AI is not just reshaping how businesses operate — it’s redefining the CFO’s role at the centre of value creation, risk management, and operational leadership.
As stewards of capital, CFOs must cut through the hype and ensure AI investments deliver measurable business returns. As guardians of risk and compliance, they must shield their organisations from new threats — from algorithmic bias to data privacy breaches with heavy financial and reputational costs. And as leaders of their function, CFOs now have a generational opportunity to modernise finance, champion AI adoption, and build teams ready for an AI-powered future.
LEAD WITH RIGOUR. SAFEGUARD WITH VIGILANCE. CHAMPION WITH VISION.
That’s the CFO playbook for AI success.
Click here to download “AI Stakeholders: The Finance Perspective” as a PDF.
1. Investor & ROI Gatekeeper: Ensuring AI Delivers Value
CFOs must scrutinise AI investments with the same discipline as any major capital allocation.
- Demand Clear Business Cases. Every AI initiative should articulate the problem solved, expected gains (cost, efficiency, accuracy), and specific KPIs.
- Prioritise Tangible ROI. Focus on AI projects that show measurable impact. Start with high-return, lower-risk use cases before scaling.
- Assess Total Cost of Ownership (TCO). Go beyond upfront costs – factor in integration, maintenance, training, and ongoing AI model management.
Only 37% of Asia Pacific organisations invest in FinOps to cut costs, boost efficiency, and strengthen financial governance over tech spend.
2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape
AI brings significant regulatory, compliance, and reputational risks that CFOs must manage – in partnership with peers across the business.
- Champion Data Quality & Governance. Enforce rigorous data standards and collaborate with IT, risk, and business teams to ensure accuracy, integrity, and compliance across the enterprise.
- Ensure Data Accessibility. Break down silos with CIOs and CDOs and invest in shared infrastructure that AI initiatives depend on – from data lakes to robust APIs.
- Address Bias & Safeguard Privacy. Monitor AI models to detect bias, especially in sensitive processes, while ensuring compliance.
- Protect Security & Prevent Breaches. Strengthen defences around financial and personal data to avoid costly security incidents and regulatory penalties.
3. AI Champion & Business Leader: Driving Adoption in Finance
Beyond gatekeeping, CFOs must actively champion AI to transform finance operations and build future-ready teams.
- Identify High-Impact Use Cases. Work with teams to apply AI where it solves real pain points – from automating accounts payable to improving forecasting and fraud detection.
- Build AI Literacy. Help finance teams see AI as an augmentation tool, not a threat. Invest in upskilling while identifying gaps – from data management to AI model oversight.
- Set AI Governance Frameworks. Define accountability, roles, and control mechanisms to ensure responsible AI use across finance.
- Stay Ahead of the Curve. Monitor emerging tech that can streamline finance and bring in expert partners to fast-track AI adoption and results.
CFOs: From Gatekeepers to Growth Drivers
AI is not just a tech shift – it’s a CFO mandate. To lead, CFOs must embrace three roles: Investor, ensuring every AI bet delivers real ROI; Risk Guardian, protecting data integrity and compliance in a world of new risks; and AI Champion, embedding AI into finance teams to boost speed, accuracy, and insight.
This is how finance moves from record-keeping to value creation. With focused leadership and smart collaboration, CFOs can turn AI from buzzword to business impact.
In my last Ecosystm Insight, I spoke about the importance of data architecture in defining the data flow, data management systems required, the data processing operations, and AI applications. Data Mesh and Data Fabric are both modern architectural approaches designed to address the complexities of managing and accessing data across a large organisation. While they share some commonalities, such as improving data accessibility and governance, they differ significantly in their methodologies and focal points.
Data Mesh
- Philosophy and Focus. Data Mesh is primarily focused on the organisational and architectural approach to decentralise data ownership and governance. It treats data as a product, emphasising the importance of domain-oriented decentralised data ownership and architecture. The core principles of Data Mesh include domain-oriented decentralised data ownership, data as a product, self-serve data infrastructure as a platform, and federated computational governance.
- Implementation. In a Data Mesh, data is managed and owned by domain-specific teams who are responsible for their data products from end to end. This includes ensuring data quality, accessibility, and security. The aim is to enable these teams to provide and consume data as products, improving agility and innovation.
