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How technology leaders are
adopting emerging tools todeliver enterprise-wide AI.

The great acceleration:
CIO perspectives
on generative AI2 	 MIT Technology Review Insights

Preface“The great acceleration: CIO perspectives on generative AI” is an MIT Technology Review Insights
report sponsored by Databricks. This report, based on seven in-depth interviews with senior
executives and experts, conducted in April and May 2023, seeks to understand how technology
leaders are adopting emerging generative AI tools as part of an enterprise-wide AI strategy.
The report also draws on an MIT Technology Review Insights global survey of 600 senior data
and technology executives, conducted in May and June 2022. Adam Green was the author of
the report, Teresa Elsey was the editor, and Nico Crepaldi was the publisher. The research is
editorially independent and the views expressed are those of MIT Technology Review Insights.
We would like to thank the following individuals for their time and insights:
Andrew Blyton, Vice President and Chief Information Officer, DuPont Water & Protection
Michael Carbin, Associate Professor, MIT, and Founding Advisor, MosaicML
Owen O’Connell, Senior Vice President and Chief Information Officer (Information Digital
Services and Operations), Shell
Noriko Rzonca, Chief Digital Officer, Cosmo Energy Holdings
Richard Spencer Schaefer, Chief Health Informatics Officer, Kansas City VA Medical Center,and AI Solutions Architect, National Artificial Intelligence Institute, U.S. Department of
Veterans Affairs
Cynthia Stoddard, Senior Vice President and Chief Information Officer, Adobe
Matei Zaharia, Cofounder and Chief Technology Officer, Databricks, and Associate Professorof Computer Science, University of California, Berkeley

CONTENTS

MIT Technology Review Insights

0 1 Executive summary.................................................................4
02 AI everywhere, all at once......................................................603 Building for AI..........................................................................10
04 Buy, build? Open, closed?....................................................13
05 Workforce worries?...............................................................16
06 Risks and responsibilities....................................................19
07 Conclusion.............................................................................. 223

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4 	 MIT Technology Review Insights

Executive
summaryThe emergence of consumer-facing generative
AI tools in late 2022 and early 2023 radicallyshifted public conversation around the power
and potential of AI. Though generative AI had
been making waves among experts since
the introduction of GPT-2 in 2019, it is just now thatits revolutionary opportunities have become clear
to enterprise. The weight of this moment—and theripple effects it will inspire—will reverberate for
decades to come.

The impact of generative AI on economies andenterprise will be revolutionary. McKinsey Global
Institute estimates that generative AI will add between$2.6 and $4.4 trillion in annual value to the global

economy, increasing the economic impact of AI as awhole by 15 to 40%.1 The consultancy projects that AI willautomate half of all work between 2040 and 2060, withgenerative AI pushing that window a decade earlier thanprevious estimates. Goldman Sachs predicts a 7%—or
nearly $7 trillion—increase in global GDP attributable togenerative AI, and the firm expects that two-thirds of U.S.
occupations will be affected by AI-powered automation.2
Text-generating AI systems, such as the popular
ChatGPT, are built on large language models (LLMs).
LLMs train on a vast corpus of data to answer questions
or perform tasks based on statistical likelihoods. Rather
than searching and synthesizing answers, they use
mathematical models to predict the most likely next

“I can’t think of anything that’s been more powerfulsince the desktop computer.” Michael Carbin, Associate Professor, MIT, and Founding Advisor, MosaicML

MIT Technology Review Insights

word or output.3 “What was exciting to me, when I firstinteracted with ChatGPT, was how conversant it was,”says Michael Carbin, associate professor at MIT andfounding advisor at MosaicML. “I felt like, for the firsttime, I could communicate with a computer and it could
interpret what I meant. We can now translate language
into something that a machine can understand. I can’t
think of anything that’s been more powerful since thedesktop computer.”
Although AI was recognized as strategically important
before generative AI became prominent, our 2022
survey found CIOs’ ambitions limited: while 94% oforganizations were using AI in some way, only 14%
were aiming to achieve “enterprise-wide” AI by 2025. Bycontrast, the power of generative AI tools to democratize
AI—to spread it through every function of the enterprise,
to support every employee, and to engage every
customer —heralds an inflection point where AI cangrow from a technology employed for particular use
cases to one that truly defines the modern enterprise.
As such, chief information officers and technical leaders
will have to act decisively: embracing generative AI to
seize its opportunities and avoid ceding competitive
ground, while also making strategic decisions about data
infrastructure, model ownership, workforce structure,
and AI governance that will have long-term
consequences for organizational success.
This report explores the latest thinking of chief
information officers at some of the world’s largest andbest-known companies, as well as experts from thepublic, private, and academic sectors. It presents theirthoughts about AI against the backdrop of our global
survey of 600 senior data and technology executives.4
Key findings include the following:• Generative AI and LLMs are democratizing access to
artificial intelligence, finally sparking the beginningsof truly enterprise-wide AI. Powered by the potential of
newly emerging use cases, AI is finally moving from pilot
projects and “islands of excellence” to a generalized
capability integrated into the fabric of organizational
workflows. Technology teams no longer have to “sell”
AI to business units; there is now significant “demandpull” from the enterprise.

• A trove of unstructured and buried data is now legible,
unlocking business value. Previous AI initiatives had tofocus on use cases where structured data was readyand abundant; the complexity of collecting, annotating,and synthesizing heterogeneous datasets made wider
AI initiatives unviable. By contrast, generative AI’s new
ability to surface and utilize once-hidden data will power
extraordinary new advances across the organization.
• The generative AI era requires a data infrastructurethat is flexible, scalable, and efficient. To power
these new initiatives, chief information officers andtechnical leads are embracing next-generation data
infrastructures. More advanced approaches, such asdata lakehouses, can democratize access to data andanalytics, enhance security, and combine low-coststorage with high-performance querying.
• Some organizations seek to leverage open-source
technology to build their own LLMs, capitalizing on
and protecting their own data and IP. CIOs are alreadycognizant of the limitations and risks of third-party
services, including the release of sensitive intelligenceand reliance on platforms they do not control or havevisibility into. They also see opportunities arounddeveloping customized LLMs and realizing value fromsmaller models. The most successful organizations
will strike the right strategic balance based on acareful calculation of risk, comparative advantage,and governance.
• Automation anxiety should not be ignored, butdystopian forecasts are overblown. Generative AI tools
can already complete complex and varied workloads,
but CIOs and academics interviewed for this report do
not expect large-scale automation threats. Instead,they believe the broader workforce will be liberated
from time-consuming work to focus on higher value
areas of insight, strategy, and business value.
• Unified and consistent governance are the rails on
which AI can speed forward. Generative AI brings
commercial and societal risks, including protecting
commercially sensitive IP, copyright infringement,
unreliable or unexplainable results, and toxic content.
To innovate quickly without breaking things or getting
ahead of regulatory changes, diligent CIOs must address
the unique governance challenges of generative AI,
investing in technology, processes, and institutional
structures.

