Executive
Perspectives

The CEO’s Roadmap on
Generative AI
March 2023

0

Introduction to this document

The most focused of business models, and the strongest of brands can beblown to bits by new information technology-- Philip Evans, in his book "Blown to Bits"
Similarly, it is clear that Generative AI will bring another major
revolution across all businesses. Today companies are focused on
productivity gains and technical limitations, but CEOs need to movethe focus to business model innovation.

In this BCG Executive
Perspectives edition, we
explore how CEOs can takefull advantage of thecoming revolution with
Generative AI

This is no small task, and CEOs—who are likely several steps
removed from the technology itself—may feel they are at a
crossroads. But from our perspective, the priority for CEOs is not to be
fully immersed in the technology. It is to understand how Generative
AI will impact their organization and their industry, and what strategic
choices will enable them to exploit opportunities and manage
challenges.
1 1

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The release of ChatGPT in late 2022 is analogous to Mosaic’s launch
three decades prior. In 1993, it was clear that the internet would bringa major revolution across all businesses in less than a decade.

Human-AI augmentation of the futureWith
Traditional ML

With
Generative
AI/Foundation
Models• Humans as passive recipients of technology tools• Humans as operators of processes• Use cases around making decisions with data

• Humans actively using technology with data
• Humans as operators of processes• Use cases around augmenting human creativity• Humans supervising AI on first drafts• Humans as designers of content and auditors of AI• Making decisions based on statistics andsequencing

Focus on standardization
and routinization to reduce
costs and replace humaneffortFocus on augmenting
decision making to create
most efficient systems andprocesses

Focus on enabling greater
productivity and creativity,
to solve unsolved problems/ Might augment decision
making in some cases
2

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Prior toTraditional ML• Use cases focus around automation

No Code / Low Code"Infinite Memory"

Lack of Truth Function

With a convenient chatbot-likeinterface, Generative AI
democratizes access for all
including those not well versed intech. "English is the hottest new
programming language" according
to Andrej Karpathy1

Generative AI, trained on vast
amounts of data, offers users
access to an automated systemthat provides seemingly infinite
memory and acts as aknowledgeable personal aide2

As a probabilistic model,
Generative AI generates the most
likely output to a query. This cansometimes create hallucinations
i.e., outputs completely separated
from objective truth

Defining features that will drive Business Model Innovation
NOTE: 1. Andrej Karpathy is a premier computer scientist and one of the founding members of OpenAI. 2. While not technically infinite, GPT-3 was trained on ~500 billion tokens, which gives users animpression of an "infinite memory" database3

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CEOs don't need to understand the technology behind Generative AI to createbusiness model innovation; instead, they need to understand its key features

Executive Summary | CEOs must make choices across three key pillars

Discover your strategic advantage through experimentation
Which use cases will
differentiate yourorganization?

1

c.
d.

2

a.
b.
c.
d.

CEOs will need to address key org questions for change management, talent and operating models
Generative AI will redefine roles and responsibilities across the organization
As AI adoption accelerates, CEOs need to develop a strategic workforce plan
CEOs will need to consider new operating models, however we expect that agile (or bionic)
models will remain the most effective and scalable in the long term

Protect your business with clear policies that address the limitations of Generative AI

POLICIES
How will the company
ensure ethical guardrails
and legal protections arein place?

Generative AI is accelerating across every industry, it is time to act now or be left behind
Use cases that rely on existing large language model (LLM) applications will be important to staycompetitive, but they won't offer differentiation – CEOs need to discover the company's golden
use case
When use cases are identified plan the right implementation approach: fine-tune or train
Plan for long-term advantage through investment in talent and infrastructure

Prepare your workforce with strategic workforce planning and transforming op models

PEOPLEHow should CEOs adapt
org structures andprepare employees for
deployment?

a.
b.

3

a.
b.
c.

