By all accounts, Artificial Intelligence has a profound impact on businesses across industries around the world. A recent Forbes Advisor survey revealed that 97% of business owners believed that ChatGPT will improve their operations. This is a staggering figure considering the platform was launched just under a year ago (November 2022).
Another study by Verta Insights found that 31% of companies plan to increase their AI spend for business strategy, cloud migration, and cost savings. This trend is expected to continue as the use of AI becomes more widespread.
With this flurry of activity, serious AI platforms are emerging to provide the infrastructure that businesses and individuals will build revolutionary AI products.
We have sampled the top AI platforms right now. This list will continue to be updated as we continue to review the AI platforms, as they emerge.
The top AI platforms right now
While there are hundreds of AI use cases in business, Statista names financial service companies as the biggest AI adopters (30%); nearly all industries show a 20% increase in AI use across all their functions.
Further, a McKinsey survey indicates that AI works best in sales and marketing, supply chain management, and manufacturing. These business functions use AI platforms for content generation, data analytics, automation, and more.
Here are the top 10 AI platforms that you can start to leverage right away.
#1. Google AI Platform
Google AI Platform is a suite of tools to create AI-based applications that run on Google Cloud. Businesses with machine learning projects like sales predictions, fraud detection, sentiment analysis, and quality control can use Google’s AI tools to accelerate development and deployment. These tools include:
This is an experimental collaboration tool that uses generative AI to improve creativity and productivity. It is a large language model (LLM) designed to complement Google Search. Businesses can use Bard to automate customer service, create marketing campaigns, conduct audience research, and test new products.
2. Studio Bot
This is an AI-powered coding assistant for developers to get feedback about their code using plain English. It boosts productivity when finding bugs and vulnerabilities.
PaLM API helps developers create AI-based apps while MakerSuite streamlines their workflows. Businesses can use these tools to create app prototypes and reduce time-to-market.
This platform is designed for data scientists and machine learning engineers to create and train models for automating customer service, structuring document data, or detecting money laundering.
This is Google’s text-to-image AI generator that creates photorealistic images. Businesses can use this tool to design marketing materials, create presentations for Google Slides, or create product images without investing in a prototype.
#2. Amazon SageMaker
SageMaker is AWS’s machine learning service for data scientists, ML experts, and software engineers. It offers a simplified workflow for machine learning operations (MLOps), enabling automation from end to end.
This means that developers or engineers involved in quality assurance, project management, and CI/CD can work faster and with fewer errors. SageMaker also integrates DevOps principles into machine learning workloads, placing emphasis on collaboration, communication, and continuous testing and monitoring.
Amazon SageMaker is suitable for organizations in need of ML forecasting from in-house data. These include:
- Medical practices processing X-rays and other medical images for visual signs of diseases
- Legal practices analyzing past cases to find precedents for current and future litigation
- Financial services seeking patterns of fraud or money laundering out of millions of transactions
- Marketers looking for customer browsing habits to tailor digital ads and improve lead quality
- Manufacturers seeking seasonal and economic patterns to streamline their supply chains.
#3. Microsoft Azure Machine Learning
Microsoft Azure Machine Learning offers similar features to the AWS offering. However, Azure ML has a studio in which users can train AI models without writing code. It also enables developers to dive into AI and machine learning without having data science or analytics backgrounds.
The platform uses pre-configured drag-and-drop cloud resources with default settings to create AI models. Developers can then delete unnecessary resources to reduce charges on their cloud subscriptions.
The Azure pay-as-you-go model encourages small businesses with budget and workforce limitations to venture into AI.
Some use cases for Azure Machine Learning include:
- Automated stock trading systems that use investor data and financial trends to predict interest in buying or selling
- Product recommendation engines to upsell and cross-sell products on e-commerce websites
- Speech-to-text engines based on natural language processing (NLP) to transcribe audio into numerous languages
- Customer service chatbots to semi-automate interactions for multinational businesses with a global customer base.
