disruption in the making
Leverage the potential of the AI revolution, says Kalpana B, KPMG in India.
Artificial Intelligence (AI) is an idea that has oscillated through many hype cycles over many years, as scientists and sci-fi visionaries have declared the imminent arrival of thinking machines. But it seems we’re now at an actual tipping point. AI, expert systems, and business intelligence have been with us for decades, but this time the reality almost matches the rhetoric, driven by the exponential growth in technology capabilities (e.g., Moore’s Law), smarter analytics engines, and the surge in data*. A look at how AI has made tremendous inroads into the realm of businesses as well as our everyday lives.
Businesses these days have leapfrogged by harnessing technology, and the way they are operating in the present dynamic and competitive environment is stranger than fiction. Customer experience is improving as marketing techniques are getting increasingly scientific. Software tools were enablers until innovators created technology platforms (in a recent digital episode) that not only target the ‘how’ of the improvements but also the ‘who’. Robotic process automation employs software robots that execute repetitive and rule-based processes, while advanced technology dedicated to digital labor beckons intelligent robots to take up a portion of judgment-based decision-making. The advanced side of this digital labor consists of an array of technologies such as Artificial Intelligence (AI), machine learning, natural language processing, and so on. We will lay emphasis on the smarter aspect of the digital labor spectrum and on what the future holds for us.
Moore’s Law summarizes the observation made by Intel co-founder Gordon Moore in the year 1965 that the processing power of computers will double every two years. The Fourth Industrial Revolution by Klaus Schwab deliberates that if this holds true in the future too, computers will attain the same level of processing power as humans in 2025.
Let us first attempt to disentangle the intricacies of technology and understand its wider applicability in shaping the future of businesses.
AI is the simulation of human intelligence by computers in different ways such as vision, speech, and decision-making abilities; it encompasses self-learning, reasoning, and self-optimization. Machine learning is a branch of AI that enables computers to learn without being explicitly programmed and choose an effective course based on the learnings and experience.
To understand this phenomenon better, let us assume that an academy dealing with online courses is piloting a digital marketing campaign in different parts of a country, for instructor-led and self-learning modules. During the course of the campaign, if the machine learning program observes that the self-learning course module is not preferred in a particular region, then automatically the marketing attention will shift to instructor-led courses. In the future, the program will extend this knowledge to other regions sharing similar demographics too, for effective campaigns.
Machine vision is the ability of a computer to ‘see’ just like humans, while speech recognition is the ability of computers to ‘hear’ and understand dictation. Through vision abilities, the application can be extended to understand videos, images, and pictures. This opens a huge window of opportunity to make sense of vast amounts of data present in the format of images and videos. Speech recognition ability allows interpretation of human speech and spoken languages, thereby using the knowledge base to take business decisions. Another technology that has extensive application is natural language processing (NLP), which can understand and generate languages that humans use naturally with computers.
what has happened so far?
Banking, insurance, retail, IT, and telecom have been early adopters of technology. Since technology has been the medium and basis of executing transactions, for long, data captured over the years acts as a natural foundation to adopt smarter technologies. We can expect these sectors to adopt smarter technology sooner than others.
A few growth drivers of Artificial Intelligence are increasing number of internet and smartphone users, increasing traffic in online business, advancements in big data analytics, cloud computing, proliferation of business apps, talent, and fierce competition. AI has entered our day-to-day lives, as business, personal, and social worlds have shrunk within a device for users. Whether they are automated chats about interacting with customers to understand their problems or intelligent segregation of pictures based on weather, appearance, and personality traits, AI has numerous manifestations. Other recent applications of AI that one might have experienced are speech-to-text conversion, face recognition, age prediction, retina recognition, and fingerprint security scanners.
As individuals access bespoke applications, they cursorily permit personal data to be sent to a cloud, leaving behind a digital trail. The magnitude of personal and business-relevant data being generated is humongous and corporates are using it wisely to target the various user segments. Through a shift from data elitists to amateurs, machine learning is becoming accessible to professionals as it is being presented by innovators in a more usable, as-a-service package. With the mushrooming of startups, the idea of making it a practical solution to actual problems is becoming even more realistic.
Machine vision is the ability of a computer to ‘see’ just like humans, while speech recognition is the ability of computers to ‘hear’ and understand dictation.
Vital and time-intensive areas such as onboarding new candidates are being offered through AI-powered recruiting and hiring solutions. Fields such as telemedicine are being transformed by AI solutions for improved diagnosis and lowered costs. Certain approaches are focusing on enriching the online shopping experience through the convenience of chatting on an integrated chatbot platform. Image recognition technology finds huge applicability in e-retail for refining online purchase choices. Analyzing data has been predominantly done by humans over the years, but intelligent platforms today are not only analyzing structured data but also generating reports that communicate in spoken English text.
AI platforms also help B2C sectors such as telecom, banking, and retail in digital marketing campaigns by generating customer-specific content throughout the customer life cycle—by analyzing the demographics and customer touchpoint data. Another potent area of application is contact center automation where cognitive solutions automate inbound and outbound calls. The areas we discussed are sketchy reflections of the countless applications of AI, and this technology is increasingly making a material difference across the operating models of many sectors today.
Organizations will have to reshape themselves by making the most of the overwhelming development in technology, to rise above the noise and provide true value to the consumer and society at large.
We no longer use landline telephones, telegraphs, handheld calculators, pagers, floppy drives, and CDs. The next generation may not even be acquainted with these terms. Technology is changing fast and we have to keep pace with it to avoid being obsolete. As businesses have evolved over past decades, society has also shown keen interest in adopting newer technology. Organizations that were slow to recognize this fact became outdated and finally struggled to exist.
We are surrounded by AI—whether it is choreographed searches or automatic advertisement feed, we have experienced AI in different sizes and proportions. Moore’s Law is not just a theory that suggests the speed of advancements, but a thrilling indication that even society needs to progress in a synchronized way. Competition is no longer with just other incumbents or contestants in the market, but with oneself too. Organizations will have to reshape themselves by making the most of the overwhelming development in technology, to rise above the noise and provide true value to the consumer and society at large.
what could possibly help us in the journey
■ ■ ■ ■ ■ ■ ■ ■ ■ ■ Choose the right capturing mechanisms [Study] the relevance and reliability of data
Stay data-hungry—the accuracy of these systems improve with the variety and size of data sets Imbibe current learnings
Interlock safety with the solutions—embed control mechanisms to reduce the impact of failure Hire the right talent mix and build a motivated team Select the right partners
Stay updated—keep your ear to the ground Be innovative—linking data sets and extracting unapparent associations between them could prove progressive for your business
Make AI an enterprise-wide priority ■