Opportunity Recognition and Evaluation

Introduction and Context

Entrepreneurship begins before a product is built, a company is incorporated, or capital is raised. It begins with a judgment that a particular problem, change, or unmet need can be transformed into a valuable and defensible venture. This judgment is called opportunity recognition and evaluation.

An opportunity is not simply an idea. An idea is a possible concept, while an opportunity is a concept supported by evidence that it can create value for identifiable customers and sustain an economically viable organization. The distinction is essential because many ideas are technically possible but commercially unattractive, ethically problematic, poorly timed, or incompatible with the founder’s capabilities.

At graduate level, opportunity analysis should be treated as a process of disciplined inquiry under uncertainty. Entrepreneurs rarely possess complete information. They must therefore combine customer research, market analysis, technological assessment, financial reasoning, institutional analysis, experimentation, and judgment. The purpose is not to eliminate uncertainty—an impossible task—but to reduce avoidable uncertainty and make explicit the assumptions on which a venture depends.

Learning Objectives

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

1. Distinguish ideas, opportunities, and business models.
2. Identify opportunities arising from customer problems, technological change, market gaps, and institutional shifts.
3. Apply systematic frameworks to evaluate desirability, feasibility, viability, timing, uncertainty, and founder–opportunity fit.
4. Formulate and test critical venture assumptions using evidence and experiments.
5. Assess competition, regulation, ethics, inclusion, and defensibility.
6. Make a reasoned decision to pursue, revise, defer, or reject an opportunity.

1. The Nature of an Entrepreneurial Opportunity

An entrepreneurial opportunity exists when conditions allow an entrepreneur to introduce a product, service, process, or organizational arrangement that creates value by addressing a need more effectively than existing alternatives. Opportunities are relational: they arise from the interaction of external conditions, customer preferences, available resources, and entrepreneurial interpretation.

Three distinctions are useful:

• Idea: A proposed possibility, often expressed as a product or technology.
• Opportunity: A promising possibility supported by a problem, customer, means of delivery, and value-creation logic.
• Business model: The configuration through which an organization creates, delivers, and captures value.

A product-centered view asks, “What can we build?” An opportunity-centered view asks, “Who experiences a consequential problem, why is it inadequately solved, and under what conditions can we solve it profitably and responsibly?” The second question prevents technological enthusiasm from being mistaken for commercial potential.

Opportunity discovery and opportunity creation

Two major perspectives explain how opportunities emerge. The discovery perspective argues that opportunities result from objective changes—such as demographic shifts, price changes, or new technologies—that create mismatches between supply and demand. Entrepreneurs discover and exploit these mismatches.

The creation perspective emphasizes that opportunities are often constructed through entrepreneurial action. Entrepreneurs engage stakeholders, develop prototypes, negotiate partnerships, and shape customer preferences. The market is not always waiting in a finished form; it may be co-produced by the entrepreneur and early users.

These perspectives are complementary. A change in regulation may create a latent opening, but its commercial form may emerge only through experimentation with customers, suppliers, regulators, and investors. Graduate-level analysis therefore asks both: “What external change makes this possible?” and “What actions will make this opportunity real?”

2. Sources of Opportunities

2.1 Customer problems and jobs to be done

The most durable opportunities often originate in recurring, costly, urgent, or emotionally significant customer problems. Customer analysis should focus not only on demographic characteristics but also on the job to be done: the progress a customer is trying to make in a particular situation.

A problem is more attractive when it has:

• high frequency or high consequence;
• inadequate existing alternatives;
• customers with authority and willingness to pay;
• a clearly identifiable context of use; and
• a solution that can be delivered economically.

Entrepreneurs should distinguish a stated preference from revealed behavior. A respondent may say that a proposed solution is interesting, but actual behavior—switching suppliers, paying for a substitute, spending time on a workaround, or providing a deposit—is stronger evidence.

2.2 Technological change

Technologies create opportunities by reducing cost, increasing performance, enabling new experiences, or changing the boundaries of what can be coordinated. Relevant changes include artificial intelligence, biotechnology, cloud computing, robotics, advanced materials, digital payments, and energy storage.

Technology alone does not constitute an opportunity. An opportunity requires a use case, an adoption pathway, and a value proposition. A technically superior product may fail because it is difficult to integrate, requires behavior change, violates trust expectations, or lacks an economically attractive distribution channel.

