. For example, for self-driving cars to work, several factors must be identified, analysed and responded to at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds. All this information is calculated side by side to help a self-driving car make decisions like when to change lanes.
It would be useful to look at some examples of how AI changes customer experiences as well as making business processes and internal systems more efficient.
Sephora, the makeup brand: When a customer walks into a Sephora store to find a makeup shade without putting anything on the face. A colour IQ scans a customer's face and provides personalized recommendations for foundation and concealer shades, while Lip IQ does the same to help find the perfect shade of lipstick. A huge help to customers who know the stress of finding the perfect shade by trial and error! Smart?
Walmart, the retail giant: they plan on using robots to help patrol those vast aisles. Walmart is testing shelf-scanning robots in dozens of its stores. The robots scan shelves for missing items, things that need to be restocked or price tags that need to be changed. These robots free human employees to spend more time with customers and ensure that customers aren't faced with empty shelves. Efficient?
North Face - The company uses IBM Watson's cognitive computing technology to ask questions to customers about where they'll wear the coat and what they'll be doing. Using that information, North Face can make personalized recommendations to help customers find the perfect coat for their activities. Convenient?
Uniqlo, the clothing chain: they are pioneering the use of AI to create a unique in-store experience. Select stores have now AI-powered UMood kiosks that show customers a variety of products and measures their reaction to the colour and style through neurotransmitters. Based on each person's reactions, the kiosk then recommends products. Customers don't even have to push a button; their brain signals are enough for the system to know how they feel about each item. Scary?
Amazon Go, Amazon's cashier-less grocery store: Amazon is attempting to revolutionize not only the way people shop online but also the way we interact with brick-and-mortar stores. The company completely automates the grocery shopping experience. Once the shoppers check-in via app, the sensors throughout the store track which items they put in their basket. Once shopping is all done, customers can just take their items and leave. No checkout lines, no cashiers, no baggers; Amazon automatically charges shoppers when they leave the store.
Finally, an extended example would be DOMO, a fast-growing business management software company that's raised over $ 500 million in funding, has created a dashboard that gathers information to help companies make decisions. The cloud-based dashboard can scale with the size of the company, so it can be used by teams as few as 50 or by much larger enterprises. There are more than 400 native software connectors that let Domo collect data from third-party apps, which can be used to offer insights and give context to business intelligence.
This gives companies using Domo a way to pull data from Salesforce, Square, Facebook, Shopify, and many other applications that they use to gain insight on their customers, sales, or product inventory. For instance, Domo users who are merchants can extract data from their Shopify point-of-sale and e-commerce software, which is used to manage online stores. The extracted information can be used to generate reports and spot trends in real-time, such as in product performance, which can then be shared to any device used by the company.
To conclude, it is evident that AI brings a colossal lot to the table for a wide range of business stakeholders to add convenience and simplicity to customer experiences, while also saving time and real cost for business along with making processes and planning more efficient and future-facing. Debates, nonetheless, should continue to trigger innovative learning solutions around how to offset or reduce some of the ethical concerns that AI brings along with the benefits.