There is a tremendous amount of activity happening across the Enterprise AI landscape. We are fortunate to be partners with a few dozen portfolio companies building innovative products and solid businesses across this space. A few key themes that have been emerging lately are reflected broadly across our portfolio:

GenAI is not a silver bullet, but it’s a key piece that completes the picture: As it relates to automating complex enterprise workflows, today's Generative AI (Gen AI) stands as a promising yet potentially incomplete tool on its own. However, its true value emerges when integrated into a comprehensive intelligent enterprise software system. Such a system could include workflow and collaboration capabilities, data and machine learning (ML)-based components and an element of data moats. When overlayed with the right GenAI, such a system could close the loop and deliver outcomes of a significantly higher quality. As an example, Gen AI could be useful for generating personalized outreach emails or bullet points in a presentation slide. But such emails or slides may not hit the mark without the right context in place. A deeper ML-based system that connects to various data sources in the company and has been trained on relevant industry data may be much better at pinpointing the key messages that should go into each email or slide. When overlaid with GenAI, such a system could produce complete emails or slides that are much higher quality than what simple GenAI tools would produce.

When will Enterprise AI see the light of day? Well, it is already happening, and at meaningful scale. Enterprise AI products that solve real problems while being fully enterprise-grade are already getting deployed and delivering value, often without the corresponding hype. Offerings from several of our portfolio companies are outlined in the sections below serve as great examples. Full adoption of transformational technologies at scale is a complex phenomenon, and often happens over a long period. We are many years into the Cloud era, and only about a quarter of enterprise workloads run on the cloud today, yet this wave has already given rise to numerous players with hundreds of billions in combined annual revenues. Similarly, we believe, the full impact of the current generation of Enterprise AI will shape up over many years, and will be extensive.

Enterprise AI follows the same laws of gravity as enterprise software: The trajectory of Enterprise AI is often different from that of AI tools catering to consumers, prosumers, developers or other individuals. The latter set constitutes a large share of headlines today with its superlative growth and high valuations. Such tools can have rapid growth with strong viral loops and quick user adoption. But success for such tools can sometimes be “easy-come, easy-go”, if the audience base moves to greener pastures. On the other hand, Enterprise-focused products can take time to be adopted, whether they leverage AI or not. Enterprises are discerning, focused on real ROI, and have complex purchase and adoption processes. But once they adopt a product, the retention and expansion rates for such products can be significantly higher. The snowball often builds steadily, consistently, and over a longer period. And there is often a wider set of successful outcomes. Our portfolio leans towards enterprise-centric offerings, with data flywheels and applied AI as a core part of their respective moats.

Significant value from AI will be created in the application layer: In the first innings of GenAI, much of the attention and dollars have flowed into foundation models and GPUs. But that could change significantly. The foundation model layer is now teeming with many high-quality offerings with comparable capabilities and is potentially commoditizing. In previous platform shifts such as PC, Internet and Mobile, a large portion of value eventually accrued to applications (e.g. Google, Amazon, Facebook), software platforms (e.g. Windows, Cloud platforms, Salesforce), and full solutions (e.g. Apple, Tesla). Our belief since the early days has been that a significant part of the value from AI will accrue in the application layer, and the tooling and data infrastructure that enables enterprises to adopt, manage and secure such applications. We are investors in many companies building software to re-invent how work is done across a variety of business functions and industry verticals.

Service-as-software: B2B customers are likely to pay significantly more for outcomes than what they pay today for SaaS applications seen as enablers. While AI capabilities are expanding rapidly, today’s AI may not be sufficiently reliable for full automation of complex workflows. Our thesis has been that there is vast potential for AI + human models where AI does much of the heavy lifting and humans help close the loop and deliver outcomes reliably. Over time, as technology capabilities improve, AI can do an increasing part of the work. Over the past several years, we have partnered with companies delivering outcomes with an AI + human model in various areas, including security assessments, test certifications, and marketing campaign orchestration. Companies using this model have been able to drive high gross margins and scalability by leaning heavily on AI and automation. And this approach can work even better where companies are able to leverage high-quality offshore talent for the human element.

Abundance of opportunity, new pool of capital: We are seeing a slew of exciting opportunities across the board as Enterprise AI gets on its way to transform every business workflow and industry vertical. Emergent will soon begin investing from a new pool of capital. Stay tuned for more info on this!

As a refresher, Emergent leads pre-seed and seed rounds in capital-efficient Enterprise AI and Cloud Infrastructure startups with high-potential founders and helps them refine product-market fit and develop go-to-market. Please reach out if there are any high-quality founding teams you recommend that we speak with.

