Jun 282017
 

RobotThis is the second of a three (3) part post on the connection between Artificial Intelligence (AI) and Knowledge Management (KM). In this post I examine those industries that will or are soon to be disrupted by AI and KM, specifically in the form of Cognitive Computing. Before we look ahead, let’s take a look back. During the time I first became involved in AI (late 80’s), it’s hype and promise at that time became too much to live up to (a typical phenomenon in software see Hype Cycle) and its promise faded into the background. Fast forward to 2010 and AI is beginning to become the “next big thing”. AI had already made its presence felt in the automobile industry (robotics), as well as with decision making systems in medicine, logistics, and manufacturing (expert systems and neural networks). Now AI in the form of Cognitive Computing is making its mark on several industries. In a recent CB Insights Newsletter, it was stated that the US Bureau of Labor Statistics indicates that 10.5 million jobs are at risk of automation. Due to the rapid adoption and application of better hardware processing capabilities which facilitate artificial intelligence algorithms use on big data this is leading the change in blue and white collar jobs.

At a recent Harvard University commencement address , Facebook Chief Executive Mark Zuckerberg stated “Our generation will have to deal with tens of millions of jobs replaced by automation like self-driving cars and trucks,”. Bill Gates, the founder of Microsoft and Chairman of the Bill and Melinda Gates Foundation in a recent MarketWatch story had this to say “In that movie, old Benjamin Braddock (Dustin Hoffman) was given this very famous piece of advice: “I just want to say one word to you. Just one word …Plastics,” And today? That word would likely be “robots“, and “artificial intelligence” would have a huge impact”.

Although there are many industries where Cognitive Computing will disrupt the way business is conducted including the economics around job loss and future job creation, I have chosen to look at three (3) industries; Legal Services, Automotive Industry, and Healthcare.

Legal Services

LegalKnowledge Management (KM) is becoming more prevalent within law firms as well as legal departments as the practice of KM has become more mature. AI technologies are also making its way into the practice of law. Ability to reuse internally developed knowledge assets such as precedents, letters, research findings, and case history information is vital to a law firm’s success. Paralegals currently play a critical role in assisting attorneys with discovery. With the use of AI systems attorneys will be able to “mine” more accurately and efficiently the large volumes of documents (i.e., precedents, research findings, and case history information) located in various repositories to aid in decision making and successful client outcomes. This ability will limit the use of paralegals and attorneys currently needed to perform these tasks.

Cognitive computing, will enable computers to learn how to complete tasks traditionally done by humans. The focus of cognitive computing is to look for patterns in data, carrying out tests to evaluate the data and finding results. This will provide lawyers with similar capabilities as it provides doctors; an in-depth look into the data that will provide insights that cannot be provided otherwise. According to a 2015 Altman Weil Law Firms in Transition survey  35% of law firm leaders indicate cognitive computing will replace 1st year associates in the next ten (10) years. While 20% of law firm leaders indicate cognitive computing will replace 2nd and 3rd year attorneys as well. In addition, 50% of law firm leaders indicate cognitive computing will replace paralegals altogether. Cognitive computing capability to mine big data is the essential reason lower level research jobs will be replaced by computers. This situation is not just limited to the legal profession.

Automotive Industry

Autonomous VehicleAutonomous Vehicles and Vehicle Insurance

Autonomous vehicles, also known as a driverless car, robot car (here we go with robots again!), and self-driving car can guide themselves without human intervention. This kind of vehicle is paving the way for future cognitive systems where computers take over the art of driving. Autonomous Vehicles are positioned to disrupt the insurance industry. Let’s take a look at what coverages are a part of the typical vehicle insurance policy.

