Accenture Foresight
Generative AI
How Generative AI Will Fundamentally Transform Our World
Reinvention in the age of generative AI
Executive Summary
Report
Video: Foresight in 15
Work, workforce, workers
Reinvented in the age of generative AI
Executive Summary
Report
Video: Foresight in 15
AI for Everyone
Report
Interactive Charts
Generative AI will transform work across industries
On average, 40% of working hours can be impacted by Large Language Models
Average across all
industries
Higher potential for automation
Higher potential for augmentation
Lower potential for augmentation or automation
Non-language tasks
Work time distribution and potential AI impact based on employment levels in the U.S. in 2021
Generative AI will impact every job category, but at different levels
Companies should invest in talent for
two distinct challenges: creating and using AI
Average across all
occupations
Higher potential for automation
Higher potential for augmentation
Lower potential for augmentation or automation
Non-language tasks
Work time distribution and potential AI impact based on employment levels in the U.S. in 2021
Video
Generalizing AI
Executive Summary
Report
Interactive Charts
Global executives are motivated by future advances in AI foundation models
These changes will see businesses shift focus from building their own AI to building with AI
%
are either very or extremely inspired by the new capabilities offered by AI foundation models
96
%
agree AI foundation models will enable connections across data types, revolutionizing where and how AI is used
97
%
agree AI foundation models will play an important role in their organizations’ strategies in the next 3 to 5 years
98
Global executives are astonished by the rapid pace of AI innovation
0
Executives in Europe are particularly amazed by how quickly AI is advancing
%
of global executives are astonished by the unprecedented pace of AI innovation
North America
Europe
Growth Markets
Why Data & AI?
Interactive Charts
Getting the data strategy right is a top challenge for implementing generative AI
There's work ahead to fully capitalize on data and the potential of AI
What are the challenges your organization faces in using and implementing generative AI today?
Data strategy
Cloud infrastructure
Accuracy
Talent
Execution
56%
46%
43%
38%
33%
53%
50%
65%
APAC
Europe
North America
of companies feel organizationally ready to scale up generative AI
56%
Over half of organizations are ready to scale up generative AI within 6 months
Even so, most organizations support some level of government regulation
See more
See previous
93%
support some level of government
regulation around AI
North America
Europe
APAC
96%
88%
94%
Nearly all organizations are increasing their tech investments
Technology underpins all other strategy needs of an enterprise, and investment is ramping up
0%
of companies plan to increase investments around technology spend as a percentage of revenue in June 2023 (compared to 89% in February)
North America
APAC
Europe
97%
92%
95%
Data and AI remain top investment priorities as technology spend increases
Areas where organizations plan to increase levels of spending in technology in 2023
Data/AI
67%
56%
Security
48%
46%
Digital transformation
46%
56%
Cloud
42%
46%
Customer experience
38%
40%
Cost transformation
32%
40%
Talent and change management
24%
25%
Product engineering
22%
28%
Workforce reskilling and upskilling*
19%
Metaverse*
19%
Outsourcing of non-core activities
11%
20%
See previous
*Metaverse and Workforce reskilling and upskilling has been introduced in wave 9. Previous data is not available.
See more
Securing Generative AI
Blog
Video
The Art of AI Maturity
Executive Summary
Report
Interactive Charts
Only 12% of organizations are “AI Achievers”
But those in that category outperform in nearly all capabilities
AI INNOVATORS
0%
Struggling to materialize their AI strategy
AI ACHIEVERS
0%
Capitalizing by building on strong foundations and a differentiated strategy
AI EXPERIMENTERS
0%
No strong AI foundations or clear strategy
AI BUILDERS
0%
Strong AI foundations but unclear differentiation strategy
AI differentiation
AI capabilities derived from the model of achieving at least 30% AI influenced revenue
AI foundation
AI foundation capabilities derived from the model of achieving at least 10% AI influenced revenue
Low
Low
High
High
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 mgmt and governance
Data mgmt and governance - change
Talent & Culture
Mandatory training
Employee competency in AI skills
Innovation culture embedded
Innovation culture encouraged
AI talent strategy
Responsible AI
Responsible AI by design
Responsible data & AI strategy - change
ACHIEVERS
BUILDERS
INNOVATORS
EXPERIMENTERS
See distribution
See capabilities
AI Achievers do things differently
AI Achievers already enjoyed greater revenue growth before the pandemic
Find out more about AI Achievers
50%
Revenue growth
pre-pandemic
(2019)
Peers
AI Achievers
What is an AI Achiever?
AI Achievers capitalize by building on strong foundations and a differentiated strategy
What do AI Achievers do differently?
- Their top leaders champion AI as a strategic priority for the entire organization
- They invest heavily in talent to get more from their AI investments
- They industrialize AI tools and teams to create a strong AI core
- They design and use AI responsibly, from the start
- They prioritize long- and short-term AI investments
AI-driven transformation is faster
Transformations can take time, but AI-driven are 13% faster than digital
90%
10%
75%
50%
25%
Percentage companies mentioning AI or digital for business transformation
2022
2010
2030
Digital transformation
AI transformation
16 months
(13%)
9 years
11 months
8 years
7 months
From AI compliance to competitive advantage
Report
Interactive Charts
The roadblocks to readiness
Complexity and a lack of clarity are the primary barriers to responsible AI
Learn why
do not have a fully operationalized and integrated Responsible AI Governance Model
35%Uncertainty around rollout process/timing
34%Potential for inconsistent standards across regions
Additionally, organizations may be unsure what
to do while they wait for regulations to be defined.
This lack of clarity can lead to strategic paralysis as companies adopt a “wait and see” approach.
Reactive companies tend to prioritize the specific requirements rather than the underlying risk, which can lead to problems down the road ... and value left on the table.
0%
Back
0%
of companies are struggling to operationalize across all the key elements of responsible AI
Responsible AI readiness
Most organizations have yet to turn favorable attitudes and intentions into actions
Fully in practice
Some
None
Responsible AI capabilities
6%
69%
25%
Developed a governance framework
62.3%
Implementing risk mitigation tools and techniques
44.1%
Defined a risk management portfolio
40.7%
The upside of AI regulation
Regulation is one way to address the trust deficit that arises from AI implementation
Leaders believe regulation will bolster trust and confidence in AI, which will have a beneficial effect on their business.
0%
Believe it will create opportunities for competitive advantage/differentiation
0%
Believe it can help attract/retain talent
0%
Think it will improve their ability to industrialize and scale AI
Cloud Data: A New Dawn for Dormant Data
Report
Video
7 architecture considerations for generative AI
Blog
Audio
What begins with a strong digital core can become much more
Explore our latest insights
Here’s how:
- Create a foundation: Migrate existing applications to the cloud and invest in cloud-based applications.
- Use the cloud for change: Standardize and simplify the processes modern cloud applications manage—enabling real-time insights and creating strategic agility.
Reimagining the future of work
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