This is the 73rd article in the Profiles in Knowledge series featuring thought leaders in knowledge management. Annie Green is a knowledge strategist and architect who has led several KM initiatives. She helps individuals and organizations think and learn. Her roles have included Associate Professorial Lecturer at The George Washington University, Knowledge Valuation Portfolio Editor for VINE Journal, and Associate Fellow of the Institute of Knowledge and Innovation (IKI).
I first met Annie when I was a panelist at the KM Education Forum (KMEF) Summit held May 5–6, 2011 at The George Washington University in DC. Annie was the co-leader of the KMEF. We both spoke at KMWorld 2010 and KMWorld Connect 2020.
Annie is an author, speaker, lecturer, journal reviewer, and a member of several brain trust/think tanks. She was the co-founder of the Knowledge Management Education Forum (KMEF), served as Chair of the Association for Intelligent Information Management KM Competencies team, and served as the chair of the International Conference on Intellectual Capital and Knowledge Management (ICICKM) 2013. She is the creator of the Framework of Intangible Valuation Areas (FIVA), a model that identifies intangible assets within the business structure
Annie created the Business Reasoning, Analytics, Intelligence Network (BRAIN), a model that identifies the components to establish artificial intelligence in the business enterprise structure. She has a Doctor of Science (D.Sc.) from George Washington University and a Masters in Information Systems (M.S.I.S) from George Mason University.
Her KM focus is on building an architecture that leverages data, intelligence, and knowledge to build a thinking organization. She has built a career on the effective and efficient operations of organizations. She prides herself on being an evangelist for individual and organizational thinking that leverages data, intelligence and knowledge.
Annie has expertise in Knowledge Management; Artificial Intelligence; Architecting a Cognitive Enterprise; Strategy Formulation; Digital Transformation; Analytics & Assessments; Scorecards, Dashboards, and Professional Reports; Change Management; and Requirements Engineering.
- George Mason University — Digital Transformation, Data Governance Specialist, 2019–Present
- Seed First L.L.C. — CEO, Knowledge Strategist/Architect, 2006–Present: Knowledge Assessment, Evaluations, System Architecture, Taxonomy Development and Knowledge Measurements and Indicators.
- The George Washington University — Assistant Professorial Lecturer, 1990–Present
- Georgetown University School of Continuing Studies — Program Director, Managing Artificial Intelligence Certificate, 2018–2019
- Kent State University — Instructor, 2011–2013
- General Services Administration: PBS — Sr Consultant/KM Strategist, 2006–2007
- SRA International — Health Systems SBU Process Director, 1997–2002
- The George Washington University — D.Sc., Knowledge Management and Educational Technology Leadership, 2004
- George Mason University — MSIS, Information and Systems Engineering and Artificial Intelligence, 1988
- Virginia Commonwealth University — BS, Biology, 1979
- Measuring Organization Vitals that are Essential to Sustain the Life of the Organization
- Intangible asset knowledge: The conjugality of business intelligence (BI) and business operational data
- Intangible assets in plain business language
- Business information — A natural path to business intelligence: Knowing what to capture
- The starting block: Enterprise (business) intelligence — Evolving towards knowledge valuation
- Building blocks to a knowledge valuation system (KVS)
- The transformation of business knowledge into intangible assets
- A framework of intangible valuation areas (FIVA): Aligning business strategy and intangible assets with Julie J. C. H. Ryan
- Prioritization of value drivers of intangible assets for use in enterprise balance scorecard valuation models of information technology (IT) firms
- A framework of intangible valuation areas and antecedents with Andreas N. Andreou and Michael Stankosky
- VINE Journal of Information and Knowledge Management Systems — Knowledge Valuation Portfolio Editor
- Building Artificial Intelligence into a Business
- Value Pathways to Strategy Formulation
- Artificial Intelligence and Firm Strategy
This article starts with a basic definition of strategy. Origin provides context and perspective — ab initio, or “start from the beginning” — that which is new comes from that which is old. The content of this article applies to a company, organization, business, or enterprise, as these terms are assumed to be interchangeable. This article uses the term “enterprise.”
