Threshold Ventures’ Lisa Xu: AI and the Next Generation of Vertical Software
Contents
ABSTRACT
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The previous generation of vertical market software platforms were built to provide a system of record and streamline human tasks and activities. Today, AI and LLMs are enabling a new era of solutions that are able to perform tasks autonomously, thereby alleviating the workload of overstretched staff and helping businesses to accelerate growth, save on costs, and streamline operations. Lisa Xu, Partner at Threshold Ventures, explores how software solutions can tap into the huge market opportunity presented by these often overlooked businesses. As companies navigate shifting customer expectations and labor challenges, AI-native solutions are becoming essential for improving productivity, generating new business, and future-proofing business operations.
KEY POINTS FROM LISA XU’S POV:
Why is AI-native vertical software such an important category moving forward?
Businesses across industries are struggling to find qualified employees, while labor costs are putting immense pressure on already-thin margins. With their workforce under strain, companies are struggling to meet the rising demands of today’s customers. In addition, many industries are facing retirement surges that will put generational transfer of businesses at great risk. In these conditions, tools that not only improve employee productivity, but also help bridge the labor gap (e.g., with AI-driven training for the next generation of workers) will be embraced.
The pandemic fast-tracked the digital transformation of traditional businesses and their readiness for implementing AI-native solutions. During COVID lockdowns, traditional businesses had to rapidly pivot to e-commerce models, adopt digital marketing tactics, and transition to remote work. With companies now reaping benefits including reduced operational costs and access to wider markets, this has precipitated a permanent evolution in how these businesses operate.
Many incumbents will lag behind in delivering AI-native solutions. Due to limitations from legacy architecture or internal access to AI expertise, software incumbents across various industries may be slow to deliver AI capabilities, while AI-native entrants can quickly build solutions to capture new market opportunities and benefit from the ability to seize new data sets as they emerge.
What are some of the business models and use cases that might be attached to this category?
New solutions can become systems of action, with an AI-powered approach acting as both a wedge and a defensive moat. “LLM-powered, AI-native solutions can deliver 10x the measurable impact of the previous generation of software. Not only will they improve the customer experience and employee performance, but once integrated, they can generate proprietary data sets that can be harnessed to uncover new opportunities to create value and expand the bottom line for customers,” says Xu. Products like lead intake systems or automated scheduling tools offer quick wins with immediate ROI, helping businesses see the value of AI from the outset.
“LLM-powered, AI-native solutions can deliver 10x the measurable impact of the previous generation of software. Not only will they improve the customer experience and employee performance, but once integrated, they can generate proprietary data sets that can be harnessed to uncover new opportunities to create value and expand the bottom line for customers."
Lisa Xu~quoteblock
AI-native vertical software isn’t just a productivity tool for employees - solutions are emerging that actually perform tasks previously performed by humans (i.e., “service as software”), resulting in clear cost reduction and revenue lift for customers:
Numa’sAI allows car dealerships to meet demand and strengthen relationships with customers while reducing staff workload. It gives customers real-time updates, automatically books appointments, and uses voice technology to answer inbound calls, delivering 100x better customer responsiveness and incremental appointments and revenue.
Parspec’s Configure, Price, Quote (CPQ) platform is designed to streamline the entire procurement process for the construction industry using LLM-based document summarization. Parspec’s AI platform has enabled its customers to submit more bids, increase win rates, and ultimately increase profitability.
FleetWorks acts as an AI-powered sales agent in the fleet industry, handling all of a brokerage’s inbound and outbound communication channels. FleetWorks’s platform uses voice synthesis and generative AI to automate high value work and ultimately save carrier reps at least 4 hours per day.
Inscribe’sAI Fraud Analyst automates manual fraud reviews for fintechs, banks, and lenders. Risk teams spend less time on in-depth investigations and more time converting trustworthy customers, leading to $85K+ additional productivity per full-time equivalent (FTE).
Slang’s restaurant-centric voice AI and digital phone concierge allows restaurants to answer questions, take reservations, and satisfy callers 100% of the time. Restaurants can onboard onto the tool within minutes, and restaurants using Slang report an increase in reservations and ROI.
New players in this space can leverage clear and viable strategies to expand their TAM. AI-native startups have clear opportunities to increase revenue per customer by launching multiple products and services that address specific pain points. These companies can strategically layer additional revenue streams on top of their core offerings to expand ACV and capture market share from horizontal competitors. While the previous generation of vertical platforms like Toast, ServiceTitan, Procore, and Appfolio scaled primarily on SaaS + FinTech business models (e.g., embedded payments, payroll processing, etc.), the new generation of vertical platforms are delivering value in a completely different way. Xu believes this will unlock completely new ways of capturing value, such as replacing labor budgets, moving away from traditional seat-based SaaS.
What are some of the potential roadblocks?
The vertical software market has become increasingly saturated, with many existing solutions targeting core operational workflows across industries. The success of the new generation of vertical software startups will depend on how well they can craft differentiated value propositions that resonate with customers—focusing on unique pain points that traditional horizontal platforms overlook.
SMBs demand immediate, tangible value from new technologies. Unlike many enterprise buyers, traditional SMBs operate on thin margins with limited resources and cannot afford long implementation cycles or solutions that take time to demonstrate ROI. Software vendors targeting these customers must prioritize frictionless onboarding processes (leveraging AI to deliver this where possible), ensuring fast time-to-value to drive adoption.
Building proprietary data moats will be critical for AI-native solutions to achieve long-term differentiation and defensibility. As these platforms automate workflows and collect high-value business data—ranging from customer preferences to operational insights—they create a virtuous cycle of continuous improvement. However, scaling a proprietary data moat requires sustained customer engagement and consistent usage of the platform’s features.
IN THE INVESTOR’S OWN WORDS
LISA XU
"Vertical software startups have seen success by customizing the UX/UI of their product to fit the unique needs of their industry’s customer; offering unified data models that enhance visibility across the business and enable more informed, profitable decision-making; and implementing targeted go-to-market strategies with a clear focus on improving operational efficiency.
Now, this next generation of startups are eliminating costs and unlocking revenue opportunities for customers from day one. But most excitingly, over time, their ownership of foundational data will shine a light on opportunities for product expansion, putting these startups head and shoulders above legacy incumbents."
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