our proprietary solution

We are developing a system that will not only repair the numerous faults in the strategic planning process; it will also enhance it by adding significant new features and functionalities.

1. Fully computer-automated, cloud-based strategic planning system. Operable 24/7 without human intervention.
   

   Our system will be fully computer-automated and cloud-based so that all the strategic planning functions can happen without human intervention or initiation. This means that those functions can and will operate around the clock all year long. Some form of strategic planning can occur as frequently as circumstances demand.

2. Lightning-fast import and analysis of data
   

   The computer and algorithm-driven processes in our system will enable lightning-fast import and analysis of data, identification of patterns and trends, drawing of conclusions, and making strategic decisions. These are tasks that typically would take days or weeks. This will allow businesses to respond almost instantly to competitor  initiatives and other strategy events.

3. Constant automated search for new data
   

   The types of data utilized by the system will be clearly specified – either manually by corporate strategy officials or by computer algorithms reviewing the corporation’s existing strategic planning process. Separate search algorithms will be employed to scan a large number of additional sources to discover new relevant data. With input from the search findings, the officials will decide whether to import and use new data.

4. Constant automated data currency review and update
   

   The creation dates for the data currently being used will be checked and compared with the latest dates for the same data. Data that are out-of-date will be updated.

5. Automatic check of data validity
   

   After carefully defining what is meant by data validity (i.e., the degree to which they measure what they claim to measure, correct, accurate), rules of that validity will be established and applied to the data that go into the strategic planning process. Where the validity levels of the data are shown to be unacceptable, the data will be either cleansed or replaced.

6. Automated review of interrelationships among data types
   

   Start by manually defining the possible relationships, connections, and correlations among the various data types employed in the strategic planning process. This includes defining equations that represent those relationships. Then, use machine learning algorithms to discover other connections with existing data and with data that might yet be acquired.

7. Automated review of lines of reasoning in drawing conclusions from data
   Agree on some simple rules of logic that will be accepted in reaching conclusions from data and in making decisions on the basis of those conclusions. To evaluate the strength of the reasoning used, apply the rules to conclusions and decisions already made. For the future, the rules may be applied to new data that has been acquired (leading to new conclusions) and to the factors that will be taken into account in reaching a new decision.

   These actions will be taken automatically by algorithms in the system. When new data are imported by the system or at the request of a manager, the logic rules will be checked against data that are being used (interpreted) for the first time. When a new decision is being considered, a manager will enter the key factors and parameters, and  the system will automatically check its  logic.

   Where shortcomings are discovered in executive decision-making on strategic issues, the affected executives will receive training in the rules and practice of rational decision-making. Eventually, the training will be administered by the system’s algorithms. Furthermore, as the system is steadily enhanced, it will be able to monitor specific decision events, notice deviations from the  rules, and prompt for adjustments.

8. Constant automated review of assumptions underlying strategic decisions
   

   As executives make their strategic decisions, they will be asked or prompted to state the assumptions behind those decisions. They will be asked to express them in terms that  are as objective, practical, and quantifiable as possible. The parameters of those assumptions will be entered into the system so that at appropriate times or under appropriate circumstances, when it makes sense to test the assumptions, system algorithms will automatically reexamine them. It also will assess the degree to which the assumption has deviated and how much that may compromise the promise of the strategy.

9. Automatically-prompted creation of strategy implementation plans
   

   The implementation of each strategic plan will be described manually in the greatest detail possible, incorporating time deadlines, required resources, and performance benchmarks. The automated system may be able to prompt for certain implementation steps.

10. Constant automated reviews of strategy implementation plans for progress.
    

    Once the implementation scheme is in place, at appropriate intervals, algorithms will check on whether key steps have been reached or completed. This will provide a continuous reading on the progress in implementing each strategy.

Those are the basic steps in the strategic planning process that will be automated in our system. As we grow the system, several other more innovative strategy functions will be taken over by AI algorithms. Some of those will require further experimentation and research.