Generative AI presents critical risks for which companies will need to be prepared
Prepare for risk through clear policies and training that define roles and responsibilities on how to
use Generative AI with a measure of confidence
CEOs should ensure the organization adapts responsible AI norms for long term risk mitigation
4

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POTENTIAL

BCG Executive
Perspectives
Potential: Discover your strategic
advantage
People: Prepare your workforce
Policies: Protect your business5

Copyright © 2023 by Boston Consulting Group. All rights reserved.

AGENDA

Interest in Generative AI is exploding, fueled by the launch of ChatGPT
1a

It is time to act now

Interest in Generative AI has grownexponentially since Q4 2022

This is driven by the release of ChatGPT,
which has taken the world by storm

Google Search Interest (100 = max interest)

Wall Street
Journal100
90Interest in
ChatGPT

80

60

Even at it's peak, interest in
the metaverse does notcompare to interest in
ChatGPT today

50
40
30

87

100

Fortune

59

20
10
0

1

14 11 11 13

95

09/21 10/21 11/21 12/21 01/22 02/22 03/22

ChatGPT3
11/22 12/22 01/23 02/23

TechCrunch…and many more

Metaverse
6

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Companies are already seeing a transformative effect fromusing Generative AI
1a

It is time to act now

Technology~88%
Of software developers
reported higher productivity when using agenerative AI code assistant1

Consumer
Automated on-model fashion image
generation resulted in

1.5X
Increase in retailer conversion rate2

Biopharma
Generative AI Identified a novel drugcandidate for the treatment of Idiopathic
Pulmonary Fibrosis in

21 days

Financial Institutions

Entertainment

Insurance

Synthetic GAN-enhance training set for
fraud detection achieved aGenerate novel animated motions from asingle training motion sequence with

InsureTech platforms leveraging
generative AI to reduce up to~98%~97.2%~30%

accuracy ratequality score on natural movementsof customer service costs6(vs. 97% with unprocessed original data)4(vs. 84.6% with traditional methods)5

1. Quantifying GitHub Copilot’s impact on developer productivity and happiness 2. Vue.ai helps fashion retailers create high-quality on-model product photos 3.Deep learning
enables rapid identification of potent DDR1 kinase inhibitors 4. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection
5. GANimator: Neural Motion Synthesis from a Single Sequence 6. Insurtech COVU Leverages OpenAI to Streamline Insurance Agency Operations

7

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(vs. years with traditional methods)3

Total addressable market is expected to reach ~$120B by 2027
It is time to act now

Generative AI TAM ($B)

CAGR
2022-2025

121

61
15

35
9
2
2

1

18
4
3
3

3

3
2

6
5
6

75%

23

21

Consumer

64%

15

22

Healthcare

85%

16

Media

59%

12
9

12

Public sector

52%

14

19

Other1

61%

15

11
9

8
5
4

BFSI2

88

+66%

2
2
1

32

9
10

7

2022

2023

2024

2025

2026

2027

131%

94%

91%

74%

44%

38%

YoY growth of Gen. AI market

NOTE: 1. Other includes Industrial Goods, Energy, and Telecom markets; 2. BFSI includes Insurance (~$2B 2025) and Financial Institutions (~$13B 2025) including retail andwholesale banking, asset and wealth management, and private equity
Source: AI TAM research; Expert interviews; BCG analysis

8

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1aDiscover the company's golden use case

For the CEO, thekey is to identify
the company’s
so-called golden
use cases thatdrive competitive
advantage
Non-core use
cases are tablestakes, everyone
will adopt them

CORE OR"GOLDEN"
Use Cases

NON-CORE
Use Cases

Strengthen competitive positioning with truly unique use
cases that both drive value and are challenging to adopt(i.e., have a barrier to entry for competitors)• For example, in pharmaceuticals companies,
Generative AI can drive core R&D to produce new
drugs/molecules at record pace

There is low barrier to adopting use cases that rely on
existing LLM applications, but they will be important tokeep pace with other organizations• For example, purchasing Generative AI tools that create
automatic summaries of meeting notes

Table-Stakes to use cases
will improve efficiency9

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1b

Focus on Building
Long-Term Advantage

Golden use cases will add to a company's unique competitive advantage
in the marketplace, while non-core use cases are readily adopted by all
Discover the company's golden use case

