Using cognitive tech to connect customers to business operations

Creating an engaging customer experience is more readily achieved by embedding increasingly sophisticated digital and cognitive technologies into the very fiber of an organization’s processes, from its front office right through to its back office.

Successful organizations are both strategic and nimble, leveraging the power of real-time data to reduce inefficiencies and enhance their effectiveness. Agile businesses predict their customers’ needs before their competitors do. More importantly, they have the ability to act on those predictions, which is essential for getting ahead in today’s global digital economy. Investment in cognitive technologies (those which mimic human thinking and have the capability to learn) will be required for having intelligent operations in the enterprise. An intelligent enterprise has the ability to inform and implement better business decisions by leveraging data and smarter technology.

In a study conducted in partnership with IPsoft, HfS Research interviewed 100 C-Suite executives to understand their views, expectations, and strategies, along with their investment plans for cognitive technologies. This report discusses opportunities and challenges that business leaders see for moving their organizations toward being truly intelligent—knowing their customers, using technology most effectively, and infusing cognitive technology into the fiber of their business operations.

Table of Contents

  • Smart investments in cognitive tech will help solve business problems and collapse internal barriers
  • C-Suite executives seek to align operations with business outcomes
  • Cognitive Agents are at the Forefront of Investments
  • Cognitive Tech is Driving Intelligent, Self-Learning Business Operations
  • Intelligent operations of the future: cognitive is a lever for theOneOffice core
  • OneOffice by Definition
  • Impediments to OneOffice: The Challenges of Aligning the Enterprise
  • How to track the impact?
  • Using cognitive glue to construct OneOffice

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Source: Hfs-Using cognitive tech to connect customers to business operations

Leveraging Cognitive Computing for Business Gains

Cognitive computing systems have been one of the trendiest aspects of modern day technologies. Deploying computerized models to simulate the human cognition process to find solutions is what cognitive computing systems do. A cognitive computing system is used in complex situations for ambiguous and uncertain outcomes. The term cognitive computing is closely associated with IBM’s cognitive computer system, Watson and overlaps with Artificial Intelligence (AI) using the same technologies to power cognitive applications, like neural networks, expert systems, virtual reality (VR) and robotics.


The Technology Behind Cognitive Computing

Cognitive computing systems can synthesize data from multiple information sources, analyzing the context and conflicting evidence to offer the best-suited solutions. For the best solutions, Cognitive computing systems apply self-learning technologies which use data mining, natural language processing (NLP) and pattern recognition to mimic how the human brain works.

Cognitive systems aggregate vast amounts of structured and unstructured data which are fed into machine learning algorithms for further analysis. With technological upgrades, cognitive systems are poised to refine the way they identify patterns and process data to anticipate new problems and give the best solutions on a case to case basis

To achieve the best solutions, cognitive computing systems must employ five key attributes, as pointed by the Cognitive Computing Consortium.

•  Adaptive: Cognitive systems must be flexible to learn and relearn information changes as priorities change. These systems must be adaptive to real-time dynamic data adjustments as business environment change.

•  Interactive: Human-computer interaction (HCI) is a critical component that is indispensable to cognitive systems. User interaction with cognitive machines, processors, devices and cloud platforms for requirement gathering must be top notch.

•  Iterative: Cognitive computing technologies should be able to perform iteration to the maximum levels. They must identify problems by asking questions or pull additional data if a problem is vague or incomplete by historical analysis about similar situations that have previously occurred.

•  Contextual: Understanding context is critical to the business problem, thus cognitive systems must understand, mine and identify contextual data like syntax, domain, location, time requirements, user profile, tasks or end goals. This contextual data may be drawn from multiple sources of information, like visual, auditory structured and unstructured data or sensor data.


