The smart tax function: leveraging generative AI 

Tax functions can strategically leverage their organisation’s existing generative AI solutions to enhance efficiency and build a compelling case for future investments. This is especially relevant in today’s challenging economic climate, where securing budgets for new tax transformation initiatives is difficult. By concentrating on maximising the value of existing generative AI solutions and refining processes, tax functions can demonstrate tangible efficiency gains from, and improved utilisation of, existing investments, thereby building a strong foundation for subsequent transformation projects.

Strategic implementation of generative AI for routine tasks frees up tax professionals for higher-value work such as strategic planning, business partnering, and value creation. This allows tax teams to apply their expertise to complex analysis, nuanced interpretation, and strategic advisory roles, thereby enhancing their ability to deliver added value.

To effectively leverage existing generative AI solutions, it is important to understand its specific strengths: it excels at flexible, interpretive tasks with unstructured data, whereas traditional automation handles precise, repetitive operations. Understanding this fundamental difference is key for effective resource allocation.

Successfully leveraging the value of initial investment in generative AI technology requires utilising a flexible and actionable strategy, often informed by internal or external experts. This strategy must define appropriate use cases and specific application opportunities with practical examples, providing a foundation for experimentation and deployment. Alongside this strategic foundation, success also requires a robust tech platform and cultivating a culture of innovation.

Leading tax functions that successfully integrate generative AI strategically use robust, evolving technology platforms. These platforms ensure access to a diverse range of leading models from various providers and incorporate essential supporting capabilities, facilitating early adoption of the latest advancements. Importantly, these leading functions select specific generative AI solutions based on technical capability, user-friendliness, scalability, and integration potential.

Maximising the impact of generative AI adoption hinges on successfully fostering a culture of innovation built on practical applications and standard output verification. This involves incentivising experimentation, where lessons from both successes and failures are valued. Continuous learning programmes – progressing from basics to advanced applications, supported by ongoing knowledge sharing through regular follow-ups and forums for teams to discuss insights and challenges – underpin this culture.

Another key feature of many organisations successfully harnessing the power of generative AI is the ability to leverage motivated champions within the organisations who understand both technology and organisational goals. Creating an environment where these champions can drive adoption and transparent communication about AI capabilities helps manage expectations and guide practical usage.

To demonstrate tangible improvements, a flexible, iterative approach to generative AI implementation must be adopted. In practice, this typically means beginning by identifying ‘time thieves’ – processes consuming disproportionate manual effort or involving repetitive, high-volume tasks. These become ideal candidates for initial generative AI pilots. Once a target process is selected, its steps are mapped to pinpoint exactly where generative AI can deliver the most impact, serving as a focused testing ground without disrupting entire workflows.

The authors’ observations from working with successful adopters show that small pilot projects are crucial to successful generative AI endeavours. They provide critical insights, reveal generative AI’s practical strengths and limitations, and allow for testing capabilities before scaling.

This iterative process of piloting process improvement projects, combined with the consistent and early capture of lessons learnt, is vital not just for the immediate use case but for shaping the broader adoption strategy and future implementations. These learnings enable strategic decisions; for example, if a current generative AI solution falls short, leading functions maintain the flexibility to explore advanced versions or even non-AI alternatives, ensuring their overall approach accommodates emerging capabilities and evolving organisational needs.

Cross-functional collaboration, often including internal or external specialists where beneficial, is essential for effective generative AI adoption. Drawing on diverse perspectives and proven strategies accelerates learning, avoids duplicating efforts, and ensures that the entire tax function benefits. This collaborative approach helps to maximise value provided by existing technology. This approach also facilitates the identification of future automation opportunities and the development of well-founded cases for further investments.

Through pilot projects and continuous learnings, leading tax functions gain a deep understanding of process steps in which the deployment of generative AI provides the biggest efficiency gains, while also learning the limitations. Based on this information, they maximise value for investment by strategically integrating generative AI workflows with other established technologies, rather than using it as a standalone solution.

These tax functions integrate generative AI’s unique strengths into workflows that rely on proven tools already in use for precision tasks such as data validation, complex calculations, or rule-based automation, offsetting the weaknesses and boosting the strengths of both. This layered approach ensures generative AI’s outputs are refined, verified, and operate within controlled, auditable frameworks, thereby creating trustworthy and highly effective end-to-end tax processes.

Ultimately, effective generative AI implementation in tax is accomplished by utilising a flexible, methodical, and strategic approach that fosters a culture of innovation, emphasises practical application, and embraces learning-by-doing. Leading tax functions demonstrate how leveraging generative AI tools can achieve measurable gains with comparatively small investments.

This balanced methodology – strategically targeted, realistic, and adaptable – empowers tax professionals to show tangible returns, strengthening stakeholder confidence and paving the way for broader transformation. By concentrating on focused, iterative pilots that quickly deliver visible impact, tax leaders secure immediate wins while positioning their departments for ongoing evolution in an increasingly complex global environment.