When considering artificial intelligence adoption, you surely wonder how much money this undertaking will cost you. Do AI projects cost millions of dollars and take years of development? Or a few thousand bucks and a few weeks of work? There is one important thing you have to be aware of here: it depends – and on a number of factors.

Estimating an AI development cost without knowing any details is pointless. You can just as well guess a random number. Today, we’ll try to have a closer look at the costs related to AI projects – but it’s still not an accurate estimate for a given project. Don’t expect exact figures – the numbers part is very individual, and reliable estimates are only available when details on the project are known. 

What we’re looking at in this article is the type of project that utilizes machine learning to solve a given business problem – a custom AI solution, not off-the-shelf AI software. When it comes to off-the-shelf AI software, there are some that are great: take OCR or some instances of face recognition, where you don’t need your own data set to train the model. But if the problem you’re trying to solve with artificial intelligence is about the processes within your company – like sales, marketing, pricing, recommendations, etc. – your data (specific to the use case: about your company, customers, employees, etc.) is a crucial element of the project and you have to be ready to incur additional costs related to data collection and cleansing.

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Whether you're developing machine learning tool, utilize natural language processing, computer vision, or other AI solutions, you need to consider several factors that will impact the AI cost.

Difficulties of Estimating AI Project Costs

Because so many factors influence AI projects – like team composition, use case, data, selected AI model, and more – it’s impossible to provide a fixed price cost estimation for a large AI project. With smaller projects, like a minimum viable product, that is possible, but remember that there are two things to estimate at the beginning of the project: 1) the development cost and 2) the development time.

Here are some things you need to remember when trying to estimate the cost of implementing AI:

Unreliable benchmarks

The fact that company X developed their AI model in 6 weeks doesn’t have to mean your model will be developed in the same amount of time – so consult machine learning engineers who build models, not people with limited AI know-how.

Operational costs

Team composition is tricky. If you haven’t worked with AI engineers or data scientists before, you may have difficulties assembling your team quickly – and time is money. Not to mention the recruitment costs.

R&D costs

The project will require several iterations. You can’t get it all right the first time around, so you’ll have to keep improving the model. Treat AI implementation as an experiment. It may fail multiple times, and it will surely impact the AI development costs.

Enhancing human intelligence

The engineers/data scientists working on your project need to understand the business logic behind it. Otherwise, they won’t be able to develop an AI solution that helps your business.

Building an end-to-end custom solution

Building the models is not the end of your project. If you want humans to interact with them – and I bet you do! – then you will also need software developers and designers who will create the user interface. This may heavily impact the total cost of custom AI development.

Gathering historical data

You may be lacking data for training models and need time to collect it or obtain it from third parties.

Initial AI costs

The real “AI work” is the last part of the development process, after you build the use case, create a data strategy, and preprocess data. All these preparations take time and impact the development costs of your AI system.

Maintenance costs

When estimating the AI development costs, consider the maintenance costs. Let’s face it. AI algorithms require ongoing maintenance. Once you have a basic version of your AI system, you will need to continuously optimize the models, maintain the infrastructure and carry other software costs.

Various factors can impact how much AI projects cost

Estimating an AI Software Development Cost – a How-to Guide

It’s funny, but the pricing of building AI systems doesn’t start with numbers at all. Sure, it’s good to know that your budget is between X and Y, or maybe just X and no more, but in general, you’ll get to the numbers at the very end. The good thing is that the steps you follow when estimating the development costs will help you outline some general information for your data strategy. Let’s reuse the work you’ve done, it can’t be wasted!

Step 1: AI Solution Scope

What does your AI development project consist of? What’s the goal? Which AI features should it have? What are the success and failure criteria? What data are you going to use?

When the use case is identified, you should define the scope of the project. It can get tricky, and you may not realize some things should be within the scope – but you can use the help of an AI advisor or a data science partner. Together with an expert, you will also be able to prioritize work and estimate given tasks in hours to be able to better evaluate how much a project will cost. With that, you move on to step number 2.

Step 2: Action items

When you create a data strategy, it should include core activities and action items. Those will be assigned to given team members to make sure that everyone knows what they’re responsible for. At this point, you can also separate the non-AI work from the AI work. If you can do something yourself, like retrieve the data, do it. That’s because a clear division of work will help you move forward faster – and at this stage, estimate the timeframe of your project and the cost of the work that has to be done. Divide the tasks wisely, taking into consideration the skills and capabilities each team member has. Efficiency is a money-saver.