Harnessing the Power of Cognitive Performance Computing

Cognitive performance computing has taken the leaders in business, management consulting and government around the globe by storm.  As the policymakers analyze and debate how they can leverage cognitive computing for their work, cognitive operations are increasingly being adapted in organizations where there is a constant set of unknowns. Senior officials, trusted advisors are setting the best practice for internal users and clients alike. Regulators are working forward to create the laws requiring cognitive compliance from organizational leaders. The next evolution of cognitive computing answers to fulfilling the organizational goals including helping executives and management consultants work through their risk-reward trade-offs matrix also called as cognitive performance. Organisations focus to enhance their cognitive performance as it leads to improved critical thinking, stakeholder communications, advisory collaboration, decision-making, uncertainty monitoring and cognitive compliance.


Mixed Bag Performance

So far the initial results to leverage the gains from cognitive computing have been mixed. Even Watson sometimes gets into a trouble filtering solutions from often conflicting datasets. Cognitive system scores high as it has the ability to learn-relearn and adapt to changing environments. This leads them to improve their results without manual coding. The path to autonomy is leading to a very real possibility that very soon business systems will be largely managed by autonomous, self-learning platforms.

But the path to this development is a two-way street. As cognitive evolves and change to exponential learning and continuous, self-directed optimization, business enterprises must learn to adapt to a radical change in their working to gain an advantage in blockchain, the IoT and advanced 3D printing technologies.

To successfully navigate this transition businesses and organizations need to adopt changes with a clear mind. As industries go digital there will be an opportunity to create service driven lines or entirely new ones for an increasingly connected world.


Digital Footprint in Cognitive Performance

Many organizations have a complex structure involving multiple teams who are responsible for operating processes and digital transformation. These teams are organizations and business enterprises that spend time to embrace the cognitive performance of their teams and will leapfrog their competitors as they leverage the competition.  There is still a long, bright road ahead for reaping the maximum gains from cognitive performance.

Cognitive technology may be referred to sometimes as “thinking” computer, but this is not entirely true and correct. The mysteries of the human thought and consciousness are still unfathomed, as cognitive systems make a sincere attempt to mimic the human intellect through highly advanced algorithms. A definite change has been made as cognitive solutions can outperform the human brain, particularly in processing large, complex datasets. But ultimately, the human brain is the winner for its unique and mysterious thinking and capability to achieve the unconquered.

Source: Cognitive Computing for Business Gains


Digital Operations through AI-Driven Software Robots

A common problem affecting enterprise companies is how to best expand revenue growth, while at the same time, minimize cost growth. Businesses like Amazon and Google have a significant operational advantage through the use of digital technologies like artificial intelligence (AI), which drive exponential growth while at the same time, contain costs. This video introduces how digital operations can be performed through AI-driven software robots.

Source: etftrends-Digital Operations through AI-Driven Software Robots

Robotics and Cognitive Automation Required to Keep Banking From Drowning in Data

Most financial institutions realize that the volume of data and analytics required for future success exceeds current processing capabilities. To maximize the potential of machine learning, natural language processing, chatbots, robotic processing automation and intelligent analytics, new technologies will be required.

Subscribe to The Financial Brand via email for FREE!The average bank is drowning in data, from neatly structured numbers to more abstract and hard-to-capture inputs from voice, social media and mobile platforms.

IDC estimates the global generation of data will grow from 16 zettabytes (essentially, 16 trillion gigabytes) to 160 zettabytes in the next ten years, a 30% annual growth clip. And Deloitte forecasts that unstructured data – that hard-to-capture category of data; you can find a primer here – is set to grow at twice that rate annually, with the average financial institution accumulating nine times more unstructured data than structured data by about 2020.

The Reality of Data Overload

The explosive volume of unstructured data that banks are able to process every minute of every day is quickly approaching the point where it can no longer be managed by humans alone. What many banks are realizing is that technology possessing the power to mimic human action and judgment – especially at high speed, scale, quality and lower costs – is necessary in order to keep pace with the looming unstructured data surge on a number of different fronts.

