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Technology’s generational moment with generative AI: A CIO and CTO guide
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TECHNOLOGY’S GENERATIONAL MOMENT WITH GENERATIVE AI: A CIO AND CTO GUIDE
July 11, 2023 | Article
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CIOs and CTOs can take nine actions to reimagine business and technology with
generative AI.
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Article (12 pages)
Hardly a day goes by without some new business-busting development related to
generative AI surfacing in the media. The excitement is well deserved—McKinsey
research estimates that generative AI could add the equivalent of $2.6 trillion
to $4.4 trillion of value annually.1“The economic potential of generative AI:
The next productivity frontier,” McKinsey, June 14, 2023.
Sidebar
ABOUT THE AUTHORS
This article is a collaborative effort by Aamer Baig, Sven Blumberg, Eva Li,
Douglas Merrill, Adi Pradhan, Megha Sinha, Alexander Sukharevsky, and Stephen
Xu, representing views from McKinsey Digital.
CIOs and chief technology officers (CTOs) have a critical role in capturing that
value, but it’s worth remembering we’ve seen this movie before. New technologies
emerged—the internet, mobile, social media—that set off a melee of experiments
and pilots, though significant business value often proved harder to come by.
Many of the lessons learned from those developments still apply, especially when
it comes to getting past the pilot stage to reach scale. For the CIO and CTO,
the generative AI boom presents a unique opportunity to apply those lessons to
guide the C-suite in turning the promise of generative AI into sustainable value
for the business.
Share
Sidebar
A QUICK PRIMER ON KEY TERMS
Generative AI is a type of AI that can create new content (text, code, images,
video) using patterns it has learned by training on extensive (public) data with
machine learning (ML) techniques.
Foundation models (FMs) are deep learning models trained on vast quantities of
unstructured, unlabeled data that can be used for a wide range of tasks out of
the box or adapted to specific tasks through fine-tuning. Examples of these
models are GPT-4, PaLM 2, DALL·E 2, and Stable Diffusion.
Large language models (LLMs) make up a class of foundation models that can
process massive amounts of unstructured text and learn the relationships between
words or portions of words, known as tokens. This enables LLMs to generate
natural-language text, performing tasks such as summarization or knowledge
extraction. Cohere Command is one type of LLM; LaMDA is the LLM behind Bard.
Fine-tuning is the process of adapting a pretrained foundation model to perform
better in a specific task. This entails a relatively short period of training on
a labeled data set, which is much smaller than the data set the model was
initially trained on. This additional training allows the model to learn and
adapt to the nuances, terminology, and specific patterns found in the smaller
data set.
Prompt engineering refers to the process of designing, refining, and optimizing
input prompts to guide a generative AI model toward producing desired (that is,
accurate) outputs.
Through conversations with dozens of tech leaders and an analysis of generative
AI initiatives at more than 50 companies (including our own), we have identified
nine actions all technology leaders can take to create value, orchestrate
technology and data, scale solutions, and manage risk for generative AI (see
sidebar, “A quick primer on key terms”):
1. Move quickly to determine the company’s posture for the adoption of
generative AI, and develop practical communications to, and appropriate
access for, employees.
2. Reimagine the business and identify use cases that build value through
improved productivity, growth, and new business models. Develop a “financial
AI” (FinAI) capability that can estimate the true costs and returns of
generative AI.
3. Reimagine the technology function, and focus on quickly building generative
AI capabilities in software development, accelerating technical debt
reduction, and dramatically reducing manual effort in IT operations.
4. Take advantage of existing services or adapt open-source generative AI
models to develop proprietary capabilities (building and operating your own
generative AI models can cost tens to hundreds of millions of dollars, at
least in the near term).
5. Upgrade your enterprise technology architecture to integrate and manage
generative AI models and orchestrate how they operate with each other and
existing AI and machine learning (ML) models, applications, and data
sources.
6. Develop a data architecture to enable access to quality data by processing
both structured and unstructured data sources.
