Leaders are excited by GenAI—but first, they must cross the readiness gap

Enterprise leaders recognize generative artificial intelligence (GenAI) as a transformative force capable of reshaping industries. However, bridging the gap between this promise and organizational readiness is imperative.

In a roundtable co-hosted by Genpact, HFS convened executive leaders from diverse industries, including finance, insurance, pharmaceuticals, advertising, and more. Participants represented companies like Chubb, Dentsu, Estée Lauder, JPMorgan, Prudential Financial, Pfizer, and Tinuiti.

The conversation revolved around how leaders evaluate opportunities to implement GenAI effectively and harness its full potential.

The following highlights key insights from the discussion, offering actionable points to advance with GenAI.

Organizations are excited to adopt GenAI, yet they grapple with feeling unprepared

Across our roundtable delegates, there is a clear recognition of the transformative potential of GenAI. Eighty percent of participants express genuine excitement about the profound impact GenAI could have on their organizations, and less than 7% feel anxious (see Exhibit 1). This collective optimism stems from the belief in the technology’s potential to enhance human abilities and transform every function, from financial risk management to drug development and beyond.

Exhibit 1: 80% of delegates are excited about the impact of GenAI on their organization

Sample: 15 Delegates
Source: HFS Research, 2024

Amidst this optimism, there was also a sobering realization of the gap between the potential of GenAI and the current state of organizational readiness. One delegate noted, “While the vision of the generative enterprise represents what’s possible, our current reality lags by five to 10 years. In achieving and deploying this vision, most companies are still in the experimental stages with RPA [robotic process automation].”

In fact, many executives see themselves in Stage 1 of GenAI maturity in HFS’ maturity model (see Exhibit 2).

“We are stuck in the early phases of maturity, and we aren’t getting to that fully integrated piece yet. It feels like we are squeezing lemons and are yet to move to the right-hand side of the diagram where you get new value,” noted one delegate.

Exhibit 2: Many organizations recognize they are in Stage 1 in the maturity diagram

In a survey we ran with attendees before the roundtable, we found that most organizations are still in the early stages of deploying GenAI. Specifically, a third of respondents are in the pilot stages, and 50% have deployed the technology across one business function. Only 17% have deployed GenAI across multiple business functions (see Exhibit 3).

Exhibit 3: Delegates have deployed GenAI in at least one business function

Sample: 14 Delegates
Source: HFS Research, 2024

Leaders are prioritizing productivity use cases as a short-term win while recognizing GenAI’s broader impact

As enterprises begin their GenAI adoption journeys, they’ve taken a pragmatic approach by prioritizing productivity use cases. As the delegates highlighted, these use cases encompass enhancing productivity in technology teams, optimizing marketing strategies, improving financial management, facilitating risk assessments and legal support, and efficiently handling customer queries through chatbots.

This prioritization of internal improvement and productivity serves four essential purposes:

• Gain firsthand experience with GenAI: By focusing on productivity use cases, leaders can understand its potential and address challenges internally before wider deployment, as echoed by a delegate: “We need to prove the effectiveness of productivity use cases internally before we go and promote it to clients.”
• Minimize risk during the critical stages: Prioritizing productivity use cases offers a secure space for innovation amidst evolving regulations, reducing public exposure risks.
• Do more with less: Address the challenge of achieving more with fewer resources by enhancing productivity without staff reductions, as noted by one leader: “Most companies are not reducing headcount but reducing effort fractally, for now.”
• Offer measurability for tangible results: Productivity use cases are often compelling among leaders seeking quantifiable metrics like time saved, cost reductions, or increased efficiency. Of course, that’s not always the case, as one participant noted: “The highest value use cases for GenAI often lie in areas that are challenging to measure… Expressing this to leadership can be challenging when productivity metrics take precedence.”

As organizations delve deeper into the GenAI landscape, they recognize that the technology’s highest-value use cases often exist in areas beyond productivity. These use cases involve improving the employee experience, enhancing customer satisfaction, contributing to societal impact, or even enabling the creation of entirely new business models.

Moving beyond productivity gains requires data readiness, aligning use cases with business needs, navigating regulations, and reskilling employees

Transitioning beyond productivity and moving up the GenAI maturity ladder requires a thoughtful approach to digital innovation. Our delegates highlighted some of the most common challenges and shared strategies to tackle those obstacles.

• Structured and clean data are vital for effective GenAI implementation: Our delegates agreed that organizations should invest in robust data governance, integration, and quality control measures. Centralizing data management, ensuring standardization, and establishing data pipelines are critical steps to provide clean and structured data for GenAI applications. As Sanjay Srivastava, Chief Digital Strategist of Genpact, emphasized: “Getting the data right is 90% of the work.”
• Aligning GenAI with specific business objectives is the key to success: Organizations must avoid succumbing to the allure of the “shiny tool syndrome,” where “people think GenAI is magic to solve all problems,” as one delegate noted. To overcome this, aligning GenAI use cases with defined business objectives is critical.
• Navigate AI’s intricate regulations and ethical considerations strategically: Achieving an equilibrium between AI regulations and innovation is crucial. Organizations should develop a comprehensive understanding of the regulatory landscape and proactively address ethical concerns through responsible AI practices to effectively mitigate risks.
• Prepare all employees for the digital revolution: Organizations should identify skills beyond data science proficiency and proactively invest in workforce education, developing other essential skills such as critical thinking and problem solving. The combination of technical and analytical skills is crucial for the jobs of the future.

The Bottom Line: Embrace the excitement surrounding GenAI while acknowledging the readiness gap. Prioritize productivity use cases as a starting point, but don’t overlook the broader potential impact.

Leaders are undoubtedly excited about the limitless possibilities with GenAI. But first, they must bridge the readiness gap to capture its full value. This vision will empower organizations to handle adoption complexities more effectively while addressing challenges such as data quality, aligning use cases with business needs, regulatory compliance, and employee upskilling.

Ultimately, while productivity gains are important, acknowledging the broader impact of GenAI across the organization is essential.