As businesses delve deeper into machine learning and artificial intelligence, they are actively seeking practical use cases for these transformative technologies. As with any tech evolution, early adopters start by working with the foundational layers of the stack. Over time, however, software vendors emerge, offering solutions that address common challenges and simplify adoption.
Major software companies are now bundling Generative AI (Gen AI) solutions into their ecosystems to solve enterprise-level challenges. Examples include Microsoft’s Copilot, Adobe’s Firefly, Salesforce’s Einstein GPT, and ServiceNow’s Now Assist. These offerings abstract away complex, lower-level issues, enabling enterprises to focus on outcomes rather than infrastructure.
One critical hurdle in enterprise adoption of large language models (LLMs) or foundation models is the issue of "hallucinations." This refers to AI generating confident but inaccurate or entirely fabricated responses. For decision-makers, deploying AI systems prone to errors or that provide unverified information is not an option, especially when considering the potential for embedded biases in training data.
To mitigate this, we anticipate software companies introducing highly refined AI features (some marketed explicitly as AI, others seamlessly integrated) designed to address user pain points with precision and reliability (i.e. RAG - Retrieval- Augmented Generation). Much like the early internet era or the rise of smartphones, businesses that fail to embrace AI-enhanced functionality risk losing relevance in a rapidly evolving landscape.
We believe these advancements in generative AI and machine learning will significantly enhance productivity, especially in high GDP per capita economies, driving innovation and operational efficiency across industries.
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