understanding context is crucial in AI development and implementation
In the rapidly evolving world of artificial intelligence (AI), the goal is to eventually develop AI that seamlessly integrates with a user's context, transitioning from being an adopted technology to becoming invisible infrastructure, much like electricity or the internet today.
However, current AI tools, such as those used in the legal field, face challenges in understanding user context, leading to inefficiencies and limitations in unlocking real value and recurring revenue. For example, tasks like redlining contracts take longer with AI tools than for an experienced lawyer to do manually.
To address these issues, AI tools are now adopting advanced natural language processing (NLP) and contextual machine learning models to understand user intent and domain-specific nuances automatically. This transformation is crucial for securing recurring revenue in AI.
The conversion process involves several steps. First, **contextual understanding** allows AI models to infer the meaning of terms like "vibe revenue," which refers to a type or snapshot of recurring revenue or a particular revenue metric relevant to subscription or SaaS businesses.
Next, **intent recognition** detects the user's intent to convert an informal revenue measure into a normalized annual recurring number by assessing the full user query and related inputs.
**Data normalization and calculation** then extract numeric values or sales figures from the context and apply domain rules, such as multiplying monthly recurring revenue by 12 to get ARR. AI tools also adjust for seasonal or contextual factors if mentioned.
To help users, AI systems generate natural language summaries explaining how the conversion was done, often including clarifying questions or suggestions to refine inputs. Integration with CRM and analytics systems ensures that the data reflects ongoing contracts and churn rates, enhancing the accuracy of ARR estimation.
Platforms like Outreach.io and 6sense use AI-powered workflows and predictive analytics to understand deal stages, revenue activities, and customer engagement, providing sales and finance teams with actionable insights without manual calculations.
Despite these advancements, many professionals find AI prompting challenging, and companies that can create a 'contextual layer for AI' to make it easier to use will likely be the next wave of unicorns. There is also a concern among investors about "vibe revenue," which refers to revenue from AI tools that see initial growth but have high churn rates due to customers not renewing after trying them out. A significant portion of AI revenue is still considered "vibe revenue" rather than "recurring revenue."
The future of AI lies in handling specific nuanced context, a nascent field that is expected to be the next big wave in AI investment and product development. Building verticalized AI agents that focus on specific domains is a hot area of focus for venture capital. An example of this is a loan underwriting AI agent that incorporates a contextual layer, capturing human underwriter's working knowledge like client emails, Slack conversations, calendar patterns, and decision history.
In summary, the future of AI is about more than just processing documents; it's about understanding the underwriter's judgment calls, learning from past patterns, and human decision-making. The key to unlocking the full potential of AI lies in solving contextual challenges and making AI tools more user-friendly.
- The transformative advancements in AI tools are now focusing on incorporating contextual understanding, intent recognition, and data normalization to provide a seamless user experience, similar to how electricity and the internet are integrated into our daily lives.
- To make AI tools more accessible and valuable for businesses, the development of a 'contextual layer for AI' is crucial, enabling users to effortlessly interact with these tools without encountering obstacles, ultimately reducing 'vibe revenue' and increasing recurring revenue.
