Technology Intelligence, Foresight and Roadmapping

Introduction

Entrepreneurial opportunities rarely emerge from a single invention. They arise when entrepreneurs interpret changes in science, technology, markets, regulation, society and business models more effectively than competitors. Technology intelligence provides the evidence for that interpretation; technology foresight helps explore possible futures; and technology roadmapping connects future possibilities with present decisions.

For an entrepreneurial firm, these activities answer questions such as:

• Which technologies are becoming technically and commercially viable?
• Which customer problems may create demand for new solutions?
• Who owns relevant intellectual property?
• How quickly are competitors and substitutes developing?
• What capabilities, partnerships and investments will be required?
• Which uncertainties could invalidate the business model?

This chapter treats intelligence, foresight and roadmapping as related but distinct managerial practices. Intelligence emphasizes evidence about the present and near future. Foresight explores alternative futures and their implications. Roadmapping translates selected strategic intentions into a time-phased plan of markets, products, technologies, capabilities and milestones.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain technology intelligence, environmental scanning, foresight and roadmapping.
2. Conduct a structured scan of technological, market, regulatory and social environments.
3. Interpret patent and market evidence without confusing activity with commercial success.
4. Apply technology lifecycle models and technology readiness levels.
5. Use forecasting, horizon scanning and scenario planning to manage uncertainty.
6. Construct and evaluate a technology roadmap.
7. Identify ethical, legal, data-quality and governance risks in technology analysis.

1. Technology Intelligence as an Entrepreneurial Capability

Technology intelligence is the systematic collection, analysis, interpretation and communication of information about technologies and their environments for decision-making. It is not simply searching for news. Intelligence requires a decision purpose, credible sources, analytical methods and communication to people who can act.

A useful distinction is:

Technology intelligence commonly covers five domains:

1. Scientific and technical development: research publications, prototypes, standards, patents and engineering performance.
2. Market development: customer needs, adoption, prices, competitors, channels and substitutes.
3. Industrial structure: suppliers, complementors, platforms, production capacity and ecosystems.
4. Institutional environment: regulation, public procurement, policy, standards and funding.
5. Societal context: public acceptance, workforce effects, sustainability, privacy and cultural expectations.

The central managerial danger is mistaking a measurable signal for a reliable conclusion. A large number of patents may indicate intense competition, defensive filing or legal uncertainty rather than an attractive market. A rapidly growing market may be unprofitable because prices are falling faster than demand is increasing. Intelligence therefore combines multiple forms of evidence and explicitly records uncertainty.

2. Environmental Scanning and Horizon Scanning

Environmental scanning is the broad and continuing examination of external conditions that could influence an organization. Horizon scanning is a forward-looking form of scanning that seeks emerging issues, weak signals, discontinuities and developments outside the organization’s current assumptions.

2.1 A structured scanning process

A practical scanning cycle contains six stages:

1. Define the decision: For example, whether to enter a market, invest in a platform or develop a new product.
2. Set the boundary: Specify technologies, customer groups, geographies, time horizons and relevant uncertainties.
3. Collect signals: Use scientific databases, patent databases, market reports, standards bodies, trade publications, expert interviews, startup databases, regulatory consultations and customer research.
4. Filter and classify: Remove duplicates, assess source quality, classify by technology, application, maturity and likely impact.
5. Interpret and prioritize: Examine convergence, disruption, barriers, dependencies and strategic relevance.
6. Review continuously: Update assumptions, record changes and communicate implications.

A weak signal is an early, incomplete or ambiguous indication of a potentially important development. Examples include a small number of unusual research papers, a new standards committee, pilot projects by an unexpected industry or a regulatory consultation concerning a previously unregulated application. Weak signals should not be treated as predictions. Their value lies in prompting investigation and preparedness.

2.2 PESTLE and STEEP analysis

PESTLE organizes external factors into political, economic, social, technological, legal and environmental categories. STEEP uses a similar structure but generally emphasizes social, technological, economic, environmental and political factors. These frameworks prevent an analysis from becoming narrowly technical.

