Insights from the Frontlines of GenAI Adoption

As the CEO of the AI-native company leading the AI Security field, I have witnessed firsthand the incredible pace at which generative AI (GenAI) technology is evolving. While the buzz surrounding this technology is undeniable, there remains a critical need to distill insights from real-world data to guide strategic decision-making. Everest Group recently used the latest research from CalypsoAI to cut through the noise, offering a grounded view of GenAI adoption across various horizontal functions in large enterprises.

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Current State of GenAI Adoption in Horizontal Functions

The Everest Group report, Generative AI Adoption–Examining Real-World Use Cases in Horizontal Functions and Future Outlook, highlights a significant trend: Despite the high levels of interest in GenAI, more than 85% of Proofs of Concept (PoCs) have struggled to transition into full-scale production. This statistic underscores a growing impatience I have witnessed among executives eager to see tangible returns on their AI investments. In response, enterprises are shifting to specialized models, such as Small Language Models (SLMs), to drive specific use cases, but this has introduced a new challenge: Model sprawl. As more models are deployed for targeted purposes, the need for a robust governance and orchestration layer becomes critical for managing dispersed AI systems effectively.

Leading Functions in GenAI Adoption

It’s no surprise that the Everest Group report found IT and Security, being technology-centric functions, leading the charge in GenAI adoption. These areas account for the majority of enterprise spending on GenAI, reflecting their pivotal role in driving digital transformation. Notably, functions such as Legal and Human Resources, which traditionally command smaller budgets, are also showing a strong appetite for GenAI. For instance, Legal departments are leveraging GenAI to process large volumes of unstructured documents, enhancing efficiency and accuracy. Although the current data set used in the report does not cover Customer Support, Sales, or Marketing, other evidence shows these functions also adopt GenAI at high rates.

Top Use Cases Across Functions

Everest Group identifies the most prominent use cases for GenAI across functions. In IT, automated code generation and debugging stand out as the leading applications, providing significant boosts in efficiency. In the Security domain, GenAI is used extensively for threat detection and response, capitalizing on the technology’s ability to identify patterns that could signal potential risks. Human Resources functions use GenAI to enhance recruitment and talent acquisition processes, with resume screening and candidate matching emerging as top use cases. GenAI has revolutionized Legal processes such as document creation, review, and analysis.

Industry-Specific GenAI Adoption Trends

Looking at industry-specific trends, the report notes Financial Services and Pharma lead the way, together accounting for about half of GenAI adoption in horizontal functions. Financial Services firms deploy GenAI primarily for fraud detection and management, while pharma uses it for drug discovery and development. The retail, Manufacturing, Telecommunications, and Energy sectors are also showing significant adoption, with applications ranging from personalized marketing to predictive maintenance.

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Barriers and Challenges in GenAI Adoption

Despite the promising advancements, enterprises face substantial challenges in GenAI adoption, with 73% of enterprise leaders identifying the lack of clarity on success metrics as a top concern. Budget constraints, the rapidly evolving technology landscape, and data security and privacy concerns also rank high on the list of challenges. Additionally, CIOs are grappling with key risks such as data security, ownership and responsibility, interpretability, and bias. These issues highlight the need for a GenAI implementation approach that addresses the technological aspects and the ethical and governance challenges.

Corporate Governance and Future Outlook

In response to these challenges, organizations are beginning to establish corporate governance strategies  for GenAI, including internal and third-party AI auditing processes, to ensure safe, responsible implementation of GenAI use cases and tools. There is also a growing emphasis on AI provider accountability, with some enterprises seeking indemnity from AI vendors to mitigate legal risks associated with AI-generated intellectual property.

The GenAI landscape is poised for further evolution. As models become smaller and more efficient, and as AI compute moves to the edge, we can expect to see even greater personalization and automation in business processes. The rise of Agentic AI, which shifts the focus from knowledge-based assistance to action-oriented automation, will further enhance productivity and reshape the nature of work across functions.

The insights Everest Group gleaned from CalypsoAI research underscore the importance of grounding GenAI adoption strategies in real-world data. As enterprises navigate this rapidly changing landscape, they must balance the excitement of technological innovation with the practical realities of implementation. By continuing to refine the general understanding of GenAI through ongoing research and collaboration, CalypsoAI will ensure organizations have the ability to unlock the full potential of this transformative technology.

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