Productivity Gains

Efficiency Gains

Innovation

First drafts
with Jasper AI

Predictive maintenancewith an Equipment Manufacturer

Building novel proteins
with ProFluent

What is Jasper Doing?
Web-based application for businesses powered byGenerative AI that helps teams create tailoredcontent up to 10x faster

What is the Equipment Manufacturer Doing?
Building proof-of-concept for global end-to-end
predictive maintenance of fleet with IoT sensors
powered by Generative AI

What is ProFluent Doing?
Creating novel proteins that do not exist in nature,
aimed at advancing drug treatment. Proof-ofconcept shown with creation of novel proteins
with anti-microbial properties

How is Jasper Doing it?
Built a model on top of OpenAI's GPT-3, finetuned on 50+ use-cases such as writing,copyediting, advertising, and content creation

How is the Equipment Manufacturer Doing it?
IoT sensors constantly monitor key indications of
performance through signals from parts, and relaythat information back to a Generative AI powered
back-end software

How is ProFluent Doing it?
Using "inverse design", i.e., working backwards
from desired properties to create proteins.
Gartner believes that by 2030, 30% of new drugs
will be discovered using this method

Why is generative AI better vs traditional ML?
Identification of anomalies in sensor data isdifficult since failure data is rare in real-world.
Generative AI can generate synthetic data, andbetter predict failures before occurrence

Why is generative AI better vs traditional ML?
Similar to Jasper, traditional ML does not have"generative" capabilities and thus is not great at
creating never before seen protein structures by
self-learning from training dataset

Why is generative AI better vs traditional ML?
Traditional ML incapable for such a task. It does
not have any "generative" capabilities for new text
adapted to use-case

Non-core use caseGolden use cases

Productivity improvement will be table stakes
since all businesses will adopt

For the equipment manufacturer, high quality of maintenance is a core part of their businessmodel. Similarly for ProFluent, protein synthesis is at the heart of their business. Generative
AI strengthens competitive positioning for both companies in their core business activities

10

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1bWhile foundation models today are used for generative use cases, this may
expand to include discriminative use cases as well in the future
1b

Discover the company's golden use caseNot exhaustive

Any writing task

Algorithmic
trading

Dynamic pricing
engines

Currently in domain of
Traditional ML

Demandforecasting

Ad spend
optimization

RecommendationengineNew material
synthesis throughinverse design

Design drafts(e.g., architecture
designs)

Fraud
detection

Generative
uses of AI
Currently in domain ofFoundation models

Customer facing
chatbots

Foundation models are currently being used for generative use cases; however, this may expand in the future to cover certain
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Discriminative
uses of AI(e.g., meeting notes,
editing)

Once use cases are selected, CEOs should make strategic choices
about whether to fine-tune existing LLMs or to train a custom model
1c

Fine tune or train

Decision Tree for Foundation Model Choice

Consider several tradeoffs –performance, budget, in-house
talent to modify/maintain,
safety guardrails etc.

NO

Models from providers more expensive, but
likely better along other dimensions

Desired Functionality does not already exist
in developed models
OR
Use case necessitates creation of proprietarymodel to win competitive advantage

NO

YES
YES

Large budget forcompute and In-house
talent available for build& maintenance of model?

Consider open-source model

Cheaper

Consider data privacy implications ofstoring it in the cloud vs. on-premise

Fast to implement

Buy access to foundation
model provider

Finetune
ModelsDependent on coremodel

Buy from provider with service agreement

Consider a partnership
Partner with model provider who supports
training a new proprietary model

Consider designing aproprietary model
Build/Train model in-house
Likely limited to select companies due tocosts, compute, and talent requirements

Limited flexibility

Train
Models

Flexibility andpotential fordifferentiation
Expensive and timeconsuming
Technical know-how
needed12

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Open-source likely cheaper tomodify/operate, but requires more talentTraining a custom LLM will offer greater flexibility, but that comes withhigh costs and capability requirements
Fine tune or train

