10 Companies That Offer AI as a Service (AIaaS)

There's a wake-up call for businesses in 2023: adopt artificial intelligence or risk falling behind.

According to a McKinsey report, about 30% of tasks in 60% of occupations could be automated, and current generative AI has the potential to absorb 60% to 70% of employees' time today.

For many, though, the thought of integrating AI might seem daunting. That's where Artificial Intelligence as a Service (AIaaS) comes in.

In this post, we'll guide you through the emerging AIaaS market and introduce 10 companies you can start working with.

Table of Contents

• What is AI as a service?
• How can businesses leverage AIaaS?
• 10 Companies That Offer AI as a Service

What is AI as a service?

AI as a Service, or AIaaS, makes advanced artificial intelligence solutions readily available to businesses through cloud computing.

Companies use these services to access machine learning algorithms, data pattern recognition, natural language processing, predictive analytics, and more. Think of it as AI without a huge data science team.

Much like SaaS (Software as a Service), AIaaS products allow businesses to use their solutions on a subscription basis.

How can businesses leverage AIaaS?

Coca-Cola

Coca-Cola's vending machines powered with AI analytics and its collaboration with ChatGPT and Open AI are straightforward examples of how businesses can be creative with AIaaS.

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Coca-Cola and AI Vending Machines

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Coca-Cola Bottlers Japan (CCBJ), Asia's leading Coca-Cola bottler, turned to data analytics to optimize product distribution in its 700,000 vending machines across the region.

They developed a predictive model to determine optimal vending machine locations, the right product lineup within each machine, pricing strategy, and expected sales volume.

In doing so, Coca-Cola utilized Google's Vertex AI, BigQuery analytics data warehouse, and AutoML for tabular data.

This AIaaS implementation highlighted how data analytics and machine learning could drive operational efficiency and business insight.

Coca-Cola and OpenAI

In February 2023, Coca-Cola pioneered a partnership with OpenAI, utilizing its DALL-E2 model and ChatGPT for innovative marketing activities, like its AI-powered "Masterpiece" campaign.

The campaign wove together iconic artworks from different eras, narrating the journey of a Coca-Cola bottle as it travels to a student seeking inspiration.

This amalgamation of live-action shots, digital effects, and AI was created by Electric Theatre Collective's VFX team and the creative agency Blitzworks.

Starbucks

In collaboration with Microsoft, Starbucks has developed an innovative, AI-based recommendation engine named "Deep Brew."

This tool was designed to provide customers with pertinent product suggestions across digital menu boards and in-app ordering.

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Deep Brew employs advanced reinforcement learning techniques, enabling it to adapt to customer preferences and various situational factors such as time of day, weather, and location.

This sophisticated AI platform is supported by Microsoft's Azure infrastructure, known for its scalability and flexibility.

Also, the Deep Brew project enhanced Starbucks' line of Mastrena super-automatic espresso machines.

These machines are sensor-enabled, meaning every espresso shot is recorded and centrally analyzed to optimize brewing processes and identify maintenance needs.

By integrating this Internet of Things (IoT) technology with Deep Brew, Starbucks can predict and proactively address machine issues.

HubSpot

We've developed ChatSpot, an AI bot that uses chat-based commands to interact with your CRM data.

ChatSpot will allow you to perform tasks (like sending emails) or pull data insights (like creating custom reports) by entering a text prompt. With it, you accelerate yourself and accomplish everything you already do in HubSpot twice faster.

See how the Co-founder of HubSpot, Dharmesh Shah, uses ChatSpot to carry out routine marketing and sales tasks with the bot.

Try it out for free.

10 Companies That Offer AI as a Service

1. Google Cloud AI

Google's AIaaS offering, known as Google Cloud AI, provides developers with a suite of machine learning services.

Its unique offerings include AutoML, which allows developers to train custom machine learning models with minimal coding, and AI Hub — a one-stop destination for AI content.

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It's perfect for businesses looking to improve their analytics, develop AI-powered applications, or incorporate AI into their existing systems.

On top of that, Google Cloud offers brands to enable a more intelligent supply chain, smart factory, sustainability, systemic agility, and productivity.

In a nutshell, Google Cloud AI is a versatile means to fulfill any task for retail, media and entertainment, financial services, telecommunications, and more. You can reduce costs and optimize your value chain.

Case Study: P&G

Procter & Gamble (P&G) has been utilizing Google Cloud to enhance and personalize the consumer experience and leverage other Google Cloud capabilities for several tasks:

• To offer consumers the best selection of products at their local stores and reach them via their preferred channels.
• To store and analyze vast amounts of brand and marketing information.

On top of that, P&G turned to Google Cloud's BigQuery and developed a data lake, allowing a unified view of consumers and the creation of omnichannel consumer journeys.

Moreover, thanks to Google Cloud services, you can see connected products offering personalized services.

Examples include Lumi by Pampers, which helps parents monitor their babies' sleep habits and diapers, and the Oral-B iO toothbrush that aids users in improving their cleaning routines.

2. Microsoft Azure AI

Microsoft Azure AI is a comprehensive suite of AI services and cognitive APIs. With services like Azure Machine Learning, businesses can build, train, and deploy machine learning models for any task.