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6 	 MIT Technology Review Insights

AI everywhere,
all at once

hanks to its flexibility and range, anduser-friendly, natural language–based
interface, generative AI is showing its mettle
everywhere from copywriting to coding. Its
power and potential to revolutionize how
work is done across industries and business functions
suggests it will have a reverberating impact like that ofthe personal computer, the Internet, or the smartphone,launching entirely new business models, birthing new
industry leaders, and making it impossible to remember
how we worked before its spread (see Figures 1 and 2).

This sudden focus on generative AI’s power and potential
represents a marked shift in how enterprise thinks
about AI: until recently, AI adoption was uneven acrossindustries and between functions within companies.

Figure 1: Enterprise applications and use cases for generative AI
These are just a few of the business functions compellingly addressed by generative AI.
Generating forecasts forcomplex scenarios

Automating and personalizing
customer service

Analyzing massive volumes ofcomplex and unstructured data

Learning from previous andunresolved support tickets

Aggregating key metrics
across production systems
Automating creation of text,
charts, and graphs for reports

Providing intelligent scripts foragent interactions

generative AI

Analyzing operational concerns,
such as inventory and staffing
Optimizing pricing strategies
Auto-generating and adapting contracts,
purchase orders, and invoices
Understanding user preferences,behaviors, and contextual cues

Developing personalized
marketing
Increasing precision andeffectiveness of ad targeting
Detecting security threats andaccount takeover attempts

Analyzing communications for phishingattempts and social engineering

Source: Compiled by MIT Technology Review Insights, based on data from “Retail in the Age of Generative AI ,”5 “The Great Unlock: Large Language Models in
Manufacturing,”6 “Generative AI Is Everything Everywhere, All at Once,” 7 and “Large Language Models in Media & Entertainment,”8 Databricks, April–June 2023.

MIT Technology Review Insights

Our 2022 survey found just 8% of respondents sayingthat AI was a critical part of three or more business
functions. Only in finance and IT did more than half of
respondents say AI was a critical or widespread part of
the function.
Before the advent of generative AI, only a rare few
organizations had made AI a critical capability across thebusiness, or even aspired to. While 94% of organizations
were already using AI in some way, only 14% of themaimed to achieve “enterprise-wide AI,” defined as having
AI being a critical part of at least five core functions, by
2025 (see Figures 3 and 4).
But now generative AI is changing the conversation.
Demonstrating applications in every business function,
AI is poised to spread enterprise wide. AI is now evenventuring assuredly into creative applications, once
considered a uniquely human endeavor. Adobe, forexample, has launched Firefly, a family of creative
generative AI models that act as a copilot to creativeand design workflows, according to Cynthia Stoddard,
the company’s senior vice president and chief
information officer. Firefly tools can recolor existing
images, generate new images, and edit new objects
into and out of images, all based on a text description.
The energy and chemical industries are applying AI indomains that had previously been inaccessible. Multiindustrials giant DuPont, for instance, had worked on
chatbots for both employee and consumer interfaces
previously, but found their inaccuracy too frustrating.
“LLMs now have the capability to achieve the necessary
accuracy, and at a faster pace,” says Andrew Blyton, vicepresident and chief information officer at DuPont Water& Protection. The company is now using AI in production
scheduling, predictive reliability and maintenance, andsales price optimization applications.

Figure 2: Industry-specific applications and use cases
Every industry—and business—will find its own custom applications of generative AI technology.
Consumer goods and retail

Manufacturing• Providing virtual fitting rooms• Serving as expert copilot for technicians• Scheduling delivery and installation• Allowing conversational interactions with machines• Providing in-store product-finding assistance• Providing prescriptive and proactive field service• Optimizing demand prediction and inventory planning• Enabling natural language troubleshooting• Generating novel product designs• Assessing warranty status and documentation• Understanding process bottlenecks and devising
recovery strategies

Media and entertainment• Providing intelligent search and tailored content
discovery

Financial services• Uncovering potential trading signals and alerting
traders to vulnerable positions• Writing engaging headlines and copy

• Accelerating underwriting decisions• Providing real-time feedback on content quality• Optimizing and rebuilding legacy systems• Curating personalized playlists, news digests, and• Reverse-engineering banking and insurance models

recommendations• Enabling interactive storytelling, driven by viewer
choices• Monitoring for potential financial crimes and fraud• Automating data gathering for regulatory compliance• Extracting insights from corporate disclosures• Delivering targeted offers and subscription plans
Source: Compiled by MIT Technology Review Insights, based on data from “Retail in the Age of Generative AI,” 9 “The Great Unlock: Large Language Models in
Manufacturing,” 10 “Generative AI Is Everything Everywhere, All at Once,” 11 and “Large Language Models in Media & Entertainment,” 12 Databricks, April–June 2023.

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Figure 3: The great reconsideration
Before generative AI, few organizations had adopted AI as a critical part of any business function.

Current AI adoption by function (2022)
Marketing and advertising

4%

Sales

4%

Finance6%14%Product development

6%

17%

IT

34%
26%

19%

6%

16%

20%

5%

20%

46%
25%

3%
8%

46%
42%

22%

2% 9 %

HR 4%

Supply chain and manufacturing

37%

11%

23%

20%

47%
23%

42%
32%

34%

10%

2%

11%

1%

Not using

Piloting use cases

Limited adoption of use cases

Widescale adoption of use cases

AI is now a critical part of the function

Not applicable/not sure

Figure 4: Limited AI ambitions
Before generative AI, few organizations aimed to make AI a critical capability across key functions by 2025.

