The Art of AI Maturity | Accenture

Source: Accenture Research.

Note: Our estimate is derived from a natural language processing analysis of investor calls of the world’s 2,000 largest companies (by market cap), from 2010 to 2021, that referenced “AI” and “Digital” in tandem with “business transformation,” respectively. Data was sourced from S&P earnings transcripts.

Only 12% of companies are AI Achievers

Discover the varying levels of AI Maturity across different industries, company sizes and geographies using the filters below. Click reset to return to the global view.

Filter by

• No. of companies in category

Select

Reset

AI DIFFERENTIATION

AI capabilities identified as key drivers to

achieve at least 30% AI influenced revenue

LOW HIGH

LOW HIGH

AI FOUNDATION

AI capabilities identified as key drivers to achieve at least 10% AI influenced revenue

AI INNOVATORS

2%

Companies that have mature AI strategies but struggle to operationalize

AI ACHIEVERS

2%

Companies that have differentiated AI strategies and the ability to operationalize for value

AI EXPERIMENTERS

8%

Companies that lack mature AI strategies and the capabilities to operationalize

AI BUILDERS

2%

Companies that have mature foundational capabilities that exceed their AI strategies

AI Achievers outperform in nearly all capabilities

Explore more below to better understand the AI capabilities and what sets each group apart.

Achievers Builders Innovators Experimenters

Strategy and Sponsorship

Senior sponsorship

AI Strategy

Proactive vs. Reactive

Readily available AI and ML tools

Readily available developer networks

Data and

AI Core

Build vs. Buy

Platform and technology

Experimentation data - Change

Data management and governance

Data management and governance - Change

Talent and

Culture

Mandatory training

Employee competency in AI-related skills

Innovation culture embedded

Innovation culture encouraged

AI talent strategy

Responsible

AI

Responsible AI by design

Responsible data & AI strategy - Change

Senior Sponsorship

Organizations have an AI strategy that is developed by the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an equivalent. The CEO and the Board actively sponsor and share accountability for the strategy and associated AI initiatives.

AI Strategy

Organizations not only have a core AI strategy aligned to the overall business strategy, but they also dedicate tools and tactics to execute it and continuously track their performance against that strategy.

Proactive vs. Reactive

Organizations have the resources (such as technology, talent, and patents) to proactively define and demonstrate how AI can create value vs. apply AI as a reaction to a need. They’re first-movers instead of fast followers in terms of applying AI for business value.

Readily available AI and ML tools

Organizations work with an ecosystem of technology partners to access machine learning models and tools to help innovate new products and services.

Readily available developer networks

Organizations tap into an ecosystem of technology partners to access developer networks that support the development of new products and services.

Build vs. Buy

Organizations develop custom-built AI applications or work with a partner who offers solutions as-a-service, vs. purchase “off-the-shelf” AI solutions with little-to-no customization.

Platform and Technology

Organizations apply the necessary cloud, data and AI infrastructure, software, self-serve capabilities and industry best practices, and they adopt the latest tools available from platform and technology partners.

Experimentation Data — Change

Organizations improved their use of experimentation data between 2018 and 2021, effectively translating into a higher data and AI maturity. Experimentation data is the use of internal and external data to design new models and generate new insights. To do that, organizations use enterprise-grade cloud platforms to keep data clean and trustworthy, and to support decision making at greater speed and scale.

Data Management and Governance

Organizations scale their data management and governance practices to increase data quality, trust, and ethics across entities —e.g., by implementing master data management and ensuring security, compliance and interoperability.

Data Management and Governance — Change

Organizations improved their data management and governance practices between 2018 and 2021, effectively translating into a higher data and AI maturity.

Mandatory AI Training

Organizations enforce AI-specific training programs to improve AI fluency, which are tailored for senior leadership and specific functions, e.g., salesforce, product engineers, etc. They also create deliberate opportunities for employees to learn and apply AI in their roles.

Employee Competency in AI-Related Skills

Organizations regularly measure the competency level of employees to determine where further training is needed to improve overall acumen. They measure and build acumen in critical areas like coding, data processing and exploration, business analytics, domain and business expertise, ML, visualization and more.

Innovation Culture Embedded

Organizations ensure innovation is part of the day-to-day work environment. They encourage mindsets, behaviors and routines that all serve as a vehicle for experimentation, collaboration and learning from ideation to product development to market launch.

Innovation Culture Encouraged

Organizations promote and reward innovative mindsets and behaviors including entrepreneurship, collaboration and thoughtful risk-taking.

AI Talent Strategy

Organizations have an AI talent strategy - hiring, acquiring, retention - that evolves to keep pace with market or business needs. They also have an AI talent “roadmap” for hiring diverse AI-related roles, beyond “just” ML engineers—such as behavioral scientists, social scientists, and ethicists.

Responsible AI

Organizations have an industrialized, responsible approach to data and AI across the complete lifecycle of their AI models—an approach that can meet changing regulatory requirements, mitigate risks, and support sustainable, trustworthy AI.

Responsible AI—Change

Organizations have improved their responsible data and AI practices between 2018 and 2021, effectively translating into a higher data and AI maturity.

Note: Each square represents one of the 17 key capabilities. The square is filled in where the AI Maturity profile is out-performing against peers (higher than the average across all companies in terms of % of companies reaching the mature level).

Out-performing

Under-performing

Deep Dive: The Elements of AI Maturity

Practice makes progress

Accenture Foresight app

Stay ahead of change – explore our new thought leadership

app for a personalized feed of insightful perspectives

that prepare you for what’s to come.