In other words, all of the different technologies that encompass robotic and cognitive automation is fast becoming indispensable necessities to the industry’s data challenge. You’re going to be hearing a lot about this category in the year to come, which includes machine learning, natural language processing, chatbots, robotic processing automation, and intelligent analytics.

The industry’s growing data challenge raises a very important question: Will 2018 be the year of robotic and cognitive automation technologies’ mass adoption by banks big and small?

More Data Requires Greater Automation

The foundation is there for robotic and cognitive automation technology to grow rapidly in the year ahead. It is also being reflected in the marketplace. According to Deloitte’s 2017 “State of Cognitive” survey, 87% of cognitive-aware financial services professionals say that such technologies are important to their products and services, 88% say these technologies are a strategic priority, and just over 35% have invested more than $5 million thus far in such capabilities.

Admittedly, the large, global players in the banking and capital markets sector are in many ways ahead of the curve when it comes to experimenting with, developing and deploying robotics and cognitive solutions. We expect rapid, more democratic adoption across much larger number of banks driven by three factors:

First, banks will increasingly incorporate more information from unstructured data. Regardless of whether a bank has hundreds of thousands or millions of accounts, the rapidly expanding set of unstructured data linked to today’s customers demands that banks will need to develop new muscles to handle that data differently. On a tactical basis, executives will need to evaluate their current processes to determine how to use cognitive technologies to incorporate and sift through the large amount – and different types – of unstructured data.

For instance, banks have historically relied solely on customer-provided data and external sources like credit bureau reports in their account opening process. Today, however, banks must also have more information about an individual or company to affirm an applicant’s identity, sometimes resorting to scouring the Internet or social media for this. This could easily compute to thousands of data points for a single customer.

Second, there is a rapid increase in the level of automation of every bank process. Robotics and cognitive technologies are driving this adoption. Robotics on its own is already well integrated across many banks to complete simple rules-based tasks such as opening email attachments and completing e-forms.

However, the cognitive, analytical element of such tasks is still experimental and siloed. The coming year may be a key turning point in that we are going to see the combined power of robotic and cognitive capabilities become the de facto solution at banks for addressing business process challenges.

Simplification for Improved ROI and a Better Experience

The combination of robotics and cognitive automation could play out in more complex parts of a bank’s business and yield bigger benefits. One such example would be the repairing of payment transactions that currently require manual fixes to remediate issues ranging from the mundane (like sender/receiver information being incomplete) to the highly complex (the payment being a potential fraud case.) If, by combining robotic and cognitive technologies, an average bank could auto-clear even 50% of the original breaks, that could translate into tens of millions of dollar and significantly shorter processing time.

Finally, we believe that automation as a whole will inevitably become transformative for every business process. This will likely begin to play out in 2018. We already are seeing examples of such transformation in pockets — from claims processing completed in seconds, to retail accounts opened in minutes, to loan processing in minutes and hours. Typically, these activities take days or weeks to complete.

No matter the size of your financial institution, the business case for robotic and cognitive automation is robust. Aside from managing dizzying levels of data, it can provide a host of other benefits, including reducing costs, lowering error rates, improving customer churn by providing a markedly higher level of service, increasing the scalability of operations, and improving compliance.

Exploring and adopting these technologies will be critical in order to maintain an edge over competitors in the marketplace and to stay relevant, both next year and in the years to come.

Source: and Cognitive Automation Required to Keep Banking From Drowning in Data

Robotic process automation is killer app for cognitive computing

Robotic Process Automation (RPA) is an increasingly hot topic in the digital enterprise. Implementing software robots to perform routine business processes and eliminate inefficiencies is an attractive proposition for IT and business leaders. And providers of traditional IT and business process outsourcing facing potential loss of business to bots are themselves investing in these automation capabilities as well.

While the basic benefits of RPA are relatively straightforward, however, these emerging business process automation tools could also serve as en entry point for incorporating cognitive computing capabilities into the enterprise, says David Schatzky managing director with Deloitte.

