How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues
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The document discusses how cognitive computing enhances business process management (BPM) by providing increased flexibility, agility, and adaptability in complex business environments. It describes a systematic approach to achieve cognitive BPM, which combines advanced technologies, such as artificial intelligence and machine learning, to automate decision-making and improve operational efficiency. Examples across various industries illustrate how cognitive BPM can transform traditional processes, enabling smarter, data-driven decisions and enhanced customer experiences.
How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues
- 1. How Cognitive ComputingUnlocks Business Process Management’s Performance- Enhancing Virtues Extending process management to business logic offers enterprises increased flexibility, agility and adaptability in evolving and complex business ecosystems. COGNIZANT 20-20 INSIGHTS Executive Summary The ability to successfully maintain momentum in any economy requires processes that can sustain growth at scale. To achieve such status, supporting technology and IT administration must be evaluated in the context of present and future states. What’s more, how business processes manage complex activities, while also supporting con- tinuous awareness of situations and real-time decisions, requires a seamless partnership between humans and the machine environments in which they operate. By extending process management from process logic to business logic, a cognitive approach to business process management (BPM) offers flexibility, agility and adaptability in evolving and complex business ecosystems. This white paper describes a systematic approach to achieving cognitive BPM. Cognizant 20-20 Insights | June 2018
- 2. BPM & COGNITIVE COMPUTING IN A NUTSHELL BPM refers to the systematic method to improve business processes. More often, it involves cohe- sive systems that go beyond the management of people and information. BPM experts study, rec- ognize, manage, optimize and monitor business processes that support enterprise goals. Over the years, these activities have evolved significantly to include systems that can learn at scale, employ logic and reason, and interact nat- urally with human beings. We call this extended form of BPM “cognitive computing.” Cognitive computing systems (referred to vari- ously as robotic process automation, intelligent process automation, cognitive automation, cognitive agents, etc.) are not explicitly pro- grammed, but instead are trained like humans. They gain experience and hone processes over time, and manage both structured and unstruc- tured data using artificial intelligence (AI) and machine learning (ML) algorithms. They can also adapt to new usages and formats in real-time, just as humans do. Cognitive computing accelerates, enhances and scales human expertise by: • Understanding natural language (or sensory data) and interacting naturally with humans. »» It provides non-biased advice, autono- mously. • Reasoning (forming hypotheses, making arguments and planning). »» It interacts with and assists users by ana- lyzing both content and context. • Learning (sensing and applying meaning). »» It creates new insights and value. • Offering progressive support. »» It improves operational efficiency. Cognitive systems can simulate human brain activity to solve the most complex problems in business process management. DEFINING COGNITIVE PROCESS A cognitive process is one that makes BPM more dynamic and probabilistic by enabling decision management systems to understand, evaluate and comprehend business events. A cognitive process is one that makes BPM more dynamic and probabilistic by enabling decision management systems to understand, evaluate and comprehend business events. Cognizant 20-20 Insights
- 3. Over the years, these activities have evolved significantly to include systems that can learn at scale, employ logic and reason, and interact naturally with human beings. We call this extended form of BPM “cognitive computing.” Cognizant 20-20 Insights 3How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues |
- 4. 4How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | For instance, cognitive data management can fuel process automation with machine learning, offer- ing businesses the potential for massive returns. In conjunction with social, mobile, analytics and cloud (SMAC),smarterprocessesaremorethanjustatool for planning and execution; they are an intelligence engine that delivers cognitive decision-making based on operational insights in context. Combining business processes with machine learning produces cognitive solutions that can enhance customer experiences. These solutions compare the data that business activity gen- erates with data from other sources, enabling dynamic and context-sensitive decision-making. This opens the door to an entirely new level of straight-through processing. For example, in a predictive maintenance process, machine-learn- ing algorithms can be applied to sensor data to identify situations, which indicate that a machine breakdown is imminent. The cognitive business solution ensures that insights from various devices are processed efficiently and that the field service team is utilized in the best possible way. These insights also help to optimize the pro- cess by automatically releasing a remote patch to a device, or by providing guidance to customers with self-service steps. The evolution of business process management is synonymous with the decades-long evolution of the automobile industry. Just a few years back, driverless cars seemed like fiction, but now they are nearing reality. In the same way, basic workflows are evolving into cognitive automated processes (see Figure 1). 