A useful sequence is:

1. Identify the technological capability.
2. Identify tasks or constraints it changes.
3. Determine which users experience meaningful benefit.
4. Assess adoption barriers, complementary assets, and regulation.
5. Test whether the benefit exceeds switching and implementation costs.

2.3 Market gaps and underserved segments

A market gap occurs when customer needs are inadequately served by current offerings. Gaps may involve price, quality, accessibility, speed, convenience, geographic coverage, cultural fit, or service to a neglected segment.

Entrepreneurs should avoid defining a gap merely as “no competitor exists.” Absence of competitors may indicate weak demand, difficult economics, or legal barriers. A stronger gap is evidenced by customers using imperfect substitutes, combining several products, tolerating long delays, or creating informal workarounds.

2.4 Institutional and societal shifts

Institutions include laws, regulations, standards, professional norms, public agencies, and cultural expectations. Institutional shifts can generate opportunities by changing what organizations must do, what they may do, or what customers consider legitimate.

Examples include data-protection requirements, carbon reporting, health and safety standards, public procurement reforms, demographic aging, migration, and changing expectations about inclusion. Such opportunities require careful analysis because regulatory dependence can create both demand and vulnerability. A venture built solely on a temporary subsidy or unresolved legal interpretation may have weak durability.

3. From Insight to Opportunity Hypothesis

An opportunity hypothesis is a provisional statement linking a target customer, problem, proposed solution, value, and business logic. A useful structure is:

For [target customer] who experiences [important problem] in [context], [solution] will provide [measurable or observable benefit] because [mechanism]. The venture will capture value through [revenue or other economic mechanism].

This statement is not a marketing slogan. It is a set of testable assumptions. The entrepreneur should identify which assumptions are most critical and least known. These are the leap-of-faith assumptions. For example, a venture may assume that hospitals will share data, that users will change routines, or that a regulator will approve a new process. If a critical assumption is false, refinement of the logo or interface has little value.

Assumption categories

4. Evaluating Opportunity Quality

A rigorous evaluation considers several dimensions rather than relying on one attractive feature.

4.1 Desirability

Desirability concerns whether a sufficiently important group of customers wants the proposed outcome. It includes functional, economic, social, and emotional value. The relevant question is not whether customers like the idea but whether they will allocate scarce resources—money, time, attention, data, or organizational authority—to obtain it.

Evidence should progress from opinions to behavior. Interviews are useful for learning vocabulary, context, and pain points, but they are vulnerable to courtesy bias and hypothetical bias. Stronger tests include landing-page experiments, paid pilots, letters of intent, pre-orders, usage retention, and switching behavior.

4.2 Feasibility

Feasibility asks whether the venture can produce and deliver the promised value. It includes technical performance, access to talent, supply, infrastructure, distribution, intellectual property, and organizational capability.

A feasibility assessment should identify dependencies. For example, an artificial-intelligence service may depend on data rights, computing capacity, model reliability, cybersecurity, human oversight, and integration with a customer’s workflow. Technical feasibility must therefore be evaluated as a system, not as an isolated prototype.

4.3 Viability

Viability concerns whether the opportunity can support a sustainable economic model. A simple contribution calculation is:

Price and variable cost must be measured in the same currency per unit. The calculation assumes that variable cost changes approximately with each additional unit and excludes fixed costs. It is useful for short-run decisions but does not by itself establish profitability.

If fixed costs are F and contribution per unit is C, the accounting break-even volume is:

This formula assumes a constant selling price, constant variable cost per unit, and a single relevant product or a stable sales mix. F must include the fixed costs relevant to the period, while C must be positive and expressed per unit. If contribution is zero or negative, there is no finite break-even volume under these assumptions.

Worked example. A service charges $120 per subscription per month. Variable service cost is $45, and monthly fixed costs are $30,000.

Contribution = $120 − $45 = $75 per subscription.

Break-even volume = $30,000 ÷ $75 = 400 subscriptions per month.

At 500 subscriptions, monthly operating contribution after fixed costs is (500 × $75) − $30,000 = $7,500, before financing costs, taxes, and other excluded items.

The model should also include acquisition cost, retention, refunds, payment fees, working capital, compliance, and founder compensation. Attractive gross margins can conceal high customer-acquisition costs or weak retention.

