In the hyper-evolving world of generative AI, enterprises are racing to harness the potential of this transformative technology. Yet, the path to success is fraught with challenges, requiring a delicate balance between innovation and risk management. PwC’s recent “Early Days” Generative AI report highlights six key priorities that organizations at the forefront of AI adoption are focusing on to navigate these complexities and set the stage for long-term success.

1. Managing the AI Risk/Reward Tug-of-War

Generative AI has sparked both excitement and concern within the business community. On one hand, the technology promises unprecedented opportunities for competitive advantage; on the other, it raises significant ethical, legal, and technical risks. As PwC emphasizes, achieving a healthy balance between these competing forces is crucial.

“There’s a fascinating parallel between the excitement and anxiety generated by AI in the global business environment writ large, and in individual organizations,” PwC notes. The report underscores that while some companies have managed this tension effectively, others have struggled, leading to either paralysis or reckless decision-making with potentially disastrous consequences.

One example provided in the report involves a company that found itself at an impasse because the only team capable of validating its AI models was the same team that had developed them—a clear conflict of interest. In contrast, another company made swift progress by establishing a cross-functional leadership team early on to ensure enterprise-wide consistency and risk alignment. This proactive approach not only addressed the ethical implications of AI but also maintained momentum, allowing the company to leverage AI responsibly and effectively.

2. Aligning Generative AI with Digital Strategy

The second priority for successful AI adoption is aligning generative AI strategies with existing digital transformation efforts. PwC points out that many organizations are still grappling with their digital journeys, and the introduction of generative AI adds another layer of complexity.

“Generative AI’s primary output is digital—digital data, assets, and analytic insights, whose impact is greatest when applied to and used in combination with existing digital tools,” the report states. To maximize the benefits of AI, organizations must ensure that their AI initiatives complement and enhance their broader digital strategies.

A case study from the report illustrates this point vividly. A global consumer packaged goods company initially deployed generative AI in its customer service operations, automating the process of filling out service tickets and answering customer queries. However, as the company’s leaders explored further, they realized that the same AI models could be applied to other business functions, such as procurement and HR, leading to much greater gains than initially anticipated. This cross-functional approach exemplifies how integrating AI with digital strategies can unlock significant value.

3. Experimenting with an Eye for Scaling

Experimentation is key to discovering high-value applications of generative AI, but without a plan for scaling, even the most promising pilots can fall short of their potential. PwC’s report highlights the importance of engaging senior leadership early on to ensure that AI experiments are aligned with the organization’s broader strategic goals.

“Experimentation is valuable with generative AI because it’s a highly versatile tool, akin to a digital Swiss Army knife,” the report notes. However, the risk of “pilot purgatory”—where AI projects get stuck in the experimental phase without delivering tangible value—is real. To avoid this, PwC recommends identifying patterns and repeatable processes that can be scaled across the organization.

One example provided involves a financial services company that initially focused its AI efforts on automating HR tasks. By involving the CIO and CISO in these early experiments, the company was able to identify broader applications for the technology, leading to a more comprehensive and scalable AI strategy.

4. Developing a Productivity Plan

Generative AI’s ability to boost productivity is well-documented, but organizations need a clear plan for how to leverage these gains. PwC identifies three main approaches: reinvesting productivity gains to boost output, reducing labor input to cut costs, or pursuing a combination of both.

The report highlights several companies that have successfully implemented AI-driven productivity improvements. For instance, PwC firms in mainland China and Hong Kong reported efficiency gains of up to 50% in code generation and 80% in internal translation tasks, thanks to small-scale AI pilots. These productivity gains not only enhance operational efficiency but also improve employee satisfaction by eliminating repetitive, mundane tasks.

However, PwC warns that automating low-value work can shift the burden onto more strategic tasks, potentially leading to burnout. “Organizations will want to take their workforce’s temperature as they determine how much freed capacity they redeploy versus taking the opportunity to reenergize a previously overstretched employee base,” the report advises.

5. Putting People at the Heart of Your AI Strategy

One of the most critical priorities for generative AI success is ensuring that employees are fully engaged in the AI journey. PwC’s report highlights the importance of early and ongoing communication with employees about the role of AI and the benefits it can bring to their work.

“Our 26th Annual Global CEO Survey found that 69% of leaders planned to invest in technologies such as AI this year,” the report states. Yet, many employees remain uncertain or unaware of how these technologies will impact them. To bridge this gap, PwC recommends involving employees in the creation and selection of AI tools, providing customized training and upskilling opportunities, and fostering a culture that embraces human–AI collaboration.

One example of successful employee engagement comes from a financial services firm that encourages its employees to experiment with AI tools and even celebrates small-scale failures as part of the innovation process. “Failures mark innovation and are expected, and even celebrated,” the report notes, underscoring the importance of creating a safe environment for AI experimentation.

6. Collaborating with the Ecosystem

The final priority identified by PwC is the importance of working with external partners to unlock the full potential of generative AI. “Companies with a clear ecosystem strategy are significantly more likely to outperform those without one,” the report asserts.

In the healthcare and pharmaceutical industries, for example, collaboration between organizations has long been limited by privacy concerns and regulatory hurdles. However, AI is beginning to change that dynamic by enabling safe, large-scale data-sharing and data-pooling. PwC highlights the potential for generative AI to revolutionize drug development and precision medicine through the creation of synthetic datasets that can be shared across organizations.

By working closely with suppliers, service providers, customers, and other partners, organizations can not only enhance their own AI capabilities but also drive broader industry innovation. “The holy grail of healthcare and pharmaceutical firms is the ability to access patient records at scale and identify patterns that could uncover routes to more effective treatments,” the report notes, emphasizing the transformative potential of ecosystem collaboration.

Strategic GenAI Adoption is Key

As generative AI continues to reshape the business landscape, organizations must be strategic in their approach to adoption. PwC’s six key priorities—managing risk, aligning AI with digital strategy, experimenting with scaling in mind, developing a productivity plan, putting people at the center, and collaborating with the ecosystem—provide a roadmap for enterprises looking to succeed in this new era.

While it’s still early days for generative AI, the lessons learned from these early adopters will be invaluable as the technology matures. By following these priorities, organizations can not only navigate the complexities of AI adoption but also position themselves as leaders in the next wave of digital transformation.