Introduction — The Origin of Strategy
The origin of strategy is a plan of action to achieve a goal or set of goals and/or objectives. Enterprise Strategy is the brainchild of leadership and management to optimize the performance of their enterprise. Their strategy identifies the empirical games that an enterprise should play to achieve its goals and objectives. The strategy definition above, states “empirical games,” because generally, the goals and objectives within an enterprise’s strategy are based on observation, practical experience, and experimentation, rather than fact-based evidence. Although many organizations do have key performance indicators (KPIs) that are captured, modeled, and analyzed, it is questionable if these KPIs adequately represent the performance drivers within the enterprise. Current KPIs are sometimes thought to be proxy based or anecdotal; a casual or informal collection of data used in strategy formulation.
Strategy formulation is the foundation to gain insights into strengths, weaknesses, opportunities, and threats (SWOT) that require attention to sustain or improve enterprise performance. To capture data and information in a manual or semi-manual process given the complex structure of the enterprise is a very daunting task — timely and laborious. Thus, the capture of enterprise data and information should be the introduction of artificial intelligence (AI) into the enterprise strategy. The enterprise generates so much data and information that is being captured or should be captured and analyzed to determine the breadth and depth of mechanisms that contribute to enterprise performance — the era of big data and data analytics.
The Relationship Between Artificial Intelligence and Enterprise Strategy
There is a synergistic relationship between AI and building the strategic plan to continuously guide the enterprise to success. What needs to be known is how and where AI fits into the existing strategic management process and the changes necessary to achieve successful integration. Strategy starts with the definition of a vision and mission, which is then supported by strategy formulation to define the goals and objectives to meet the vision. However, prior to beginning the strategy formulation process, enterprise leaders and managers must first perform an internal and external analysis of their environment. These analyses seek to surface information about occasions, patterns, trends, and relationships, which uncovers SWOTs that help to decide the path of action for the enterprise. Internal and external analysis identifies SWOTs from interactions that occur within or outside of the enterprise, respectively. The SWOT analyses provide enterprise leadership and management with insights into the present and future positions of the enterprise. The results of the analyses are the input to strategy formulation, and an entry point to use machine learning to automate the strategy formulation process.
Strategy formulation forces enterprise leadership and management to look at the changing environment and position themselves to handle the changes that may occur. Imagine if there is a framework that facilitated the automation of this process to expedite access to the intelligence and knowledge necessary to build enterprise strategy — an automated process that emulates the collective minds of enterprise leadership and management.
A Framework to Automate Enterprise Strategy — AI, Neural Network, Intelligence, Memory, Process Automation, Machine Learning
Enterprises are implementing AI solutions, not knowing if they are formulated on an inclusive set of measures and indicators that are used to build algorithms that provide insights into the enterprise’s performance. The ultimate goal is to capture relevant and essential measures and indicators (financial and non-financial) that collectively represent the vital or essential components of an enterprise. The goal should be to build an Enterprise Intelligence Repository (EIR) that captures relevant and essential measures and indicators in a structure that supports the complexity of the enterprise, which aligns with its vital components. This is a first step to enhancing the integrity of strategy formulation and subsequently enterprise strategy. The many moving internal and external parts of an enterprise, or its vital components are:
- Customer: The associations (e.g., loyalty, satisfaction, longevity, etc.) an enterprise has built with consumers of its goods and services
- Competitor: The position (e.g., reputation, market share, name recognition, image) an enterprise has built in the business marketplace
- Employee: The collective capabilities (e.g., knowledge, skill, competence, know-how) of an enterprise’s employees
- Information: An enterprise’s ability to collect and disseminate its information and knowledge in the right form to the right people at the right time
- Partner: the associations (financial, strategic, authority, power) an enterprise has established with external individuals and organizations (e.g., consultants, customers, suppliers, allies, competitors) in pursuit of advantageous outcomes
- Process: An enterprise’s ability (e.g., policies, procedures, methodologies, techniques) to leverage the ways in which the enterprise operates and creates value for its employees and customers
- Product/Service: An enterprise’s ability to develop and deliver its offerings (i.e., products and services) that reflects an understanding of market and customer requirements, expectations, and desires
- Technology: The databases, hardware, and software an enterprise has invested in to support its operations, management, and future growth and renewal
These enterprise vital components are a validated taxonomy. A 2004 study identified that an enterprise that uses a standard and consistent taxonomy to define and develop models and algorithms increases its ability to identify, measure, account for, and validate its performance. This taxonomy establishes a path for enterprise leadership and management to identify assets (Figure 1), and subsequently, their related measures and indicators. It also provides a foundation to construct a network of interactions. The key to defining the network of assets, measures, and indicators or enterprise performance information is to ask the right questions and to know what is needed to answer the questions. Each of the enterprise vital components and assets introduces key questions that align with performance measures and indicators. Defining the measures and indicators beneath each asset builds a neural network of performance information, or enterprise memory. This structure paves the way to construct algorithms, machine learning, for the strategy formulation process that augments the thought process of enterprise leadership and management when developing enterprise strategy.
