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


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



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


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


Key insights



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



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

Presenter

Presentation Notes

Sources: Pooja Singh, “Enterprise use of AI has grown 270 percent globally over the past four years,” Entrepreneur Asia Pacific, January 22, 2019. https://www.entrepreneur.com/article/326737 International Data Corporation (IDC), “Worldwide spending on artificial intelligence systems will grow to nearly $35.8 billion in 2019, according to new IDC spending guide,” March 11, 2019. https://www.idc.com/getdoc.jsp?containerId=prUS44911419


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?


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%


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


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


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




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


year

Natural language processingused by

Computer visionused by

Currently using


year

Currently using


year



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



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



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:




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


https://www.idc.com/getdoc.jsp?containerId=prUS45481219

https://www.gartner.com/document/3979185?ref=solrAll&refval=239708319


“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



Pursue creative approaches



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



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



“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



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



“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



Become a smarter consumer



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%)




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


“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



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



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



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%



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



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



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


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%



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



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%


“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



Recommendations



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.



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.


Become smarter consumers


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.


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

About DeloitteDeloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.


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