Vehicle insurance typically addresses six (6) coverages. These coverages include (1) Bodily Injury Liability, which typically applies to injuries that you, the designated driver or policyholder, cause to someone else; (2) Medical Payments or Personal Injury Protection (PIP), which covers the treatment of injuries to the driver and passengers of the policyholder’s vehicle; (3) Property Damage Liability, which covers damage you (or someone driving the car with your permission) may cause to someone else’s property; (4) Collision, which covers damage to your car resulting from a collision with another car, object or and even potholes; (5) Comprehensive, which covers you for loss due to theft or damage caused by something other than a collision with another car or object, such as fire, falling objects, etc.; (6) Uninsured and Underinsured Motorist Coverage, which reimburses you, a member of your family, or a designated driver if one of you is hit by an uninsured or hit-and-run driver. The way these coverage’s are applied (or not) to a vehicle policy will be disrupted by the use of autonomous vehicles.

According to an 2016 Forbes article by Jeff McMahon  about 90 percent of car accidents are caused by human error. However, it is estimated that autonomous vehicles will significantly reduce the number of accidents. This will significantly disrupt the insurance revenue model, effecting all six (6) types of coverage identified above. When the risk of accidents drops, the demand for insurance will potentially drop as well (this will not happen unless the states no longer require insurance that covers accidents). So, there will be no doubt that auto insurance companies will the type of coverage and the language effecting the policies.

Some Unintended? Side Effects

The autonomous vehicle with its multiple sensors have the potential to eliminate accidents due to distractions and drunk driving. This will disrupt the vehicle repair industry by largely eliminating crashes so collision repair shops will lose a huge portion of their business. Indirectly, the decreased demand for new auto parts will hurt vehicle part manufacturers. According to the U.S. Department of Transportation in 2010 approximately 24 million vehicles were damaged in accidents, which had an economic cost of $76 billion in property damages. The loss of this revenue will put a strain on these manufacturers.

Healthcare

CogMedThe healthcare delivery process presents a consistent flow of data, information and knowledge for the delivery of healthcare. These areas include Patient Intake, Data Collection, Decision Support, Diagnosis and Treatment, and Patient Closeout. The areas of the healthcare delivery process that will be disrupted by cognitive computing include Patient Intake, Data Collection and Diagnosis and Treatment.

Patient Intake and Data Collection: The patient intake process is the first opportunity to capture knowledge about the patient and his/her condition at the time of arrival at the healthcare facility. Cognitive computing executed through natural language processing (NLP) tools will capture medical insurance information, method of payment, medical history and current vital condition. All of this data is transitioned to the facilities database. This presents an opportunity for the data, information and knowledge about the patient to be automatically shared. NLP tools will limited or eliminate the need for a receptionist/admin to initially capture patient information.

Diagnosis and Treatment: Making a diagnosis is a very complex process, which includes cognitive tasks that involves both logical reasoning and pattern recognition. The development of Artificial Neural Networks that incorporates deep-learning capabilities are being developed to mine health related big data repositories. This innovation is providing clinicians and researchers with effective tools for improving and personalizing patient treatment options. It has been established that big-data analysis could help to identify which patients are most likely to respond to specific therapeutic approaches versus others. Analysis of such data may also improve drug development by allowing researchers to better target novel treatments to patient populations.

The clinical trials that pharmaceutical companies rely on for FDA approval and drug labeling capture too little of the information patients and physicians need. The trials only enroll a small percentage patients and can take years and tens of millions of dollars to finish. Many trials never enroll enough patients to get off the ground. Using cognitive computing will assist physicians to understand which patients are most likely to respond with standard approaches, and which need more aggressive treatment and monitoring. Enabling cognitive computing to harness the genetic and clinical data routinely generated by hospitals and physicians would also accelerate drug development, by rapidly matching targeted treatments sitting in companies’ research pipelines with the patients who are most likely to respond. In addition, the sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to analyze the resulting big data without the use of cognitive computing tools (for more information see Forbes article: IBM and Microsoft are Disrupting The Healthcare Industry with Cognitive Computing).

Where do we go from here!

