The assessment and development of managerial strategic thinking will be performed concurrently as explained in the next component.

If an organization is going to approach strategic planning seriously, it needs to have confidence in the “strategic thinking” skills of its managers. If it wishes, it can ignore this issue, and carry out its strategic planning through less skilled leaders. This piece of the strategic planning process can be implemented at any time and with as many or as few managers as the organization chooses. An organization may elect to evaluate the strategic thinking skills of existing managers or manager candidates (through hiring or promotion), then use the results to make decisions about hiring, promotion, or delegation of responsibility. If the organization is dissatisfied with the level of strategic thinking skills in its managers, it can ask/require them to engage with the skill development tool.

The initial Skill Assessment component consists of a series of computer-administered questions (objective and essay), and case studies designed to determine a person’s ability to think strategically. Algorithms in the component will not only score the manager’s performance, but also ask follow-up questions wherever it detects weakness or uncertainty in the manager’s answers. Among the data used to train these algorithms are proven real-world indicators of a strategic management mindset. These will be gathered from journal articles on related research, professional management treatises, commercial research reports, consulting firm white papers, and personal stories about successful managers and businesses. More sophisticated analytical algorithms will recommend steps that a manager might take to improve his/her strategy skills.

The Skill Development component may be used independently or it may follow results generated by the Skill Assessment component. It will rely on established evidence on how individuals have been effectively trained to think and act in a strategic manner. Examples are the curricula of business school courses on strategic planning, the textbooks used in those courses, executive education programs on strategy, and academic research on this topic. Using those data, algorithms will prompt the user-manager to complete certain exercises and engage in certain activities, continually evaluate the progress he/she is making, and adjust the development plan until a desired skill level is reached.

The data gathered for this component will define what the organization is capable of accomplishing through its strategic plans. These are the assets of the organization, encompassing Resources and Competencies. These data inform the strategies that initially are created as well as any subsequent adjustments made to them as they are carried out.

The data will be found or generated within the organizations – in existing records, reports, and databases. For some types of resources or competencies, in some organizations that have never tried to quantify what they are capable of doing, it may be necessary to create systems to begin collecting data for the first time.

Decide which of the organization’s assets have the potential to contribute to or detract from its strategies. Start with tangible Resources. The items on the initial version of this list will be based on the organization’s previous efforts at strategic planning. As management gains a more nuanced appreciation of the factors affecting the success of their strategies, the list will grow and become more granular. For some organizations, the list could be quite long. These are a few more tangible examples (in most cases, they can be enumerated or assigned a dollar value):

• number of employees in specific job categories with specific skill sets, experience, and certifications(utilized and unutilized)
• number and types of equipment with specific capacities and capabilities (utilized and unutilized)
• number and location of physical facilities with available space and amenities
• number, size and location of real estate properties owned
• current product/service lines with characteristics, production costs, and customer needs to be served
• control and reporting systems, affecting cost, quality, productivity
• traditional financial performance metrics (absolute and trends)
• accumulated financial resources (free cash flow, cash reserves, borrowing capacity, credit rating)
• proprietary technologies (patents, copyrights, trademarks, trade secrets) with potential applications and projected market value

There also are intangible assets that may be just as important. They often are harder to quantify. Here are a few examples:

• specific staff skills and capabilities
• recognized level of management judgment
• recognized congenial labour-management relations
• recognized organizational reputation for innovation with an established track record
• recognized research capabilities
• recognized reputation with various stakeholder groups

Another group of difficult-to-measure assets are Competencies. These are typically tasks that people and machines, working together, can perform. They are the products of various combinations of the organization’s resources. They will look a lot like the intangible Resources just above.

It is not important whether an asset is called a Resource or a Competency; it is only important that it is possessed by the organization and available for utilization in executing its strategies.

Note: The resources and competencies possessed by each organization will be unique. No two organizations will have identical bundles of assets. This is just one example of why there cannot be a single, standard version of this AI-driven product.

Describe the data about these assets in the greatest possible detail, including the ways in which the assets may impact the organization’s strategies.

Determine the sources/locations of each data type within the organization.

Determine for which of these assets the organization currently and systematically collects data in a form that is clean and accurate enough to use for training algorithms. Make initial judgments about the accuracy and reliability of each data type.

Reengineer the existing data-collection systems for those data to i) perform the collection automatically and ii) bring the data together in a central repository.

Establish systems and protocols for collecting data about the other valuable assets not currently measured, and direct them to the same central repository.

Where necessary, “clean” and reframe the data to make them suitable for training algorithms and generating strategies.

