Finance leaders are facing a dilemma—they know how important artificial intelligence (AI) is to help secure the company’s future. Yet we’re seeing organizations struggle to scale AI. At risk? The potential to lower costs, increase productivity and create sustainable business value. 

Accenture research shows a vast majority of executives understand the value of AI. Our study revealed that 86% of CFOs understand that AI is an absolute essential for finance to meet growth targets. And 81% said they fear going out of business entirely if they can’t scale AI within five years. But here’s a big part of the dilemma: While many CFOs are embracing AI and successfully piloting it within their department, our research shows a clear majority of executives—76%—have difficulty scaling it across the business. 

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A template for success

Correctly implementing AI makes data and tools reusable and scalable. The underlying architecture is about more than creating automated processes. A thoughtfully constructed AI environment is also about solving complex problems. AI will contribute the strongest business outcomes if it is fully integrated into an intelligent operating model. What does that mean? Adopting an intelligent operating model means combining data, human-machine augmented capabilities and talent to maximize assets and stay flexible. Intelligent operations are critical for finance to move away from being strictly transactional to becoming a strategic business partner.

Companies with intelligent operations make the most of human + machine partnerships, freeing up the finance team to focus on more strategic activities. And they increase accuracy through AI and machine learning algorithms to mimic human behaviors and build more precise forecasting models. They also have access to real-time, diverse data—a single source of truth.

Think of intelligent operations as the architecture that lays the groundwork so assets such as AI can offer real benefits. For example, CFOs who adopt human-machine augmented solutions powered by AI and robotics have outpaced their peers in recovering and stabilizing operations. During the COVID crisis, human + machine environments were hugely beneficial when teams were working remotely and had to compensate for staff shortages.

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Adopting an intelligent operating model means combining data, human-machine augmented capabilities and talent to maximize assets and stay flexible.

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Digital assistance is also a major asset. It supports complex reconciliations or cash application through AI-driven logic. Among other benefits, this frees up capacity to drive savings or offer people new capabilities to support the business more proactively during highly volatile periods.
Humans working with AI create capabilities far beyond either just human or just automated processes alone. Among other things, using AI provides faster processing, more rapid, accurate judgement, reduced fatigue, higher quality results and makes operations more proactive vs. reactive.

Higher productivity 

Companies the world over are using AI to boost productivity and heighten insights, among other benefits. For example, a global logistics company teamed with Accenture to help improve its finance and accounting operations. The company redesigned processes to increase efficiencies and productivity by 36%. This marked top-quartile industry performance in efficiency and productivity. By introducing AI and automation, the company has standardized 80% of its finance processes—up from 1%. It has also cut the time to process invoices from 15 days down to one. Additionally, the company more than doubled what moves through the finance process—from 30% to 75%—easing processes for customers and vendors. Working capital is up by $100 million, costs are down by 30%, and the company is well positioned for growth.

Elsewhere, we’re seeing finance organizations vastly improving compliance and controls by using AI to audit expense claims. AI advisors can audit 100% of their claims and send only questionable claims to a manager, saving time and costs. By monitoring its travel and expense claims, for instance, a large tech company:

  • Improved its non compliance detection rate by 95%
  • Reduced manual audit efforts 30% to 50%
  • Lowered its travel and expense costs by 3% to 5%

And AI has proved invaluable for mapping risks. Risk based reconciliations and reconciliation advisors can help identify high risk accounts more accurately and prioritize reconciliations for those accounts during the close process. AI-powered journal entry advisors, for example, can complete SAS 99 validation for the five W's—Who, Where, When, What and Why.

Processes move from the reactive to the proactive. Based on user preferences, a “cognitive collections concierge,” for example, can drive a more proactive collections process that will have a direct impact on day sales outstanding and optimize working capital through faster cash collection.

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These benefits will be the launch pad for finance to scale AI throughout the business—improving decision-making and resilience across the company.

Putting AI at the center of your finance function is a huge step in the right direction. Adopting an intelligent operating model means combining AI and other advanced technologies, cloud-based systems and collaboration tools with an agile workforce, decisive leadership, simplified governance and a strong ecosystem of partners to deliver the highest value business outcomes, including resilient, sustainable growth.

Manoj Shroff

Managing Director, Finance and Accounting Business Process Lead – Accenture Operations

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