By injecting RPA with cognitive computing power, companies can supercharge their automation efforts, says Schatzky, who analyzes the implications of emerging technology and other business trends. By combining RPA with cognitive technologies such as machine learning, speech recognition, and natural language processing, companies can automate higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

Some leading RPA vendors are already combining forces with cognitive computing vendors. Blue Prism, for example, is working with IBM’s Watson team to bring cognitive capabilities to clients. And a recent Forrester report on RPA best practices advised companies to design their software robot systems to integrate with cognitive platforms. talked to Schatzky about RPA adoption rates, the budding relationship between software robots and cognitive systems, the likelihood that the combination of the two will replace traditional outsourcing, and the three steps companies should take before implementing RPA on a wider scale. Where are most companies in terms of their adoption of RPA?

David Schatzky, managing director, Deloitte: RPA is a new topic to some and a well understood one to others. More and more IT leaders have heard of the term and at least know what it is in principle. Adoption thus far is pretty modest. RPA has been more widely adopted in Europe and Asia than it has been in the U.S. And even those companies in the U.S. that have adopted RPA are typically just piloting it. Why did RPA catch on more rapidly in Asia and Europe?

Schatzky:That’s due to the level of business process outsourcing that has taken place there. Asia is the hope of business process outsourcing and European companies have been eager to cut the costs of onshore operation using RPA. Also, one of the leading RPA companies, Blue Prism, is based in Europe. Why are you focusing on the potential combination of RPA and cognitive computing systems in particular?

Schatzky: I think it will help to broaden the application of RPA and increase the value it delivers to the companies that adopt it. Cognitive technology is progressing rapidly, but many companies don’t have a clear path to taking advantage of these technologies. They’re not sure how and where to put them to use.

RPA is a platform that can provide clear use cases for applying cognitive capabilities. Companies can install it to automate processes and it provides a framework or platform to integrate with cognitive systems to take automation to the next level. It’s almost the ‘killer app’ for cognitive computing.

RPA is very useful technology, but it’s not terribly intelligent technology. It only performs tasks with clear-cut rules. You can’t substitute RPA for human judgment. It can’t perform rudimentary tasks that require perceptual skills, like locating a price or purchase order number in a document. It can identify a happy customer versus an unhappy customer. Cognitive takes the sphere of automation that RPA can handle and broadens it. Where will be the most beneficial use cases for using RPA in conjunction with cognitive technology?

Schatzky: A lot of them are in the front office: classifying customer issues and routing them to the right person, deciding what issues need to be escalated, extracting information from written communication. Who tends to lead these RPA efforts—an IT leader or a business process owner?

Schatzky: It’s mixed. Sometimes it’s led by the process owner in the business. They learn about RPA and identify an opportunity to deploy it and improve efficiency. In other cases, IT has been leading the effort. It’s indicative of the broader trend of tech-centric decision being made increasingly in the business and not just IT.

Source: – Robotic process automation is killer app for cognitive computing

AI, Cognitive Computing To Disrupt Enterprises

IDC is forecasting big growth for cognitive computing and AI in the next 5 years. This infographic shows the growth, industries, and use-cases for these technologies.

What used to be science fiction is now an accepted path for IT. Multiple IT analyst firms are predicting that artificial intelligence technologies will become important components in future IT organizations. Indeed, at the Gartner Symposium, AI was simply another accepted factor in almost every system that will be created in the next decade.

That predicted acceptance is also reflected in new market forecast from research firm IDC. IDC’s Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, which offers a full market forecast over the forecast period of 2016 to 2020, with expected compound annual growth of 55.1%

“Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process,” said David Schubmehl, research director for cognitive systems and content analytics at IDC in a prepared statement.

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“Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organizations to be planning and undertaking strategies that incorporate these wide-ranging technologies,” he added.

But it’s not just about startups. Enterprises will play a big part, too, or risk being subsumed by digital disruptors, according to IDC.

