Abstract

Enterprise artificial intelligence investments present unique economic challenges that traditional capital budgeting frameworks fail to adequately address. This article develops a comprehensive economic framework specifically designed for AI investment decisions, integrating uncertainty quantification, option value analysis, and dynamic portfolio optimization. Drawing from fourteen years of software engineering practice and seven years of AI research, I present a decision-making architecture that accounts for the probabilistic nature of AI project outcomes, the 80-95% failure rates documented in enterprise deployments, and the path-dependent characteristics of machine learning system development.

The framework introduces three novel components: (1) a Risk-Adjusted Net Present Value (RA-NPV) methodology calibrated to AI-specific uncertainties, (2) a Real Options Valuation (ROV) approach capturing the embedded flexibility in staged AI investments, and (3) a Monte Carlo-based scenario analysis tool integrating technical, organizational, and market risks. Through analysis of 127 enterprise AI projects across manufacturing, financial services, and healthcare sectors, I demonstrate that organizations applying this framework achieve 2.3x higher risk-adjusted returns compared to those using traditional DCF methods.

Keywords: AI investment framework, economic decision-making, risk-adjusted returns, real options valuation, enterprise AI economics, capital budgeting, Monte Carlo simulation, portfolio optimization

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1. Introduction: The Investment Challenge

In my experience at Capgemini Engineering, I have observed a troubling pattern: organizations approach AI investments with the same analytical tools they use for traditional IT projects, then express surprise when outcomes diverge dramatically from projections. This fundamental mismatch between methodology and domain explains much of the documented 80-95% failure rate in enterprise AI initiatives (as I explored in the first article of this series).

The core problem lies in the nature of uncertainty itself. Traditional software projects exhibit what economists call “risk” — outcomes that can be probabilistically characterized from historical data. AI projects, by contrast, operate under conditions closer to Knightian uncertainty, where the probability distribution of outcomes is itself unknown. This distinction, first articulated by Frank Knight in 1921, carries profound implications for investment analysis.

1.1 Why Traditional Frameworks Fail

Consider a standard Discounted Cash Flow (DCF) analysis applied to an AI project:

NPV = Σ (CFt / (1 + r)^t) - Initial Investment

This formulation assumes:

• Cash flows can be estimated with reasonable confidence
• The discount rate adequately captures project risk
• The investment is a single, irreversible commitment
• Success is binary (the project either works or fails)

None of these assumptions hold for AI investments. Cash flows depend on model performance, which cannot be known until training is complete. The discount rate cannot capture the multi-dimensional risk profile spanning technical, organizational, and market domains. AI investments are inherently staged, with multiple decision points where projects can be expanded, contracted, or abandoned. And success exists on a continuum — a model with 85% accuracy may deliver positive ROI, while one at 70% may destroy value, yet both “work” in a technical sense.

1.2 The Framework Architecture

This article develops an integrated economic framework addressing these limitations through three interconnected components:

graph TB
    subgraph "Economic Decision Framework"
        A[Investment Proposal] --> B[Risk-Adjusted NPV Analysis]
        B --> C[Real Options Valuation]
        C --> D[Monte Carlo Simulation]
        D --> E[Portfolio Optimization]
        E --> F[Investment Decision]
    end
    
    subgraph "Risk Dimensions"
        R1[Technical Risk] --> B
        R2[Organizational Risk] --> B
        R3[Market Risk] --> B
        R4[Regulatory Risk] --> B
    end
    
    subgraph "Decision Outputs"
        F --> G[Stage-Gate Criteria]
        F --> H[Abandon Thresholds]
        F --> I[Expansion Triggers]
    end

Figure 1: Integrated Economic Framework Architecture

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2. Component I: Risk-Adjusted Net Present Value

The first component transforms traditional NPV into a risk-adjusted methodology specifically calibrated for AI investments. During my research at Odessa Polytechnic National University, I analyzed 127 enterprise AI projects to derive empirically-grounded risk adjustments.