2025 forecast for AI adoption by function
Marketing and advertising

3% 12%

21%

44%

20%

Sales

2% 12%

26%

37%

24%

Finance

3% 10%

22%

Product development

2% 13%

18%

IT

2% 9 %

HR

3% 11%

Supply chain and manufacturing

4 % 8%

Source: MIT Technology Review Insights survey, 2022.

17%

21%
46%
22%

21%
49%

19%
18%

43%

39%
30%

27%
38%

MIT Technology Review Insights

Multinational organizations with assets stretching backdecades have historically struggled to unify their digital
infrastructure. Mergers and acquisitions have resulted
in fragmented IT architectures. Important documents,from research and development intelligence to design
instructions for plants, have been lost to view, locked
in offline proprietary file types. “Could we interrogate
these documents using LLMs? Can we train models
to give us insights we’re not seeing in this vast worldof documentation?” asks Blyton. “We think that’s an
obvious use case.” Language models promise to
make such unstructured data much more valuable.

“LLMs now have thecapability to achieve thenecessary accuracy, andat a faster pace.”

Energy giant Shell concurs: “We’ve started to see
benefits because a lot of documentation previously
in repositories is now starting to come together,”says Owen O’Connell, senior vice president and chief

Andrew Blyton, Vice President
and Chief Information Officer,
DuPont Water & Protection
information officer for information digital services
and operations. The firm is also streamlining legal,
regulatory, and human resources paperwork covering
its many jurisdictions, and even surfacing insightsfrom unstructured data in areas like recruitment
and performance.

High hopes
for health care
Health and medicine, a deeply human-centeredfield, has also been a productive testing groundfor AI. In the lab, AI-powered tools have proven
their mettle at predicting protein structures,
aiding in drug discovery, and tracking the spreadof outbreaks, including the covid-19 pandemic.
Generative AI is now showing its promise asa powerful assistant to front-line staff. Natural
language processing tools, for instance, cantranscribe and summarize medical notes, whilechatbots might be trained to answer consumer
medical questions.
Richard Spencer Schaefer, chief healthinformatics officer at the Kansas City VA Medical
Center, which manages health care for American
military veterans, and AI solutions architect at
the VA’s National Artificial Intelligence Institute, is
optimistic that automation and predictive analytics
could help the sector. The VA recently validated

a model that identified the 24-hour risk of apatient admitted to hospital subsequently beingtransferred to a higher level of care. “We found avery significant improvement in the accuracy andpredictability of using that AI model, to a point
where there could be significant reduction in
mortality,” he reports.
Schaefer is also excited about the power of AI
to alleviate health-care employee fatigue whilebuilding their trust, via small, practical operational
improvements. “I think where you’ll see muchquicker adoption in health care,” he says, “is in very
specific automated process-type improvements.”
The VA, for example, is currently working on an
AI-driven project to reduce unnecessary alarms
and alerts in its medical centers. Machine learningmodels can help by defining dynamic and patientcentered thresholds for provider interventions,rather than relying on the alarm systems’ traditionaland static guardrails and thresholds. “For this towork,” says Schaefer, “we have to build trust andhelp health-care workers understand, hey, this iswhat AI can do.”

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MIT Technology Review Insights

Building for AI

I applications rely on a solid data
infrastructure that makes possible thecollection, storage, and analysis of its
vast data-verse. Even before the businessapplications of generative AI became
apparent in late 2022, a unified data platform foranalytics and AI was viewed as crucial by nearly 70%
of our survey respondents (see Figure 5).
Data infrastructure and architecture covers softwareand network-related infrastructure, notably cloud or
hybrid cloud, and hardware like high-performance GPUs.
Enterprises need an infrastructure that maximizes thevalue of data without compromising safety and security,especially at a time when the rulebook for dataprotection and AI is thickening. To truly democratize
AI, the infrastructure must support a simple interface
that allows users to query data and run complex tasks
via natural language. “The architecture is moving in a
way that supports democratization of analytics,”
says Schaefer.

Data lakehouses have become a popular infrastructure
choice. They are a hybrid of two historical approaches—data warehouses and data lakes. Data warehouses cameto prominence in the 1980s to systematize businessintelligence and enterprise reporting. However,
warehouses do not offer real-time services, operating
on a batch processing basis, and cannot accommodate
emerging and non-traditional data formats. Data lakes,
favored for their ability to support more AI and data
science tasks, emerged more recently. But they arecomplex to construct, slow, and suffer from inferior data
quality controls. The lakehouse combines the best of
both, offering an open architecture that combines theflexibility and scale of data lakes with the managementand data quality of warehouses.

“We have aggregated data acrossa lot of different technologies overtime, and I think what we’re finding
now is that the lakehouse has thebest cost performance straight off.”
Andrew Blyton, Vice President andChief Information Officer, DuPont Water& Protection

MIT Technology Review Insights

For the VA, the lakehouse is appealing because it
minimizes the need to move data, which creates privacyand governance risks. “We face a significant challenge
in data movement, which is why we are dedicating
substantial resources and expertise to address it,”says Schaefer.

Controlling thedata faucet
Training Open AI’s GPT-3 cost more than $4 million,
while the training of Meta’s LLaMA model took
1 million GPU hours and cost over $2.4 million.13
Training an AI model is energy-consuming; it can
expend more electricity than 100 typical U.S. homes
over an entire year.14 The training of Open AI’s GPT3 consumed 1,287 MWh of electricity and emitted
over 550 tons of carbon dioxide.15 “These models
are some of the most compute-heavy models you
can train,” says Shell’s Owen O’Connell.
Larger models are more expensive to train and run,with the cost of each interaction roughly related tothe model’s size, says Michael Carbin, of MIT and
MosaicML. OpenAI is reportedly spending $40
million a month to process the queries entered byusers, while Microsoft’s Bing chatbot reportedly
needs $4 billion worth of infrastructure to serve its
potential user base.16 More efficient smaller models(see next chapter) may be a promising alternative.
Companies will need to ramp up their attention
to efficiency while startups and researchers are
pioneering new ways to improve AI model efficiency.
“As our costs grow, we’re hiring engineers who
know how to optimize and control costs,” says
DuPont’s Andrew Blyton. “At some point, we getmuch more efficient on a query or compute basis
because we can hire experts to optimize.” DuPont
also tracks and analyzes cloud computing costs at
a granular level. “We have some talented internal
capability, and we rely on our vendors to ensure
we’re optimizing everything. The wrong approachhere can have a significant impact on both timeand performance,” says Blyton.

