In the tax domain, data is not just an asset but an instrument that unlocks value when correctly orchestrated. However, the value may be unrealized without effective data governance and accurate analysis. Tax leaders should harness the right tools, approaches and standards to create business value from data. Businesses should also be aware of the disruption AI will potentially bring.
Tax functions strive for improved processes and technology enhancements that facilitate robust data collection, analysis and conversions into actionable insights. However, they often encounter data-related challenges. What are these challenges, and how can leaders create sustainable solutions?
The tax function should use data to be a value creator for the organization, "reinforcing the concept of being a value driver rather than just a compliance function," says Charles Brayne, EY Global Tax Platform Product Management Leader.
Embracing AI is the key to future innovation
The influence of AI on the contemporary business environment is profound and will be transformative. Those who are able to harness its potential are poised to achieve remarkable results. AI lays the groundwork for extraordinary innovation and expansion when integrated across various business areas, such as tax. As Lyn Bird, Corporate Vice President, Sales Activation & Transformation for Microsoft, "Companies with a strong foundation for AI innovation will excel, and those that don't will fail."
Finance and tax leaders should strategically align their value creation and technical feasibility approach to leverage AI. Understanding the key drivers of value, and aligning AI initiatives with overarching strategic goals, can guide decision-making processes and facilitate improved resource utilization. "Insights emerge when value-driven evaluation and technical viability intersect to deliver on specific use cases," Bird says.
Data is the backbone of AI, playing an instrumental role in unveiling its true capabilities. However, the data's quality, availability and management significantly influence the speed and effectiveness of deriving value from AI initiatives.
Understanding the data engineering required to manage, govern and use the data effectively is essential. Despite AI's massive potential, the human element remains pivotal. As Brayne explains, "The value delivered by tax professionals will only increase if they learn how to harness the power of AI to drive different types of insight and value creation.” For many, this will require the development of new skills, including the ability to manage complex data sets with confidence, as well as the ability to detect situations where the risk of bias in AI-generated insight necessitates human validation and review.
The challenges of data management
The role of structured and unstructured data in AI cannot be overstated. According to Richard Clough, EY Global Tax Chief Data Officer, "A tax function is probably 80% a data consumer, and so therefore, to do tax well, you must have access to accurate and trusted data."
The importance of data quality, availability and management on the value derived from AI initiatives should not be underestimated. The tax sector provides a unique arena where AI can amalgamate structured and unstructured data from multiple sources to extract deeper insights to bolster decision-making processes. Leveraging AI, tax professionals need to rely on more than just data scientists or delve into the complexities of algorithms.
However, capitalizing on data goes beyond merely analyzing it; it requires a use-case-oriented approach to data management and governance. It's not about "fixing" data haphazardly but identifying specific use cases and managing data based on its value and technical feasibility. Bird advocates for a targeted, value-centric approach to data management.
"Focusing solely on the destination without considering the journey can often lead us on a longer path,” Bird says. “Traditionally, the world has been cleaning data by any means necessary, not necessarily focusing on the route. What generative AI (GenAI) now offers, and what the world needs, is a GPS-like system that can provide the shortest path to the destination, effectively prioritizing the route."
Balancing risk and value creation
Embracing AI initiatives necessitates a delicate equilibrium. Leaders must balance the promise of value creation against potential risks, including trust, compliance, regulatory obligations and brand reputation.
"Generative AI and even more advanced technologies bring the need for trust in your data governance into greater focus. Technologies like generative AI will interpret patterns they see in the data. If that data is not trusted or well governed, that could be quite dangerous." Clough says.
Data readiness for AI, therefore, becomes an important evaluation and ongoing data governance need. Thinking about enhancing data management to treat data as a product becomes a critical focus with associated levels of data quality, data curation, and robust governance between both producers and consumers of data.
Looking towards the future, the evolution of data layers and metadata management, coupled with technological advancements, promises to enhance AI implementation's efficiency and effectiveness.
"The future revolves around how metadata is applied to manage the data assets, models, algorithms and its relation to specific data assets" Bird says. Automated data extraction is rapidly gaining popularity, offering numerous benefits such as preserving data attributes, enhancing data trust and improving data governance.
As AI and advanced technologies evolve, they will disrupt traditional tax activities. This disruption poses potential risks to professionals, mainly in data processing or content creation tasks. To truly capitalize on AI-driven transformations, financial and tax leaders must efficiently orchestrate and govern data. By prioritizing value, improving data accessibility and extraction, embracing advanced data governance and fabric technologies, and driving unique insights and value creation, these leaders can secure a place at the forefront of innovation. "Understanding data territoriality and data sovereignty is crucial for managing risks in data value chains, especially when dealing with cross-border data restrictions and regulations," says Brayne.
To enhance efficiency, a strategic approach to data management is critical. This not only entails cleaning some data simultaneously but prioritizing data based on specific value use cases. This ensures the focus is on the most valuable data assets, thereby improving value extraction.