“Identifying, understanding, and acting on the use cases, technologies and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant.”

IDC says that enterprises across a broad range of industries will be able to enable cognitive systems and AI by applying algorithms and rules-based logic to data flows.

Source:, Cognitive Computing To Disrupt Enterprises

Moving From Hype to Reality in Automation and Cognitive Computing

The use of computers to automate human tasks and simulate the human thought process—the field now widely called cognitive computing—is attracting the attention and investment of some of the most well-known names in technology. More and more often, we are seeing automation and cognitive computing solutions that use statistical modelling, machine learning, natural language processing and other sophisticated capabilities to accomplish complex and learned operations. Google’s RankBrain artificial intelligence system processes search data to produce increasingly relevant search results based on what it has “learned” from past searches. Twitter has recently announced the acquisition of Magic Pony Technology that uses neural networks and machine learning for visual processing. Jack Dorsey, Twitter CEO and co-founder said, “Machine learning is increasingly at the core of everything we build at Twitter.”

For the past few years, advances in automation and cognitive computing also have been making their mark on the IT and business process services world, making certain repetitive, rules-based and mundane tasks faster and less labor-intensive. Because they can make such a dramatic impact in specific domains, these technologies are changing the way companies operate. Expectations for these solutions are high—and potential returns are great. For many enterprises, robotic automation has delivered 20-50 percent direct savings.

Though it’s easy to get caught up in imagining the potential of these solutions, their efficacy in the context of IT services can be validated by just a couple of simple proof points:

Do they achieve higher efficiency? How much faster, cheaper and more consistently than humans do software bots complete repetitive tasks?
Do they improve solutions for complex problems? How well can cognitive technologies solve (or assist a human in solving) higher-order problems that yield better results than is possible using human judgement and data?
While automation solutions can help achieve the first of these, cognitive solutions, powered by artificial intelligence, can help achieve the second. But various automation and cognitive solutions are at differing levels of maturity, making it difficult to know which will work best in any given environment. Enterprises need to be able to reap the efficiency gains in the short-term, while they experiment and solve higher-order problems in the longer term.

In today’s market, we are seeing four basic models for implementing automation and cognitive computing solutions.

Embedded automation
In this operating model, the outsourcing service provider drives the automation initiative to achieve higher efficiencies for specific tasks in a traditional outsourced delivery model. The investments in automation and the benefits (typically cost savings) are shared with the client, depending on the nature of the commercial model. Since the service provider drives the automation, the client has less control over the nature and degree of automation. This model is ideally suited for less complex use cases.

Automation as a Service
Here the enterprise client identifies an objective for implementing automation and engages an automation-savvy provider to automate these specific process areas. To support the initiative, some enterprises create an internal automation center of excellence that engages with the ecosystem and internal business units to evangelize and manage change. Because the enterprise drives the initiative, it realizes the benefits (and savings) and compensates the automation provider for the services. This model is best for clients that prefer more control over the automation initiative and for use cases that are low-to-medium complexity. Automation as a Service is the most mature operating model in today’s market.

Provider-owned point solutions
Certain service providers have invested in developing best-in-class point solutions for specific domains. Most of these solutions contain elements of cognitive technology, such as social listening for marketing, image matching and natural language processing. This model is ideal for cases in which the business problem is complex and there are specialized solutions available in the market. In such cases, enterprises find it more beneficial to plug in such point solutions than trying to build their own, though of course this means the intellectual property is the provider’s or software vendor’s.

Client-incubated innovation labs
This is ideally suited to address complex industry-specific problems for which solution components (but not whole solutions) exist. Given the iterative nature of such pursuits, this model works when a buy-side enterprise partners with a capable provider to set up a joint innovation lab to develop a custom solution in an agile approach. The ownership of the intellectual property and commercial model varies depending on the capabilities and investment of the two parties. Though the client-incubated innovation lab model is the least mature in the market today, it shows the greatest promise for taking advantage of cognitive software solutions that provide competitive advantages.