Figure 1: Enterprise Vital Components with Assets (Note: This is not an inclusive set of assets; these were extracted from current component-based valuation models)
The neural network structure of enterprise information and its complementing algorithms align with common business language and advances the enterprise information to enterprise intelligence. The enterprise intelligence is further grouped or decomposed into three sub-categories (Figure 2). These categories and algorithms provide a more granular focus and establish reason and basis, as well as knowledge for actions. The automation of the strategy formulation process provides the ability to dynamically monitor and evaluate leading and lagging indicators as well as the achievement of strategic goals and objectives in enterprise strategic plans. The results of the algorithms are fact- or evidence-based answers to performance-based questions.
Figure 2: Sub-Categories of Enterprise Intelligence
Plan and Know the Data and Information That Drives Enterprise Strategy
Until this point, the discussion has focused on the structure — network, machine learning, and process automation, which are the necessary technologies to integrate AI into strategy management. The question now switches to “What data goes into these measures and indicators?,” which is a critical function to ensure the authenticity, integrity, and completeness of the data used in the analysis and captured in the Enterprise Intelligence Repository.
In The Information Executives Truly Need, Peter Drucker’s chapter of Harvard Business Review on Measuring Corporate Performance, Drucker presents the following four categories of diagnostic information required by a business to manage wealth creation:
- Foundation Information: Routine measures such as cash flow and liquidity projections, which, when normal, basically do not tell much; however, when not normal, indicate a problem that needs to be identified.
- Productivity Information: Measures that deal with performance of key resources. These measures must also include the total-factor productivity, which means that they should provide the value-add of all costs, including the cost of capital.
- Competence Information: Measures associated with core competencies that link market or customer value with special skills and abilities of the producer or supplier of products and services.
- Resource-Allocation Information: Measures associated with the allocation of scarce resources, such as capital and performing people.
Drucker identifies that results of these four kinds of information inform and direct tactics, which provide organized information for strategy. The data and information that is identified to go into these buckets and used in the algorithms must be intentionally planned. Regardless of what AI solution is introduced to the enterprise, if the data is erroneous, the risk of moving the enterprise along a catastrophic path is elevated. Therefore, it is critical to know and validate the origin and sources of the data and information driving the strategic decisions of enterprise leadership and management.
The Real Perspective: Artificial Intelligence — Enterprise Brain, Mind, and Body
If the brain were a company, its mission statement would be: “Promoting the highest quality of individual life by regulating stress to maintain homeostasis (balance).” To achieve this, the brain reconciles stimuli from our five senses with our internal milieu, i.e., observes the outside world and responds to it internally.
In the conclusion, the reality becomes prevalent. The brain and the mind in the human is not artificial, however — it is a real model that is used to create artificial intelligence within the enterprise. The brain is a tangible organ and the mind is intangible neurons that control many colloidal effects, which regulates all vital human functions throughout the human body. In addition, there are hormones vital to the brain for protection and adaptation. In an enterprise, the strategy is the brain as it is a tangible document. However, the enterprise mind represents the belief systems, social influence, thought processes, education, innate intelligence, moods, emotions, institutional knowledge, etc., possessed by the leadership and management of the enterprise.
Imagine the influence of the mind over the brain, by visualizing enterprise leadership and management gathering in a boardroom to direct corporate strategy and establish corporate policy. Is the pertinent data and information being captured to emulate this collective mind in an artificial mechanism? Perhaps 50 percent is captured and the other 50 percent is augmented by the real human. If the answer to this is not clear and tasks to be augmented by human are unknown, then there should be a concern for the readiness to implement AI within the enterprise.