1 The Evolution of Workflow Automation Basic Automation • Workflow • Macros • Screen scraper • Auto eMailer Basic Automation The driver controls everything inside the vehicle – steering, brakes, throttle and power. Driver Assistance The driver mostly has control over everything, but the car automati- cally performs some specific functions. Limited Self-Driving An automated driver assistance system for steering and acceleration uses information about the driving environment. Full Self-Driving Under Certain Conditions Fully autonomous vehicles perform all critical safety driving functions and monitor roadway conditions. Full Self -Driving Under All Conditions A fully autonomous system enables the vehicle to perform at the same level as a human driver. Robotic & CX-Aligned Process Automation • Experience aligned workflow • Extensible and adaptive prebuilt process object libraries Autonomic Process Automation • Address common obstacles of unstructured data and complex rules • Automation of complex rules, electronic data capture Cognitive Automation • Autonomous decision-making • New insights and data discovery • Personal and interactive support driven by insights & contexts Linkage to Artificial Intelligence Unstructured Data/Complex Rules Structured Data/Simple Rules Structured Data/ Simple Rules Figure 1
- 5. 5How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | Specifically, these capabilities can be bifurcated by: • Enhanced decision-making and optimization: Cognitive computing enables a business pro- cess to make decisions on behalf of humans based on rich experience and large amounts of unstructured data. By digesting a myriad of unstructured information, cognitive sys- tems can inject intelligent insights into the decision-making process. Using predictive and adaptive decision capabilities, they also add value to preventive decision-making. Cognitive interaction improves channels of communication by supporting new chan- nels and devices. For instance, the system can guide an individual through a process or conversation and effectively leverage that person’s channel preferences. After the inter- action, it can then communicate the results for a clear and orderly post-analysis. • Advanced intelligent automation: Cognitive agents can process human interactions over any preferred communications channel. Using AI and ML, these systems can effectively cap- ture insights and codify process specifications, revealing new automation opportunities that can be leveraged using robotic process automation (RPA) to augment and mimic human intelligence. When combined with application programming interface (API)-enabled business processes, existing assets can be made available to business partners, supporting innovative business models that shift the step cycle from “define-execute- analyze-improve” to “learn-plan-act.” COGNITIVE PROCESS AUTOMATION PATTERNS Cognitive decision support, cognitive interaction, cognitive process learning and cognitive process enablement help to automate business processes. Figure 3 explains several high-level patterns of cognitive business automation, including: • Robotic desktop automation. • Automated decision-making based on rules and analytics. • Robotic process automation. • Deep integration to automate system steps. • Smart integration to leverage cognitive ser- vices and organizational APIs. • Seamless hand-off between machines and humans. • Cognitive agents for contextual conversations and anytime, anywhere interactions. CognitiveProcessEnablement Cognitive Agent Cognitive Decision Support Workflow Engines Routine Structured Process Unstructured Processes Business Automation Intelligent Insights BPM Engines Cognitive Process Learning Based on AI & ML Predictive and Adaptive Decision Case Management Knowledge- Intensive Processes Mobile, Social, Communication (email, voice, video), Documents, Notes, Sensors API-fication of Process Cognitive Interactions Email, Chat, Messaging Ad-hoc, Unstructured Processes Raw Materials that Fuel Cognitive Computing Figure 2
- 6. Cognizant 20-20 Insights 6How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | COGNITIVE PROCESS: A CASE ILLUSTRATION The concept of cognitive BPM can best be understood through the lens of a real-life retail example. Consider a retailer that has auto- mated an inventory system to manage stock inventory at a given time. The retailer uses BPM and operational decision management to carry out the operations. Additionally, it maps business events to the process to capture the data required for monitoring the activity and preparing reports. All these help the retailer’s inventory manager understand, in real time, historic information and, eventually, make better business decisions. This can be summa- rized as follows: Let’s say an inventory manager applies business processes, decision management and reporting capabilities to determine present stock and future reorder levels. What happens if the manager wants this system to also make informed decisions based on customer feedback about a product? He may deploy a cognitive system to analyze inter- nal data and