Take a quick browse through LinkedIn and you’re likely to see countless AI influencers promoting the ease and benefit of creating an AI model, making it sound like a doddle to do. AI advancements like ChatGPT have indeed democratized the creation of basic AI models with attributes similar to personal assistants, and it’s also true that they can now be created in a short space of time. However, there’s a big difference between a rudimentary model designed for personal use and an advanced, scalable AI model designed for business needs.
In this article, we’ll look at the complexities of developing an AI model for business, and why opting for an off-the-shelf model often makes more sense.
Understanding AI models
Imagine an AI model as a savvy friend who excels at making decisions or predictions based on the data they’ve absorbed. This friend could be as simple as a decision tree or as complex as a neural network. After all, the AI world spans a wide range of tasks, from facial recognition to processing spoken language.
In the past 18 months, we’ve seen huge advancements in the capabilities of AI and the models we’re seeing today can be customised for specific tasks such as matching payments to invoices with just a bit of fine-tuning. This customisation allows AI models to not only perform standard functions but also meet unique business needs effectively.
It’s about more than just having the right tools
Having the right technological tools might seem like enough to build an AI model, but the reality is quite different. Crafting a functional and efficient AI system requires a deep understanding of both the technology and the specific business context in which it will operate.
The importance of high-quality data
The backbone of any AI model is the data it uses. The quality, organisation, and labelling of this data are paramount. AI models thrive on high-quality data that is well-structured and meticulously tagged. This is particularly critical when dealing with specialised data sets tailored for specific business needs.
The task of organising and preparing data to avoid issues like overfitting, where a model performs well on training data but poorly on new, unseen data, is both crucial and challenging. Techniques such as regularisation, cross-validation, and diverse training data sets are employed to combat overfitting and ensure the model is robust enough to handle the job.
Collaboration is everything
Developing AI is not a one-person job. It requires a collaborative effort. A team of data scientists, engineers, and business analysts must work together to prepare the data, develop the model, and continuously refine it to ensure it meets the business needs. This collaboration extends beyond the initial development phase, as ongoing adjustments and improvements are often necessary to keep the model effective in a changing business environment. For this reason alone, the cost and time involved in building a custom model often leads businesses to choose a ready-made solution that they can buy off the shelf instead.
Navigating bias and regulatory compliance
Another significant aspect of custom AI development is addressing potential biases and adhering to stringent regulatory standards. AI systems can inadvertently learn biases from their training data, which can lead to unfair or unethical outcomes. Regulatory frameworks like the EU AI Act set to take effect in 2025 impose strict guidelines on AI deployment, focusing on privacy, security, and ethical considerations.
Weighing up the costs
As I mentioned earlier, the cost of developing a custom AI model is not limited to financial expenditure. It includes significant investments of time and resources, which could otherwise be directed towards other strategic areas. Companies must consider whether the benefits of a custom AI model outweigh these costs or if a pre-configured solution might meet their needs just as effectively.
Defining business objectives
Before integrating AI technologies, businesses must clearly define what they aim to achieve with AI. Workflow orchestration lays the groundwork by providing a clear view of business processes and helps you to pinpoint exactly where AI can add the most value by automating tasks and improving efficiency.
In cases where AI implementation is deemed beneficial, appointing a dedicated Chief AI Officer can help ensure that the AI strategy aligns seamlessly with business goals and that the entire team is prepared for and supportive of the integration.
The evolution of data usage
Our approach to data has evolved dramatically over the centuries, from primitive cave paintings to sophisticated digital data analysis. Today, AI offers us the capability to interpret vast amounts of data in ways that mimic human cognitive processes.
We’ve gone from narrow-field AI to General AI (seen in large language models like those powering some of the most advanced AI platforms) and it’s particularly transformative. These models are trained on diverse datasets and can perform a broad range of tasks immediately upon deployment.
The exception to the rule: Highly specialised data types
Even with all the advancements in AI, there are still times when a pre-built model just doesn’t cut it. This is especially true in industries that work with really specific kinds of data that your average AI just isn’t built to handle. Take the Oil and Gas industry, for instance. This is an area I’ve got some real-world experience in. Companies in this sector often need AI to help them find smaller, yet valuable oil pockets. They’re not looking for a one-sise-fits-all solution but rather a more tailored approach that prioritises precision and quick responses over more generic, large-scale methods. The kind of data they use isn’t your typical text stuff; we’re talking about complex geological and seismic data, along with detailed satellite images. Big language models, as smart as they are with words, just aren’t up to the task of making sense of this kind of information.
Deciding between custom and ready-made AI solutions
While building a custom AI model from scratch may seem like an exciting project, it’s essential to weigh up the practicalities and pros and cons. The journey involves extensive data collection, rigorous model training, and the assembly of a skilled team to handle the complexities of AI development. It’s also crucial to ensure that the AI model adheres to all applicable laws and regulations while remaining free from bias. For these reasons, in most cases, I would recommend choosing an off-the-shelf model, but recognise there are instances where a custom model may be preferable.
When considering the best AI action plan for your business, remember the primary goal: to streamline operations, speed up work, and free up resources so you can spend less time on grunt work and more time on the bigger picture. Whether to develop a custom model or utilise a pre-built solution should be based on a thorough understanding of your business needs and the potential return on investment.
