New Guideline Helps Determine AI Projects Cost and Timescales 2021 - 2022 (Updated)

Standard software development outsourcing project costs can be easily estimated. Previously, we have issued an article related to the offshore software development services cost & guideline for the year 2020 / 2021. If you haven’t checked it out, make sure to read it to have a general idea of how much you’ll expect to pay a software outsourcing service provider. However, it is a different story when it comes to estimating the delivery time and cost of AI projects. Many variables are affecting the delivery time and price of such projects. 

Most AI projects include adding a small AI feature to the existing software. The goal is to maximize the impact of the collected data. By understanding how to estimate the cost of such projects, you’ll be able to optimize your budget when going through a digital transformation stage. This article will discuss the three-necessary-step of defining the price of AI projects

The fundamentals of IT Project Estimating

Before diving into AI cost, let’s have a quick reminder of IT project estimation fundamentals since AI project costs are an adaptation and evolution from that. 

6 key aspects that influence one project estimation: time, cost, scope, quality, benefits, risks. In the traditional Waterfall model, the estimation will occur in the planning phase after project initiation, while in Agile, the estimation occurs during the sprint retrospective

New Guideline Helps Determine AI Projects Cost and Timescales 2021 - 2022 (Updated)

In recent decades, despite IT projects still running late, serious and big setbacks are far less common. One of several reasons behind this is the widespread existence of more flexible development processes and shorter project cycles. Another reason is many adopt project quality indicators and continuously improve. The debate about IT processes and project management methods remains intense. However, IT projects' quotations will remain widely the same information from most suppliers. This will include a similar analysis of how the overall work is broken down into phases or activities. 

This is quite similar to AI projects. The AI project's cost estimation now is like IT projects were years ago: inconsistent and often unreliable. A big reason is a difference in the AI development process and the perception of what AI product quality means. Therefore, AI project estimations are often driven by personal experience or project constraints more than objective considerations.

Creating and maintaining consistency in the way AI is constructed will provide a more objective basis for evaluating future AI projects. This will also enable you to improve and optimize your AI work while maintaining your supplier discussion position.

Three steps to define the whole cost of the AI project

1. Defining the scope of AI work before determining the price

Getting started with the basics and asking the following questions will help you achieve what you want without any uncertainty.

  1. What data will I use for training?
  2. What does the output look like?
  3. How can I measure the quality of the model?
  4. What is the acceptable accuracy?
  5. Is the input actually connected to the output?

By having these questions answered, you’ll have a more realistic understanding of the entire project. It’s time to get rid of the price estimation of all non-AI products.

2. Separate AI tasks from non-AI tasks

In most cases, the project requires a lot of work that is not directly related to AI. Unless someone says, “Let’s create an AI function to detect this,” there is a lot to unpack.

At this stage, you’ll have to find all aspects of the project that don’t require AI-related skills, so you can easily estimate the price yourself.

Some of these questions below are to determine which parts of your project are not related to AI.

  1. Can you retrieve your data easily? How will you provide data for your AI model?
  2. Is your data ready to use? Do you have to work a lot to transform the feeds?
  3. How can the end-user interact with the output? Should you create a reporting dashboard or API?
  4. How will you host the model, and should you prepare a server for it?
  5. Should you use external data sources? How will you connect these data sources?

Over time, you will see a list full of software development or business intelligence services.

3. Choosing the development approach

Unless you’ve already had a skilled professional who has experience working with similar problems onboard, evaluating the part of the project requiring AI is a challenge. Therefore, you’ll have to think of which approach you will take since different methods will have different prices, times, and risk ratios.

High-level APIs / ready-to-use tools

Plenty of companies have decided to hire programmers without any data science background to use high-level APIs and ready-to-use tools to get their work done. This approach saves time and can be considered one of the best options among the most common problems. However, it is difficult to guess if the API will be a good fit, and there is a significant risk of losing time without achieving anything because it is not much you can edit in a model that you have not created yourself.

  • Advantages: fast and inexpensive
  • Disadvantages: Unexpected results - cannot fix badly performing model or cannot find the right APIs/tools
  • Recommended: simple projects based on common AI problems.

Ask your analyst to work with your custom machine learning model.

Inexperienced analysts without solid machine learning and AI experience often spend weeks investigating and trying different approaches and come up with models with questionable accuracy. We do not recommend using this approach at all.

  • Advantages: none
  • Disadvantages: high probability of delays, low-quality model
  • Recommendation: don't do this.

Hiring experienced professionals in the field of data science

If you think about developing a multitude of different models and determine that AI is your key selling point in the future, we recommend you hire an expert in data science.

Time and money are definitely a matter. On Average, it takes up to 3 months to hire an AI partner in the US.

  • Advantages: support the project, grow with the team and be a strong team player
  • Disadvantages: hiring process takes time, evaluating his/her work is hard, high wage
  • Recommendation: we strongly recommend if you want to change into an AI-based company gradually.

Outsourcing from an AI company

If your AI project is not an important part of your software, you can save money and time by hiring a skilled professional who can get the job done quickly and easily.

Of course, it is not cheap to hire AI professionals in the U.S market. But you can consider an offshore software development center to save cost. Not only you’ll have complete confidence that the task will be completed quickly, but also you’ll know that an expert like this who can help your team solve the problem is hired at a lower cost compared to having a team learn the entire skill from scratch.

  • Advantages: fast delivery with work well completed
  • Disadvantages: not many of them; therefore hiring process takes time since, in most cases, they are remote experts.
  • Recommendation: companies that are looking to add AI to their software or operations quickly.


To conclude, there is no standard price when it comes to AI projects, but by having a solid analysis of all tasks, understanding which approach you should take, and being aware of the scope of work, you can have a general idea of how to price AI projects.

Because each AI feature is different, requiring different costs, time, and risk ratios, the best way to move forward with clarity is to talk to an expert; you can reach out to a data science consulting company or a team of machine learning engineers. That way will enable you to figure out what approach best suits your project’s needs.