social media content, such as Twitter, Facebook, Instagram, etc. to formulate a response. Based on that response, he can then modify the cognitive process to either place an order for additional stock or suggest a better line of prod- ucts to enhance the operations. Consequently, key benefits of this process include incorporation of customer-centric decision-making, enhanced customer satisfaction and smarter investment in products by companies. Deconstructing Cognitive Process Automation Automation Leveraging Robotic Desktop Manual Operator Uses Robot to Automate Manual Steps Final Decision is Made by Human Either Pull/Push Task by Robot Automate Decision-Making Leveraging Rules, Analytics Automation Leveraging Robotic Process Automation Deep Integration (Service) Smart Integration (API) Cognitive Agent CLIENT MACHINE Cognitive Decision Robotic Desktop Automation/ Business Step Automation CLIENT MACHINE ESB Gateway Contextual Coversation IoT Blockchain Org.API Cognitive API Cognitive application capabilities: Understands human interaction Generates & evaluates evidence-based hypothesis Adopts and learns from user selection BPM PLATFORM Web Automation to Complete Task Final Decision is Made by Robot Figure 3
- 7. Cognizant 20-20 Insights 7How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | COGNITIVE PROCESS TRANSITION AND ADOPTION Transition from traditional business processing to cognitive business processing requires system- atic execution and adoption. Figure 4 describes our proven roadmap to cognitive BPM. To be cognitive, the process must think and learn on top of the traditional framework. We break this down into processes that: • Do, by enriching the traditional process with knowledge. • Think, by enhancing the system with deci- sion-making. • Learn, by expanding the business with insights. The overall approach can be subdivided into four high-level phases: • Discover: On a high level, the journey to cog- nitive processing starts with collaborative discovery to learn and define existing busi- ness processes in a launch workshop (the “do” part). This requires assessing organizational readiness and identifying process candidates through a cognitive opportunity assessment. • Define: The next phase is to define actionable insights captured from actual process usage and business pain points. These findings will help catalog potential areas for cognitive capa- bilities, leading to plans based on the list and associated technology needs. • Design: In the design phase, the future cog- nitive process model is identified along with a strategy to extract insights (“think” and “learn”) from non-structured data. The “think” strategy integrates available input sources and decision modules to support timely decisions based on relevant data. The “learn” strategy depends on capturing decision outcomes and leveraging those insights to rectify issues. • Develop: Finally, the identified, recognized and explored capabilities are implemented using prototypes for testing in real-life use cases. Transition from traditional business processing to cognitive business processing requires systematic execution and adoption. Cognitive Process Automation Traditional BPM Cognitive BPM Process that Does Process that Thinks Process that Learns Core BPM Capabilities Decision & Optimization Capabilities Cognitive Capabilities Cognitive Process Automation: A journey toward automated natural processing Figure 4 The Automated Natural Processing Journey
- 8. Cognizant 20-20 Insights 8How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | From here, the iterative journey repeats the discover, define, design and develop cycle, enabling continuous improvement (as revealed in Figure 5). AS IS Process Discover Process Discover Knowledge Discover Cognitive Need Define Process Models Define Cognitive Capabilities Define Automation Strategy Design Cognitive Process Models Design Cognitive Services Design Automation Model Enrich the Processes with Knowledge Learn, Discover Plan Next Steps Interact, Integrate Develop, Probe, Sense Enhance the System with Decision-Making Expand the Business with Learning Insights DISCOVERY METHODOLOGY DEFINE DESIGN DEVELOP Define To-Be Processes Design Cognitive Process Develop Prototype, Future Recommendations Complete Discovery: 15% of total effort Complete Discovery: 15% of total effort Discovery Completed Complete Design: 35% of total effort Complete Develop: 35% of total effort Define Completed Discovery Completed Define Completed Design Completed Discovery Completed • Process discovery leveraging current artifacts workshops • Check digital enable- ment readiness • Validate process leveraging cognitive checklist • Identify agent-oriented, cognitive and intelligent business process • Gather appropriate samples for model learning • Define to-be processes based on levers • Define intersystem dependency • Define scenarios for process automation, context extraction, cognitive interactions and experiences • Define KPIs & recommendations • Define cognitive BPM model and collaborate with system and cognitive (AI) models • Design to-be processes • Identify cognitive services • Design appropriate models (e.g., conversa- tion, decision, entity, learning, etc.) • Design cognitive & automation experience • Design user hand-off scenario • Design contextual model • Develop to-be processes, UX, appropriate models, integration & data services • Use cognitive API toolkits for quick & easy integration • Train models with sample interactions • Apply continuous integration, deploy- ment, testing & improvement cycles Iterative Journey with Continuous Improvements 15% 30% 65% 100% A Transformation Approach to Cognitive Processes Figure 5
