Manager | Audit
Manager, Risk & Controls
"We test a slice and hope it represents the rest, and sometimes it does not."
Quick Facts
Role
Manager | Audit
Level
Manager
Dept
Audit
Industry
Audit
Env
Hybrid GRC
Tools
SAP GRC, Excel, Power BI
Sound familiar?
Control and risk data sits across multiple systems and assembling a current unified view requires manual work every time
Control testing is based on sampling and problems pass undetected until the next cycle catches them months later
Risk registers are maintained by hand and are out of date almost as soon as they are completed
Deteriorating controls and rising risk indicators do not surface until they have already created a problem
Evidencing control effectiveness to auditors and the board is slow, manual, and does not reflect the current position
AI systems are entering business processes without defined controls and the risk framework has not caught up
You are not alone
40%
rise in generative AI use in audit activities over the past year, from 15% to 40% (IIA, Pulse of Internal Audit).
78%
of internal audit teams use data analytics in some or all of their audits (2024 study, via ACCA).
40%
reduction in control-testing time that AI can deliver, freeing auditors for strategic work (Deloitte).
84%
of internal auditors would consider AI skills as important when recruiting for their function (Wolters Kluwer, 2025).
Join those who are leveraging data to move from financial stewardship to strategic business leadership.
How is AI raising the stakes
AI is being adopted in tax functions at large organisations at a pace that is leaving mid-sized organisations behind.
Machine learning tools for automated tax data extraction, classification, and validation are compressing provision timelines from weeks to days in the organisations that have deployed them. Transfer pricing documentation that previously required months of manual data collection is being generated from connected data sources in a fraction of the time. Tax Managers at organisations that have not made this investment are competing for talent with organisations that offer more interesting, less administratively burdened tax work - and losing.
The strategic advisory role that Tax Managers aspire to - modelling the tax implications of M&A, restructuring, or market entry decisions before they are executed - requires the same integrated, real-time financial data that the compliance role needs, but used for scenario modelling rather than historical reporting.
Without this data infrastructure, the tax function is perpetually reactive: providing tax analysis on decisions that have already been made rather than influencing the structuring of decisions that are still being shaped. The opportunity cost of that gap is significant.
Tax is one of the financial functions where the data quality and integration problem is most acutely felt, and where the consequences of getting it wrong are most directly financial.
Global minimum tax requirements, digital services taxes, and mandatory disclosure regimes are simultaneously increasing the volume of tax data that needs to be processed and the quality standard it needs to meet. Tax Managers who are still managing this complexity through manual ERP extracts and spreadsheet-based provisions are running on processes that were not built for this regulatory environment and will not survive the next wave of compliance requirements without a significant data infrastructure upgrade.
Manager | Audit
How Bronson can help
Data Strategy and Governance
Bronson.AI builds the data architecture, ownership model, and governance framework that connects operational data into a single, governed layer, so that decisions are made from one version of the truth rather than competing reports.
- Data standards framework covering metric definitions, KPI structures, and cross-functional data taxonomy.
- Data ownership and stewardship model assigning accountability for each data domain.
- AI governance policy ensuring automated decisions are auditable, explainable, and compliant.
Generative AI and LLMs
Bronson.AI implements generative AI and large language model solutions that accelerate workflows, from drafting and summarisation to intelligent search and recommendation, grounded in the organisation's own governed data.
- Generative AI use case design identifying where LLM capability delivers genuine productivity and quality gains.
- Retrieval-augmented generation connecting LLM outputs to internal knowledge bases and governed data sources.
- Output governance framework ensuring AI-generated content is accurate, auditable, and aligned with organisational standards.
AI and Agentic Automation
Bronson.AI implements the AI and automation capability that turns data into action, identifying inefficiencies, flagging anomalies, and triggering workflow responses without manual intervention. We help the function move from monitoring to orchestrating.
- Process automation across high-volume, rule-based workflows to reduce manual effort and error rates.
- Predictive anomaly detection that flags deviations before they escalate into failures or cost overruns.
- AI-powered forecasting and prioritisation that connects data signals to operational resource allocation.
Unlock your potential
Unlock the Power of Data in Tax
Data is the backbone of a well-managed and strategically valuable tax function. For the Tax Manager, harnessing accurate, integrated, and real-time financial data across all entities and jurisdictions is what enables the function to move from deadline-driven compliance administration to genuine strategic tax management.
Overcome Data Challenges Effortlessly
One of the primary challenges facing Tax Managers is tax-relevant financial data that is fragmented across multiple ERP instances, entity management systems, and spreadsheet-based provisions - requiring significant manual effort to compile and reconcile before any compliance or analytical work can begin. Building the integrated tax data layer that eliminates this preparation burden is the investment that frees the function to focus on value-adding work.
The Promise of Data, Analytics, and AI Advancements
Imagine a tax function where provision is computed automatically from connected financial data, where transfer pricing documentation is generated from live intercompany transaction data, and where the tax implications of strategic decisions are modelled in real time rather than analysed after the fact. This is not just a vision but the very real value proposition that our Data, Analytics, and AI Consulting and Solutions offer.
Realize the Value of Advanced Data Solutions
Our services are designed to guide Tax Managers through:
- Integrated Tax Data: A unified view of tax-relevant financial data across all entities, jurisdictions, and ERP systems.
- Automated Compliance: AI-assisted tax provision, data classification, and regulatory monitoring that reduces manual effort and error risk.