For example, the commercial potential of autonomous delivery vehicles depends not only on navigation technology. It also depends on labor costs, insurance rules, pedestrian safety, municipal permissions, public acceptance, battery performance and the economics of delivery density.

PESTLE is a checklist rather than a forecasting model. It does not show causality or probability automatically. Analysts should therefore supplement it with questions such as:

• Which factors reinforce one another?
• Which factors could impose a threshold or constraint?
• Which developments are within management control?
• Which assumptions are most uncertain and consequential?

2.3 Signal assessment

A signal can be assessed using qualitative dimensions such as:

• Novelty: Is it genuinely different from established activity?
• Evidence strength: Is it supported by independent, credible sources?
• Momentum: Is activity accelerating, stable or declining?
• Potential impact: If it develops, how significant could the effect be?
• Time to impact: Could consequences appear within months, years or decades?
• Strategic fit: Does it affect the organization’s customers, assets or capabilities?

A simple prioritization score may be used for an initial workshop:

where P is the priority score, I is estimated potential impact, U is uncertainty, and R is relevance to the organization. Each factor may be rated, for example, from 1 to 5. This is not a probability calculation and should not be interpreted as an objective measurement. It is a transparent device for deciding which signals deserve further investigation.

3. Patent Intelligence and Intellectual Property Analysis

Patents disclose technical information in exchange for time-limited legal rights, subject to national or regional law. Patent analysis can reveal technological directions, active organizations, inventors, collaboration patterns and possible freedom-to-operate concerns. It cannot, by itself, establish market demand or guarantee that a patent is valid or enforceable.

3.1 The patent document

Important parts of a patent include:

• Title and abstract: A rapid but incomplete description.
• Claims: The legally significant definition of the protected invention.
• Description: Technical explanation and embodiments.
• Priority information: Earlier filings from which later applications may claim priority.
• Classification codes: Standardized categories that improve searching beyond keywords.
• Citations: Earlier patents and other documents referenced by applicants or examiners.
• Legal status: Information about applications, grants, expiration, assignments or abandonment.

A sound search combines keywords, classification codes, assignee names, inventor names, citation links and related applications. Searchers should account for synonyms, translation differences, changes in company names and deliberately broad or narrow claim language.

3.2 Patent indicators and their limitations

Useful indicators include:

• Filing counts by year or technology category.
• The number of distinct applicants.
• Geographic distribution of filings.
• Citation counts and citation networks.
• Family size, meaning related filings in multiple jurisdictions.
• Grant, abandonment and maintenance patterns.
• Concentration among major owners.
• Collaboration or co-assignment networks.

A basic growth rate can be calculated as:

where N₁ is the earlier count and N₂ is the later count. The measure is meaningful only when the counting rules, time intervals, jurisdictions and patent-family treatment are consistent. Small denominators can produce very large percentages, and filing counts can be distorted by changes in law or filing strategy.

Patent counts should be interpreted cautiously because:

• One invention may generate many related filings.
• A single patent may have little commercial value.
• Some important innovations are protected by secrecy, speed, design or know-how.
• Patent databases may contain incomplete or delayed legal-status data.
• Citation practices vary across jurisdictions and technologies.
• A patent may be valid but irrelevant to a proposed product, or relevant but blocked by another patent.

3.3 From patent landscape to business decision

A patent landscape should move beyond a visual map. The analyst should ask:

1. What technical problems are being solved?
2. Which claims appear central to important product architectures?
3. Are there crowded areas, open spaces or possible design-around routes?
4. Which firms control complementary rights?
5. What jurisdictions matter commercially?
6. Is specialist legal analysis required?

Patent landscaping is not a substitute for a formal freedom-to-operate opinion. Entrepreneurs should obtain qualified intellectual-property advice before making high-stakes decisions.

