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Vertical AI – The future of generative AI

The specialization and hyper-customization in the use of generative AI is paving the way for the rise of a new type of AI: vertical AI. What does it mean and why should you care?
Daniel Zacharias

Code Power Team

October 23, 2023
Team working with vertical AI

Artificial intelligence may have been around since the 1950s but it’s only recently that it has really exploded onto the public scene. The advent of generative AI has created a commotion and sparked interest across multiple industries. What’s more, it’s already revolutionizing the ways we work and even go about our daily lives.

However, the technology is still in its embryonic phase. There’s a long way to go for it to become a reigning tech in our lives. Of course, platforms such as ChatGPT and MidJourney are already giving some impressive results. Crafting elaborate stories or intricate images is well within reach. Still, this is all a bit general-purpose, a ‘Jack of all trades, master of none’ sort of approach.

Many organizations and businesses are waiting to see what will happen next. Ideally, they’d like to see industry-specific solutions. Fortunately, it’s exactly that that’s lurking just around the riverbend of the modern business landscape. Another tech revolution is on the cusp of happening and we call it vertical AI.

The two types of generative AI

For those unfamiliar with the topic, generative AI is a type of artificial intelligence technology that can produce various types of content, including text, images, audio, and synthetic data, in response to prompts. This sort of artificial intelligence leverages deep learning models to create original, realistic outputs.

In order to achieve this, AI trains on large volumes of data, enabling it to capture patterns and correlations that it can later replicate and combine in new ways. This allows it to extrapolate from learned knowledge and create unique content. Ideally, the results are comprehensible to the human user. 

We can differentiate between the two subdivisions of generative AI:

  1. Horizontal AI. It refers to the development of AI models that can be applied across different industries or use cases. Horizontal AI models are use-case agnostic and can be used to develop any AI-based product. This AI is simpler to create and able to address broadly defined business functions. It can generate text, and images, or perform accounting or customer service-related tasks, among many other uses. 
  2. Vertical AI. It refers to the development of AI models designed to solve a specific problem or address a particular use case in a particular industry. Thus, vertical AI is an advanced option with a more narrow specialization. It’s highly specific, fine-tuned, and well worth elaborating on.

Defining vertical AI

Like I’ve mentioned above, this type of generative artificial intelligence system aims to solve particular challenges and address unique requirements within an industry with the help of tailored algorithms. It derives its narrow specialization from training on industry-specific, private data for increased effectiveness.

This approach promises personalized and intelligent solutions, improving both operational performance and user satisfaction. That’s because vertical AI has a laser-focused approach to the issues faced by that particular industry. So, instead of a generic, “one-size-fits-all” kind of method, vertical AI is all about specificity. 

Why specialize AI?

But wait, why can’t a large, comprehensive, horizontal AI model cover this ground? Surely, if it grows a bit more, horizontal AI models can deal with any task, right? Well, not quite. Horizontal AI has some downsides which the vertical approach could mitigate easily enough.

  • Better performance. General-purpose models have become too large and difficult to maintain. They’re also susceptible to error levels you simply wouldn’t tolerate, especially in highly-sensitive sectors like engineering or healthcare. It’s in those fields that vertical AI can truly shine, as it can offer fine-tuned outcomes with potentially less mistakes. 
  • Improved user experience (UX). Vertical AI offers functionality improvements. For example, an accounting model with local and national tax rules embedded into it will perform drastically better than a general-purpose platform trained on more general datasets. 
  • Precise and cost-efficient training. Vertical models won’t require you to feed them billions of parameters about virtually everything. They rely on more specific training datasets instead, which means that the models will be more precise while also requiring less curated datasets and less resources to process them. 
  • Higher data security. Lower resource requirements are easier to keep safe, even if run on a cloud-based infrastructure.

The use of AI in vertical SaaS

Speaking of which, there’s no denying that a lot of today’s tailor-made services are cloud-based. In fact, we could go so far as to say that vertical AI is simply the logical heir to vertical Software-as-a-Service (vSaaS).

After all, vertical solutions are deeply embedded in the end-user workflow. So, for example, if we’re talking about e-commerce, vendors are able to harvest data and observe behavioral patterns not available elsewhere. That presents a highly valuable opportunity for deploying vertical AI models. This can lead to better and faster services, and bring tangible value for both companies and customers.

A few examples

We’re at the cusp of something truly groundbreaking. Chances are high that the demand for vertical AI will skyrocket across several industries in the near future. 

There are few sectors in which this is more apparent than in the realm of law. Legal language models (LLMs) could be a true game changer. They are likely to automate tedious and repetitive work processes and substantially reduce the costs while maintaining (or even increasing) quality. 

There’s also healthcare, where vast amounts of data (medical research, patient info, etc.) could leverage AI to identify patterns and help in providing personalized treatments and care. This can facilitate the predictions of potential risks, lead to more accurate diagnoses, and improve overall outcomes.

Of course, we shouldn’t forget finance. Risk management, investment strategies, and fraud detection could reap the benefits of generative ( especially vertical) AI. The tech can help gauge out accurate and timely insights from complex data sets and allow managers to make the most informed decisions, anticipating trends and risks. 

The manufacturing sector, in turn, can leverage AI to optimize operations, supply chain management, and quality control — and remove bottlenecks.

How to make it happen

Since this is a fairly novel technology, chances are you’ll start your transformative journey from scratch. We recommend a few steps to incorporate a vertical AI strategy into your operations.

  1. Identify the verticals. Perform a thorough analysis of relevant use cases within your company. Cover internal operations and customer needs. Don’t neglect market trends. Ask yourself: Where could vertical AI add the greatest value? Do you wish to enhance decision-making or generate new opportunities for growth?
  1. Invest in internal expertise. To truly make the most out of vertical AI models, you’ll need professionals with the know-how to implement those models. Upskilling existing team members is always a safe (yet time-consuming) bet. If you don’t have the time to go for that, then you can always hire specialists, and consultants, or outsource the entire project.
  1. Consider pilot projects. Start small, within identified verticals. Test the feasibility, assess the challenges, and only then commit to implementation. Once you get the hang of vertical AI, you can move forward with a more ambitious plan. 

The main challenges

Since artificial intelligence came into the limelight, it’s been plagued by controversies and predictions of societal collapse. Most of these are gross exaggerations — but this doesn’t mean that implementation isn’t without its risks.

Perhaps the biggest issue is of an ethical nature. The main problem here is related to the model training itself. If you don’t properly curate datasets or if you work with incomplete ones, you can lead your AI model to become biased, favoring certain outcomes in detriment of other (more precise) ones.

That’s not all. Vertical AI models can be complex and difficult to interpret, which can make it difficult to understand how they are making decisions. This can lead to a lack of transparency and accountability, which can be problematic in areas such as healthcare and criminal justice. 

And that’s without mentioning the potential use of the data you feed into the model. If you aren’t careful, the AI might give you more than a headache when it comes to data privacy and security, especially in sectors like the healthcare and financial industries. 

Don’t ignore it

Just because vertical AI isn’t big yet doesn’t mean you should skip it altogether. Explore this exciting new frontier while it’s still in its inception. Such a significant breakthrough in law, healthcare, finance, manufacturing, and other industries can play a pivotal role in the years to come.

Those who overlook it are likely to face significant setbacks. We’re talking about rising operational costs, slower internal processes, and missed opportunities for automation.
Leveraging industry-specific knowledge through AI will soon become imperative for anyone looking to create an enduring enterprise. The trick is to start slow. Identify the relevant verticals, invest in both internal and external expertise, and consider pilot projects before making any big moves. The time is now.

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