7. Create a centralized, cross-functional generative AI platform team to
provide approved models to product and application teams on demand.
8. Invest in upskilling key roles—software developers, data engineers, MLOps
engineers, and security experts—as well as the broader nontech workforce.
But you need to tailor the training programs by roles and proficiency levels
due to the varying impact of generative AI.
9. Evaluate the new risk landscape and establish ongoing mitigation practices
to address models, data, and policies.
1. DETERMINE THE COMPANY’S POSTURE FOR THE ADOPTION OF GENERATIVE AI
As use of generative AI becomes increasingly widespread, we have seen CIOs and
CTOs respond by blocking employee access to publicly available applications to
limit risk. In doing so, these companies risk missing out on opportunities for
innovation, with some employees even perceiving these moves as limiting their
ability to build important new skills.
Instead, CIOs and CTOs should work with risk leaders to balance the real need
for risk mitigation with the importance of building generative AI skills in the
business. This requires establishing the company’s posture regarding generative
AI by building consensus around the levels of risk with which the business is
comfortable and how generative AI fits into the business’s overall strategy.
This step allows the business to quickly determine company-wide policies and
guidelines.
Once policies are clearly defined, leaders should communicate them to the
business, with the CIO and CTO providing the organization with appropriate
access and user-friendly guidelines. Some companies have rolled out firmwide
communications about generative AI, provided broad access to generative AI for
specific user groups, created pop-ups that warn users any time they input
internal data into a model, and built a guidelines page that appears each time
users access a publicly available generative AI service.
2. IDENTIFY USE CASES THAT BUILD VALUE THROUGH IMPROVED PRODUCTIVITY, GROWTH,
AND NEW BUSINESS MODELS
CIOs and CTOs should be the antidote to the “death by use case” frenzy that we
already see in many companies. They can be most helpful by working with the CEO,
CFO, and other business leaders to think through how generative AI challenges
existing business models, opens doors to new ones, and creates new sources of
value. With a deep understanding of the technical possibilities, the CIO and CTO
should identify the most valuable opportunities and issues across the company
that can benefit from generative AI—and those that can’t. In some cases,
generative AI is not the best option.
McKinsey research, for example, shows generative AI can lift productivity for
certain marketing use cases (for example, by analyzing unstructured and abstract
data for customer preference) by roughly 10 percent and customer support (for
example, through intelligent bots) by up to 40 percent.2“The economic potential
of generative AI: The next productivity frontier,” McKinsey, June 14, 2023. The
CIO and CTO can be particularly helpful in developing a perspective on how best
to cluster use cases either by domain (such as customer journey or business
process) or use case type (such as creative content creation or virtual agents)
so that generative AI will have the most value. Identifying opportunities won’t
be the most strategic task—there are many generative AI use cases out there—but,
given initial limitations of talent and capabilities, the CIO and CTO will need
to provide feasibility and resource estimates to help the business sequence
generative AI priorities.
Providing this level of counsel requires tech leaders to work with the business
to develop a FinAI capability to estimate the true costs and returns on
generative AI initiatives. Cost calculations can be particularly complex because
the unit economics must account for multiple model and vendor costs, model
interactions (where a query might require input from multiple models, each with
its own fee), ongoing usage fees, and human oversight costs.
3. REIMAGINE THE TECHNOLOGY FUNCTION
Generative AI has the potential to completely remake how the tech function
works. CIOs and CTOs need to make a comprehensive review of the potential impact
of generative AI on all areas of tech, but it’s important to take action quickly
to build experience and expertise. There are three areas where they can focus
their initial energies:
* Software development: McKinsey research shows generative AI coding support
can help software engineers develop code 35 to 45 percent faster, refactor
code 20 to 30 percent faster, and perform code documentation 45 to 50 percent
faster.3Begum Karaci Deniz, Martin Harrysson, Alharith Hussin, and Shivam
Srivastava, “Unleashing developer productivity with generative AI,” McKinsey,
June 27, 2023. Generative AI can also automate the testing process and
simulate edge cases, allowing teams to develop more-resilient software prior
to release, and accelerate the onboarding of new developers (for example, by
asking generative AI questions about a code base). Capturing these benefits
will require extensive training (see more in action 8) and automation of
integration and deployment pipelines through DevSecOps practices to manage
the surge in code volume.