DuPont has invested in multiple technology
infrastructures and has found the data lakehousepromising. “We have aggregated data across a lot ofdifferent technologies over time,” says Blyton, “andI think what we’re finding now is that the lakehouse
has the best cost performance straight off, and hence
we’ve started investing more heavily in scaling.” For
DuPont, a data lakehouse provides visibility into acomplex organizational portfolio that includes yearsof M&A activity and technical debt. “Making sense of
the data of our business has been the main reason toinvest in the data lakehouse approach,” says Blyton.
“How do I take multiple enterprise resource planningsystems (ERPs) of data, merge them together, andgive people almost real-time access to informationthat previously was being done manually?”
The lakehouse abstracts complexity in a way thatallows users to perform advanced activities regardlessof technical competency. Shell has built an “enterpriselayer” that allows users to interact dynamically.
“Previously, you had to go to data stores, extract thedata, cleanse it, and do multiple transform activities,”says O’Connell. The lakehouse approach allows users
to run workloads and do historical analysis and trendmatching themselves, while cloud services provide
computational elasticity.
The data lakehouse supports both structured andunstructured data, says O’Connell. “Typical databases
are designed for only one type of data,” he says.
Lakehouse “allows us to move much quicker. We have
all that data coming together into a common architecturethat people can mix together and find business value
from.” This is crucial for a data-intensive company like
Shell. For predictive maintenance alone, the company
has 17,000 models and 4 trillion rows of data—adding
another 20 billion per day from 3 million sensors acrossfacilities around the world.
Data lakehouse adopters are using the technology
as part of a data strategy that enables practicaland impactful data and AI use cases. Says Schaefer,

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MIT Technology Review Insights“Democratizing the data is pretty expensive to do, andhistorically there weren’t good resources for that. But
we’re rapidly entering the era of lakehouse and hybridlake transactional models and software tools that are
finally playing together well enough. I think in health careparticularly we’ll see a pretty rapid adoption of tools that
improve operational efficiencies because we have a lot ofopportunity to improve the way that we do things and a
lot of opportunity for process automation.”

Blyton adds that, at DuPont, “we are using the lakehouseand tools like the data lake to build a data foundation andapply ML algorithms to it. We are expanding predictive
maintenance using algorithms with third-party sensors tofigure out when a machine’s going to break before it
breaks, as opposed to after it’s broken. These are goodrock-solid business cases, real value being generated
from the investment that’s producing a tangible and ameasurable return for us.”“Typical databases are designed for
only one type of data. Lakehouseallows us to move much quicker.” wen O’Connell, Senior Vice President and Chief Information
O
Officer (Information Digital Services and Operations), Shell

Figure 5: AI priorities for executives

78%

Scaling AI/ML use cases
to create business value
is a top priority

DISAGREE 8%
NEUTRAL 14%

AGREE

Unifying our data
platform for analytics
and AI is crucial to
our enterprise data
strategy

68%
AGREE

DISAGREE 12%
NEUTRAL 20%

We favor a multi-cloudapproach as a flexible
foundation for AI/MLDISAGREE 12%
NEUTRAL 16%

72%
AGREE

72%
AGREE
AGREE

Data problems are
the most likely factor
to jeopardize our
AI/ML goals

DISAGREE 9%
NEUTRAL 19%
Source: MIT Technology Review Insights survey, 2022.

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MIT Technology Review Insights

Buy, build?
Open, closed?

oday’s CIOs and leadership teams are
re-evaluating their assumptions onownership, partnership, and control as they
consider how to build on the capabilities ofthird-party generative AI platforms.

Over-leveraging a general-purpose AI platform is unlikely
to confer competitive advantage. Says Carbin, “If you
care deeply about a particular problem or you’re goingto build a system that is very core for your business, it’sa question of who owns your IP.” DuPont is “a scienceand innovation company,” adds Blyton, “and there is aneed to keep LLM models internal to our organization,
to protect and secure our intellectual property—this is a
critical need.”

Creating competitive risk is one worry. “You don’tnecessarily want to build off an existing model where
the data that you’re putting in could be used by thatcompany to compete against your own core products,”
says Carbin. Additionally, users lack visibility into the data,
weightings, and algorithms that power closed models,
and the product and its training data can change at any
time. This is particularly a concern in scientific R&D
where reproducibility is critical.17
Some CIOs are taking steps to limit company use of
external generative AI platforms. Samsung banned
ChatGPT after employees used it to work oncommercially sensitive code. A raft of other companies,
including JP Morgan Chase, Amazon, and Verizon,“If you care deeply about a particular
problem or you’re going to build asystem that is very core for yourbusiness, it’s a question of who
owns your IP.”
Michael Carbin, Associate Professor, MIT,and Founding Advisor, MosaicML