4. Market and Competitive Analysis

Technology creates value only when it solves a recognized or developable problem for a customer or stakeholder. Market analysis therefore examines demand, willingness to pay, adoption barriers, competitive structure, substitutes and the conditions required for profitable scale.

Important evidence sources include customer interviews, observation, surveys, pilot usage, procurement records, industry statistics, competitor disclosures, pricing studies, channel interviews and regulatory data. Market reports can provide useful estimates but should be checked for definitions, sampling methods, assumptions and conflicts of interest.

A technology-market assessment should distinguish:

• Available market: Customers who could technically be served.
• Addressable market: Customers the business can reach with its resources and business model.
• Obtainable market: The realistic share that may be captured within a specified period.

Market size is not the same as revenue opportunity. Revenue depends on price, volume, repeat purchase, retention, cost-to-serve, channel margins and timing. Entrepreneurs should test the problem before overinvesting in technical development.

Competitive analysis should include direct competitors, substitute technologies, internal customer solutions, potential entrants, suppliers and complementors. Porter’s five forces can structure this analysis, but digital platforms and ecosystems may require additional attention to network effects, data access, interoperability and platform governance.

A strong market analysis triangulates three questions:

1. Desirability: Do users or buyers value the solution?
2. Feasibility: Can it be produced, delivered and supported?
3. Viability: Can the business capture sufficient value to survive?

5. Technology Lifecycle and Maturity Assessment

Technology development is not a single linear process. Several models help analysts discuss maturity, but each represents a different aspect of development.

5.1 The technology S-curve

The S-curve describes a common pattern in which performance improves slowly during early experimentation, rapidly during a period of learning and then more slowly as physical, economic or architectural limits are approached. It is a heuristic, not a universal law.

The model supports questions such as:

• Is further investment likely to yield substantial performance improvement?
• Is a competing technology entering a steeper improvement phase?
• Has the current design reached diminishing returns?
• Are performance measures still the ones valued by customers?

A technology can be mature technically but immature commercially, organizationally or institutionally. Conversely, a technically modest technology may diffuse rapidly because it is inexpensive, compatible and easy to adopt.

5.2 Technology life cycle

A simplified technology life cycle contains emergence, growth, maturity and decline or substitution. The boundaries are not fixed, and multiple generations may coexist. Early stages usually involve uncertainty, experimentation and high unit costs. Growth involves dominant designs, increasing investment and expanding applications. Maturity brings process efficiency, standardization and stronger price competition. Decline may involve substitution, regulation, changing customer preferences or resource constraints.

5.3 Technology Readiness Levels

Technology Readiness Levels (TRLs) provide a staged vocabulary for technical maturity. The widely used nine-level structure is:

TRLs primarily address technical demonstration. They do not automatically measure manufacturing readiness, cybersecurity, regulatory approval, customer adoption, unit economics or organizational capability. A useful assessment therefore records evidence for each claim and adds complementary dimensions such as manufacturing readiness, market readiness and regulatory readiness.

For example, a medical device may be TRL 7 but commercially unavailable because clinical evidence, reimbursement approval or manufacturing validation is incomplete.

6. Technology Forecasting

Technology forecasting estimates possible future developments, performance, adoption or timing. Forecasts should be expressed with assumptions and uncertainty rather than false precision.

6.1 Qualitative methods

• Expert panels: Gather informed judgments, ideally from diverse and partly independent experts.
• Delphi method: Conducts multiple anonymous survey rounds, providing summarized feedback between rounds to seek considered convergence.
• Morphological analysis: Decomposes a system into dimensions and explores combinations of possible states.
• Analogy: Uses development patterns in related technologies, while testing whether the analogy is structurally valid.
• Trend extrapolation: Extends observed patterns, but requires caution where discontinuities are plausible.

