Superagency in the Workplace

Empowering people to unlock AI’s full potential

Hannah Mayer Lareina Yee Michael Chui Roger Roberts

January 2025

Introduction 2

Chapters:

An innovation as powerful as the steam engine 5
Employees are ready for AI; now leaders must step up 11
Delivering speed and safety 18
Embracing bigger ambitions 26
Technology is not the barrier to scale 35

Conclusion: Meeting the AI future 40

Acknowledgments 42

Methodology 43

Glossary 44

Introduction

Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research finds the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast enough.

A rtificial intelligence has arrived in the workplace and has the potential to be as transformative as the steam engine was to the 19th-century Industrial Revolution.1 With powerful and capable large language models (LLMs) developed by Anthropic, Cohere, Google, Meta, Mistral, OpenAI, as the steam engine was to the 19th-century Industrial Revolution.1 With powerful and capable large language models (LLMs)developed by Anthropic,Cohere,Google,Meta, Mistral, OpenAl, and others, we have entered a new information technology era. McKinsey research sizes the long-term AI opportunity at \$4.4 trillion in added productivity growth potential from corporate use cases.2

Therein lies the challenge: the long-term potential of AI is great, but the short-term returns are unclear. Over the next three years, 92 percent of companies plan to increase their AI investments. But while nearly all companies are investing in AI, only 1 percent of leaders call their companies “mature” on the deployment spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes. The big question is how business leaders can deploy capital and steer their organizations closer to AI maturity.

This research report, prompted by Reid Hoffman’s book Superagency: What Could Possibly Go Right with Our AI Future, 3 asks a similar question: How can companies harness AI to amplify human agency and unlock new levels of creativity and productivity in the workplace? AI could drive enormous positive and disruptive change. This transformation will take some time, but leaders must not be dissuaded. Instead, they must advance boldly today to avoid becoming uncompetitive tomorrow. The history of major economic and technological shifts shows that such moments can define the rise and fall of companies. Over 40 years ago, the internet was born. Since then, companies including Alphabet, Amazon, Apple, Meta, and Microsoft have attained trillion-dollar market capitalizations. Even more profoundly, the internet changed the anatomy of work and access to information. AI now is like the internet many years ago: The risk for business leaders is not thinking too big, but rather too small.

Superagency: By the numbers

Employees are more ready for the change than their leaders imagine

3×

more employees are using gen AI
for a third or more of their work
than their leaders imagine;
more than 70% of all employees
believe that within 2 years gen AI will
change 30% or more of their work

1.4×

more likely for millennials to report
extensive familiarity with gen AI tools
than peers in other age groups;
they are also 1.2× more
likely to expect workows
to change within a year

Companies need to move fast—employees trust their leaders to balance speed and safety

of the C-suite say their companies are developing gen AI tools too slowly, even though 69% started investing more than a year ago

1.3×

more likely for employees to trust their own companies to get gen AI deployment right than they are to trust other institutions

Companies are investing in gen AI but have not yet achieved maturity

92%

of companies plan to invest more in gen AI over the next 3 years

1%

believe their investments have reached maturity

Leaders need to recognize their responsibility in driving gen AI transformation

2.4×

more likely for C-suite to cite employee readiness as a barrier to adoption vs their own issues with leadership alignment, despite employees currently using gen AI 3× more than leaders expect

McKinsey & Company

48%

of employees rank training as the most important factor for gen AI adoption; yet nearly half feel they are receiving moderate or less support

This report explores companies’ technology and business readiness for AI adoption (see sidebar “About the survey”). It concludes that employees are ready for AI. The biggest barrier to success is leadership.

Chapter 1 looks at the rapid advancement of technology over the past two years and its implications for business adoption of AI.

Chapter 2 delves into the attitudes and perceptions of employees and leaders. Our research shows that employees are more ready for AI than their leaders imagine. In fact, they are already using AI on a regular basis; are three times more likely than leaders realize to believe that AI will replace 30 percent of their work in the next year; and are eager to gain AI skills. Still, AI optimists are only a slight majority in the workplace; a large minority (41 percent) are more apprehensive and will need additional support. This is where millennials, who are the most familiar with AI and are often in managerial roles, can be strong advocates for change.

Chapter 3 looks at the need for speed and safety in AI deployment. While leaders and employees want to move faster, trust and safety are top concerns. About half of employees worry about AI inaccuracy and cybersecurity risks. That said, employees express greater confidence that their own companies, versus other organizations, will get AI right. The onus is on business leaders to prove them right, by making bold and responsible decisions.

Chapter 4 examines how companies risk losing ground in the AI race if leaders do not set bold goals. As the hype around AI subsides, companies should put a heightened focus on practical applications that empower employees in their daily jobs. These applications can create competitive moats and generate measurable ROI. Across industries, functions, and geographies, companies that invest strategically can go beyond using AI to drive incremental value and instead create transformative change.

Chapter 5 looks at what is required for leaders to set their teams up for success with AI. The challenge of AI in the workplace is not a technology challenge. It is a business challenge that calls upon leaders to align teams, address AI headwinds, and rewire their companies for change.

About the survey

To create our report, we surveyed 3,613 employees (managers and independent contributors) and 238 C-level executives in October and November 2024. Of these, 81 percent came from the United States, and the rest came from five other countries: Australia, India, New Zealand, Singapore, and the United Kingdom. The employees spanned many roles, including business development, finance, marketing, product management, sales, and technology.

All the survey findings discussed in the report, aside from two sidebars presenting international nuances, pertain solely to US workplaces. The findings are organized in this way because the responses from US employees and C-suite executives provide statistically significant conclusions about the US workplace. Analyzing global findings separately allows a comparison of differences between US responses and those from other regions.

An innovation as powerful as the steam engine

‘Scientific discoveries and technological innovations are stones in the cathedral of human progress.’

– Reid Hoffman, cofounder of LinkedIn and Inflection AI, partner at Greylock Partners, and author

magine a world where machines not only perform physical labor but also think, learn, and make autonomous decisions. This world includes humans in the loop, bringing people and machines together in a state of superagency that increases personal productivity and creativity (see sidebar “AI superagency”). This is the transformative potential of AI, a technology with a potential impact poised to surpass even the biggest innovations of the past, from the printing press to the automobile. AI does not just automate tasks but goes further by automating cognitive functions. Unlike any invention before, AI-powered software can adapt, plan, guide—and even make—decisions. That’s why AI can be a catalyst for unprecedented economic growth and societal change in virtually every aspect of life. It will reshape our interaction with technology and with one another.

Many breakthrough technologies, including the internet, smartphones, and cloud computing, have transformed the way we live and work. AI stands out from these inventions because it offers more than access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI holds the potential to shift the way people access and use knowledge. The result will be more efficient and effective problem solving, enabling innovation that benefits everyone.

Over the past two years, AI has advanced in leaps and bounds, and enterprise-level adoption has accelerated due to lower costs and greater access to capabilities. Many notable AI innovations have emerged (Exhibit 1). For example, we have seen a rapid expansion of context windows, or the short-term memory of LLMs. The larger a context window, the more information an LLM can process at once. To illustrate, Google’s Gemini 1.5 could process one million tokens in February 2024, while its Gemini 1.5 Pro could process two million tokens by June of that same year.4 Overall, we see five big innovations for business that are driving the next wave of impact: enhanced intelligence and reasoning capabilities, agentic AI, multimodality, improved hardware innovation and computational power, and increased transparency.

AI superagency

What impact will AI have on humanity? Reid Hoffman and Greg Beato’s book Superagency: What Could Possibly Go Right with Our AI Future (Authors Equity, January 2025) explores this question. The book highlights how AI could enhance human agency and heighten our potential. It envisions a human-led, future-forward approach to AI.

