AI Crowdsourcing: An In-Depth Guide for 2024

Hey there! Artificial intelligence (AI) is transforming businesses – but it‘s not easy to jump on the AI bandwagon. Developing AI systems requires scarce skills, complex processes, and substantial investments. This is where crowdsourcing comes in!

In this guide, we‘ll explore how crowdsourcing provides a scalable, cost-efficient approach to enable AI adoption. I‘ll share insights on:

• What is AI crowdsourcing and how it works
• Key use cases and examples
• Tangible benefits over in-house teams
• Leading crowdsourcing platforms
• Trends and future outlook

Let‘s get started!

The Growing Importance of AI

AI is going mainstream. According to Gartner, the number of organizations implementing AI grew from 4% in 2018 to 37% in 2019. This trend continues upward – a recent IBM study found 29% of SMEs have adopted AI in 2020.

What‘s driving this growth? AI enables businesses to operate more efficiently, save costs, boost productivity, and drive revenue growth. As per a McKinsey report, AI has the potential to create $13 trillion of value for the global economy by 2030.

However, becoming an AI-powered enterprise has significant barriers. Let‘s look at the key challenges involved:

• Data hunger: AI models need vast volumes of high-quality, labeled training data. Collecting and preparing this data requires substantial effort and cost.
• Talent crunch: Only 28% of enterprises report having the skills needed to implement AI, as per an MIT report. Hiring scarce and expensive AI talent is tough.
• Long time-to-value: On average, enterprises take 9 months to deploy a single AI use case in production. The long development cycles delay ROI realization.

This is where crowdsourcing comes to the rescue!

What is Crowdsourcing for AI?

Crowdsourcing leverages the collective inputs of a large, distributed pool of contributors via the internet to complete work. Contributors can be paid or volunteer based on the nature of work.

Crowdsourcing has become extremely relevant for developing AI systems. Organizations use crowdsourcing for:

• Data collection and labeling
• Model development
• Testing and quality assurance

This "human-in-the-loop" approach combines the scalability of crowdsourcing with the judgment of human contributors. The result? AI solutions that are higher quality, safer, and more robust.

Let‘s look at some real-world examples of how crowdsourcing supercharges each stage of the AI lifecycle.

Data Collection and Labeling

Data is the fuel for AI algorithms. The more high-quality training data, the better AI models become at "learning" and performing tasks accurately.

But where do enterprises get this data from? Collecting millions of data points for model training is complex and expensive. Crowdsourcing provides a scalable solution.

For instance, Scale offers data labeling via a global network of contractors. Clients submit datasets, which Scale distributes to contractors for labeling based on problem complexity and skills required.

Benefits? Scale delivered 2.5 million annotated images to its client Osaro in just 3 weeks for training robots. The cost was 70% lower than in-house labeling.

Such crowdsourced data powers AI behind self-driving cars, medical imaging diagnosis, predictive maintenance, and much more.

According to research by MarketsandMarkets, the data labeling market is projected to grow from $1.6 billion in 2021 to $6.3 billion by 2027 due to high demand for quality training data.

Algorithm Development

Developing AI models requires specialized data science skills. But the stark talent shortage poses a key barrier. Crowdsourcing helps bridge the skills gap.

Kaggle, for instance, hosts data science competitions to crowdsource solutions for analytics and modeling problems posed by companies.

Here‘s an example: DataRobot hosts competitions where participants develop models to predict key metrics on Kaggle using structured datasets shared by DataRobot clients. The top performers get monetary prizes and recognition.

For clients like Expedia, this crowdsourcing model delivered high-accuracy solutions at a fraction of the cost and time taken through traditional consulting models.

As per Kaggle, their crowdsourced data scientists achieve highly accurate solutions – the winning models often outperform industry expert benchmarks.

The data science competition market is forecast to grow from $636 million in 2021 to $1.4 billion by 2028 as per Valuates Reports, highlighting the value crowdsourcing brings.