1

Develop New,
Cutting-edge
foundation model2

Enhance
Existing
foundation model3

Fine-tune
Existing
foundation model

Create a new foundation model
in-house from scratch. Costs scalewith model complexity

Partner with LLM provider to significantly
enhance existing model (e.g., feeding
complex company-proprietary data)

Fine-tune existing foundation model for
related tasks (e.g., fine-tuning ChatGPT forlegal memo writing)$50 - $90M+$1 - $10M$10 - $100k+

Estimated cost for complex models

Estimated cost

Estimated cost

Main drivers of cost:

Main drivers of cost:

Main drivers of cost:•• Training runs: $10M+2• People and R&D costs: variable•• Partnership costs: variable• Data gathering and labelling: $10k+4• Computational costs: minimal

Hardware (i.e. GPUs or TPUs): $30M1

Training runs: $1M -$5M3

Usage Costs – $7M to $15M yearly (costs 30x to 50x lower if not using the most advanced model)
GPT4 costs $0.06 for ~750 words. 5k to 10k employees each using the technology 100 times a day costs ~$7M to $15M
1. Meta's LLaMA used 2048 A100 GPUs for training, each of which can cost ~$20k. See https://wandb.ai/vincenttu/blog_posts/reports/Meta-AI-Released-LLaMA-VmlldzozNjM5MTAz?galleryTag=ml-news. 2. A single training run for GPT-3 is projected to cost $12M. See https://venturebeat.com/ai/ai-machine-learning-openai-gpt-3-size-isnteverything/. 3. Training runs here likely less intensive than full-scale model training, leading to lower costs.

13

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1cCarefully assess the timing of Generative AI investments considering tech andtalent; move too soon and risk wasting money, too slow and risk falling behindPlan for long-term advantage

It could take 5+ years for low error tolerance use cases to be feasible1 …
Error tolerance
Key metric to evaluate readiness of Generative AI is the error tolerance of chosen use-case

Adoption

Illustrative1

Low error
tolerance
Medium error
tolerance
High error
tolerance(3-5+ yrs.)
Long remaining
runway for generative
AI adoption(0-2 yrs.)(0-1 yrs.)
321

Market maturity

Open-source:
OpenAI's GPT-2

Near-term | HIGH
error tolerance

Use cases where errors are OK• e.g., drug development sincescientists review every
molecule suggested by AI for
safety and efficacy
3…but research is becoming
proprietary

Longer-term | LOW
error tolerance

Use cases with low room for error• e.g., doctors using chatbots toretrieve and query a patient's
medical history for easy access1. Sequoia expects first drafts produced by Generative AI in certain domains to be better than human professionals by 2030
See https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/

Proprietary:
OpenAI's GPT-3;
Meta's LLaMA

Research is also
moving very quickly:
Meta's LLaMA
released 2/24/23,
outperforming GPT-3
on many tasks
GPT-4 released on
3/14/23

Waiting too long to invest into Generative AI
today may mean that businesses risk fallingbehind. Research into high-performing
foundation models is increasingly proprietaryand guarded as a source of competitive
advantage.
14

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1d

BCG Executive
Perspectives
Potential: Discover your strategic advantage
People: Prepare your workforce
Policies: Protect your business

15

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AGENDATo achieve the Human-AI augmentation of the future, CEOs should answer
questions for change management, workforce planning, and op model design
2aAddress key organizational questions

Managing Culture andChange in Company

Strategic
Workforce Plan

Organization andOperating Model Design

Overarching
Goal

Cultivate a culture that embraces
AI like another coworker

Build a workforce that will becompetitive 10 years from now

Create an efficient operating modelthat balances scale and agility

Key
Questions
Addressed:• How can professional identity concerns be
managed to encourage AI adoption?

• What new skills and talent will be crucial for
long-term advantage?

• What existing roles and responsibilities willchange because of Generative AI?

• How can a culture of human and AI
collaboration be fostered?

• What new competencies will managers needto lead an AI-augmented workforce?

• How should I organize my departments for
efficient collaboration with AI• How can management communication
create positive momentum• How should training/recruiting be adjusted
to build a high-performing workforce?

