Defined Crowd

AI for the Win: How Companies Gain the Competitive Advantage with AI

Artificial intelligence can benefit your business, if it’s backed by high-quality data.  

Whether through speech recognition, translation or other natural language processing techniques, AI is becoming a powerful tool for businesses of all sizes, across all industries. Companies are recognizing the benefits of AI, using the technology to increase productivity, better understand customers, and mine data to provide valuable business insights.  

AI is quickly becoming as ubiquitous as software, and soon, like software, every business will use it.

The role of data is fundamental to the AI revolution. Data is to AI what code is to software: without data, AI is nowhere.

However large amounts of high-quality, bias-aware, and ethically sourced data is in short supply. And while large volumes of unstructured data are currently floating around in the digital world, AI professionals need high-quality, structured data to train high-performing, bias-free, and accurate AI models.  

In the following article, we’ll take a closer look at how AI is creating business growth opportunities, examine latest AI trends, and explain why high-quality data is so essential to successfully implementing AI.  

Brain with printed circuit board (PCB) design and businessman representing artificial intelligence (AI), data mining, machine and deep learning and another modern computer technologies concepts.

From automating repetitive tasks to facilitating global expansion, AI is creating more business growth opportunities now than ever before.

AI is personalizing the customer experience 

AI models are helping businesses to provide customers with a hyper-personalized experience. Algorithms allow businesses to show customers relevant, context-sensitive products and offers, based on their purchase history and preferences. Studies show that this level of personalization can prevent cart abandonment, ensure loyalty and increase ROI. In fact, New Epsilon research indicates that 80% of customers are more likely to make a purchase with a company if it offers a personalized experience.

Global expansion 

Before the global pandemic hit, businesses were already on the path of digital transformation to improve worker productivity, customer service, and product quality.

The advent of COVID-19 accelerated this transformation exponentially, especially in the field of artificial intelligence. As millions of people around the world were forced to work and shop from home, businesses jumped at the opportunity to connect with customers in new markets. In particular, machine translation has broken down the language barrier, allowing businesses to engage across borders, in multiple languages.

Operational efficiency

Automating everyday businesses processes with AI may not sound as exciting as self-driving cars, for example, but it can have the biggest impact on a company’s bottom line. By reducing the time needed to complete repetitive, administrative tasks, automation allows employees to focus on tasks that have a direct impact on profitability. Automation increases efficiencies and saves time and money.


One of the downsides of digital transformation and the increased use of AI and other technologies is that opportunities for cybercrime have also increased. In one report, global losses from cybercrime reached nearly $1 trillion in 2020. However, the good news is that the capabilities of AI-powered cybersecurity have increased as well, allowing companies to detect fraud, find holes in security systems, and fight cybercrime more effectively.  

Impressive Growth Ahead 

AI is predicted to grow quickly in the next 5 years: PwC found that AI could contribute up to $15.7 trillion to the global economy by 2030, of which $6.6 trillion will likely come from increased productivity and $9.1 trillion from consumption-side effects. These are exciting numbers and show a big growth trajectory for AI.  

Companies are taking note as well, as global spending on artificial intelligence (AI) is forecast to double over the next four years, growing from $50.1 billion in 2020 to more than $110 billion in 2024. This spending is taking many forms, but in particular, businesses are investing in chatbots for better customer engagement, enhanced voice search and product recommendation tools and automation.  

Here are a few examples of companies using AI to explore and expand business opportunities:


Language learning app Duolingo is firmly rooted in AI. Users begin with an AI-driven placement test to determine their knowledge level of the language they want to study.

As learning increases and users gain more proficiency, they interact with the app in different ways, while deep learning algorithms predict what users need to keep practicing. Algorithms also analyze user data to personalize the learning experience even more.

The app also makes use of AI-powered chatbots that teach users through text-based conversations. The more the chatbots are used, the faster they learn, and the better they become at teaching humans.


Google has made major investments in AI. Probably the most familiar is Google Assistant, Google’s conversational voice-activated digital assistant. Using voice recognition and deep learning NLU networks, Google is constantly improving the Assistant to better understand and respond to users. According to TechRepublic, the Google Assistant is the “linchpin” in the company’s AI-first strategy for the future, and will “come to define how users interact with almost all of Google’s core products”. Just as technology moved from the web to mobile, so will it now move from mobile to AI. And Google plans to lead the charge in embracing the evolution.

Google Assistant can open apps, tell you the weather, set alarms, play music, and even call and schedule appointments for you.