Superagency, a term coined by Hoffman, describes a state where individuals, empowered by AI, supercharge their creativity, productivity, and positive impact. Even those not directly engaging with AI can benefit from its broader effects on knowledge, efficiency, and innovation.

AI is the latest in a series of transformative supertools, including the steam engine, internet, and smartphone, that have reshaped our world by amplifying human capabilities. Like its predecessors, AI can democratize access to knowledge and automate tasks, assuming humans can develop and deploy it safely and equitably.

Exhibit 1
Gen AI capabilities have evolved rapidly over the past two years.
Illustrative capabilities of gen Al platforms from select frontier labs, nonexhaustive
2022-231 Jan 2025²
Anthropic Claude · Not multimodal (text only) →Claude 3.5
Limited contextual understanding (difficulty with ·Multimodal (text,audio,and images) ·Enhanced contextual understanding and coherence
complex conversations) ·No tool usage during long interactions ·Experimental computer usage capability for some users
Google Bard
Google Gemini · Not multimodal (text only) →Gemini 2.0 Flash ·Multimodal (text,audio,and images)
·Advanced reasoning (capable of multistep problem-
·Fair reasoning Limited contextual understanding (difficulty with solving and nuanced analysis)
complex conversations)
Limited real-time data integration Enhanced contextual understanding (maintains
coherence in long dialogues)
·Low personalization (limited adaptability) ·Real-time data integration (from Google Search)
·Advanced personalization (user context)
Llama 1 →Llama 3.3
·Not multimodal (text only) ·Text-based (earlier versions were multimodal,
Fair reasoning LLaMa 3.2)
·Limited contextual understanding (dificulty with complex conversations) ·No APl access ：Advanced reasoning (capable of multistep problem-
solving and nuanced analysis) ·Enhanced contextual understanding (maintains
coherence in long dialogues) ·APl access (tools for model and agent development)
→ Phi-4
Not multimodal (text only) ·Multimodal (text,audio,and images)
·Fair reasoning (ie,limited to coding tasks) ·Advanced reasoning (capable of multistep problem-
·Focused training (smaller, coding-focused data set) solving and nuanced analysis)
·Comprehensive training (diverse data)
GPT-3.5 →OpenAlo1
OpenAl ·Not multimodal (text only) ·Multimodal (text and images)
Fair reasoning ability (eg,scored high on SAT, ·Advanced reasoning (eg,top 1O% on bar examination)
but bottom 1O% on bar examination) ·Enhanced contextual understanding (maintains
coherence in long dialogues)
Limited contextual understanding (difficulty with
·Advanced APl access (supports multimodal inputs)
coherence in complex conversations)
Standard APl access (for text generation)
Note: Exhibit is not intended as an evaluation or comparison but as an illustration of the rapid progress in capabilities.
1 Initial models released between Mar 2022 and Mar 2023.
2 Latest models released between Nov and Dec 2024.
Source: Company websites and press releases; McKinsey analysis
Intelligence and reasoning are improving
AI is becoming far more intelligent. One indicator is the performance of LLMs on standardized tests. OpenAI’s Chat GPT-3.5, introduced in 2022, demonstrated strong performance on high-school-level exams (for example, scoring in the 70th percentile on the SAT math and the 87th percentile on the SAT verbal sections). However, it often struggled with broader reasoning. Today’s models are near the intelligence level of people who hold advanced degrees. GPT-4 can so easily pass the Uniform Bar Examination that it would rank in the top 10 percent of test takers,5 and it can answer 90 percent of questions correctly on the US Medical Licensing Examination.6
The advent of reasoning capabilities represents the next big leap forward for AI. Reasoning enhances AI’s capacity for complex decision making, allowing models to move beyond basic comprehension to nuanced understanding and the ability to create step-by-step plans to achieve goals. For businesses, this means they can fine-tune reasoning models and integrate them with domain-specific knowledge to deliver actionable insights with greater accuracy. Models such as OpenAI’s o1 or Google’s Gemini 2.0 Flash Thinking Mode are capable of reasoning in their responses, which gives users a human-like thought partner for their interactions, not just an information retrieval and synthesis engine.7
Agentic AI is acting autonomously
The ability to reason is growing more and more, allowing models to autonomously take actions and complete complex tasks across workflows. This is a profound step forward. As an example, in 2023, an AI bot could support call center representatives by synthesizing and summarizing large volumes of data—including voice messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an AI agent can converse with a customer and plan the actions it will take afterward—for example, processing a payment, checking for fraud, and completing a shipping action.
‘I’ve always thought of AI as the most profound technology humanity is working on . . . more profound than fire or electricity or anything that we’ve done in the past.’
– Sundar Pichai, CEO of Alphabet
‘AI, like most transformative technologies, grows gradually, then arrives suddenly.’
– Reid Hoffman, cofounder of LinkedIn and Inflection AI, partner at Greylock Partners, and author
Software companies are embedding agentic AI capabilities into their core products. For example, Salesforce’s Agentforce is a new layer on its existing platform that enables users to easily build and deploy autonomous AI agents to handle complex tasks across workflows, such as simulating product launches and orchestrating marketing campaigns.8 Marc Benioff, Salesforce cofounder, chair, and CEO, describes this as providing a “digital workforce” where humans and automated agents work together to achieve customer outcomes.9
Multimodality is bringing together text, audio, and video
Today’s AI models are evolving toward more advanced and diverse data processing capabilities across text, audio, and video. Over the last two years, we have seen improvements in the quality of each modality. For example, Google’s Gemini Live has improved audio quality and latency and can now deliver a human-like conversation with emotional nuance and expressiveness.10 Also, demonstrations of Sora by OpenAI show its ability to translate text to video.11
Hardware innovation is enhancing performance
Hardware innovation and the resulting increase in compute power continue to enhance AI performance. Specialized chips allow faster, larger, and more versatile models. Enterprises can now adopt AI solutions that require high processing power, enabling real-time applications and opportunities for scalability. For example, an e-commerce company could significantly improve customer service by implementing AI-driven chatbots that leverage advanced graphics processing units (GPUs) and tensor processing units (TPUs). Using distributed cloud computing, the company could ensure optimal performance during peak traffic periods. Integrating edge hardware, the company could deploy models that analyze photos of damaged products to more accurately process insurance claims.
Transparency is increasing
AI is gradually becoming less risky, but it still lacks greater transparency and explainability. Both are critical for improving AI safety and reducing the potential for bias, which are imperative for widescale enterprise deployment. There is still a long way to go, but new models and iterations are rapidly improving. Stanford University’s Center for Research on Foundation Models (CRFM) reports significant advances in model performance. Its Transparency Index, which uses a scale of 1 to 100, shows that Anthropic’s transparency score increased by 15 points to 51 and Amazon’s more than tripled to 41 between October 2023 and May 2024.12
Beyond LLMs, other forms of AI and machine learning (ML) are improving explainability, allowing the outputs of models that support consequential decisions (for example, credit risk assessment) to be traced back to the data that informed them. In this way, critical systems can be tested and monitored on a near-constant basis for bias and other everyday harms that arise from model drift and shifting data inputs, which happens even in systems that were well calibrated before deployment.
All of this is crucial for detecting errors and ensuring compliance with regulations and company policies. Companies have improved explainability practices and built necessary checks and balances, but they must be prepared to evolve continuously to keep up with growing model capabilities.
Achieving AI superagency in the workplace is not simply about mastering technology. It is every bit as much about supporting people, creating processes, and managing governance. The next chapters explore the nontechnological factors that will help shape the deployment of AI in the workplace.
Employees are ready for AI; now leaders must step up
‘People are using [AI] to create amazing things. If we could see what each of us can do 10 or 20 years in the future, it would astonish us today.’
– Sam Altman, cofounder and CEO of OpenAI
E mployees will be the ones to make their organizations AI powerhouses. They are more ready to embrace AI in the workplace than business leaders imagine. They are more familiar with AI toolthey want more support and training, and they are more likely to believe AI will replace at least a embrace Al in the workplace than business leaders imagine.They are more familiar with Al tools, they want more support and training,and they are more likely to believe Al willreplace at least a third of their work in the near future. Now it’s imperative that leaders step up. They have more permission space than they realize, so it’s on them to be bold and capture the value of AI. Now.
Beyond the tipping point
In our survey, nearly all employees (94 percent) and C-suite leaders (99 percent) report having some level of familiarity with gen AI tools. Nevertheless, business leaders underestimate how extensively their employees are using gen AI. C-suite leaders estimate that only 4 percent of employees use gen AI for at least 30 percent of their daily work, when in fact that percentage is three times greater, as self-reported by employees (Exhibit 2). And while only a total of 20 percent of leaders believe employees will use gen AI for more than 30 percent of their daily tasks within a year, employees are twice as likely (47 percent) to believe they will (see sidebar “Who is using AI at work? Nearly everyone, even skeptical employees”).
The good news is that our survey suggests three ways companies can accelerate AI adoption and move toward AI maturity.
Exhibit 2
Employees are three times more likely to be using gen AI today than their leaders expect.

