To help analysts do BA4AI, I created the FABR Framework (Five AI Business Risks Framework) — a practical tool that helps identify, analyze, and mitigate the main risks associated with AI initiatives. In this article, I’ll illustrate its application through four real-world case studies.
#BA4AI (Business Analysis for Artificial Intelligence) is a movement that invites business analysts to take the lead in shaping the adoption of AI. Instead of reacting to innovation, analysts must connect technology capabilities to business goals, anticipate risks, and ensure AI solutions deliver real value to stakeholders.
Artificial Intelligence is transforming how organizations operate, but success doesn’t come from the technology itself — it comes from how people use it to generate meaningful outcomes.
These 4 case studies exemplify how business analysis professionals can define expected outcomes for AI transformation initiatives and what strategies they could implement for each identified risk.
🏙️ Case 1 – Smart City Surveillance
Scope (Outputs): Implement an AI-powered camera network to detect crimes and dangerous situations in real time.
Expected Outcomes: Faster police response, reduced incidents, and safer communities.
Risks and Analyst Strategies
- 🧮 Decision Errors: False alarms could lead to unnecessary interventions or missed emergencies.
→ Validate models with real-world data and ensure human confirmation before action. - ⚖️ Bias & Discrimination: The system may unfairly target specific groups due to biased training data.
→ Ensure diverse datasets and perform fairness audits regularly. - 🔐 Privacy Violation: Citizens’ images and movements may be recorded without consent.
→ Apply strict data governance, anonymization, and transparency policies. - 🤖 Over-Reliance on AI: Authorities might act automatically on system alerts.
→ Keep human supervision in all critical responses. - 🧩 Explainability & Accountability: Residents may question why certain actions were taken.
→ Document decision logic and communicate how AI supports public safety.
💡 Insight: Security without ethics becomes surveillance.
🎓 Case 2 – AI in Education Assessment
Scope (Outputs): Deploy AI tools to automatically grade student exams and assignments.
Expected Outcomes: Fairer, faster, and more consistent evaluations to improve learning.
Risks and Analyst Strategies
- 🧮 Decision Errors: Misinterpretation of creative or subjective answers.
→ Train models with diverse examples and maintain manual review options. - ⚖️ Bias & Discrimination: Different writing styles or accents may affect AI grading.
→ Calibrate models to represent varied linguistic and cultural backgrounds. - 🔐 Privacy Violation: Students’ performance data could be misused.
→ Ensure secure data storage and informed consent from families. - 🤖 Over-Reliance on AI: Teachers may lose their role in evaluating progress holistically.
→ Design a hybrid model where AI supports, not replaces, human judgment. - 🧩 Explainability & Accountability: Students deserve to know why they received a grade.
→ Provide transparent grading criteria and accessible explanations.
💡 Insight: Fairness in education requires both precision and empathy.
🏦 Case 3 – AI for Credit Scoring
Scope (Outputs): Implement an AI system to automate credit risk assessment and loan approval.
Expected Outcomes: Faster loan processing, improved accuracy, and reduced default rates.
Risks and Analyst Strategies
- 🧮 Decision Errors: The system may incorrectly reject or approve loan applications.
→ Continuously monitor accuracy and apply human review for edge cases. - ⚖️ Bias & Discrimination: Historical data may reflect systemic inequality.
→ Audit data sources and test for fairness across demographics. - 🔐 Privacy Violation: Sensitive financial data could be leaked or reused.
→ Apply encryption, consent policies, and secure model training. - 🤖 Over-Reliance on AI: Agents may stop questioning automated outcomes.
→ Encourage validation processes and independent human oversight. - 🧩 Explainability & Accountability: Customers should understand why credit was denied.
→ Implement explainable AI tools and document decision criteria.
💡 Insight: Trust in finance depends on transparency and accountability.
🌍 Case 4 – AI Travel Concierge
Scope (Outputs): Develop a virtual assistant to plan trips — booking flights, hotels, restaurants, and events.
Expected Outcomes: Personalized, seamless travel experiences with minimal user effort.
Risks and Analyst Strategies
- 🧮 Decision Errors: Booking wrong dates or destinations.
→ Add confirmation checkpoints and feedback loops. - ⚖️ Bias & Discrimination: Recommending only certain brands or locations.
→ Ensure diverse recommendation sources and neutral algorithms. - 🔐 Privacy Violation: Leaking personal or payment information.
→ Use strong encryption and data anonymization. - 🤖 Over-Reliance on AI: Users depend entirely on the system, losing flexibility.
→ Allow easy manual adjustments and override options. - 🧩 Explainability & Accountability: Users may not understand why certain options were chosen.
→ Show transparent reasoning and decision factors.
💡 Insight: Convenience must never replace human judgment.
Connecting the Dots with FABR – A Framework for Conscious AI Adoption
Across all four cases, the same five risks appear — sometimes technical, sometimes ethical — but always human in nature.
The FABR Framework helps analysts recognize that building AI responsibly is not only about technology; it’s about understanding context, anticipating consequences, and designing the expected outcomes that align with human values.
🚀 Leading AI the BA Way
As AI becomes part of every business process, business analysts are called to play a new role: not as users of AI, but as designers of how AI is used.
Use the FABR Framework to anticipate risks, shape responsible solutions, and ensure that every AI initiative delivers value that truly matters — outcomes that are fair, transparent, explainable, and aligned with stakeholder needs.
💡 Because the future of AI depends not only on how machines learn — but on how we, as humans, guide them.
References
- The FABR Framework : A Guide to Lead AI Adoption with Safety and Value
- IIBA – International Institute of Business Analysis. (2024). BA4AI: Business Analysis for Artificial Intelligence. The Corner – Special Article.
- Other articles and videos from The Brazilian BA about Artificial Intelligence.
- This article in Portuguese: FABR Framework aplicado: Estudos de Caso
