A recent survey of large enterprises reveals a significant shift towards in-house application development, driven by the rise of foundation models offering accessible APIs. This move away from reliance on external vendors for AI-driven solutions has major implications for the industry. For instance, companies that once relied on third-party chatbots and custom recommenders can now develop these tools in-house, potentially saving costs and increasing customization. As a result, teams and investors at the forefront of GenAI and LLM innovations must adapt to this changing landscape to remain competitive.
According to the U.S. Census Bureau, AI adoption among U.S. businesses is rapidly growing:
- Usage rates are expected to nearly double from 3.7% in Fall 2023 to 6.6% by Fall 2024, according to the U.S. Census Bureau.
- The employment-weighted use rate suggests a broader impact on the workforce.
- Larger and younger firms are leading the way in AI adoption across various industries and states.
To understand the current state of GenAI and LLM usage, I delved into recent U.S. online job postings related to these technologies. The analysis revealed a diverse range of applications, spanning from content creation and business operations to communication, education, and even ethical considerations.
Technology and development applications dominate the landscape, showcasing the immense potential and practical utility of LLMs in streamlining and enhancing a wide array of software development processes and system-related tasks. Business and marketing applications follow closely behind, demonstrating the growing recognition of GenAI’s potential to revolutionize marketing strategies and customer experiences.
Notably, while customer-facing applications like chatbots and content creation are gaining traction, many companies are initially focusing on internal use cases such as code generation, data analysis, and knowledge management. This suggests that enterprises are still navigating the challenges of deploying GenAI in sensitive external-facing scenarios, opting to first leverage the technology to streamline internal processes and improve efficiency.
A recent analysis of job postings reveals the varying degrees of GenAI adoption across different industries and companies. To further understand the state of GenAI adoption, I examined job postings from two contrasting examples: TikTok and KPMG US. As a consumer-facing social media giant, TikTok is heavily investing in GenAI for content creation, moderation, and personalization. From generating ad creatives to developing intelligent recommendation systems, TikTok is pushing the boundaries of what’s possible with GenAI. The company’s job postings reflect this focus, with numerous positions related to machine learning, natural language processing, and computer vision.
On the other hand, KPMG US, a professional services firm, is taking a more measured approach, focusing on integrating GenAI into its audit, tax, and advisory offerings. KPMG’s use cases revolve around enhancing automation, improving decision-making, and ensuring ethical AI usage. The firm’s job postings emphasize the importance of AI governance, explainable AI, and the integration of GenAI with traditional business processes. These examples showcase the diverse ways in which companies are adopting and leveraging GenAI technology.
Recommendations for Enterprises Adopting GenAI and LLMs
The current landscape of GenAI and LLM adoption reveals several key trends. Enterprises are primarily focusing on in-house development, citing the lack of mature, market-ready solutions and the increased accessibility of foundation models through APIs. Internal use cases, such as code generation and text summarization, are leading the way, as concerns surrounding hallucination, safety, and public perception drive a focus on applications where risks can be more easily managed. Successfully deploying these technologies requires close collaboration between diverse teams, including engineers, product managers, UX designers, and domain experts. Additionally, addressing bias and ensuring responsible AI usage is crucial for building trust and mitigating potential risks.
For teams building GenAI and LLM applications, there are several key takeaways to consider. First, focus on solving real problems by identifying specific pain points within your organization or industry that can be addressed with these technologies. Second, prioritize user experience to ensure your applications are user-friendly and accessible to a wide range of users. Third, invest in talent and infrastructure, as building and deploying these technologies requires skilled personnel and robust computing resources. Finally, embrace ethical AI principles by mitigating bias, ensuring fairness, and being transparent about the limitations of your models.
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