#4. IBM Watson Studio
The IBM Watson Studio platform is the go-to solution for businesses that have valuable raw data in silos, i.e., information that one department can access but not others.
IBM Watson Studio is built on the Cloud Pak for Data infrastructure which connects siloed data in a multi-cloud environment. Watson Studio then builds AI models using all business data no matter where it resides (on-premise, cloud, multi-departments). Like SageMaker, Watson Studio is an MLOps service that enables data scientists to collaborate on their AI model development lifecycle. It also offers flexible subscription models to deploy AI models, including long-term commitment pricing and pay-as-you-go.
IBM Watson Studio offers businesses the following key benefits:
- Utilizing natural language interfaces and interactive templates to create predictive AI models
- Simultaneously training data scientists and software developers about AI modeling
- Creating AI-based risk models that meet compliance requirements, protecting businesses from risk exposure
- Using drag-and-drop AI models, making coding languages optional in machine learning.
TensorFlow is a free, open source library developed by Google for developers to learn AI and ML. This platform is the easiest way to build and train AI models, which explains its recent popularity. It allows developers to deploy models on Android devices, web browsers, and servers. It also supports a range of programming languages and cloud vendors. Notable businesses use TensorFlow to build and power their AI solutions, including:
TensorFlow was used to train the MyCokeRewards loyalty program app to recognize product codes. Users scan the codes on soda bottle caps and fridge-packs using their smartphones to participate in promotions.
Machine learning helps MRI scan operators and radiologists to identify anomalies in brain anatomy. The TensorFlow platform contributes to GE Healthcare’s ambition to create a fully autonomous MRI scanner. It places more focus on patients than machine settings.
TensorFlow orchestrated Spotify’s data pipelines and running experiments to improve end user experience. It enabled the platform to give personalized music recommendations for millions of users from millions of tracks, albums, and playlists.
PyTorch is the ideal AI framework for Python developers to create deep learning models. It is a production-ready platform that’s user-friendly and compatible with all leading cloud vendors.
Businesses can use PyTorch when they’re ready to scale their applications and workflows as their databases grow. The best uses of PyTorch include:
Image recognition and classification
If a business has vast product inventories, it can use PyTorch to:
- Identify low stock
- Implement self-checkouts
- Avoid double-counting
- Eliminate human errors.
Text classification and translation
Businesses can use PyTorch to automate tasks that require natural language processing (NLP). These include translating websites and marketing content or integrating customer service chatbots.
PyTorch also has pre-made models with multi-language databases for converting audio into text. You can use PyTorch to transcribe meetings and customer service calls for record-keeping.
The H20.ai platform prioritizes speed and agility in building AI/ML solutions to accelerate business growth. Its main selling point is low-code and no-code development using web-based applications. H2O.ai can turn unstructured data, e.g., images and documents, into AI-ready models. It then extracts valuable information from the data to improve productivity and decision-making. The platform’s use cases include:
Businesses can use H2O.ai to automate essential financial workflows like:
- Cyber-threat detection
- Money laundering detection
- Real-time accounting
- Loan predictions.
For providers, i.e., doctors, hospitals, and medical suppliers, H2O.ai can:
- Automate referrals
- Predict hospital capacities
- Predict patient stays and readmissions
- Detect anomalies in X-rays.
For health insurance companies, H2O.ai can create models to detect medical claim fraud, overpayment, and pricing inaccuracies.
H2O.ai can improve business processes such as:
- Predicting component wear and tear
- Streamline transportation and supply chains
- Designing products with the highest likelihood of success.
H2O.ai models can score leads, segment customers, and personalize marketing content and offers using e-commerce data.
Dataiku is one of the leading data science AI companies today, with clients such as Cisco, Ubisoft, Unilever, and GE.
The Dataiku platform comes with data visualization features, MLOps and Data Ops workflows, and collaboration tools. These tools are accessible to everyone across the enterprise. The platform mainly targets business analysts, data scientists, and CEOs.
