Future Winners Will be Businesses That Get Smarter, Bring Agility

A new economic paradigm is beginning to dawn with emergence of the smart economy, the sharing economy, the circular economy and the platform economy. It is the synergies of these movements which is causing huge social and economic changes in our lives. The impact of technology in our daily lives are unprecedented due to which our businesses and their functioning are being changed. Virtually every single business realm is beginning to transform from an old economic order into a new reality. The digitization of society and the economy has an impact on everything and everyone. Policy makers and decision makers are roused to the transformational process and impact on businesses. All over the world regulators and policy makers are bravely attempting to get a hold on the upheaval through all kinds of initiatives. After all businesses, governments, financial and insurance institutions ought to be able to adapt to consumer needs as they rapidly change not next year or next week, but preferably today, this minute.

Smart business emerges when all players involved in achieving a common business goal — are coordinated in an online network and use machine learning technology to efficiently leverage data in real time, for example online retail, ride sharing, dating and so on. These types of technology enabled models in which most operational decisions are made by machine that allows companies to adapt dynamically and rapidly to changing market conditions and customer preferences, gaining tremendous competitive advantage over traditional businesses.

Technology waits for no one. Without a second thought people start using new machines and devices in their daily lives. We have changed our old landlines and cell phones for a smartphone, adopted to GPS navigation systems in our cars to find our ways, Wikipedia has smoothly conquered Encyclopedia from our mind space just like Google Earth has made Atlas obsolete. From physical banking we moved to digital banking and now into mobile banking and turned to Google Translate instead of dictionary for translating massive amount of text. E-readers and tablets have found a comfortable home in our laps and we stream our music, films and television shows on demand when we feel for it. Past decade has completely redefined our social lives, how we learn, solve problems, help each other and make decisions. Outdated thermostats we replaced by smart ones, old TV sets were replaced by smart television sets offering online films and music access with social media connectivity. Social Networks are apparently offering a way of communicating that satisfy every human urge to present self and leave a mark. People are not hooked to the internet or gadgets, they are hooked onto each other and their social needs are being met right there.

All these technologies are connected online over the internet with people like us, but also with each other, with other technology, other machines and new smart home devices. The common denominator is that they help simplify our lives, making life more comfortable and budget friendly. These are the first set of applications of Internet of Things (IoT) in our homes. As per research firm Gartner, by 2020 there will be near around 700 plus million plus smart connected homes. Huge business opportunities for Virtual Reality (VR) and Augmented Reality (AR) as they are closing the gap between virtual and real world. And the distinction between virtual and real has started to blur. AR will bring us extra (verbal and visual) information which is added to what we see on our smartphone and tablet screens. In the next year or so we might stop differentiating between personal conversation and digital interactions as we are going to share our experience with our robot assistant. We will try our dresses in a virtual dressing room, our cars will drive themselves (Google and Tesla almost reached there) and we stated using crypto currencies already. Block chains are further building transparency in all our business processes.

The boundaries between virtual and physical are being erased in our daily lives. There is no need to talk about online versus offline anymore. They both are merging into one which is bringing a seamless experience. It is becoming unclear with every passing day to differentiate between what is real, what is virtual or augmented in our lives. How are we going to manage to distinguish between what is real and what is not. What is natural and what is artificial is becoming less defined.

Formal dimensions are becoming less defined. 4D printers are about to debut to produce objects without any human guidance or interface, that can change shape in particular circumstances such as rise or fall in temperature. Time and space are becoming less defined. Virtual and Augmented reality and new hologram technology are about to make us believe that we are somewhere else than we actually are. Seems like time and space are becoming one.