* Technical debt: Technical debt can account for 20 to 40 percent of technology
budgets and significantly slow the pace of development.4Vishal Dalal, Krish
Krishnakanthan, Björn Münstermann, and Rob Patenge, “Tech debt: Reclaiming
tech equity,” McKinsey, October 6, 2020. CIOs and CTOs should review their
tech-debt balance sheets to determine how generative AI capabilities such as
code refactoring, code translation, and automated test-case generation can
accelerate the reduction of technical debt.
* IT operations (ITOps): CIOs and CTOs will need to review their ITOps
productivity efforts to determine how generative AI can accelerate processes.
Generative AI’s capabilities are particularly helpful in automating such
tasks as password resets, status requests, or basic diagnostics through
self-serve agents; accelerating triage and resolution through improved
routing; surfacing useful context, such as topic or priority, and generating
suggested responses; improving observability through analysis of vast streams
of logs to identify events that truly require attention; and developing
documentation, such as standard operating procedures, incident postmortems,
or performance reports.
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4. TAKE ADVANTAGE OF EXISTING SERVICES OR ADAPT OPEN-SOURCE GENERATIVE AI MODELS
A variation of the classic “rent, buy, or build” decision exists when it comes
to strategies for developing generative AI capabilities. The basic rule holds
true: a company should invest in a generative AI capability where it can create
a proprietary advantage for the business and access existing services for those
that are more like commodities.
The CIO and CTO can think through the implications of these options as three
archetypes:
* Taker—uses publicly available models through a chat interface or an API, with
little or no customization. Good examples include off-the-shelf solutions to
generate code (such as GitHub Copilot) or to assist designers with image
generation and editing (such as Adobe Firefly). This is the simplest
archetype in terms of both engineering and infrastructure needs and is
generally the fastest to get up and running. These models are essentially
commodities that rely on feeding data in the form of prompts to the public
model.
* Shaper—integrates models with internal data and systems to generate more
customized results. One example is a model that supports sales deals by
connecting generative AI tools to customer relationship management (CRM) and
financial systems to incorporate customers’ prior sales and engagement
history. Another is fine-tuning the model with internal company documents and
chat history to act as an assistant to a customer support agent. For
companies that are looking to scale generative AI capabilities, develop more
proprietary capabilities, or meet higher security or compliance needs, the
Shaper archetype is appropriate.
There are two common approaches for integrating data with generative AI
models in this archetype. One is to “bring the model to the data,” where the
model is hosted on the organization’s infrastructure, either on-premises or
in the cloud environment. Cohere, for example, deploys foundation models on
clients’ cloud infrastructure, reducing the need for data transfers. The
other approach is to “bring data to the model,” where an organization can
aggregate its data and deploy a copy of the large model on cloud
infrastructure. Both approaches achieve the goal of providing access to the
foundation models, and choosing between them will come down to the
organization’s workload footprint.
* Maker—builds a foundation model to address a discrete business case. Building
a foundation model is expensive and complex, requiring huge volumes of data,
deep expertise, and massive compute power. This option requires a substantial
one-off investment—tens or even hundreds of millions of dollars—to build the
model and train it. The cost depends on various factors, such as training
infrastructure, model architecture choice, number of model parameters, data
size, and expert resources.
Each archetype has its own costs that tech leaders will need to consider
(Exhibit 1). While new developments, such as efficient model training approaches
and lower graphics processing unit (GPU) compute costs over time, are driving
costs down, the inherent complexity of the Maker archetype means that few
organizations will adopt it in the short term. Instead, most will turn to some
combination of Taker, to quickly access a commodity service, and Shaper, to
build a proprietary capability on top of foundation models.
