Thriving in the era of pervasive AI · Thriving in the era of pervasive AI. Deloitte’s State of...
Transcript of Thriving in the era of pervasive AI · Thriving in the era of pervasive AI. Deloitte’s State of...
Thriving in the era of pervasive AIDeloitte’s State of AI in the Enterprise, 3rd Edition
A report by the Deloitte AI Institute & the Deloitte Center for Technology, Media & Telecommunications
Copyright © 2020 Deloitte Development LLC. All rights reserved. 2
About the survey 3
Key insights 5
The significance of AI 12
Pursue creative approaches 20
Become smarter consumers 26
Actively address risks 32
Recommendations 41
Copyright © 2020 Deloitte Development LLC. All rights reserved.
Contents
Copyright © 2020 Deloitte Development LLC. All rights reserved. 3
To obtain a global view of how organizations are adopting, benefiting from, and managing artificial intelligence (AI) technologies, Deloitte surveyed 2,737 IT and line-of-business executives in nine countries, between October and December, 2019.
All companies were adopters of AI technologies and respondents were required to either: • Determine AI technology spending and/or approve AI
investments• Develop AI technology strategies• Manage or oversee AI technology implementation• Serve as an AI technology subject matter expert • Make or influence decisions around AI technology
To complement the blind survey, Deloitte also conducted in-depth interviews with AI experts from various industries.
About the survey
Copyright © 2020 Deloitte Development LLC. All rights reserved.
Copyright © 2019 Deloitte Development LLC. All rights reserved.
Deloitte surveyed business and IT leaders to understand how organizations are using artificial intelligence (AI) technologies.
Respondent profile2,737 IT and business executives
Country N %
United States 1104 40%
Canada 300 11%
China 300 11%
United Kingdom 218 8%
France 203 7%
Japan 203 7%
Germany 201 7%
Australia 108 4%
Netherlands 100 4%
Industry %
Technology, Media and Telecom 33%
Energy, Resources and Industrials 20%
Consumer 19%
Financial Services 16%
Life Sciences and Health Care 10%
Education 2%
About the survey
Copyright © 2020 Deloitte Development LLC. All rights reserved.
Other
10%Senior Dir/Dir
8%SVP/VP
32%CIO/CTO
23%CEO/
President
13%Owner/partner
9%500-999
36%1,000-4,999
27%5,000-9,999
28%10,000+
Company size
12%$50M - $500M
17%$500M - $1B
33%$1B - $5B
25%$5B -…
13%$10B+
47%IT
53%LOB
Annual revenue
Title Role
72% from C-suite
Source: State of AI in the Enterprise, 3rd Edition, Deloitte 4
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Key insights
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Thriving in the era of pervasive AI – Key insights
Adopters continue to have confidence in AI technologies’ ability to drive value and advantage
Early-mover advantage may fade soon
Virtually all adopters are using AI to improve efficiency; mature adopters are also harnessing the technologies to boost differentiation
AI adopters tend to buy more than they build, and they see having the best AI technology as key to competitive advantage
Adopters recognize AI’s risks, but a “preparedness gap” spans strategic, operational, and ethical risks
Copyright © 2020 Deloitte Development LLC. All rights reserved. 6
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Adoption is continuingto accelerate
Current adopters see AI as essential
However, their advantage may diminish
The “early adopter” phase is ending –the market is moving into the “early majority” phase
Adopters are continuing to invest and gain competitive advantage
Barriers to AI adoption are lowering –it's becoming easier to implement and integrate
37% of organizations have now deployed AI – a 270% increase from four years ago
Global spending on AI to top US$35 billion in 2019 and more than double to US$79.2 billion by 2022
73% of adopters say AI is very or critically important to their business success today
71% of adopters expect to increase their investment next fiscal year
74% of adopters say AI will be integrated into all enterprise applications within three years
61% say AI will substantially transform their industry in next 3 years
AI adoption is maturing – competitive advantage may be harder to keep
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To maintain their edge, adopters should focus on three things:
Pursue creative approaches Become a smarter consumer Actively address risks
Most adopters are using AI technologies for optimizing the efficiency of processes and improving current products
Because the barriers have lowered and AI technology is easily available, "choosing right" is more important than ever
The vast majority of adopters feel somewhat prepared to address AI risks, but not enough are implementing specific practices to address them
The functional areas where AI is used are dominated by IT and cybersecurity – other areas are underdeveloped
Only 47% of all adopters say that they have a high level of skill around selecting AI technologies and technology suppliers
56% of adopters agree that their organization is slowing its adoption of AI technologies because of the emerging risks
What is needed to compete and win in a future where AI is ubiquitous?
