Generative AI: The 21st-century power play

In brief

Which industries will benefit from generative AI? The short answer is: all of them. However, our analysis suggests a range of impacts across industries.

The opportunity

Three keys to success

GenAI is sparking a surge
of innovation akin to
the advent of electricity. Discover how to channel
its reinvention potential.

In depth

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Generative AI illuminates opportunities for growth and innovation that perhaps shine as bright as those presented by the arrival of electricity. It’s important, however, that executives don’t get blinded by the light of possibility and start utilizing the technology without a value-driven plan that incorporates strategies to address the formidable risks.

With the right approach, businesses can realize the ultimate promise for GenAI: igniting the type of organizational reinvention that enables companies, their workforces, and the customers they serve to achieve new levels of efficiency and prosperity.

The future’s…electric.

In conclusion

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August 2024

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7 min

Dive deeper:
Productivity or pioneering? Your industry’s GenAI adoption play

The

The approach

opportunity

Fast forward to today, and we find ourselves at another pivotal moment heralded by a transformer: this time, it’s the generative pre-trained transformer (GPT) that powers most GenAI. Other forms of AI have been enabling a variety of organizational capabilities and consumer experiences for several years now. However, the general-purpose nature of generative AI means it can serve as the foundation for profound innovations across all elements of day-to-day life.

Though we’re still in the early days of development, the lightning speed at which GenAI is evolving and integrating into our societal and business fabric suggests we’re about to experience change at a pace never before witnessed. Already, nearly one-third of organizations have adopted the technology since it emerged less than two years ago. And in many cases, the results these early movers are seeing lead to believing: adopters are much more likely than those waiting on the sidelines to view generative AI as a way to improve products and services, build trust, and even completely transform their business models.

In brief

3 min

Generative AI (GenAI) is poised to become the catalyst for era-defining transformation in the global economy. It stands to reshape industries, enhance productivity, and forge new paradigms of innovation and human–machine collaboration.

Further reading: Go deeper on the generative AI revolution

As a strategic partner for TED in 2024, we’re working together to lead the global conversation on AI.

PwC and TED

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Productivity or pioneering? Your industry’s GenAI adoption play

Responsible AI for generative AI

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The path to generative AI value: Setting the flywheel in motion

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—Marcia Griffiths

It’s electric!”

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Joe Atkinson
Global Chief AI Officer,
PwC US

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Nicki Wakefield
Global Clients and
Industries Leader,
PwC UK

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Jennifer Kosar
Trust and Transparency
Solutions Leader,
Partner, PwC US

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Scott Likens
Global AI and Innovation Technology Leader,
Principal, PwC US

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Colin Light
EMEA and UK
Strategy& Leader,
Partner, PwC UK

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Bret Greenstein
Data and Analytics and Generative AI,
Principal, PwC US

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Learn more

This moment in time is reminiscent of the twilight of the 19th century, when humanity stood on the cusp of a different kind of revolution that would redraw the contours of daily life and commerce:

the advent of electricity.

Common lore credits Thomas Edison’s invention of the durable light bulb with providing the spark for world electrification. However, the transformer played an equally vital role. This unsung hero enabled electricity to reach any business or home, no matter its distance from a power plant. Only then could a slew of electricity-powered inventions ignite a major shift in the way people lived and worked, ranging from factory equipment that bolstered business productivity to household appliances that revolutionized domesticity.

To thrive while navigating these electrifying times, it’s critical to develop a nuanced strategy for generative AI adoption:

10 min

With generative AI, we’re powering ahead into a new era that presents both vast opportunities and formidable challenges for business. Leaders must consider the possibilities and risks involved—and we’ve come up with a formula for success.

Sectors like software and luxury goods could see the biggest boosts from GenAI on a percentage-point basis. But the possible uplift for even those sectors in the lower-benefit ranges remains substantial considering their razor-thin margins.

Take the transportation and logistics sector (excluding government) for instance, where standard freight carriers often see margins as low as 2%. Some companies in the sector could see their margins double once they put generative AI to work.