- Use Cases. Data Mesh is particularly effective in large, complex organisations with many independent teams and departments. It’s beneficial when there’s a need for agility and rapid innovation within specific domains or when the centralisation of data management has become a bottleneck.
Data Fabric
- Philosophy and Focus. Data Fabric focuses on creating a unified, integrated layer of data and connectivity across an organisation. It leverages metadata, advanced analytics, and AI to improve data discovery, governance, and integration. Data Fabric aims to provide a comprehensive and coherent data environment that supports a wide range of data management tasks across various platforms and locations.
- Implementation. Data Fabric typically uses advanced tools to automate data discovery, governance, and integration tasks. It creates a seamless environment where data can be easily accessed and shared, regardless of where it resides or what format it is in. This approach relies heavily on metadata to enable intelligent and automated data management practices.
- Use Cases. Data Fabric is ideal for organisations that need to manage large volumes of data across multiple systems and platforms. It is particularly useful for enhancing data accessibility, reducing integration complexity, and supporting data governance at scale. Data Fabric can benefit environments where there’s a need for real-time data access and analysis across diverse data sources.
Both approaches aim to overcome the challenges of data silos and improve data accessibility, but they do so through different methodologies and with different priorities.
Data Mesh and Data Fabric Vendors
The concepts of Data Mesh and Data Fabric are supported by various vendors, each offering tools and platforms designed to facilitate the implementation of these architectures. Here’s an overview of some key players in both spaces:
Data Mesh Vendors
Data Mesh is more of a conceptual approach than a product-specific solution, focusing on organisational structure and data decentralisation. However, several vendors offer tools and platforms that support the principles of Data Mesh, such as domain-driven design, product thinking for data, and self-serve data infrastructure:
- Thoughtworks. As the originator of the Data Mesh concept, Thoughtworks provides consultancy and implementation services to help organisations adopt Data Mesh principles.
- Starburst. Starburst offers a distributed SQL query engine (Starburst Galaxy) that allows querying data across various sources, aligning with the Data Mesh principle of domain-oriented, decentralised data ownership.
- Databricks. Databricks provides a unified analytics platform that supports collaborative data science and analytics, which can be leveraged to build domain-oriented data products in a Data Mesh architecture.
- Snowflake. With its Data Cloud, Snowflake facilitates data sharing and collaboration across organisational boundaries, supporting the Data Mesh approach to data product thinking.
- Collibra. Collibra provides a data intelligence cloud that offers data governance, cataloguing, and privacy management tools essential for the Data Mesh approach. By enabling better data discovery, quality, and policy management, Collibra supports the governance aspect of Data Mesh.
Data Fabric Vendors
Data Fabric solutions often come as more integrated products or platforms, focusing on data integration, management, and governance across a diverse set of systems and environments:
- Informatica. The Informatica Intelligent Data Management Cloud includes features for data integration, quality, governance, and metadata management that are core to a Data Fabric strategy.
- Talend. Talend provides data integration and integrity solutions with strong capabilities in real-time data collection and governance, supporting the automated and comprehensive approach of Data Fabric.
- IBM. IBM’s watsonx.data is a fully integrated data and AI platform that automates the lifecycle of data across multiple clouds and systems, embodying the Data Fabric approach to making data easily accessible and governed.
- TIBCO. TIBCO offers a range of products, including TIBCO Data Virtualization and TIBCO EBX, that support the creation of a Data Fabric by enabling comprehensive data management, integration, and governance.
- NetApp. NetApp has a suite of cloud data services that provide a simple and consistent way to integrate and deliver data across cloud and on-premises environments. NetApp’s Data Fabric is designed to enhance data control, protection, and freedom.
The choice of vendor or tool for either Data Mesh or Data Fabric should be guided by the specific needs, existing technology stack, and strategic goals of the organisation. Many vendors provide a range of capabilities that can support different aspects of both architectures, and the best solution often involves a combination of tools and platforms. Additionally, the technology landscape is rapidly evolving, so it’s wise to stay updated on the latest offerings and how they align with the organisation’s data strategy.
