I recently had the privilege of participating as a panel speaker in an Accenture webcast entitled Are You Ready for the Next Black Swan? alongside colleagues Cherene Powell and Todd Rebella. The focus of the webcast – hosted by David Davidson, head of Accenture’s CFO and Enterprise Value practice for North America – was how to digitize finance for improved capability and agility. The pandemic, in fact, is a perfect example of the “black swan” event that is unpredictable and has extreme consequences.

My fellow panelists discussed strengthening the digital core of finance and how to get finance talent ready for this new era. I talked about the importance of data and analytics. Like my colleagues, I’m working with finance leaders who are trying to get the best possible basis for making crucial decisions.

Companies still in early stages of analytics development

CFOs need to make the right decisions, and that means optimizing data through analytics and harnessing artificial intelligence (AI). But, while most companies today are using analytics to support decision-making and advanced business process automation, fewer than five percent have reached broad scale across the enterprise.

<<< Start >>>



<<< End >>>

I’m seeing some signs that the pandemic is serving as a catalyst to get companies to move from experimenting with analytics and AI to harnessing the full power of these technologies. When Accenture surveyed 1500 C-suite executives across 16 industries last year, we found that 80 to 85% of companies were on what we called the proof of concept factory path. That means they conduct AI experiments and pilots but have a low scaling success rate and low return on investments.

Only 15 to 20% of companies were on the strategic scaling path. This means going beyond proof of concept to achieve a much a higher success rate, more scaling of AI, and a much higher return (nearly three times higher than the proof of concept group). But there is a smaller group – less than 5 percent – who are industrialized for growth. They create a culture of AI with data and analytics democratized across the organization.

These organizations promote product and service innovation and realize benefits from increased visibility into customer and employee expectations. Our research indicates that industrializing AI this way supports competitive differentiation. That correlates with significantly better financial results.

<<< Start >>>

80 to 85%

Companies on the proof of concept factory path

<<< End >>>

<<< Start >>>

15 to 20%

Companies were on the strategy scaling path

<<< End >>>

<<< Start >>>

< 5%

Companies that are industrialized for growth

<<< End >>>

When we polled webcast participants online, 50% said they were in the experimental stage, 29% in the proof of concept factory, and 21% described themselves as strategic scalers. No participants described themselves as industrialized for growth.

Harnessing the power of data and analytics

So how do organizations get on the path to strategic scaling and, ultimately, industrializing for growth?

Here are three critical success factors that set the leaders apart:

1. Treat AI as a team sport. The leaders we have looked at have a structure and governance in place, with a clearly defined model and strategy for scaling AI. They have owners with clear accountability and dedicated AI champions for initiatives that are closely linked to business strategy.

2. Recognize the importance of managing data. Strategic scalers and industrializers recognize the importance of business-critical data and they invest heavily in data quality, data management, and data governance frameworks on the cloud.

3. Position needed talent and skills. We found that scaling calls for multidisciplinary teams, along with clear sponsorship from the top and alignment with the C suite’s vision are key. Strategic scalers’ teams are usually headed by a chief AI data or analytics officer. Organizations still in the proof of concept stage are more likely to rely on a lone champion to drive AI efforts. Organizations further along the maturity curve are also using new skills such as human-centric design or are tapping into the social and behavioral sciences to establish responsible business practices.

<<< Start >>>

Enterprise analytics isn't about a single problem in a vacuum. It's about optimizing and connecting information across the whole network. 

<<< End >>>

Enterprise analytics isn’t about a single problem in a vacuum. It’s about optimizing and connecting information across the whole network. The CFO might ask, for example, how can we get a holistic view of working capital and improve overall cash conversion? This requires gathering data sets from payables, receivables, and inventory. These data sets support questions from procurement, supply chain, risk, and other areas.

When you connect the data, you have an integrated data architecture. When you connect the analytic models that consume the data, the models talk, share insight, become scaled, and become industrialized. You have a digital brain, an enterprise neural network that provides integrated decision support. This is enterprise analytics, industrialized for growth.

Black swans are inevitable. Companies that can stabilize quickly in uncertain environments and shift their focus to investment are the ones that tend to recover and outperform markets after these events. It’s not easy getting there. The C-suite needs to view AI investment as the cost of doing business. The investments should be aligned with the intention of driving large-scale change. The organization needs multidisciplinary teams with the right skills and mindsets. But the results – including greater resiliency and a faster rebound in a post-black swan environment – are worth the effort.

Lauren Kroes

Senior Manager – CFO & Enterprise Value, Enterprise Analytics

Subscription Center
Subscribe to Business Functions Blog Subscribe to Business Functions Blog