6.2 Quantitative methods

Time-series extrapolation may be useful when data are sufficiently stable and the process generating the trend is not changing materially. A simple linear trend is:

where yₜ is the observed measure at time t, a is the intercept, b is the estimated change per time period, and εₜ is the unexplained error. The equation assumes a reasonably stable relationship over the forecast interval. It should not be used mechanically when the series has structural breaks, saturation, strong seasonality or changing definitions.

A diffusion model may represent adoption that begins slowly, accelerates and then saturates. The logistic form is:

where A(t) is cumulative adoption at time t, K is the saturation level, r is the growth-rate parameter, t₀ is the midpoint time and e is the base of natural logarithms. The model assumes an upper limit and a broadly symmetric growth pattern; real markets may violate both assumptions because of network effects, policy changes, supply constraints or competing technologies.

Forecast error should be monitored. For actual value yₜ and forecast ŷₜ, the error is:

A mean absolute error over n observations is:

MAE has the same units as the forecast variable. It is useful for comparing forecasts of the same measure, but it does not reveal whether errors are systematically too high or too low. Metrics should be selected according to the decision and data properties, not simply because they are familiar.

7. Scenario Planning and Strategic Uncertainty

Scenario planning develops several internally consistent descriptions of how the future might unfold. Scenarios are not predictions. They are tools for challenging assumptions, testing strategies and identifying early-warning indicators.

A disciplined scenario process is:

1. Define the focal issue and decision.
2. Identify driving forces.
3. Separate predetermined trends from critical uncertainties.
4. Select two high-impact, substantially independent uncertainties.
5. Construct a scenario matrix or a richer set of narratives.
6. Describe consequences for customers, technologies, competitors, regulation and operations.
7. Test strategies across scenarios.
8. Identify signposts and contingent actions.

For example, an energy-technology venture might consider uncertainty about the speed of grid decarbonization and the degree of regulatory support. The resulting scenarios could include rapid supported transition, rapid market-led transition, slow regulated transition and fragmented transition. Each should contain a causal story, not merely an optimistic or pessimistic label.

A robust strategy performs acceptably across several plausible futures. A flexible strategy includes options, staged investment, modular architecture and trigger points for escalation or withdrawal.

8. Technology Roadmapping

A technology roadmap is a visual and analytical representation of how market needs, products, technologies, capabilities and milestones relate across time. It aligns technical work with strategic intent.

8.1 Roadmap layers

A commonly used roadmap has three connected layers:

1. Market and environment: Customer needs, regulations, societal expectations and competitive conditions.
2. Products and services: Product generations, features, value propositions and applications.
3. Technology and capabilities: Components, platforms, processes, skills, suppliers and research projects.

Time is usually represented horizontally, while the layers or themes are arranged vertically. Dependencies and decision gates should be explicit. A roadmap should show not only what the organization intends to develop, but also what must be true for the plan to succeed.

8.2 Roadmap construction

A practical process includes:

• Establish the strategic question and planning horizon.
• Gather evidence from customers, markets, patents, technology assessments and operations.
• Identify future customer outcomes and performance requirements.
• Map current products, technologies and capability gaps.
• Generate alternative paths and assess dependencies.
• Sequence initiatives using milestones, resources and learning objectives.
• Assign owners and decision gates.
• Review the roadmap as assumptions change.

A roadmap is not a project schedule. A project schedule focuses on tasks and dates for a defined project. A roadmap coordinates multiple projects and uncertain future conditions. It should therefore contain ranges, assumptions and review points rather than pretending that every date is known.

8.3 Prioritization

Organizations may rank initiatives using strategic fit, customer value, technical feasibility, risk, required investment, learning value and option value. A weighted score can be useful if the criteria and weights are openly discussed:

Here S is the total score, wᵢ is the weight assigned to criterion i, and xᵢ is the normalized score for that criterion. The weights are dimensionless and should sum to 1. The result is ordinal or decision-support information, not an objective truth. Sensitivity analysis should test whether a small change in weights reverses the ranking.