2.1 The RA-NPV Formulation

The Risk-Adjusted NPV for AI investments takes the form:

RA-NPV = Σ [E(CFt) × Pt × Ot × Mt] / (1 + rf + λσ)^t - I0 × (1 + c)

Where:
- E(CFt) = Expected cash flow at time t
- Pt = Technical success probability at time t
- Ot = Organizational adoption probability at time t
- Mt = Market relevance probability at time t
- rf = Risk-free rate
- λ = Risk aversion parameter
- σ = Project volatility
- I0 = Initial investment
- c = Contingency factor for AI projects (typically 0.3-0.5)

2.2 Empirical Calibration of Success Probabilities

The key innovation lies in the empirical calibration of success probabilities. My analysis of 127 projects revealed the following distributions:

These probabilities align with the risk profiles discussed in Article 3 of this series, where I established that narrow AI systems exhibit fundamentally different economic characteristics than general-purpose implementations.

2.3 Case Study: Manufacturing Predictive Maintenance

To illustrate the RA-NPV methodology, consider a predictive maintenance AI project at a European automotive manufacturer (anonymized from my Capgemini engagement):

Traditional NPV Calculation:

NPV = -2.4M + 1.2M/1.035 + 1.2M/1.035² + ... + 1.2M/1.035⁵
NPV = -2.4M + 5.42M = €3.02 million

Risk-Adjusted NPV Calculation:

Pt = 0.65, Ot = 0.55, Mt = 0.70
Adjusted discount rate = 0.035 + 0.8 × 0.45 = 0.395
Contingency factor = 0.35
Adjusted investment = 2.4M × 1.35 = €3.24M

RA-NPV = -3.24M + Σ [1.2M × 0.65 × 0.55 × 0.70] / 1.395^t
RA-NPV = -3.24M + 1.01M = -€2.23 million

The traditional NPV suggests a highly attractive investment with €3.02 million in value creation. The risk-adjusted analysis reveals expected value destruction of €2.23 million. This dramatic reversal explains why so many “compelling” AI business cases result in failed deployments.

graph LR
    subgraph "Traditional Analysis"
        T1[Investment: €2.4M] --> T2[NPV: +€3.02M]
        T2 --> T3[Decision: PROCEED]
    end
    
    subgraph "RA-NPV Analysis"
        R1[Adjusted Investment: €3.24M] --> R2[RA-NPV: -€2.23M]
        R2 --> R3[Decision: DECLINE or RESTRUCTURE]
    end

Figure 2: Traditional vs. Risk-Adjusted Analysis Comparison

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3. Component II: Real Options Valuation

The RA-NPV methodology, while more realistic than traditional DCF, still treats AI investments as single, irreversible commitments. In practice, AI projects unfold through stages, with decision points where management can expand successful initiatives, contract underperforming ones, or abandon failures entirely. Real Options Valuation captures this embedded flexibility.

3.1 The Option Value of Staged Development

During my PhD research, I developed an adaptation of the Black-Scholes framework for AI project valuation. The key insight is that each stage of AI development creates options on subsequent stages:

graph TD
    subgraph "Stage 1: POC"
        A[Data Assessment
Investment: €150K] --> B{Viable?}
        B -->|Yes| C[Option to Proceed
Value: €200K]
        B -->|No| D[Abandon
Loss: €150K]
    end
    
    subgraph "Stage 2: Pilot"
        C --> E[Model Development
Investment: €400K]
        E --> F{Performs?}
        F -->|Yes| G[Option to Scale
Value: €1.2M]
        F -->|No| H[Abandon
Loss: €550K]
    end
    
    subgraph "Stage 3: Production"
        G --> I[Full Deployment
Investment: €1.8M]
        I --> J{Adopted?}
        J -->|Yes| K[Full Value
NPV: €4.5M]
        J -->|No| L[Partial Value
NPV: €0.8M]
    end

Figure 3: Staged AI Investment with Embedded Options

3.2 Valuing the Options

The call option value at each stage can be approximated using a modified Black-Scholes formula:

C = S × N(d1) - K × e^(-rT) × N(d2)

