15 Companies That Offer AI as a Service (AIaaS)

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In today's booming AI landscape, I've often wondered, "Is there a way for me to automate this totally boring task?" The answer is usually yes! There are Artificial Intelligence as a Service (AIaaS) companies that can make my job easier. I've used AI to help me transcribe interviews, generate metadata for my blog posts, and caption images so they're accessible and SEO-optimized.

I'm not alone in making the most of the AI tools at my fingertips. 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. It's all about finding the right AIaaS tool for the job.

In this post, I'll guide you through the emerging AIaaS market and introduce 15 companies you can start working with today.

Table of Contents

• What is AI as a service?
• 15 Companies That Offer AI as a Service
• How Businesses are Leveraging AIaaS

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. I could just find a service that meets my needs and plug it into my workflow.

Often, that convenience comes with a price. Much like SaaS (Software as a Service), AIaaS products allow businesses to use their solutions on a subscription basis. However, there are tools that are free or offer a free version with limited capabilities.

15 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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Google Cloud AI is perfect for businesses looking to improve their analytics, develop AI-powered applications, or incorporate AI into their existing systems.

In my opinion, Google Cloud meets a real need in the market. Businesses want to leverage the power of AI, but not every team will have an AI developer in-house who can build new systems from scratch. I'm excited to see how medium-sized businesses will use Google Cloud's models to level up their operations, much like the larger players.

I'm also excited about what Google Cloud can accomplish related to sustainability. Currently, it's tough for businesses to see how materials are sourced and manage their supply chain every step of the way. Google Cloud has use cases specific to intelligent supply chain and smart factory. That can help teams ensure materials are ethically and sustainably sourced.

Case Study: P&G

Procter & Gamble (P&G) has been using Google Cloud capabilities to enhance and personalize the consumer experience. The AI service provider helps with tasks like:

• Offering consumers the best selection of products at their local stores and reaching them via their preferred channels.
• Storing and analyzing vast amounts of brand and marketing information.

On top of that, I love how 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, I found personalized product offerings when researching this initiative. Thanks to Google Cloud services, I noticed new connected products. 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. BigML

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

What I like: 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.

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.

I want to take a moment to note that IBM Watson isn't new. The tech company launched this AI offering in 2010. In fact, Watson was first developed to answer Jeapordy!-style questions, even competing on the show in 2011.

Today, Watson includes pre-trained AI services like Watson Assistant, which allows for building conversational interfaces into any application, device, or channel. Watson has grown into a full suite of offerings that make day-to-day business a breeze.

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Case study: LegalMation

I have friends who are lawyers, and from what I can tell, the job involves a ton of paperwork. That's why my interest was piqued by LegalMation's use case for Watson.

LegalMation is a suite of AI solutions for lawyers and litigation attorneys. The company turned to Watson to liberate lawyers from mundane tasks, such as early-phase documentation drafting.

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.

I was impressed by the results. 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.

4. Alibaba Cloud

Similar to Azure and AWS, Alibaba Cloud offers cloud computing and AI services. Founded in 2009 by the ecommerce giant Alibaba Group, it has since become a major player in the cloud computing market, particularly in China and the Asia-Pacific region.

The platform has key features such as databases, networking, image recognition, analytics, and security solutions that allow you to harness AI's power for valuable data, enhanced decision-making, and efficient operations.

Alibaba Cloud became the Olympics partner back in 2017. The goal is to allow the Games event to operate more efficiently, effectively, and securely.

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5. Altair RapidMiner

Altair 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.
• Machine learning operations.
• Visual analytics workflow.
• Automated data science.

These are helpful for optimizing prices, detecting fraud, preventing churn, collating large data sets and retrieving patterns, or customer segmentation for targeted ads.

Case Study: Fraud Detection

Patients, medical providers, and healthcare businesses are often prone to healthcare fraud, which may be hard to detect and identify.

In a detailed piece by CIOCoverage, they mention that one of such healthcare companies used the Altair RapidMiner platform to collect data and flag fraud cases on a bigger scale, as well as integrate various new methods to catch fraud quickly.

The outcome was the fast identification of a whooping $20 million fraud. Plus, the healthcare company now detects and prioritizes high-risk cases, reduces the time spent on inspection, monitors fraud patterns, and prevents new fraud cases.

I was fascinated by this AI use case. Artificial intelligence is able to parse through large sets of data, finding patterns faster than most people. I love how this case study showcases how AI can save so many people money and headaches.

6. 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.

7. Salesforce Einstein AI

Salesforce Einstein AI combines CRM, AI, and data to help businesses streamline internal processes, gain valuable insights, make smarter decisions, and provide personalized customer experiences.

Embedded with key features such as predictive analytics, natural language processing (NLP), chatbots, and virtual assistants, it's a great AI tool for accelerating growth and transforming business operations.

Einstein AI is integrated into various Salesforce products, including Sales Cloud, Marketing Cloud, and Service Cloud, allowing you to harness its capabilities across your sales, marketing, and customer service processes. The result? Improved efficiency, increased productivity, reduced costs, and enhanced customer trust.

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Case Study: Gucci

Gucci, renowned for its luxurious fashion products, wanted to provide personalized customer experience across all touchpoints, including its digital channels. So, they turned to Salesforce to provide a unified platform that connects their stores and mobile app.

This helped them create "brand-ready messages" to Gucci advisors, ensuring consistent brand voice and faster response times. It also offers omnichannel support, allowing clients to connect with advisors via their preferred channels, such as WhatsApp, SMS, or WeChat, for efficient issue resolution and product information.