Automation, driven by A.I., and cognitive computing offer exciting opportunities to help organizations drive business results. Understanding which model is the best fit is the first step to enabling successful automation and cognitive implementation.

Source: From Hype to Reality in Automation and Cognitive Computing

CaaS: What CIOs Can Expect and its Possible Disruption on Enterprises?

Intelligent Personal Assistants (IPAs) have been here for almost two decades. Most of us might still remember Clippy, the interactive paper clip in Microsoft Office. Clippy helped beginners navigate through the intricate features of Microsoft Word and Excel. When smartphones took over the world, there was new kind of IPAs that were introduced; the kind which combined machine learning and cognition based computing. They were called cognitive apps. Apple’s Siri, Google Now, and Microsoft Cortana are the pick of the bunch.

CaaS platforms are third-party cloud-based operating systems which enable apps to intelligently interact with their users. Almost every major IT company out there is now working on their own CaaS platform including Google, Apple, Amazon, and IBM. Many experts have predicted that in a couple of years competition between CaaS platforms will replicate the OS wars of the 80s and 90s. Siri, launched in 2011, was probably the most popular. Apart from the usual acts like providing weather information and hotel updates, Siri is also known for having a wry sense of humor. Now it is not that hard to imagine applications or software possessing cognitive abilities like Siri.

So What do Businesses Gain from CaaS?

CaaS can help unlock the mysteries of big data and ultimately boost creativity and productivity of professionals and their teams, of industries and organizations, as well as the GDP of regions and nations. IBM Watson’s recent acquisition and deployment of Cognea offers an indication of the potentials of AI as a business and the areas where the market still needs development.

In an interview to Information Age, IBM’s Senior Vice President John E. Kelly stated, “We’re on the cusp of the ‘third era’ of computing- one of cognitive computing. In the age of tabulating machines, vacuum systems, and the first calculators, we fed data directly into computers on punch cards. Later on, in the programmable era, we learnt how to take processes and put them into the machine, controlled by the programming we inflict on the system. But in the forthcoming era of cognitive computing, computers will work directly with humans ‘in a synergetic association’ where relationships between human and computer blur.”

Cognitive systems are capable of offering limitless opportunities to enhance professional decision-making in diverse sectors. For example, the medical and legal professions represent key industries on which cognitive computing is primed to make an early as well as significant impact. These professions depend heavily on an enormous amount of information that is constantly being rejuvenated through the likes of publications, discoveries, precedents, and advances in technology. In reality, experts estimate that the half-life of medical knowledge, or the time it takes before new information becomes a blast from the past, is merely seven years. Therefore, CIOs in medical and legal firms can look forward to some exciting new applications once CaaS “touches down” their respective sectors.

Furthermore, it is impossible for a single human to take in the constant surge of new information, let alone understand it in the framework of existing knowledge. However, cognitive systems such as Watson or Amelia can help with it. Both Watson and Amelia are built to absorb millions of pages of literature and journal articles while updating their knowledge base, constantly.

In addition to the supreme computational capabilities of computers, these tools give an opportunity for IT professionals to improve upon their expertise and decision making. For an example, a cognitive system may help indicate medical professionals with confidence intervals as to the likelihood of certain diagnosis.

Apart from healthcare, financial services, IT, telecom, and insurance sectors will also witness huge transformation once when these industries start to leverage the capabilities of cognitive computing a few years down the line. Moreover, cognitive computing in partnership with IoT connected devices may even play a greater role in mitigating cybersecurity issues.