Applied Case: Illustrative Case of a Smart Cold-Chain Venture

Background

NorthStar, a fictional early-stage venture, is considering a sensor platform for monitoring temperature-sensitive food during transport. The proposed system combines low-cost sensors, wireless connectivity, analytics and alerts for distributors and retailers.

Problem

The founders initially assumed that superior sensor accuracy would create a defensible advantage. A preliminary scan revealed several competing sensor suppliers, open-source analytics tools, changing data-protection requirements and customers more concerned with reducing spoilage and proving compliance than with laboratory accuracy alone.

Analysis

The team conducted four workstreams. First, environmental scanning identified stricter food-traceability expectations, variable connectivity in rural transport routes and rising interest in reducing food waste. Second, a patent search found many sensor and packaging filings but fewer claims focused on workflow integration. The team treated this finding as an opportunity hypothesis, not proof of freedom to operate. Third, interviews with distributors showed that installation time, battery life, dashboard integration and actionable alerts were more important than small improvements in sensor precision. Fourth, a TRL assessment placed the sensing hardware near demonstration maturity, while the integrated service had lower market and operational readiness.

Scenario planning considered two uncertainties: the speed of regulatory digitization and the willingness of logistics firms to share shipment data. The scenarios indicated that a technically excellent but isolated device would be vulnerable in every scenario. An interoperable service with staged data permissions performed better across the set.

Approach and solution

NorthStar developed a roadmap with three horizons. In the first horizon, it would validate the customer problem through paid pilots, integrate with existing logistics software and demonstrate reliable alerts. In the second, it would add predictive spoilage models, reusable devices and partner channels. In the third, it would explore automated quality certification and cross-company data services, subject to governance and consent requirements.

The roadmap included decision gates: continue only if pilot customers reduced avoidable spoilage, installation time met a defined operational threshold and data permissions were acceptable. Patent counsel reviewed relevant claims, while technical staff documented sensor calibration, connectivity limitations and cybersecurity controls.

Outcome

The analysis redirected investment from marginal sensor improvement toward interoperability, workflow design and evidence of economic value. The venture did not eliminate uncertainty, but it reduced the risk of solving the wrong problem and created measurable learning milestones.

Learning points

1. Technical performance is only one dimension of readiness.
2. Patent activity must be interpreted with legal and market evidence.
3. Scenarios help identify strategies that remain useful under uncertainty.
4. A roadmap should include assumptions, dependencies and decision gates.
5. Entrepreneurial intelligence is valuable when it changes resource allocation or experimentation.

9. Governance, Ethics and Responsible Use

Technology intelligence can create legal, ethical and reputational risks. Analysts should respect database licenses, copyright, confidentiality, trade secrets and privacy law. Competitive intelligence must not involve deception, unauthorized access, theft of confidential information or misuse of personal data.

Important governance practices include:

• Record source, date, methodology and confidence level.
• Distinguish fact, interpretation, assumption and speculation.
• Use multiple independent sources for consequential claims.
• Protect personal and commercially sensitive information.
• Document search strategies so results are reproducible.
• Disclose conflicts of interest and sponsored research.
• Test analytical tools for bias and systematic omissions.
• Maintain human review of AI-generated summaries and classifications.
• Avoid treating algorithmic rankings as neutral or definitive.

Artificial intelligence can accelerate patent clustering, document classification, trend detection and scenario drafting. However, models may hallucinate sources, miss technical context, reproduce historical bias or expose confidential information through inappropriate tools. AI outputs should be treated as analyst assistance, not verified evidence. Human experts remain responsible for legal interpretation, strategic judgment and consequential decisions.

Environmental sustainability should also be included. A technology may create operational efficiency while increasing energy demand, electronic waste, extraction pressure or rebound effects. Roadmaps should examine lifecycle impacts, repairability, supply-chain resilience and end-of-life responsibilities.