‍

Portfolio Fuels Up‍

Coverage of selected recent funding rounds and exits in the Emergent portfolio

Loft Labs announced $24 million in a Series A funding round led by Khosla Ventures. This investment will enable Loft to accelerate the development of its Kubernetes platform, focusing on improving multi-tenancy and cost efficiency for large-scale cloud-native deployments. Emergent initially invested in Loft at the early seed stage. The company grew 4.6X year over year and has several enterprise customers and strong traction with its open source projects. (Link)

UptimeAI raised $14 million in Series A financing led by WestBridge Capital. The funding will accelerate the company's expansion across North America, the Middle East, and Asia, enhancing its AI-driven operational excellence platform that optimizes industrial processes for increased reliability and efficiency. Emergent led UptimeAI’s seed round a year and a half back. The company has demonstrated rapid growth and deep customer value since then. (Link)

SageTap, a pioneering AI-driven marketplace for matching SaaS buyers and sellers, announced $6.8M in financing from NFX, Emergent Ventures, a number of its customers, and others. The company achieved 3X year-over-year revenue growth and is cash-flow positive. Emergent led Sagetap’s pre-seed round earlier. (Link)

Arcion Labs was acquired by Databricks. This was a strong exit in our Fund 1 and helped us to continue driving meaningful liquidity back to our fund investors. Arcion built a product with an innovative approach to real-time data replication, and Databricks expects to generate significant revenue by coupling this with their distribution capabilities and existing products. (Link) 

A few other financing rounds that closed recently are yet to be announced. While the VC financing market for larger rounds is highly discerning and nuanced at present, we are seeing that companies with solid fundamentals and strong metrics are able to raise capital and often have multiple high-quality options.

‍

Portfolio Features

Latest in the world of Enterprise AI, from the lens of Emergent portfolio entrepreneurs

Transforming contact center experience with GenAI: Observe.AI announced a slew of GenAI-powered enhancements to its Conversation Intelligence platform. The platform is designed to transform customer interactions by providing deep insights and actionable recommendations to contact center operators. These new enhancements leverage generative AI to analyze and understand customer conversations in real-time, enhancing agent performance and improving overall customer experience. Observe’s platform is powered by the only contact center-specific 40-billion-parameter LLM, wrapped in secure, supervisory layers and fine-tuned with over 100 years of contact center data, driving accuracy lifts of 35% for call summarization and 33% for sentiment detection compared with generic models (Link). Observe has been recognized as a ‘Strong Performer’ in The Forrester Wave for Real-Time Revenue Execution Platforms. 

AI that can help you communicate better: Prezent launched ASTRID, a new AI-powered communication assistant. Prezent’s products can help enterprises save 90% of employee time spent building and transforming presentations and reduce agency spending by 70%. ASTRID brings science and AI to storytelling. It helps business professionals create highly impactful slides and business communications quickly by integrating audience empathy, structured storylines, training and learning, relevance to context, industry insights, and on-brand design (Link). For its innovative contributions to AI, Prezent was named a winner of the 2024 Artificial Intelligence Excellence Awards, and recognized as a high performer by G2.

Towards better data observability: Acceldata launched an innovative AI copilot to enhance its data observability platform, designed to help DataOps teams monitor data pipelines for anomalies and streamline the creation of data policies and rules. This AI copilot aims to reduce manual configuration, improve data reliability, and control cloud costs by learning consumption patterns and automating error-prone tasks. (Link)

Optimizing marketing spend: Factors.AI launched Segment Insights, a feature designed to provide detailed analysis of segmented marketing data. This tool enables marketers to gain deeper insights into campaign performance, identify trends, and optimize strategies based on segmented audience behavior. (Link)

AI-native customer intelligence: Statisfy announced its new AI-native customer intelligence product. The platform aims to transform customer engagement by leveraging advanced AI to provide deeper, actionable insights for businesses. (Link)

Streamlining privacy compliance: Privado is utilizing GenAI to automate the creation of Records of Processing Activities (RoPA) reports, streamlining compliance with privacy regulations. This innovative approach reduces the manual effort required to document data processing activities, enhancing accuracy and efficiency in privacy management. (Link)

Improving observability across customer interactions: SupportLogic launched its Data Cloud on Snowflake to help businesses drive unified observability across customer interactions. Supportlogic’s platform helps businesses monitor and improve their post-sales processes, ensuring better customer support and satisfaction. For large businesses, customer signals and insights are typically spread across multiple systems of record, predominantly in unstructured data. SupportLogic connects disparate data sources, normalizes the data, extracts signals, and makes accurate predictions. SupportLogic was one of the earliest companies leveraging GenAI commercially in the enterprise and counts some of the world’s most innovative companies as its customers. (Link) 

‍