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MIT Technology Review Insights

have enacted restrictions or bans. “We can’t allow things
like Shell’s corporate strategy to be flowing through
ChatGPT,” says O’Connell. And since LLMs harness thetotality of the online universe, he believes that companies
will in the future be more cautious about what they put
online in the first place: “They are realizing, hang on,
someone else is going to derive a lot of value out of
my data.”
Inaccurate and unreliable outputs are a further worry.
The largest LLMs are, by dint of their size, tainted byfalse information online. That lends strength to theargument for more focused approaches, according toMatei Zaharia, cofounder and chief technology officer at
Databricks and associate professor of computer scienceat the University of California, Berkeley. “If you’re doing
something in a more focused domain,” he says, “youcan avoid all the random junk and unwanted informationfrom the web.”Companies cannot simply produce their own versions ofthese extremely large models. The scale and costs put
this kind of computational work beyond the reach of all
but the largest organizations: OpenAI reportedly used
10,000 GPUs to train ChatGPT.18 In the current moment,
building large-scale models is an endeavor for only thebest-resourced technology firms.
Smaller models, however, provide a viable alternative.
“I believe we’re going to move away from ‘I need half atrillion parameters in a model’ to ‘maybe I need 7, 10, 30,
50 billion parameters on the data that I actually have,’”says Carbin. “The reduction in complexity comes bynarrowing your focus from an all-purpose model thatknows all of human knowledge to very high-quality
knowledge just for you, because this is what individuals
in businesses actually really need.”Thankfully, smaller does not mean weaker. Generativemodels have been fine-tuned for domains requiring lessdata, as evidenced through models like BERT—for
biomedical content (BioBERT), legal content (LegalBERT), and French text (the delightfully named
CamemBERT).19 For particular business use cases,
organizations may choose to trade off broad knowledge
for specificity in their business area. “People are reallylooking for models that are conversant in their domain,”says Carbin. “And once you make that pivot, you start torealize there’s a different way that you can operate andbe successful.”

Dolly: The $30
conversational LLM
In March 2023, Databricks released Dolly, an opensource LLM aimed at democratizing the powerof generative AI.20 Trained for less than $30, it
demonstrated ChatGPT-like conversational ability(namely, the ability to follow user instructions). It isbased on the LLaMA tool from Meta, fine-tuned onhigh-quality inputs crowdsourced from Databricks
employees. Dolly, named after the world’s firstcloned mammal, has just 6 billion parameters—less than 3.5% of the 175 billion used in GPT-3.
“The highest-end models, with hundreds of billionsof parameters, can be very expensive to train andrun,” notes Databricks’s Matei Zaharia. These
costs leave many companies relying on proprietary
tools like ChatGPT or spending huge sums andresources to build their own. While improved
hardware and software efficiencies will lower
costs over time, building models with trillionsof parameters will still be beyond reach for
many, he observes.
To provide an alternative to AI tools becoming
centralized in just a few large companies,
Databricks sought to develop an open-sourcechat model that used open-source code and data
but also allowed commercial use. “The main thing
we wanted to investigate,” says Zaharia, “is how
expensive is it to create one using just public data
sets that are out there, using open-source codeand using or collecting training data that is opensource and is runnable by everyone.”
Databricks open-sourced the training code,
data set, and model weights for Dolly. Its April
2023 release, Dolly 2.0, is the first open-source
LLM licensed for commercial use.21 This allows
companies to combine their own data with
the Databricks data set to build personalized
applications that will not compromise their IP
or corporate information.
By enabling companies to cheaply build andcustomize their own tools, platforms like Dolly
can democratize access to generative AI.

MIT Technology Review Insights“All the large models that you can get from third-party
providers are trained on data from the web. But withinyour organization, you have a lot of internal concepts
and data that these models won’t know about.”
Matei Zaharia, Cofounder and Chief Technology Officer, Databricks, andAssociate Professor of Computer Science, University of California, Berkeley“People are starting to think a lot more about data as
a competitive moat,” says Zaharia. “Examples like
BloombergGPT [a purpose-built LLM for finance]
indicate that companies are thinking about what they
can do with their own data, and they are commercializing
their own models.”“Companies are going to extend and customize thesemodels with their own data, and to integrate them into
their own applications that make sense for theirbusiness,” predicts Zaharia. “All the large models thatyou can get from third-party providers are trained ondata from the web. But within your organization, you havea lot of internal concepts and data that these models
won’t know about. And the interesting thing is the model
doesn’t need a huge amount of additional data or training
time to learn something about a new domain,” he says.

Smaller open-source models, like Meta’s LLaMA,
could rival the performance of large models and allow
practitioners to innovate, share, and collaborate. One
team built an LLM using the weights from LLaMA at a
cost of less than $600, compared to the $100 million
involved in training GPT-4. The model, called Alpaca,
performs as well as the original on certain tasks.22 Open
source’s greater transparency also means researchers
and users can more easily identify
biases and flaws in these models.
“Much of this technology can be within the reach ofmany more organizations,” says Carbin. “It’s not just the
OpenAIs and the Googles and the Microsofts of theworld, but more average-size businesses, even startups.”

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MIT Technology Review Insights

Workforce worriesF

rom the telephone to the desktop computer,every leap in everyday technology sparks
worries about unemployment and threats
to human craft and skill. But generative AI
appears to mark a new phase given theunprecedented range of tasks that can now feasibly
be automated. Uniquely, some of the most automationvulnerable sectors today are in high-end technicaland creative fields.

The CIOs and academics interviewed for this reportoffer an optimistic view on generative AI’s effects forenterprises and society at large. They believe AI could
help sectors like health care where the workforce isstretched, and that human experts will remain essential.
“People are worried that they will lose their jobs becauseof AI, but that’s not the primary concern in health care,”says Schaefer. “The technologies that we’re putting
in place are enabling physicians to be a part of the

An Accenture analysis determined that 40% of
working hours across industries could be automated oraugmented by generative AI, with banking, insurance,
capital markets, and software showing the highest
potential.23 McKinsey projects that generative AI andrelated technologies could automate activities thatcurrently take up 60 to 70% of worker time—up from
50% before the advent of generative AI.24“The technologies that
we’re putting in place areenabling physicians to bea part of the developmentof AI, and because of theGoldman Sachs predicts that two-thirds of U.S.
occupations will be affected by AI-powered automation,level of validation involved,
but does not expect this to lead to widespread job loss:
I think there will be more
“Most jobs and industries,” the firm says, “are only
partially exposed to automation, and are thus more likely
trust in the models thatto be complemented rather than substituted by AI.” Itgoes on to note that technology-related job losses tend
we develop.”
to be balanced—and exceeded—by technology-related
job growth: “more than 85% of employment growth overthe last 80 years is explained by the technology-driven
creation of new positions,” the authors write.25

Richard Spencer Schaefer,
Chief Health Informatics Officer,
Kansas City VA Medical Center

MIT Technology Review Insights

development of AI, and because of the level ofvalidation involved, I think there will be more trust in themodels that we develop.” Humans are still necessary toannotate, curate, expose models to data sets, andprovision for responsible AI. “We’ve got a way to go
where we can unleash AI to do our research because
there is so much hand-holding that happens at the
moment,” adds O’Connell.
Generative AI will also empower by democratizing
access to technical capabilities once confined to a sliceof the workforce. Companies are already taking steps toencourage engagement. “We have created communities
of practitioners internally who do not necessarily havemainstream IT backgrounds,” says Blyton. “We wantto empower our business users to craft their own
dashboards and drive their own insights from data.” Infact, this type of data democratization was already a top
way that companies were generating tangible benefits
from AI in 2022, according to our survey (see Figure 6).

