How Accenture leverages AI as a growth engine

answer: Taking a leadership position on AI is critical to continuing to move forward and shape new services and capabilities. For example, at a company our size, despite our focus on emerging technologies, we sometimes find small issues across technologies. Processes and data exist in different locations, and silos develop over time. Most large companies face this challenge. But these are valuable processes, and the business data we have is especially valuable. AI bridges these gaps, opening up new opportunities for better end-to-end outcomes and enabling our finance functions to meet the growing business expectations of our stakeholders.

For many companies, the key to achieving impactful results from business AI is to start with one capability that is central to business performance. Why was finance a good place to start for you and what did you hope to achieve?

I always say that finance is the heart of our organization. Hearing one of our global IT leaders use this phrase was both inspirational and thought-provoking: Don’t accidentally cause a heart attack in your organization.

All jokes aside. He was right. Transactional and operational data flows through finance and management decisions rest on top of it. By starting there, we can now have an end-to-end impact on the entire process around sourcing, liquidity, forecasting, accounts receivable, and more. And SAP provides the digital core where all transaction data is harmonized.

The bottom line is that if you want to move from reactive reporting to more proactive, AI-driven insights that you can use to drive your business forward, finance is a natural starting point. So we set out to transform our finance processes in a way that integrated data and scaled across the value chain.

Cash and liquidity are critical not only to the finance function, but also to the company as a whole. But managing it requires gathering data, making predictions, and making decisions across many teams. How has AI helped?

When finance is the heart of a company, cash and liquidity are the lifeblood of the system. Here’s a good example: Because Accenture makes many acquisitions and operates operating capital in more than 50 countries, decisions often rely on historical manual reviews. This is what was happening at Accenture until a forward-thinking leader walked in and asked if we could apply machine learning to this problem. Great leaders often ask great questions, and this one really got us thinking.

[AI] This freed up 20% of idle cash, which could be moved to global operations to fund acquisitions and strategic growth.

eli lambert

We took inspiration from retail, how stores handle inventory based on discounts and sales. If you treat cash like stocks, you can apply the same learning model to figure out how much you actually need to hold at any given time. This is how we built what we call an “intelligent cache.” Bring all your business data together into one data mart (a repository of structured data for a specific department or line of business) and use machine learning to generate actionable recommendations for your team.

AI is very good at this, and here’s what’s surprising: AI has freed up 20% of our idle cash, which we can now move into our global operations to fund acquisitions and strategic growth. What once took months or even a year or more to build can now be built in days or weeks thanks to SAP’s data cloud. [SAP] Bring together your Datasphere, Databricks, and machine learning workloads in one place. The result is faster decision-making, better visibility, and more accurate forecasts.

I’m excited to hear how they leveraged the benefits of strategic AI innovation and redirected them to high-value activities for their organizations. I believe you were also dealing with accounts receivable, which impacts cash flow and customer relationships. What pain points did you face and how has automation and machine learning changed your processes?

Accounts receivable was much more manual than accounts payable. Clearances were inconsistent, with payments incomplete or containing only partial data, which took time to reconcile. Anyone who works in or near the financial industry knows exactly what I’m talking about. So we co-developed a machine learning-based accounts receivable management solution on the SAP platform. Automation rates for accounts receivable processing more than doubled, and automatic reconciliations tripled, an increase of approximately 300%.

As part of this, we introduced reliable one-click match recommendations that reduce errors and reduce manual effort. The cash application scheduler built on the SAP platform improved automated payments by 7% and matched approximately 77% faster. All of this leads to more efficient accounts receivable processing, better cash flow visibility, and increased team productivity.

For a global organization like Accenture, collating financial data to uncover meaningful insights can be a daunting task. We leveraged generative AI, which is really smart. What led you to that approach, and how has it changed your team’s day-to-day experience?

We were working on balancing balance sheets across more than 50 countries, and the process was decentralized. I know many companies are facing this problem. So, the first thing we did was move everything online. We then deployed machine learning and generative AI to analyze cost categories, summarize data, and uncover important changes.

[Our] Built on the SAP platform, Intelligent Financial Advisor can generate highly accurate descriptive comments, resulting in over 90% approvals with little or no modification. This will save approximately 57,000 hours worldwide. Our team can focus on higher-value analysis instead of manual adjustments.

eli lambert

Next, we introduced Intelligent Financial Advisor, which is built on the SAP platform. This advisor can generate descriptive comments that are so accurate that over 90% of them are approved with little or no modification. This saved approximately 57,000 hours globally in administrative tasks alone and enabled us to shorten global store closures to three days instead of five. Insights are faster and clearer, allowing your team to focus on higher-value analysis rather than manual adjustments. It also helps create more consistent rollups across regions, allowing for more strategic use of talent.

I’m hearing this theme about the ability to better allocate time from manual, mechanical tasks to tasks that provide much more value to the business, rather than just having tangible benefits to the business from the output. This also applies to planning and forecasting. How did you implement AI into that part of the finance function?

Our planning efforts were becoming too complex. Remember, we are a large, multifaceted, global business. So they replaced their old model with SAP Analytics Cloud, delivering an AI-powered multi-year planning model.

We first applied it to modeling mergers and acquisitions, where accuracy is critical. This allows you to model the most complex datasets and allows finance teams to more easily collaborate across the business. As a result, forecasts are more accurate, the risk of error is reduced, and collaboration between management and practitioners is significantly improved. Initial results have been positive, encouraging broader expansion of the use of AI in planning.

What advice do you have for leaders who are less familiar with leveraging AI to significantly improve business performance?

Start with the high-impact functions that lead to real results. Next, focus on data quality and harmonization early on. It is the basis for everything that happens after that. Then get the rhythm right and get your team working together. Focus on the use cases that really matter to you. The best vendor will help you identify that use case. And make sure you get the support you need from those vendors and their partners.

Use AI to drive growth. At Accenture, we were able to use AI to save a lot of cash in one area and invest that money in another high-growth process, in our case, acquisitions. Here’s how you can use AI to completely rethink your business and take it to the next level.

Eli Lambert on advising other companies

Adopt a crawl, walk, run approach as you work through your work. Start slowly and increase the scale and pace of adoption over time. Be sure to invest in change management and upskilling to accelerate learning and adoption. We also work closely with established technology providers and system integrators. That accelerates everything.

My final suggestion is to use AI to drive growth. At Accenture, we were able to use AI to save a lot of cash in one area and invest that money in another high-growth process, in our case, acquisitions. Here’s how you can use AI to completely rethink your business and take it to the next level. And it is possible today in a way that was not possible five years ago. Let’s grab that chance.

I couldn’t agree more with Lambert. AI offers a real opportunity to rethink entire business processes for greater impact.

To continue exploring what’s possible, learn more about what Lambert’s team has done with AI at Accenture. Next, see more use cases for AI in finance and all major functions including procurement, supply chain, manufacturing, and more.

Brenda Bown is Chief Marketing Officer at SAP Business AI.