AI, KM and Cognitive Computing will continue to evolve and more areas of disruption will be coming. So, the question is… how can we address the loss of jobs; how can we prepare for the new jobs; and how must business and government evolve to meet the challenges that cognitive computing present? It is clear that we must retrain/retool the current workforce and at the same time infuse our vocational schools, trade schools, colleges and universities with the right tools and experienced instructors/professors to teach the concepts and applications of AI, KM and Cognitive Computing. Businesses must continue to innovate. Innovating in the same old way will cause a business to become extinct. However, I’m talking about innovating by bringing a diversification of thought and experiences, including cultural into the innovation community. Creating your innovation intersection (for more on finding your innovation intersection – read The Medici Effect by Frans Johansson). Only by innovating differently will your business not only survive but thrive in this new world where interacting with computers (yes robots too!) will be an everyday occurrence in life!

May 312017
 

AI and KMThis is the first of a three (3) part post on the connection between Artificial Intelligence and Knowledge Management.

Artificial Intelligence (AI) has become the latest “buzzword” in the industry today. However, AI has been around for decades. The intent of AI is to enable computers to perform tasks that normally require human intelligence, as such AI will evolve to take many jobs once performed by humans. I studied and developed applications in AI from the mid to late 1980’s through the early 2000’s. AI in the late 1980’s and early 1990’s evolved into a multidisciplinary science which included expert systems, neural networks, robotics, Natural Language Processing (NPL), Speech Recognition and Virtual Reality.

Knowledge Management (KM) is also a multidisciplinary field. KM encompasses psychology, epistemology, and cognitive science. The goals of KM are to enable people and organizations to collaborate, share, create, use and reuse knowledge. Understanding this KM is leveraged to improve performance, increase innovation and expand what we know both from an individual and organizational perspective.

KM and AI at its core is about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process. I believe that AI and KM are two sides of the same coin. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.

The connection of KM and AI has lead the way for cognitive computing. Cognitive computing uses computerized models to simulate human thought processes. Cognitive computing involves self/deep learning artificial neural network software that use text/data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing is leading the way for future applications involving AI and KM.

In recent years, the ability to mine larger amounts of data, information and knowledge to gain competitive advantage and the importance of data and text analytics to this effort is gaining momentum. As the proliferation of structured and unstructured data continues to grow we will continue to have a need to uncover the knowledge contained within these big data resources. Cognitive computing will be key in extracting knowledge from big data. Strategy, process centric approaches and interorganizational aspects of decision support to research on new technology and academic endeavors in this space will continue to provide insights on how we process big data to enhance decision making.

Cognitive computing is the next evolution of the connection between AI and KM. In future post, I will examine and discuss the industries where cognitive computing is being a disruptive force. This disruption will lead to dramatic changes on how people will work in these industries.

Mar 312017
 

CognitiveThere are approximately 22,000 new cases of lung cancer each year with an overall 5-year survival rate of only ~18 percent (American Cancer Society). The economic burden of lung cancer just based on per patient cost is estimated $46,000/patient (lung cancer journal). Treatment efforts using drugs and chemotherapy are effective for some, however more effective treatment has been hampered by the inability of clinicians to better target treatments to patients. It has been determined that Big Data holds the key for providing clinicians with the ability to develop more effective patient centered cancer treatments.

Analysis of Big Data may also improve drug development by allowing researchers to better target novel treatments to patient populations. Providing the ability for clinicians to harness Big Data repositories to develop better targeted lung cancer treatments and to enhance the decision-making process to improve patient care can only be accomplished through the use of cognitive computing. However, having a source or sources of data available to “mine” for answers to improve lung cancer treatments is a challenge!

There is also a lack of available applications that can take advantage of Big Data repositories to recognize patterns of knowledge and extract that knowledge in any meaningful way. The extraction of knowledge must be presented in a way that researchers can use to improve patient centric diagnosis and the development of patient centric treatments. Having the ability to use cognitive computing and KM methods to uncover knowledge from large cancer repositories will provide researchers in hospitals, universities, and pharmaceutical companies with the ability to use Big Data to identify anomalies, discover new treatment combinations and enhance diagnostic decision making.

