Self-writing, self-learning code, “no touch” decision making, and large language models. These and other promising applications of generative AI (gen AI) offer a transformative opportunity for companies. Indeed, the business world is abuzz with gen AI’s ability to create value by changing how work gets done across functions and workflows.
But like any technology, generative AI is not a panacea. Most organizations struggle to capture the full value of their digital investments, and generative AI could play out the same way if leaders deploy it without priming their organizations to reap the benefits. And it comes with challenges, including the use of public gen AI offerings that could result in the flow of intellectual property or proprietary data into the public domain, along with privacy and bias concerns.
In a 2023 McKinsey Global Survey on digital strategy involving more than 1,000 respondents, we found a startlingly strong connection between organizations that have built a strong innovative culture and operating model and their ability to increase value through the newest digital technologies, including generative AI.
In this article, we examine five actions that top innovators are taking to put strategic distance between themselves and their peers and surpass them when it comes to using gen AI, and how these factors help create business value.
Action, not just talk, on deploying gen AI
Everyone is talking about generative AI, but top innovators are actually doing something about it. Thirty percent of top innovators we surveyed said they are already deploying generative AI at scale in their innovation and R&D functions, more than six times the rate of companies that are lagging behind on innovation (Exhibit 1).
This is an acceleration from 2022, when top innovators deployed gen AI at scale at 3.2 times the rate of trailing peers, according to our research. The widening gap is important, since organizations increasingly look to innovation as a primary driver of growth and resilience in challenging economic times. Companies that can harness the speed and granularity of generative AI to find new growth opportunities are likely to have a formidable competitive advantage.
A strong innovative culture does multiple things well at once. It emphasizes vision and strategy by giving initiatives the resources they need to succeed, and it focuses on pursuing new and bigger ideas grounded in differentiated business insights—even embracing failures along the way. Innovative cultures also reach scale more quickly in relevant markets. And they mobilize the organization by both capitalizing on external networks and motivating and rewarding their people to innovate.
Committed innovators craft operating models that reflect these practices and others, including investing strongly in R&D and in digital tools such as advanced analytics, AI, digital platforms, and knowledge management. Overall, they have a higher willingness to disrupt themselves and pursue breakthroughs.
In the current uncertain global context, most organizations are looking for new sources of growth across all growth pathways. In our new survey, 78 percent of respondents say their current business model would need to change “moderately to completely” in order to remain economically viable by 2025. Innovation is central to survival, and innovative companies are using tech to beat the odds (Exhibit 2).
While companies typically look to their core to drive the majority of their growth, most organizations in our latest survey are focused as much or more on “innovating new” as on “scaling old”—even within their core.
Top innovators are reaping significantly better business outcomes from their AI investments
Innovation has long been a source of growth and competitive advantage for companies that are able to get it right. Top innovators win not just by spotting new opportunities faster and coming up with ideas that address those emerging needs. They are also better at developing them quickly and scaling them ahead of peers, whether launching a new product to market or innovating their operating model. Indeed, we found in our June 2022 research that top innovators are three times more likely than others to encourage experimentation.
Generative AI can rapidly accelerate this competitive edge and further widen the gap between top innovators and others. The proliferation of available data, as well as the exponential learning curve of AI algorithms, is creating a seismic shift in their favor (Exhibit 3).
The need for an innovative operating model
Critically, these organizations are already hardwiring their operating models to be AI-led, allowing them to capture the value of these investments to drive growth.
McKinsey research shows that experimentation and a commitment to evolving ideas, businesses, and technology are paramount and underpin success at driving new sources of growth. Those same factors are central to deriving value from gen AI. Examples include reorganizing from functional silos to integrated, cross-functional teams aligned to products or platforms; making product management a core competency across the organization; and frequently reviewing the allocation of talent and resources to align with the highest-impact areas.
