Vertical AI
Vertical AI
What is vertical AI?
- AI services built for a specific industry or job function
- The moat is domain data and workflow, not the model
- Low setup effort and easy-to-measure results
What vertical AI really changes is the question asked in the adoption meeting. The unit of discussion shifts from “which model is smarter?” to “who is accountable for this one task from start to finish?” General-purpose AI hands over a draft and leaves verification, formatting, and entry into core systems to people. Vertical AI is sold with that follow-up work built into the product, so what you buy is no longer a tool but a finished work product. The impact metrics naturally become volume processed, reply turnaround time, and rework rate.
Vertical AI splits into three layers by depth. Knowledge-based products simply add domain documents and specialist terminology on top of a model. Workflow-based products redesign the entire business process, from intake through approval. Embedded products plug into industry core systems such as EMR, TMS, and ERP, reading and writing data directly. The further down the list, the bigger the impact, but switching costs and vendor lock-in grow too. A chatbot that just adds RAG over internal documents doesn’t qualify here; it is more accurately classed as an internal AI tool. More vendors now offer per-transaction or per-success pricing instead of a monthly per-seat fee, so estimate your monthly volume before signing.
Judge adoption on two axes: repetition volume and exception rate. Start with work where the same type of task repeats several hundred times a month and exceptions account for 20% or less. By contrast, for work where exceptions exceed half of cases, constant human review actually increases total effort. To find out which side your work falls on, don’t rely on gut feeling; count the last 3 months of processing logs by type.
The most common point of failure in the field is the expectation that “it’s built for our industry, so it must know our company’s work too.” What vertical AI has learned is the industry average, not your company’s forms, abbreviations, or approval chains, so expect 2–3 months of tuning on your own data at the start. Imported products add another gap: they may not reflect Korean legal amendments, tax rules, or local forms. If you use an embedded product and omit a data-export clause on termination, the correction history and labeling assets your staff built up over two years will stay with the vendor.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Seolyeong ParkGenerative AI and AX training specialist with 17 years of hands-on experience and 200+ lectures, helping non-technical staff put AI to work right awayThe term comes from “vertical market,” meaning a specific industry market, and carries over the “vertical SaaS” category from the SaaS industry into AI. Since 2023, amid the generative AI boom, it has become established investment and industry shorthand for the wave of industry-specific AI startups that stand apart from general-purpose chatbots.
Consider a hypothetical case. Company A makes auto parts, with 520 employees and annual revenue of KRW 180 billion (≈ US$134,670,100). Its two in-house lawyers review 2,400 contracts a year, yet even for urgent requests from the purchasing team, replies took 5 business days on average. When production schedules slipped because of contracts every quarter, management asked for alternatives.
- The head of legal counted the review-queue logs from the past 3 months by type. Of all contracts, 68% were revised versions of standard purchase contracts with only a few clauses changed, and the head reported, “Lawyers shouldn’t have to review that 80% from scratch.”
- We first piloted a global legal-specialized AI for two months, but it failed to flag the payment due date clause under the Subcontracting Act at all. The assistant manager doing the reviews concluded, "It's useless in practice except for English NDAs."
- They brought in two Korean contract-review vendors and ran a blind test, feeding in 50 of the company’s own contracts that had already been reviewed. They then compared risky-clause detection rates and false positives clause by clause.
- The procurement lead went into contract negotiations and added two sentences to the agreement: on termination, the original review history is exported in Excel and PDF, and the company's data is not used to train the vendor's models. Integration was narrowed to a single existing system, the electronic approval tool.
- Six months after adoption, reply time on standard contracts fell from 5 business days to 1.2, and the two lawyers’ time on M&A and dispute matters rose from 6 to 19 hours a week.