Agree on rules and standards that define superior/inferior and improving/declining performance or status for each of the asset types.

Write algorithms to accept regular data inputs in order to make predictions about their effects on initial strategy design and subsequent adjustments.

Automatically collect data from the identified sources and feed them into the algorithms for training purposes.

Evaluate the resulting predictions for relevance, plausibility, and usefulness.

Continue the training until reliable, effective algorithms have been developed or it is determined that the algorithm will not serve its purpose.

Deploy reliable, effective algorithms in the service of planning new strategies and updating existing strategies.

Design a user interface that does two things: automatically reports changes in the data (measured against pre-established benchmarks and trigger points) and allows management queries about different configurations of the data and comparisons among the data.

With the passage of time, as more knowledge accumulates about the implications of these asset data for strategic factors relevant to the organization’s mission, develop algorithms that describe in detail those implication effects and, eventually, suggest adjustments to the assets in order to optimize the strategic factors.

This components operates very much like the previous component dealing with resources and competencies. Strategies are affected by almost everything that goes on in the external environment in which an organization operates. These are some of the factors that comprise that environment.

• Legal environment (laws, regulations, court decisions, enforcement vigor, at levels of city, state, national, other countries)
• Political environment (specific politicians, ideological mood, lobbying opportunities, public policies, government spending/grants, government regulations)
• Economic conditions (interest rates, inflation rates, consumer income, employment levels, government spending, at local, regional, national, international levels)
• Technological innovations
• Funding sources (financial markets, research grants, charitable contributions)
• Market conditions (local, regional, national)
• Consumer demographics (numbers, geographic location, age, employment, income, education, purchase behaviour, lifestyle)
• International markets
• Emerging markets
• Potential consumer demographics
• Consumer product preferences (product features, price)
• Competitors (number, size, strategies, competitive intensity)
• Vendors (prices, service, product features, reputation)

Identify the external environmental factors that the organization believes may have a general impact upon the planning and success of its strategies. This list of factors will be significantly different for each organization, even organizations in the same industry. The list will be very long. Over time, as the list is tested, some factors will be dropped for lack of strategic relevance. The organization also will continuously discover new factors that have implications for its strategic planning.

Describe the data about these factors in the greatest detail possible, including the ways in which the factors may impact the organization’s strategies.

Describe the sources where data about the factors may be found. These will be extremely diverse and located outside the organization. The most common sources are likely to be:

• trade and professional journals
• industry associations and publications
• government agencies (regulatory, financing)
• consulting firms
• NFP research institutions
• academic research centers
• individual academicians and their research publications
• FP data gathering and market research firms
• news media
• research and data gathering conducted or contracted for by the organization

Determine the terms under which the data may be accessed and used. There may be limitations on who may access the data, for how long they will be able to access them, the uses to which they may put the data, the format in which the data are accessible, and the cost of accessing the data.

Create an acquisition and access plan for the desired data. Prioritize the quest for data types by their importance to the strategic planning process and their easy availability. For each data type define these characteristics:

• Owner of the data
• Precise description of what the data measures
• How were the data originally collected?
• When were the data collected? (How up-to-date are the data)
• Who may be given access to the data (anyone, institution members, subscribers, payers)?
• Technical difficulty or ease of accessing the data
• What is the cost of accessing the data?
• In what condition or format are the data?
• What further “cleaning” will be necessary before the data can be used?
• What will the organization have to do to be prepared to accept and process the data from each source?

Establish the sequence in which the organization will acquire/access the various data types, using the previously determined priorities.

Initiate the import of data according to the sequence and the overall acquisition and access plan. This is likely to require the development of organization systems tailored to particular data types. The import function will be performed automatically.

Include data cleaning mechanisms in the data import systems.

Agree on rules and standards that describe the relationship that each data type has with the organization’s strategies and critical success factors.

Use those rules and standards to write algorithms that make predictions about the effects of the data on initial strategy design and subsequent strategy adjustments. [See component 16.]

Evaluate the resulting predictions for relevance, plausibility, and usefulness.

Continue the training until reliable, effective algorithms have been developed or it is determined that the algorithms will not serve their purpose.

Deploy reliable, effective algorithms in the service of planning new strategies and updating existing strategies.

Design a user interface that does three things: i) automatically reports changes in the data (measured against pre-established benchmarks and trigger points), ii) automatically suggests their impact on existing and prospective strategies, and iii) enables management to make queries about the strategy effects of hypothetical configurations of the data (and their underlying factors).

Using third party research on the cross-effects among the various environmental factors, draft rules and standards describing how changes in one factor may lead to changes in other factors.