- 9. Cognizant 20-20 Insights 9How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | Business automation is integral to the improvement cycle and requires four additional steps, including: 1. Optimized and integrated processing. 2. Robotic processing to mimic human action. 3. Enhanced processing to augment human intelligence. 4. Cognitive processing to mimic human intelli- gence (as depicted in Figure 6). Figure 7 highlights the steps and actions neces- sary to go from traditional business processing to cognitive, automated business processing. Automation Maturity Levels A Recipe for Process Automation Figure 6 Figure 7 Process Discovery Check Readiness Collect Current Utilization and Customer Satisfaction Index Candidate Identification DISCOVER DEFINE DESIGNDEVELOP Workshop & Data Collection Process Maturity Process Prioritization Feasibility Study Define Road- map Product Selection Capacity Environment Setup ROI Analysis Complexity Analysis RPA Fitment Analysis Benefit Analysis Process Prioritization Checklist 4 Quadrant Process Mapping Eligible Processes Define Physical & Deployment Arch. Security Intersystem Dependency, Enterprise Data Flows Start Point End Point Hands-on Design Process, System, UI, Data Extraction Study Queue Evaluation Concurrency Check, Work Separation Human Hand-off Design Process Flow Task Assignment Strategy Common Steps Cognitive Flow Design Screen Switch, Rule Interpretation Auditing, Integration Graph Decision Integration API Invocation Cognitive Flow Design Data Source Management Cognitive API Identification Robotic Architecture Automation Design Analysis Flow Design Reusability & Source Control Management Security Preparing Infrastructure Monitoring Plan Log Configuration Configurability Design Utility Building Flow Building Robotic Step Recording, Data Extraction, Handle Decision Cognitive Step Integration Continuous Unit Testing Development Step Testing Regression Testing Automation Testing VerificationDeployment Continuous Monitoring Review Audit Data Validate KPI Identify Improvements Continuous Improvements Interactive Journey with Continuous Improvements • Rules engine • Screen scraping • Workflow • Processing of unstructured data and base knowledge • Natural language processing • Big data analytics • Artificial intelligence • Machine learning • Large-scale processing ROBOTIC PROCESS ENHANCED PROCESS COGNITIVE PROCESS • Structured data interaction • Optimized process Improves Workflow Mimics Human Action Augments Human Intelligence Mimics Human Intelligence COGNITIVE BUSINESS AUTOMATION STAGES IN ENTERPRISES
- 10. 10How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | COGNITIVE BUSINESS PROCESSES APPLICATIONS Cognitive business operations can be applied across industries and functional areas of an enterprise. For example: • Healthcare. Hospital care management sys- tems can leverage data from social media to examine the spread of diseases and track the outbreak of epidemics. For example, during the outbreak of dengue fever in a city, hos- pitals can monitor Twitter feeds to identify symptoms experienced by the public. Tech- nologies such as geolocation can identify local tweets; natural-language processing can be applied to determine which tweets concern a particular ailment. Such real-time analyses can help health insurance providers to track and predict outbreaks and take proactive measures, such as urging community mem- bers to get vaccinated or stock up on supplies. • Banking. In the field of banking, cognitive BPM is widely used to determine customer satisfaction. For example, when customers are approved for a loan, they are directed to the bank’s loan-servicing department, which ensures proper payment collection, as well as any changes to the payment plan. This involves inbound and outbound calls that gen- erate call transcripts. By applying cognitive analysis to this process, the bank can then determine whether its employees are asking the right questions, how polite they are, and whether they are working efficiently. The net result is inevitably a better experience for the customer and the bank. • The human touch. Companies can use cog- nitive technologies to analyze information from customers in the form of letters, email or other communication. For instance, when handling customers with strong negative sentiments, companies can deploy sentiment analysis. This will help direct those customers to the employees who can best serve them, which will in turn boost customer satisfaction. In traditional BPM systems, this process eats up hours of labor, but with cognitive BPM, it can be automated and expedited. For example, our digital contact center solution uses cognitive automation to provide intelligent assis- tance and preemptive capabilities to proactively anticipate customer grievances. It delivers a supe- rior customer experience, enhanced cross-selling insights and reduced customer churn. • Improved decision-making. In recruiting, man- agers faced with hundreds of applications for dozens of openings typically spend enormous amounts of time trying to identify the best candidates, using just simple intuition and other limited tools. Cognitive BPM can change all this, as it looks beyond the formal attri- butes of candidates (such as their degrees or years of work experience) and incorporates The “think” strategy integrates available input sources and decision modules to support timely decisions based on relevant data. The “learn” strategy depends on capturing decision outcomes and leveraging those insights to rectify issues. Cognizant 20-20 Insights
