KPMG's recent announcement was particularly noteworthy from my perspective, because it indicated that the audit firm would be deploying IBM's Watson "cognitive computing technology" to KPMG's professional services offerings.--According "One current initiative is focused on employing supervised cognitive capabilities to analyze much larger volumes of structured and unstructured data related to a company's financial information, as auditors'teach' the technology how to fine-tune assessments over time. This enables audit teams to have faster access to increasingly precise measurements that help them analyze anomalies and assess whether additional steps are necessary." IBM is, of course, one of KPMG's biggest software-auditing clients. All of these recent reports mention that the AI technologies currently are being contemplated for use in connection with financial audits.
Like any algorithm- and data-driven process, artificial intelligence (AI) presents internal audit with a clear role in ensuring accuracy and reliability. It is important that boards and other stakeholders understand stages in the AI life cycle where internal audit's focus can provide positive effect. How can the board best support the CAE's efforts to ensure that the internal audit team is prepared to provide assurance over AI and capitalize on its efficiencies in appropriate audit plans and activities?
If you had to choose one word that strikes fear in your heart, what would it be? Here's one I can think of… Unfortunately, we have no good news to share about lessening the drudgery of tax audits (sorry!). Recently, XebiaLabs introduced the first and only Release Audit Report that covers all release activities in an enterprise's software delivery pipeline at the push of a single button. The report provides evidence for every single manual and automated task in the software delivery process, instantly: who did what, when, and where. You can create report filters by date, folder, keywords, and more, and export information for one or many releases.
A wide range of claims and news can be found online, from the technology "not being ready" to vendors declaring that AI is already within their tools. Ultimately, firms must decide when to invest in AI, or rather, which AI-enabled tool provides the best return on investment. By understanding how AI fits into their technology infrastructure and audit processes, audit managers and partners can most effectively choose the right solution. It's critical to understand what real AI and machine learning techniques mean, and the implications for the firm. So how do firms measure their readiness for AI and assess potential vendors?
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