As a marketer, I appreciate when AI can give me a boost, providing me a launch pad for consistent messaging. That's especially true if I'm trying to craft copy quickly. I can only imagine how helpful this would be for my colleagues in the service world.

8. AWS AI

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

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

What I like: 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 is used to recognize facial expressions and emotions.
• Content moderation for unsafe or inappropriate inclusions across videos or imagery.
• Text detection.
• Celebrity detection.

9. ServiceNow

ServiceNow is a cloud-based platform that helps businesses automate tasks, improve efficiency, and deliver a better employee and customer experience. In fact, if you work in the tech world, you've probably encountered ServiceNow somewhere in your workflow.

It boasts features such as IT service management, HR service delivery, customer service management, and an integration hub, making it an excellent tool for businesses looking to improve efficiency and boost productivity.

One unique feature we like about the ServiceNow platform is its virtual agent. It uses intelligent chatbots to boost customer support and employee productivity by providing instant resolutions to common queries.

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Case Study: Coca-Cola Hellenic Bottling Company (CCHBC)

CCHBC leveraged the ServiceNow platform to improve its IT operations and employee experience.

The bottling company previously relied on an old-fashioned, legacy system for managing IT requests and changes. As such, tracking issues, automating processes, and providing a smooth experience for employees proved challenging.

However, after implementing ServiceNow's AI solutions, CCHBC achieved significant results such as a 20% increase in project efficiency, an average of 3.5 to 4 hours for resolving critical incidents, and 150k hours given back to employees.

I remember the days of old-fashioned ticketing (which, in the grand scheme of things, wasn't that long ago). AI has already made it faster for teams to manage requests, and I'm grateful for the tools that power that experience.

10. 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 I like: 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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Case Study: TomTom's Digital Cockpit

When I think of TomTom, I think of the company's GPS technology back when location technology was all the rage. It helped me get from point A to B without getting lost, technology that felt like magic at the time.

Well, the company is once again using new technology to level up. And this time, they're using technology from Azure. With a boost from Azure, TomTom developed the Digital Cockpit, an immersive in-car infotainment system that car manufacturers can customize. AI allows the drives to interact with their cars seamlessly - no need to use a smartphone.

According to Microsoft Azure's website, Azure allowed the product team to shift from 10 to 3 people. Beyond that, query response times improved from 12 seconds to 2.5 seconds.

11. OpenAI

OpenAI is one of the most famous AI service providers. It is known for powerful AI language models like GPT-3, 3.5, and 4.

As a content marketer, of course I've taken ChatGPT out for a spin. I've used it to help make social copy, to generate emails, and I've even tried to see if it could write me a full article. Here's what I've found: ChatGPT is a powerful tool when leveraged property. Human intervention is required.

I've also found ChatGPT very helpful when augmenting skills that I'm still learning. For example, I've used ChatGPT to help me write code to modify my website. I could have figured out the right string of commands myself, but that would have taken lots of Googling and testing. ChatGPT eliminated these steps and saved me time.

So, how does that play into AIaaS? 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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12. H2O.ai

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

Its 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.

13. DataRobot

DataRobot is an AI service provider 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.

Case Study: FordDirect

Cars are big purchases that require careful thought and consideration. If I were on the market for a new set of wheels, I would want the sales experience to be highly personalized. Marketing and sales collateral would ideally be catered to my needs so I could make the best decision.

Enter FordDirect, a partnership between Ford and Lincoln dealerships and Ford Motor Company. This digital marketing solution provider helps dealers understand who their customers are and well-timed personalized touch points. This insight means understanding thousands of customer signals from across Ford. That's where DataRobot stepped in.

DataRobot's AI Platform improved FordDirect's Customer Journey Platform, powering recommendations, optimizations, and direct signals that teams rely on. According to a DataRobot case study, the switch allowed FordDirecto to decrease its technology debt by $3 million. Further, the time it takes from data access to implementation is now 75% faster.

14. Oracle Cloud Infrastructure

Oracle Cloud Infrastructure (OCI) provides a robust cloud computing platform for businesses of all sizes that seek a robust, secure, and cost-effective AI solution.

Focusing strongly on performance and reliability, Oracle's range of cloud services include compute, storage, networking, analytics, databases, and security, which support businesses in building, deploying, and managing various applications.

It's compatible with Oracle's on-premises software and applications, making migrating your IT infrastructure to the cloud a breeze.

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15. MonkeyLearn​​

MonkeyLearn is a cloud-based text analysis platform that allows businesses to extract valuable insights from their text data. Think customer reviews, social media posts, emails, or surveys.

You can choose from various pre-trained machine learning models or train your custom models to handle tasks such as sentiment analysis, topic classification, and entity extraction.

MonkeyLearn's simple, easy-to-use interface and extensive customization options make it a dream tool for businesses looking to analyze data efficiently, make informed decisions, improve customer experience, and identify new opportunities.

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How Businesses are Leveraging 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.

Want to hear this story? I found it fascinating.

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 as fast.

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

Try it out for free.

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.

As a marketer who has embraced AI into my workflow, I get two core benefits: time and cost savings. The above tools offer both (and more). So, if you're looking to optimize your processes, automate repetitive and mundane tasks, and drive business growth, consider including AI in the mix.

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

Editor's note: This post was originally published in July 2023 and has been updated for comprehensiveness.

Topics: Artificial Intelligence

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