Where:
d1 = [ln(S/K) + (r + σ²/2)T] / (σ√T)
d2 = d1 - σ√T

For AI projects:
- S = Expected present value of subsequent stages (if successful)
- K = Investment required to exercise the option (next stage cost)
- σ = Volatility of AI project value (empirically 0.4-0.6)
- T = Time to decision point
- r = Risk-free rate

3.3 Case Study: Financial Services Fraud Detection

A major European bank (client engagement through Capgemini) evaluated an AI-based fraud detection system. The traditional NPV analysis suggested marginal attractiveness:

Traditional NPV: €1.8 million
Real Options Value: €3.7 million

The €1.9 million difference represents the value of managerial flexibility — the ability to abandon the project at each stage if results prove unsatisfactory. This “abandonment option” is particularly valuable in AI projects given their high failure rates.

3.4 Stage-Gate Decision Criteria

The real options framework naturally generates stage-gate criteria. At each decision point, proceed only if:

Option Value > Remaining Investment Present Value

I have codified these criteria into a decision protocol used across multiple Capgemini engagements:

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4. Component III: Monte Carlo Simulation

The third framework component addresses the multi-dimensional uncertainty inherent in AI investments through Monte Carlo simulation. While RA-NPV adjusts for risk and Real Options captures flexibility, Monte Carlo generates the full distribution of possible outcomes.

4.1 The Simulation Architecture

My research established a Monte Carlo framework with four correlated risk dimensions:

graph TB
    subgraph "Risk Inputs"
        T[Technical Risk
Model Performance
Data Quality
Integration Complexity]
        O[Organizational Risk
Change Resistance
Skill Gaps
Process Alignment]
        M[Market Risk
Competitive Dynamics
Customer Adoption
Regulatory Change]
        F[Financial Risk
Cost Overruns
Benefit Delays
Resource Constraints]
    end
    
    subgraph "Monte Carlo Engine"
        T --> MC[10,000 Iterations]
        O --> MC
        M --> MC
        F --> MC
    end
    
    subgraph "Output Distributions"
        MC --> NPV[NPV Distribution]
        MC --> IRR[IRR Distribution]
        MC --> PT[Payback Distribution]
        MC --> VaR[Value at Risk]
    end

Figure 4: Monte Carlo Simulation Architecture

4.2 Correlation Structure

A critical insight from my empirical analysis is that AI project risks are correlated. Technical failures often trigger organizational resistance, which amplifies market adoption challenges. I model these correlations using a Cholesky decomposition of the following correlation matrix:

4.3 Simulation Parameters

Each risk dimension is modeled with appropriate probability distributions derived from the 127-project empirical dataset:

4.4 Case Study: Healthcare Diagnostic AI

I applied the full Monte Carlo framework to a diagnostic imaging AI project for a healthcare network (engagement through Capgemini’s healthcare practice). The project aimed to deploy AI-assisted diagnosis for chest X-rays across 14 hospitals.

The simulation reveals that while the expected NPV is positive (€2.1 million), there is a 37.6% probability of value destruction. The 5th percentile outcome shows potential losses of €5.2 million — information completely obscured by traditional single-point analysis.

4.5 Value at Risk Analysis

For risk-averse organizations, I calculate AI-specific Value at Risk (VaR) metrics:

• AI-VaR(95): The loss that will not be exceeded with 95% confidence
• AI-CVaR(95): The expected loss given that losses exceed VaR(95)

For the healthcare project:

• AI-VaR(95) = €5.2 million
• AI-CVaR(95) = €6.8 million

These metrics enable direct comparison with alternative investments and inform capital allocation decisions. For deeper analysis of healthcare AI economics, see my Cost-Benefit Analysis for Ukrainian Hospital AI Implementation.

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5. Portfolio Optimization for AI Investments

Individual project analysis, however sophisticated, misses the portfolio effects that emerge when organizations pursue multiple AI initiatives simultaneously. The fourth component of the framework addresses portfolio-level optimization.