• Where should LLMs and data scientists sit
within the organization?

A successful Generative AI adoption plan is customized to each organization, driven by theindustry the company operates in, its current AI readiness, and the golden use cases it selects
16

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Key considerations to craft a Generative AI adoption plan

While traditional AI has augmented the capabilities of managers and decision
makers, Generative AI will augment the capabilities of individual contributors
Redefine roles and responsibilities

Traditional AI/ML
empowers individuals to
make decisions, changing
the role of managers

Generative AI creates
first draft content,changing the role ofindividual contributors

Traditional AI and ML algorithms
augments decision making

Generative AI augments
content creation

Lower-level individuals can now make datadriven decisions without management
supportIndividuals will spend less time creating
first-drafts and more time revising or
supervising AI generated content

This changes the role of the manager fromdecision maker to a manager of teaming
and relationship dynamics• For e.g., at ExxonMobil, geoscientists
use ML algorithms to decide where andhow to extract oil at maximum
efficiency with limited guidance ofmanagers

VS.

This changes job tasks of individual
contributors to include auditor orsupervisor of Generative AI• For e.g., Andrej Karpathy, a founding
member of OpenAI, said "Copilot has
dramatically accelerated my coding…
I don't even really code [anymore], I
prompt & edit"

This is the first time that atechnology developed in
Silicon Valley benefits the
lives of everyday people soquickly and so tangibly-- Satya Nadella,
CEO of Microsoft

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While traditional AI has augmented the capabilities of managers and decision
2b Redefine roles and responsibilities
makers, Generative AI will augment the capabilities of individual contributors

FROM: Key roles today

TO: New roles tomorrow• Creating marketing content and ideas• Supervising AI for first drafts of creative
briefs and brand guidelines and overall
better and faster marketing content

A supervisor role with AI on content, withincreased time devoted to strategic thinking

from scratch• Managing social media accounts,• Building deeper relationships withcustomers, suppliers, and brandambassadorsscheduling and uploading posts• Writing creative briefs to interface withadvertisement agencies• Increased focus on brand strategy,
positioning, and target audience
identification• Tracking ad campaign performance metrics• Creating brand guidelines to drivealignment across all stakeholders• Increased focus on personalized marketing
campaigns using Generative AI-powered
toolsCore role changes for a marketer
18

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A role centered around creating marketing
content and executing campaigns

2bRedefine roles and responsibilities

Generative AI will redefine roles across the organization
Carefully consider the professional identity of your employees when making changes to role definitions

Social MediaSpecialist

Creating social media content, schedulingand uploading posts

Building relationships with customers and followers

Advertisers

Developing creative material (e.g., videos)

Exploring new advertising channels and opportunities

Accountant

Preparing and maintaining financial
accounts

Identifying and implementing new accounting policies andprograms

Payroll Specialist

Processing employee payroll and taxes

Ensuring compliance with labor laws and regulations,providing guidance and support to employees

Software Engineers

Low-value coding and debugging, codetranslation

Reviewing code safety, designing new complex algorithms(e.g., better recommendation engines)

Help Desk Support

Troubleshooting common issues

Resolving system-wide problems, supporting complex
technical issues

Sales Rep

Lead generation, follow-ups, logging
customer interactions in CRM systems

Build relationships with customers, understand their needs
and pain-points

Deals Desk Support

Log quotes, and request sales approvals

Develop complex pricing models, customized deals forcustomers

FinanceIT

Sales

Future tasks(in addition to verifying first drafts)

19

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Marketing

Sample roles

Tasks today that Generative AI
can provide first drafts forWhile traditional AI has augmented the capabilities of managers and decision
2b Redefine roles and responsibilities
makers, Generative AI will augment the capabilities of individual contributors

The Atlantic

To successfully adopt Generative AI,
CEOs must alleviate these concerns
Work with HR to understand how roles will
evolve and regularly pulse check employee
sentiment as their AI initiatives roll out

Develop a transparent change
management initiative that will both help
employees embrace their new AI coworkers andensure employees retain autonomy

TIME
Magazine

FortuneWhile some roles will be adversely impactedby Generative AI, overall Humans aren’t going
anywhere — and in fact are needed to
deploy AI effectively and ethically20

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Employees are expressing concern about
the impact to their professional identity

As Generative AI adoption accelerates, CEOs need to use their learnings
to develop a strategic workforce plan
Develop a strategic workforce plan

ANTICIPATE
Understand talent and skills needed
to deliver on business strategy

DEVELOP
Upskill and reskill talent at speed withhigh reach and high richness• What workforce changes are neededas the company steadily adopts GenerativeAI?