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To identify a group of AI leaders to emulate, we will explore what “Seasoned” AI adopters are thinking and doing“Seasoned” adopters have:
1–5 6–10 11+
High
Low
Number of AI production deployments
Expertise in building, integrating,
and managing AI
Seasoned26%
Skilled47%
Starters27%
built multiple AI systems
shown a high level of maturity selecting appropriate technologies, identifying use cases, building and integrating AI solutions, and staffing
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
Seasoned26%
Skilled47%
Starters27%
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There are varying levels of maturity across industries – Technology, Media & Telecommunications has the highest proportion of Seasoned adopters
31%
34%
23%
28%
28%
24%
54%
46%
54%
46%
45%
44%
15%
19%
23%
25%
25%
31%
Education
Consumer
Financial Services
Life Sciences & Health Care
Energy, Resources & Industrials
Technology, Media & Telecom
Starters Skilled SeasonedN=2,737Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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There’s been a shift since the State of AI in the Enterprise, 2nd edition –Skilled and Seasoned have grown and Starters have diminished
36% 43% 21%27% 47% 26%
Starters Skilled Seasoned
Shifting maturity
2018 2020Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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The significance of AICopyright © 2020 Deloitte Development LLC. All rights reserved. 12
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13%
20%
42%
50%
41%
23%
In 2 years
Today
No/minimal strategic importance Somewhat importantVery important Critical strategic importance
Adopters believe that AI is key to leading today and in the future
Strategic importance of AI to organization’s business success
N = 2,737
83%of adopters say AI will be very or
critically important to their business success in next 2 years
90% of Seasoned adopters say AI is very or critically important to their organization's business success today
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Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Organizations are embracing key AI technologies – with almost universal adoption in the next year
67%
97%
54%
95%
58%
94%
56%
94%
Machine learningused by
Currently using
Using + plan to use in next
year
Deep learningused by
Currently using
Using + plan to use in next
year
Natural language processingused by
Computer visionused by
Currently using
Using + plan to use in next
year
Currently using
Using + plan to use in next
year
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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1%
15%
30%
37%
17%
< $1M
$1M to $10M
$10M to $20M
$20M to $50M
> $50M
AI investment (people + hardware + software + consulting)
Increasing investment
N = 2,737
71%of adopters expect to increase
their investment next fiscal year(by an average of ~26%)
Adopters are investing considerably and are planning to increase their investment in the near term
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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AI investment last fiscal year (people + hardware + software + consulting)
Typical payback period for AI projects
N = 2,737
65%
43%
32%
34%
57%
68%
Starters
Skilled
Seasoned
Less than $20 million $20 million and more
10% 20%
23%
17%
44%
47%
47%
25%
25%
35%
Starters
Skilled
Seasoned
Too early > 2 years 1-2 years < 1 year
More mature AI adopters invest more and report shorter payback periods
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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The majority of adopters believe that AI will substantially transform both their organization and industry in the next 3 years
Note: Percentages may not total 100 percent due to a small number of respondents who answered “Don’t know"N = 2,737
9%
23%
43%
21%
3%8%
17%
36% 26%
9%
Now Less than 1 year In 1-3 years In 3-5 years Beyond 5 years
AI will transform our organization AI will transform our industry
75% say AI will transform their organization 61% say AI will transform their industry
Within 3 years:
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Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Although adopters are still bullish on AI, their early advantage may wane as barriers to adoption fall and deployment spreads
The future of AI
74%of adopters say AI will be integrated into all enterprise applications within three years
N = 2,737
Gartner – according to their 2020 CIO survey, more than two of five enterprises plan to deploy AI by the end of 2020
SOURCES: IDC, Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, Sept 2019; Gartner, 2020 CIO Agenda: Industry Perspectives Overview, Jan 2020
IDC – spending on AI systems will reach $97.9B in 2023, more than 2.5x the $37.5B spent in 2019
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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“One of the things that I’m most excited about is the proliferation of AI platforms so that everybody starts off not at ground zero but in a place where another researcher has ended. That is going to be one of the fundamental reasons why we will have rapid progress over the next few years.”
Manohar PaluriAI researcher
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Pursue creative approaches
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AI technologies are still primarily used in IT and cybersecurity related functions
Functions where AI is primarily used (percent rating each a top-two function in which AI is used)
5%6%7%8%8%9%
11%12%13%13%15%16%22%47%
Procurement
Legal/Compliance
HR
Marketing
Finance
Strategy
Sales/Business Development
Supply Chain/Logistics/Distribution
Customer Service
Operations
Engineering/Product development
Production/Manufacturing
Cybersecurity
IT
N = 2,408 (those using AI technologies internally)
For each functional area, AI is primarily used to automate or optimize the efficiency of functions – versus enhancing them in new ways
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Adopters are seeking and achieving efficiency through AI
Lowering costs
Reducing headcount
Improving decision-making
Making processes more efficient
Enhancing relationships with clients/customers
Making employees more productive
Discovering new insights
Enhancing existing products and services
Creating new products and services
Enabling new business models
30%
32%
34%
36%
38%
40%
42%
10% 15% 20% 25% 30%
% who reported as top two potential benefits pursued
% w
ho a
chie
ved
outc
ome
to a
hig
h de
gree
Blue dotted lines represent the average of respective dimensions
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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“Many large pharma are thinking about AI in [terms of] cost savings and efficiency. When you’re running a massive organization that has hundreds of active clinical trials costing millions of dollars, there are low-hanging fruit that are not necessarily scientifically complex where AI can save the organization hundreds of millions of dollars.”
