AI and Data Strategy

• Business Alignment: Tie your AI initiatives directly to specific, measurable outcomes like reducing operational costs by 30% or increasing customer satisfaction scores.
• Scalable Data Architecture: Move toward “Lakehouse” or hybrid multicloud environments that handle massive, unstructured datasets—such as emails, call transcripts, and research papers—which fuel modern generative AI.
• Operationalized Governance: Shift from passive policy-making to a “control layer” that ensures AI is explainable, ethically grounded, and compliant with regulations like the EU AI Act or India’s DPDPA.
• Execution-First Mindset: Prioritize “Agentic AI” that doesn’t just summarize data but executes workflows, such as automatically adjusting supply chains or processing insurance claims.

• Decision Intelligence: Shift from looking at “what happened” in a dashboard to receiving prescriptive recommendations on “what to do next”.
• Operational Efficiency: Automate 75% of manual tasks, such as creating retail listings or handling 94% of common HR inquiries.
• Hyper-Personalization: Leverage customer behavioral data to provide real-time, 24/7 personalized shopping experiences that can drive up to 35% of revenue.

• Middle management barriers
• Excessive caution
• Legacy systems and complexity
• Fragmented strategy and ownership
• Cultural inertia and outdated processes

In a recent BDO survey of more than 1000 C-suite executives around the world, the respondents said one of the biggest restrictions to resilience and getting the maximum benefit out of AI is messy and siloed data.

Strong data governance is essential to the resilience and adaptability of an organisation’s systems – and to enable it to develop the capacity to respond to change.

In practice many organisations will end up with the same sort of generative AI using large language models (LLMs) as their competitors, but what will set them apart and give them a competitive advantage is the quality of the data they feed into those LLMs.

Much attention is usually placed on the financial, product and customer master data held in your ERP systems, but your unstructured data (such as emails, reports, data sheets, process definitions, etc.) is often the forgotten part of the ecosystem but can make up around 80 percent of an organisation’s information. …this provides your GenAI the context for your business.

The most important thing is that chief executives acknowledge that someone needs to own it, and identify the best person to do that in their particular organisation – it shouldn’t by default be the CIO.

1. Assess Maturity: Audit your current data quality, accessibility, and lineage to identify gaps that could “crush” legacy systems under AI workloads.
2. Define Use Cases: Use Value-vs-Feasibility Scoring to prioritize high-impact, low-risk “quick wins” before scaling to complex enterprise transformations.
3. Build a Data Culture: Invest in Data Literacy Training for cross-functional teams, ensuring business leaders and data scientists speak the same language.
4. Iterate and Optimize: Implement MLOps to continuously monitor model performance and data drift, ensuring your AI remains accurate over time.