Exploring Alternative Futures: Using AI to Bring Shell-Type Scenario Analysis to SMEs

🧭 Dojo Compass

Module: Decision-Making, Innovation and Lateral Thinking

Focus Area: Decision-Making and Judgment

Businesses operate in environments characterized by uncertainty. Changes in economic conditions, customer behavior, technology, competition, regulation and geopolitics can significantly affect their prospects. Yet many companies approach strategic planning by developing a business plan based primarily on what they expect to happen.

An alternative is to examine several plausible futures and consider how the company might perform under each of them.

This approach, commonly known as scenario planning or scenario analysis, has been used by major corporations, most notably Shell, to explore alternative futures and prepare for significant changes in their operating environments.

Historically, however, sophisticated scenario analysis has often been beyond the reach of small and medium-sized enterprises (SMEs). It can require substantial research, specialist expertise, management time and analytical resources.

Artificial intelligence creates an opportunity to change this.

AI can help SMEs gather relevant information, develop alternative scenarios, examine them from different perspectives, assess their implications and continuously update them as new information becomes available.

This article introduces a practical framework for using AI to make sophisticated scenario analysis accessible to SMEs, transforming it from an occasional strategic exercise into an ongoing management capability.

🎯 Key Challenge

Most companies recognize that their business plans are based on assumptions about the future.

They may assume that:

Customer demand will continue growing.
Existing suppliers will remain available.
Input costs will remain relatively stable.
Competitors will behave in predictable ways.
New technologies will develop gradually.
Regulatory requirements will not change significantly.
Financing will remain accessible.

These assumptions may be reasonable, but they are not necessarily correct.

The difficulty is that business plans frequently concentrate on a single expected future. Management develops revenue projections, estimates costs, allocates resources and establishes performance targets around a particular set of expectations.

This creates several vulnerabilities.

1. The company may be insufficiently prepared for alternative developments.

A business may have a carefully prepared plan but little understanding of what would happen if important assumptions proved incorrect.

For example, what if a major customer reduced its purchases, a new competitor entered the market, financing became more expensive or a technological development fundamentally changed customer expectations?

Management may recognize these possibilities individually without having examined how they could affect the business or what responses would be appropriate.

2. Management may underestimate the interaction between different developments.

Business developments rarely occur in isolation.

An increase in energy prices may coincide with currency depreciation, higher financing costs and reduced customer demand. Individually, each development may be manageable. Together, they may create a much more serious challenge.

Scenario analysis helps examine these interactions rather than treating each risk as an independent event.

3. Companies may reinforce their existing assumptions.

Management teams often develop expectations based on their experience, industry knowledge and understanding of their customers.

This knowledge is valuable, but it can also create blind spots. People naturally tend to favor information that supports their existing views and may overlook developments that challenge them.

A good scenario planning process should deliberately introduce alternative perspectives and question assumptions that management might otherwise take for granted.

4. Traditional scenario analysis can be expensive and time-consuming.

Sophisticated scenario planning requires research, analytical capabilities and the ability to consider developments across multiple disciplines.

Large corporations may have dedicated strategy teams, external consultants and substantial research budgets.

SMEs generally do not have these resources. Their management teams are often occupied with immediate operational demands, leaving little time for extensive analysis of alternative futures.

5. Scenario analysis can quickly become outdated.

Even when a company undertakes a good scenario planning exercise, the resulting analysis may become stale as circumstances change.

New market information, technological developments, regulatory changes and competitive activity may invalidate important assumptions.

Unless scenarios are periodically reviewed, they can become historical documents rather than useful management tools.

The challenge is therefore to make scenario analysis both accessible and continuous.

🥋 Dojo Solution

Use AI to democratize sophisticated scenario planning

One of the most interesting characteristics of AI is its ability to combine information gathering, analytical reasoning and the examination of different perspectives.

These capabilities make AI particularly well suited to supporting scenario analysis.