• What key skills will be needed to work
effectively with Generative AI?
• What training programs can upskill theworkforce at speed?

DEVELOPANTICIPATEENGAGEENGAGEATTRACT• What are the risks associated withworkforce changes, and how to mitigate
them?

ATTRACT

Deliver unmatched talent value
proposition and experience

Source creatively securing
best-in-class candidate experience• How to create a culture of continuous
learning and development that encourages
employees to use Generative AI?

• How should the interviewing processchange to surface the talents needed in a
Generative AI dominated world?

• What is the company's value proposition
to employees in a Generative AI world?

• How should the sourcing process change
to ensure candidates with new skillsets are
attracted to the company?
21

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2c

Consider centralizing the IT/R&D function supplying LLMs and data engineers
Consider new operating models
Senior C-suite role (e.g., Chief AI Officer)
Elevates the importance of Generative AI to the C-suiteand signals importance across the organization

Finance
Each functional department
interfaces with the Central IT/
R&D to:HRCentral IT/R&D
Collects data, trains LLMs,
and supplies data engineers

Sales• Supply all collected data formodel training• Embed data scientists
within their departments tobuild functional expertise• Request data engineers tofine-tune LLMs for specificuse-cases

Accounting

Operations

This creates a scalable modelwith a central authority for data
ownership and model control

22

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2d

We expect that agile (or platform) models will remain the mosteffective and scalable in the long term
Consider new operating models

Decentralized

Sample model for a platform organization

Front-end

Business EP

Data EP

Technology EP

Front end teams have autonomy toserve customers

Division 1

Division 2

Division 3(e.g., B2B business)(e.g., B2C business)(e.g., Mfg. business)

BD team

BD team

BD teamOperation teamOperation teamOperation teamProduct teamProduct team…

…

Merchandise
centerUser
centerProduct team

…

…
Order
center

Payment
centerCustomer Data

Transaction data…

SafetyET Brain

Cloud Computing

Scalable
Processes are identified and scaled toserve front end teams and to learn

Flexible
Technology allows for personalizationand localization, to create the pull…

Integrated
One source of all data andinformation…

Responsive
Modular technology available to all23

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2d

BCG Executive
Perspectives
Potential: Discover your strategic advantage
People: Prepare your workforce
Policies: Protect your business24

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AGENDA

25

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Risks associated with Generative AI are showing up in the real world rapidly

GenAI creates fundamental shifts impacting the Responsible AI (RAI) approach•

Anybody (even non-technical staff ) can
use these capabilities with very few
technical resources (e.g., data,
compute, expertise)•

Capability overhang can emerge in
unexpected corners of the organization(e.g., compared to only technical divisions
before)•Smaller need for large teams andbudgets limiting visibility for
managers and governance mechanisms•

Time to detect, resolve, and mitigate
incidents is much higher violating theprinciple of surprise aversion in risk
management

Democratization

Buying / renting
from 3PP:

3PP Reliance

Shadow AI will be on steroids:

Latent and opaque risks
outside of in-house scope:•

Foundation models require a lot ofcompute, data, and expertise and are
overwhelmingly procured rather thanbuilt in-house•

Limited visibility on data lineage (e.g.,
copyright infringement) and model training(e.g., using confidential information to
upgrade models)•

Small set of entities can provide
these foundation models•

Limited control on functionality changes
on the technical roadmap
26

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Ease of use is much higher now:

Companies must be wary of critical risks of Generative AI today before
adopting the technology
Generative AI presents critical risks
Energy use andenvironmental harm
Generative AI uses more energy on
compute, both during model training
and usage than traditional ML. While
more efficient computation techniques
are being developed, mitigation today islimited to usage of moreenvironmentally sustainable energy
sources

Lack of
Truth Function
Generative AI can sometimes produce
factually incorrect responses presented
in a very convincing manner. To
mitigate risks from using incorrect
information, companies must mandate
double checking all Generative AI
outputs, and limiting its use to noncritical tasks today

Not Exhaustive

Capability
Overhang
Due to its probabilistic nature,
Generative AI can sometimes show
unexpected capabilities upon
deployment (e.g., several users tricked
ChatGPT and bypassed its security toaccess kernel model). This risk isdifficult to fully mitigate, but extensive
pre-launch testing will help

SophisticatedPhishing and Fraud
Generative AI makes cybercrime easier– generating convincing phishingemails or deepfakes instantly. To
mitigate this risk, companies muststrengthen cybersecurity protocols,
train employees on new safety risks,and consider deploying Generative AI
themselves to to catch fraud

BiasedOutputs
Real world data is often biased.
Without oversight, the Generative AI
models trained on this data also carrybias. Mitigation techniques include
Reinforcement Learning with Human
Feedback (RLHF) where the model istaught to be unbiased, yet this method
is not perfect

Leaks ofproprietary data
When training Generative AI models in
the cloud, companies transmit
proprietary data which the data may beleaked in a security breach. To mitigate
this risk, companies can instead choose
to train models on-prem vs. cloud,although this necessitates othertradeoffs

Copyright
Infringement
Generative AI is trained on publicly
available data, much of which is
copyright protected. This can lead tolawsuits by IP holders. Mitigation
strategies rely heavily on foundation
model providers to obey copyright laws,
and for governments to create new laws
for Generative AI

Shadow AI
Employee application of external
generative AI tools without adequate
guidance or supervision creating risk
and causing harm. To mitigate this risk,
companies must create detailed andclear Generative AI use guidelines andpolicies
27

Copyright © 2023 by Boston Consulting Group. All rights reserved.

3aNot all risks are created equal, with some posing a higher business risk
while also being harder to mitigate
3aGenerative AI presents critical risks

Not Exhaustive

Mitigation ischallenging

4

Energy use andenvironmental
harmCapability overhang

2

1Companies should focus on risks
with high severity of business
impact, that are easily mitigatedfirstSimple mitigation
steps possiblethat are either difficult to
2 Risks
mitigate, or that have low potential
business impact can be tackled
3 second

Copyright
infringement
Lack oftruth
function
Sophisticatedphishing & fraud

3
Low

Source: BCG Henderson Institute

Severity ofbusiness risk

Leaks ofproprietary data
Shadow AI

1
High

4

Risks with low business impact are
toughest to mitigate, butnonetheless important to tackle
after low hanging challenges
are addressed28

reserved.
reserved.
rightsAllrights
Group.All
ConsultingGroup.
BostonConsulting
byBoston
2023 by© 2022
Copyright ©

Ease ofmitigation

Biased outputs

Timeline to potential resolution of issues varies based oncomplexity and urgency
Generative AI presents critical risks

Potential Resolution

Description

Short-termLong-term

Medium-term

1

2

3

4

5

6

7

8

ShadowAI

Sensitive data
leaks

Enhanced
Fraud

Lack of Truth
Function

Copyright
challenges

Biasedoutputs

Environmental
Harm

Capability
OverhangMore widespreadand unconventional/
non-traditional usesof AI invisible totop-down
governanceapproaches

Sensitive data canpotentially be
extracted frommodels via prompt
engineering or moresophisticatedabuse/attacks

Constantly evolving
capabilities todeceive anddetect with higher
stakes over timefrom morepervasivenessDynamicallychanging
information
ecosystem makes it
difficult to mitigate
this issue without
extensive humanintervention

Unclear data
provenance frompublicly scraped
datasets and quality
issuesGenerative AI
models trained onreal world data cancarry over bias to
outputs in hard to
detect ways