Ample computing power and ever emerging digital data are becoming the fuel for machine learning. The more data and more iterations the algorithmic engine goes through, the better its output gets. Data experts come up with probabilistic prediction models for specific actions and then the algorithm churns out loads of data to produce better decisions in real time with every iteration. These prediction models become the basis for most business decisions. Thus, machine learning is more than a technological innovation that is transforming the way business is conducted as human decision making is increasingly replaced by algorithmic output that provides real time actionable insights. Data scientists are identifying and testing on the data points that is providing the insights they seek and then re-engineering algorithms to mine the data. This requires a deep understanding of the business and expertise in machine learning algorithm. Analysts can easily check their assumptions on parameters to be added or removed, weightage for the specific and respective user behavior, etc. The future of business will be highly dependent on certain digital

Re-calibrated algorithms produce increasingly accurate predictions. Today’s data experts essentially identify and test which data points provide the insights they seek and then engineer algorithms to mine the data. This requires both a deep understanding of business and expertise in machine learning algorithms to smart business model driven by digital. To become a smart business, organizations must enable as many operating decisions as possible to be made by machines fueled by live data rather than by humans supported by their own data analysis.

Data capture processes for many businesses is becoming utmost priority. Live data is becoming essential to create the feedback loop which is the basis of machine learning. Considering every emerging business; for example the rental business in Goa for car and bikes, emergence of digital channels have leveraged mobile telephony, the internet of things (in the form of smart bike locks) and existing mobile payments and credit systems to verify with live data on the entire rental process. The process is simple, intuitive and only takes several seconds. Data driven rental process highly improves the consumer experience. On the basis of live data rental operators dispatch bikes to the requested destination with all the technology integrations where the users want them, making it much cheaper than the earlier years. Most of these type of businesses that seek to be more data-driven typically collect and analyze information in order to create a casual model. The model then isolates the critical data points from the mass of information available. That is not how smart businesses using data now. Instead they are capturing all information generated during exchanges and communications with customers and other users as the business is operating and letting the algorithms figure out which data relevant.

In a small business all activities not just knowledge management and customer relations are configured using software to automate the decisions affecting them. In a traditional business software made processes and decisions flow more rigid and often became a confinement. In contrast the dominant logic for smart business is becoming reactivity in real time. The first step is to build a model of how humans currently make decisions and find ways to replicate the simpler elements of that process using software, which is as per Martin Lindstorm’ sBuylogy not always easy given that many a times human decisions are built on common sense or even subconscious neurological activity. The example Amazon’s online and physical retail going to the next level with mobile apps (apps only shopping experience) is driven by continuous software integration of the retaining process with advanced algorithm. Through various bot integration it has enables various tools on its platform. Using the tools, the sellers greet buyers, introduce products, and negotiate prices and bargain, just as people do in a traditional market place. Various software providers like Magento (taken over by Adobe), Shopify, Big Commerce, etc. have developed sophisticated tools using advanced algorithms from real-time user behavior wherein the sellers greet buyers, introduce products, conduct sampling and surveys, negotiate prices and so on just as merchants and customers do in a traditional market place. Also these platform enablers have developed set of software tools that help sellers design and launch a variety of sophisticated online shop fronts. Once these online shops are up and running, sellers are accessing other software products to issue coupons, offer discounts, run loyalty programs and conduct other customer relationship activities, all of which are coordinated with one another. Because most software today are running online as a service, an important advantage of software modeling of a business activity is that the live data is being collected naturally as part of the business process, building the foundation for the application of machine learning technologies.

While working on various digital transformation projects for different verticals and consumer segments in APAC and US markets, I have realized that with many interconnected players, business decisions require complex coordination. The recommendation engines of the above mentioned hybrid ecommerce platforms works with the inventory management systems of sellers and with the consumer profiling system of various social media platforms. And the transaction system works with discount offers and loyalty programs as well as feed into associated logistics network. Communication standards such as TCP/IP and APIs (application programming interfaces) are playing critical role in getting the data flowing among multiple players while ensuring strict control of who can access and edit data throughout the entire ecosystem. APIs are allowing different software systems to talk and coordinate with each other online and are becoming central to the online development platforms, be it eCommerce, marketing automation and central CRM of a business. Businesses are needing more and more support from the third party developers. Like Amazon developed various APIs through its AWS platform for use by independent software suppliers. Today merchants on AWS subscribe to 100+ software modules on average and the live data services they enable are drastically decreasing the merchants cost of doing business. During 2017 while working for an ecommerce major in Middle East, it took tremendous efforts for me and my team to build a common standard so that data could be used and interpreted in the same way across all of the business units (based in multiple geographies) of the organization. Next challenge was figuring out the right incentive structures to persuade business units to share the data they have is an important and ongoing challenge. We found out that the more data flowed across the network, the smarter the business units became smarter bringing more value to the customers and to the businesses entire business ecosystem.

