Six CIOs/CTOs/CDOs from India, Sri Lanka, Uganda, Nigeria and Ecuador delve into driving smarter decisions using Gen AI:
Generative AI is rapidly moving beyond tactical applications to become a powerful tool for strategic decision-making. By harnessing the ability to analyze massive datasets, simulate scenarios, and predict future trends, AI/ML solutions are empowering business leaders to make smarter, faster, and more informed choices. But realizing this potential requires a strategic approach. Executives across industries share their insights on the key success factors for leveraging AI in the C-suite.
Alignment, Data, and Risk: Foundational Pillars
Isaiah Etuk, Chief Digital Officer at Capital Express Life Assurance, emphasizes the critical importance of aligning AI initiatives with specific, high-impact strategic goals, such as market expansion, product diversification, or major capital investments. He stresses the need for measurable targets, like a 30% reduction in operational costs, to ensure focus and demonstrate ROI. Etuk underscores the crucial role of high-quality data and regulatory compliance under the Nigeria Data Protection Regulation (NDPR), noting that strong data governance not only improves model performance but also builds stakeholder trust. He cites a specific example of data cleansing improving model accuracy. Finally, he highlights process optimization and proactive risk management, including bias detection frameworks and collaboration with NAICOM, as essential for maintaining operational integrity and ensuring the reliability of AI-driven insights. He points to NAICOM’s mandatory data API for Nigerian insurance companies as an example of increasing regulatory engagement with data and AI, noting its mandatory nature for 100% of insurance companies in Nigeria.
Data Integration, Scenario Simulation, and Continuous Learning
Bhagvan Kommadi, CIO of Capri Global Capital, highlights data integration and analysis as a key success factor. He emphasizes the power of AI/ML models to uncover valuable insights from diverse data sources, facilitated by creating a data lake for advanced dashboarding, reporting, and analytics, supporting informed strategic decisions. Kommadi also stresses the importance of scenario simulation, enabling decision-makers to evaluate potential outcomes and assess risks through AI-powered “what-if” analyses. He provides a more specific example of how this helps preparedness for various challenges. Finally, he emphasizes continuous learning and adaptation, noting that generative AI/ML models require ongoing training with new data to remain relevant in dynamic market environments. He highlights the importance of feedback loops that incorporate unseen scenarios to ensure models evolve for greater accuracy and adaptability.
Strategic Alignment, Data Quality, and Scenario Planning
Manav Mengi, CIO of Diamond Trust Bank in Uganda, reiterates the importance of aligning AI/ML models with strategic objectives and stakeholder collaboration. He emphasizes the need for data-driven insights based on high-quality, diverse, and well-curated datasets and the use of AI for scenario simulation and predictive capabilities. He gives specific considerations for success: training AI models on high-quality, industry-relevant data and incorporating internal and external datasets for broader contextual analysis.
He provides examples of using AI-based application monitoring tools to track model performance, detect anomalies, and mitigate risks like model drift, ensuring continuous performance and accurate insights. He notes the importance of open communication channels between stakeholders to refine AI strategies over time. He provides examples of using AI for real-time monitoring, market expansion analysis (analyzing regional economic indicators, customer demographics, and competitive landscapes), and simulating the impact of interest rate changes on loan portfolios, providing strategic foresight. He also mentions using predictive analytics to enable cross-selling and upselling opportunities to the existing customer base.
Data-Driven Insights, Scenario Planning, and Real-Time Decision-Making
Mithila Abeysekara, Chief Technology & Digital Officer at First Capital Holdings in Sri Lanka, emphasizes the transformative power of AI-driven decision support tools. He highlights data-driven insights derived from processing vast amounts of data, citing JPMorgan Chase’s use of AI to predict market trends. He also highlights scenario planning and simulation to assess risks and rewards, and real-time decision-making capabilities for navigating fast-paced financial environments, citing Citigroup’s use of AI in high-frequency trading.
Business Needs, Monitoring, and Teamwork
Carlos Cordova, Chief Digital and Data Officer at a leading insurance company in Ecuador, emphasizes the importance of accurately identifying business needs through close collaboration with business units, mentioning workshops conducted to understand their strategic decision-making processes. He highlights the value of a robust monitoring and evaluation framework, recalling a CEO emphasizing the importance of focusing on models that truly deliver results. Cordova also stresses the importance of teamwork and cross-functional collaboration, particularly with actuarial and technical departments, to validate progress and the insights generated by AI models. He mentions the actuarial team’s support in developing predictive models for claims and cancellations, and the successful delivery of five predictive models over a year.
Data Access, Scenario Modeling, and Explainable AI
Saranga Wijayarathne, Chief Digital Innovation Officer at Softlogic Life, Sri Lanka, prioritizes high-quality data access from diverse sources, enabling analysis of claims patterns and demographic data. He also highlights scenario modeling and simulations for evaluating potential outcomes and risks, and emphasizes the importance of explainable AI and trust in AI outputs for high-stakes decisions, ensuring transparency and interpretability.
The Bottom Line
The consensus is clear: AI is no longer a futuristic concept but a strategic imperative for businesses looking to thrive in today’s complex and rapidly changing world. By focusing on strategic alignment, data quality, scenario planning, and continuous learning, organizations can unlock the full potential of AI-powered decision-making and gain a significant competitive edge.
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