How data strategy can activate AI’s fit with oil and gas tax functions

AI can accelerate tax transformation, but only when data, talent and operating models are aligned.

In brief

• True AI value emerges when tax teams move beyond compliance, leveraging technology to unlock strategic insights and resiliency amid complexity.
• Increasingly, managed service models enable alignment between service delivery and technology and data investment, with incentives for continuous improvement.
• Ambition alone won’t transform tax functions. Without a solid data foundation, AI risks amplifying existing weaknesses, not solving them.
• Cutting tax headcount while demanding AI-driven results creates a paradox that only integrated data and operating model shifts can resolve.

Oil and gas (O&G) companies have made the decision: Tax and finance functions need to transform. The pace and scale of regulatory changes, cost pressures and talent constraints effectively made the decision unavoidable.

What has changed is the pressure around execution. The EY 2025 Tax and Finance Operations Survey found nearly two-thirds of O&G leaders cite the inability to execute a sustainable data and technology strategy as the biggest barrier to tax and finance functions delivering on their purposes, outplacing budget or talent concerns.

This gap between ambition and outcomes is widening just as the pressure intensifies. Regulatory complexity continues to grow, cost reduction programs are reshaping finance organizations and workforce constraints are becoming more acute.

AI is often positioned as the catalyst that will resolve these tensions. In practice, however, AI is less of a silver bullet than an accelerant, magnifying both the strengths and weaknesses of existing tax and finance operating models. For most organizations, leadership buy-in isn’t the barrier. The hard part is aligning upstream data, governance and delivery models so AI can translate the intent into an impact.

Why execution has become the defining challenge

The data problem tax cannot solve on its own

In many organizations, tax functions feel the impact of poor data quality, but lack direct control over how that data is created, governed or prioritized.

One large, vertically integrated O&G company serves as an example of how data readiness can shape AI success in tax. While the company had a strong interest in applying AI across its tax function, it became clear that fragmented systems, spreadsheet-driven workflows and limited coordination were restraining what could realistically be achieved, especially under a compressed timeline.

Rather than deploy AI in isolation, the company sought to strengthen its fundamentals. The early work centered on simplifying core processes and making entity and finance data easier to trust and reuse. Once tax logic and standard definitions were embedded into the broader finance environment, AI became a practical next step — supporting targeted automation and improving insights into the provision results. Efficiency and confidence grew as capacity was built for higher-value tax analyses.

Tax teams rely on enterprise data that originates across the business, yet they rarely control how that data is created or governed. Survey findings show that only about one in five O&G respondents consider their tax or finance functions “very effective” at accessing, organizing and reusing data. When systems and master data aren’t aligned, governance gaps show up downstream as delays, rework and inconsistent outcomes.

This is why many AI initiatives stall. Intelligent automation and agent-based workflows can accelerate compliance, analysis and reporting, but only when fed accurate, timely and structured inputs. When underlying data is incomplete or inconsistent, AI tends to magnify data quality issues rather than eliminate them.

As a result, leading companies are reframing tax transformation as part of a broader finance and enterprise data agenda. Instead of treating tax as a downstream consumer of information, they are embedding tax requirements earlier into finance transformations and data architecture decisions. This integrated approach can improve AI outcomes, as well as strengthen compliance, auditability and transparency across the organization.

Redefining the value of tax transformation

Another theme emerging from industry discussions is a broader understanding of value. Historically, tax and finance transformation programs were justified almost entirely through cost reduction. While cost remains important, O&G leaders increasingly recognize that transformation delivers value in more nuanced and incremental ways.

Survey respondents point to several outcomes that matter just as much as cost takeout: improved data quality, faster reporting cycles, greater confidence in regulatory compliance, enhanced insight into effective tax rates and cash taxes, and the ability to respond more quickly to legislative change. Nearly three-quarters of O&G respondents say tighter alignment between the tax strategy and the overall finance and organizational strategy is now a top priority.

AI plays a role here, not by replacing professional judgment but by shifting the effort away from manual, repetitive tasks and toward higher value, judgment-intensive strategic work. When routine data collection, reconciliation and compliance processes are automated, tax professionals gain capacity to focus on planning. They can think more about risk management and be more strategic in their decision-making. For many companies, this “release valve” is the most compelling part of the transformation story.

A three-pillar roadmap for sustainable progress

Across industry conversations, a practical roadmap for progress is taking shape. Leading companies are aligning around three mutually reinforcing pillars.

How one integrated O&G company made AI-enabled transformation stick

Experience at a large, vertically integrated O&G company shows how operating model decisions can sustain AI-enabled transformation. With the regulatory complexity rising, provision timelines tightening and resource constraints persisting, the company needed a more integrated delivery approach. Traditional in-house execution and fragmented co-sourcing left it with limited capacity to fully leverage emerging technologies.

The company adopted a managed services model that combined operational execution, technology enablement and continuous improvement. Provision and compliance processes became more standardized, data was centralized and investment in automation became more consistent.

With day-to-day execution stabilized, internal tax professionals gained more capacity to step out of manual, spreadsheet-driven work. They were able to spend more time on complex issues, apply judgment where it mattered the most and work more closely with the business.

The impact went beyond operational efficiency. The tax function became more resilient, better able to keep up with regulatory change, expand AI use over time and maintain confidence in the provision outcomes. For many O&G companies, this combination of operating model shift and technology enablement is essential to translating their AI ambition into sustained results.

What “good” looks like in practice

O&G companies making the most progress integrating AI into their tax functions share a few common traits.

In these environments, tax functions evolve from compliance engines into insight generators. Improved data enables better scenario analysis, more proactive risk management and clearer visibility into tax outcomes across jurisdictions. Over time, this creates a virtuous cycle: Better data supports better decisions, which, in turn, strengthen the confidence in AI-enabled processes.

For O&G leaders under pressure, the path forward does not require wholesale reinvention overnight. Practical first steps include assessing the data readiness, identifying high-friction compliance processes suitable for automation and re-evaluating operating models to determine where external support can create some breathing room.

A global O&G company’s capital projects organization was facing execution challenges driven by inconsistent project templates, manual documentation, and variation across contractors and vendors. Over time, it became clear that the underlying issue was not a lack of technology but the absence of consistent, well-governed data across the project lifecycle.

The company focused on establishing a common data model for capital projects, covering everything from planning and execution through handover to operations. This created greater consistency across teams and third parties, supported more digital project management processes and laid the groundwork for enterprise-wide data governance.

With core project data standardized at the source, the company was better positioned to apply analytics, automation and ultimately AI. The result was value created for capital projects, as well as downstream tax and finance functions.

Successful transformation begins by recognizing that AI is not a stand-alone solution. It is a catalyst — one that rewards organizations willing to align their data, talent and operating models around a shared vision. Those that bridge the gap between intent and execution will be best positioned to navigate complexity, manage risk and extract lasting value from AI in tax and finance. In practice, that often requires deliberate operating model choices that align incentives, create capacity and allow AI capabilities to mature over time. For many O&G organizations, those choices are already being shaped by sustained pressures on cost, talent and execution capacity, not technology ambition alone.