- 11. Cognizant 20-20 Insights 11How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | more modern techniques of data collection. By integrating IBM’s Watson Personality Insights API – which uses linguistic analyt- ics and personality theory to infer attributes from a person’s unstructured text – with IBM BPM, a candidate’s digital fingerprints (or Code Halo as we call it) can be quickly ana- lyzed for character traits and potential red flags. This information can help managers zoom in on the right candidates, quickly. QUICK TAKE Applications in Use Across Industries One of our banking customers is leveraging cognitive capabilities such as cog- nitive interaction and expert bots as part of a contact center makeover that has reduced average call handling time by more than 8%, while supporting 5,000 users and 400,000 interactions per month. Cognitive BPM is helping the organization to reduce new agent training time by more than 20%. Similarly, we helped a healthcare organization in leveraging decision-making capabilities based on historical insights to assign claims to appropriate pro- cessors who can better serve similar types of claims more accurately and in less time. An insurance customer is applying cognitive process automation for pay- ment processing. In this case, bots are interacting with various downstream systems such as mainframes, as well as machines running Windows-based applications such as Microsoft Excel, to reduce claims processing times.
- 12. 12How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | Cognizant 20-20 Insights COGNITIVE PROCESS: GOALS AND BENEFITS Cognitive BPM supports self-learning and adap- tive BPM systems, enabling seamless interaction between systems and humans to achieve better results. Cognitive BPM achieves this in the fol- lowing ways: • Connecting customers, contexts and content through omnichannels. • Implementing an anytime, anywhere and any- thing (AAA) process model. • Developing automated, adaptive and predic- tive decision-making capabilities. • Establishing intelligent, connected and con- textual interactions with users. • Automating agent-oriented processes. Organizations that have successfully imple- mented cognitive computing can expect the following benefits: • Improved productivity and reduced costs: »» Automation leveraging decision-making and smart integrations. »» Contextual digital agent advisor for aug- mented work capability. »» Process automation leveraging cognitive agents. • Increased revenues: »» API-fication of business assets such as processes and rules. »» Intelligent business model leveraging cog- nitive capabilities. »» Support for additional workloads without increasing SME headcounts. • Better results and customer experiences: »» Accurate real-time process insights based on events across the enterprise. »» Removal of manual errors and discretion. »» Aggregation of process insights across channels, devices, systems, geographies and lines of business. »» Connected and personalized customer experiences. The question is not whether organizations should adopt cognitive computing, but how. To achieve suc- cess, organizations must identify existing in-house expertise in the context of an opportunity assess- ment, define areas where cognitive computing can addvalue,clarifyusecasestoleveragethesesystems across the enterprise, build a business case around the application, assess technology vendors that understand cognitive services, and then implement and integrate the solution with existing business pro- cesses to modernize platforms and applications.
- 13. 13How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | Cognizant 20-20 Insights LOOKING AHEAD Traditional workflows based on predefined pro- cess logic offer little support in today’s complex and dynamic business environment. Now, in the cognitive era, BPM extends beyond traditional process automation and optimization, and is vital to the digital business goals of both large and small organizations. To get ahead with cognitive BPM, companies must have advanced end-user interfaces (UIs), and access to large volumes of business data that can generate the cognition capabilities nec- essary to scale human expertise. The continuous awareness, gradual learning and real-time decision-making of a cognitive approach are essential to managing modern business needs, and can drive any process auto- mation, no matter how complex. Software vendors offer cognitive capabilities that keep BPM platforms informed, innovative and intelligent. Seamless integration of current systems with platforms, such as IBM Watson, can truly digitize BPM. Through high-volume data analysis, rational thinking and self-learning, these technologies can unlock benefits previ- ously unattainable for businesses. Cognitive technologies make automation possible across all enterprise domains. The organizations that adopt these technologies today will have a distinct competitive advantage once the cogni- tive BPM revolution spreads. Cognitive technologies make automation possible across all enterprise domains. The organizations that adopt these technologies today will have a distinct competitive advantage once the cognitive BPM revolution spreads.
