Key Takeaways
- • Deloitte argues that internal audit functions remain constrained by limited auditor headcount and predominantly manual, time-consuming working methods, and that digitalisation offers a route to greater agility and higher-quality findings.
- • Improvements in audit management systems, including high levels of integration and API capabilities, now allow internal audit teams of varying sizes to build customised technology environments and tailored visualisation layers.
- • Generative AI is cited as having clear potential to raise productivity, quality and impact in internal audit, though it remains at proof-of-concept stage for a small number of functions.
- • Deloitte recommends that internal audit leaders define the function's purpose before investing in digitalisation, start with available data and tools, set a digital strategy with measurable outcomes, collaborate with technology teams, and build an innovation culture.
Summary
Deloitte's publication on internal audit digitalisation contends that many audit functions still rely on predominantly manual processes, and that while leaders have invested in analytics and visualisation training, few have used digital change as an opportunity to rethink existing approaches. The firm questions whether cyclical coverage strategies, point-in-time assessments and limited non-statistical sampling remain adequate, and whether reports that take weeks or months to finalise meet current needs. It also notes that teams often spend more time identifying issues than analysing root causes, and that institutional knowledge is frequently concentrated in key individuals rather than shared across the function.
Deloitte points to several enablers. Audit management systems have improved substantially in quality and integration, and their API capabilities allow functions to connect with other applications and build tailored visualisation layers. The software is no longer limited to the largest functions. Organisations are also investing more in data and technology, improving the reliability and availability of data that internal audit can use in its assurance work. Low- and no-code automation tools have become more accessible, and the rise of large language models and generative AI has broadened the range of possible applications. Examples cited include using AI and machine learning to identify themes across thousands of audit reports, analysing stakeholder meeting minutes to highlight risk areas, automated audit committee reporting, AI-generated first drafts of audit reports, automated working papers, and QA chatbots.
According to Deloitte, generative AI remains at proof-of-concept stage for a small number of functions, but its potential to improve productivity, quality and impact is clear. The firm argues that functions which fail to engage with digitalisation risk lacking agility in responding to emerging risks, redirecting resources, delivering efficiency and improving insight quality. Its recommendations include clarifying the function's purpose so that digitalisation serves that objective, starting with existing data sources and tools, setting a digital strategy with a defined roadmap and progress measures, collaborating closely with technology teams, drawing on agile development approaches to foster an innovation culture, and developing enough digital fluency among leaders to identify use cases and communicate requirements to specialist teams.
Deloitte points to several enablers. Audit management systems have improved substantially in quality and integration, and their API capabilities allow functions to connect with other applications and build tailored visualisation layers. The software is no longer limited to the largest functions. Organisations are also investing more in data and technology, improving the reliability and availability of data that internal audit can use in its assurance work. Low- and no-code automation tools have become more accessible, and the rise of large language models and generative AI has broadened the range of possible applications. Examples cited include using AI and machine learning to identify themes across thousands of audit reports, analysing stakeholder meeting minutes to highlight risk areas, automated audit committee reporting, AI-generated first drafts of audit reports, automated working papers, and QA chatbots.
According to Deloitte, generative AI remains at proof-of-concept stage for a small number of functions, but its potential to improve productivity, quality and impact is clear. The firm argues that functions which fail to engage with digitalisation risk lacking agility in responding to emerging risks, redirecting resources, delivering efficiency and improving insight quality. Its recommendations include clarifying the function's purpose so that digitalisation serves that objective, starting with existing data sources and tools, setting a digital strategy with a defined roadmap and progress measures, collaborating closely with technology teams, drawing on agile development approaches to foster an innovation culture, and developing enough digital fluency among leaders to identify use cases and communicate requirements to specialist teams.
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