10. Common Failure Modes and Best Practices

Common failures include:

• Starting with a favorite technology rather than a decision or customer problem.
• Counting patents or publications without examining quality and relevance.
• Treating a forecast as a fact rather than a conditional estimate.
• Confusing TRL with commercial readiness.
• Producing a roadmap that lists projects but not outcomes or dependencies.
• Ignoring substitutes, regulation and ecosystem partners.
• Using a single optimistic scenario.
• Hiding uncertainty behind precise scores.
• Failing to update analysis when assumptions change.

Best practice is to maintain an evidence register, use explicit confidence ratings, combine quantitative and qualitative methods, involve technical and commercial stakeholders, test assumptions through experiments, and review roadmaps at regular strategic intervals. The most useful output is not the longest report; it is a clear set of choices, uncertainties, indicators and next actions.

Chapter Summary

Technology intelligence supplies structured evidence about technologies, markets, competitors, institutions and society. Environmental and horizon scanning identify trends, weak signals and emerging risks, while PESTLE helps ensure that analysis extends beyond technical performance. Patent intelligence can reveal technical directions and ownership patterns, but patent counts do not establish commercial value or freedom to operate. Market analysis connects technology with customer needs, adoption conditions, competition and value capture.

Technology lifecycle models, S-curves and TRLs provide useful maturity language, but they measure different aspects of development and should not be confused with market, manufacturing or regulatory readiness. Forecasting methods range from expert and Delphi approaches to trend and diffusion models; all require assumptions and uncertainty assessment. Scenario planning explores plausible futures rather than predicting one future. Technology roadmaps integrate markets, products, technologies, capabilities, milestones and decision gates across time.

Effective practice is evidence-based, transparent about uncertainty, ethically governed and linked to action. For entrepreneurs, the value of these methods lies in improving timing, prioritizing experiments, avoiding premature commitment and building strategies that remain adaptable as conditions change.

Key Terms

Technology intelligence: Systematic analysis of technology-related information for decision-making.

Environmental scanning: Examination of external conditions affecting an organization.

Horizon scanning: Forward-looking search for emerging issues, weak signals and discontinuities.

Weak signal: An early, ambiguous indication of a potentially important development.

PESTLE: Framework covering political, economic, social, technological, legal and environmental factors.

Patent landscape: Structured analysis of patents, owners, technologies and relationships in a defined field.

Patent family: Related patent applications or grants associated with the same invention or priority claims.

Freedom to operate: Assessment of whether a product can be made, used or sold without infringing relevant rights.

Technology forecasting: Estimation of possible future technology performance, timing or adoption.

Scenario planning: Exploration of several plausible and internally consistent futures.

Technology roadmap: Time-phased representation linking markets, products, technologies and capabilities.

S-curve: Heuristic model of slow, rapid and slowing improvement or adoption.

Technology lifecycle: Pattern of emergence, growth, maturity and decline or substitution.

Technology Readiness Level: Staged measure of technical demonstration maturity from TRL 1 to TRL 9.

Commercial readiness: Degree to which a solution can be adopted and supported in a viable market.

Signpost: Observable event or indicator used to monitor which future may be emerging.

Dominant design: Configuration that becomes a widely accepted basis for further development.

Strategic uncertainty: An important unknown that may materially affect strategic choices.

Technology ecosystem: Network of firms, users, institutions, platforms and complementors involved in value creation.

Decision gate: Explicit point at which evidence is reviewed before continuing, changing or stopping an initiative.

Review Questions

5 Short Answer Questions

1. Distinguish technology intelligence from technology forecasting.
2. What is a weak signal, and why should it not be treated as a prediction?
3. Identify three limitations of using patent counts as a measure of innovation.
4. What does TRL 7 generally indicate?
5. Why is a technology roadmap different from a project schedule?

5 Long Answer Questions

1. Explain the environmental scanning process and show how PESTLE can be used without becoming a purely descriptive checklist.
2. Discuss how patent, market and customer evidence should be combined when evaluating a new technology venture.
3. Compare qualitative and quantitative technology forecasting methods, including their assumptions and limitations.
4. Explain the relationship between technology lifecycle models, S-curves and Technology Readiness Levels.
5. Describe the stages involved in constructing a technology roadmap and explain the importance of decision gates.