The approach pioneered by Shell is instructive. Rather than attempting to forecast a single future, scenario planning explores several plausible ways in which the world might develop. Each scenario provides a coherent account of how different developments could interact and what their consequences might be.

The purpose is not to predict which scenario will occur. It is to help management understand the range of possible futures and prepare the company to respond.

AI can help SMEs undertake this process through four important capabilities.

1. Information access and synthesis

AI can gather and analyze information from a wide range of sources, including economic data, industry reports, market developments, technological research, regulatory announcements and competitor activity.

This can substantially reduce the research burden associated with scenario development.

2. Development of alternative perspectives

AI can examine potential developments from different perspectives, including those of economists, customers, suppliers, competitors, investors, regulators, technologists and industry specialists.

This is particularly valuable because high-quality scenario planning should challenge management’s assumptions and expose it to alternative interpretations of the same information.

3. Analysis of consequences and strategic responses

AI can help examine how different scenarios might affect the company’s revenue, costs, customers, suppliers, competitive position, financing requirements and operations.

It can also help identify potential responses and evaluate their advantages and disadvantages.

4. Continuous monitoring and updating

AI can help monitor developments in the external environment, identify changes that may affect existing scenarios and recommend when assumptions or scenario narratives should be reconsidered.

This introduces an important additional dimension: scenario analysis can become a continuously evolving strategic resource rather than a periodic planning exercise.

The central idea is to use AI not simply to generate scenarios, but to create an ongoing process that connects external developments with the company’s internal circumstances and strategic decisions.

🏗️ Putting It into Practice

The following framework provides a practical approach for SMEs wishing to introduce AI-supported scenario analysis.

Step 1: Identify the strategic question

Scenario analysis should begin with a business question, not with a general request to AI to predict the future.

The question should relate to an important uncertainty or strategic decision facing the company.

Examples include:

How might our industry evolve over the next five years?
What could happen to our business if our principal export market becomes less accessible?
How might AI change the economics of our industry?
What would happen if a major competitor introduced a substantially lower-cost product?
How might climate change affect our agricultural supply chain?
What could happen if interest rates remained elevated for an extended period?
How should we prepare for changes in customer preferences or purchasing behavior?

The time horizon should also be considered.

Some questions may require an analysis of developments over the next 12–24 months, while others may be better examined over three to five years.

The company should avoid trying to analyze every possible uncertainty simultaneously. It is generally more useful to begin with a small number of strategically important questions.

Step 2: Establish the company’s starting position

For AI to produce meaningful scenarios, it needs to understand the company being analyzed.

A general analysis of an industry may be interesting, but the real value comes from understanding how potential developments could affect the particular business.

The company should therefore provide relevant internal information, which might include:

Its business model and principal revenue sources.
Products, services and customer segments.
Geographic markets and competitive position.
Major suppliers and distribution channels.
Financial performance and cost structure.
Existing business plans and strategic priorities.
Operational capabilities and resource constraints.
Major dependencies and vulnerabilities.

The information does not necessarily need to be comprehensive at the outset. An SME can begin with a relatively simple description of its business and progressively improve the information available to the AI system.

More sophisticated analysis may incorporate financial statements, sales information, management reports, customer data, inventory information and operational performance indicators.

Appropriate safeguards should be established when providing confidential or personal information to AI systems.

The objective is to enable AI to connect external developments with the company’s actual circumstances.

Step 3: Identify the principal drivers of change

The next step is to identify the developments most likely to influence the company’s future operating environment.

AI can assist by examining relevant information across several dimensions.

Economic factors

Inflation and interest rates.
Currency movements.
Employment and wage trends.
Consumer purchasing power.
Access to financing.
Economic growth or recession.

Market and competitive factors

Changes in customer preferences.
New competitors or business models.
Changes in distribution channels.
Industry consolidation.
Pricing pressure.
Shifts in customer demand.

Technological factors

New AI capabilities.
Automation and robotics.
Changes in software and digital infrastructure.
New production technologies.
Technological substitution of existing products or services.

