In today’s business environment with an unprecedented explosion of data and rapidly emerging applications for Artificial Intelligence (AI), the internal audit function finds itself facing a tremendous opportunity. Traditional, manual audit methodologies, often reliant on limited sampling, are increasingly insufficient to provide the assurance and insights that businesses demand. The power of audit analytics, supercharged by AI, is no longer a futuristic concept; it's today’s reality for internal audit teams striving to work smarter, not harder.
Embracing this transformation isn't as simple one might think. As a recent webinar, "The Future of Audit Analytics – Working Smarter, Not Harder" hosted by the IIA highlighted, navigating the hype and implementing practical solutions requires a nuanced understanding of critcal aspects beyond just technology. Featuring insights from Alban Clot, Founder and co-CEO of Supervizor, the webinar provided an actionable roadmap for leveraging audit analytics effectively. Success hinges on more than just tools – it requires addressing deep-seated challenges in data, skills, and relationships.
Alban kicked off the webinar by discussing what he calls the "AI Paradox." While AI is being massively adopted across corporations, revolutionizing business operations and even creating new business models, its adoption within internal audit practices is still in its nascent stages. Polling during the webinar underscored this, revealing that more than a third of internal audit attendees are not yet actively using AI in their daily work.
This disparity, Alban warned, comes with significant risks of falling behind:
To maintain talent, address emerging risks, and ensure continued relevancy, internal audit simply must embrace audit analytics.
An additional polling question reinforced Alban's observations about the hurdles internal audit teams face in their analytics journey. The top challenge cited by nearly 50% of attendees was accessing and ensuring the quality of relevant data. This was closely followed by a lack of team skills or training in analytics tools and techniques.
Alban elaborated on these persistent problems:
To overcome these challenges, Alban shared a handful of tips gleaned from implementing hundreds of audit analytics programs:
This is non-negotiable. As the old adage goes, "garbage in, garbage out." If you feed your analytics program (or even an AI tool) bad data, you'll get garbage insights. For this reason, it is estimated that 80% of an audit team's time is spent on data capturing, cleaning, and preparation—not on actual analysis.
While you don't need to turn every auditor into a data scientist, a certain level of data literacy is crucial. Understand the meaning and context of data sets, even technical terms like "BKPF" in SAP. A basic understanding of data fields (like the difference between internal and external invoice numbers) can make or break the effectiveness of a simple routine check for duplicates.
Share Your Wins: Foster a culture of continuous learning and knowledge sharing. When someone on the team achieves an analytics success, share it widely. This not only educates peers but also builds momentum and enthusiasm for the program.
Gaining trust and support from data owners and IT teams is paramount. Spend time with them, explain your objectives, and demonstrate how audit analytics can benefit them directly. Alban shared an example of an automotive company where demonstrating how analytics could help IT teams gain insights from audit routines unlocked their support. Talk to everyone involved—from management to developers—to understand the business process and gain holistic support.
Don't try to do everything at once. Select a supportive geography, a small ERP system, or a well-understood process where you can achieve quick wins and demonstrate value. This early success builds confidence and secures the buy-in needed to scale the program globally. An engineering firm started their program in France, achieved success in three months, and then recently expanded globally to 25 countries. Focus on building standard checks first, as these are easier to deploy and demonstrate immediate value.
The webinar emphasized that while human elements are crucial, purpose-built technology plays a vital role in overcoming the inherent challenges of data quality and traditional sampling. Solutions that can automatically standardize and normalize data from disparate systems are game-changers.
This kind of technology, like that from Supervizor, connects directly to transactional systems (like SAP, Oracle, and other applications) and retrieves data from various modules (e.g., A/P, A/R, G/L). The platform then standardizes this fragmented data into a unified data lake. This automated process handles the complex task of reconciling data from different models and formats, turning unusable datasets into a single, cohesive source for analysis. For example, whether an invoice is entered in SAP in the US or in Germany, such a platform ensures it's stored identically, allowing for consistent routine checks across the globe. This significantly reduces the 80% of time typically spent on data capturing, cleaning, and preparation, allowing auditors to focus on analysis and insights.
Such solutions also provide a library of pre-set, ready-to-use controls and routine checks (e.g., Supervizor currently offers 350+ out-of-the-box checks categorized by process like P2P, R2R, R2C). This empowers audit teams to move beyond traditional sampling to full-population testing, continuously monitoring data and identifying risks and anomalies much more rapidly.
An effective audit analytics program delivers tangible benefits for the entire organization:
Establishing a successful audit analytics program is a journey that requires a blend of strategic vision, investment in human capital, and the right technological enablers. It's about transforming internal audit from a reactive, sample-based function to a proactive, data-driven strategic partner. By prioritizing data quality, fostering data literacy, building strong relationships, starting small, and leveraging purpose-built technology, internal audit teams can navigate the complexities of the modern data landscape and work smarter, not harder.
For a deeper dive into these critical insights and to see practical examples of how internal audit teams are leveraging analytics today, we encourage you to watch "The Future of Audit Analytics – Working Smarter, Not Harder."