Content Curation

An important aspect to cognitive computing and Big Data is the ability to perform a measure of content curation. The lung cancer Big Data environment that will be analyzed should include both structured and unstructured data (unstructured being documents, spreadsheets, images, video, etc.). In order to ingest the data from the Big Data resource the data will need to be prepared. This data preparation includes applying Information Architecture (IA) to the unstructured data within the repository. Understanding the organization and classification schemes relating to the data both structured and unstructured is essential to unifying the data into one consistent ontology.

Are We Up for the Challenge!

Even if a Big Data source was available and content curation was successful, the vast amounts of patient data is governed by HIPAA laws which makes it difficult for researchers to gain access to clinical and genomic data shared across multiple institutions or firms including research institutions and hospitals. According to Dr. Tom Coburn in his January 14th article in the Wall Street Journal ‘A Cancer ‘Moonshot’ Needs Big Data; gaining access to a big data repository all inclusive of patient specific data is essential to offering patient centered cancer treatments. Besides the technology challenges, there are data and regulation challenges. I’m sure that many of these challenges are being addressed. Thus, far there have been no solutions. Are we up for the challenge? Big Data analysis could help tell us which cancer patients are most likely to be cured with standard approaches, and which need more aggressive treatment and monitoring. It is time we solve these challenges to make a moonshot a certain reality!

Jan 112017
 

KM Wizard2As we enter into 2017, I am dedicating this first blog post to answering some of the pressing KM questions presented by my clients.

Do you recommend any standard model of KM to follow/implement? If so, please describe the model in summary.

There are several models of KM that I have worked with at various organizations to implement KM strategies, methods, processes and solutions, these include; The SECI Model (knowledge capture); The Continuous Knowledge Model (Connect, Collect, Catalog, Reuse, & Learn); KM Maturity Model (Maturity Level 1 – Initial: – Knowledge management is a one-time process with no formal KM practices within the organization, Maturity Level 2 – Repeatable: – KM processes are implemented and tested, Maturity Level 3 – Defined: KM roles are created, defined, and filled, Maturity Level 4 -Managed: – KM is more standardized and Organization-wide KM practices are defined and measured regularly, Maturity Level 5 – Optimized: – KM is mastered and flexible to external and internal changes ) for assessment, understanding knowledge gaps, implementation of KM and the evolution of KM within an organization.

How has the evaluation of effectiveness of KM actions been accomplished, such as readiness assessment, maturity assessment and ROI?

When my firm is engaged with an organization to assess KM we tailor the KM Maturity Model activities to assess where the organization is with KM as well as develop activities in which to resolve gaps that have been identified. This leads to the development and operationalization of a strategy/roadmap to achieve a desired level of maturity.

What are the main reasons of KM success in an organization?

There are many reasons (or factors) for KM success. These reasons include:

Implementing a KM Strategy: The KM Strategy must be positioned to drive specific initiatives that align with the mission and objectives of the organization. The KM Strategy includes formal procedures, methods and processes to collect knowledge throughout the organization, a well-established infrastructure, networks for transferring knowledge between employees, and tools to facilitate the process. The KM Strategy will lay the foundation to align specific tools/technology to enhance individual and organizational performance.

Besides implementing a KM Strategy, other reasons for KM success are:

  • Having Executive Leadership/Sponsorship
  • Having adequate Budgeting and Cost Expectations
  • Having participation from all levels of the organization
  • Having adequate (or developing) processes and technology that support KM
  • Having adequate (or access to) Resources
  • Having adequate education and understanding of KM
  • Implementing sufficient metrics to measure the impact of KM on the corporation
  • Having adequate monitoring and controls in place to ensure the knowledge is relevant and is current and accurate
  • These reasons can be applied to all companies implementing KM and the absence of one or more of these factors may cause the KM effort (i.e., KM Program or KM Project) to fail.