Exhibit 1
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5. UPGRADE YOUR ENTERPRISE TECHNOLOGY ARCHITECTURE TO INTEGRATE AND MANAGE
GENERATIVE AI MODELS
Organizations will use many generative AI models of varying size, complexity,
and capability. To generate value, these models need to be able to work both
together and with the business’s existing systems or applications. For this
reason, building a separate tech stack for generative AI creates more
complexities than it solves. As an example, we can look at a consumer querying
customer service at a travel company to resolve a booking issue (Exhibit 2). In
interacting with the customer, the generative AI model needs to access multiple
applications and data sources.
Exhibit 2
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For the Taker archetype, this level of coordination isn’t necessary. But for
companies looking to scale the advantages of generative AI as Shapers or Makers,
CIOs and CTOs need to upgrade their technology architecture. The prime goal is
to integrate generative AI models into internal systems and enterprise
applications and to build pipelines to various data sources. Ultimately, it’s
the maturity of the business’s enterprise technology architecture that allows it
to integrate and scale its generative AI capabilities.
Recent advances in integration and orchestration frameworks, such as LangChain
and LlamaIndex, have significantly reduced the effort required to connect
different generative AI models with other applications and data sources. Several
integration patterns are also emerging, including those that enable models to
call APIs when responding to a user query—GPT-4, for example, can invoke
functions—and provide contextual data from an external data set as part of a
user query, a technique known as retrieval augmented generation. Tech leaders
will need to define reference architectures and standard integration patterns
for their organization (such as standard API formats and parameters that
identify the user and the model invoking the API).
There are five key elements that need to be incorporated into the technology
architecture to integrate generative AI effectively (Exhibit 3):
* Context management and caching to provide models with relevant information
from enterprise data sources. Access to relevant data at the right time is
what allows the model to understand the context and produce compelling
outputs. Caching stores results to frequently asked questions to enable
faster and cheaper responses.
* Policy management to ensure appropriate access to enterprise data assets.
This control ensures that HR’s generative AI models that include employee
compensation details, for example, cannot be accessed by the rest of the
organization.
* Model hub, which contains trained and approved models that can be provisioned
on demand and acts as a repository for model checkpoints, weights, and
parameters.
* Prompt library, which contains optimized instructions for the generative AI
models, including prompt versioning as models are updated.
* MLOps platform, including upgraded MLOps capabilities, to account for the
complexity of generative AI models. MLOps pipelines, for example, will need
to include instrumentation to measure task-specific performance, such as
measuring a model’s ability to retrieve the right knowledge.
Exhibit 3
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In evolving the architecture, CIOs and CTOs will need to navigate a rapidly
growing ecosystem of generative AI providers and tooling. Cloud providers
provide extensive access to at-scale hardware and foundation models, as well as
a proliferating set of services. MLOps and model hub providers, meanwhile, offer
the tools, technologies, and practices to adapt a foundation model and deploy it
into production, while other companies provide applications directly accessed by
users built on top of foundation models to perform specific tasks. CIOs and CTOs
will need to assess how these various capabilities are assembled and integrated
to deploy and operate generative AI models.
6. DEVELOP A DATA ARCHITECTURE TO ENABLE ACCESS TO QUALITY DATA
The ability of a business to generate and scale value, including cost reductions
and improved data and knowledge protections, from generative AI models will
depend on how well it takes advantage of its own data. Creating that advantage
relies on a data architecture that connects generative AI models to internal
data sources, which provide context or help fine-tune the models to create more
relevant outputs.
In this context, CIOs, CTOs, and chief data officers need to work closely
together to do the following:
* Categorize and organize data so it can be used by generative AI models. Tech
leaders will need to develop a comprehensive data architecture that
encompasses both structured and unstructured data sources. This requires
putting in place standards and guidelines to optimize data for generative AI
use—for example, by augmenting training data with synthetic samples to
improve diversity and size; converting media types into standardized data
formats; adding metadata to improve traceability and data quality; and
updating data.