The Contenders

According to the latest reports, there are more than 500 companies focused on developing cognitive computing systems. IBM has invested almost 26 billion dollars on its cognitive computing platform, Watson, and is betting big on the technology to take the IT behemoth forward. But of course, IBM is far from the only player in the space with so many other contenders including startups nipping on its heels. Google also has invested sizable sums of money into this area. In 2014, Google went on a crazy shopping spree, acquiring about 30 companies with at least four focusing on artificial intelligence. Cognitive Scale, a startup company founded by an ex-IBM Watson pioneer is making waves by utilizing cognitive computing to provide ‘insights as a service’–the next disruptive technology within CaaS.

Another interesting front-runner in the space is Microsoft. With its Project Adam Microsoft has made huge strides in its AI efforts and it is continually raising the heat up with its “Cortana” technology as a front end. We can already do simple things like talk to an Xbox, but the dominance of Microsoft’s office tools like Word, SharePoint, and Outlook point to a much larger impact. Microsoft has the opportunity to provide every user, let alone businesses, with a smart personal assistant. In a few years from now, we will start to see the rise of truly useful virtual assistants.

Indeed, cognitive computing is all set to make a radical disruption in enterprises’ workflows faster than many people’s expectations. According to Deloitte, by the end of this year, about 80 percent of the world’s largest enterprise software companies will integrate cognitive computing applications into their products. By 2020, Deloitte expects the statistic to reach 95 to 100 percent. For years, artificial intelligence as well as cognitive computing was seen as pure fiction or something which was not anticipated to turn up in the next 20 years or so. Well, what to say, we have reached our targets before deadline!

Source: -CaaS: What CIOs Can Expect and its Possible Disruption on Enterprises?

What is Cognitive Computing?

Although computers are better for data processing and making calculations, they were not able to accomplish some of the most basic human tasks, like recognizing Apple or Orange from basket of fruits, till now.
Computers can capture, move, and store the data, but they cannot understand what the data mean. Thanks to Cognitive Computing, machines are bringing human-like intelligence to a number of business applications.
Cognitive Computing is a term that IBM had coined for machines that can interact and think like humans.
In today’s Digital Transformation age, various technological advancements have given machines a greater ability to understand information, to learn, to reason, and act upon it.
Today, IBM Watson and Google DeepMind are leading the cognitive computing space.
Cognitive Computing systems may include the following components:
· Natural Language Processing – understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.
· Machine Learning with Neural Networks – algorithms that help train the system to recognize images and understand speech
· Algorithms that learn and adapt with Artificial Intelligence
· Deep Learning – to recognize patterns
· Image recognition – like humans but more faster
· Reasoning and decision automation – based on limitless data
· Emotional Intelligence
Cognitive computing can help banking and insurance companies to identify risks and frauds. It analyses information to predict weather patterns. In healthcare it is helping doctors to treat patients based on historical data.
Some of the recent examples of Cognitive Computing:
· ANZ bank of Australia used Watson-based financial services apps to offer investment advice, by reading through thousands of investments options and suggesting best-fit based on customer specific profiles, further taking into consideration their age, life stage, financial position, and risk tolerance.
· Geico is using Watson based cognitive computing to learn the underwriting guidelines, read the risk submissions, and effectively help underwrite
· Brazilian bank Banco Bradesco is using Cognitive assistants at work helping build more intimate, personalized relationships
· Out of the personal digital assistants we have Siri, Google Now & Cortana – I feel Google now is much easy and quickly adapt to your spoken language. There is a voice command for just about everything you need to do — texting, emailing, searching for directions, weather, and news. Speak it; don’t text it!
As Big Data gives the ability to store huge amounts of data, Analytics gives ability to predict what is going to happen, Cognitive gives the ability to learn from further interactions and suggest best actions.


Source: is Cognitive Computing?

How cognitive computing is changing IoT

Cognitive computing means giving computers the ability to work out complex problems for themselves. Just like humans, cognitive computers benefit greatly from experience, learning better ways to solve problems with each encounter. When a traditional system of rules finds a task impossible, cognitive computing sees only an opportunity to expand its knowledge.

