Cognitive Computing simply means tapping into the power of a computing system that can work smarter than us. It is the next step forward in digital evolution where we move further from programmatic computing to self-learning systems.
In the initial days, tabulating data was regarded as computing. Then came the age of programming languages with which software programs were written and deployed.
Now, cognitive computing is pushing the envelope towards an era where systems can learn, think, decide and act on their own without human intervention.
The Brain Power that Propels Cognitive Computing
Cognitive Computing borrows its brain power from Machine Learning algorithms and Artificial Intelligence. Using these technologies, they learn continuously from data received as input during the regular course of operations.
Pattern recognition systems, Natural Language Processing, Data Mining add to the efficiency of these systems. With intelligence that is continuously built up, they are able to foresee patterns and arrive at proactive decisions by anticipating problems and deriving possible solutions. With time, they become completely autonomous and can handle operations without human interference, thus, making real the much awaited future of complete automation.
Cognitive Computing Systems (CCSs) will forge a new winning partnership between machines and humans that will cut costs and improve service models.
It will help:
- Enhance the current level of efficiency by quickening decision making
- Scale the quantum of processes rapidly and consistently
- Accelerate the level of performance by capturing knowledge from real-world
This automation can contribute massive reforms to existing businesses practices that are error-prone or inefficient.
91 percent of retail industry executives familiar with cognitive computing believe it will play a disruptive role in the industry, and 94 percent are likely to invest in cognitive capabilities in the near future. IBM Thinking Like a Customer Report.
Industries where cognitive computing can add machine intelligence include:
- Health Care – Deep analytics of historical patient data and clinical workflow
- Retail – Analysing customer behaviour and refining product suggestions at POS
- Finance – Suggesting stock market movements based on social media behaviour
- Customer service – Chatbots with Artificial Intelligence & Machine Learning that treat customers with proactive suggestions
- Insurance – Usage based insurance, faster claim processing, historical data based underwriting, etc.
- Manufacturing – solves everyday manufacturing challenges ranging from managing production cycles, equipment maintenance to labor safety.
#1. Cognitive Healthcare
Hand written notes, long gestation period to identify disease symptoms, and lack of information have remained primary causes that cripple the efficiency of healthcare professionals. Cognitive Computing can sweep away all those inefficiencies in a single shot by providing insightful information that was impossible with programmatic computing.
Proactive Diagnosis
Cognitive computing can give a computerized approach to search past patient records, prescriptions and disease statistics to predict disease patterns. Big Data analytics helps in improving clinical workflows thus improving healthcare services on a global scale.
Real-life example:
Researchers at University of California, Los Angeles (UCLA), were able to quickly identify people with changes of diabetes by mining thousands of patient records in digital form. The data mining also revealed patterns which helped identify the chances of disease patterns that were previously unknown.
#2. Cognitive Retail
Omnichannel Retail will be one of the biggest benefactors of cognitive computing. From analyzing and understanding fluctuating customer behaviour to forecasting inventory and making possible smart shelves, cognitive retail will change the way business is conducted in the retail industry.
Omni-channel Intelligence
Cognitive retail will help retailers to conquer their customers through providing services that help customers find the right product when they want it and where they want it. It will bring about a paradigm shift in omnichannel eCommerce where data from multiple points like warehouses, vendors, logistics, customer point of sale, etc. should be analyzed and broken down into meaningful information on a real-time basis.
Real-life examples:
Coop Danmark was able to profit by using Cognitive abilities to identify SKUs that can be marked down for a specific period of time for maximum profitability. Similar to Coop Danmark, Lindt – the global chocolate brand and City Beach – the Australian based retailer were also able to boost their retail administration prowess using Cognitive Computing.
#3. Cognitive Finance
The finance industry is one of the toughest industries to decipher real-time information. Currency exchange rates, stock market indices, valuation norms, stock values – there is a massive volume of information which an investor has to digest before making a rational decision.
Predictive Analysis of Markets
With the coming of cognitive finance, investors, CFOs and everybody else relating to BFSI (Banking, Financial Services and Insurance) industry will be able to keep a steady pace with changing financial environment. Thus, it will reduce the amount of risk taken in a financially volatile economy and help improve business profitability.
