A decade after ICAEW first examined the potential of data analytics in external audit, find out how adoption has evolved across UK firms of different sizes. Explore the growing role of platforms, vendors, new delivery models, and emerging AI technologies as well as the practical barriers to adoption that remain.
ICAEW’s new report and accompanying webinar look at these themes.
Key takeaways
- Analytics is becoming embedded in audit workflows. Firms are moving away from standalone tools towards integrated platforms. that support analytics across the audit cycle.
- Firm size influences adoption but does not determine it. Larger firms often have more resources to build bespoke solutions, while mid-tier and smaller firms benefit from a growing vendor market and more accessible tools.
- Data quality remains a major barrier. Extracting, transforming and validating client data is still one of the most time-consuming and technically challenging parts of the analytics process.
- Methodology alignment is critical. Firms are more confident using analytics when tools are clearly linked to audit methodology and approach.
- Skills need to evolve. Auditors do not need to become data specialists, but they do need the confidence using tools, including AI, to understand when to use tools, how they work, and how to interpret results.
- Regulatory uncertainty can slow innovation. Many firms want more guidance and practical examples showing how analytics can be used to obtain sufficient appropriate audit evidence.
In 2016, when ICAEW published its initial publication Data analytics for external auditors, the discussion was largely centred on whether data analytics could transform audit. Now in 2026, the conversation has changed towards how firms can embed analytics into audit platforms, methodologies, training programmes and regulatory frameworks. While firms still face practical barriers around data quality, methodology alignment, and regulatory confidence, staying up to date with analytics and the evolving landscape of AI are vital. Understanding the risks and opportunities for analytics across the audit life cycle helps firms, vendors, methodology providers, regulators and standard setters improve audit quality, efficiency and stay relevant in a more technologically advanced world.
What’s changed in the last decade?
Analytics has moved from being a separate, optional procedure to becoming embedded in audit platforms, workflows and methodologies. This means it is increasingly part of how audits are planned, performed and reported. While larger firms have more resources and more advanced internal platforms, mid-tier and smaller firms are increasingly adopting analytics through third-party vendors, integrated audit platforms and tools.
Embedded workflows
The most significant development since 2016 is the integration of analytics into the audit process. Previously, analytics was often treated as a separate procedure. Today, leading firms have embedded analytics within audit platforms, allowing data to flow through planning, execution, and reporting stages. Examples from larger firms include EY Helix within EY Canvas, KPMG Clara and Deloitte Omnia. Moore Kingston Smith's CARE platform is also a a good example of internally developed capabilities in the mid-tier space. Rather than relying entirely on external specialists, the firm has developed a self-service analytics environment that allows audit teams to access and analyse client data more directly.
Azets' approach, on the other hand, shows that firms do not need a proprietary platform to embed analytics into audits, as they have demonstrated that the use of externally sourced tools such as Alteryx and Power BI can support audit procedures and data analysis.
A broader market for analytics
In 2016, analytics at scale was largely associated with the largest firms. However, 10 years on, a growing vendor market has made analytics tools more accessible to mid-tier and smaller firms. Solutions such as Inflo, MindBridge, Validis, DataSnipper and Alteryx offer a range of capabilities that allow firms to adopt analytics without developing extensive internal systems.
New delivery models
Firms have adopted a range of approaches to deliver audit analytics. Some firms empower auditors directly through self-service tools, while others use specialist data teams to support engagement teams. In a self-service or citizen-led model, auditors use analytics tools directly and only bring in specialists for more complex work. In a two-team model, the audit team decides when analytics should be used, but data specialists usually handle data extraction, transformation and analysis before the audit team interprets the results. Hybrid approaches are also emerging, combining auditor experience with the specialist’s skills.
Skills now needed
Technology and data skills have become increasingly important, but auditors are not expected to become data scientists. The key skills now needed are more practical: knowing when analytics should be used, understanding how tools are configured, interpreting responsibly, and applying professional judgement to decide whether the results provide useful audit evidence. This is especially important as analytics moves into risk assessment, journal testing, substantive procedures and AI-enabled audit work.
ICAEW is supporting this shift in several ways, for example, by recognising the role it plays in improving auditor training and awareness around analytics. This includes embedding data analytics software into the Next Generation ACA and introducing a Technology Learning Hub. ICAEW also offers practical learning resources such as the Analytics in Power BI Certificate, which helps finance professionals build skills in analysing trends, modelling data and producing clear reports. This means the skills gap is not just being left to individual firms to solve. ICAEW is helping students, members and firms build a more consistent foundation in data, analytics and technology.
What are the main challenges?
Data
Despite significant progress, data quality remains a major obstacle. Firms, especially on the smaller end, continue to face challenges with data extraction, completeness, consistency, and access. While a wide range of tools are becoming available to aid this, better tools alone cannot solve the issue of poor-quality data.
Methodology and regulation
Methodology is important because firms need to show how analytics fits into their audit approach and how the results provide appropriate audit evidence. If tools are not aligned with methodology, auditors may lack confidence in using them or may duplicate work through traditional testing.
Furthermore, while regulators and standard setters are generally supportive of wider analytics adoption. Their concerns are more about how analytics is applied, how results are interpreted, and whether firms have documented the process and evidence properly.
Some firms are cautious because they worry about regulatory scrutiny, unclear guidance, and uncertainty over how analytics-based procedures fit into existing audit standards. This can lead firms to use analytics alongside traditional testing rather than replacing or reducing other procedures.
Looking ahead at AI’s impact on data analytics
Artificial intelligence is expected to accelerate the development of audit analytics. Firms are now exploring AI-enabled testing, automated data preparation, and intelligent risk assessment. However, future success will depend on maintaining trust, quality, and professional judgement.
Calls to action
- Firms: Benefit from greater investment in upskilling, in tools, and by making bolder decisions about when to use analytics.
- Methodology providers: Prioritise developing work plans that better support and encourage the use of analytics, without introducing unnecessary burden.
- Regulators and standard setters: Provide better support by articulating and clarifying where and how current auditing standards support the use of analytics, and by sharing more specific, detailed examples of best practice.
- Software providers: Focus on meaningful engagement with all stakeholders to ensure that their software and products comply with the requirements of auditing standards and integrate well with other platforms.
- Professional bodies: Use central role among stakeholders, to represent and advocate for the needs of firms and members by facilitating debate about the three key barriers, and explore solutions to challenges around data quality.
Author
Yodahey Belete, intern at ICAEW with a background of study in Accounting and Finance.