You can't succeed with AI without a machine learning engineer

Step 3: The team required to implement AI

Action items won’t be done without a team. Do you want to build an in-house data science team or outsource? Both approaches have their pros and cons – you can read more about that in our article about building your data science team. If you want to build an in-house team, you have to account for recruitment and training costs – which, reportedly, can be about 15 thousand dollars. Then, you have to remember that you have to pay their salary, which on average is 120K dollars yearly, according to Glassdoor. In this case, you estimate your project by counting the months of work needed for implementing AI – since the salaries are the same every month.

If you outsource, you will most probably be charged on an hourly or daily basis – so you will pay for the actual time spent on your project. The hourly rates of outsourced data scientists vary and can fall anywhere between 60 and 400 dollars per hour. Or even more, depending on the skills, experience, location, etc. The number of hours required to build your AI system depends on how much preparation has to be done, how complex the AI solution is, what output is expected, and so on. 

Step 4: Consider additional costs

Is the cost of a data science team the only one you’ll have to pay? You have to consider other costs: infrastructure (e.g. cloud, data storage), integration costs (e.g. API development, documentation), and maintenance.

An artificial intelligence project doesn’t end with building AI algorithms. The costs of production (infrastructure, integration, maintenance) are often overlooked, but they’re there, and they’re not avoidable. Depending on the complexity of the model and AI technology stack, these costs don’t have to be huge. However, you need to remember that your model needs continuous support, that new data has to be cleaned and annotated (unless we are talking about generative AI, but that’s a different story), and that even if much of the recurring work is automated, the model can’t be left unsupervised.

Read also: How Much Does It Cost to Use GPT Models? GPT-3 Pricing Explained

Bad news now: artificial intelligence cost can vary from a few thousand dollars to a few hundred thousand. I wish I could estimate your project magically without having to look at it, but it’s simply impossible. For that, it really is necessary to have a closer look at your needs, requirements, and possibilities to assess the complexity of the solution and the work that it requires. But wait, don’t lose hope. You don’t need a million-dollar budget to start with AI.

How much do AI projects cost? Consider all of the costs of building AI systems

Artificial intelligence costs are huge! Can I start on a smaller scale?

The good news is that starting on a small scale is actually recommended. That’s because artificial intelligence has to prove its worth. It needs to be a viable solution that improves something in your organization, solves a real business problem, and generates cost savings.

So you choose one segment to work on – say you want to stop customers from leaving your company, so your model will be churn prediction. End of story. Don’t overcomplicate things. Start with one small AI project to test your assumptions. Then, learn how to work in a more data-driven way, and mitigate the risk of wasting time and money. So you create a piece of your data strategy and a project roadmap, you identify action items and define success and failure criteria, and the work begins. After a few weeks, the first model is ready, and you can see how it works. Sounds cool, right? And more good news: a pilot project of building a PoC AI system like that will usually cost between 5 and 20 thousand dollars and take only a few months to deliver.

The AI Sprint

Because artificial intelligence projects rely on a good alignment of business and tech, it’s safer to start small. That’s also why we developed our AI Sprint to get our clients on board with artificial intelligence.

The AI Sprint was designed to help companies quickly gain a deeper understanding of the opportunities and limitations of AI, identify the right use case, create a data strategy and an action plan. During a 2-day AI discovery workshop, we work closely with the client’s team to understand their business, the processes, and the requirements, to share our knowledge on the tech side, and provide advice on the next steps.

After the workshop, we start developing a PoC of a chosen model that will deliver quick wins – this way, we make sure the delivered AI project is viable, that it supports the client’s business, and brings tangible value.

artificial intelligence cost estimation is always a question

Artificial intelligence cost estimation is still unknown

Well, it’s not totally unknown, but it’s difficult to estimate, especially if you’re not sure how your project will be evolving and what the development of it will include. And it’s fine not to know these things if you’re just starting with artificial intelligence – that’s why we recommend consulting specialists or companies experienced in AI development. They will be able to scope the project and evaluate how much work is required and what other resources are needed.

Do you need to buy data from third parties? Do you need to pay for the cloud? How much will the maintenance cost be? There’s a variety of factors that need to be taken into consideration when pricing an AI project, so it’s not an easy task. Luckily, that doesn’t mean you need an endless budget to keep spending more and more money on the project. Just start making a small first step and validate the idea of utilizing artificial intelligence in your organization before you move forward with company-wide AI adoption. 

Artificial intelligence cost analysis – key takeaways

Summing up, the costs of an AI project can be drastically different, depending on the size and complexity of the AI system, the required resources, the quality of data, and the data science team. Before jumping to any conclusions, make sure you have the basics covered: identify the area you want to improve with AI, and set up the goals: both long-term and quick wins. Then, build and train the models and put them to work. With a small project proving its worth (meaning it gives you a competitive advantage while being cost-effective), it’s easier for you to plan for a bigger scale with a bigger budget: you already know what AI development looks like and how to work on data science projects.

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