4.4 Timing

An opportunity can be desirable and feasible but poorly timed. Timing depends on technology maturity, customer readiness, infrastructure, regulation, macroeconomic conditions, and the availability of complementary assets. “First mover” status is not automatically advantageous; early entrants often educate the market and bear high development costs, while later entrants can learn from them.

A timing assessment should ask:

• What changed recently?
• Why is adoption possible now rather than five years ago?
• What must change before adoption accelerates?
• How long is the window likely to remain open?
• Can the venture survive until the market is ready?

4.5 Uncertainty and downside exposure

Risk is often represented as a known probability distribution; uncertainty refers to situations in which outcomes or probabilities are poorly known. Early ventures face both. Rather than assigning false precision, entrepreneurs should map assumptions, plausible scenarios, leading indicators, and irreversible commitments.

A decision matrix can compare scenarios without pretending to predict them precisely. For example, a venture may model conservative, base, and favorable adoption cases, then identify which assumptions cause the largest change in cash requirements. Sensitivity analysis is especially valuable for price, conversion rate, churn, production yield, and regulatory delay.

4.6 Founder–opportunity fit

Founder–opportunity fit concerns the match between the opportunity and the founder’s knowledge, relationships, credibility, motivation, resources, and ability to learn. Fit is not a claim that only insiders can innovate. Outsiders may challenge assumptions, while insiders may be constrained by conventional thinking. The relevant issue is whether the founding team can obtain the missing knowledge and build legitimacy.

A strong fit may provide:

• privileged access to users or distribution;
• domain knowledge that improves problem diagnosis;
• technical or operational capability;
• motivation to persist through uncertainty; and
• credibility with stakeholders.

Fit should not become an excuse for overconfidence. Founders close to a problem may mistake personal frustration for a large market. External validation remains necessary.

5. Frameworks for Structured Evaluation

5.1 Desirability–feasibility–viability

The three-part framework is a useful screening device. Desirability asks whether people want the outcome; feasibility asks whether it can be delivered; viability asks whether value can be captured sustainably. A fourth dimension—responsibility—should be added in graduate practice: whether the opportunity avoids unacceptable harm and distributes benefits and risks fairly.

The dimensions interact. A desirable service may be infeasible at the promised price. A feasible technology may be undesirable because it creates surveillance concerns. A viable business model may be socially unacceptable or legally impermissible.

5.2 Industry and ecosystem analysis

Industry analysis should include customers, competitors, substitutes, suppliers, complementors, regulators, and platform owners. Competitive advantage is not determined only by the number of direct competitors. A venture can be threatened by a substitute behavior, a powerful platform, a supplier controlling a scarce input, or a customer capable of developing the solution internally.

Ecosystem analysis is particularly important for digital, health, energy, and mobility ventures. Value may depend on interoperability, standards, data access, physical infrastructure, and coordinated adoption by multiple actors.

5.3 Total addressable market and the danger of inflated numbers

Market sizing commonly uses three concepts:

• Total addressable market (TAM): demand if the venture could serve the entire theoretically relevant market.
• Serviceable available market (SAM): the portion compatible with the venture’s geography, segment, capability, and offering.
• Serviceable obtainable market (SOM): the realistic share the venture may capture over a defined period.

A top-down estimate begins with an industry total and applies assumptions. A bottom-up estimate uses observable drivers such as number of target organizations × annual price × expected adoption. Bottom-up estimates are generally more useful for operating plans, but both approaches depend on credible assumptions. Market size is not the same as accessible demand, and a large market does not guarantee attractive economics.

5.4 Real-options reasoning

Early entrepreneurial investments often have option-like characteristics. A small experiment purchases information and preserves the right, but not the obligation, to invest more later. This logic supports staged commitment: conduct a low-cost test, examine evidence, and scale only when critical uncertainties decline.

However, real-options reasoning should not justify endless experimentation. A test must have a decision rule, a time limit, and a clearly defined implication. Otherwise, the venture may continue because of escalation of commitment rather than evidence.

6. Evidence, Experimentation, and Decision Quality

Opportunity evaluation is an evidence-building process. A useful experiment changes one important assumption, measures a relevant behavior, and has a predefined interpretation. Examples include a manual concierge service before automation, a paid pilot before full product development, or a prototype used in a real workflow.

A minimum viable product is not necessarily a minimal software product. It is the smallest credible intervention that generates valid learning about a critical assumption. A manual service may be appropriate where automation is uncertain; a simulation may be appropriate where physical production is expensive.