1. Assess “strategic thinking” in managers
   

   An interactive program for assessing a person’s ability to think strategically. There are established characteristics of the thought processes of a person who is capable of thinking strategically. The program asks questions that elicit a person’s thoughts and attitudes about strategic or long-term or futuristic issues. Many will be multiple choice or yes-no. But the most useful questions will describe short (or longer) case situations and seek the person’s interpretation or suggestions for further action. The program will score the person’s strategic thinking IQ, perhaps iteratively as he proceeds through the questions.

2. Develop managers’ ability to think strategically.
   

   This feature builds on the assessment program; it aims to improve a person’s competence in strategic thinking. It might be combined with separate offline reading of papers and case studies. It would ask questions and require completion of exercises. Feedback would be provided on the answers; a regularly updated score of strategic thinking IQ would be shown, indicating the person’s learning progress. In effect, the program would be a fairly aggressive form of online learning.

3. Gather and apply data on resources and competencies
   

   Through persistent real-time links to internal corporate reporting systems (in finance, manufacturing, human resources) the system will gather data on organizational resources and competencies, and feed them into the strategic planning process. One of the most important functions that AI can contribute to the strategic planning process is gathering, analyzing, interpreting, and regularly updating data about factors in the business’s internal environment that affect strategy. These data inform the strategies that initially are created as well as any subsequent adjustments made to them as they are being implemented.
   

   • The data that explain each of resources and competencies are described in the greatest possible detail, including the ways in which they impact strategy planning.
   • Sources of each type of data are identified – location (system, databank) throughout the organization. This may require considerable research and inquiry to discover data that may be unseen within department and division silos.
   • Initial judgments are made about the accuracy and reliability of each data type.
   • An algorithm is written to accept regular inputs of each data type in order to then make predictions about the corresponding effects on the initial strategy design or subsequent strategy adjustments.
   • Once a reliable, effective algorithm has been developed, it is deployed in the service of planning new strategies and updating existing strategies.
   • The next stage is to evolve the proven algorithms to take into account the interactions and interdependencies among the factors.

4. Generate a strategic vision
   

   Generation of a strategic vision based on views of multiple stakeholders and on environmental data.

   • The first step is to identify all the stakeholders whose attitudes, policies, activities, plans, opinions, and values that the organization would like to take into account in defining its future direction. Paramount among those stakeholders are the original founders, the current leaders, other top executives, and major shareholders/owners of the organization.
   • The specific ways in which those stakeholder characteristics will be allowed to influence the future strategic vision is described.
   • The environmental factors that bear on the strategic vision also are identified.
   • Decisions are made about the ways and the extent to which the environmental factors will be taken into account.
   • Logarithms are developed to translate stakeholder preferences and environmental factors into basic principles of a statement of strategic vision or alternative statements.

   There are two problems with this system component. It will be extremely challenging to develop algorithms that can read the preferences of stakeholders and translate them into a strategic vision for the organization. Those preferences often are not clearly and explicitly expressed. Stakeholder preferences are likely to change frequently over a short span of time. Yet, it is a rule of thumb that strategic visions ought not to be changed very much or very often. They usually remain constant for decades. There are similar difficulties with the environmental factors.
   

   We believe that eventually, as the science of artificial intelligence advances, it will be possible to generate  a workable strategic vision for a business.

5. Propose competitor-focused strategies
   

   The ultimate purpose of the strategic planning function (and this AI-driven system) is the formulation (and constant update) of strategies that fulfill an organization’s mission and move it toward realization of its strategic vision. A valuable feature of this system will be one that proposes competitor-focused strategies. That is, strategies that are responsive to the threats (or opportunities) that competitors present.
   

   • This begins by identifying all those firms that might be considered competitors of the organization doing the planning. The system prompts the planners to think outside the traditional group of competitors, to include other businesses offering product/services that meet similar customer needs in different ways.
   • It identifies the current, specific strategic threats and opportunities posed by each competitor.
   • It identifies and begins drawing data on a regular basis from information sources that report reliably on the activities of these competitors.
   • Generic principles (rules of action) of response to competitor strategies are defined. These are principles that the organization’s leaders would follow when confronted by particular competitor behaviors and actions.
   • Those principles are combined with situational factors and written into algorithms that suggest courses of action the organization may choose to follow.
   • Because of the complexity of matching one organization’s strategic response to another’s strategic initiative, and because of the scope and consequences of that response, this is a component of the system that will invite/require managerial approval of decisions that may be made.