Ron Alfa Senior VP of Translational Discovery, Recursion Pharmaceuticals
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Efficiency is the top desired outcome for all segments, but Seasoned adopters focus more on product creation, while Starters focus on cost reduction
N = 2,737
Making processes more efficient
Lowering costs
Enhancing existing products and services
Creating new products and services
Rank of top desired outcomes of AI efforts (from list of 10 outcomes) Starters Skilled Seasoned
1
2
4 84
3
2
3
11
3
1
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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“Once you get used to automating business processes with technologies such as RPA, AI and machine learning, you’re not going to go back. It changes the way you do business. As much as doing things faster and better, it allows you to do things that were not possible before. If you can take a three-week-long mortgage application process and reduce that task to five minutes, how can you go back? You have changed the business model. Suddenly, it has become a differentiator for you.”
Prince KohliCTO, Automation Anywhere
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Become a smarter consumer
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17% 33% 30% 13% 8%
Adopters tend to buy AI technologies more than they are building them
Build or buy?
N = 2,737
Buy all Buy more than build Even blend Build more than buy
Build all
Build moreBuy more
Seasoned (53%) and Skilled (51%) adopters are more likely to buy the AI systems they need than Starters (44%)
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Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Adopters say optimizing their data infrastructure for AI and having the best AI technologies are key to boosting competitive advantage
Top AI initiative to increase competitive advantage
6%
9%
14%
16%
17%
19%
20%
Using lower-code or AutoML
Hiring world-class AI experts
Developing partnerships that will help execute our AI initiatives faster
Deploying data science and AI development platforms
Utilizing cloud-based AI services and capabilities
Gaining access to the newest and best AI technologies
Modernizing our data infrastructure for AI
N = 2,737Source: State of AI in the Enterprise, 3rd Edition, Deloitte
Less than half of adopters (47%) say that they have a high level of skill around selecting AI technologies and technology suppliers
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“To the customer, it is not AI that matters—it’s the fact that magically, the business process got done. People don’t buy AI. What they buy is the solution to a real problem that they have. If it happens to use AI, then it happens to use AI, but that doesn’t matter to them in the end as long as the task is completed on time and with accuracy.”
Prince KohliCTO, Automation Anywhere
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Most adopters are using some sort of cloud-based or open-source AI capabilities – learning how to integrate these tools and techniques can be essential
AI development techniques and tools used
30%
34%
37%
40%
40%
48%
Low-code/no-code AI tools
Self-service data preparation tools
Tools for developing conversational interfaces
Tools to provide transparency into AI systems
Automated machine learning tools
Data science/ machine learning platforms or frameworks
N = 2,737
93% using some sort of cloud-based AI capability
78% using some sort of open-sourced AI capability
54%of adopters say that they
have a no/low/medium level of skill around integrating AI technology into their existing IT environment
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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The Seasoned take a more centralized approach to their AI technology and vendor selection
40%47%
11%
32%
45%
18%
28%
38%
26%
Centralized Hybrid Decentralized
Seasoned Skilled Starters
N = 2,718
Approach to AI technology and vendor selection
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Actively address risksCopyright © 2020 Deloitte Development LLC. All rights reserved. 32
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A majority of adopters are worried
Impact of risks
agree that their organization is slowing its adoption of AI technologies because of the emerging risks
agree that negative public perceptions will slow or stop adoption of some
AI technologies
N = 2,737
Public concern
56% 56%
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Actively managing AI-related risks is as worrisome as perennial AI challenges such as data management, cost, integration and implementation
Top AI challenges (select up to 3)
18%
20%
22%
23%
27%
28%
29%
30%
30%
30%
Lack of executive commitment
Difficulty identifying use cases
Challenges proving business value
Lack of skills
Choosing the right AI technologies
Challenges implementing AI technologies
The high cost of AI technologies / solutions
Data challenges
Integrating AI into the organization
Managing AI-related risks
N = 2,737
Organizational
Technology
Operational
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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95 percent of adopters have some sort of concern around ethical risks –with the safety of AI systems selected the most
Top ethical risk (select one)
14%
19%
19%
19%
24%
Potential bias of AI algorithms
Using AI to manipulate people’s thinking and behavior
Elimination of jobs due to AI-driven automation
Lack of explainability and transparency in AI-derived decisions
Safety concerns about AI-powered systems
N = 2,737Note: 5% had no ethical concerns
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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While cybersecurity is adopters’ most worrisome AI risk – AI failures, misuse of personal data, and regulatory uncertainty are also top areas of concern