A disciplined evidence process includes:

1. State the assumption and why it matters.
2. Define the observable behavior that would support or weaken it.
3. Choose a test that minimizes cost and bias.
4. Establish thresholds before observing results.
5. Record evidence and alternative explanations.
6. Decide whether to persevere, pivot, pause, or abandon.

The quality of evidence depends on sample selection, measurement validity, duration, and context. A small number of enthusiastic early adopters may not represent mainstream customers. A high click-through rate may not predict payment. A pilot subsidized by a grant may not reveal willingness to pay.

7. Ethical, Legal, and Governance Considerations

Opportunity recognition has ethical content because entrepreneurs decide which problems deserve attention, whose data are collected, and who bears risk. Evaluation should address:

• privacy, consent, and data minimization;
• safety and reliability;
• accessibility and discriminatory effects;
• labor conditions and supply-chain impacts;
• environmental externalities;
• truthful marketing and responsible experimentation; and
• accountability when automated systems make consequential decisions.

For AI-enabled opportunities, founders should evaluate data provenance, model limitations, explainability appropriate to the context, cybersecurity, human oversight, and procedures for correcting harmful outputs. Compliance is a baseline, not a substitute for responsible design. A legally permissible opportunity may still damage trust or produce unjust outcomes.

Institutional stakeholders should be engaged early where they can approve, block, or materially affect adoption. This includes regulators, professional bodies, communities, employees, and affected noncustomers. Stakeholder engagement is not merely public relations; it can reveal hidden constraints and improve the opportunity itself.

8. Applied Case: CareLink Home Monitoring

The following is a fictional illustrative case.

Background

A multidisciplinary team proposes CareLink, a home-monitoring service for older adults recently discharged from hospital. Low-cost sensors and a mobile application would alert family members and care coordinators to changes in mobility, medication routines, or emergency risk.

Problem

Hospitals experience readmissions, families struggle to monitor relatives remotely, and professional caregivers have limited time. The team initially defines the opportunity as “an AI platform for elderly care.” This framing is technology-led and too broad.

Analysis

Customer interviews reveal four distinct stakeholders: older adults, family members, hospitals, and home-care providers. Their objectives differ. Older adults value independence and privacy; families value reassurance; hospitals value lower readmissions; providers value actionable alerts rather than additional false alarms.

The team identifies several assumptions:

• families will consent to monitoring;
• older adults will accept sensors in the home;
• alerts will be sufficiently accurate;
• hospitals will pay or refer patients;
• the service can meet health-data requirements; and
• savings or improved outcomes will exceed implementation costs.

A feasibility test uses a small number of households and human-reviewed alerts rather than fully automated prediction. A desirability test measures continued use and caregiver response, not merely stated interest. A viability analysis compares subscription revenue with installation, monitoring, customer support, compliance, and acquisition costs.

Approach and solution

The team narrows the initial segment to post-discharge patients with a family caregiver and a participating clinic. It offers a six-week paid pilot, obtains informed consent, minimizes collected data, and provides a human escalation pathway. The first version monitors only two high-value events rather than attempting comprehensive health prediction.

The clinic will continue only if the pilot demonstrates reliable installation, meaningful caregiver engagement, acceptable privacy perceptions, and evidence of reduced avoidable escalation. The team also establishes a stop rule if false alarms exceed a predefined level or if participants report unacceptable intrusion.

Outcome

The pilot shows strong family interest but weak adoption among patients living alone because installation and support are burdensome. Alert accuracy is acceptable for mobility changes but poor for medication inference. The team abandons the broader predictive-health claim and focuses on mobility and post-discharge support through clinics with existing care coordinators.

Learning points

1. A broad technology concept became a specific opportunity only after stakeholder and workflow analysis.
2. Desirability differed across users, buyers, and affected individuals.
3. Human-in-the-loop experimentation reduced technical and ethical risk.
4. Narrowing the initial scope improved feasibility and credibility.
5. Evidence supported a pivot, not a simple go/no-go judgment.