5.1 The Portfolio Challenge

In my consulting experience, organizations rarely consider AI investments in isolation. A typical enterprise might simultaneously pursue:

• 2-3 “moonshot” projects with high potential but low probability
• 5-7 “core” projects with moderate risk and return profiles
• 10-15 “incremental” projects with lower potential but higher certainty

5.2 Mean-Variance Optimization for AI

I adapt Markowitz portfolio theory to AI investments, with modifications for the unique characteristics of AI projects:

Maximize: E[Rp] - (λ/2) × Var[Rp]

Subject to:
Σ wi = 1 (fully invested)
wi ≥ 0 (no short positions)
Σ Ii × wi ≤ Budget (capital constraint)
n_moonshot ≤ 0.25 × n_total (diversification constraint)

Where:
- E[Rp] = Expected portfolio return
- Var[Rp] = Portfolio variance
- λ = Risk aversion parameter
- wi = Weight allocated to project i
- Ii = Investment required for project i

5.3 Correlation Benefits in AI Portfolios

Unlike financial assets, AI projects can exhibit both positive and negative correlations depending on their relationship:

Optimal portfolios include complementary projects to reduce overall variance while maintaining expected returns.

5.4 Case Study: Multi-Industry AI Portfolio

A European conglomerate with operations in manufacturing, retail, and logistics approached my team for portfolio-level AI investment planning. They had identified 23 potential AI initiatives with a combined investment requirement of €47 million, against a budget of €20 million.

The optimized portfolio achieves:

• Expected total NPV: €24.3 million
• Portfolio standard deviation: €6.8 million (vs. €9.2M without optimization)
• Sharpe Ratio: 0.47 (vs. 0.31 for equal-weighted selection)
• Probability of positive returns: 78% (vs. 64% for equal-weighted)

pie title "Optimal AI Portfolio Allocation"
    "Moonshot Projects" : 24
    "Core Projects" : 51
    "Incremental Projects" : 25

Figure 5: Optimized Portfolio Composition

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6. Integration: The Decision Protocol

The four framework components integrate into a unified decision protocol that I have implemented across multiple enterprise engagements:

6.1 The Five-Step Protocol

graph TD
    A[1. Initial Screening
Basic viability check] --> B{Pass?}
    B -->|No| C[Reject]
    B -->|Yes| D[2. RA-NPV Analysis
Risk-adjusted valuation]
    D --> E{RA-NPV > 0?}
    E -->|No| F[Restructure or Reject]
    E -->|Yes| G[3. Real Options Valuation
Stage flexibility value]
    G --> H{ROV > RA-NPV × 1.2?}
    H -->|No| I[Consider staged approach]
    H -->|Yes| J[4. Monte Carlo Simulation
Full distribution analysis]
    J --> K{P NPV>0 > 55%?
VaR acceptable?}
    K -->|No| L[Risk mitigation or Reject]
    K -->|Yes| M[5. Portfolio Optimization
Cross-project effects]
    M --> N{Improves portfolio
risk-return?}
    N -->|No| O[Defer or Reject]
    N -->|Yes| P[APPROVE with Stage Gates]

Figure 6: Integrated Decision Protocol

6.2 Decision Criteria Summary

6.3 Governance Structure

The framework requires appropriate governance to function effectively:

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7. Empirical Validation

The framework has been applied to 127 AI projects across manufacturing, financial services, and healthcare sectors. This section presents validation results.

7.1 Methodology

I tracked projects through the framework over a 36-month period, comparing outcomes against projections. Projects were categorized by whether they followed the full framework or used traditional analysis methods.

7.2 Results

The most striking finding is not the higher success rate (42% vs. 18%), but the quality of abandonments. Framework-guided projects that were abandoned demonstrated “valuable failure” — they were terminated at early stages, preserving capital for redeployment. Traditional projects often continued to completion despite deteriorating economics, destroying value.

7.3 Statistical Significance

Applying a two-sample t-test to risk-adjusted returns:

• t-statistic: 4.72
• p-value: < 0.001
• Cohen’s d: 0.89 (large effect size)

The framework demonstrates statistically significant improvement in investment outcomes.

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8. Practical Implementation

For practitioners seeking to implement this framework, I provide actionable guidance based on my implementation experience.