Which solutions do you recommend be utilized in order to motivate and to increase the participation of people in KM?

The organizational culture plays an important role in motivating people to participate in KM. Creating an environment where informal networks are encouraged within the organization play a key role in the level of participation in KM activities. I have found the following solutions help increase participation of people in KM:

  • Incentivize people by awarding those who ideas create value for the organization
  • Highlighting those that participate in KM related activities (communities of practice, contributing to Wikis, active in blogs, learning a new skill important to the enterprise, attending conferences and presenting key learnings to the organization (knowledge transfer)
  • Tie participation in KM related activities to opportunities in which employee bonuses are increased
  • Communicating successful KM initiatives and the people who worked on them throughout the organization.

Creating knowledge-driven culture is one of the most important challenges of KM implementation, what is your suggestion for tackling this problem?

Here are some suggestions for creating a knowledge-driven culture:

The structure of the organization plays an important role in determining how knowledge is distributed, how decisions are made, the degree to which people feel comfortable sharing. It is important to remove barriers that exist between different groups and individuals. Organizational structure strongly influences the ability and willingness of people and communities to share and create knowledge, create an environment both physical and mental (open and trusting of individuals to share ideas) will help in developing your knowledge-driven culture.

Centralization: The degree to which decision making is centralized. In highly centralized

Organizations, decisions are made by few managers at the top of the organization. This puts a heavy demand on the cognitive capacity of these managers. Research and experience have identified that decentralized structures as being more suited for KM.

Formalization: The extent to which behaviors in an organization are governed by rules, policies, will have an effect on developing your knowledge-driven culture. In general, rigid, formal structures are regarded as being detrimental to KM.

A simpler organizational structure, which leads to less silos tend to make it easier for KM to be implemented. The complexity of the organizational structure also affects how it must be managed and what managerial roles are necessary to effectively implement KM.

What is your idea about trends and orientation of KM in 2017 and the near future?

One of the major areas in which KM will make an impact in 2017 and in the near future is within the customer service industry. Customer Service is the area in which most customers will have their only connection and interaction with your organization. It is this area where customers will form their opinions about the organization and determine if they remain a customer or move on to another competitor. Customer service is where organizations are investing a major portion of their revenue and attention to improving their customer service. Knowledge structures to support cognitive engagement in customer service is a future trend. Cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect data, information and knowledge along with having the ability to understand and communicate in natural ways are all aspects of future customer service.

BIG Data continues to make an impact and present a challenge in the industry, which specifically points to how KM will be positioned to gleam knowledge from the various repositories of structured and unstructured data contained within the organization. Infusing Big Data with KM will provide organizations with a competitive edge to not only bring about significant innovations, but deliver knowledge across the enterprise to the right people at the right time and in the right context.

Social media is another future impact where KM will make a difference. Younger employees and customers having grown up in the social media era, and are more open to sharing information than previous generations. With adoption of enterprise collaboration tools on the rise, new streams, formats and sources of enterprise knowledge are being created. This consists largely of unstructured content (social chats, team forums, etc.) and this must be incorporated into a broader KM strategy, and be made easily accessible/findable by customers and employees. This will see a future impact of the use of Information Architecture in the delivery of knowledge.

Personal KM and Wearable Technology, with all of the advances in technology becoming accessories for us to wear is producing a multitude of data, information and knowledge accessible to the user. This includes Fitbits, Apple Watch, Google Glasses and more… all deliver and collect information that allow us to make personal decisions during the course of the day. What to eat, drink, wear, how much we’re exercising (or not) are all decisions in some part influenced by our wearable tech!

Wearable technology is gathering information not only about us but also the environment around us. Where is this all taking us? Will our physicians have the capability to tap into all of this personal information? How about potential advertisers? KM will be at the center of how can we capture the decisions we make from this information to improve our lives. Personal Knowledge Management the key to taking control of our personal information created by these devises.