Figure 6: Most instrumental investmentareas for data and AI

When asked which investments have been instrumental tocreating benefits from AI/ML, business leaders are most
likely to say democratizing data access and building
business intelligence infrastructure have already paid off.
Percentage of respondents who ranked each option
among their top three investments.
Democratizing data (internal/external data sharing)
0%

32%

50%

Business intelligence/analytics infrastructure and tools
0%

32%

50%

Data governance and management
0%

29%

50%

29%

50%

Machine learning infrastructure0%
Talent and skills development

Blyton also predicts that ideas for AI will start comingfrom the workforce, marking the start of a more selfservice and entrepreneurial era within organizations.
According to O’Connell, business unit leaders, aware of
AI thanks to the blizzard of coverage over recent months,
are already creating a “demand pull” for AI applications,rather than technology teams having to sell AI ideas tothe organization.
Over the last decade, software programming has beenamong the most sought-after, highly paid roles in business.
Yet this is one domain where generative AI tools aredemonstrating utility, prompting concerns. CIOs andtechnical leads interviewed for this report offer a more
optimistic view. They expect rising demand for specialties
such as operational efficiency and responsible AI, andthey believe programmers will shift their attention tohigher value and less tedious work. Blyton asks, “Do I
need someone to be laboriously typing six hours’ worth ofcode into an engine and trying to debug it for three days?
I can see some huge efficiency by not doing that.”
Some expect programmers shift to chaperoning raw
initial ideas into more mature and business-readyoutputs. “It’s not hard to imagine that in the near future,
you could use the English language to generate code,”
predicts Blyton. “In a year or so, my workflow could
involve business users doing parts of the analysis in
English language, and then I take the output code and

0%

26%

50%

Improving data quality
0%

24%

50%

24%

50%

24%

50%

Data acquisition0%
Unified data infrastructure0%Open-source standards and formats
22%

0%

50%

Process re-engineering
0%

20%

50%

19%

50%

Cloud migration0%

Acquisition of AI start-ups
0%19%

Source: MIT Technology Review Insights survey, 2022.

50%

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MIT Technology Review Insights“In the next five to ten years we will see how quickly
we can adapt, and companies that fail to adapt,no matter how big, are going to disappear.”
Noriko Rzonca, Chief Digital Officer, Cosmo Energy Holdings

incorporate it into something more robust.” Stoddard
says AI is accelerating a trend already underway: “Youneed people who understand math and science to workwith the models, and you need people who have deep
analytic ways of thinking. In my area, the roles have been
migrating towards analytics, working with models,understanding deviations and coding.”
CIOs largely frame AI as a copilot rather than a rival tohuman employees. Zaharia predicts that LLMs will allow
executives to acquire high-quality data for decisions,saving time otherwise spent by analysts responding totheir on-demand report requests. But he adds, “I don’t
think they’ll be able to do extremely complicated workthat requires a lot of planning fully automatically. I don’t
think there’s anyone whose job is just the super-simple
stuff that can be done by a language model.” Stoddard
concurs: “Generative AI lets creators at all levels use
their own words to generate content,” she says. “But I
don’t think it will ever replace humans. Instead, it’sgoing to be an assistant to humans.”
She adds, “We internally view AI/ML as being a helper,
truly helping our people, and then allowing them to spend
more time on other value-added activities.” At Adobe, achange management process was necessary to win over
the workforce. Says Stoddard, “Back when we started
this, I would say people were skeptical, but when they
saw that they were truly able to spend more time on value
added, and in some cases, their jobs were upskilled,
people became believers, and they still are believers.”

The democratization of access to technical tools
could be society-wide. “All these people who couldn’t
program or manipulate design software, what are thenew opportunities for them?” asks Carbin. “This is goingto lower the barrier for people accessing technology
and programming or manipulating software. Now
you have this natural-language interface for design
software allowing you to navigate what was otherwise
an extremely difficult tool used only by the best
creative professionals.”
Risk aversion and cultural factors, like fear of failure,also need to be addressed to drive AI adoption in theworkforce. “Sometimes those who have been in or havemanaged organizations in Japan for a very long time
and who are used to a certain style, don’t welcome
risk-taking as much as the newer members of thebusiness and commercial realm who constantly offer
unique ideas and are eager for change,” says Noriko
Rzonca, chief digital officer, Cosmo Energy Holdings.
“As a business, it may take a longer time to adapt toand use new initiatives, but we are fortunate in the factthat improvement is something the Japanese are very
good at. As we adjust to using AI in business, I am
certain we will see great improvement and change tothe way businesses in Japan operate. I believe there
is huge potential in AI for Japan.”Rzonca says that organizations will have to embracedata and AI if they are to thrive: “In the next five toten years we will see how quickly we can adapt, andcompanies that fail to adapt, no matter how big, aregoing to disappear.”