8.1 Quick-Start Version

Organizations new to structured AI investment analysis can begin with a simplified version:

8.2 Common Implementation Errors

Based on observations across multiple organizations:

8.3 Tools and Templates

I have developed tools implementing the framework components:

• RA-NPV Calculator (linked from Cost-Effective AI Development analysis)
• Real Options Valuation Template
• Monte Carlo Simulation Engine
• Portfolio Optimization Dashboard

These tools are available through the Stabilarity Research Hub’s enterprise AI resources section.

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9. Advanced Topics

9.1 Dynamic Option Exercise

The basic real options framework treats exercise decisions as static. In practice, optimal exercise timing depends on resolved uncertainty. I model this using stochastic dynamic programming:

V(t, S) = max{Exercise Value, (1+r)^(-1) × E[V(t+1, S')]}

This Bellman equation captures the trade-off between immediate exercise (proceeding to next stage) and waiting to resolve additional uncertainty.

9.2 Learning Effects

AI projects generate learning that benefits subsequent initiatives. I model this as:

Cost(project n) = Cost(project 1) × n^(-b)

Where b is the learning rate (empirically 0.15-0.25 for AI projects). This learning curve effect should be incorporated into portfolio valuation, particularly for organizations building AI capabilities.

9.3 Strategic Option Value

Beyond financial returns, AI investments create strategic options — the ability to pursue future opportunities that would be inaccessible without the capability. Valuing these strategic options requires scenario analysis of future competitive landscapes (a topic I explored in Anticipatory Intelligence research).

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10. Limitations and Future Research

10.1 Framework Limitations

Empirical base: The 127-project dataset, while substantial, represents primarily European enterprises. Generalization to other markets requires additional validation.

Parameter stability: Risk parameters may shift as AI technology matures. The framework requires periodic recalibration.

Organizational factors: The framework quantifies but cannot resolve organizational dysfunction. Companies with poor AI governance will still struggle regardless of analytical sophistication.

Emerging architectures: The rapid evolution of AI architectures (particularly generative AI) may invalidate some calibrated parameters. The framework requires continuous updating.

10.2 Future Research Directions

My ongoing research addresses:

1. Real-time risk recalibration using Bayesian updating as project data accumulates
2. Multi-agent simulation of competitive AI investment dynamics
3. Regulatory impact modeling particularly for EU AI Act compliance costs (building on the Failed Implementations analysis)
4. Cross-industry transfer of risk parameters and success factors

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11. Conclusion

The economic framework presented in this article addresses a critical gap in enterprise AI investment analysis. Traditional capital budgeting methods systematically underestimate AI project risks while failing to capture the option value embedded in staged development approaches.

The integrated framework — combining Risk-Adjusted NPV, Real Options Valuation, Monte Carlo Simulation, and Portfolio Optimization — provides a rigorous foundation for AI investment decisions. Empirical validation across 127 projects demonstrates that organizations applying this framework achieve 2.3× higher risk-adjusted returns compared to traditional analysis methods.

The framework does not guarantee success — the fundamental uncertainties of AI remain. But it enables organizations to make decisions that are calibrated to these uncertainties, preserving capital for the highest-potential opportunities while avoiding the systematic overinvestment that explains the documented 80-95% failure rate.

In the next article, I will apply this framework specifically to Total Cost of Ownership (TCO) models for enterprise AI, providing detailed cost structures for the full AI lifecycle.

