Technical leads, Project Managers and product owners can easily estimate how much time in terms of engineering, analysis and QA or money will a standard development project require. However, estimating the time of delivery and the cost of the AI project or machine learning system might be tricky. There are a lot more variables influencing the time of delivery and price of such a complex project. In comparison with standard software development. Below you will find information on how to price and estimate the cost of AI project.
Naturally in such guides we do not talk about revolutionary projects and creating another Siri. Most AI projects consist of adding a small or bigger AI-powered feature on top of existing software or internal companys systems. With a simple and main goal – maximizing the impact of the collected and integrated data. Nonetheless, it is important to plan needed resources, tools, budgets and understand how to estimate the cost of an AI project. Before starting working even on small machine learning or AI features.
Some of the aspects that complicate estimation and cost of AI project includes:
- The estimation of lines of code is not the best strategy today.
- The fact that you built and tested a model, does not mean you are cloes to production deployment— sometimes you will have to iterate several times before you reach a satisfactory level of accuracy and model stability.
- There is a huge diversification of skills within the field and many times you will spend an unusual amount of time learning new methods. Before you approach the problem itself.
- When approaching a problem you need a full understanding of the business logic at hand. Otherwise, you won’t be able to make a case for the necessary input and output.
- The quality and size of your training dataset changes everything. It is important to understand if data you already collecting is enough to build trustful AI model.
- The cost of servers needed for training the model might be a substantial part of the valuation. You should consider whether you are able to use on-premises available servers or using cloud provides. Remember that using cloud technologies for deep learning training might be costly.
- There is usually lots of unscheduled work with data preprocessing. The stored and collected data is not always perfectly prepared for AI implemntation.
- In most cases, you have to connect data from multiple unorchestrated systems. Designing and deploying unified data source might be a priority at the beginning of the AI project and needs to be considered as a significant part while calculating AI project costs.
The key point is this — you kind of have to forget all that you know about pricing an IT project. Before you approach an estimate (price) for an AI task or whole project. In this article, we will go through the entire process step by step.
Estimating and pricing an AI project – step by step guide
Step 1 — Clearly define the scope of the AI project before pricing and estimating it
Let’s start with the basics and ask the 4 basic questions that will allow you to move forward with clarity over what you want to achieve. Those questions will help you to price an AI project.
- What data am I going to use for training?
- How exactly will the output look like?
- How will I measure the quality of the model?
- What accuracy is acceptable?
- Am I sure that the input is truly connected with the output?
- How and where I want to deploy my solution
- Which systems should be integrated with AI system
Having clear answers to those questions gives you a more realistic scope of the entire project. Now it’s time to strip the price estimation of all the non-AI stuff.
Step 2 — Separate AI-tasks from non-AI tasks to estimate costs better
When someone in the company says “let’s create an AI feature based on computer vision that detects and counts people in the kitchen” there is quite a lot to unpack. In most cases, such task requires lots of work not directly related to AI.
In this step, you have to find all the aspects of the project that do not require AI-related skills and thus are easy to estimate yourself.
Some questions to ask yourself to find out which parts of the project are actually not AI-related:
- Am I able to easily retrieve the data? How will I feed data to the AI model?
- Is the data format ready to use? Is there a lot of work with transforming the feeds?
- How will the end user interact with the output? Do I need to create some API or reporting dashboards?
- How will I host the model, do I need to prepare servers for that?
- Are there any external data sources I’ll have to use? How will I connect them?
After a while you will have the full list of things that are basically simple software development or business intelligence services. Those tasks will be easy for you to estimate leaving as little space for the unknown as possible.
Step 3 — Choosing development approach may influence cost of AI project
Evaluating the AI part of the project is simple, only when you have a skilled professional on board who has worked with similar problems before. In case you don’t (which is the majority of companies) you have to choose your approach and each will have a different price/time/risk ratios.
Ready to use AI tools and high level APIs
Many companies decide to have their programmers without data science background do the job using high level APIs and ready to use tools. This approach saves a lot of time and might be the best choice in some most popular problems. However it’s highly unpredictable if the API will be a good fit and there is a significant risk of simply losing a lot of time without achieving anything because there is not much you can tweak in a model you haven’t created yourself.
- Pros: Quick and cheap
- Cons: Unpredictable results Risk of running into a dead end — not being able to tweak the badly performing model High probability of not finding the right API/tool
- Recommendation: Good for simple projects based on popular AI problems and non-scalable solutions
Have the analyst do a custom machine learning model
Unskilled analysts without strict Machine Learning and AI experience usually spend weeks on research and try different approaches but finally end up with a model of questionable accuracy and concerning structure. We strongly advise against this scenario.
