Building a custom AI model for your business? Leave it to the experts

Customisation bars for business AI model

Lately, there’s been a lot of noise in the technology space about how easy it is to build your own AI model. And, while it’s true that platforms like ChatGPT have made it possible to whip up a basic model (think of it like a personal assistant) in just a few minutes, there's a world of difference between piecing together a simple model and developing a sophisticated, scalable AI machine learning model suitable for business needs. Our Head of AI Research and Development, Sam Ward, is here to peel back the layers on the complexity of AI model development, shedding light on why using a pre-built model is often the smarter move.

So, what’s an AI model, anyway?

First off, let’s begin by unpacking what an AI model actually is. In the simplest terms, an AI model is a mathematical way to predict or decide something based on data. Think of an AI model like a really smart cookie that learns from loads of examples of data to make predictions or decisions. These models can range from simple decision trees to complex neural networks, all designed to do a range of tasks ranging from facial recognition to understanding what you’re saying.

In the last year and a half, the way we talk about AI models has evolved. With advancements in tech, we're now seeing models that can be fine-tuned to do specific tasks like payment reconciliation or invoice matching with just a bit of extra training. It’s a bit like how you might adjust a pair of new headphones to better fit your ears.

The challenges of building your own AI model

Image displaying challenges of building your own AI Model:  Data and overfitting Skills Bias and regulation hurdles Costs

Building an AI model for your business isn't just about having the right tools. It’s like baking; having a kitchen doesn't instantly make you Delia Smith. Let’s walk through why.

Data and overfitting

At the heart of any AI model is data. Not just any data, but high-quality, well-organized, and meticulously labeled data that the AI can learn from. The need for this kind of data becomes even more integral when dealing with narrow fit data: information that's highly specialized and not broadly applicable. Collecting, organising, and labelling this data to prevent overfitting - a scenario where the model performs well on training data but poorly on new, unseen data - can be an immense undertaking.


There’s also the fact that implementing AI isn’t a one-person job. It requires a team of specialists: data scientists to parse and prepare the data, machine learning engineers to build and refine the models, and domain experts to ensure the AI's outputs are relevant and accurate. Plus, tweaking and tailoring these AI models to work just right for your business (a critical step in adapting them to fit unique business requirements) is an iterative and time consuming process.

Bias and regulation hurdles

Then there's bias and regulations. AI can unintentionally learn biases present in the data it's trained on, leading to unfair or unethical outcomes. For example, if your model has only been trained on American data, it may be biased towards other countries. And depending on your industry, you might face strict regulations around data privacy and security - for example, the EU AI Act which will come into effect in 2025 is a hefty piece of legislation with many rules and caveats. Implementing safeguards and processes to address such concerns is non-negotiable.


When you weigh up all these factors together, creating your own custom model translates into significant costs. Beyond the financial investment, there's the opportunity cost of time and resources diverted from other potential investments. Before you know it, your custom DIY AI project will cost so much more than investing in a ready-to-use solution.

Getting your AI ducks in a row

Before you can make a decision on AI solutions, you first need to figure out exactly what you want AI to do for your business. A technology base layer such as workflow orchestration enables you to organise work, see all your processes in one place and identify use cases where automation can help you become leaner and more efficient.

Once you’ve got the fundamentals in place and have an idea on specific use cases, consider bringing in a Chief AI Officer to lead the charge, making sure your AI strategy aligns with your business goals and employees are on board, too.

And, when it comes to the AI itself, look into off-the-shelf solutions. There are plenty out there, designed to fit a variety of needs, without the hassle and cost of building something from scratch. Products like EnateAI, for instance, offer a ready-made solution for traditionally manual service tasks such as email triage, sentiment analysis, intelligent document processing and data analysis.

Why a ready-made AI model might be best

We’ve been trying to make sense of data for centuries, from cave paintings to the vast digital landscapes of the internet. Our journey has taken us from focusing on narrow, specialized data sets to embracing the power of General AI, which can understand and process information in a more holistic, human-like way.

This shift towards General AI, like what you see in large language models (LLMs), has been a game-changer. These models don’t need to be custom-built for each task; they’re trained on a wide range of data (hello, internet) and can handle a variety of tasks right out of the box.

Sure, there are some exceptions. For example, if your business deals with very specific, non-textual data then you might need something tailored. But for the vast majority of business needs, especially when dealing with text and standard data types, an off-the-shelf General AI model can offer incredible insights and efficiency, without the need for a bespoke solution.

The exception to the rule: highly-specialized data types

With all this said, there are certain scenarios where building your own AI might be necessary. Industries dealing with highly specialized data types, far removed from the realm of general language understanding tend to be the exception.

Take, for instance, the Oil and Gas industry - a sector I've had personal experience with. Companies in this field might seek AI solutions to identify smaller yet rich oil pockets, favouring agility and precision over broad, large-scale extraction methods. The data involved here is not textual but consists of geological, seismic, and dense satellite imagery. Large Language Models (LLMs), as advanced as they are in understanding and generating human language, fall short when it comes to interpreting these specific data types.

In conclusion

Building your own AI model from scratch might sound like a viable project, but when it comes to business applications, especially in areas like service operations, it's essential to weigh the practicalities against the allure of customization. The road to developing a bespoke AI model is paved with challenges: from the intensive data collection and labeling required, to assembling a team of experts who can navigate the intricate dance of machine learning, to ensuring your AI remains unbiased and compliant with ever-tightening regulations. These hurdles can translate into significant costs and resources, potentially diverting focus from your core business objectives.

Sam Ward, as the Head of Research and Development at Enate, leads the forefront of AI innovation in orchestrating human and digital workforces. With over a decade of experience in data science and artificial intelligence, Sam drives the development of Enate's platform, focusing on enhancing business processes, customer experience, and overall growth through AI and intelligent automation.
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Sam Ward