AIMultiple ResearchAIMultiple Research

Top Benefits, Use Cases & Vendors to Crowdsource AI in 2024

Top Benefits, Use Cases & Vendors to Crowdsource AI in 2024Top Benefits, Use Cases & Vendors to Crowdsource AI in 2024

Artificial intelligence is gaining popularity. According to Gartner, 37% of organizations in 2019 use AI in the workplace. And as of 2020, even 29% of SMEs have deployed AI. However, AI transformation is neither cheap nor easy. AI talent is scarce and required investment for AI adoption can take a long time to pay off. Most companies (excluding tech giants such as Google and Facebook) are experiencing these challenges and trying to find ways to make cost effective AI investments.

Crowdsourcing option is a cost effective solution. Between 2012 and 2017, in the United States alone, the market for crowdsourcing grew by about 37% and its value was estimated at $ 6.5 billion. Though these numbers include a wide variety of tasks such as text translation and survey, the prevalence of crowdsourcing for AI is increasing.

Since the machine learning lifecycle contains tedious processes such as data preparation, model building and testing, we expect more businesses to choose the crowdsourcing approach to reduce costs and time-to-market while building an AI system.

What is crowdsourcing AI?

Crowdsourcing is a workforce approach that aggregates information or input into a task or project by using the services of a large number of people via the internet.

Crowdsourced labor can be paid or voluntary depending on the project. Yet, in the artificial intelligence world, crowdsourced labor is mostly a paid service.

What are crowdsourcing use cases in AI?

AI systems require these components in order to function effectively:

  1. Labeled clean data to help the system work accurately
  2. Data science effort to build effective models
  3. Testing to check if the system works as intended

Data Labeling

Data is the fuel of AI systems. As machine learning algorithms are fed by more data, the accuracy of AI systems improve. However, collecting a sufficient amount of real-world data to use in algorithm training is not as easy as it sounds. According to high-level guidelines, machine learning problems that have an average complexity require 10.000-100.000 data points while highly complex problems demand 100.000-1.000.000 data points. Handling the labeling of such a high volume data with in-house resources is an expensive and time-consuming task. Therefore businesses can prefer crowdsourcing options when labeling data points to train their machine learning models.

We have a detailed guide to data labeling where we examined the advantages and disadvantages of data labeling workforce options and highlighted the quality trade-off in outsourced and crowdsourced data labeling, feel free to check it out.

Algorithm Design

Attracting top AI talent is difficult and expensive. According to GlobeNewswire’s survey, 59 % of respondents named ‘shortage of data science talent’ as the primary barrier to realizing value from their big data technologies. And if they find the right talent for their needs, businesses may have a tough time allocating the necessary budget since the average annual salary of a full-time data scientist is $120k in the US. For instance, finding freelance talent from Toptal, an on-demand recruiting agency, costs between $60 – $210 per hour. This corresponds to $100k-$350k per year excluding discounts which would be possible when recruiting the data scientists full time.

If businesses need an AI talent to design machine learning algorithms, they can rely on crowdsourcing by launching data science competitions. Data science competition platforms enable businesses to gather crowdsourced data scientists for a specific task. Businesses define their problem and present data to help data scientists design an algorithm that solves their business problem. Data scientists from different regions submit their solution and the most accurate solution wins the prize.

Competitions are advantageous for businesses because

  • The prize they pay to the winner is much less than they would pay to a data scientists
  • Since multiple data scientists work on the same problem and there is a competition among them, the solutions can be more accurate

Testing / Quality assurance

Software testing helps organizations provide an objective, independent view of the software to allow the business to understand the obstacles of software implementation.

Each AI system requires testing to improve the accuracy of products. Crowdsourced testing enables businesses to carry out tests by using a number of different testers from different places that provide unique advantages to organizations such as testing software for the target audience by recruiting specific target groups from the crowd.

Below infographics highlights why crowdsourced testing is a reliable option for businesses who want to measure the effectiveness of AI software:

Crowdsourced testing vs In-house testing infographics
Source: Global App Testing


Data collection is one of the most crucial steps of AI development, and it can also be crowdsourced. Clickworker is a data collection specialist and can fulfill your project needs with a crowdsourcing model. They work with over 4 million registered workers who are proficient in 45 languages and cover more than 70 target markets. Their offerings include:

  • Collecting and providing datasets for AI/ML training
  • SEO content services/text creation
  • Data categorization and tagging
  • Conducting surveys and web research
  • Gathering customer insights from PoS
  • Product data maintenance

You can also check our data-driven list of data collection/harvesting services.

For more in-depth knowledge on data collection, feel free to download our comprehensive whitepaper:

Get Data Collection Whitepaper

Finally, AI also helps testing. As we explained in our article, AI technologies can be used to automate tedious tasks in software quality assurance.

What are the benefits of a crowdsourced workforce compared to in-housing?


Biases in artificial intelligence are one of the top concerns of the AI community, yet, the diversity in the machine learning algorithm design and data collection teams is the most common way to reduce these prejudiced assumptions from AI systems. Crowdsourcing enables businesses to gather individuals from different backgrounds that eventually help reduce bias in AI solutions.

Faster time-to-market

With crowdsourcing platforms, businesses can scale a workforce from 0 to the number they needed. With this speed of recruitment and an almost unlimited number of workers, organizations can launch their AI products faster to the market.

Cost-efficient and Quality Work

Businesses pay based on the work done by individuals rather than agreeing on a contract with fixed terms. This business model help organizations save money while encouraging the crowd to provide quality work.

Which companies can help you find crowdsourced labor?

Data Labeling Crowdsourcing Companies

  • Amazon Mechanical Turk offers a crowdsourced workforce via its platform. On Amazon Mechanical Turk, businesses can design, publish, and coordinate a wide range of human intelligence tasks, such as text/ image/ video classification, writing product descriptions, or answering questions.
  • LionBridge AI provides localization and AI training data services in 350+ languages. Their crowdsources community collects data and labels it based on the specific task. Services LionBridge AI offers are image/ text classification, product categorization, sentiment analysis, audio transcription and more.
  • Clickworker provides scalable solutions in 18 languages with their crowdsourcing workforce includes 2.2 million people from 136 countries. Solutions Clickworker offers include training data for ML and AI systems, classifications,  product data management, sentiment analysis, surveys, tagging, text Creation, SEO texts, transcriptions and web research. Clickworker also offers both standardized and custom solutions for implementing data-oriented projects to its customers.

Data Science Competition Companies

  • bitgrit is a platform offering a global network and community for data scientists to interact with each other. Businesses launch competitions using bitgrit’s platform and aggregate the wisdom of the data science crowd.
  • Kaggle offers both public and private data science competitions and on-demand consulting by a global talent pool.

Crowdsourced Testing Companies

  • Global App Testing uses the crowd to help QA and Engineering teams overcome the challenges and release high-quality software at speed. Their crowdsourcing team contains 40,000 vetted professionals from +100 countries.
  • Digivante is a crowdsourced testing company, with high-profile clients such as Audi, GymShark, and Calvin Klein. Their mission is to provide a flawless experience for software users by using the crowd. They currently work in over 149 countries and have a community of over 55,000 professional testers.

You can also read our other articles about AI if you want to learn more. These articles can also interest you:

You can also check out our list of AI tools and services:

If you have questions about how you can integrate AI into your business, don’t hesitate to contact us:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.