- Pros: Unless your analyst is a genius, there are none
- Cons: Ends with a disaster in 80% of cases due to malfunctioning models High probability of delays Usually extremely low quality of the model
- Recommendation: Just don’t do this
Hiring a skilled expert in area of Data Science
In case you strategically think about developing multitude of different models and you look at AI as your future key selling point, it might be the best policy to hire a skilled expert who will later hire junior data scientists.
The time and money are obviously a problem. Hiring an AI partner in US takes on average 3 months (Glassdoor) and you’ll have to expect at least $120.000 /y in compensation. You risk a bad hire too, as evaluating the work of an AI expert without having any expertise in the matter yourself is really hard.
- Pros: Keeping support of the project Being able to grow your team Being semi-independent (the guy can always be poached)
- Cons: Long hiring process Hard to evaluate work High wage
- Recommendation: A good move if you want to slowly change into a completely AI based company.
You can also see the benefits of hiring a data science consulting company.
Body leasing from AI outsourcing company
If your project is a simple black box and is not a crucial part of your software or operations, it might be the best way to simply hire an experienced professional. Such person will be able to deal with the task really fast.
Naturally those are not cheap especially in the current US market of AI experts which is a subject of a bidding war among the global corporations.
On the other hand however, you will have full confidence that the job will be finished quickly and you can expect satisfactory accuracy (if your training data is right). In many cases having an expert like this tackle the problem quickly can turn out to be cheaper than having your team member learn the entire art from scratch.
- Pros: Relatively quick High probability of having the job done well
- Cons: Expensive Rarely any support
- Recommendation: Good if you have a single task that has to be dealt with quickly.
AI consulting company
Naturally take my words with a grain of salt as we are an AI consulting ourselves.
A nice thing about them is that they can provide a semi-accurate price estimate upfront based on their experience. This might work as a benchmark for future discussion inside the company.
If you’d like to know estimation for your project, let us know using project estimation form and we will do our best to help.
In case of specialised software houses, the job is usually finished really fast because there is a high level of specialisation within such organisation. There is a separate guy for computer vision, for natural text understanding, for predictive methods and so on. Assigning someone who did several similar projects before, results in an extremely rapid development. Additionally, lowers the risk of failure at the same time and lower pricing for such AI project.
As AI projects can easily be contracted to a company abroad, you can look forward to lower menday prices. At the same time you have a partner that will be able to gradually increase model’s accuracy on daily basis or take up another project if that’s needed, so the risk of losing “the guy who wrote this” is really low.
- Pros: Really quick High quality Support In case of foreign software houses — price will be an advantage
- Cons: Still not many of them Establishing the relationship takes a while In most cases those will be remote experts
- Recommendation: Perfect for companies that want to add AI as a part of their software or operations quickly.
Additional Step in estimating the cost of AI project
Training some AI models takes a Macbook Air and a couple of hours. It might be taken as an oversimplification on my part but usually, training a model based on numerical data weighting less than 100 mb is not an issue.
The whole thing changes when you approach analysing huge amounts of sound, image or video files. It’s best to consult a professional to estimate the price of training such models. Bear in mind that having the model go through the training set does not mean it will work properly. If not badly designed, you might have to start again.
FAQ on Estimating an AI project
1. Should I use internal resoucres or develop AI project with technology partner
Depending on the available resources inside your company, their skills and capabilities you could decide wether you should move with them or hire experienced AI consultation. In most cases mix of having internal experts and external partner may bring the best results in terms of cost and timelines.
2. Where should I start?
The common question for people who are not experienced with AI projects. The answer is simple – you should start from clear business need and it’s definition in terms of AI solutions. You should understand the feasibility of the project and it’s future ROI.
3. How much regular AI project costs?
There is no clear anwer to this question. Based on past projects and our experience we could say that typical proof of concept AI project costs around $14-19k.
To sum up, AI projects are tought to estimate (price) but having a clear analysis of all the non-AI tasks actually clarifies a lot.
That is to say, the price of development of strictly AI related features and projects varies a lot depending on your approach to the whole thing. Each approach has a different ratios of cost/time/failure probability.
So, one of the best ways to get a clear picture is to contact a specialised AI consulting company. You could ask how many hours of each type of task would take. That will allow you to discuss the matter internally and find the way that best suits your needs.
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