9. A Practical Opportunity Evaluation Protocol

A graduate entrepreneur can use the following sequence:

1. Frame the change: Identify the customer, technological, market, or institutional shift.
2. Define the problem: Describe the context, current workaround, consequence, and affected stakeholders.
3. Specify the customer and buyer: Distinguish user, payer, beneficiary, and decision maker.
4. Map alternatives: Include direct competitors, substitutes, internal solutions, and nonconsumption.
5. State the value proposition: Explain the outcome and mechanism in observable terms.
6. List assumptions: Rank them by importance and uncertainty.
7. Assess desirability, feasibility, viability, timing, responsibility, and founder fit.
8. Design evidence-generating experiments: Prefer behavioral evidence and reversible commitments.
9. Model economics and scenarios: Include costs, adoption, retention, cash needs, and sensitivity.
10. Make a decision: Pursue, revise, defer, partner, or reject, with explicit reasons.

A high-quality evaluation document should show not only why the opportunity might work but also what would prove it wrong. This is a mark of analytical maturity.

Challenges and Limitations

Opportunity evaluation has unavoidable limitations. Market research can be biased by leading questions, nonrepresentative samples, and respondents’ inability to predict future behavior. Financial projections are sensitive to assumptions and can create an illusion of precision. Competitive analysis may miss emerging substitutes or a powerful incumbent response. Experiments can produce misleading results when incentives, samples, or contexts differ from the eventual market.

Entrepreneurs also face cognitive biases: confirmation bias, overconfidence, availability bias, sunk-cost effects, and the tendency to interpret ambiguous evidence in favor of a preferred idea. Governance mechanisms such as independent review, documented assumptions, precommitted thresholds, and diverse stakeholder input can improve judgment.

Contemporary Developments

Digital platforms have lowered the cost of testing and distribution but increased dependence on algorithms, data access, and platform rules. Generative AI can accelerate customer research, prototyping, and scenario analysis, but it may also produce fabricated evidence, reproduce bias, expose confidential information, or encourage superficial validation. AI should support—not replace—primary research, domain expertise, and accountable decision making.

Climate adaptation, demographic change, geopolitical disruption, and changing regulation are generating opportunities in resilience, circular production, health, education, and sustainable infrastructure. These fields often involve public value and long time horizons, so evaluation must include externalities, policy dependence, and distributional effects rather than relying solely on private financial returns.

Chapter Summary

Opportunity recognition is the process of identifying possibilities for value creation from customer problems, technological change, market gaps, and institutional shifts. An idea becomes an opportunity hypothesis when it specifies a customer, problem, solution, value mechanism, and economic logic that can be tested.

Effective evaluation examines desirability, feasibility, viability, timing, uncertainty, responsibility, and founder–opportunity fit. Customer evidence is strongest when it demonstrates behavior rather than opinion. Technical feasibility requires attention to dependencies, integration, reliability, and regulation. Viability requires realistic unit economics, cash requirements, retention, and sensitivity analysis. The contribution and break-even formulas are useful screening tools but depend on explicit assumptions.

Entrepreneurs should use experiments and staged commitments to reduce uncertainty. A minimum viable product is the smallest credible intervention that tests a critical assumption, not necessarily the smallest software package. Structured analysis must also consider competition, ecosystems, ethics, privacy, safety, inclusion, and institutional legitimacy. The objective is not certainty but better-informed action: pursue, revise, partner, defer, or abandon.

Key Terms

Opportunity: A value-creating possibility supported by a meaningful problem and a viable means of addressing it.

Idea: A proposed concept that may or may not have commercial potential.

Business model: The logic by which an organization creates, delivers, and captures value.

Opportunity recognition: Identifying a possible venture opportunity from changes or unmet needs.

Opportunity evaluation: Assessing the attractiveness, feasibility, viability, timing, and risks of an opportunity.

Jobs to be done: The progress a customer seeks in a particular situation.

Desirability: The extent to which customers value and seek a proposed outcome.

Feasibility: The extent to which a solution can be produced and delivered.

Viability: The extent to which an opportunity can sustain an economic model.

Market gap: An inadequately served customer need or segment.

Institutional shift: A change in law, regulation, norms, standards, or public expectations.

Leap-of-faith assumption: A critical, uncertain assumption on which the opportunity depends.

Minimum viable product: The smallest credible intervention that generates useful learning.

TAM: Total addressable market under ideal theoretical access.

SAM: Serviceable available market compatible with the venture’s scope.

SOM: Serviceable obtainable market realistically capturable in a specified period.

Founder–opportunity fit: The match between venture requirements and the founding team’s capabilities and access.

Contribution: Price per unit minus variable cost per unit.

