As AI moves from experimentation to business-enabling capabilities, companies are committing billions of dollars to initiatives that promise transformative impact. Yet many leadership teams struggle to answer a simple but fundamental question: what data do we actually have to leverage these opportunities effectively? Without clarity, accessibility, and understanding of data quality and lineage, AI investments risk underperformance, duplication of effort, and missed opportunities for value creation.
While awareness of AI readiness is growing at the board level, the pace of AI investment often outstrips organisations’ understanding of their own data landscape. Across many enterprises, critical datasets remain siloed, duplicated, or underutilised. In some cases, organisations unknowingly purchase the same external data multiple times, wasting resources and creating inefficiencies that erode both budgets and operational speed. The FT Board Director Programme has highlighted the importance of these questions, urging directors to understand how prepared their organisations truly are – and to act before gaps in readiness undermine the promise of AI.
The boardroom challenge
Boards must understand that AI is a tool to deliver business outcomes, not a strategy in itself. AI initiatives succeed only when data is treated as a strategic asset: complete, accessible, and governed. Leadership teams need to focus on how AI contributes to business results, rather than chasing AI as an end goal.
Siloed or inconsistent data is one of the most common barriers. We have seen, for example, in two different financial services companies, multiple databases held overlapping information about customers. When introducing “next best action” recommendations, systems struggled to identify that a customer in one system (mortgages) was the same as a customer in another (insurance), resulting in inappropriate offers being made. Similar patterns occur in pharma, where separate research teams license the same external dataset multiple times at millions of dollars each, or with location/geographic data pulled into different technology stacks, creating redundancy and inefficiency.
Duplicated datasets are not just a cost issue; awareness of data quality and currency – how fresh and complete the data is – is equally crucial. It is less about having the highest-quality or newest data and more about understanding what data goes into AI models, so the outputs are trustworthy and meaningful. Without this understanding, AI outputs risk inconsistency, undermining executive confidence in insights and slowing adoption.
What does AI-ready data look like?
AI-ready data is clean, discoverable, and structured to support reliable AI outputs and business decisions. Beyond accessibility, boards must also ensure data is governable and auditable, meaning its use is controlled, traceable, and compliant with governance frameworks. Simply making data discoverable is not enough; boards must maintain visibility of how data is being accessed and used.
Key characteristics include:
Accessibility, governable, and auditable: Data should be discoverable by authorised users across the organisation, with strong governance around access and usage. Spreadsheets or untracked data, even if accessible, fail the auditable test.
High-quality and consistency: AI-ready data should be cleansed, de-duplicated, and standardised. Metrics such as completeness (% of required fields populated), accuracy (% of records passing validation rules), consistency (% of records matching across systems), and timeliness (% of data refreshed within agreed timeframes) allow boards to measure readiness.
Alignment with strategic priorities: Organisations should prioritise datasets that contribute most to AI-enabled business outcomes.
Data lineage: Knowing where data came from, how it has been transformed, and its governance history is crucial for reliability, regulatory compliance (e.g., EU AI Act), and operational trust.
Understanding what’s missing: Awareness of gaps or underrepresented areas ensures AI models are representative and less prone to bias, improving ethical and accurate decision-making.
Practical implications for boards
Boards can translate AI readiness into measurable actions by focusing on three areas:
Eliminating duplicate spend: Identify redundant internal or external datasets, ensuring that resources are not wasted on multiple subscriptions or overlapping storage. Centralising or standardising data does not mean gathering everything into a single team; tools like Harbr Data enable cross-team collaboration while maintaining governance frameworks, access controls, and shared infrastructure.
Prioritising high-value datasets: Identify which datasets drive the most strategic impact and focus investment on improving quality, accessibility, and readiness for AI initiatives.
Linking data strategy to measurable outcomes: Beyond anecdotal progress, boards can track KPIs such as:
These metrics create visibility and accountability, enabling boards to evaluate AI-readiness and the true effectiveness of investments.
The hidden cost of inaction
Failing to establish AI-ready data carries significant operational, financial, and regulatory risks. Poorly governed or incomplete data leads to suboptimal AI outcomes, wasted resources, and delayed insights. Competitors with disciplined data governance can gain strategic advantage.
A growing concern is “shadow AI” – employees using unapproved AI tools with sensitive company data to meet business needs. Without governed, auditable, and AI-ready data, companies risk losing visibility, control, and compliance, while accelerating exposure to reputational and regulatory harm.
Regulatory frameworks, such as the EU AI Act, increasingly mandate that high-risk AI systems use data that is relevant, representative, free of errors, and complete. Boards must ensure data readiness to comply and to enable AI adoption that drives real outcomes.
Leading organisations restructure data governance practices by centralising data catalogues, standardising metadata, and implementing audit processes – not to silo data in a single team, but to facilitate collaboration across domain experts and maintain governance, standards, and access controls. This approach creates AI-ready data products, ready to power strategic initiatives.
Preparing for the next wave of AI
The next wave – agentic AI – will require autonomous systems capable of real-time decision-making across multiple data sources. Systems will need to evaluate data quality, relevance, and lineage on the fly. Companies unprepared today with human-analyst-ready data will struggle to adopt these capabilities tomorrow.
The era of “winning by volume” is ending; success will depend on data readiness, not the sheer quantity of data. Boards that prioritise AI-ready data now position their organisations for sustainable, scalable, and ethical AI-driven transformation.
