Artificial Intelligence & Machine Learning

Only 5% Organizations Have a Mature Gen AI Investment Plan

Generative AI is rapidly transforming industries, prompting businesses to invest in this technology to gain a competitive edge. To progress in their generative AI journeys, enterprises must align their AI strategy with growth and market-oriented goals. But, time is of the essence as organizations only have two years to capitalize on gen AI's benefits, according to a joint research report by Genpact and HFS Research. The report titled "The Two-Year Gen AI Countdown: How Businesses Are Scaling Gen AI Adoption and Avoiding the Productivity Trap" showed that organizations are raising fresh capital or reallocating budgets to invest in gen AI and gain a competitive edge (See Exhibit 1).

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Genpact and HFS Research surveyed 550 senior executives from organizations with revenues of $1 billion or more across 12 countries and 8 industries. While most enterprises were in the early stages of gen AI adoption, their significant commitment to the technology was evident in their investments. Nearly 61% of executives allocated up to 10% of their technology budgets for gen AI. The respondents expect an average of 30% increase in gen AI investments over the next year, with those in the "wait-and-watch" category being the most aggressive in their forecasts, the report said.

In terms of generating funds for gen AI investments, 51% of respondents plan to reallocate capital from sources such as IT infrastructure, software development, and research and development. Fifty percent of respondents have set aside dedicated funds for gen AI initiatives, and 42% plan to use anticipated savings from gen AI efficiency gains (See Exhibit 2).

Globally, 70% of CEOs recognize the urgency to act on gen AI to prevent competitors from gaining a strategic advantage. A global survey by McKinsey showed that 23% of organizations have quantified the impact of AI, attributing at least 5% of their earnings before interest and taxes, or EBIT, to AI initiatives.

Gen AI Maturity Levels

The research categorizes enterprises into four gen AI maturity levels, reflecting their investment commitment and deployment extent:

• Pioneers (5%): Leading integration and seeking direct growth outcomes from gen AI, such as increased market share and competitive advantage;
• Fast Followers (27%): Strategically deploying gen AI for efficiency gains and enhancing customer experience;
• Wait and Watch (45%): Delaying investment, primarily aiming to leverage gen AI for operational efficiency and productivity;
• Deniers (23%): Skeptical of gen AI and missing out on potential innovations.

Diversification of Gen AI Uses

Gen AI applications are expanding across various sectors, moving beyond chatbots and AI assistants to more complex tasks such as underwriting insurance policies, detecting stress in loan accounts, and aiding retail chains in strategizing store layouts and personalizing product descriptions for customers.

The Genpact-HFS report showed that healthcare, retail and high-tech industries intend to fund their gen AI investments using existing budgets. Respondents from the banking, capital markets and insurance sectors, however, plan to allocate new dedicated funds for gen AI initiatives, reflecting a strong commitment to the technology's potential. The life sciences sector aims to take a two-fold approach: relying partially on anticipated gen-AI-driven savings while also exploring external funding sources and partnerships.

"As companies step into gen AI's uncharted waters, the journey holds huge promise, but it is not without its perils," said Sreekanth Menon, global lead for AI at Genpact. "The challenge for most enterprises is that they remain anchored in proofs of concept that can be impressive but rarely reach operational scale. Having access to insights that can chart a course toward gen AI’s full benefits - beyond productivity alone - is invaluable."

Although 74% of respondents anticipate gen AI driving productivity gains, 52% caution against prioritizing productivity over broader business goals, which may potentially affect employee experience.

The Importance of Data Quality for Gen AI Success

The success of the gen AI initiatives hinges on high-quality data. Organizations aiming for outcomes based on gen AI within two years are facing significant challenges related to data quality and strategy, highlighting the urgent need for robust data management practices.

"Many tech leaders and CIOs struggle to develop effective data strategies due to silos within data science and engineering departments, resulting in disjointed approaches and redundant investments," said Sarfraz Nawaz, product executive in residence at Mighty Capital and former director of digital and data science at Johnson & Johnson.

"Our research indicates that the majority of executives view generative AI as a catalyst for value creation, fostering not only productivity but also customer satisfaction, revenue growth, and competitive advantage," said Balkrishan Kalra, president and CEO of Genpact. "The fundamental shift will be in how enterprises think about data and technology; this will shape the future competitiveness and success of organizations globally."

Echoing Kalra's sentiments, Phil Fersht, CEO and chief analyst, HFS Research, highlighted the substantial investment being made in gen AI and its pivotal role in future value creation. He also cautioned enterprises ought to gauge the influence of gen AI by its capacity to forecast and tailor experiences before solely focusing on productivity gains.