Political, regulatory and geopolitical factors

Changes in trade policy.
New regulations and compliance requirements.
Taxation and government policy.
Political instability.
International conflicts and supply chain disruption.

Environmental and social factors

Climate-related developments.
Resource availability.
Demographic changes.
Consumer attitudes.
Sustainability requirements.

AI can help identify relationships between these factors and determine which developments deserve closer attention.

For example, a company importing agricultural inputs may be affected by currency movements, international commodity prices, shipping costs, weather conditions and changes in agricultural regulation.

An AI-supported analysis could help identify how these factors interact rather than examining them separately.

The company should ultimately select the drivers that are both strategically relevant and sufficiently uncertain to justify scenario analysis.

Step 4: Construct alternative scenarios

Once the principal drivers have been identified, AI can help develop several plausible future scenarios.

A useful starting point is to distinguish between relatively predictable developments and significant uncertainties.

Relatively predictable developments might include demographic trends, already enacted regulatory changes or known contractual commitments.

Significant uncertainties might include the future direction of interest rates, the speed of technological adoption, changes in geopolitical relationships or the behavior of major competitors.

The combination of these factors can produce different plausible futures.

For example, consider an SME that manufactures and distributes agricultural products.

It might examine three alternative scenarios:

Scenario A: Gradual growth and stable markets

Economic conditions remain relatively favorable, input prices fluctuate within manageable limits and customer demand grows gradually.

The company can continue investing in its existing business model, focusing on productivity, customer relationships and measured expansion.

Scenario B: Cost pressure and market contraction

Currency depreciation, higher financing costs and increased input prices coincide with weaker customer demand.

Margins become compressed, customers delay purchases and working capital requirements increase.

The company may need to renegotiate supplier arrangements, reduce discretionary expenditure, adjust pricing and strengthen cash management.

Scenario C: Accelerated technological transformation

New AI-enabled agricultural technologies and improved data services change how farmers purchase inputs and manage production.

Traditional distribution relationships become less important, while integrated advisory, financing and monitoring services become more attractive.

The company may need to develop new capabilities, establish technology partnerships and reconsider its commercial model.

These are simplified illustrations. A serious scenario analysis would develop each scenario in greater depth, explaining the sequence of developments, the conditions under which it might emerge and the implications for the company.

Importantly, scenarios should be internally coherent and plausible. They should not simply be optimistic, pessimistic and neutral versions of the same forecast.

Step 5: Introduce different perspectives to challenge the scenarios

This is one of the most valuable applications of AI.

High-quality scenario planning should not merely generate different descriptions of the future. It should examine those futures from different perspectives and challenge the assumptions underlying each one.

AI can be instructed to examine a scenario as if it were:

An economist assessing macroeconomic developments.
A customer considering future purchasing decisions.
A competitor evaluating market opportunities.
A supplier examining its own commercial interests.
A technology specialist assessing emerging capabilities.
A regulator considering possible policy changes.
An investor evaluating the company’s financial prospects.
A risk manager identifying vulnerabilities.

Each perspective may reveal different implications.

For example, management might assume that customers will respond to higher prices by reducing purchases. A customer-oriented analysis might reveal that customers are more concerned about reliability of supply than price, creating an opportunity to strengthen long-term commercial relationships.

Similarly, management may view a new technology as a threat, while a competitor-oriented analysis may identify opportunities to use it to develop entirely new services.

AI can also be instructed to act as a critical reviewer, deliberately challenging the assumptions, identifying missing considerations and developing arguments against the prevailing management interpretation.

This is particularly important because AI can otherwise reinforce the assumptions contained in the information and instructions it receives.

The objective is to use AI to broaden the company’s thinking, not simply to make its existing thinking more elaborate.

AI-generated perspectives should not be confused with independently verified expert opinions. Where an issue is material, the company should validate the analysis through reliable external sources, specialist advice or direct engagement with relevant stakeholders.