Testing and Quality Assurance

Before deploying AI systems, they need to be rigorously tested. Does the system work as expected across diverse scenarios? Are the outputs correct and free of bias?

Answering these requires large-scale testing using a diverse workforce. Crowdsourcing provides exactly that.

Companies like Google, Microsoft, etc. rely on crowdsourced testing services like Applause to assess AI capabilities across parameters like functionality, usability, localization, and more.

For example, Appen provided Uber with crowdsourced testers to evaluate the language recognition accuracy of the Uber Voice feature across different languages, accents, and contexts.

Such crowdsourced testing enables going from thousands of test cases to millions – delivering comprehensive, unbiased feedback to enhance AI quality.

MarketsandMarkets estimates the crowdsourced testing market will grow from $1.4 billion in 2020 to $4 billion in 2025.

Key Benefits of Crowdsourcing for AI

Now that you understand how crowdsourcing powers the AI lifecycle through practical examples, let‘s discuss the tangible benefits it provides compared to in-house teams:

1. Cost Saving

Crowdsourcing offers significant cost advantage over hiring full-time resources, since you pay only for work done. Kaggle estimates crowdsourcing can lower costs by 50-75%.

2. Scalability

Crowdsourcing provides on-demand access to skills. You can easily scale the workforce across geographies and skills as needed.

3. Speed

By leveraging crowds globally in parallel, you can drastically reduce project timelines compared to in-house teams.

4. Quality

With diverse inputs, crowdsourcing improves coverage and accuracy. Services like Kaggle even offer "money-back guarantee" if quality thresholds aren‘t met.

5. Innovation

Crowdsourcing taps into talent globally, resulting in innovative solutions. Problems get a diversity of perspectives.

6. Compliance

Established platforms ensure contributors meet requirements like NDAs, data privacy, security protocols etc.

7. Reduced Bias

The diversity of contributors minimizes individual biases that can creep into AI algorithms and data.

Crowdsourcing enables organizations to mitigate the key barriers in AI adoption around data, skills, bias, and quality. The results? Higher ROI, faster time-to-value, and reduced risks.

Top AI Crowdsourcing Platforms

Many reputed platforms exist today to access on-demand crowdsourcing talent for AI projects. I‘ve compiled some of the top players below across 3 key categories:

Data Annotation

Algo Development & Model Training

Testing & Quality Assurance

This is by no means an exhaustive list – many more excellent crowdsourcing providers exist. Evaluate your specific needs to choose the right partner.

Future Outlook

The crowdsourcing industry is poised for massive growth, with AI as a key driving force. Here are two key trends to watch:

• Enterprise Adoption: Crowdsourcing is gaining acceptance as a serious enterprise capability. As per Deloitte, 42% of global enterprises already leverage crowdsourcing today. We‘ll see wider mainstream adoption.
• Specialization: Platforms will offer more specialized services for AI focused on particular domains like retail, manufacturing, healthcare etc. AI crowdsourcing will become more tailored.

Gartner estimates that by 2025, 60% of data science work will leverage crowdsourcing in some form for skills augmentation.

Crowdsourcing is also evolving to become more "intelligent" – using AI itself to automate tasks like weighting contributions or validating work. There is fantastic potential for collaboration between AI and collective human intelligence!

The Power of Crowdsourcing for AI

We‘ve covered a lot of ground here! Let‘s summarize the key points:

• AI is delivering tremendous value but has key barriers around skills, data, and timelines
• Crowdsourcing provides a scalable, cost-efficient model to overcome these barriers
• It helps in data collection, algorithm development, and testing for AI systems
• Benefits include reduced costs, better quality, faster execution, and access to innovation
• Many robust platforms exist today to access vetted crowdsourcing talent on demand
• Crowdsourcing for AI will see massive growth as it becomes a core enterprise capability

The bottom line? AI success requires a thoughtful human-machine symbiosis. With the right strategy, crowdsourcing enables organizations to build AI capabilities quickly, economically and safely.

I hope you found this guide useful! Do let me know if you have any other questions. I‘m always happy to help explain concepts or discuss ideas.