A full 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. Nearly all C-suite executives view AI as an enabler of their strategic priorities. And an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. What’s more, three out of four C-suite executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely.
With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
How Crucial Is Scaling AI to Your Business?
Compare to see what respondents in your country are saying.
Compare to see what global respondents in your industry are saying.
Nail it, then scale it
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The study focused on determining the extent to which AI enables the business strategy, the top characteristics required to scale AI, and the financial results when done successfully. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Three distinct groups of companies with increasing levels of capability required to successfully scale AI emerged from the research—Proof of Concept Factory, Strategically Scaling, and Industrialized for Growth.
Analytics buried deep and not a CEO focus
Siloed operating model typically IT-led
Unable to extract value from their data
Struggle to scale as unrealistic expectations on time required
Significant under investment, yielding low returns
CEO focus with advanced analytics and data team solving big rock problems
Multi-disciplinary teams of 200+ specialists championed by Chief AI, Data or Analytics Officer
Able to tune out data noise and focus on essentials
Intelligent automation and predictive reporting
Catch up on digital/AI/data asset debt
Experimental mindset achieving scale and returns
Digital platform mindset and enterprise
culture of AI democratizing real-time
insights to drive business decisions
Clear enterprise vision, accountability,
metrics, and governance breaking
‘What if’ analysis enabling improved
acquisition, service and satisfaction
Responsible business practices
enhancing brand perception and trust
Competitive differentiator and value
creator driving higher P/E multiples
The Great Divide
Considering that the companies in our study collectively spent US$306 billion on AI applications in the past three years, the ROI gap amongst them is significant. How significant? US$110 million between companies in the Proof of Concept stage and Strategic Scalers.
Companies that strategically scale AI can achieve triple the return of their AI investments compared to companies pursuing siloed proof of concepts.
– Managing Director, Technology Strategy
While the C-suite executives surveyed reported positive ROI on their AI investments, we wanted to dig deeper. Was there any relationship between successfully scaling AI across the enterprise and key market valuation metrics? What was the “premium” for being a leader?
Using survey data combined with publicly available financial data, our team of data scientists created a model to identify the premium for companies in our sample that successfully scale AI, controlling for various characteristics of the companies.
We discovered a positive correlation between successfully scaling AI and three key measures of financial valuation with an average lift of 32% on Enterprise Value/Revenue Ratio, Price/Earnings Ratio, and Price/Sales Ratio.
How to succeed at scaling
The research revealed three critical success factors that separate the Strategic Scalers from organizations in the Proof of Concept stage. Strategic Scalers:
01. Drive “intentional” AI
02. Tune out data noise
03. Treat AI as a team sport
01. Drive “intentional” AI
Strategic Scalers pilot and successfully scale more initiatives than their Proof of Concept counterparts—at a rate of nearly 2:1—and set longer timelines. They are 65% more likely to report a timeline of one to two years to move from pilot to scale. And even though they achieve more, Strategic Scalers spend less. At first glance it may seem paradoxical. But the data indicate that these leaders are more intentional, with a more realistic expectation in terms of time to scale—and what it takes to do so responsibly.
To successfully scale, companies need structure and governance in place. And the Strategic Scalers have both. Nearly three-quarters of them (71%) say they have a clearly-defined strategy and operating model for scaling AI in place, while only half of the companies in Proof of Concept report the same.
Strategic Scalers are also far more likely to have defined processes and owners with clear accountability and established leadership support with dedicated AI champions. Initiatives not firmly grounded in business strategy and lacking a governance construct to oversee and manage are slower to progress. Turf wars break out over who “owns” AI. And, regardless of the AI platforms used, or the knowhow recruited, misaligned efforts fall flat.
Sizing up the situation
The “smaller” companies in our study generated revenues between US$1 and 5 billion a year. The largest had revenues of more than US$30 billion. When it comes to scaling AI, are there any major differences between these two groups of companies? Do the largest companies face lower scaling success rates due to their organizational complexity? Or, quite the opposite, do they achieve higher returns as they untap greater value potential?
When we grouped the surveyed companies by size, we found no significant differences in scaling success rate or return on AI investments. So, size is not a factor. It’s all about instilling the right AI capabilities and mindset in the organization.
02. Tune out data noise
My organization recognizes the importance of our core data as the foundation for scaling AI.
54% vs 37% Strategic ScalersProof of Concept
Ninety percent of the data in the world was created in just the past 10 years. One-hundred and seventy-five zettabytes of data will be created by 2025. Yet after years of collecting, storing, analyzing, and reconfiguring troves of information, most organizations struggle with the sheer volume of data and how to cleanse, manage, maintain, and consume it.
Strategic Scalers tune out “the noise” surrounding data. They recognize the importance of business-critical data, identifying financial, marketing, consumer, and master data as priority domains. And Strategic Scalers are more adept at structuring and managing data. The research shows they are much more likely to wield a larger, more accurate data set (61% versus 38% of respondents in Proof of Concept). And 67% of Strategic Scalers integrate both internal and external data sets as a standard practice compared to 56% of their Proof of Concept counterparts.
What’s more, they use the right AI tools—things like cloud-based data lakes, data engineering/data science workbenches, and data and analytics search—to manage the data (60% compared with 47%) for their applications. From creation to custodianship to consumption. Strategic Scalers understand the importance of using more diverse datasets to support initiatives.
From creation to custodianship to consumption, Strategic Scalers focus on data assets that underpin their AI efforts.
03. Treat AI as a team sport
The effort of scaling calls for embedding multi-disciplinary teams throughout the organization—teams with clear sponsorship from the top ensuring alignment with the C-suite vision. For Strategic Scalers, these teams are most often headed by the Chief AI, Data or Analytics Officer. They’re comprised of data scientists; data modelers; machine learning, data and AI engineers; visualization experts; data quality, training and communications, and other specialists.
It’s a lesson Strategic Scalers have learned well. In fact, a full 92% of them leverage multidisciplinary teams. Embedding them across the organization is not only a powerful signal about the strategic intent of the scaling effort, it also enables faster culture and behavior changes. In contrast, those still in Proof of Concept are more likely to rely on a lone champion within the technology organization to drive AI efforts.
92% of Strategic Scalers leverage multi-disciplinary teams
Industrializing for Growth is a dynamic destination.
From our experience, we know of three additional variables that speed companies along their journey to the ultimate destination: A data-driven culture where AI is driving exponential returns.
Focus on the ‘I’ in ROI
Adopt a digital platform mindset to scale
Build trust through Responsible AI
Scaling to new heights of competitiveness
There are reams of information on the “what” of AI. But scaling new heights of competitiveness with AI requires understanding the “how.” And at times eschewing conventional wisdom that continues to emerge as AI evolves:
It’s not just about SPEED
It’s about moving deliberately, in the right direction.
It’s not just about MONEY
It’s about aligning your investments to the right places with the intention of driving large-scale change.
It’s not just about MORE DATA
It’s about investing in your data, deliberately yet pragmatically, to drive the right insights.
It’s not just about a SINGLE LEADER
It’s about building multidisciplinary teams that bring the right capabilities.
Scaling the exponential power of AI across the enterprise is a journey. Those that learn the lessons on each path will reach a place where the business is seamlessly fused with intelligence that boosts productivity and effectiveness.
The result: industrialized growth through unassailable competitive strength in everything from organizational effectiveness to brand perception and trust.
Our research involved 1,500 C-suite executives from companies with a minimum revenue of US$1 billion in 12 countries around the world across 16 industries, with the aim to uncover success factors for scaling AI.