SOTA achievablethrough larger models
with more complex
architectures withhighercomputational
needs

Latent capabilities andhow they might beleveraged depends on
the interacting
ecosystem anddevelopment of new
systems

Clear guidelines
captured in
enforceable
policies
accompanied bycultural change
and top-down
communication

Sensitive data
should beanonymized and/or
encrypted

Better detection
methods withverifiable claims
on performance andreduced false
negatives and false
positives

Fact-checking
services andproblematic
information
fingerprinting canprovide stop-gap
measures to stem
issuesLegal precedents
will be set as courts
figure out IP andcopyright issues
which will shape
response anddevelopment
approaches

Reinforcement
Learning with Human
Feedback (RLHF) cancatch some biases
but not all. Tech.
advancement likely
needed forcomprehensive solve

Research work in
TinyML with anemphasis on edgedevice deployment,
faster inference, andcost +
environmental
considerations

Better governancemechanisms andunderstanding of
how capabilities
emergeChained models canbe used to blockleakage of sensitive
data

29

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3a

3b

Adapt responsible AI norms

To manage risk with Generative AI, companies can adopt thefollowing policies today:
Enact Responsible Research/Release Norms

Companies are behind ifthey haven't alreadyinstituted such policies

Set and Communicate Clear Generative AI Use Policies• To manage IP and hallucination risks set clear guidelines on when Generative
AI can and cannot be used

Sanitize Sensitive Data Before Training Models• To minimize losses during data breaches, sanitize sensitive data (such as
names and addresses) before training foundation models

Improve Generative AI Risk Assessment Capabilities• Consider setting up a "red-team" to deliberately find failure models andvulnerabilities with Generative AI applications

Leaders will need to revisit these policies continuously,as the pace of innovation with Generative AI is high andproduces new capabilities (and correspondingly new risks)
30

Copyright © 2023 by Boston Consulting Group. All rights reserved.

Key
Generative AI
policies toadopt today• Like academia, set up an institutional review board to a priori assess impactof any Generative AI use cases

Connect with our Generative AI leadership team with any questions

Abhishek Gupta

David Martin

Nicolas de Bellefonds

Senior Solution Delivery Manager, RAI
Gupta.Abhishek@bcg.com
Montreal

Managing Director & Partner
Martin.David@bcg.com
DallasManaging Director & Senior Partner
deBellefonds.Nicolas@bcg.com
Paris

Akash Bhatia

François Candelon

Steve Mills

Managing Director & Partner
Bhatia.Akash@bcg.com
San Francisco

Managing Director & Senior Partner
Candelon.Francois@bcg.com
ParisManaging Director & Partner
Mills.Steven@bcg.com
Washington D.C.

Amanda Luther

Leonid Zhukov

Suchi Srinivasan

Managing Director & Partner
Luther.Amanda@bcg.com
AustinVice President – Data Science
Zhukov.Leonid@bcg.com
New York

Managing Director & Partner
Srinivasan.Suchi@bcg.com
SeattleDaniel Sack

Matthew Kropp

Managing Director & Partner
Sack.Dan@bcg.com
Stockholm

Managing Director & Partner
Kropp.Matthew@bcg.com
San Francisco – Bay Area

Click here for our latest insights on Generative AI

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Copyright © 2023 by Boston Consulting Group. All rights reserved.

Generative AI TeamThe materials contained in this presentation are designed for the sole use by the board of directors or senior
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contained in these materials are based upon standard valuation methodologies, are not definitive forecasts, and are notguaranteed by BCG. BCG has used public and/or confidential data and assumptions provided to BCG by the Client.
BCG has not independently verified the data and assumptions used in these analyses. Changes in the underlying data or
operating assumptions will clearly impact the analyses and conclusions.

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The services and materials provided by Boston Consulting Group (BCG) are subject to BCG's Standard Terms(a copy of which is available upon request) or such other agreement as may have been previously executed by BCG.
BCG does not provide legal, accounting, or tax advice. The Client is responsible for obtaining independent advice
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