Of course, many of the low-hanging productivity gains available today will erode over time. They’re largely derived from applying GenAI to achieve efficiencies in an organization based on how it operates right now. Once every player in the market starts using the technology in these ways, it will catalyze reinvention, and customer expectations will inevitably advance, causing the bar to rise.

That’s why companies will want to use today’s work integrating the technology as the foundation for the more transformative applications, which will move them toward new operating models—and into new markets.

David Andre,
Chief Science Officer
at Alphabet’s X,
the moonshot factory, discusses how he approaches innovation.

1. Master the balancing act

Although generative AI presents organizations with numerous high-value applications, they won’t always come easy. Here are some keys to success.

success

hree
keys to

Dive deeper:
How will GenAI transform the workforce?

Dive deeper:
Set the generative AI flywheel in motion

There’s a way for companies to capture GenAI productivity gains while laying the groundwork for more transformative applications. It requires an implementation approach that relies largely on pattern recognition.

What we mean by a “pattern” is the common-model architecture, tooling, and design elements that enable each of GenAI’s six primary capabilities: net-new content creation, augmentation, transformation, dialogue, information retrieval, and summarization.

Because a single AI model can be adapted and tuned for many specific tasks, applying a GenAI pattern to a use case can unlock pathways to similar use cases. Build these elements out for one, and they can be repurposed for others that deliver the same capability.

approach

The

And simply thinking about applying generative AI in this broader and more efficient way, as opposed to focusing on what specific tasks it can automate, helps set a flywheel in motion. This approach can both reveal and provide the foundations for implementations that trigger reinvention.

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Industry dynamics will play a starring role in determining the pace at which companies are likely to capitalize on generative AI efficiency and reinvention opportunities. Two variables matter most: the degree of disruption GenAI brings to an industry, and the industry’s ability to adopt and integrate the technology.

To see this dynamic in action, we plotted 22 industries against the two variables:

Already we’re seeing the interaction of these factors at work. Take the pharmaceuticals industry, for example, which faces a high level of disruption and a somewhat smoother road to adoption than other industries. Both new entrants and established companies, in many cases working together, are already using generative AI drug-discovery platforms. In fact, Insilico Medicine used its Pharma.AI platform to develop the world’s first drug that was fully designed by generative AI—and it’s already in Phase II human clinical trials.

Of course, regardless of the quadrant in which an organization falls, there’s not a lot of time to lose. The emergence of AI-native disruptors can’t ever be reliably predicted—to which retailers that were around for the emergence of e-commerce can attest.

Previous introductions of groundbreaking technology, like electricity or the internet, provide indications of what lies ahead for the workforce. New workflows and tasks will surely emerge—as will new kinds of jobs. But employees need new skills to capably fill them. Governments, businesses, and individuals all have a role to play in the massive upskilling effort that’s only just beginning.

Every organization’s generative AI strategy needs to include an upskilling program that starts with assessing employees’ current AI proficiency and providing role-specific training programs, learning resources, and certifications to address the gaps. Companies might consider teaming up with educational institutions or AI-training providers to offer these options. Either way, employees require guidance to use generative AI effectively and responsibly as it becomes an increasingly ingrained part of daily work.

2. Manage the skills transition

—Jerry Kaplan, AI expert and entrepreneur

What jobs involve or benefit from the use of electricity? Obviously, an awful lot of them do. I think we’re going to see the same thing [with AI].”

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A comprehensive approach to responsible AI is indispensable for ensuring those creating and using generative AI applications engage in the daily practices needed to keep risks at bay.

3. Tackle the risks

societal biases that generative AI can propagate

sustainability concerns due to the energy demands of the massive large-language models powering some applications

the potential spread of misinformation stemming from both the technology’s propensity to provide incorrect facts on occasion and its ability to create deepfakes.

Generative AI presents some age-old technology risks along with new considerations. Like other digital technologies, it will need an effective governance structure and targeted security measures. New risks that are emerging include:

—Marc Mathieu, Head of AI Transformation, Salesforce

We often say that trust is not just a layer; it’s a road map. Build that muscle of trusted, ethical AI now, because the road map is going to, if anything, become more and more complex as we go toward autonomous AI.”