* Ensure existing infrastructure or cloud services can support the storage and
handling of the vast volumes of data needed for generative AI applications.
* Prioritize the development of data pipelines to connect generative AI models
to relevant data sources that provide “contextual understanding.” Emerging
approaches include the use of vector databases to store and retrieve
embeddings (specially formatted knowledge) as input for generative AI models
as well as in-context learning approaches, such as “few shot prompting,”
where models are provided with examples of good answers.
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7. CREATE A CENTRALIZED, CROSS-FUNCTIONAL GENERATIVE AI PLATFORM TEAM
Most tech organizations are on a journey to a product and platform operating
model. CIOs and CTOs need to integrate generative AI capabilities into this
operating model to build on the existing infrastructure and help to rapidly
scale adoption of generative AI. The first step is setting up a generative AI
platform team whose core focus is developing and maintaining a platform service
where approved generative AI models can be provisioned on demand for use by
product and application teams. The platform team also defines protocols for how
generative AI models integrate with internal systems, enterprise applications,
and tools, and also develops and implements standardized approaches to manage
risk, such as responsible AI frameworks.
CIOs and CTOs need to ensure that the platform team is staffed with people who
have the right skills. This team requires a senior technical leader who acts as
the general manager. Key roles include software engineers to integrate
generative AI models into existing systems, applications, and tools; data
engineers to build pipelines that connect models to various systems of record
and data sources; data scientists to select models and engineer prompts; MLOps
engineers to manage deployment and monitoring of multiple models and model
versions; ML engineers to fine-tune models with new data sources; and risk
experts to manage security issues such as data leakage, access controls, output
accuracy, and bias. The exact composition of the platform team will depend on
the use cases being served across the enterprise. In some instances, such as
creating a customer-facing chatbot, strong product management and user
experience (UX) resources will be required.
Realistically, the platform team will need to work initially on a narrow set of
priority use cases, gradually expanding the scope of their work as they build
reusable capabilities and learn what works best. Technology leaders should work
closely with business leads to evaluate which business cases to fund and
support.
8. TAILOR UPSKILLING PROGRAMS BY ROLES AND PROFICIENCY LEVELS
Generative AI has the potential to massively lift employees’ productivity and
augment their capabilities. But the benefits are unevenly distributed depending
on roles and skill levels, requiring leaders to rethink how to build the actual
skills people need.
Our latest empirical research using the generative AI tool GitHub Copilot, for
example, helped software engineers write code 35 to 45 percent
faster.5“Unleashing developer productivity with generative AI,” June 27, 2023.
The benefits, however, varied. Highly skilled developers saw gains of up to 50
to 80 percent, while junior developers experienced a 7 to 10 percent decline in
speed. That’s because the output of the generative AI tools requires engineers
to critique, validate, and improve the code, which inexperienced software
engineers struggle to do. Conversely, in less technical roles, such as customer
service, generative AI helps low-skill workers significantly, with productivity
increasing by 14 percent and staff turnover dropping as well, according to one
study.6Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, Generative AI at
work, National Bureau of Economic Research (NBER) working paper, number 31161,
April 2023.
These disparities underscore the need for technology leaders, working with the
chief human resources officer (CHRO), to rethink their talent management
strategy to build the workforce of the future. Hiring a core set of top
generative AI talent will be important, and, given the increasing scarcity and
strategic importance of that talent, tech leaders should put in place retention
mechanisms, such as competitive salaries and opportunities to be involved in
important strategic work for the business.
Tech leaders, however, cannot stop at hiring. Because nearly every existing role
will be affected by generative AI, a crucial focus should be on upskilling
people based on a clear view of what skills are needed by role, proficiency
level, and business goals. Let’s look at software developers as an example.
