The next phase of AI value creation will be driven by narrow, deeply embedded systems that understand how specific industries actually operate. This is why I see vertical AI emerging as one of the most investable niches as we’re heading into 2026.
Why I’m cautious about horizontal AI and model companies
Vertical AI solutions are designed to solve clearly defined problems within specific industries. Horizontal AI, on the other hand, refers to general-purpose solutions that are not tailored to the needs of a particular industry. They can be powerful, but as foundational models continue to evolve, many risk becoming obsolete. We have already seen this play out. Prompt engineering tools, for example, went extinct when models improved; users could simply ask the system to generate a better prompt on its own.
On paper, companies that develop AI models are an obvious target for investment. In practice, there are two major challenges.
First, funding in this space is highly concentrated, both geographically and at the company level. In 2025, approximately 60% of all AI capital was invested in major AI labs. Of that funding, around 90% went into mega-rounds clustered around a small group of players (OpenAI, Anthropic, xAI) and primarily within the US. Second, access to this market is limited. For most VC firms, this is not a repeatable or diversified investment strategy.
Unlike horizontal AI applications, which often suffer from weak defensibility, or foundational model companies, which are defined by extreme concentration and high barriers to entry, vertical AI operates in a far healthier investment landscape. Capital is distributed across industries and geographies. And, most importantly, strong moats emerge naturally.
The most investable niches in vertical AI in 2026
Legal vertical AI
Lawyers spend 40–60% of their time on manual document-related tasks, and much of this work is repetitive and of low value, making it an ideal environment for AI-driven automation and decision support. Also, 30% of lawyers already use AI in some form in their workflow, which is unusually high for a traditionally conservative profession. Legal tech is already a multibillion-dollar segment, with revenue at $29.7 billion in 2023, growing at around 5% year over year.
1. Litigation AI
Litigation is a document-heavy and time-sensitive process. Vertical AI litigation platforms focus on tasks like discovery, case strategy, evidence analysis, and procedural forecasting. One example of a startup within the litigation-specific niche is Wexler AI, a litigation analysis and preparation company based in the UK, which closed a $5.3M seed round in 2025.
2. Intellectual Property AI
IP is another vertical where specialization matters. Patent analysis, prior art searches, and infringement assessment are highly technical and data-intensive, making them well-suited for AI. In the UK, Ankar AI raised a £3M seed round in 2025, focusing on AI-driven patent intelligence. Sweden-based Lightbringer also raised €4.2M in 2024.
3. SMB and retail legal AI
Unlike large enterprises, SMBs cannot afford large legal teams but still face recurring legal needs, from contracts and employment law to compliance and disputes. Vertical AI platforms targeting this segment focus on accessibility, cost efficiency, and standardized workflows. Italian Lexroom raised $19M in Series A, while in Germany, Jupus secured a €6.5M seed round.
Finance vertical AI
At its core, financial processes are characterized by repetitive, rule-based workflows. These functions are highly structured, compliance-driven, and time-intensive, making them ideal candidates for vertical AI automation.
1. Bookkeeping for SMBs
Many SMBs find traditional accounting software too complex and poorly aligned with their needs. While 71% of SMBs use some form of accounting software, a significant portion still relies on spreadsheets, pen and paper, or fragmented tools for key financial tasks. Recent funding rounds prove investor interest in this niche. Recent deals include Quanta raising $15M and Bluebook securing €2.4M to automate accounting workflows with AI.
2. Audit
Both external and internal audits rely on repetitive evidence gathering, control testing, document review, and report drafting. These are precisely the tasks where vertical AI can deliver immediate value. One example from the Roosh Ventures portfolio is Kobalt Labs, which raised $11M in Series A in December 2025. Kobalt replaces manual document review and assessment processes with intelligent, audit-ready automation.
Vertical AI in procurement
Procurement is one of the least glamorous areas of the enterprise and one of the most broken. It sits between strategy and execution, touching finance, sales, and supply chain. And yet, in most small and mid-sized companies, it is still run on spreadsheets, email threads, and manual approvals.
1. Procurement planning for SMBs
Large enterprises rely on heavy ERP systems such as SAP and Oracle, which are powerful but expensive, complex, and slow to adapt. SMBs and mid-market companies, by contrast, are largely left behind.
Vertical AI presents a clear opportunity for intelligent procurement planning tools built for SMBs. Recent funding activity signals growing investor interest in this niche. Procure.ai raised $13M to build AI-native procurement planning for modern businesses. In Switzerland, Scalera.ai raised €5.7M to create an AI assistant focused on procurement for construction and industrial projects.
Vertical AI is not a long-term inevitability; it is a near-term opportunity. The next 24–36 months will define category leaders as incumbents move too slowly and horizontal tools fail to adapt to real operational constraints.
Once vertical systems become embedded into legal, financial, and procurement workflows, switching costs will rise quickly, and markets will consolidate. The window to back early category leaders is narrow. For investors willing to go deep rather than broad, vertical AI offers one of the few remaining areas where conviction, not capital scale, still determines outcomes.