Break-even volume: Sales volume at which contribution covers relevant fixed costs.

Real-options reasoning: Staged investment that preserves the right to invest further as uncertainty declines.

Review Questions

5 Short Answer Questions

1. How does an opportunity differ from an idea?
2. What is meant by a job to be done?
3. Name four major sources of entrepreneurial opportunities.
4. Why is behavioral evidence generally stronger than stated customer interest?
5. What is founder–opportunity fit?

5 Long Answer Questions

1. Explain how desirability, feasibility, and viability interact in opportunity evaluation.
2. Compare discovery and creation perspectives on entrepreneurial opportunities.
3. Discuss how technological change can create opportunities without automatically creating markets.
4. Explain the role of staged experimentation in managing uncertainty.
5. Evaluate the ethical and governance issues that should be considered in an AI-enabled opportunity.

3 Analytical / Application Questions

1. Select a problem in a university, workplace, or community. Develop an opportunity hypothesis, identify five assumptions, and rank them by importance and uncertainty.
2. A proposed service charges $80 per unit, has a variable cost of $30 per unit, and fixed monthly costs of $25,000. Calculate contribution per unit and break-even volume, then explain two limitations of the calculation.
3. Using the CareLink case, recommend whether the team should pursue, pivot, or abandon the opportunity. Support your recommendation with evidence concerning desirability, feasibility, viability, timing, and responsibility.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes an entrepreneurial opportunity from an idea?

A. An opportunity is always based on patented technology.

B. An opportunity is an idea supported by evidence of value creation and a viable means of delivery.

C. An opportunity requires no customer research.

D. An opportunity is any concept that a founder personally prefers.

Correct Answer: B. An opportunity is an idea supported by evidence of value creation and a viable means of delivery.

Question 2

Which source of opportunity is most directly associated with unmet needs and inadequate existing alternatives?

A. Customer problems

B. Accounting changes

C. Founder intuition alone

D. Random experimentation without a user context

Correct Answer: A. Customer problems

Question 3

A service sells for $100 per unit and has a variable cost of $40 per unit. What is its contribution per unit?

A. $40

B. $60

C. $100

D. $140

Correct Answer: B. $60

Question 4

If fixed costs are $18,000 and contribution per unit is $60, what is the accounting break-even volume?

A. 180 units

B. 240 units

C. 300 units

D. 1,080,000 units

Correct Answer: C. 300 units

Question 5

Which evidence most strongly demonstrates willingness to pay?

A. A respondent says the idea is interesting.

B. A visitor spends several seconds on a webpage.

C. A target customer signs a paid pilot agreement.

D. A founder believes the market is large.

Correct Answer: C. A target customer signs a paid pilot agreement.

Question 6

What does feasibility primarily evaluate?

A. Whether customers find the idea emotionally attractive only

B. Whether the solution can be produced and delivered as promised

C. Whether the founder has already earned a profit

D. Whether competitors have no products at all

Correct Answer: B. Whether the solution can be produced and delivered as promised

Question 7

Which statement about TAM, SAM, and SOM is correct?

A. SOM is always larger than TAM.

B. SAM is the entire global economy.

C. SOM represents the realistically obtainable portion of the relevant market.

D. TAM measures only current revenue of the startup.

Correct Answer: C. SOM represents the realistically obtainable portion of the relevant market.

Question 8

Why can first-mover status fail to provide an advantage?

A. Early entrants never educate customers.

B. Early entrants may bear market-education costs and face immature technology or infrastructure.

C. Customers always reject new products.

D. Regulation automatically protects later entrants.

Correct Answer: B. Early entrants may bear market-education costs and face immature technology or infrastructure.

Question 9

Which practice best reduces confirmation bias in opportunity evaluation?

A. Ignoring evidence that contradicts the business idea

B. Predefining decision thresholds and documenting disconfirming evidence

C. Interviewing only enthusiastic friends

D. Increasing investment whenever results are ambiguous

Correct Answer: B. Predefining decision thresholds and documenting disconfirming evidence

Question 10

For an AI-enabled opportunity, which issue is especially important?

A. Using as much personal data as possible regardless of consent

B. Eliminating all human oversight

C. Assessing data provenance, model limitations, privacy, and accountability

D. Treating regulatory compliance as unnecessary

Correct Answer: C. Assessing data provenance, model limitations, privacy, and accountability