There are two ways in which gen AI can accelerate an organization’s growth strategy. The first is its ability to rapidly scan and process huge amounts of certain types of information and synthesize it. Put more simply, it can answer questions incredibly quickly. But the quality of the answers depends on both the quality of the question and access to the data that would inform an accurate answer.
The second accelerant relates to self-learning. Gen AI can write code that will write more code and improve itself. This can be deployed not only to refine the questions it is being asked but also to move directly from idea to execution with no human touch. An organization has to adapt its workflows to allow this to happen, since a process is only as fast as its slowest step.
With services like ChatGPT open to anyone, and as public platforms scale at exponential rates, access to actual gen AI technology is no longer a differentiator. That means the way to compete with gen AI is changing. However, there are five steps that innovative organizations can take to create a competitive edge with this technology (Exhibit 4):
- Know how to ask the right questions. Top innovators are already ahead on using other forms of AI, and their organizations are already trained to understand how to use algorithms to accelerate gen AI. They recognize the “garbage in, garbage out” trap that gen AI can lay and are already adept at understanding the roles it can play—and those it cannot. Top innovators are four to five times more likely than their weakest peers to have tech-savvy business leaders who understand how to take advantage of new technologies in their business and deploy them in the right spot to create value. They are also three times as likely to have agile teams that are already used to writing their own code. With gen AI allowing faster self-writing code, these organizations are primed for rapid deployment into teams that already understand the limitations and benefits of such tech.
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Spot “wrong” answers fast and weed them out. The concept of “fail fast,” a cornerstone of top innovators’ thinking, is critical in the age of gen AI. These companies already have systems and a culture in place to be able to pull the plug on something that isn’t working and move onto the next idea. Equally critical, they are nine times as likely to already have hundreds or even thousands of cross-functional teams that are taking a holistic, end-user view of their business, which means they are more likely to not just ask the right questions but also spot “bad” answers.
These organizations also test and learn at “all levels of the organization.” Executives are three times more likely to believe their organization encourages risk taking than manager-level employees and below. As gen AI democratizes test-and-learn practices across organizations, management practices must democratize too. Leading innovators are already doing this.
- Continually build proprietary data. Top innovators are also five times more likely to already have internal processes, products, and customer interactions that are designed for data capture, leveraging both internal and external data. This means that when they run gen AI, they have sources of insight that others don’t to help get better answers. They also are more than five times as likely to have established DevSecOps practices in place to ensure that they know how to properly protect their own data, comply with complex regulations on how to handle the data they are accessing externally and internally, as well as prevent the accidental leak of their proprietary data into the broader “source material” for public algorithms like ChatGPT.
- Create an organizational ability to learn quickly. The fact that top innovators are succeeding here is no surprise, since the ability to learn quickly is almost the definition of what it takes to be a successful innovator. The data bears this out, with top innovators more than eight times as likely to have adopted agile practices organization-wide, not just in information technology.
- Wire key “no human touch” workflows to take advantage of gen AI’s speed. In addition to providing organizations with the ability to rapidly search through huge data sets to find answers to questions, gen AI’s ability to “self-learn” and evolve, including code that “writes itself,” is one of the most talked about benefits of the technology. Organizations that have already identified key workflows that can benefit from this ability and have put in place the people, mindsets, and processes to enable it are ahead in harnessing the speed of gen AI, going from idea to execution with no human touch.
Top innovators are more than eight times as likely to have already put this nimble operating model in place. What’s more, they already have agile teams embedded in their organizations. These teams write their own code, increasing the potential speed and depth of gen AI to permeate their business. Finally, they have a deep bench of tech-savvy talent that will understand the limits of the technology and how to ensure it doesn’t go off track.
The world is changing fast, and organizations have to change with it to keep up. While innovation, as always, remains a choice, the business case for an innovative culture is stronger than ever. In the context of generative AI, leaders have distinguished themselves with actions over words, and we believe the stage is set for many more organizations to follow suit.