The Conclusion: Open the Enterprise Mind
A strategic plan enables an organization to evaluate its resources, allocate budgets, and determine the most effective plan for maximizing return on investment (ROI). A company without a strategic plan is not utilizing its brain and mind to provide its workforce with direction or focus. The enterprise leadership and management are choosing to be reactive, rather than being proactive in the face of business conditions. The enterprise is then put in a position to address unanticipated pressures as they surface and the enterprise is placed in a disadvantageous position. This is likened to the human brain, which has to regulate the stress of the individual when things become out of balance in the body and it has not been prepared by the mind. For example, if an enterprise decides to include autonomous cars into its operations, then it is hopeful that the enterprise strategy has identified the goals and objectives that align with the inclusion of the autonomous cars, along with where they are to be added or what they are to replace in the current operations. Understanding its current functions and the requirements of the drivers (there are explicit rules that drivers must follow for public safety) are critical to establishing the measurements and indicators that drive the goals and objectives. If enterprise strategy does not exist or its content has not been prepared from adequate validated and verified data, then the enterprise has made a decision to circumvent the protection of the strategy and to act arbitrarily with guess and gosh, which could significantly impact public safety and place the enterprise in a disadvantageous position.
Constructing artificial intelligence in an enterprise is not an overnight project. It must be thoroughly planned and introduced in a phased-in approach that targets high- and low-level gains. Implementers of AI cannot void the very components that are being emulated. If intelligence is to be automated, then it is critical that functions and operations of intelligence be understood. An enterprise should:
- Understand the context of AI, as AI has many guises.
- Understand the role of the strategy, as it is what is transferred to the essential functions and workforce of the entire enterprise.
- Have an architecture of the enterprise, which may or may not be a major impact, as enterprises have employed enterprise architecture for years. It would depend on the integrity of the architecture and if it adequately represents the enterprise current state.
- Engage its leadership and management to ensure it does not work in a vacuum of what it perceives to be the mind of its leadership and management or that it does not build theory void of practical experience.
- Just like in a healthy human, the mind, brain, and body, interact to enhance functionality in humans. Recognize that the enterprise leadership and management (mind), strategy (brain), and workforce (body), interact to improve the performance of the enterprise.
- Know that the strategy consolidates, simplifies, and anticipates, as it is the monitoring tool that promotes efficiency and serves a purpose to protect and/or adapt a dynamic living enterprise.
- Be cognizant of the vulnerability of the data and the algorithms, as their variables and interactions affect the efficacy of the enterprise strategy.
- Just as the brain’s job is to regulate all stress and return balance to the internal milieu, the enterprise strategy’s job is to regulate all strengths, weaknesses, opportunities, and threats, to sustain or improve the enterprise environment.
- Understand that the optimum state is to have leadership and management and the workforce to share the same goals and objectives, to communicate well, and to complement each other.
- Because You Need To Know — Pioneer Knowledge Services with Edwin K. Morris
- Convergence Conversation: Unpacking Knowledge Management with Deborah Westphal
Conferences and Presentations
- Knowledge Engineering/Science Curricula — What is it?
- 6th European Conference on Knowledge Management 2005 (ECKM 2005): A Framework of Intangible Valuation Areas & Antecedents with Andreas N. Andreou
- 4th European Conference on Intellectual Capital (2012): Measuring Organization Vitals that are Essential to Sustain the Life of the Organization
- 9th ICICKM (2012): Challenges and Opportunities: Designing and Delivering a 21st Century Knowledge Management Education Program with Denise Bedford
- 930gov Technology Tradeshow & Conference: Knowledge Management Comes of Age
- Systems Engineering Conference DC 2012: A System Approach to Knowledge Creation and Valuation
- KMGN: The Digital Transformation: Convergence of AI & KM
- Knowledge Management Education Forum (KMEF)
- PLANT the Right Seeds to GROW: A Harvest of Knowledge
- Onsite Event
- Summary by Guy St. Clair
- Summary by Kent State University
- LinkedIn Group
- In Focus | Artificial Intelligence interviewed by Jeff Warner
- In Search of Knowledge Management: Pursuing Primary Principles with Michael Stankosky and Linda Vandergriff
- Chapter 8: The Organizational Body Gets an Intelligent Brain
- Chapter 19: Driving Change Using Knowledge Management — Lessons Learned from an Unidentified Organization
- Chapter 10: The Cognizant Organization
- Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management, and Organizational Learning — ICICKM 2013
- Creating the Discipline of Knowledge Management: The Latest in University Research edited by Michael Stankosky — Chapter 12: A Framework of Intangible Valuation Areas (FIVA) — Event at The George Washington University
- Identifying, Measuring, and Valuing Knowledge-Based Intangible Assets: New Perspectives edited by Belen Vallejo-Alonso. Arturo Rodriguez-Castellanos, and Gerardo Arregui-Ayastuy — Chapter 11 Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN): A New Approach to Intangible Asset Valuation Based on Einstein’s Perspective