Generative AI ingenuity at scale

Generative AI seemed too good to be true. It reduces coding time from days to minutes, personalizes products down to the smallest detail, and detects security vulnerabilities almost as soon as they appear. This has helped to rapidly increase the return on investment in AI from 13% to 31% from 2022.

While this largely reflects the success of pilot projects, sandbox experiments, and other small-scale investments, these early results have caused business leaders to rethink what is possible. Our latest proprietary survey conducted among 5,000 managers from 24 countries and 25 industries shows that the majority of managers are more optimistic about the opportunities created by generative artificial intelligence than last year. More than three-quarters (77%) say Gen AI is ready for the market, up from just 36% in 2023, and almost two-thirds (62%) now say Gen AI is more reality than hype.

From skepticism to certainty: Executives see the true value of generative AI taking shape

More than three-quarters of executives say they need to quickly adopt generative AI to keep up with the competition. According to the study, 72% of top-performing CEOs say competitive advantage depends on who has the most advanced generative AI. IBM Institute for Business Value (IBM IBM) 2024 CEO Survey.

Already, business leaders have begun to discover how generative AI increases profits. Operating profits directly attributable to AI doubled to almost 5% in 2022-2023, and executives expect that number to reach 10% by 2025. Embedding generative AI into existing enterprise software workflows can also provide a more sustainable return on investment, according to an upcoming IBM IBV study.

One in three companies pause an AI use case after the pilot phase, but two in three do not.

However, despite these early signals, some analysts are skeptical. They predict that this hype-driven surge in adoption will be followed by a “trough of disillusionment” where organizations retreat from the complexities associated with implementing generative AI across core business functions. And in some cases it’s true. One in three companies pause an AI use case after the pilot phase, but two in three do not. In this environment, how can business leaders best translate successful experiments into enterprise-level investments that can deliver value at scale?

Where does generative AI provide the most value today?

Generative AI can prove to be a powerful catalyst for business transformation, but it is not a panacea. They need to be implemented taking into account costs, data management and ethical implications, as well as taking into account talent and skills. Since the greatest power of generative AI is to enhance human work rather than automate it, culture change is essential to deliver lasting value. In fact, 64% of CEOs let’s say that success with generative AI will depend more on human adoption than on the technology itself.

Instead of applying generative AI as a solution to every problem, leaders need to understand how different tools work together, with traditional AI techniques, generative AI models, and automation playing their roles. They have to move beyond use-case thinking and focus on using generative AI to transform the way employees work every day. Getting to your destination is a journey, and how much experience an organization has with AI influences where it should start.

Focusing the use of generative AI on key business functions helps organizations achieve transformational, first-class growth.

Organizations are taking two main approaches to making the systemic changes needed to ensure a sustainable return on their AI investment.

1. Experimentation: Finding efficiencies in low-risk, non-critical features. Prioritizing generative AI adoption in low-risk areas where traditional AI already provides clear business value helps accelerate transformation and can increase profitability. Roughly two-thirds of executives say their organizations are implementing generative AI in customer service (70%), IT (65%), and product development (65%), which is consistent with what we saw in mid-2023.
2. Goal: Strengthen core business functions to drive broader transformation. The risks of applying generative AI to business operations closer to the core may be higher, but this is where the promise of business transformation begins to take shape. Willing to focus on previously insufficiently explored sales areas; information Safety; and supply chain, logistics and fulfillment see higher ROI.

However, for some organizations, such transformation opportunities seem out of reach. That’s why some are considering a platform approach to generative AI, which pools resources and profits across departments or partner organizations, as a cheaper and easier-to-implement option. This way, leaders can avoid starting from scratch in every area and quickly and more strategically deploy generative AI in functions with the greatest potential, including finance, supply chain and manufacturing, human resources, and sales and marketing. However, leaders taking this approach must also consider the unique needs of each function and find ways to tune generative AI applications accordingly.

Productivity gains that provide an advantage today will matter tomorrow.

By providing an AI platform that enables employees to experiment safely, organizations can unlock the collective genius of their employees and enable data-driven decision-making. Leaders will need to foster a growth and innovation mindset and encourage employees to look beyond what has worked in the past to pioneer breakthrough innovations, stay ahead of the curve, and drive transformational growth at scale with generative AI.

Download the report to learn where generative AI is delivering the highest ROI today, how executives can leverage its long-term potential and overcome key challenges, and what steps you can take to scale generative AI and transform your business – no matter where you are on an AI journey.