Note: Figures may not sum to 100%, because of rounding. Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118) ; McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
Our research looked at people who self-identify as “Zoomers,” “Bloomers,” “Gloomers,” and “Doomers” in their attitudes toward AI—a set of archetypes introduced in Superagency. We find that 39 percent of employees identify as Bloomers, who are AI optimists that want to collaborate with their companies to create responsible solutions. Meanwhile, 37 percent identify as Gloomers, who are more skeptical about AI and want extensive top-down AI regulations; 20 percent identify as Zoomers, who want AI to be quickly deployed with few guardrails; and just 4 percent identify as Doomers, who have a fundamentally negative view of AI (exhibit).
Even those with a skeptical take on AI are familiar with it; 94 percent of Gloomers and 71 percent of Doomers say they have some familiarity with gen AI tools. Furthermore, approximately 80 percent of Gloomers and about half of Doomers say they are comfortable using gen AI at work.
Exhibit
Employee segments dier, but all indicate a high familiarity with gen AI.
US employee sentiment on gen Al, by archetype, % of respondents

1 Dened as those who have “extensive experience (use several tools for complex tasks)” and “experts.” 2 Dened as those who have “some familiarity (use 1–2 tools a few times)” and “extensive experience (use several tools for complex tasks)” and “experts.” Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
Leaders can invest more in their employees
As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work. Now they would like their companies to invest in the training that will help them succeed. Nearly half of employees in our survey say they want more formal training and believe it is the best way to boost AI adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that incentives such as financial rewards and recognition can improve uptake.
Yet employees are not getting the training and support they need. More than a fifth report that they have received minimal to no support (Exhibit 3). Outside the United States, employees also want more training (see sidebar “Global perspectives on training”).
Exhibit 3
Employees long for more support and training on gen AI.
Share of US employees agreeing that a company initiative would make them more likely to increase day-to-day usage of gen Al tools, %

US employees'perceived level of support for gen Al capability building at their organizations, %of respondents

Note: Figures do not sum to 100%, because of rounding. ¹Objectives and key results. Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Global perspectives on training
To get a clearer picture of global AI adoption trends, we looked at trends across five countries: Australia, India, New Zealand, Singapore, and the United Kingdom. Broadly speaking, these employees and C-suite leaders—the “international” group in this report—have similar views of AI as their US peers. In some key areas, however, including the topic of training, their experiences differ.
Many international employees are concerned about insufficient training, even though they report receiving far more Exhibit <5> of <21> support than US employees. Some 84 percent of international employees say they receive significant or full organizational support to learn AI skills, versus just over half of US employees. International employees also have more opportunities to participate in developing gen AI tools at work than their US counterparts, with differences of at least ten percentage points in activities such as providing feedback, beta testing, and requesting specific features (exhibit).
Exhibit
International employees get more encouragement to use gen AI tools.
Sources encouraging employees’ use of gen AI tools at work, % of respondents reporting practice in place at their organization
Employee involvement in developing gen AI tools, % of respondents

Source: McKinsey international employee survey, Oct–Nov 2024 (Australia and New Zealand, n = 139; India, n = 134; Singapore, n = 140; UK, n = 201) ; McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
C-suite leaders can help millennials lead the way
Many millennials aged 35 to 44 are managers and team leaders in their companies. In our survey, they selfreport having the most experience and enthusiasm about AI, making them natural champions of transformational change. Millennials are the most active generation of AI users. Some 62 percent of 35- to 44-year-old employees report high levels of expertise with AI, compared with 50 percent of 18- to 24-yearold Gen Zers and 22 percent of baby boomers over 65 (Exhibit 4). By tapping into that enthusiasm and expertise, leaders can help millennials play a crucial role in AI adoption.
Exhibit 4
Millennials aged 35 to 44 are AI optimists, with 90 percent indicating condence in their gen AI abilities.
US employee sentiment on gen AI, by age group, % of respondents

1 Dened as those who have “extensive experience (use several tools for complex tasks)” and “experts.” Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Not at all
Exhibit 5
Two-thirds of managers regularly act as sounding boards for their teams on gen AI.
Frequency of team inquiries about using new gen AI tools at work, % of US manager respondents (n = 1,440)
Use of gen AI tools to resolve a team member’s challenge, % of US manager respondents (n = 1,440)

Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Since many millennials are managers, they can support their teams to become more adept AI users. This helps push their companies toward AI maturity. Two-thirds of managers say they field questions from their team about how to use AI tools at least once a week, and a similar percentage say they recommend AI tools to their teams to solve problems (Exhibit 5).
Since leaders have the permission space, they can be bolder
In many transformations, employees are not ready for change, but AI is different. Employee readiness and familiarity are high, which gives business leaders the permission space to act. Leaders can listen to employees describe how they are using AI today and how they envision their work being transformed. They also can provide employees with much-needed training and empower managers to move AI use cases from pilot to scale.
It’s critical that leaders meet this moment. It’s the only way to accelerate the probability that their companies will reach AI maturity. But they must move with alacrity, or they will fall behind.
Delivering speed and safety
‘Soon after the first automobiles were on the road, there was the first car crash. But we didn’t ban cars—we adopted speed limits, safety standards, licensing requirements, drunk-driving laws, and other rules of the road.’
– Bill Gates, cofounder of Microsoft
A I technology is advancing at record speed. ChatGPT was released about two years ago; OpenAIreports that usage now exceeds 300 million weekly users13 and that over 90 percent of Fortune500 companies employ its technology.14 The internet did not reach this level of usage until the early 2000s, nearly a decade after its inception. reports that usage now exceeds 3OO million weekly users13 and that over 9O percent of Fortune 500 companies employ its technology.14 The internet did not reach this level of usage until the early 20oOs, nearly a decade after its inception.
The majority of employees describe themselves as AI optimists; Zoomers and Bloomers make up 59 percent of the workplace. Even Gloomers, who are one of the two less-optimistic segments in our analysis, report high levels of gen AI familiarity, with over a quarter saying they plan to use AI more next year.
Business leaders need to embrace this speed and optimism to ensure that their companies don’t get left behind. Yet despite all the excitement and early experimentation, 47 percent of C-suite leaders say their organizations are developing and releasing gen AI tools too slowly, citing talent skill gaps as a key reason for the delay (Exhibit 6).
Exhibit 6
Half of business leaders believe the development and release of gen AI tools is too slow in their organizations.
US C-suite categorization of the pace at which their organizations are developing and releasing gen AI tools, % of respondents
Too slow About right Too fast
47 45 9
US C-suite’s perceived top reason organizations are developing and releasing gen AI tools too slowly, % of respondents reporting development pace as “too slow”
Talent skill gaps Resourcing constraints
46 38 8 8
Complex approval process
Technical complexity
Note: Figures may not sum to 100%, because of rounding. Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
Business leaders are trying to meet the need for speed by increasing investments in AI. Of the executives surveyed, 92 percent say they expect to boost spending on AI in the next three years, with 55 percent expecting investments to increase by at least 10 percent from current levels. But they can no longer just spend on AI without expecting results. As companies move on from the initial thrill of gen AI, business leaders face increasing pressure to generate ROI from their gen AI deployments.
We are at a turning point. The initial AI excitement may be waning, but the technology is accelerating. Bold and purposeful strategies are needed to set the stage for future success. Leaders are taking the first step: One quarter of those executives we surveyed have defined a gen AI road map, while just over half have a draft that is being refined (Exhibit 7). With technology changing this fast, all road maps and plans will evolve constantly. For leaders, the key is to make some clear choices about what valuable opportunities they choose to pursue first—and how they will work together with peers, teams, and partners to deliver that value.
Exhibit 7
Most C-suite respondents have road maps to guide their gen AI strategies and have begun identifying use cases.
Presence of a dened gen AI road map, % of US C-suite respondents

