AI Road Map: How Accenture Uses AI as a Growth Engine
Nearly every enterprise leader today thinks about how to leverage AI to accelerate business outcomes—where to get started is another matter.
A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. Eli Lambert, a managing director of Finance in the Global IT division at Accenture, is one of those people.
The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture’s scale might be intimidating to some, but not Lambert. He’s leading an ongoing transformation of Accenture’s finance function, which he calls “the heartbeat” of the company.
The results he’s achieved— including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting—shine a spotlight on what’s possible. And he’s just getting started.
Accenture is a multinational professional services firm that specializes in IT and management consulting
- 780,00 employees in 52 countries
- 350 partners
- 9,000 clients
- Recognized for 20 years by Fortune’s “most admired companies” list
- Ranked first in industry, and fifth overall, on “Just Companies” list
I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation
Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?
A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we’re hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.
For many companies, the key to getting impactful results from business AI is to start with one function that’s central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?
I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, “Let’s not accidentally cause a heart attack for the organization.”
Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And SAP gives us a digital core where all that transactional data is harmonized.
The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.
Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?
If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here’s a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it’s easy for decisions to default to historical, manual reviews. That’s what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.
[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.Eli Lambert
We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That’s how we built what we call “Intelligent Cash.” It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.
AI is so good at this, and here’s what’s incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because SAP’s data cloud brings [SAP] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.
I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization. I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?
Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I’m talking about. So, we co-developed on the SAP platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.
As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the SAP platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.
In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team’s day-to-day experiences?
We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.
[Our] Intelligent Financial Advisor, built on the SAP platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That’s saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.Eli Lambert
We then deployed an Intelligent Financial Advisor built on the SAP platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That’s saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It’s also helping create more consistent roll-ups across regions and letting us use our talent more strategically.
I’m hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?
Our planning work had grown too complex. Remember, we’re a large-scale, multifaceted global business. So, we replaced old models with SAP Analytics Cloud, which gives us multi-year planning models enhanced by AI.
We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.
What advice do you have for leaders who are not as far along in using AI to supercharge business results?
First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it’s the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you—the best vendors can help you identify those—and make sure to get the help you need from those vendors and their partners.
Use AI to spur growth. At Accenture, we’ve been able to use AI to save significant cash in one area, which we then invest in another, high-growth process—acquisitions in our case. That’s how you use AI to really rethink your business and move it to the next level.
Eli Lambert, on advice to other enterprises
As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who’ve been there before. That accelerates everything.
The final suggestion I have is to use AI to spur growth. At Accenture, we’ve been able to use AI to save significant cash in one area, which we then invest in another, high-growth process—acquisitions in our case. That’s how you use AI to really rethink your business and move it to the next level. And that’s possible today in ways that were not, even five years ago. Seize that opportunity.
I couldn’t agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.
To keep exploring what’s possible, learn more about what Lambert’s team has done with AI at Accenture. Then see more AI use cases in finance and across all your key functions, including procurement, supply chain, manufacturing, and more.
Brenda Bown is chief marketing officer for SAP Business AI.