39% 62%
37%
37%
37%
39%
38%
37%
38%
38%
36%
40%
58%
57%
57%
55%
54%
53%
53%
53%
53%
52%
Cybersecurity vulnerabilities
AI failures affecting business operations
Consequences of using personal data without consent
New and changing regulations
Liability for decisions and actions made by AI systems
Making bad decisions based on AI recommendations
Lack of transparency
Ethics issues
Potential job losses from AI-driven automation
Negative employee reactions
Backlash from customersFully prepared Major/extreme concernSource: State of AI in the Enterprise, 3rd Edition, Deloitte
Copyright © 2020 Deloitte Development LLC. All rights reserved. 37
There is a great deal of uncertainty around the regulation of AI –organizations want structure, but are worried about impacts
Concern around regulation Demand for action Inhibiting innovation
have a major or extreme worry around how new and changing regulations could impact their
AI initiatives
agree that AI technologies should be heavily regulated by the
government
believe that new government regulations will hamper the
ability of companies to innovate with AI in the future
N = 2,737
China – 82%, Australia – 78%, US –57%, Netherlands – 48%
57% 62%62%
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Auditing and testing AI systems is the top practice, but not enough adopters are actively managing the risks around AI
Risk management practice adoption Number of risk management practices
26%
33%
34%
35%
36%
36%
37%
39%
40%
Having a single executive in charge of AI-related risks
Ensuring that AI vendors provide unbiased systems
Keeping a formal inventory of AI implementations
Using outside vendors to conduct independent audit and testing
Establishing policies or a board to guide AI ethics
Collaborating with external parties on leading practices
Aligning AI risk management with broader risk efforts
Training practitioners how to recognize and resolve ethical issues
Conducting internal audit and testing
N = 2,7375%
65%
23%
7%5+ practices
2-3 practices
1 practice
4-5 practices
Source: State of AI in the Enterprise, 3rd Edition, Deloitte
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Seasoned adopters are more likely to actively manage risks, though still not at a high level
What is your organization currently doing to actively manage the risks around your AI implementations?
Starters Skilled SeasonedImproved knowledge Keeping a formal inventory of all AI implementations 32% 35% 35%
Better alignment
Aligning AI risk management with broader risk management efforts 32% 37% 43%
Having a single executive in charge of AI-related risks 22% 27% 28%
Auditing and testing
Conducting internal audit and testing 39% 38% 43%
Using outside vendors to conduct independent audit and testing 32% 37% 36%
Addressing ethics
Training practitioners how to recognize and resolve ethical issues around AI 36% 39% 43%
Collaborating with external parties on leading practices around AI ethics 31% 35% 43%
Ensuring that our AI vendors provide unbiased systems 29% 32% 39%
Establishing policies or a group/board to guide AI ethics 35% 34% 37%
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“Our customers care very much about transparency and explainability. Often, it’s not that they care how the model works—it’s just that they want to be able to correct it. We found that implementing very clear feedback mechanisms is a way to do that.”
Kevin WalshAI Product Group Lead, HubSpot
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Recommendations
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Push boundaries expand your view of the possible and differentiate yourself
Create the newbuild new products and services powered by AI, don’t simply improve what you have
Expand the circlemove AI out of the realm of just the IT department, involve more of the business in AI efforts
Pursue creative approaches
Improving efficiency and automation is a laudable goal, but businesses will likely soon need to go beyond and use AI technologies to differentiate themselves.
Take inspiration from inventive use cases to develop solutions that are both useful and novel.
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Leverage a diverse teaminclude both technical and business experts in selecting AI technologies and suppliers
Take a centralized approach coordinate experimentation and AI technology and vendor selection
Focus on integrating and scaling make sure things fit into the bigger picture – whether on-prem or cloud-based, proprietary or open-source
As more AI-powered capabilities become available from partners and vendors, organizations should become more savvy and scrutinize vendors best equipped to provide access to the latest and greatest technologies.
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Become smarter consumers
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Align risk-related effortsintegrate AI-related risk management with broader risk efforts to drive business value
Challenge your vendorsthoroughly vet suppliers and partners, make sure they can prove trustworthy systems
Monitor regulatory effortsdon’t let the fear of potential regulation slow innovation, watch what is happening around the world
Actively address risks
Over the past few years, needed conversations concerning bias, transparency, and safety have become more common.
Developing a set of principles and processes to actively manage the range of AI risks can help build trust within your business and with customers and partners.
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This presentation contains general information only and Deloitte is not, by means of this presentation, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This presentation is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on this presentation
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