Some practical applications that will become commonplace includes:
- Proactive supply chain risk mitigation
- Sharpened market intelligence for arm’s length and competitive pricing
- Fraud and error prevention through automation
- Use of AI to predict patterns for cost optimization
- Analyzing social behavior to arrive at stock market outlook
Real-life example:
Trump and Dump bot is a computer program that will estimate the increase or decrease in a company’s stock value based on President Donald Trump’s tweets. Although the idea might seem a bit too far fetched, it is already proven to be realistic.
For instance, on 5th Jan the President tweeted a criticism against Toyota’s plan to build a plant in Mexico. Toyota’s stocks fell by 3.1% immediately after the tweet.
#4. Cognitive Customer Service
Routine and uncertainty alternate wildly in the customer service domain. Customer service agents have to stay updated with the product changes, understand customer perspective and deliver assistance without letting human inefficiencies get in the way. At the same time, the cost should also be maintained at a bare minimum to upkeep business profitability.
Chatbots and Self-service
Cognitive customer service helps achieve all these objectives by providing a cognition based conversation based self-service for customers. Chatbots, driven by conversational technology, is a classic example of such automated customer service. Just like other technology trends, conversational technology will give a new impetus to the Ecommerce sector.
Chatbots with pre-trained customer service skills and industry skills can provide the same, if not better customer service than humans. They offer a personalized shopping experience online, just like how a salesperson in a brick and mortar textile store aid you in selecting the products of your choice. Secondly, chatbots are also empowered with analytic skills and machine learning which improves their efficiency with time.
Real-life example:
VentureBeat surveyed the globe for popular chatbots and zeroed in on some chatbots that were surpassing human customer service.
Instalocate is one such chatbot. Instalocate makes lives easier for travelers by simplifying the task of journey-tracking, resolving compensation issues, fetching best deals from the Internet and so on. These tasks otherwise require numerous calls to the customer service, which often does not yield the best results.
#5. Cognitive Insurance
Cognitive computing in insurance industry is helping insurers like Metlife, USAA, etc. to reduce risks in underwriting, inaccuracies in insurance valuation and reduce claim costs. Predictive capabilities of cognitive computing helps estimate futuristic claim amounts with accuracy based on which financial provisions can be made.
Data-driven Business Models
Data-driven models like Usage Based Insurance are also expected to become mainstream in the near future with the help of cognitive computing. Forbes expects the insurance scene in the United States to improve a lot where legal and claim documents can be closely linked to State law and order using cognitive computing helping insurers assess risk and estimate premiums easily.
Real-life example:
USAA was able to accelerate its policy approval process rapidly based on inputs like applicant’s past clinical data and current medical policies and guidelines. the USAA deployed IBM Watson to check whether a policy application deserved approval or needs to be rejected if it is not in tune with the policies.
#6. Cognitive Manufacturing
Cognitive Manufacturing will heighten the level of interaction between manufacturing equipments and humans. CCS will help workers to know real-time the vital stats of equipment that are difficult to inspect from close quarters or are spread across a large area.
Some emerging applications of Cognitive Computing in manufacturing include:
- Deep search for critical patterns to predict downtimes
- Robotic technicians that can access past data for improving repair quality
- Weather, logistics, context data-driven parts planning
Combined with Internet of Things and the data streams that it creates, Cognitive Computing can deliver data-based strategic inputs for executives to maximize their manufacturing productivity.
Wrapping Up
Cognitive Computing is giving birth to a whole new way of doing business – Cognitive Business. Tech giants like IBM, Microsoft, Intel and a host of other brands have already placed huge bets in this technological evolution.
Cognitive Computing will enhance the way industries and their inbred business operations function. From healthcare that requires delicate attention to manufacturing that requires hard-hitting facts, Cognitive Computing will revolutionize businesses and turnaround profitability. Consequently, it will also improve human living experience in the digital era.
Computing is no longer about programming or building software solutions. Instead it is evolving into a way of living. Man and machine will merge together to arrive at decisions for everyday complex problems. It will take out the guesswork involved and introduce accuracy as a standard trait.
Additionally, it will also help businesses help achieve cost of economy since operations can be easily scaled without locking up capital or resources. Machine learning and Artificial Intelligence will help enterprises and ensure that the systems keep learning and evolving with changing patterns of data input received. A must-have for any business that has to keep up with constant changes in the Digital Transformation era.