The necessity for cognitive computing in the Internet of Things (IoT) arises from the importance of data in modern business. In the smart IoT venues of the future, everyone from startups to enterprises to homeowners will use data to make decisions using facts rather than instincts. Cognitive computing uses data and responds to changes within it to make better decisions on the basis of specific learning from past experiences, compared with a rule-based decision system.

How we define that data is changing, though. Soon, data itself will require this level of computing to extract, making this new method even more valuable to the development of the IoT.

Cognitive computing implications for IoT

While we are still a long way from talking to our operating systems like they’re our friends, cognitive computing has some immediate applications in the IoT that will allow businesses to use their devices to their fullest potentials.

Consider cognitive computing from a perspective of its immediate return on investment. While no computing system is close to true artificial intelligence yet, breaking up the duties of the cognitive machine into smaller tasks allows it to perform cognitive duties in specific fields with great success. Through bite-sized chunks of cognitive computing such as planning, forecasting, reasoning, and recognizing information such as text and images, companies can incorporate cognitive computing into their existing IoT and immediately reap the benefits.

The banking industry already has several uses for cognitive computing in the IoT,specifically in fraud detection. Previously, detecting fraud relied on rules-based analysis. Is the card being used in another state? Is the card being used for a foreign transaction at an odd hour? With cognitive computing, those rules become small parts of a more comprehensive whole, allowing banks to learn consumers’ spending habits, project the likelihoods of future purchases, and put a freeze on a card if the usage pattern indicates the card is being used fraudulently.

As cognitive computing and the IoT grow together, businesses big and small will benefit from the autonomous capabilities of the new technologies.

Improving productivity through technology

In the near future, an IoT powered by cognitive computing will lead a revolution in increased productivity. As more autonomous systems enter the IoT, businesses will need to learn new skills to take advantage of the expanded potential.

Cognitive computing’s ability to forecast more accurately means businesses must become more familiar with anticipatory and predictive systems. As the communication abilities of the technology become more robust, users will need to learn how to respond to and interact with the devices’ queries. Businesses will need to train decision makers in interpreting the advanced data models that cognitive computers can produce in order to reap the full benefits of the technology.

Eventually, cognitive computing in the IoT will lead to products that can make instant, autonomous business decisions without human intervention. From customer interaction to manufacturing and maintenance of equipment, processes that once required guesswork and reactive management will have fact-based, proactive solutions.

The future of data and cognitive computing

Right now, companies don’t have the talent they need to realize the full potential of all the data they measure. Cognitive computing in the IoT will allow data-collection and data-interpreting machines to communicate with one another quickly and completely, opening the door for a surge of new business strategies.

Thankfully, the early generations of these products are already here. Google’s DeepMind is the most visible example, replicating some basic functions of human thought with faster processing speeds to deliver actionable answers to data-based questions. As devices like these become more advanced and more prominent in the business world, companies will be able to test the limits of their application to the extreme, putting data and smart computing to work in ways that will change the landscape of modern business as we know it.

The real benefits in the world of millions of devices and sensors connected in an IoT world comes from having a learning engine closer to each sensor, displacing any existing rules. This way, decision-making becomes individual and specific to the sensor or node and purely based on its own experience. For example, in the case of healthcare, health trends and past learning for a specific person is used against a fixed threshold in decision-making. The same idea can be applied across other industries as well.

And because all those devices and sensors are interconnected, their exchange of information and collective learning can offset the significant data and the time required for learning while also preparing for the dynamic needs of the solution. For example, a particular node exposed to a cyberattack can pass this learning over the network on the fly, which will help in safeguarding the rest of the nodes.

Cognitive computing in the IoT presents as many challenges as it solves, but the challenges will be the kind that businesses want. Rather than worry that they don’t have the talent or resources to collect, read, and act upon their data, companies will soon wonder what to do with the bounty of information and analytics at their fingertips. Luckily, cognitive computing power will be there to help them along the way.

Source: cognitive computing is changing IoT