06
A

MIT Technology Review Insights

Risks and responsibilities

I, and particularly generative AI, comeswith governance challenges that may
exceed the capabilities of existing data
governance frameworks. When working
with generative models that absorb andregurgitate all the data they are exposed to, without
regard for its sensitivity, organizations must attend tosecurity and privacy in a new way. Enterprises also
now must manage exponentially growing data sourcesand data that is machine-generated or of questionable
provenance, requiring a unified and consistent
governance approach. And lawmakers and regulators
have grown conscious of generative AI’s risks, as well,leading to legal cases, usage restrictions, and new
regulations, such as the European Union’s AI Act.
As such, CIOs would be reckless to adopt AI tools without
managing their risks—ranging from bias to copyright
infringement to privacy and security breaches. At Shell,“we’ve been spending time across legal, finance, data
privacy, ethics, and compliance to review our policies andframeworks to ensure that they’re ready for this andadapted,” says O’Connell.

“If your entire businessmodel is based on the
IP you own, protection
is everything.”
 ndrew Blyton, Vice President
A
and Chief Information Officer,
DuPont Water & Protection

One concern is protecting privacy at a time when reams of
new data are becoming visible and usable. “Because thetechnology is at an early stage, there is a greater need forlarge data sets for training, validation, verification, and drift
analysis,” observes Schaefer. At the VA, “that opens up a
Pandora’s box in terms of ensuring protected patient
health information is not exposed. We have invested
heavily in federally governed and secured high-compute
cloud resources.”
Commercial privacy and IP protection is a related concern.
“If your entire business model is based on the IP you own,
protection is everything,” says Blyton of DuPont. “There
are many bad actors who want to get their hands on ourinternal documentation, and the creation of new avenues
for the loss of IP is always a concern.”
Another data governance concern is reliability. LLMs
are learning engines whose novel content is synthesizedfrom vast troves of content and they do not differentiate
true from false. “If there’s an inaccurate or out-of-datepiece of information, the model still memorizes it. This is
a problem in an enterprise setting, and it means thatcompanies have to be very careful about what goes into
the model,” says Zaharia. Blyton adds, “It’s an interesting
thing with ChatGPT: you ask it the same question twiceand you get two subtly different answers; this does
make science and innovation company people raise
their eyebrows.”
Model explainability is key to earning the trust of
stakeholders for AI adoption and for proving thetechnology’s business value. This type of explainability
is already a priority in AI governance and regulation,
especially as algorithms have begun to play a role inlife-changing decisions like credit scoring or reoffending
risk in the criminal justice system. Critics argue that AI

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MIT Technology Review Insights

systems in sensitive or public interest domains should not
be a black box, and “algorithmic auditing” has received
growing attention.
“It’s pretty obvious there’s a lot riding on health-caremodel explainability,” says Schaefer. “We’ve beenworking on model cards”—a type of governancedocumentation that provides standardized info abouta model’s training, strengths, and weaknesses—“aswell as tools for model registries that provide some
explainability. Obviously algorithm selection is an
important consideration in model explainability, as well.”“Generative AI evolves thepossibility and promise of
AI exponentially. You cantransform the conversation
between the creator andthe computer.” ynthia Stoddard, Senior Vice President
C
and Chief Information Officer, Adobe

At Cosmo Energy Holdings, Rzonca considers how tobalance strong AI governance with empowerment. “We
are developing governance rules, setting them up, andtraining people,” she says. “In the meantime, we aretrying to get something easy to adapt to, so people canfeel and see the results.” To strike the balance, she pairs
democratized access to data and AI with centralized
governance: “Instead of me doing everything directly,
I’m trying to empower everyone around and help themrealize they have the individual ability to get things done.
Thanks to an empowered team, I can be freed up to

The creative copilot: AI at Adobe
The creative industries are embracing“Generative AI evolves the possibility

departments, rationalizing spending

generative AI to empower design

and promise of AI exponentially,” says

and simplifying the technology stack.

professionals with fascinating new

Adobe senior vice president and CIO

abilities. The software firm Adobe is

Cynthia Stoddard. “The tools are

now offering a suite of generative AI

becoming a lot stronger, and you canplatform that identifies real ordesign models called Adobe Firefly,

do more, so you can transform theemerging technical issues—the kinds

offering creative capabilities thatconversation between the creator andof faults a system administrator might

extend the powers of Photoshop,

the computer into something that’s

get called out in the middle of the night

Illustrator, Express, and its enterpriseeasy, more natural to use, intuitive.”

to fix—and fixing them automatically.

Adobe has also built a “self-healing”

offerings to new heights.
Adobe is also using AI to optimize its

Stoddard says, “as a company, AI is

The company has also developed

own back-office processes. Internally,

core to delivering experiences to our

Adobe Sensei GenAI to deliver more

the company is deploying automation

customers. Then as the IT organization,

speed and productivity in Adobe

to sift customer query tickets that can

we’re following those same principles

Experience Cloud, as a copilot forbe solved without human intervention,

to inject capabilities into what we do tocustomer experience workflows.

reducing workloads and giving

make it easier for our constituents.”

This can improve enterprisecustomers a speedier resolution.

productivity and efficiency in areas

It has employed AI to catalog thelike personalized content, editing,sprawling and often overlappingand conversational experience.

software solutions used acrossMIT Technology Review Insights“We are seeing the needto have very integrated
governance models,integrated governancestructures for all data
and all models.” ichard Spencer Schaefer,R
Chief Health Informatics Officer,
Kansas City VA Medical Center
focus on security and ensure data governance to avoid
any type of unfortunate data-related issue.”
The importance of unified governance to manage therisks of generative AI was a common theme in ourinterviews. Schaefer says, at the VA, “we are seeing
the need to have very integrated governance models,integrated governance structures for all data and allmodels. We’re putting a strong increased focus on very
centralized tools and processes that allow us to have anenterprise data structure.” And while unified governance
was always a need, generative AI has raised the stakes.
“The risk of having non-standardized non-well-defined
data running through a model, and how that could leadto bias and to model drift, has made that a much more
important aspect,” he says.
Stoddard notes the importance of including a wide rangeof voices throughout the AI oversight process at Adobe:“It’s important to have the element of diverse oversight
through the whole process and to make sure that wehave diversity not only of things like ethnicity, gender,and sexual orientation, but also diversity of thought andprofessional experience mixed into the process and the
AI impact assessment.” And organization-wide visibility
matters, too. Schaefer adds: “High on our list is getting
governance tools in place that provide a visual
overview of models that are in development, so thatthey can be spoken to by leadership or reviewed bystakeholders at any time.”