- Strategic Tax Analytics: Scenario modelling and real-time tax impact analysis that enables the function to contribute to business decisions before they are made.
See Results
4x ROI
payback with AI is guaranteed
90 DAYS
to a funded, board-ready AI roadmap
18 MONTHS
from pilots to
AI-centric enterprise
Get started today!
Frequently asked questions
Establish secure, well governed data management for tax, because consistent, connected, governed tax data is what makes the function's work efficient and its outputs reliable, and informed tax decisions depend on a foundation that pulls the scattered pieces together. The work is connecting the tax-relevant data from the source systems into a consolidated environment, aligning it so it is consistent and complete for tax purposes, with the governance that keeps it reliable. This is what lets tax work from a single dependable source rather than assembling data by hand from multiple systems each time it is needed.
The reason scattered data is so costly for tax specifically is that nearly everything tax does, provision, compliance, transfer pricing, planning, requires pulling together data from across the business, and when that data is scattered, every task begins with a manual assembly that is slow, error-prone, and repeated each cycle. The function spends its time gathering data rather than applying tax expertise, which is both inefficient and a poor use of skilled people.
The payoff is a tax function that works from reliable, consolidated data rather than perpetually assembling it, which makes everything faster and more accurate. Provision closes more quickly because the data is ready, compliance is less error-prone because the data is consistent, and the function has capacity for the planning and advisory work that actually adds value rather than spending its time on data gathering. The consolidated foundation also supports the modelling and automation that depend on data being connected. Fixing the fragmentation through a governed foundation is what turns tax from a function consumed by manual data assembly into one that works from reliable data and applies its expertise where it matters.
Automate the routine provision work to streamline the process and reduce the errors that manual handling introduces, because the data collection and calculation are mechanical while the judgement is what genuinely needs tax expertise, and automating the mechanical part both speeds the provision and removes the transcription and formula errors that creep into manual work under deadline pressure. The setup automates the data collection from entities, the consolidation, and the standard calculations, flagging the items that need judgement rather than processing everything by hand.
This depends on the underlying tax data being connected and consistent, which is usually the real constraint, because automating a provision built on scattered, inconsistent data just automates the assembly of a mess. Getting the tax data onto a connected, consistent footing is part of making the automation work, and it addresses the root of the error-proneness rather than just speeding up the error-prone process.
The payoff is a provision that closes faster and with fewer errors, freeing the tax team for the judgement and review that actually require their expertise. The provision that took weeks of manual data gathering and calculation compresses substantially when that work runs automatically, and the errors that manual handling introduces, the mis-keyed figure, the broken formula, the inconsistent treatment, largely disappear because automation applies the same logic the same way every time. The time reclaimed goes into reviewing the provision, addressing the genuine judgement calls, and the planning that adds more value than data assembly ever could. Automating the manual provision work is what turns the provision from an error-prone endurance test into a controlled process the team manages rather than races through.
Turn your tax data into forward-looking insight, because the shift from calculating tax after decisions are made to modelling it before is what lets tax inform decisions rather than just report their consequences, and that depends on data and modelling capability the function can use on demand. A genuine modelling capability connects the relevant tax data and applies the tax treatment of a proposed decision, a restructuring, an acquisition, a change in operations, so you can see the tax consequence before committing, and compare alternatives on their tax outcomes.
The reason this matters strategically is that tax is too often consulted after a decision's structure is set, when the options to optimise have already closed. By the time tax sees a restructuring, the structure is decided and tax can only calculate the consequence rather than shape it. A modelling capability that lets tax quickly assess the impact of options means tax can be involved while decisions are still open, which is where it adds the most value.
The payoff is tax positioned as a strategic input rather than a downstream calculator. When tax can model the impact of decisions quickly and credibly, it gets involved earlier, shapes decisions toward better tax outcomes, and demonstrates value beyond compliance. The modelling also makes tax planning proactive, identifying opportunities and risks before they crystallise rather than reacting to them afterwards. Building the capability to model tax impact on connected, reliable data is what turns tax from a function that reports what decisions cost into one that helps make better decisions, which is a markedly more valuable and influential position for the tax function to occupy.
Use AI to revolutionise how the documentation gets produced and drive real efficiency, because the labour-intensive assembly and drafting that makes transfer pricing documentation such a burden is exactly where AI can transform the effort, provided it works from your reliable intercompany data rather than generating in a vacuum. The approach uses AI to pull together the relevant data, apply the analysis, and draft the documentation in the required structure, with the tax team reviewing, refining, and applying the judgement that the documentation ultimately depends on.
The critical discipline is grounding the AI in accurate intercompany data and treating its output as a draft to be reviewed rather than a finished product, because transfer pricing documentation has to be correct and defensible, and AI that generates plausible but unverified content creates risk rather than removing it. The AI accelerates the assembly and drafting; the tax professional ensures the result is accurate, complete, and defensible, which is where their expertise is genuinely needed.
The payoff is documentation produced far faster and with far less manual effort, freeing the tax team from the labour-intensive drafting that consumes so much time each cycle. Transfer pricing documentation is notoriously burdensome precisely because it combines extensive data assembly with structured writing, both of which AI handles well under expert supervision, so the time saved is substantial. The team's effort shifts from assembling and drafting to reviewing and refining, which is a better use of tax expertise and produces documentation that is both faster to produce and consistent in quality. Using AI to draft transfer pricing documentation, grounded in reliable data and supervised by tax judgement, is what turns one of the function's heaviest recurring burdens into a manageable, efficient process.
