Artificial Intelligence in Feasibility Studies: Enhancing Business Decisions Through Data-Driven Analysis

Artificial Intelligence (AI) is transforming feasibility studies by improving the speed, accuracy, and quality of business analysis. Instead of relying only on historical data and manual evaluation, organizations now use AI to analyze large datasets, identify patterns, generate forecasts, and simulate alternative business scenarios. With more than 70% of organizations already applying AI in at least one business function, AI has become an essential tool for evidence-based feasibility assessment.

1. AI Technologies in Administrative Feasibility

AI enhances administrative feasibility by supporting organizational planning, workforce analysis, and operational management. Machine Learning (ML) predicts staffing needs and optimizes resource allocation, while Natural Language Processing (NLP) analyzes policies, contracts, and regulatory documents. Generative AI tools such as Microsoft Copilot and ChatGPT assist in drafting organizational structures, preparing project documentation, and automating routine administrative tasks, improving efficiency and managerial decision-making.

2. AI Technologies in Technical Feasibility

Technical feasibility evaluates the resources required to establish and operate a project. AI improves estimates of construction costs, equipment and machinery requirements, operating expenses, and implementation schedules. Machine Learning models generate more accurate cost estimates from historical projects, while Generative AI supports equipment specification, supplier comparison, and procurement planning. Predictive Analytics also evaluates implementation scenarios and identifies potential cost overruns before project execution.

3. AI Technologies in Marketing Feasibility

AI strengthens marketing feasibility by analyzing customer behavior, market trends, and future demand. Machine Learning forecasts sales and identifies target customer segments, while NLP analyzes customer reviews and social media sentiment. Recommendation algorithms, widely used by Amazon and Netflix, help evaluate pricing strategies, product positioning, and promotional opportunities, enabling more accurate market assessments.

4. AI Technologies in Financial Feasibility

Financial feasibility benefits from AI through revenue forecasting, cash flow prediction, investment appraisal, and risk analysis. Machine Learning and Predictive Analytics estimate financial performance under different scenarios, while Anomaly Detection identifies unusual financial transactions and potential risks. Financial institutions also use AI for credit scoring, fraud detection, and portfolio

optimization, producing more reliable investment evaluations.

AI is reshaping every stage of modern feasibility studies. By combining technologies such as Machine Learning, Natural Language Processing, Generative AI, Predictive Analytics, and Recommendation Systems, organizations can prepare faster, more accurate, and data-driven feasibility studies. As AI adoption continues to grow, these technologies are becoming indispensable tools for reducing uncertainty and supporting smarter investment and business decisions.