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References

1. Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin. https://doi.org/10.1017/CBO9780511817410
2. Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654. https://doi.org/10.1086/260062
3. Dixit, A. K., & Pindyck, R. S. (1994). Investment under Uncertainty. Princeton University Press. https://doi.org/10.1515/9781400830176
4. Trigeorgis, L. (1996). Real Options: Managerial Flexibility and Strategy in Resource Allocation. MIT Press. ISBN: 978-0262201025
5. Copeland, T., & Antikarov, V. (2003). Real Options: A Practitioner’s Guide. Thomson Texere. ISBN: 978-1587991868
6. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974
7. Kaplan, R. S., & Norton, D. P. (2004). Strategy Maps: Converting Intangible Assets into Tangible Outcomes. Harvard Business School Press. ISBN: 978-1591391340
8. McGrath, R. G., & MacMillan, I. C. (2000). Assessing Technology Projects Using Real Options Reasoning. Research-Technology Management, 43(4), 35-49. https://doi.org/10.1080/08956308.2000.11671367
9. Amram, M., & Kulatilaka, N. (1999). Real Options: Managing Strategic Investment in an Uncertain World. Harvard Business School Press. ISBN: 978-0875848457
10. Damodaran, A. (2000). The Promise of Real Options. Journal of Applied Corporate Finance, 13(2), 29-44. https://doi.org/10.1111/j.1745-6622.2000.tb00052.x
11. Fichman, R. G., Keil, M., & Tiwana, A. (2005). Beyond Valuation: “Options Thinking” in IT Project Management. California Management Review, 47(2), 74-96. https://doi.org/10.2307/41166296
12. Benaroch, M., & Kauffman, R. J. (1999). A Case for Using Real Options Pricing Analysis to Evaluate Information Technology Project Investments. Information Systems Research, 10(1), 70-86. https://doi.org/10.1287/isre.10.1.70
13. Schwartz, E. S., & Trigeorgis, L. (2001). Real Options and Investment under Uncertainty: Classical Readings and Recent Contributions. MIT Press. ISBN: 978-0262194464
14. Jorion, P. (2006). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill. ISBN: 978-0071464956
15. Rockafellar, R. T., & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-42. https://doi.org/10.21314/JOR.2000.038
16. Gartner. (2024). Magic Quadrant for Data Science and Machine Learning Platforms. Gartner Research.
17. McKinsey & Company. (2024). The State of AI in 2024: A Year of Transformation. McKinsey Global Institute.
18. Ransbotham, S., et al. (2020). Expanding AI’s Impact With Organizational Learning. MIT Sloan Management Review.
19. Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-Powered Organization. Harvard Business Review, 97(4), 62-73.
20. Deloitte. (2023). State of AI in the Enterprise (5th ed.). Deloitte Insights.
21. European Commission. (2024). The EU AI Act: A Risk-Based Approach to Artificial Intelligence Regulation.
22. OECD. (2023). Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications. OECD Digital Economy Papers, No. 341. https://doi.org/10.1787/7babf571-en
23. IEEE. (2021). IEEE 7010-2020: Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being. https://doi.org/10.1109/IEEESTD.2020.9084219
24. Sculley, D., et al. (2015). Hidden Technical Debt in Machine Learning Systems. NeurIPS 2015.
25. Amershi, S., et al. (2019). Software Engineering for Machine Learning: A Case Study. ICSE-SEIP 2019. https://doi.org/10.1109/ICSE-SEIP.2019.00042
26. Polyzotis, N., et al. (2018). Data Lifecycle Challenges in Production Machine Learning. ACM SIGMOD Record, 47(2), 17-28. https://doi.org/10.1145/3299887.3299891
27. Paleyes, A., Urma, R. G., & Lawrence, N. D. (2022). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys, 55(6), 1-29. https://doi.org/10.1145/3533378
28. Lwakatare, L. E., et al. (2019). A Taxonomy of Software Engineering Challenges for Machine Learning Systems. XP 2019. https://doi.org/10.1007/978-3-030-19034-7_14
29. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185
30. Wright, T. P. (1936). Factors Affecting the Cost of Airplanes. Journal of the Aeronautical Sciences, 3(4), 122-128. https://doi.org/10.2514/8.155
31. Flyvbjerg, B. (2006). From Nobel Prize to Project Management: Getting Risks Right. Project Management Journal, 37(3), 5-15. https://doi.org/10.1177/875697280603700302
32. Ross, J. W., Beath, C. M., & Mocker, M. (2019). Designed for Digital: How to Architect Your Business for Sustained Success. MIT Press. ISBN: 978-0262042888
33. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press. ISBN: 978-1625272478
34. Ng, A. (2016). What Artificial Intelligence Can and Can’t Do Right Now. Harvard Business Review Digital Articles.
35. Bughin, J., et al. (2018). Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. McKinsey Global Institute.

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