6. Analyze and propose multi-SBU portfolios

   For corporations with multiple strategic business units (SBUs), this system feature analyzes the composition of their SBU portfolios and proposes adjustments to maximize strategic goals. It works this way.

   • The organization’s criteria for both the overall makeup of its portfolio and for the individual SBUs in the portfolio are expressed. The criteria are unique to each organization but typically SBUs are assessed for financial and market performance while the entire portfolio evaluated for the average financial performance across all the SBUs, the particular industries and markets served by those SBUs, and the portfolio diversification among industries and markets.
   • The criteria are incorporated into algorithms that receive data about financial, market, and industry performance, calculate the related metrics, and suggest appropriate adjustments in the portfolio (divest SBUs, acquire SBUs in certain new industries or markets, increase investment in certain existing SBUs, overall reconfiguration of the portfolio).
   • Because of the possible scope and impact of major portfolio changes, this component will allow for managerial intervention before decisions are made.

7. Define and recommend market segments
   

   Organizations rarely try to sell their goods or services to the entire market of prospective customers. Their strategies focus on segments of the total market. This component defines and recommends segments for the organization’s strategic attention.

   • The organization specifies the market characteristics that are important in determining the demand for its goods/services – i.e., population, location, income, education, lifestyle, buying habits. These characteristics are used in distinguishing the market segments.
   • Sources are identified for current data on these characteristics and arrangements are made to import those data into the system on a regular basis.
   • Management sets levels for each of these characteristics – i.e., minimum levels below which strategies toward a currently served segment may be adjusted or the segment abandoned entirely, target levels that signal that an unserved segment may deserve reconsideration. Noteworthy changes in any characteristics of any segment will prompt a review of the organization’s market segment profile.
   • Rules are written into algorithms that are triggered when characteristics reach certain predetermined points, recommending specific management actions to take advantage of them.

8. Prompt for functional area sub-plans
   

   To be implemented effectively, organization-wide strategies must be backed up by sub-plans within individual functional areas (e.g., finance, marketing, human resources, operations).. The algorithms in this component take information from already formulated corporate-level strategic plans and, with knowledge of operational activities in each of the functional areas, propose the basic terms of those sub-plans.

9. Suggest operational decisions for implementation
   

   Eventually, AI logarithms will recommend decisions at the operational level needed to implement the corporate-level strategic plans. Effectively tying strategic plans to operational activities. This component relies on a great deal of data and understanding about operational activities at the most fundamental levels within the organization, and the contributions they make to achievement of strategic goals.
   

   This particular feature will be tied directly to two inter-related trends that are re-inventing the way businesses operate. The more modest of the two is Digital Transformation which aims to replace manual or non-digital business processes with digital technologies. This switch will make it easier for our automated strategic planning to interact with and control operational activities.
   

   The far more revolutionary second trend is labeled Industry 4.0, representing the Fourth Industrial Revolution. It focuses heavily on interconnectivity, automation, machine learning, and real-time data and emphasizes these four design principles:

   • Interconnection — the ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things
   • Information transparency —transparency that provides operators with comprehensive information to inform decisions. Inter-connectivity allows operators to collect immense amounts of data and information from all points in the manufacturing process, identify key areas that can benefit from improvement to increase functionality
   • Technical assistance — the technological facility of systems to assist humans in decision-making and problem-solving, and the ability to help humans with difficult or unsafe tasks
   • Decentralized decisions — the ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interference, or conflicting goals, are tasks delegated to humans

   In summary, Industry 4.0 combines physical production and operations with smart digital technology, machine learning, and big data to create a more holistic and better connected ecosystem for companies that focus on manufacturing and supply chain management

10. Check for availability of resources and competencies
    

    After several attractive strategic options have been developed and tentatively approved, this feature assesses the resources and competencies that they require and matches them with what is available. It also suggests additional amounts of these assets that should be acquired and where they can be found. When compared .with the available resources, this makes it easier for managers to choose among the options

11. Check for effects of environmental factors
    

    A successful strategy must take into account numerous external factors like market characteristics, customer preferences, competitor activities, and various environmental elements. As strategies are being formulated, this feature automatically evaluates those factors that may hinder or encourage their implementation – including potential responses from competitors. It then suggests steps to overcome any barriers.