No, there is no road map yet 0
Level of identifying revenue-generating use cases for gen AI, % of US C-suite respondents
McKinsey & Company
The dilemma of speed versus safety
There’s a spanner in the works: Regulation and safety often continue to be seen as insurmountable challenges rather than opportunities. Leaders want to increase AI investments and accelerate development, but they wrestle with how to make AI safe in the workplace. Data security, hallucinations, biased outputs, and misuse (for example, creating harmful content or enabling fraud) are challenges that cannot be ignored. Employees are well aware of AI’s safety challenges. Their top concerns are cybersecurity, privacy, and accuracy (Exhibit 8). But what will it take for leaders to address these concerns while also moving ahead at light speed?
Exhibit 8
Employees have concerns about gen AI—namely cybersecurity risks, inaccuracies, and data leaks.
Share of US employees citing risk as a concern associated with gen AI, %

Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Employees trust business leaders to get it right
While employees acknowledge the risks and even the likelihood that AI may replace a considerable portion of their work, they place high trust in their own employers to deploy AI safely and ethically. Notably, 71 percent of employees trust their employers to act ethically as they develop AI. In fact, they trust their employers more than universities, large technology companies, and tech start-ups (Exhibit 9).
According to our research, this is in line with a broader trend in which employees show higher trust in their employers to do the right thing in general (73 percent) than in other institutions, including the government (45 percent). This trust should help leaders act with confidence as they tackle the speed-versus-safety dilemma. That confidence also applies outside the United States, even though employees in other regions may have more desire for regulation (see sidebar “Global perspectives on regulation”).
Exhibit 9
Employees trust their employers most for a safe rollout of gen AI.
Share of US employees who highly trust institution to deploy gen Al tools responsibly, safely, and ethically,1%

1 High trust is dened as a 4 or 5 on a scale of 1 to 5. Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Global perspectives on regulation
A high percentage of international C-suite leaders we surveyed across five regions (Australia, India, New Zealand, Singapore, and the United Kingdom) are Gloomers, who favor greater regulatory oversight. Between 37 to 50 percent of international C-suite leaders self-identify as Gloomers, versus 31 percent in the United States. This may be because top-down regulation is more accepted in many countries outside the United States. Of the global C-suite leaders surveyed, half or more worry that ethical use and data privacy issues are holding back their employees from adopting gen AI.
However, our research shows that attitudes about regulation are not inhibiting the economic expectations of business leaders outside the United States. More than half of the international executives (versus 41 percent of US executives) indicate they want their companies to be among the first adopters of AI, with those in India and Singapore being especially bullish (exhibit). The desire of international business leaders to be AI first movers can be explained by the revenue they expect from their AI deployments. Some 31 percent of international C-suite leaders say they expect AI to deliver a revenue uplift of more than 10 percent in the next three years, versus just 17 percent of US leaders. Indian executives are the most optimistic, with 55 percent expecting a revenue uplift of 10 percent or more over the next three years.Web <2025>
Exhibit
Half of international C-suite respondents want to be early adopters.
C-suite sentiment on adoption of new technologies, % of respondents

Source: McKinsey international CxO survey, Oct–Nov 2024 (Australia and New Zealand, n = 17; India, n = 40; Singapore, n = 6; UK, n = 57); McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
Risk management for gen AI
In Superagency, Hoffman argues that new risks naturally accompany new capabilities—meaning they should be managed but not necessarily eliminated.15 Leaders need to contend with external threats, such as infringement on intellectual property (IP), AI-enabled malware, and internal threats that arise from the AI adoption process. The first step in building fit-for-purpose risk management is to launch a comprehensive assessment to identify potential vulnerabilities in each of a company’s businesses. Leaders can then establish a robust governance structure, implement real-time monitoring and control mechanisms, and ensure continuous training and adherence to regulatory requirements.
One powerful control mechanism is respected third-party benchmarking that can increase AI safety and trust. Examples include Stanford CRFM’s Holistic Evaluation of Language Models (HELM) initiative—which offers comprehensive benchmarks to assess the fairness, accountability, transparency, and broader societal impact of a company’s AI systems—as well as MLCommons’s AILuminate tool kit on which researchers from Stanford collaborated.16 Other organizations such as the Data & Trust Alliance unite large companies to create cross-industry metadata standards that aim to bring more transparency to enterprise AI models.
While benchmarks have significant potential to build trust, our survey shows that only 39 percent of C-suite leaders use them to evaluate their AI systems. Furthermore, when leaders do use benchmarks, they opt to measure operational metrics (for example, scalability, reliability, robustness, and cost efficiency) and performance-related metrics (including accuracy, precision, F1 score, latency, and throughput). These benchmarking efforts tend to be less focused on ethical and compliance concerns: Only 17 percent of C-suite leaders who benchmark say it’s most important to measure fairness, bias, transparency, privacy, and regulatory issues (Exhibit 10).
Exhibit 10
More than a third of C-suite respondents use benchmarks for gen AI, but with less focus on ethical metrics.
US C-suite use of benchmarking for gen Al tools,% of respondents

Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
The focus on operational and performance metrics reflects the understandable desire to prioritize immediate technical and business outcomes. But ignoring ethical considerations can come back to haunt leaders. When employees don’t trust AI systems, they are less likely to accept them. Although benchmarks are not a panacea to eliminate all risk and can’t ensure that AI systems are fully efficient, ethical, and safe, they are a useful tool.
Even companies that excel at all three categories of AI readiness—technology, employees, and safety—are not necessarily scaling or delivering the value expected. Nevertheless, leaders can harness the power of big ambitions to transform their companies with AI. The next chapter examines how.
Embracing bigger ambitions
‘This is a time when you should be getting benefits [from AI] and hope that your competitors are just playing around and experimenting.’
– Erik Brynjolfsson, professor at Stanford University and director of the Digital Economy Lab at the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
M ost organizations that have invested in AI are not getting the returns they had hoped. They arenot winning the full economic potential of AI. About half of C-suite leaders at companies that have deployed AI describe their initiatives as still developing or expanding (Exhibit 11). They not winning the full economic potential of Al. About half of C-suite leaders at companies that have deployed Al describe their initiatives as still developing or expanding (Exhibit 1). They have had the time to move further. Our research shows that more than two-thirds of leaders
launched their first gen AI use cases over a year ago.
Pilots fail to scale for many reasons. Common culprits are poorly designed or executed strategies, but a lack of bold ambitions can be just as crippling. This chapter looks at patterns governing today’s investments in AI across industries and suggests the potential awaiting those who can dream bigger.
Exhibit 11
Only 1 percent of C-suite respondents describe their gen AI rollouts as mature.
US C-suite's categorization of organizations' level of gen Al maturity, % of respondents
AI investments vary by industry
Different industries have different AI investment patterns. Within the top 25 percent of spenders, companies in healthcare, technology, media and telecom, advanced industries, and agriculture are ahead of the pack (Exhibit 12). Companies in financial services, energy and materials, consumer goods and retail, hardware engineering and construction, and travel, transport, and logistics are spending less. The consumer industry—despite boasting the second-highest potential for value realization from AI—seems least willing to invest, with only 7 percent of respondents qualifying in the top quartile, based on self-reported percentage of revenue spend on gen AI. That hesitation may be explained by the industry’s low average net margins in mass-market categories and thus higher confidence thresholds for adopting costly organization-wide technology upgrades.
Companies’ gen AI spend does not match the economic potential in their industries.
Representation in top quartile of gen Al spending vs overall survey representation

McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
In some industries, employees are cautious
Employees in the public sector, as well as the aerospace and defense and semiconductor industries, are largely skeptical about the development of AI’s future. In the public sector and aerospace and defense, only 20 percent of employees anticipate that AI will have a significant impact on their daily tasks in the next year, versus roughly two-thirds in media and entertainment (65 percent) and telecom, at 67 percent (Exhibit 13). What’s more, our survey shows that just 31 percent of social sector employees trust that their employers will develop AI safely. That’s the least confidence in any industry; the cross-industry average is 71 percent.
Employees in the social sector, aerospace and defense, and public sector are least optimistic about gen AI.
US employee sentiment on gen AI, by sector, % of respondents
Expects workflows Receive support High level of trust that Believe gen Al wito change by30% from organization employer will safely havea net benefiin the next year on gen Al roll out gen Al in next 5 yearsExtensive Perceive gen Al Comfortablefamiliarity results as being using gen Alwith gen Al ≥70% accurate results in work
Metals and mining
Real estate
Telecommunications
Oil and gas
Media and entertainment
Technology
Pharmaceuticalsand medical products
Automotive and assembly
Engineering,construction,and building materials
Semiconductors
Chemicals
Consumer andpackaged goods
Advanced electronics
Financial services
Travel,logistics,and infrastructure
Retail
Agriculture
Business, legal, andprofessional services
Healthcare
Public sector
Aerospace and defense
Social sector
Note: Level of familiarity is dened as those who have “extensive experience (use several tools for complex tasks)” and “experts.” High trust is “Level 4” and “Level 5” on a scale of 1 to 5. Perceived accuracy is based on past gen AI usage in a workplace setting. Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002); The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023
McKinsey & Company
Employees’ relative caution about AI in these sectors likely reflects near-term challenges posed by external constraints such as rigorous regulatory oversight, outdated IT systems, and lengthy approval processes.
There’s a lot of headroom in some functions
Our research finds that the functional areas where AI presents the greatest economic potential are also those where employee outlook is lukewarm. Employees in sales and marketing, software engineering, customer service, and R&D contribute roughly three-quarters of AI’s total economic potential, but the selfreported optimism of employees in these functions is middling (Exhibit 14). It may be the case that these functions have piloted AI projects, leading employees to be more realistic about AI’s benefits and limitations. Or perhaps the economic potential has made them worry that AI could replace their jobs. Whatever the reasons, leaders in these functions might consider investing more in employee support and elevating the change champions who can improve that sentiment.
Exhibit 14
The employees most optimistic about gen AI do not represent the most economic value potential.
Potential economic value from gen Al and share of employees with a positive outlook on gen Al,1 by function

Share of employees with positive outlook on gen AI, %
Optimism dened as share of Bloomer (believes in gen AI’s potential and supports iterative deployment) and Zoomer (believes in speedy gen AI deployment by technologists) respondents.
McKinsey US employee survey, Oct–Nov 2024
(n = 3,002
Gen AI has not delivered enterprise-wide ROI, but that can change
Across all industries, surveyed C-level executives report limited returns on enterprise-wide AI investments. Only 19 percent say revenues have increased more than 5 percent, with another 39 percent seeing a moderate increase of 1 to 5 percent, and 36 percent reporting no change (Exhibit 15). And only 23 percent see AI delivering any favorable change in costs.
Despite this, company leaders are optimistic about the value they can capture in the coming years. A full 87 percent of executives expect revenue growth from gen AI within the next three years, and about half say it could boost revenues by more than 5 percent in that time frame (Exhibit 16). That suggests quite a lot could change for the better over the next few years.
Exhibit 15
Gen AI has not yet delivered signi-cant return on investment for enterprises.
US C-suite's perception on how gen Al has affected revenues and costs, % of respondents

Note: Figures may not sum to 100%, because of rounding. Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
Exhibit 16
Half of C-suite respondents expect gen AI to deliver more than 5 percent revenue growth in the next three years.
US C-suite's perception on how gen Al will affected revenue, next 3 years, % of respondents

Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118)
McKinsey & Company
Big ambitions can help solve big problems
To drive revenue growth and improve ROI, business leaders may need to commit to transformative AI possibilities. As the hype around AI subsides and the focus shifts to value, there is a heightened attention on practical applications that can create competitive moats.
To assess how far along companies are in this shift, we examined three categories of AI applications: personal use, business use, and societal use (see sidebar “AI’s potential to enhance our personal lives”). We mapped over 250 applications from our work and publicly shared examples to understand the spectrum of impact levels, from localized use cases to transformations with more universal impact. Our conclusion? Given that most companies are early in their AI journeys, most AI applications are localized use cases still in the pilot stages (Exhibit 17).
‘[It] is critical to have a genuinely inspiring vision of the future [with AI] and not just a plan to fight fires.’
– Dario Amodei, cofounder and CEO of Anthropic
‘It is in [the] collaboration between people and algorithms that incredible scientific progress lies over the next few decades.’
– Demis Hassabis, cofounder and CEO of Google DeepMind
In many cases, that’s perfectly appropriate. But creating AI applications that can revolutionize industries and create transformative value requires something more. Robotics in manufacturing, predictive AI in renewable energy, drug development in life sciences, and personalized AI tutors in education—these are the kinds of transformative efforts that can drive the greatest returns.17 These weren’t created from a reactive mindset. They are the result of inspirational leadership, a unique concept of the future, and a commitment to transformational impact. This is the kind of courage needed to develop AI applications that can revolutionize industries.
To truly harness the potential of AI, companies must challenge themselves to envision and implement more breakthrough initiatives. Success in the era of AI hinges not just on technology deployment or employee willingness but also on visionary leadership. The ingredients are here. The technology is already highly capable and rapidly advancing, and employees are more ready than leaders think. Leaders have more permission space than they realize to deploy AI quickly in the workplace. To do so, leaders need to stretch their ambitions toward systematic change, laying the foundation for real competitive differentiation. If they want to be more ambitious about AI, companies must increase the proportion of transformational initiatives in their portfolios. The next chapter examines the headwinds that leaders must overcome—and how they can do so.
AI’s potential to enhance our personal lives
Outside of the business context, individuals are increasingly using AI in their personal lives. In previous research, we analyzed the potential impact of AI across 77 personal activities and across age, gender, and working status in the United States. While individuals have limited desire to automate certain personal activities, including leisure, sleeping, and fitness, the data shows significant opportunity for AI combined with other technologies to help with chores or labor-intensive tasks. Already in 2024, our research identified about an hour of such daily activities with the technical potential to be automated. By 2030, expansion of use cases and continued improvements in AI safety could increase automation potential up to three hours per day. When people use AI-enabled tools—say, an autonomous vehicle for transportation or an interactive personal finance bot—they can repurpose time for personal fulfillment activities or being productive in other ways.
Using human-centric design and tapping into gen AI’s potential for “emotional intelligence” are unlocking new personal AI applications that go beyond basic efficiencies. Individuals are beginning to use conversational and reasoning AI models for counseling, coaching, and creative expression. For example, people are using conversational AI for advice and emotional support or to bring their artistic visions to life with only verbal cues. Further, to the notion that AI superagency will advance society, AI has potential to become a democratizing force, making experiences that were previously expensive or exclusive—such as animation generation, career coaching, or tax advice—available to much wider audiences.
Technology is not the barrier to scale
‘As we build this next generation of AI, we made a conscious design choice to put human agency both at a premium and at the center of the product. For the first time, we have the access to AI that is as empowering as it is powerful.’
– Satya Nadella, chairman and CEO of Microsoft here is no question: AI offers a rare and phenomenal opportunity. Almost 90 percent of leaders anticipate that deploying AI will drive revenue growth in the next three years. But securing that growth entails corporate transformation, and businesses have a poor track record in this area. Nearly 70 percent of transformations fail.
To make their companies part of the minority that succeed, C-level executives must turn the mirror on themselves. They need to embrace the vital role their leadership plays. C-suite leaders participating in our survey are more than twice as likely to say employee readiness is a barrier to adoption as they are to blame their own role. But as previously noted, employees indicate that they are quite ready.
This chapter looks at how leaders can take the reins, recognizing and owning the fact that the AI opportunity requires more than technology implementation. It demands a strategic transformation. There is no denying that companies face a set of AI headwinds. To tackle these challenges, leadership teams will need to commit to rewiring their enterprises.
The operational headwinds that slow execution
Business adoption of AI faces several operational headwinds. Our interviews and research surfaced five that are most challenging: aligning leadership, addressing cost uncertainty, workforce planning, managing supply chain dependencies, and meeting the demand for explainability.
Leadership alignment is a challenging but critical first step
Securing consensus from senior leaders on a strategy-led gen AI road map is no simple task. The key to meeting this challenge is first recognizing that leadership alignment cannot be oversimplified or assumed. The process requires ongoing engagement from senior leaders across business domains, each of which may have distinct objectives and risk appetites. Together, leaders must clearly define where value lies, how AI will drive this value, and how risk will be mitigated. They must collectively establish metrics for performance evaluation and investment recalibration. To facilitate alignment, they may want to appoint a gen AI value and risk leader or institute an enterprise-wide leadership and orchestration function. These actions can enhance collaboration among business, technology, and risk teams. Although challenging, aligning leadership is a crucial step to ensure that AI projects are not disparate, avoid liability, and deliver transformative business outcomes.
Cost uncertainty makes it difficult for enterprises to predict ROI
Many companies are still determining if they can “take” AI solutions off the shelf from tech vendors or if they need to “shape” and customize them, which can be more costly but brings the potential for greater differentiation from competitors. Additionally, while leaders can budget for AI pilots, the full cost of building and managing AI applications at scale remains uncertain. Planning for a limited pilot is very different from assessing the costs of a mature solution that helps most employees multiple times a day. These factors lead to tough tradeoffs. But to move at the pace of AI, technology leaders must prioritize accelerated decision-making.
Workforce planning is more difficult than ever
There is still a world of uncertainty to manage. Employers do not know how many AI experts they will need with what type of skills, whether that talent bench even exists, how quickly they can source people, and how they can remain an attractive employer for in-demand hires after they come aboard. On the other hand, they do not know how fast AI may depress demand for other skills and thus require workforce rebalancing and retraining.
Supply chain dependencies can wreak havoc
Fragile supply chains can expose enterprises to disruptions and technical, regulatory, and legal challenges. The AI supply chain is global, with significant R&D concentrated in China, Europe, and North America and with semiconductor and hardware manufacturing concentrated in East Asia and the United States. Today’s geopolitics are complex. Furthermore, models and applications are increasingly created in open-source forums spanning many countries.
Demand for greater explainability is a central challenge
Safe AI deployment is increasingly a must-have. Yet most LLMs are often black boxes that do not reveal why or how they came to a certain response, nor what data was used to make it. If AI models cannot provide clear justifications for their responses, recommendations, decisions, or actions—showing the specific factors that led to a credit card application denial, for example—they will not be trusted for critical tasks.
These AI-specific headwinds are formidable but addressable. Companies are pushing ahead. For example, they might use dynamic cost planning or look at procuring NVIDIA clusters to secure the infrastructure they expect to need.18 Chief HR officers (CHROs) are developing training programs to upskill their current workforces and support some employees in job transitions. But lasting success will take more than that.
To capture AI value, leaders must rewire their companies
McKinsey’s Rewired framework includes six foundational elements to guide sustained digital transformation: road map, talent, operating model, technology, data, and scaling (Exhibit 18). When companies implement this playbook successfully, they cultivate a culture of autonomy, leverage modern cloud practices, and assemble multidisciplinary agile teams.
McKinsey & Company
Six enduring success factors enable tech-business transformations.
Framework for the coordinated execution of value creation
While these six elements are universally applicable, AI has introduced a few important wrinkles for leaders to address:
Adaptability. AI technology is advancing so rapidly that organizations must adopt new best practices quickly to stay ahead of the competition. Best practices may come in the form of new technologies, talent, business models, or products. For example, a modular approach helps future-proof tech stacks. As natural language becomes a medium for integration, AI systems are becoming more compatible, allowing businesses to swap, upgrade, and integrate models and tools with less friction. This modularity allows enterprises to avoid vendor lock-in and put new AI advancements to use quickly without constantly reinventing their tech stacks.
Federated governance models. Managing data and models can give teams autonomy to develop new AI tools while centrally controlling risk. Leaders can directly oversee high-risk or high-visibility issues, such as setting policies and processes to monitor models and outputs for fairness, safety, and explainability. But they can set direction and delegate other monitoring to business units, including measuring performance-based criteria such as accuracy, speed, and scalability.
Budget agility. Given technological advances across models, as well as the opportunity to curate an optimal mix of LLMs, small language models (SLMs), and agents, business leaders should keep their budgets flexible. This helps enterprises optimize their AI deployments simultaneously for costs and performance.
— AI benchmarks. These tools can serve as powerful means to quantitatively assess, compare, and improve the performance of different AI models, algorithms, and systems. If technologists come together to adopt standardized public benchmarks—and if more C-level executives start employing benchmarks, including ethical ones—model transparency and accountability will improve and AI adoption will increase, even among more skeptical employees.
— AI-specific skill gaps. Notably, 46 percent of leaders identify skill gaps in their workforces as a significant barrier to AI adoption. Leaders will need to attract and hire top-level talent, including AI/ML engineers, data scientists, and AI integration specialists. They will also need to commit to creating an environment that is attractive to technologists. For example, this can mean providing them with plenty of time to experiment, offering access to cutting-edge tools, creating opportunities to engage in opensource communities, and promoting a collaborative engineering culture. Upskilling existing employees is just as critical: Research from McKinsey’s People and Organizational Performance Practice underscores the importance of tailoring training to specific roles, such as offering technical team members bootcamps on library creation while offering prompt engineering classes to specific functional teams.19
— Human centricity. To guarantee both fairness and impartiality, it is important that business leaders incorporate diverse perspectives early and often in the AI development process and maintain transparent communication with their teams. As it stands, less than half of C-suite leaders (48 percent) say they would involve nontechnical employees in the early development stages of AI tools, specifically ideation and requirement gathering. Agile pods and human-centric development practices such as design-thinking and reinforcement learning from human feedback (RLHF) will help leaders and developers create AI solutions that all people want to use. In agile pods, technical team members sit alongside employees from business functions such as HR, sales, and product, and from support functions such as legal and compliance. Further, leaders can empathize with employees’ uneasiness about AI’s impacts on potential job losses by being honest about new skill requirements and head count changes. Forums where employees can provide input on AI applications, voice concerns, and share ideas are valuable for maintaining a transparent, human-first culture.