Its distinguishing feature, Azure Cognitive Services, enables developers to add intelligent features like vision, speech, and language understanding into applications.

What we really like are the Spatial Anchors and Azure Digital Twins services. The former allows for the creation of rich, immersive, 3D mixed-reality apps. The latter is for creating digital models of entire environments.

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3. IBM Watson

IBM Watson is a powerful AI service for businesses to predict and shape future outcomes, automate complex processes, and optimize employees' time.

Watson includes pre-trained AI services like Watson Assistant, which allows for building conversational interfaces into any application, device, or channel.

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Case study: LegalMation

LegalMation turned to Watson to liberate lawyers from mundane tasks, such as early-phase documentation drafting.

The organization saw an estimated 80% reduced costs and an unimaginable drop in drafting time from 6-10 hours to under 2 minutes for a document.

To accomplish such results, LegalMation assembled a team of subject matter experts (SMEs).

They used IBM Watson Knowledge Studio and IBM Watson Natural Language Understanding to create a domain-specific model focused on legal terminology and concepts.

4. AWS AI

Amazon Web Services (AWS) offers a wide range of AI servicesto help businesses build machine learning models and add intelligence to applications.

Key offerings include Amazon SageMaker for developing, training, and deploying machine learning models. Further, Amazon Rekognition can add image and video analysis to applications.

AWS AI is a great way to leverage AI without machine learning expertise.

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For example, with Amazon Rekognition, developers can amplify their products with:

• The Face liveness feature — to detect real users and deter bad actors.
• To search and compare faces.
• The Face detection and analysis feature — to recognize facial expressions and emotions.
• Content moderation for unsafe or inappropriate inclusions across videos or imagery.
• Text detection.
• Celebrity detection.

5. DataRobot

DataRobot provides an AIaaS solution that streamlines the process of creating, implementing, and managing artificial intelligence and machine learning systems at scale. Its cornerstone feature is to automate machine learning.

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Consider a financial services company that wants to leverage AI to improve its credit risk assessment process.

The traditional approach would require a team of data scientists to manually develop, test, and refine many models. With DataRobot's platform, however, the company can automate much of this work.

Developers can simply feed historical loan data into the platform and let DataRobot's automated machine-learning algorithms train hundreds of models and identify the one with the highest predictive accuracy.

6. OpenAI

OpenAI is famous for AI models like GPT-3, a powerful language model, as a service.

OpenAI's API allows developers to build applications that can draft emails, write code, create written content, answer questions, and even generate images.

Some companies that use OpenAI models:

• Stripe leverages GPT-4 to streamline the user experience and combat fraud.
• Jasper AI's engine combines a cross-section of the best models out there — OpenAI's GPT-4, Anthropic, and Google's models to enrich the generated text with recent search data and mimic your brand voice.
• Duolingo uses GPT-4 for conversation practice and contextual feedback on mistakes.

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7. Clarifai

Clarifai provides AI as a service with a focus on computer vision. It offers pre-trained image and video recognition models and allows developers to build, manage, and deploy machine learning models.

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Clarifai is ideal for businesses seeking AI solutions for visual recognition tasks like:

• Semantic segmentation.
• A moving object tracking.
• Image classification.
• Visual search.
• Large geographical scanning.
• Surveillance and reconnaissance.

8. BigML

BigML offers an AI platform that enables users to create and deploy machine learning models. It has a user-friendly interface that simplifies the process of training models, making it accessible to non-experts.

It's perfect for businesses wanting to adopt machine learning but lacking the necessary expertise.

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With BigML, you can:

• Build and integrate machine learning models using BigML's REST API.
• Automate and share workflows with BigML's domain-specific language for machine learning.
• Approach supervised and unsupervised learning: classification and regression and time series forecasting. Plus, anomaly detection and topic modeling.
• Build partial dependence plots to effectively generate and display thousands of model predictions.

9. H2O.ai

H2O.ai is an automated machine-learning platform for enterprises.

Thus, H2O Driverless AI empowers data scientists or analysts to work on projects faster by automating data visualization, feature engineering, model development and validation, model documentation, and machine learning interpretability.

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H2O Driverless AI operates on CPUs and GPUs for high-performance computation and examines thousands of model variations and combinations, swiftly pinpointing the optimal model in minutes or hours.

Likewise, AI Wizard explores your data and business requirements and gives instructions on the appropriate machine-learning techniques to select based on your unique data and use case requirements.

10. RapidMiner

RapidMiner is a data science platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. Among customers are Sony, Canon, Domino's, Bloomberg, and BMW.

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RapidMiner's key capabilities include:

• Model building.
• Data engineering.
• ML Ops.
• Visual analytics workflow.
• Automated data science.

So, what can you do with all of these?

For example, optimize prices, detect fraud, prevent churn, collate large data sets and retrieve patterns, or customer segmentation for targeted ads.

Case Study: 160over90

Creative agency 160over90 resorted to data mining to conduct market research and uncover high-performing messaging, customer segments, and more.

The Future of AI as a Service

The AIaaS market is growing and for good reasons. With the exponentially increasing amount of data, businesses will need more advanced AI tools to process and derive meaningful insights from it.

The future is now, and it's time to act.