Strategic Leadership for AI-Powered Business Transformation
Essential Skills for Business Leaders
Key points to share:
1. AI Strategy and Competitive Advantage
Content Focus: How to move beyond pilot projects to a unified, enterprise-wide AI strategy.
Key Learning Outcomes: Understanding where AI creates new business models (not just incremental efficiency). Leaders will learn to identify and prioritise high-value AI use cases that align with the 3-year growth targets (e.g., the 10-30% profitability target mentioned in ACI's plan).
Case Study: Analysing companies that successfully used AI for market differentiation vs. those who only used it for cost-cutting.
2. AI Governance and Risk Management (Responsible AI)
Content Focus: The executive's role in governing AI and mitigating ethical, legal, and operational risks.
Key Learning Outcomes: Implementing a Responsible AI (RAI) framework (like RaiDOT.ai) to manage bias, ensure transparency (XAI), and maintain data privacy. Training on the five key cyberattack risks on AI systems (e.g., data poisoning, model theft) and the corresponding resilience strategies.
Actionable Takeaway: Establishing an AI Governance Committee and defining audit procedures before model deployment.
3. Data Ecosystem as a Strategic Asset
Content Focus: Viewing data not as a technical byproduct but as the core strategic asset for AI.
Key Learning Outcomes: Understanding the necessity of integrating real-time ERP, CRM, and contextual data (weather, economic indicators, social sentiment) to enable dynamic predictive models. Focus on data acquisition strategy, data quality standards, and building a unified, cloud-native data platform.
Concept: The importance of "data literacy" among non-technical leaders to support data-driven investment decisions.
4. Leading Organisational and Cultural Transformation
Content Focus: The human and cultural challenges of implementing AI—a focus on leadership, not technology.
Key Learning Outcomes: Strategies for upskilling and reskilling the workforce (creating "AI Champions"). How to structure agile, cross-functional teams ("AI Squads") for rapid development and deployment. Overcoming organisational resistance and fostering a "fail-fast, learn-faster" innovation culture.
Actionable Takeaway: Defining a clear change management communication plan to transition from traditional processes to AI-powered workflows.
5. Measuring, Scaling, and Commercialising AI
Content Focus: Establishing an ROI-driven approach for all AI investments and scaling proven pilots.
Key Learning Outcomes: Defining the right KPIs for AI success (e.g., reduced time-to-decision, increase in sales uplift from recommendations, or cost savings from automation). Leaders will learn how to identify models with local success (like the Demand Forecasting Tool) and structure them for global scale-up and commercialisation (IP sharing, co-creation models).
Tool: Using an ROI framework to continuously evaluate the value delivered by the AI portfolio and justify ongoing investment.
Date and Time:
30th of October 2025
10:00 AM - 12:00 PM GMT
04:00 PM – 06:00 PM BD Time
Speaker:
Prof. Dr Alamgir Hossain, CEO, D-Ready, Durham, UK, AI Advisor ACI Ltd, Prof of AI for Research and Innovation, BUHS, BD. Former Professor of AI, Vice President, & Led Research Centres/ Institutes in several UK & International universities.
