Leaders are maximizing the return on enterprise AI investments

NOVEMBER 14, 2019

Scaling to new heights of competitiveness

  • 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale.
  • Three out of four C-suite executives believe that if they don’t scale artificial intelligence (AI) in the next five years, they risk going out of business entirely.
  • Companies in our study that are strategically scaling AI report nearly 3X the return from AI investments compared to companies pursuing siloed proof of concepts.
  • Further analysis validated a positive correlation between Strategic Scaling and a premium of 32%, on average, for three key financial valuation metrics.


The numbers tell the story

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?

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.

01 Proof of Concept Factory

  • 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
  • In our experience, 80-85% of companies are here

02 Strategically Scaling

  • 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
  • We estimate that 15-20% of companies are here

03 Industrialized for Growth

  • Digital platform mindset and enterprise culture of AI democratizing real-time insights to drive business decisions
  • Clear enterprise vision, accountability, metrics, and governance breaking down silos
  • ‘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
  • Less than 5% of companies have evolved to this point
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. 3XStrategic Scalers achieved nearly triple the return from AI investments than companies in the Proof of Concept stage of their AI journey

Paying dividends

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 Scalers  Proof 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.


*We are here to help you navigate so schedule a call to discuss your specific business goals

How to build a responsible future for Emotional AI

How to build a responsible future for Emotional AI


In brief

  • Emotional AI will redefine products and services as we know them. But how can companies use the technology responsibly?
  • Emotional AI is helping businesses detect peoples’ emotions in real time—by decoding facial expressions, analyzing voice patterns and more.
  • This technology offers opportunities for Communications, Technology and Platform companies to reinvent customer engagement, but there are risks.
  • We share key considerations companies should take into account and next steps businesses can take to address risks associated with Emotional AI.

Learning to feel

Emotional AI technology can help businesses capture peoples’ emotional reactions in real time—by decoding facial expressions, analyzing voice patterns, scanning e-mails for the tone of language, monitoring eye movements and measuring neurological immersion levels.

Emotional AI will not only offer new metrics to understand people; it will redefine products and services as we know them. But Emotional AI also brings risks. The data collected using Emotional AI technology will test companies with a whole new set of ethical challenges that require responsible actions.

The data and responsibility opportunity

Emotional AI will be a powerful tool that will force businesses to reconsider their relationships with consumers.


Companies that lead in both responsibility and intensity of emotional data usage experience on average a 63% gain in total revenue compared to the average company within the industry.


The same companies experience on average a 103% gain in EBITDA (earnings before interest, taxes, depreciation and amortization) compared to the average company within the industry.Leaders from the technology and platforms industries have a responsibility to act now to prepare for the Emotional AI era. No doubt privacy battles will erupt as our inner lives become a currency.

Coming to our senses

Emotional AI applications can lead to better experiences, better design and better service for customers. They also hold the potential to open up a completely new world of opportunities for Communications, Technology and Platform companies.


In brick-and-mortar stores across the world, cameras and sensors are gauging shoppers’ sentiments to target offerings and help make future decisions.


Enterprises are working on emotional-recognition training for digital voice assistants to aid in health monitoring and consumer sentiment analysis.

Video + audio

Emotional AI is improving auto safety. For example, cameras and microphones can pick up on passenger drowsiness—and jolt the seatbelt as a result.


Many applications have been created to not only detect emotion in text but make editorial recommendations based on reading level and other criteria.

Feeling the risks

Reading people’s emotions is a sensitive science. The data collected using Emotional AI technology will test companies with a whole new set of ethical challenges. Based on our research, we see four aspects of data collection and usage that merit close attention: Systems Design, Data Usage, Transparency and Privacy.

1. Systems design

As more and more companies incorporate Emotional AI in their operations and products, it’s going to be imperative that they’re aware of and actively work to prevent bias in their systems.

2. Data usage

Consumers may cry foul if their emotional data is being used for the company’s gain and not their own. The more value a consumer derives from sharing their data, the more they trust service providers.

3. Transparency

Businesses often fail to explain the benefits to the user of collecting their emotional data. Companies should be transparent with the user about what is being collected, how and why.

4. Privacy

As emotional data collections become more sophisticated, privacy and data protection will become more complex, and clarity around data ownership, usage and meaningful consent will become more urgent.

A new sense of responsibility

Given the role of the communications, technology and platform industries in the design of emotional data collection and usage across all industries, their approach towards responsibility in the use of emotional data becomes central to how responsibility is woven into Emotional AI as its use expands across all industries. This role needs to be taken seriously. To become a responsible steward of Emotional AI, companies need guiding principles for how data is captured and leveraged. In addition, there is a set of actions firms can take to drive stronger responsibility across three layers—individual, company and industry ecosystem—of operations.

Listen to your employees

As shown in employee protests, there’s a strong sense of ethics and doing the right thing. Empower these employees to drive an ethical mindset.

Lean on diversity

Diversity in the workplace can help executives ensure that AI systems are designed and trained with the least possible biases.

Bring responsibility to startup culture

A start-up mentality can take businesses a long way, but firms need to build a responsibility mindset into the “minimal viable product” culture.

Draw on outside experts for responsible design

Embedding an ecosystem of experts and collaborators into business processes ensures a better lens into the impact of collecting emotional data.

Extend the reach of risk assessment

Be honest and explicit about worst-case scenarios. Continuously revisiting risk assessment questions builds foresight and helps detect shifting trends.

Build responsibility into partnership agreements

Ensuring ethical principles can be fulfilled requires the entire partner ecosystem to operate on the same principle

*We are here to help you navigate so schedule a call to discuss your specific business goals