Conclusion: Meeting the AI future
‘Learn from yesterday, live for today, hope for tomorrow.’
– Albert Einstein, theoretical physicist
he pace at which AI has advanced over the last two years is stunning. Some react to that pace by seeing AI as a challenge to humanity. But what if we take the advice of Reid Hoffman and imagine what could possibly go right with AI? Leaders might realize that all the pieces are in place for AI superagency in the workplace.
They might notice that their employees are already using AI and want to use it even more. They may find that millennial managers are powerful change champions ready to encourage their peers. Instead of focusing on the 92 million jobs expected to be displaced by 2030, leaders could plan for the projected 170 million new ones and the new skills those will require.20
This is the moment for leaders to set bold AI commitments and to meet employee needs with on-the-job training and human-centric development. As leaders and employees work together to reimagine their businesses from the bottom up, AI can evolve from a productivity enhancer into a transformative superpower—an effective partner that increases human agency. Leaders who can replace fear of uncertainty with imagination of possibility will discover new applications for AI, not only as a tool to optimize existing workflows but also as a catalyst to solve bigger business and human challenges. Early stages of AI experimentation focused on proving technical feasibility through narrow use cases, such as automating routine tasks. Now the horizon has shifted: AI is poised to unlock unprecedented innovation and drive systemic change that delivers real value.
To meet this more ambitious era, leaders and employees must ask themselves big questions. How should leaders define their strategic priorities and steer their companies effectively amid disruption? How can employees ensure they are ready for the AI transition coming to their workplaces? Questions like the following ones will shape a company’s AI future:
— For business leaders:
• Is your strategy ambitious enough? Do you want to transform your whole business? How can you reimagine traditional cost centers as value-driven functions? How do you gain a competitive advantage by investing in AI?
• What does successful AI adoption look like for your organization? What success indicators will you use to evaluate whether your investments are yielding desired ROI?
• What skills define an AI-native workforce? How can you create opportunities for employees to develop these skills on the job?
— For employees:
• What does achieving AI mastery mean for you? Does it extend to confidently using AI for personal productivity tasks such as research, planning, and brainstorming?
• How do you plan to expand your understanding of AI? Which news sources, podcasts, and video channels can you follow to remain informed about the rapid evolution of AI?
• How can you rethink your own work? Some of the most innovative ideas often emerge from within teams, rather than being handed down from leadership. How would you redesign your work to drive bottom-up innovation?
These questions have no easy answers, but a consensus is emerging on how to best address them. For example, some companies deploy both bottom-up and top-down approaches to drive AI adoption. Bottomup actions help employees experiment with AI tools through initiatives such as hackathons and learning sessions. Top-down techniques bring executives together to radically rethink how AI could improve major processes such as fraud management, customer experience, and product testing.
These kinds of actions are critical as companies seek to move from AI pilots to AI maturity. Today only 1 percent of business leaders report that their companies have reached maturity. Over the next three years, as investments in the technology grow, leaders must drive that percentage way up. They should make the most of their employees’ readiness to increase the pace of AI implementation while ensuring trust, safety, and transparency. The goal is simple: capture the enormous potential of gen AI to drive innovation and create real business value.
Acknowledgments
Hannah Mayer is a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner, Michael Chui is a knowledge developer and senior fellow, and Roger Roberts is a partner.
The authors wish to thank Alex Panas, a senior partner in the Boston office; Eric Kutcher, a senior partner in the Bay Area office; Kate Smaje, a senior partner in the London office; Noshir Kaka, a senior partner in the Mumbai office; Robert Levin, a senior partner in the Boston office; and Rodney Zemmel, a senior partner in the New York office, for their contributions to this report.
The authors were inspired by the impact delivered by our QuantumBlack, AI by McKinsey, colleagues, led by Alex Singla and Alex Sukharevsky, and our gen AI lab leaders, especially Carlo Giovine and Stephen Xu.
The research was led by consultants Akshat Gokhale, Amita Mahajan, Begum Ortaoglu, Estee Chen, Hailey Bobsein, Katharina Giebel, Mallika Jhamb, Noah Furlonge-Walker, and Sabrina Shin. This report was edited by executive editors Kristi Essick and Rick Tetzeli.
Thank you to Reid Hoffman, along with his chief of staff Aria Finger and representatives at Superagency: What Could Possibly Go Right with Our AI Future publisher Authors Equity, especially Deron Triff, for their ongoing collaboration. Working together with Hoffman, who brings the distinctive perspective of being both an investor in and a mentor to the creators of AI, we looked at a central question: How can businesses win with AI in the medium and long terms? We benefited from working sessions with Hoffman, CEOs, and AI industry thought leaders.
We would like to thank the members of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) who challenged our thinking and provided valuable feedback.
This report contributes to McKinsey’s ongoing research on AI and aims to help business leaders understand the forces transforming ways of working, identify strategic impact areas, and prepare for the next wave of growth. As with all McKinsey research, this work is independent and has not been commissioned or sponsored in any way by any business, government, or other institution. The report and views expressed here are ours alone. We welcome your comments on this research at SuperagencyReport@McKinsey.com. Learn more about our gen AI insights and sign up for our newsletter.
Methodology
To create our report, we surveyed 3,613 employees (managers and independent contributors) and 238 C-level executives in October and November 2024. Of these, 81 percent came from the United States, and the rest came from five other countries: Australia, India, New Zealand, Singapore, and the United Kingdom. The employees spanned many roles, including business development, finance, marketing, product management, sales, and technology.
All the survey findings discussed in the report, aside from two sidebars presenting international nuances, pertain solely to US workplaces. The findings are organized in this way because the responses from US employees and C-suite executives provide statistically significant conclusions about the US workplace. Analyzing global findings separately allows a comparison of differences between US responses and those from other regions.
Due to rounding, percentages may not always sum to 100 percent.
Three-quarters of survey respondents in the United States work at organizations generating at least \$100 million in annual revenue, and half work at companies with annual revenues exceeding \$1 billion. All US C-suite leader respondents work at organizations with annual revenues of at least \$1 billion. Looking at workforce size, 20 percent of US respondents work at companies with fewer than 10,000 employees, 49 percent work at companies with between 10,000 and 50,000, and 31 percent work at companies with more than 50,000.
The analysis extended far beyond surveys. The researchers also conducted interviews with dozens of C-level executives and industry experts to understand their perspectives on AI’s transformative potential and the steps they are taking to lead their organizations through this transition. The report was further enriched by discussions with experts at Stanford HAI, the Digital Economy Lab at HAI, and McKinsey’s leading AI experts. Our survey and research primarily focus on gen AI; however, it is important to note that participants in the survey may not have consistently differentiated between gen AI and other forms of AI.
Additionally, we developed a comprehensive database featuring more than 250 potential AI use cases, building on the 63 gen AI use cases identified by McKinsey’s Digital Practice. This database also incorporates proprietary McKinsey research on personal productivity as well as industry reports, along with secondary research from the US government’s Federal AI Use Case Inventories, NASA, press articles, and public interviews with technology leaders.
Glossary
The following terms in this report are defined specifically for its context.
Adoption and deployment: Deployment typically refers to the extent to which an organization rolls out a technology product (whether developed in-house or purchased off the shelf), and adoption reflects how extensively these products are used to generate measurable business value. Given the emerging nature of AI, many companies are simultaneously deploying and adopting, iterating as they go. Therefore, this report often uses adoption and deployment interchangeably to refer to the overall uptake of AI tools.
Agentic AI: Systems with autonomy and goaldirected behavior capable of making independent decisions, planning, and adapting to achieve specific objectives without direct, ongoing human input.
Application programming interface (API): Intermediary software components that allow two applications to talk to each other; a structured way for AI systems to programmatically access (usually external) models, data sets, or other pieces of software.
Artificial Intelligence (AI): The ability of software to perform tasks that traditionally require human intelligence, mirroring some cognitive functions usually associated with human minds.
Deep learning: A subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. Deep learning is especially effective at learning from unstructured data such as images, text, and audio.
Digital workforce: A collaborative ecosystem where humans and automated agents work together, leveraging digital platforms, AI, and cloud computing to enhance productivity, efficiency, and scalability across various industries.
Employee: A worker in a corporate setting, either a manager or independent contributor. Examples of the type of employees represented in this report include people working in product management, marketing, technology, business development, sales, and finance.
Foundation models: 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 DALL-E 2, GPT-4, PaLM, and Stable Diffusion.
Generative AI (gen AI): AI that is typically built using foundation models and has capabilities that earlier forms Iacked, such as the ability to generate content. Foundation models can also be used for nongenerative purposes (for example, classifying user sentiment as negative or positive based on call transcripts).
Graphics processing units (GPUs): Computer chips originally developed for producing computer graphics, such as for video games, that are also useful for deep learning applications. In contrast, traditional machine learning usually runs on central processing units (CPUs), normally referred to as a computer’s “processor.”
Hallucination: A scenario where an AI system generates outputs that lack grounding in reality or a provided context. For instance, an AI chatbot may fabricate information or present a false narrative.
Large language models (LLMs): 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. GPT-4 (which underlies ChatGPT) and the Llama family of models from Meta are examples of LLMs.
Modality: A high-level data category such as numbers, text, images, video, and audio.
Multimodal capabilities: The ability of an AI system to process and generate various types of data (text, images, audio, video) simultaneously, enabling complex tasks and rich outputs.
Productivity (from labor): The ratio of GDP to total hours worked in the economy. Labor productivity growth generally comes from increases in the amount of capital available to each worker, the education and experience of the workforce, and improvements in technology.
Prompt engineering: The process of designing, refining, and optimizing input prompts to guide a gen AI model toward producing desired and accurate outputs.
Reasoning AI: AI systems that perform logical thinking, step-by-step planning, problem solving, and decision making using structured or unstructured data, going beyond pattern recognition to draw conclusions and solve complex problems.
Superagency: A state where individuals, empowered by AI, amplify their creativity, productivity, and positive impact. Even those not directly engaging with AI can benefit from its broader effects on knowledge, efficiency, and innovation.
Unstructured data: Data that lack a consistent format or structure (for example, text, images, video, and audio files) and typically require more advanced techniques to extract insights.