Training for novices needs to emphasize accelerating their path to become top
code reviewers in addition to code generators. Similar to the difference between
writing and editing, code review requires a different skill set. Software
engineers will need to understand what good code looks like; review the code
created by generative AI for functionality, complexity, quality, and
readability; and scan for vulnerabilities while ensuring they do not themselves
introduce quality or security issues in the code. Furthermore, software
developers will need to learn to think differently when it comes to coding, by
better understanding user intent so they can create prompts and define
contextual data that help generative AI tools provide better answers.
Beyond training up tech talent, the CIO and CTO can play an important role in
building generative AI skills among nontech talent as well. Besides
understanding how to use generative AI tools for such basic tasks as email
generation and task management, people across the business will need to become
comfortable using an array of capabilities to improve performance and outputs.
The CIO and CTO can help adapt academy models to provide this training and
corresponding certifications.
The decreasing value of inexperienced engineers should accelerate the move away
from a classic talent pyramid, where the greatest number of people are at a
junior level, to a structure more like a diamond, where the bulk of the
technical workforce is made up of experienced people. Practically speaking, that
will mean building the skills of junior employees as quickly as possible while
reducing roles dedicated to low-complexity manual tasks (such as writing unit
tests).
9. EVALUATE THE NEW RISK LANDSCAPE AND ESTABLISH ONGOING MITIGATION PRACTICES
Generative AI presents a fresh set of ethical questions and risks, including
“hallucinations,” whereby the generative AI model presents an incorrect response
based on the highest-probability response; the accidental release of
confidential personally identifiable information; inherent bias in the large
data sets the models use; and high degrees of uncertainty related to
intellectual property (IP). CIOs and CTOs will need to become fluent in ethics,
humanitarian, and compliance issues to adhere not just to the letter of the law
(which will vary by country) but also to the spirit of responsibly managing
their business’s reputation.
Addressing this new landscape requires a significant review of cyber practices
and updating the software development process to evaluate risk and identify
mitigation actions before model development begins, which will both reduce
issues and ensure the process doesn’t slow down. Proven risk-mitigation actions
for hallucinations can include adjusting the level of creativity (known as the
“temperature”) of a model when it generates responses; augmenting the model with
relevant internal data to provide more context; using libraries that impose
guardrails on what can be generated; using “moderation” models to check outputs;
and adding clear disclaimers. Early generative AI use cases should focus on
areas where the cost of error is low, to allow the organization to work through
inevitable setbacks and incorporate learnings.
To protect data privacy, it will be critical to establish and enforce sensitive
data tagging protocols, set up data access controls in different domains (such
as HR compensation data), add extra protection when data is used externally, and
include privacy safeguards. For example, to mitigate access control risk, some
organizations have set up a policy-management layer that restricts access by
role once a prompt is given to the model. To mitigate risk to intellectual
property, CIOs and CTOs should insist that providers of foundation models
maintain transparency regarding the IP (data sources, licensing, and ownership
rights) of the data sets used.
--------------------------------------------------------------------------------
Generative AI is poised to be one of the fastest-growing technology categories
we’ve ever seen. Tech leaders cannot afford unnecessary delays in defining and
shaping a generative AI strategy. While the space will continue to evolve
rapidly, these nine actions can help CIOs and CTOs responsibly and effectively
harness the power of generative AI at scale.
ABOUT THE AUTHOR(S)
Aamer Baig is a senior partner in McKinsey’s Chicago office; Sven Blumberg is a
senior partner in the Düsseldorf office; Eva Li is a consultant in the Bay Area
office, where Megha Sinha is a partner; Douglas Merrill is a partner in the
Southern California office; Adi Pradhan and Stephen Xu are associate partners in
the Toronto office; and Alexander Sukharevsky is a senior partner in the London
office.
The authors wish to thank Stephanie Brauckmann, Anusha Dhasarathy, Martin
Harrysson, Klemens Hjartar, Alharith Hussin, Naufal Khan, Sam Nie, Chandrasekhar
Panda, Henning Soller, Nikhil Srinidhi, Asin Tavakoli, Niels Van der Wildt, and
Anna Wiesinger for their contributions to this article.
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