- 14. Cognizant 20-20 Insights 14How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | Harish Dwarkanhalli Senior Vice President, Cognizant Salesforce and Digital Customer Experience Harish Dwarkanhalli is the Senior Vice President and the Global Delivery Head of Integrated Process Management, Salesforce and Digital Customer Experience practices in Cognizant’s Enter- prise Application Services business unit. In his 21-year tenure at Cognizant, he has held numerous key positions in relationship management, account management and delivery management across multiple industries and technologies. In his current role, he is responsible for shaping and driving business strategy, technol- ogy innovations and delivery management across the mentioned practices. He is passionate about exploring how enterprises can transform business models and drive value innovation through the nexus of digital technologies. He can be reached at Harish.Dwarkan- halli@cognizant.com and https://www.linkedin.com/in/dvharish/. ABOUT THE AUTHORS Mohan Ananthanarayanan Chief Architect, Cognizant Enterprise Applications Services’ Integrated Process Management (IPM) Practice Mohan Ananthanarayanan is a Chief Architect within Cognizant’s Enterprise Applications Services’ Integrated Process Management (IPM) Practice. He serves as the leader for IBM (integration and BPM) as well as its digital integration practices. Mohan has about 20 years of experience in middleware integration, business process management, and other digital systems and technology-related integration, including cognitive service offerings, covering strate- gizing and incubating new business segments, product offerings development and delivering large programs. He can be reached at Mohan.Ananthanarayanan@cognizant.com and www.linkedin.com/ in/mohanananthanarayanan/. Arindam Mazumder Senior Architect, Cognizant Enterprise Application Services Arindam Mazumder is a Senior Architect within Cognizant’s Enter- prise Application Services business unit. He has over 14 years of experience in IT solutions, delivery and consulting – predominantly in the healthcare, banking and financial services, as well insurance industries. He has extensive experience in BPM and integration technologies, and serves as a connected enterprise and process architect for IBM digital transformation technologies, cognitive process management and hybrid integration. He can be reached at Arindam.Mazumder@cognizant.com and www.linkedin.com/in/arin- dam-guha-mazumder-93aa60124.
- 15. Cognizant 20-20 Insights 15How Cognitive Computing Unlocks Business Process Management’s Performance-Enhancing Virtues | REFERENCES • “Cognitive Computing Defined,” Cognitive Computing Consortium, https://cognitivecomputingconsortium.com/resources/ cognitive-computing-defined/. • “Rethinking BPM in a Cognitive World: Transforming How We Learn and Perform Business Processes,” https://researcher. watson.ibm.com/researcher/files/us-hull/Hull-Motahari--Cognitive-BPM--at-BPM-2016--v10.pdf. • “Cognitive Computing ”IBM Research, http://researcher.watson.ibm.com/researcher/view_group.php?id=7515. • Motahari Nezhad H.R., Akkiraju R. (2015) Towards Cognitive BPM as the Next Generation BPM Platform for Analytics-Driven Business Processes. In: Fournier F., Mendling J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham “Cognitive Computing: What’s in for Business Process Management? An Exploration of Use Case Ideas,” https://www.researchgate.net/publication/322522862_Cognitive_Comput- ing_What’s_in_for_Business_Process_Management_An_Exploration_of_Use_Case_Ideas. • “The Fourth Industrial Revolution – Cognitive and the Future of Work,” Medium, September 15, 2017, https://medium.com/ the-future-of-financial-services/the-fourth-industrial-revolution-cognitive-and-the-future-of-work-c07d4425c24f. FOOTNOTE 1 Malcolm Frank, Paul Roehrig and Ben Pring, Code Halos: How the Digital Lives of People, Things, and Organizations are Changing the Rules of Business, John Wiley & Sons, April 2014, http://www.wiley.com/WileyCDA/WileyTitle/pro- ductCd-1118862074.html.
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