While Dataiku has similar use cases as H2O.ai, the platform stands out because of its simple interface. It is designed to enable technical and non-technical users to leverage AI, rather than leaving it to experts. Its built-in automated machine language (AutoML) algorithms remove the complexity from model design and training. Other Dataiku features include:
- Data lifecycle management: Users can prepare, analyze, and integrate data on one platform instead of switching between different tools at each step.
- Transparency: Dataiku comes with versioning, sharing, and auditing features for data workflows, enabling compliance and accountability at every level.
- Integrations: Dataiku exchanges data from various sources, including on-premise and cloud-hosted files, APIs, and other databases to train ML models seamlessly.
MonkeyLearn is a simple but powerful AI Platform that specializes in text analysis for businesses. It requires no coding experience, so users can upload or link their data sources, choose an analysis template, and process their data in minutes.
The pre-trained ML models integrate with business intelligence tools like Tableau and Power BI. Marketing teams can then create custom dashboards and charts on the MonkeyLearn platform.
This tool can help businesses with the following types of analysis:
Net promoter score (NPS)
This involves analyzing customer sentiment from comments across marketing platforms. It can help minimize churn and boost loyalty with better messaging and targeting.
MonkeyLearn analyzes customer reviews from sources like TrustPilot, G2, or app stores and marketplaces. The AI insights can help businesses generate more leads and identify common problems with their product features.
Customer satisfaction (CSAT)
Even with open-ended responses, MonkeyLearn AI can extract and pinpoint experiences that customers have with a product or service. This data helps customer success and product management teams to respond with solutions that address customer pain points.
MonkeyLearn can analyze support tickets to identify recurring problems for each product and feature. It also enables businesses to monitor and score support agents, giving additional training or coaching to resolve customer issues and reduce tickets.
MonkeyLearn addresses the challenge of cleaning and organizing survey data through automation. Business strategists can identify the most common interests and trends from survey data, then target products and offerings to meet customer needs.
Voice of the customer (VOC)
Businesses can analyze VOC to keep their audiences engaged, creating marketing content that speaks in their voice. This boosts lead generation, closes sales, and resolves tickets.
Synthesia is a video creation platform that uses AI to simplify the production process. It can generate high-quality videos from text inputs, use realistic AI avatars, and natural-sounding voices in over 120 languages. The Synthesia video editing interface is also designed as a slide deck, eliminating the need for specialized editing software.
You can convert presentation slides and PDF documents into engaging videos, then embed them to marketing or teaching platforms. Synthesia’s case studies indicate impressive results for businesses, e.g., 70% better training outcomes and 50-80% reduction in video production costs. This translates to a significant ROI for video marketing content. Other features of this platform include:
- Built-in script generators using a native ChatGPT integration
- Drag-and-drop video editing on a clean, intuitive browser interface
- Customized voice and avatar cloning to create a realistic version of oneself
- Ready access to royalty-free images, videos, animations, and soundtracks
- Content moderation to uphold ethics and compliance for AI-generated materials.
How are AI platforms impacting businesses?
AI platforms have an undeniable impact on businesses today. What began as accelerated AI adoption due to Covid-19 disruptions has now turned AI into a must-have business resource. AI platforms are already transforming businesses in the following key areas:
- Automating routine operations, particularly using chatbots in customer support
- Making data-driven decisions in strategic planning and business problem-solving
- Improving productivity by integrating AI into workflows
- Improving talent screening and recruitment with AI chatbots, automated feedback, and ML-powered interview assessments
- Tailoring product offerings for customers who increasingly prefer brands that care for their wellbeing.
As AI platforms move toward low-code and no-code user interfaces, the entry barrier into machine learning will gradually reduce.
More businesses and individuals will harness these technologies without specialized knowledge in programming, or as in Synthesia’s case, video production.