In 2017 we have introduced an AI-powered Chabot to help queries from the field customers. This was different from the mechanical service providers familiar to most people that are programmed to match customer queries with answers in their repertoire. We trained the chatbots with the support of experienced representatives from the customer care team of the business who knew all about the products in their respective categories and were well versed with the algorithms of the ecommerce giant’s platforms, return policies, delivery costs, how to make changes to an order and other hundreds of such common questions that customers ask for. Using a variety of machine learning technologies such as semantic comprehension, contextual dialogues, knowledge graphs, data mining and deep learning, these chatbots rapidly improved their ability to diagnose and fix customer issues automatically, rather than simply returning static responses that prompt the consumer to take further action. These bots started confirming with the customers that the solution presented was acceptable and then executed the same for respective customers flawlessly. We have avoided unnecessary human work.

Currently chatbots are making significant contributions to the top line of modern online business. Take the examples of the leading banking, insurance and online travel portals for whom I have designed the marketing automation strategy, who are the early adopters to chatbots have disclosed recently that their bots sales are 18% higher than their top human sales associates. The need for human customer representatives to deal with complicated or personal issues will always be there. But the ability to handle routine queries through a chatbot is very useful, especially on a day when high volume sales is expected like Amazon Great Indian Summer Sale or Flipkart Big Billion Days. Chatbots are handling 75% of the customer queries, responding to millions of queries in a day.

Later on I realized that once the business has all its operations online, it will experience the data deluge. To assimilate, interpret and use the data to its advantage, the business must create models and algorithms which will make explicitly the underlying product logic or market dynamics that the business will put efforts to optimize. This is a huge innovative and strategic level undertaking that will require many new skills due to which the enormous demand for data scientists, statisticians and economists are increasing with every passing day. The challenge they will face is to specify what job they want the machine to do and what the humans have to do, need to be very clear about what constitutes a job well done in a particular business settings in the future. The business of future need to be very clear about the set expectations and to be tailored to each stakeholders’ needs. Without the support of machine learning this will not be possible. The market place we have created for the ecommerce major in US, today facilitates through its customized web page with a selection of products curated from the billions offered by the millions of sellers. The selection is generated automatically by the advanced recommendation engine created by me and my team. Its algorithms that are designed to optimize the conversion rate of each visit, churn data across the business’s online platform integrated with offline touch points, from operations, to customer service to security.

The future of business is at the threshold of the next digital super expressway where humans and machines will converge. We need to enrich the pool of data that our business uses to become smarter, embed the software model to put the workflow and essential actors online, institute standards and APIs to enable real time data flow and seamless coordination. To embrace these new competencies, the businesses will need new kind of leadership who need to understand what the future will look like and how their verticals/industries will evolve in response to societal, economic and technological changes. New age leaders are no longer managing, rather they are enabling their people to innovate and facilitate the core feedback loop of user responses for firm decisions and smart executions where machine learning algorithms take on much of the burden of incremental improvement by automatically making adjustments which is increasing efficiency across all business touch points. Our most important task will be to cultivate creativity to increase the success rate of innovation rather than only improving the operational efficiency.

To summarize, digital native companies such as OLA Cabs, Uber, Flipkart, Amazon, Alibaba, etc. have the advantage of born online and data-ready. In future their transformation to smarter business is a natural progression as they have proven that their model works and is transforming the old industrial economy. Hence it’s time for other organizations to understand and apply this new business logic which may look technologically intimidating but more feasible. The commercialization of cloud computing and artificial intelligence technologies has made large-scale computational power and analytical capabilities accessible to everyone. With more adoption to data driven computing, the real time applications of machine learning are now possible and affordable in overall business environment. The rapid development and adoption of internet of things (IoT) is further digitizing our physical surroundings, providing more quantum of data. These developments are accumulating in the coming future, the winners will be businesses that get smarter and agile than their competitors.

(This article was first published in The Economic Times — https://economictimes.indiatimes.com/small-biz/security-tech/technology/future-winners-will-be-businesses-that-get-smarter-bring-agility/articleshow/69370290.cms )