What are your more pressing KM questions? Feel free to join the conversation here… and/or receive KM Mentoring and Guidance through the KM Mentor.

Jan 132016
 

K15968-v2As we move into 2016 it is time to reflect on knowledge management and look at the future of this discipline. In my latest book: Knowledge Management in Practice I address what I believe is the future of KM in 2016 and beyond. An excerpt from chapter 18 follows.

Future of Knowledge Management:

One of the major areas in which KM will make an impact is within the customer service industry. Customer Service is the area in which most customers will have their only connection and interaction with your organization. It is this area where customers will form their opinions about the organization and determine if they remain a customer or move on to another competitor. Due to this scenario (and others) organizations invest a major portion of their revenue and attention to improving their customer service.

In an August 2014 Harvard Business Review article by Peter Kriss entitled “The Value of Customer Experience, Quantified” he states “Intuitively, most people recognize the value of a great customer experience. Brands that deliver them are ones that we want to interact with as customers that we become loyal to, and that we recommend to our friends and family”. Also, he states that the “value of delivering such an experience is often a lot less clear, because it can be hard to quantify” Delivering consistent and concise knowledge to provide answers to customer inquiries in an efficient way leads to providing value to the customer and improving the overall customer experience.

In support of this trend of KM in customer service, Forrester’s “Top Trends For Customer Service in 2015”, author Kate Leggett points out in trend #4, knowledge management’s impact when she states “Knowledge Will Evolve From Dialog To Cognitive Engagement. Organizations will look at ways to reduce the manual overhead of traditional knowledge management for customer service. They will start to explore cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect information, automatically build models of understanding and inference, and communicate in natural ways. These solutions have the potential to automate knowledge creation, empower agents with deeply personalized answers and intelligence, scale a company’s knowledge capability, and uncover new revenue streams by learning about customer needs.”

IBM Watson is playing a significant role in the evolution of applications that automate knowledge creation by providing deeply personalized answers and intelligence. This technology will not only effect customer service, but a multitude of industries with its capability to extract knowledge from Big Data sources. The IBM Watson ecosystem will provide deep content analytics and intensive scientific discovery that will lead to improve cognition contributing to an organization’s knowledge capabilities. This supports Kate Leggett’s research and points out that KM will continue to play a significant role in delivering knowledge and decision making capabilities to the customer service industry for the foreseeable future (2016 and beyond).

Global View of KM

In reviewing the 2015 Global Knowledge Management Observatory Report, authors David Griffiths, Abi Jenkins and Zoe Kingston-Griffiths state “The Knowledge Management function in many organizations is in a state of general decline”. This as they indicate is due to the following factors:

  • “Satisfaction in Knowledge Management’s contribution to strategic and operational objectives within organizations is often poor.”
  • “Knowledge Management lacks maturity and integration within the vast majority of organizations.”
  • “Knowledge Management continues to be predominantly seen as a technology-led function.”
  • “Satisfaction with technology-led Knowledge Management solutions is not improving.”
  • “Many Knowledge Management professionals do not appear to have the necessary awareness and/or permissions required to respond to unmet demand for KM activities in organizations.”
  • “Knowledge Management, as a field or area of practice, is argued to be suffering from a lack of specialist practitioners.”
  • “The value and/or significance of Knowledge Management activities is still not being appropriately recognized or reported within most organizations.”

Solutions that address many of the findings of the 2015 Global Knowledge Management Observatory Report are essential for KM success in 2016 and as KM evolves as a discipline. This includes producing a comprehensive KM strategy, KM education options, adopting KM programs, project and systems, and addressing why KM programs/projects fail.

All of these aspects from the 2015 Global KM Observatory Report are addressed in Knowledge Management in Practice. This book should be leveraged as a reference/guide and presents a tremendous resource to support the growth of KM at your organization.