	2022-231	Jan 2025²
Anthropic Claude	· Not multimodal (text only)	→Claude 3.5
Anthropic Claude	· Not multimodal (text only)	Limited contextual understanding (difficulty with	·Multimodal (text,audio,and images) ·Enhanced contextual understanding and coherence
	complex conversations) ·No tool usage		during long interactions ·Experimental computer usage capability for some users
	Google Bard
Google Gemini	· Not multimodal (text only)	→Gemini 2.0 Flash	·Multimodal (text,audio,and images)
			·Advanced reasoning (capable of multistep problem-
	·Fair reasoning Limited contextual understanding (difficulty with		solving and nuanced analysis)
	complex conversations)
	Limited real-time data integration		Enhanced contextual understanding (maintains
			coherence in long dialogues)
	·Low personalization (limited adaptability)		·Real-time data integration (from Google Search)
			·Advanced personalization (user context)

	Llama 1		→Llama 3.3
	·Not multimodal (text only)			·Text-based (earlier versions were multimodal,
	Fair reasoning			LLaMa 3.2)
	·Limited contextual understanding (dificulty with complex conversations) ·No APl access			：Advanced reasoning (capable of multistep problem-
				solving and nuanced analysis) ·Enhanced contextual understanding (maintains
				coherence in long dialogues) ·APl access (tools for model and agent development)

				→ Phi-4
	Not multimodal (text only)			·Multimodal (text,audio,and images)
		·Fair reasoning (ie,limited to coding tasks)	·Advanced reasoning (capable of multistep problem-
		·Focused training (smaller, coding-focused data set)	solving and nuanced analysis)
			·Comprehensive training (diverse data)

	GPT-3.5		→OpenAlo1
OpenAl		·Not multimodal (text only)	·Multimodal (text and images)

		Fair reasoning ability (eg,scored high on SAT,	·Advanced reasoning (eg,top 1O% on bar examination)
		but bottom 1O% on bar examination)	·Enhanced contextual understanding (maintains
			coherence in long dialogues)
		Limited contextual understanding (difficulty with

			·Advanced APl access (supports multimodal inputs)

		coherence in complex conversations)




		Standard APl access (for text generation)

Talent skill gaps	Resourcing constraints
46	38	8	8
		Complex approval process
		Technical complexity

Expects workflows Receive support High level of trust that Believe gen Al wito change by30% from organization employer will safely havea net benefiin the next year on gen Al roll out gen Al in next 5 yearsExtensive Perceive gen Al Comfortablefamiliarity results as being using gen Alwith gen Al ≥70% accurate results in work
Metals and mining
Real estate
Telecommunications
Oil and gas
Media and entertainment
Technology
Pharmaceuticalsand medical products
Automotive and assembly
Engineering,construction,and building materials
Semiconductors
Chemicals
Consumer andpackaged goods
Advanced electronics
Financial services
Travel,logistics,and infrastructure
Retail
Agriculture
Business, legal, andprofessional services
Healthcare
Public sector
Aerospace and defense
Social sector

Superagency in the Workplace

Contents

Introduction

Superagency: By the numbers

Employees are more ready for the change than their leaders imagine

3×

1.4×

Companies need to move fast—employees trust their leaders to balance speed and safety

Companies are investing in gen AI but have not yet achieved maturity

92%

1%

Leaders need to recognize their responsibility in driving gen AI transformation

2.4×

McKinsey & Company

48%

About the survey

An innovation as powerful as the steam engine

AI superagency

Gen AI capabilities have evolved rapidly over the past two years.

Illustrative capabilities of gen Al platforms from select frontier labs, nonexhaustive

Intelligence and reasoning are improving

Agentic AI is acting autonomously

‘I’ve always thought of AI as the most profound technology humanity is working on . . . more profound than fire or electricity or anything that we’ve done in the past.’

‘AI, like most transformative technologies, grows gradually, then arrives suddenly.’

Multimodality is bringing together text, audio, and video

Hardware innovation is enhancing performance

Transparency is increasing

Employees are ready for AI; now leaders must step up

Beyond the tipping point

Leaders can invest more in their employees

Employees long for more support and training on gen AI.

Global perspectives on training

Exhibit

C-suite leaders can help millennials lead the way

Since leaders have the permission space, they can be bolder

Delivering speed and safety

The dilemma of speed versus safety

Employees trust business leaders to get it right

Global perspectives on regulation

Exhibit

McKinsey & Company

Risk management for gen AI

Embracing bigger ambitions

AI investments vary by industry

In some industries, employees are cautious

There’s a lot of headroom in some functions

Gen AI has not delivered enterprise-wide ROI, but that can change

Big ambitions can help solve big problems

‘[It] is critical to have a genuinely inspiring vision of the future [with AI] and not just a plan to fight fires.’

‘It is in [the] collaboration between people and algorithms that incredible scientific progress lies over the next few decades.’

AI’s potential to enhance our personal lives

Technology is not the barrier to scale

The operational headwinds that slow execution

Leadership alignment is a challenging but critical first step

Cost uncertainty makes it difficult for enterprises to predict ROI

Workforce planning is more difficult than ever

Supply chain dependencies can wreak havoc

Demand for greater explainability is a central challenge

To capture AI value, leaders must rewire their companies

Six enduring success factors enable tech-business transformations.

Conclusion: Meeting the AI future

‘Learn from yesterday, live for today, hope for tomorrow.’

— For business leaders:

— For employees:

Acknowledgments

Methodology

Glossary

superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-v4