12. Guide managers to select specific strategies
    

    This feature goes to the heart of the strategic planning process. Through pointed questions (about leader preferences, corporate mission & vision, strategic objectives, supporting data), analysis of related data (market trends, consumer demographics, competitor behaviors, environmental factors), and  the application of natural language processing (NLP),  this part of the system guides the selection of specific strategies, such as low-cost leadership vs differentiation vs hybrid, and focus vs overall market. This is the most complex, most challenging of all the components in this system. It is likely to be the last one to be developed as it relies extensively on other components. But it will be developed eventually.

13. Guide understanding of internal value chain
    

    To fully appreciate its range of strategic options, an organization needs to fully comprehend its internal value chain. This knowledge will help it to find ways to lower costs or differentiate its product and service offerings. This system component automatically guides the organization to recognize and describe its internal value chain and all its interlinked value-adding activities, and to simulate modifications of the chain (i.e., same activities in different configurations, new activities).

14. Guide understanding of industry value chain
    

    Each organization’s internal value chain extends backwards and forwards into an industry value chain. The system performs the same AI-driven work for the entire sequence as it does for the internal segment, allowing the development of more comprehensive industry-wide strategies.

15. Identify value chain activities for strategy implementation (low-cost leadership)
    

    The knowledge garnered through the previous two features allows the organization to identify activities and decisions at specific points in the chain that might be modified to implement a Low-Cost Leadership strategy.

16. Identify value chain activities for strategy implementation (differentiation)
    

    Similar to the previous feature, this one identifies activities and decisions at specific points in the internal value chain that might be modified to implement a Differentiation strategy, in particular comparing the organization’s capabilities with the market’s needs and wants to find points of potential differentiation in product or service.

17. Identify key success factors
    

    After several years of machine learning-driven study of the business’s operations, the system will identify its Key Success Factors – the functions, activities, and business practices (defined by the market not the company, and as viewed by customers) that are critical to the company-customer relationship. These are the three to five areas that a company must emphasize to realize its vision. The algorithms in this feature will be engineered and then left to run on their own, gathering data, analyzing them, reaching conclusions, and making recommendations to management.

18. Quantitative assessment of strategy risks
    

    Different strategies present different levels of risk, and managerial tolerance for risk varies. The purpose of this part of  the system is  to measure that risk. Applying algorithms to relevant risk metrics enables the quantitative assessment of the perils of each strategy, the threat posed by various competitors, and the attractiveness of different market segments. It will be a challenge to develop this capability.

19. Recommend strategies to achieve competitive advantage
    

    The ultimate achievement of the strategic planning function is the creation of a competitive advantage. This will be one of the most sophisticated features of this system. It will analyze all potential sources of competitive advantage in an organization’s operations and compare them to its competitors’ similar activities. Where it sees opportunities to gain an advantage, it recommends actions and initiatives to seize them. This will be one of the more difficult system proficiencies to create.

20. Recommend external combination strategies
    

    A popular method of executing a growth strategy is some form of external combination, like strategic alliances, mergers, or acquisitions. This component makes that method more accessible by searching for potential candidates for combination, analyzing their positive and negative characteristics, recommending the best partner for the organization and the specific terms of that partnership.

21. Monitor the implementation and effectiveness of a strategy
    

    It often takes months to fully implement a strategy which then may be in effect for many years. For an optimum outcome, the strategy must be monitored and managed throughout this period. This AI-driven feature will watch the metrics of implementation and report on deviations or subpar performance, allowing management to step in and make corrections. Another feature of this component will monitor the strategy’s achievement of strategic objectives, noticing when it is falling short, as well as changes in the external environment that may call for revision in the strategy.

Note:  Each of these system features may look like it could operate autonomously. That is true of many of them, and those will be available to customers as freestanding applications. However, the full value of this system is realized when all the parts are functioning as an integrated, unified configuration. At the same time, it includes points in and between each of these features where human intervention and judgment is allowed.