A capital markets firm is exploring the use of AI to enhance its trading algorithms.

Within PMI-CPMAI’s treatment of AI business cases, the core expectation is that the project manager demonstratesclear, quantifiable value aligned with organizational goals. For a capital markets firm whose objectives are improvedtrading accuracy and profitability, the most suitable method is todevelop a financial impact assessmentthat translates AI benefits into measurable financial terms. This assessment typically compares the current trading performance (baseline) with projected AI-enhanced performance, estimating impacts on revenues, margins, risk-adjusted returns, and operational costs.

PMI’s AI-oriented business case guidance emphasizes that decision makers need a structured view ofcosts, benefits, risks, and assumptions, expressed in financial metrics such as net benefit, payback period, ROI, or expected value under uncertainty. Market trend analyses and vendor consultations can inform context and options but do not directly quantify how the AI solution improves trading results. Scenario analysis can support stress testing and complement the financial view, yet the central artifact that “meets the firm’s goals and objectives” for funding decisions is afinancial impact assessmenttied to accuracy and profitability. Thus, the method that best satisfies the firm’s needs isdeveloping a financial impact assessment.