Factors That Can Drive AI Implementation Costs Up or Down

In a previous article, we discussed what goes into building out artificial intelligence models to help give businesses a clearer idea of what goes into their cost.

 

While there is no one-size-fits-all fee for AI implementation, we estimated that it could cost businesses anywhere from R100 000 – R650 000 to launch new AI models. Those figures assume that businesses partner with AI experts and follow a three-step process of analysis, validation and production. 

 

However, these figures are an estimate because every case will be different: Every business has unique objectives, and this affects what they will need to get out of AI technology.

 

Because its implementation can be complex, many factors could influence the cost – beyond the foundational fees that businesses can expect. Here are what a few of these factors look like and why they drive implementation costs up or down. 

Training models once or training them continuously. 

There are two options you can take when building out models depending on your requirements. Each approach will impact cost differently. 

 

If you’re looking to build a “once-off” model where data is collected and processed manually with the goal being to train a model and push it to production, your upfront costs will be lower. However, because this system has been built with no consideration for training or updating, every time an update needs to be made, you will incur another cost. 

 

On the other hand, if the requirement is to stream data to the system continuously, and constantly fine-tune and update the model on new data, a lot more thought needs to be put into building and implementing data pipelines. This scoping, as well as the actual implementation of the data pipelines, will drive the price up. 

 

Although the second option is more costly, we’ve seen that dynamic systems that can easily adjust to change have a huge advantage over static systems that have only been trained once. The COVID-19 situation wreaked havoc with static models for a lot of companies, as they completely failed to adjust to the unforeseen events in their environment.

Integrating AI tooling with existing workflows

The cost of integrating AI into your existing workflows will vary depending on how complex the integration processes are, security needs and the number of touchpoints

 

While there is a lot to consider here, committing to a solid integration plan will help ensure a seamless merging of current and new systems and workflows. Standalone AI applications might be cheaper, but they will be noticeably different and getting used to how they will be disruptive and time-consuming, which could cost you money in other ways. 

Training and re-skilling employees

On top of processes changing, introducing AI into your business will also mean that your team members will have to adapt to new ways of working. Their responsibilities will likely change over time, and new opportunities may open up. 

 

To make sure that your systems are being used to their full potential, you will have to train your team to work with the new AI tooling effectively. If the systems require new workflows to be set up, you will have to train your employees on how to adapt to this. In other cases where the new technology makes roles redundant, you will have to consider reskilling individuals to fill different roles. This will take time and come with a cost, but it’s a necessary investment to keep the technology running and expand its potential within your business. 

The quality and availability of data

What your company’s data currently looks like can have a big impact on how much it’ll cost to get up and running with AI. 

 

Because many older databases haven’t been set up to integrate with technology like AI, data is often messy and scattered across many different places. This leads to the Extract Transform Load (or ETL) process taking a long time to clean and organise the data so that the AI tooling can be integrated effectively. 

 

In other cases, there is not enough data available in the current system to train the models, meaning that it will have to be collected or bought from other sources. This process of collecting and annotating data can be time-consuming and expensive, which is why most AI teams will advise you to start with projects that have enough data available.

The complexity of the data and the model required to work with it

Data is also important for determining what machine learning models can be used and how expensive building them out will be. The more complex the data, the more complex the model, will be to train, and the more complex the model, the more data is needed. 

 

For example, deep learning models are incredibly good at modelling complex data, but they also require a lot of resources for training and a lot of data. This either drives spending on cloud infrastructure up or creates the need for onsite hardware with attached GPUs. 

 

If you don’t have the budget to run models like this, there are more cost-effective ones that could be used, although these could be less accurate. 

Implementing a user interface

When building new systems that require user input or validation, we’ve always found it useful to think about what you are building from the user’s perspective. To make using the system as easy as possible, you will often need to have a solid user interface – or UI – in place.

 

Getting this done properly by a professional UI designer will add to your setup costs, but the rewards are worth it. A well thought out user interface not only empowers users to be more effective at their job but is also a powerful validation tool that can help train the increasingly accurate machine learning models. Having the ability to train your models continuously will ensure that they deliver increasingly stronger results. 

 

That being said, if you are integrating your systems into existing structures, or are building them to run in the background, then it may not be necessary to design a UI. 

 

As we’ve mentioned before, the cost of setting up AI models depends very much on your business’ context and goals. AI models are completely customisable – and that’s the beauty: If you’re looking for an easy solution to export predicted values into a spreadsheet, a simple static model that won’t break the bank can help solve your problem. On the other hand, you might have a large budget to build out a state-of-the-art computer vision system that finds defects in your manufacturing process in real-time and can afford to spend on resources that will help you get there. 

 

Both of these examples of AI technology have the potential to completely transform the businesses that they are employed in because they have been designed with the right goals and metrics in mind. Knowing what you want to achieve from the start is the key to spending your money wisely through this process. 

 

If you’d like to learn more about AI tooling for businesses in general, or the services that Deep Learning Café offers, feel free to get in touch!

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Dries is the founder of the Deep Learning Café. Designing AI and cloud strategies, supported by tools and processes, is at the core of his daily endeavours towards achieving AI excellence.

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