Incorporate the rules and standards into another set of algorithms that describe the interactions among the environmental factors, and between the environmental factors and the internal assets.

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.

Define the organization’s products/services in terms of performance characteristics, ease of purchase, ease of use, quality level, price, and needs satisfied. Do the same for the products/services of perceived competitors. (a)

Using internal and external market research data, describe the numbers, demographic characteristics, product/service preferences and needs of existing and potential customers. (b)

Combine the results of steps “a” and “b” to define the characteristics of the overall market in as much detail as possible.

Using some intuition and marketing expertise, define distinct clusters or segments within the overall marketplace. Look for criteria like similar needs, preferences, personality, lifestyle, buying behaviour, decision making patterns, demographics, and geographic location. Concentrate on defining viable segments, those in which the organization has the ability to manufacture the product at a profit. [Keep in mind that customers may be either individuals or organizations.]

Taking into account the organization’s current and future ability to create different products and services, and also taking into account the presence of competition in particular market segments, determine which segments (that exist now or might emerge in the future) the organization might conceivably be able to pursue.

Codify the organization’s manufacturing capabilities and the market presence of competition in rules or protocols that become the basis of algorithms that are designed and trained to recognize the market segments on which the organization should focus its attention.

An organization’s strategic vision should reflect three things – what it is capable of doing (its resources and competencies), the setting in which its vision will be realized (markets, competitors, external factors), and the preferences of its key stakeholders.

Identify all the stakeholders whose attitudes, policies, activities, plans, preferences, opinions, and values that the organization would like to take into account in defining its future direction. For example, BOD members/trustees, executives, managers, employees, customers, suppliers, members of the public.

Describe the specific ways in which those stakeholder characteristics will be allowed to influence the future strategic vision of the organization.

Identify the sources of information that may indicate stakeholder preferences (e.g., speeches given, personal interviews, books. articles written, press releases, personal conversations).

Design algorithms, probably based in machine learning, to interpret the information about stakeholders for signs of their preferences regarding the organization’s strategic vision.

Eventually, algorithms will be developed to extrapolate possible strategic visions from the organization’s resources and competencies, and from its external environment. However, the relationship between them is not always linear and obvious, so that it may not be worth the effort with the present level of AI technology.

Determine and describe in detail the activities that an organization performs to manufacture its products or services, focusing on one product or service at a time. In the description, note the interactions between each activity and other activities – not just the fact of an interaction but exactly how one activity affects another. Where possible, also make note of the time and costs required by each activity.

Graphically, narratively, and digitally portray the organization’s internal value chain or chains.

Write algorithms to calculate the costs, times, efficiencies, and outputs in the entire chain and in its different segments, allowing for the manipulation of the chain activities to see what improvements might be achieved. This will enable “what if” reconfigurations of the value chain to see what competitive advantages might be possible.

Write automation algorithms that recognize changes in value chain data and report them and their calculated effects in graphic forms (dashboards?) to managers so that they can react if they wish.

Determine what data about the activities of an organization’s suppliers/vendors and customers are relevant to the organization’s planning for its own internal value chain. Simultaneously determine which of those data are available and under what terms. 

Describe those external activities in detail, with emphasis on their interactions with the organization’s internal activities, as well as where the interactions occur and their practical effects. Where possible, note the costs of the external activities and the circumstances under which the activities could be modified.

Extend the portrayal created in component 7 above to include the external activities before and after the internal value chain.

Write algorithms to calculate the costs, times, efficiencies, and outputs in the entire industry value chain and in its different segments. Allow for the chain to be test reconfigured in order to see the effects on those variables.

Write automation algorithms that recognize changes in value chain data and report them and their calculated effects in graphic forms (dashboards?) to managers so that they can react if they wish.

One primary reason for manipulating an organization’s value chain is in order to implement a Low-Cost Leadership strategy.

With the manipulation tools created in the previous two components, experiment with different value chain configurations to see which result in the lowest costs of production without compromising other important product characteristics.

Differentiation is another common form of competitive strategy that is more difficult to achieve successfully than Low-Cost Leadership. It requires finding and implementing meaningful, unique, distinctive product characteristics. This is accomplished by understanding i) customer preferences and ii) products/services offered by competitors, and iii) determining the organization’s ability (through its resources and competencies) to create new products/services that differentiate it from those competitors.

Gather the organization’s own market research data, along with those available from external sources, describing the product preferences of existing and prospective customers.

Gather data through the organization’s own research and from external sources describing the products (and their characteristics) available from competitors.