Step 6: Analyze the implications for the company

Once the scenarios have been developed and challenged, the next step is to examine their implications.

This is where the analysis becomes directly relevant to business planning.

For each scenario, consider:

Business area	Questions to examine
Revenue	How might sales volumes, prices and customer demand change?
Costs	What could happen to input costs, wages, financing and logistics?
Customers	How might customer priorities and purchasing behavior evolve?
Competition	Could new competitors or business models emerge?
Operations	Would existing production, distribution or service capabilities remain adequate?
Finance	What would happen to cash flow, working capital and financing requirements?
Technology	Would the company need new systems, capabilities or partnerships?
Strategy	Would existing strategic priorities remain appropriate?

AI can help develop the analysis and, where sufficient company data is available, construct illustrative financial models.

For example, management might ask AI to examine how a 10% reduction in sales, a 15% increase in input costs and a 20% depreciation of the local currency could affect operating margins and cash requirements.

These calculations should be based on verified company data and clearly stated assumptions. AI-generated financial outputs should be checked, particularly where they influence significant investment or financing decisions.

The company should also identify which scenarios would have the most serious consequences, even if their likelihood is relatively low.

Step 7: Identify strategic responses and early-warning indicators

Scenario analysis should not end with an examination of what might happen.

Its principal value is helping management determine what it should do.

For each scenario, identify:

Actions that would help the company prepare.
Investments that could improve its resilience.
Risks that should be reduced.
Opportunities that could be exploited.
Decisions that should be postponed until more information becomes available.
Conditions that would trigger a change in strategy.

An important distinction is between no-regret actions and scenario-specific actions.

No-regret actions are improvements that make sense across several possible futures. Examples might include strengthening cash management, improving customer information, diversifying critical suppliers or developing internal AI capabilities.

Scenario-specific actions are measures that become appropriate only if particular developments occur.

For example, a company may decide to investigate an alternative supplier now but only make a substantial commitment if international supply disruptions become more likely.

AI can help identify early-warning indicators associated with each scenario.

These might include:

Changes in commodity prices.
Interest rate movements.
Customer order patterns.
Competitor announcements.
Regulatory proposals.
New technological releases.
Supplier delivery performance.

The company can then establish thresholds or conditions that prompt management to reconsider its assumptions or activate a particular response.

This turns scenario analysis into a practical decision-support system.

Step 8: Continuously update scenarios using AI

Traditional scenario planning often involves substantial research and periodic strategic reviews. Once completed, the scenarios may remain largely unchanged until the next planning exercise.

AI creates an opportunity to make this process much more dynamic.

A company can establish an AI-supported monitoring process that regularly examines relevant external information and compares it with the assumptions underlying its scenarios.

For example, the system could:

Monitor relevant economic, industry and regulatory information.
Identify developments that differ materially from existing assumptions.
Determine which scenarios may be affected.
Assess whether the new information strengthens or weakens particular scenarios.
Identify new developments that may require an additional scenario.
Prepare an updated analysis for management review.

Consider a company that has developed a scenario based on the assumption that a new technology will take five years to become commercially significant.

If AI identifies substantial new investment, product announcements, customer adoption and rapidly declining implementation costs, it may suggest that the scenario’s original timeline should be reconsidered.

Similarly, an agricultural company may discover that changing weather patterns, international prices and government policy are developing differently from its original assumptions.

AI can help management recognize these changes earlier and examine their implications.

Move toward continuous scenario intelligence

Over time, companies may develop an integrated system containing:

Current scenarios and their underlying assumptions.
External information and market indicators.
Internal company performance data.
Previous scenario assessments.
Management decisions and responses.
Historical changes in scenario assumptions.

AI can use this information to provide a continuously updated view of the company’s strategic environment.

It could also identify discrepancies between what management expected and what is actually happening, helping the company improve its understanding of the factors driving its business.

However, continuous updating does not mean constantly rewriting scenarios in response to every new piece of information. Companies should distinguish meaningful changes from short-term noise and retain a stable analytical structure where appropriate.