Constitutional AI, an approach currently advocatedby the startup Anthropic, provides LLMs with specificvalues and principles to adhere to rather than relying
on human feedback to guide content production.26
A constitutional approach guides a model to enact
the norms outlined in the constitution by, for example,
avoiding any outputs that are toxic or discriminatory.
And though the risks they bring to the enterprise aresubstantial, on the flip side, AI technologies also offer
great power in reducing some business risks. Zaharia
notes, “analyzing the results of models or analyzing
feedback from people’s comments does become easierwith language models. So it’s actually a bit easier to detect
if your system is doing something wrong and we’re seeing
a little bit of that.” Our 2022 survey found that security andrisk management (31%) was the top tangible benefit to AI
executives had noted to date, while fraud detection (27%),
cyber security (27%), and risk management (26%) were
the top three positive impacts anticipated by 2025.
A powerful new technology like generative AI brings
with it numerous risks and responsibilities. Our interviewssuggest that a motivated AI community of practitioners,
startups and companies will increasingly attend to
the governance risks of AI, just as they do withenvironmental sustainability, through a mixture of
public interest concern, good governance, andbrand protection.

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MIT Technology Review Insights

07

ConclusionA

I has been through many springs andwinters over the last half century. Until
recently, it had made only limited inroads
into the enterprise, beyond pilot projects ormore advanced functions like IT and finance.
That is set to change in the generative AI era, which issparking the transition to truly enterprise-wide AI.
Few practitioners dismiss generative AI as hype. Ifanything, its critics are more impressed by its power
than anyone. But executives and experts believe thatorganizations can reap the fruits of the new harvest andalso attend to its risks. Commercial organizations andgovernments alike have to tread a fine line between
embracing AI to accelerate innovation and productivity
while creating guardrails to mitigate risk and anticipate
the inevitable accidents and mishaps ahead.

With these precautions in mind, the most forwardlooking CIOs are moving decisively into this AI era.
“People who went through the computer and Internet
revolution talk about when computers first came online,”says Blyton. “If you were one of those people who
learned how to work with computers, you had a very
good career. This is a similar turning point: as long as you
embrace the technology, you will benefit from it.”“If you were one of thosepeople who learned how
to work with computers,
you had a very good career.
This is a similar turning
point: as long as you
embrace the technology,
you will benefit from it.”Andrew Blyton, Vice President
and Chief Information Officer,
DuPont Water & Protection

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Endnotes
1. “The economic potential of generative AI,” McKinsey & Company, June 14, 2023, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#/.
2. “Generative AI could raise global GDP by 7%,” Goldman Sachs, April 5, 2023, https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html.
3. Jim Euchner, “Generative AI,” Research-Technology Management, April 20, 2023, https://doi.org/10.1080/08956308.2023.2188861.
4. “CIO vision 2025: Bridging the gap between BI and AI,” MIT Technology Review Insights, September 2022, https://www.technologyreview.com/2022/09/20/1059630/cio-vision-2025-bridging-the-gap-between-bi-and-ai/.
5. “Retail in the Age of Generative AI,” Databricks, April 13, 2023, https://www.databricks.com/blog/2023/04/13/retail-age-generative-ai.html.
6. “The Great Unlock: Large Language Models in Manufacturing,” Databricks, May 30, 2023, https://www.databricks.com/blog/great-unlock-large-language-models-manufacturing.
7. “Generative AI Is Everything Everywhere, All at Once,” Databricks, June 7, 2023, https://www.databricks.com/blog/generative-ai-everything-everywhere-all-once.
8. “ Large Language Models in Media & Entertainment,” Databricks, June 6, 2023, https://www.databricks.com/blog/large-language-models-media-entertainment.
9. Ibid.
10. Ibid.
11. Ibid.
12. Ibid.
13. Jonathan Vanian and Kif Leswing, “ChatGPT and generative AI are booming, but the costs can be extraordinary,” CNBC, March 13, 2023, https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-avery-expensive-price.html.
14. Josh Saul and Dina Bass, “Artificial Intelligence Is Booming—So Is Its Carbon Footprint,” Bloomberg, March 9, 2023, https://www.bloomberg.com/news/articles/2023-03-09/
how-much-energy-do-ai-and-chatgpt-use-no-one-knows-for-sure#xj4y7vzkg.
15. Chris Stokel-Walker, “The Generative AI Race Has a Dirty Secret,” Wired, February 10, 2023, https://www.wired.co.uk/article/the-generative-ai-search-race-has-a-dirty-secret.
16. Vanian and Leswing, “ChatGPT and generative AI are booming,” https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-a-very-expensive-price.html.
17. Arthur Spirling, “Why open-source generative AI models are an ethical way forward for science,” Nature, April 18, 2023, https://www.nature.com/articles/d41586-023-01295-4.
18. Sharon Goldman, “With a wave of new LLMs, open-source AI is having a moment—and a red-hot debate,” VentureBeat, April 10, 2023,
https://venturebeat.com/ai/with-a-wave-of-new-llms-open-source-ai-is-having-a-moment-and-a-red-hot-debate/.
19. Thomas H. Davenport and Nitin Mittal, “How Generative AI Is Changing Creative Work,” Harvard Business Review, November 14, 2022, https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work.
20. “ Hello Dolly: Democratizing the magic of ChatGPT with open models,” Databricks, March 24, 2023, https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html.
21. “ Introducing the World’s First Truly Open Instruction-Tuned LLM,” Databricks, April 12, 2023, https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm.
22. “Just how good can China get at generative AI?” The Economist, May 9, 2023, https://www.economist.com/business/2023/05/09/just-how-good-can-china-get-at-generative-ai.
23. “Gen AI LLM—A new era of generative AI for everyone,” Accenture, April 17, 2023, https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf.
24. “The economic potential of generative AI,” McKinsey & Company, June 14, 2023, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#/.
25. “Generative AI could raise global GDP by 7%,” Goldman Sachs, April 5, 2023, https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html.
26. “Claude’s Constitution,” Anthropic, May 9, 2023, https://www.anthropic.com/index/claudes-constitution.
.

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