Using knowledge gained about the organization’s value chain in the previous two components, describe modifications that could be made in the chain to create some type of differentiation in the products or services.

Write algorithms that search the customer preference data for indications of unmet product needs, search the competitor data to see which of those needs are not being met by competitors, and analyze the value chain to determine if it is capable of manufacturing product or services that meet those needs.

In detailed quantitative terms, define what “success” means for the organization. This information usually will come from the BOD or top management. It may be reflected in organization’s strategic vision or its strategic objectives. Ultimately, success is determined by what appeals to customers.

Apply AI algorithms to the organization’s knowledge of its value chain to determine which activities seem related to the organization’s definition of success.(In this context, “activities” include numerous objective and subjective aspects of the organization’s operations.) Over a period of time, patterns will emerge to show that certain activities or combinations of activities are most critical to optimal success. Those may be considered Key Success Factors.

Identify all other organizations that might be considered competitors of the organization doing the planning. Think outside the traditional group of competitors to include organizations offering products/services that meet similar customer needs in different ways.

Identify the current, specific strategic threats and opportunities posed by each competitor.

Research and regularly collect data from information sources that report reliably on the activities of these competitors. The competitor data produced in components 4, 5, and 10 may be imported here.

Design algorithms that automatically analyze those data to look for indicators of competitors’ strategic intentions, including their ability to carry out those intentions.

Define tentative principles of response to the competitor strategies that emerge from the data. These are the ideal strategic actions that the organization would take when confronted by particular competitor behaviors and actions.

Reinterpret those tentative responses in light of what the organization is capable of (resources and competencies from component 3) and external factors that may constrain its initiatives (component 4).

The result is suggested courses of action that the organization’s leaders may choose to adopt. 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, human intervention is necessary to approve these decisions.

This will be a major, complex challenge for AI technology, because competitive advantage is so difficult to define and dependent on so many variables. I think that the best way to approach this is to gather data on what are considered to be competitive advantages in a variety of organizations, or in a group of similar organizations. Identify and begin tracking a large number and wide variety of factors that might conceivably contribute to the creation of a competitive advantage. Recognize when such advantages are emerging and look for patterns amongst the factors that may be responsible. It will take considerable time to accumulate this knowledge and for patterns to become apparent. At least a few years.

Identify a group of companies operating in the same industry or markets, or facing similar challenges … in other words, likely to have similar ideas of competitive advantage.

Describe several examples of competitive advantage that those companies might want to pursue. Do it as clearly and specifically as possible, preferably in quantitative measurable terms, so that it will be evident when the advantage has been achieved.

Using experience, intuition, informed opinion, and common sense, identify a wide variety of factors that are likely to contribute to the development of a competitive advantage. This initial list will include a lot of speculation and best guesses.

In an equally speculative way, describe some possible connections between those factors and the competitive advantages. Try to be as objective, even quantitative as possible.

Applying elementary AI to those connections, establish and track correlational relationships (if any) between the factors and the competitive advantages.

Notice when specific competitive advantages seem to be emerging, growing, strengthening, shrinking, weakening, disappearing.

Using the hypothetical correlations previously established, try to determine which of the factors is responsible for the change in the status of the competitive advantage.

Over an extended period of time, identify those factors that seem to have a clear effect on the competitive advantages as well as those that appear to be entirely unrelated.

Discard from consideration those factors that have been shown to be unrelated.

On a regular basis notice new factors that seem that they might have an impact on the formation of a competitive advantage. Include them among the factors that are being processed by the algorithms.

Over an extended period of time, perhaps approaching five years, the expectation is that a group of factors will be discovered that are reliable indicators/determinants of competitive advantage for this particular organization.

An effort is made to describe the nature of the relationship between these factors and the competitive advantage, so that the factors can be managed/manipulated to improve the likelihood and strength of an advantage.

[The AI application to this component is not very scientific. Success assumes that the right factors with the right connections to a poorly-defined competitive advantage will be stumbled upon. Still, the power of a substantial competitive advantage may be worth the effort.]

When strategies are proposed (and perhaps later approved), include in their descriptions a fairly detailed summary of the resources (financial, human, equipment, space, reputation, logistics) and competencies that they will require.

Use algorithms to compare the currently available resources and competencies described in component 3 with those required by the strategies.

Develop algorithms that compare the resource requirements of the proposed (or approved) strategies with the resources actually available within the organization, noting the discrepancies.

Design other algorithms that take into account i) the contribution that each strategy will make to the organization’s long-term objectives, ii) the level of risk inherent in each strategy, and iii) the alternative non-strategic uses of the resources, before making recommendations on how to allocate the limited resources.