Management should periodically review the AI’s findings and decide whether a scenario requires modification, whether new scenarios are needed or whether the original assumptions remain valid.

Step 9: Integrate scenario analysis into strategic management

The final step is to ensure that scenario analysis becomes part of the company’s ordinary management processes.

It should inform:

Annual business planning.
Budgeting and financial projections.
Investment decisions.
Risk management.
Product and market development.
Technology adoption.
Supplier and customer strategies.
Contingency planning.

For an SME, this does not require a dedicated scenario planning department or an elaborate analytical infrastructure.

A relatively simple arrangement could involve:

An initial scenario analysis conducted with AI assistance.
A management review of the resulting scenarios and strategic implications.
Monthly or quarterly AI-supported monitoring of important developments.
Periodic reassessment of assumptions and responses.
An annual review as part of the company’s strategic planning process.

The depth and frequency of the process should reflect the company’s circumstances.

The important thing is to establish a repeatable capability that helps management remain prepared for alternative futures.

📌 Key Takeaways

Scenario planning helps companies explore multiple plausible futures rather than relying exclusively on a single business forecast.
Sophisticated scenario analysis has traditionally been associated with large corporations because of its research and analytical requirements.
AI can make this discipline considerably more accessible to SMEs.
AI can gather and synthesize external information, reducing the resources required to develop scenarios.
One of AI’s greatest strengths is its ability to examine scenarios from multiple perspectives and challenge management assumptions.
Scenario analysis should examine how different developments interact and affect the company’s actual business model.
The purpose is not to predict which scenario will occur, but to prepare the company for a range of plausible outcomes.
AI can help identify strategic responses, no-regret actions and early-warning indicators.
Continuous AI-supported monitoring can identify changes in market conditions and help update scenarios as circumstances evolve.
Management must retain responsibility for validating information, evaluating strategic implications and deciding how the company should respond.

🌿 Reflection

Business planning inevitably involves assumptions about the future.

The difficulty is not that companies make assumptions. They have to. The difficulty is that management may become so focused on its preferred view of the future that it fails to consider how the business might be affected if circumstances develop differently.

Scenario planning provides a way to challenge that tendency.

It encourages management to consider alternative possibilities, examine the interaction of different developments and identify actions that can strengthen the company’s ability to respond.

For many years, sophisticated scenario planning was largely associated with major corporations that could afford extensive research and specialist resources.

AI changes that equation.

A relatively small company can now use AI to access information, develop alternative scenarios, examine different perspectives and monitor changes in its operating environment.

Perhaps even more importantly, AI can help companies maintain this analysis over time rather than treating it as an occasional strategic exercise.

The competitive advantage may therefore come not from knowing which future will occur, but from becoming better prepared for whichever future emerges.

This raises an important question:

Does your company have a structured understanding of how alternative futures could affect its business, or is its strategy based primarily on what management currently expects to happen?

The ability to explore and prepare for different futures may become an increasingly important source of resilience and competitive advantage.

⚔️ Dojo Mission

Choose one important uncertainty facing your company and conduct a preliminary AI-supported scenario analysis.

Define the question: Identify a strategic issue that could materially affect the company’s future.
Provide context: Give AI a description of your business model, principal markets, customers and strategic priorities.
Develop scenarios: Ask AI to construct three or four plausible alternative futures.
Challenge assumptions: Ask AI to examine each scenario from the perspectives of customers, competitors, suppliers, investors and other relevant stakeholders.
Assess consequences: Identify how each scenario could affect revenue, costs, operations, competitive position and strategic priorities.
Identify responses: Determine which actions would be useful across several scenarios and which should be triggered by particular developments.
Establish monitoring: Identify the external indicators that could signal a change in the likelihood or relevance of each scenario.

Finally, ask AI to identify what new information would cause it to reconsider the scenarios.

Do not treat the first analysis as a finished product. Treat it as the beginning of a continuing process.