A recent study by SoftServe and Forrester Consulting shows that while there is strong interest in Generative AI among businesses, only a small percentage have successfully integrated it across all functions. Challenges such as data readiness, governance, and skill development are hindering widespread adoption, pointing to a gap between ambition and execution.
Global Study Reveals Limited Adoption of Generative AI Across Business Functions
London, United Kingdom—June 27, 2024—A newly released study from SoftServe, an IT consulting and digital services provider, indicates that while Generative AI continues to attract significant interest in business circles, only a small percentage of companies are actually harnessing its full potential. The research, conducted by Forrester Consulting, surveyed 777 technology decision-makers globally and found that a mere 22% of organizations are successfully implementing Generative AI across all business functions.
Despite the widespread availability of products and services based on Generative AI since the debut of ChatGPT over 18 months ago, corporations are still grappling with internal complexities that hinder full-scale deployments. While enthusiasm for AI-driven solutions remains high, factors such as data readiness, governance, and skill development are major sticking points.
Key Findings of the Study
The study’s data suggests a significant gap between ambition and execution. Over half of the decision-makers surveyed stated that their companies had set business goals for adopting Generative AI. However, 79% are apprehensive about their abilities to achieve these goals given their current internal and external expertise levels.
Despite these challenges, companies are pushing forward with multiple AI initiatives. Most organizations have already implemented at least three use cases and plan to expand or pilot additional ones in the coming 12-18 months. Nevertheless, only 51% of respondents expressed strong confidence that their current strategies would maximize AI potential, and 77% voiced concerns about realizing business value either in the short or long term.
Training and data preparation also present substantial challenges. Only 42% of organizations reported having the capability to train AI models, and nearly 89% struggle to prepare their business data for AI applications. Governance is another focal area; only 24% of organizations have established governance plans for AI use, although 90% acknowledged the necessity of such plans to mitigate risks and ensure responsible AI implementation.
CEO Perspectives
“Despite a swift start to the Generative AI race, many initiatives get stuck in the piloting stages as more organizations realize their data infrastructure isn’t ready to adequately deploy AI technologies beyond the proof-of-concept,” commented Alex Chubay, SoftServe’s CTO. “Gaps in skills and knowledge, technical feasibility, and data readiness hinder companies from moving beyond tactical wins in pilot mode to full-scale deployments enabling novel business capabilities and experiences. To make that qualitative leap, a holistic approach is required to orchestrate business priorities, use cases, and data across the technology ecosystem from the initial strategy down to the final execution.”
Differences Between Sectors and Regions
The study also highlighted regional and sectoral differences in the adoption and success of Generative AI. In terms of unlocking AI value, the United States led the pack, followed by the UK, Singapore, and Germany. Among different industries, the retail sector was most successful in leveraging AI, particularly in training models on proprietary data. Conversely, the financial services and insurance sectors faced more hurdles before seeing tangible benefits from AI projects. These sectors also reported fewer established governance plans compared to retail and others.
In other fields such as healthcare, life sciences, and manufacturing, the results were mixed. Larger businesses with revenue exceeding $5 billion struggled more with organizing the complex hardware, software, and infrastructure needed to leverage Generative AI successfully.
Future Considerations
A significant portion of decision-makers (80%) noted that their employees are currently grappling with understanding the complexities of AI use cases, underscoring a demand for external expertise. About 90% of surveyed organizations expressed the need for partners who possess advanced technical capabilities, industry-specific knowledge, and accelerated deployment support.
The study underscores that while there is no shortage of enthusiasm for Generative AI, substantial gaps between expectations and current capacities remain. Addressing these gaps will require not just investing in technology but also in governance, skill development, and data readiness.
Conclusion
As organizations continue to explore and invest in Generative AI, success will depend on the alignment of business goals with technological capabilities and data governance. The findings from SoftServe’s study offer crucial insights into the current state of Generative AI and highlight both the challenges and opportunities that lie ahead.