3 Analytical / Application Questions

1. A startup has a technically validated product but no regulatory approval, manufacturing partner or paying customers. Assess its maturity using TRL and at least two complementary readiness dimensions.
2. Design two scenario axes for an entrepreneurial venture developing a low-carbon industrial technology. Explain how the scenarios would influence investment and partnership decisions.
3. Construct a short roadmap for a digital health product across three time horizons. Include customer outcomes, technologies, capability gaps, risks and at least two decision gates.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes technology intelligence from technology forecasting?

A. Technology intelligence concerns only patent counts, while forecasting concerns only sales.

B. Technology intelligence interprets evidence for decisions, while forecasting estimates possible future developments.

C. Technology intelligence is always quantitative, while forecasting is always qualitative.

D. Technology intelligence is internal, while forecasting is exclusively external.

Correct Answer: B. Technology intelligence interprets evidence for decisions, while forecasting estimates possible future developments.

Question 2

What is the main purpose of horizon scanning?

A. To guarantee an accurate long-term prediction

B. To identify emerging issues, weak signals and possible discontinuities

C. To replace customer research with expert opinion

D. To calculate the legal validity of every patent

Correct Answer: B. To identify emerging issues, weak signals and possible discontinuities

Question 3

Which patent element generally defines the legally protected scope of an invention?

A. The abstract

B. The title

C. The claims

D. The citation list

Correct Answer: C. The claims

Question 4

A technology assessed at TRL 7 has generally been:

A. Identified only as a basic scientific principle

B. Formulated only as a preliminary concept

C. Demonstrated as a system prototype in an operational environment

D. Proven to be commercially profitable in all markets

Correct Answer: C. Demonstrated as a system prototype in an operational environment

Question 5

In the equation g = (N₂ − N₁) ÷ N₁ × 100%, what do N₁ and N₂ represent?

A. The lower and upper prices of a product

B. The earlier and later counts measured using consistent rules

C. The first and final forecast errors

D. The minimum and maximum TRL values

Correct Answer: B. The earlier and later counts measured using consistent rules

Question 6

Which statement about scenario planning is most accurate?

A. It selects one certain future and ignores alternatives.

B. It produces internally consistent plausible futures for testing strategies.

C. It replaces all quantitative forecasting.

D. It is used only after a technology reaches TRL 9.

Correct Answer: B. It produces internally consistent plausible futures for testing strategies.

Question 7

A technology roadmap primarily connects:

A. Employee salaries, tax rates and office locations

B. Market needs, products, technologies and capabilities across time

C. Patent titles with alphabetical filing numbers

D. Only laboratory experiments with fixed project dates

Correct Answer: B. Market needs, products, technologies and capabilities across time

Question 8

Why should TRL not be treated as a complete measure of commercialization readiness?

A. TRL measures technical demonstration but not necessarily regulation, manufacturing, customers or economics.

B. TRL applies only to marketing campaigns.

C. TRL is a patent-validity scale.

D. TRL measures only employee capability.

Correct Answer: A. TRL measures technical demonstration but not necessarily regulation, manufacturing, customers or economics.

Question 9

In the weighted score S = ∑(wᵢxᵢ), what condition is specified for the weights?

A. Every weight must be greater than 10.

B. The weights must sum to 1.

C. All weights must be negative.

D. Weights must be measured in kilograms.

Correct Answer: B. The weights must sum to 1.

Question 10

Which is an appropriate use of AI in technology intelligence?

A. Accepting an AI-generated patent legal opinion without review

B. Using AI to assist document classification while humans verify important conclusions

C. Uploading confidential strategy documents to any public model

D. Treating algorithmic rankings as objective truth

Correct Answer: B. Using AI to assist document classification while humans verify important conclusions