The Chinese language search engine provider is certainly invested in AI. Their AI portfolio is huge, with machine learning products, NLP-based products and more. One of the most effective projects has been Deep Voice, a production-quality text-to-speech (TTS) system constructed entirely from deep neural networks.

Deep Voice can now recreate human speech with a small amount of data and only a short time needed to train it. Deep Voice is an essential component in many speech-enabled devices such as navigation systems. It allows for human-technology interaction without needing visual interfaces, making technology more accessible for the visually impaired.

Baidu also have released the Baidu Mobile Assistant, Duer, a voice-activated assistant designed to give recommendations and make purchases online, Little Fish, a home device that can be voice-activated and with a touch screen, and SwiftScribe, AI-powered transcription software.

Increased Demand for Training Datasets  

data packets

It’s clear that AI is growing aggressively, which has naturally increased the demand for high-quality training data.

However, data isn’t as easy to come by as code and sourcing adequate volumes of high-quality, annotated training data is a real challenge for companies.

There are several common pain points that machine learning teams run into when acquiring data:  

  • Urgency: To keep the competitive edge, companies can’t afford to spend years on AI models. They need to be deployed into the market quickly, meaning that As, AI professionals need high-quality data immediately. 
  • Bias: Just as in the real world, bias in AI is a big problem caused by datasets without diverse representation.  
  • Scalability and Augmentation: Beyond initial deployment, models need fresh data to improve. As teams expand or augment model capabilities, new data is needed to attain the next stage of development.  
  • Accuracy and Quality: To obtain large quantities of training data required, most machine learning teams use crowdsourcing; a method of dividing collection, annotation, and quality assurance jobs into a series of microtasks, completed by thousands of contributors. In this situation, how can teams ensure the accuracy and the quality of the data gathered? 

The pain points listed above cannot be brushed over or ignored. These challenges can significantly impact on a business, especially if machine learning teams implement a model that is not trained on high-quality, bias-aware data. Just a few of the potential consequences include biased models, low-performing models and a loss of time and resources spent on ineffective models. This results in dissatisfied and unhappy customers, who are likely to take their business elsewhere.

How much training data do you need?  

The short answer: a lot. However, the volume of data needed depends heavily on what type of model you are training and for what purpose. The amount could vary from 1000+ hours to much more. However, one thing is very certain – the more high-quality training data used, the better the model and performance.   

Removing AI Project Barriers 
Artificial intelligence Machine Learning Business Internet Technology Concept.

If the problem is bad data, then there is only one solution: high-quality, transparent training data. Here’s where an introduction to DefinedData is in order.  

DefinedData is DefinedCrowd’s online data marketplace. We currently have more than 15,000 hours of speech data, with plans for expansion in the upcoming months. Training datasets from DefinedData are vetted before becoming available on the platform, and are supplied by both DefinedCrowd and third-party vendors.  

The DefinedData marketplace was created to address the common pain points listed above: 

  • Datasets are immediately available.  
  • Datasets are radically transparent, ensuring diverse datasets that will reduce bias.  
  • The catalog is updated continuously with fresh, new data, particularly through subscription options. 
  • Detailed metadata allows for a better understanding of the quality of the datasets.  
DefinedData Marketplace: Speech Data and More  

The DefinedData marketplace is redefining transparency when it comes to speech data. The DefinedData catalogue users to access audio samples and basic information about the datasets including number of speakers, locale, language, country, gender and accents. Additionally, the marketplace gives detailed metadata about the phonetic, age, gender and accent distribution with visual breakdowns and detailed statistics.   

An optimized shopping experience 

There have been several updates to DefinedData that provide a better overall shopping experience. This includes the ability to search and browse datasets, listen to audio samples, and download sample voice datasets. All these features are designed around transparency: to train bias-free models, you need to know exactly what type of data you’re training the model with.

Looking towards the future 

The key to success is to keep growing, and we want to evolve DefinedData as quickly as AI itself. In the upcoming year, DefinedData will continue to develop new features to bring transparency and quality to the training data market, including catalog expansion, subscription plans and affiliate marketing options.  

The Devil is in the Data

While artificial intelligence is certainly powerful, the real power ultimately lies with humans – it’s up to us to decide how we will train AI for its potential use. From creating a super-personalized experience for a customer to promoting a more diverse and inclusive society, it all depends on the data with which we train AI models.  

DefinedData is democratizing access to training data and promoting radical transparency. Take a moment to explore datasets or request a data sample.