At this point, management is in a position to make choices among the several proposed strategies.

More sophisticated versions of the algorithms will suggest when the organization should seek to acquire or develop additional resources.

Describe with specificity the primary environmental factors most likely to have a significant effect (positive or negative) on the implementation of a particular strategy. Use information generated in component 4.

Use those descriptions in selecting among the strategies under consideration, accepting or rejecting each of them.

Decide whether redesign or adjustment of a strategy candidate is appropriate.

Decide whether an effort should be made to modify the environmental factor.

Use algorithms to track changes in key environmental factors.

Decide whether an effort should be made to modify the environmental factor or the strategy that it is affecting.

Experienced managers and strategists describe the types of risks that they believe may be inherent in the strategies that have been approved, including quantitative metrics for evaluating their levels.

Evaluate proposed strategies on the basis of the risks they present. Assign risk scores to individual strategies, allowing comparisons among strategies and between strategies and leaders’ risk tolerance.

Set up AI-driven mechanisms for monitoring the risk metrics of approved strategies and calling management’s attention when they reach certain pre-determined levels.

Develop more sophisticated algorithms to establish correlations between risk levels and the performance of specific strategies.

Begin the development of an omnibus algorithm or set of algorithms, interlaced with human interventions at key points, that draw in data from a variety of sources (particularly the outputs of components 3, 4, 5, 6, 7, and 8).

Define a large number of rules interpreting the strategic implications of each of the data types, including their joint effects and interactions with each other.

Design algorithms embodying those rules, so that the data can be read and interpreted, and conclusions reached about specific strategies that seem appropriate.

After specific strategies have been identified, define in quantitative terms the circumstances in which they would be considered to be successful. Wherever possible, define different levels of success.

Use the algorithms to follow the strategies that have been selected and the level of success that they achieve.

Attempt to correlate the success of each strategy with the data that led to its selection.

Because strategies cannot be selected on the basis of unproven algorithms, this component and its algorithms will be trained and refined on large volumes of data over an extended time period, probably years.

Using outputs on the organizational value chain from components 7 and 8, along with information on specific strategic plans selected in component 17, design algorithms that suggest topics for lower level, functional area plans that are necessary to carry out the primary strategic plan.

The challenge is to find the connections between functional responsibilities and the strategic plans. This will require an approach similar to the one in component 17 – trial and error, running the algorithms repeatedly on value chain data, until they are able to generate functional area plans that make sense.

This is the planning level located most deeply in the organization. It is the most detailed, dealing with smaller, more focused tasks. It presents the greatest AI challenge.

Strategic Level
Functional Area Level
Operational Level

Define several hypothetical strategies and the operational tasks associated with implementing them.

Define the key, distinctive features of each strategy.

Use Gantt charts or other project management methodology to show the relationships between the tasks and the strategy features, and among the tasks themselves.

Write rules that describe those relationships.

Design algorithms that capture those relationships. Run the algorithms repeatedly to test their validity.

Define another set of hypothetical strategies, define their key features, and apply the tested algorithms to see if they suggest plausible operational tasks.

After repeated tests of the algorithms over an extended period of time, determine whether they will be useful in translating strategic decisions into operational decisions

Describe the strategy of a multi-SBU conglomerate organization: the overall current makeup of the portfolio in terms of the number, size, and type (industry, markets served) of SBUs, the criteria for including an SBU in the portfolio, and the organization’s future intentions for the portfolio (expressed in terms of the number, size, and type of new entities it might be looking for).

Incorporate the criteria into algorithms that receive data about financial, market, and industry performance, calculate related metrics, and assess how well the current portfolio measures up to the organization’s strategic plans, noting specific shortfalls.

Identify data describing external businesses that meet the criteria (current and prospective) for the components of the organization’s portfolio.

Determine the availability for the acquisition of those external businesses and suggest appropriate adjustments in the portfolio (divest SBUs, acquire new SBUs in certain industries or markets, increase investment in certain existing SBU’s, or modify the overall configuration of the portfolio).

Once a strategy is approved, define the indicators or benchmarks of acceptable progress in its implementation.

When the strategy initially was approved, its success should have been defined in terms of measurable, date-delimited goals.

Design algorithms to monitor the strategy’s performance in order to compare it to those benchmarks and goals, and highlight any shortfalls.

In a more developed form, the algorithms will make suggestions for correcting the strategy or the way that it is being implemented. This includes expanding the strategy, restricting it, dropping it, or committing more resources to it.