Why are CEOs struggling to achieve enterprise value for AI at scale?

Presented by Accenture

Data and AI initiatives are no longer just for the technical masterminds of the organization — they have now taken center stage as boardroom and CEO priorities. Executives know the growing value of being a data-driven business: they’ve witnessed new business models bloom, innovations take off, and customer expectations rise in tandem — if not for their own organization, then for their competitors.

As a part of a recent research report, Make the Leap, Take the Lead, Accenture found that companies who embraced data and AI grow revenues 5x the speed of those who don’t. We found that cloud investment has accelerated across the market, which in turn enables organizations to mobilize the benefits of data and AI. But for organizations that have been successful in putting data and AI at the forefront of their business, we’ve found three common success factors:

• They know their customers better by leveraging data and AI insights in new and more robust ways.

• They optimize their business processes to improve efficiencies and allow for the flexibility to adapt with changing business requirements and priorities.

• They make intelligent products and services and foster innovation tied to business outcomes.

Each of these factors come with challenges and metrics of success unique to their business but their presence in any organization is essential to developing the three key components of data-led transformation at scale.

1. Define data & AI vision and value potential

Identify your critical data and quick-win use cases early by prioritizing the data elements that unlock the insights needed for your company to drive business value. Don’t wait to determine how you will measure success. Define a framework for metrics that will assess both your quick-wins and medium-term goals. Keep these measures central to your strategic communication around business outcomes and ensure they are used to continually measure throughout your journey. Prioritize your initiatives by function and the effort, timeline, and impact if achieved. Select projects that can deliver ROI quickly and deliver immediate business value to gain momentum for the data-driven evolution.

2. Build intelligent (and scalable) data and AI platforms on cloud with the right ecosystem partners

One of the biggest challenges companies face is a proliferation of data across platforms and systems. They have data in myriad forms across legacy systems, on-premise or hybrid environments, and a complex partner ecosystem. There is often no single place people can go to consume data or get data-driven insights, which makes it difficult for companies to make data easily accessible to people in the right form and context.

It also makes it virtually impossible to apply AI at scale across the enterprise to uncover new sources of value — which will ultimately hinder any significant transformation efforts. We’ve been saying this a lot: cloud is the enabler, data is the driver, and AI is the differentiator that will help you do something different with your data.

Cloud technologies continue to be an essential prerequisite for AI, but a cloud migration alone won’t enable organizations to scale or drive value from their investments at the speed required. Furthermore, considering relationship complexities and selecting the right cloud ecosystem partner for your business is critical to ensuring the capabilities can be fully realized. What organizations do once they have enabled cloud technologies determines how much business value will be realized.  

Strategic organizational operationalization and use of data and AI is the key to unlocking business value from cloud. And in practice, Data-driven Transformation enables better decision-making because it allows organizations to scale AI and data across their business.

3. Enable talent and a culture of data and AI literacy

Change the culture, change the mindset. Quite simply, data literacy needs to be improved across the workforce for a business to break away from its peers. But change doesn’t come easy, especially when introducing new and rapidly evolving data and AI topics to a broader audience. Accenture surveyed hundreds of data and AI leaders across industries to see what’s changed.

• Talent and culture were overwhelmingly the top two challenges faced by leaders when delivering on the data vision of the organization.

• 80% of CDOs felt that the main change in their job responsibilities over the last 12 months was a shift to focusing on business value, with 70% citing that the criticality of this shift is due to needing to leverage data as a competitive advantage for market differentiation.

• CDOs identified that the top skills required to be successful in their role are the ability to serve as an agent of change and as an evangelist of data and AI across the organization.

• 50% of the CDOs we surveyed said that they lacked the right skills and talent for their desired operating model.

Leaders are able to find and retain the best talent and instill data and AI literacy at every level. Their key to success is changing the way organizations think and in turn, how its talent acts. This critical shift means embracing a new mindset and understanding that data enables everyone — not just those with tech-centric roles.

But finding a way to generate value from data and AI won’t happen without intentionality. It takes a combination of the right foundation, the right people, and strategic decisions to put data and AI at the center of everything you do. The result? A cloud enabled, data-driven business that taps into data value and uses AI in new ways for sustainable business growth — whether that’s improving operational efficiency, delivering more exceptional customer experiences, or creating new revenue streams through intelligent products and services.​

Joseph Depa is Global Lead, Data-led Transformation at Accenture.