Since OpenAI launched ChatGPT, generative AI technology has rapidly expanded across industries.1 This spread led businesses to utilize various services to build and implement generative AI tools effectively.
Here, we explore seven types of generative AI services that help businesses gain a competitive edge:
Table 1. All generative AI service categories and vendors
| Service Category | Service Provider Examples |
|---|---|
| Generative AI strategy & use case identification services | McKinsey & Company Bain & Company Accenture Boston Consulting Group (BCG) |
| Generative AI training data collection services | |
| Generative AI hardware solution providers | |
| Generative AI foundation model providers | OpenAI |
| Generative AI training and development services | H2O.ai DataRobot Microsoft Azure AWS SageMaker |
| Generative AI applications | List of generative AI applications |
| Reinforcement Learning with Human Feedback (RLHF) | Clickworker Prolific |
How did we select the service provider for this article
The list above is not comprehensive; only leading products are listed based on publicly available data, such as the number of employees and the number of user reviews on leading B2B review platforms.
1. Generative AI strategy & use case identification services
A sound strategy is essential for any business planning to integrate generative AI into its business processes. This can be challenging because it requires a deep understanding of both AI technologies and the specific business context, including:
- Operational needs
- The skills of the existing workforce
- Ethical considerations
- The potential impacts and risks of AI deployment.
Strategy services help develop this roadmap, and top players include:
McKinsey & Company
McKinsey helps businesses leverage generative AI by advising on the selection and application of AI tools for tasks like content creation, automated design, and product development. It also provides strategic support for implementing and scaling these tools to improve operational efficiency and enhance competitiveness.
Bain & Company
Bain & Company assists businesses in utilizing generative AI by developing customized strategies that integrate AI-driven creative and predictive capabilities into their existing workflows. Additionally, they provide advisory services aimed at ensuring the ethical, effective, and scalable deployment of these AI technologies, thereby stimulating innovation and growth.
Accenture
Accenture’s AI strategy services are aimed at helping businesses identify and implement AI use cases, including generative AI applications.
Boston Consulting Group (BCG)
BCG supports businesses in using AI by advising on how to integrate AI into their processes and decision-making. The company helps organizations identify relevant AI applications, implement solutions, and scale technologies to improve operational efficiency and performance.
2. Generative AI training data collection services
Generative AI models require large amounts of data to be trained. Software developers can work with data collection services to fulfill their data needs without facing the data collection challenges and solutions. These services focus on data collection, preprocessing, annotation, and other services involved in preparing a training dataset for generative AI models.
Popular names in the market are:
Clickworker
Clickworker is a prominent crowdsourcing platform that provides human-generated data collection and annotation services to support the training of generative AI models and LLMs.
This service provider is good for large-scale projects due to its large network of contributors.
Appen
Appen offers data annotation and model training for generative AI, supporting tasks like natural language processing, image generation, and speech synthesis. Appen is well-suited for small to mid-sized projects, supported by its moderate-sized network of global contributors.
Amazon Mechanical Turk
Amazon Mechanical Turk offers a marketplace for data labeling and verification services for generative AI models, including text generation, image tagging, and content moderation. MTurk is suitable for small-scale projects due to its small network of participants.
3. Generative AI hardware solution providers
During training, generative AI systems require high-performance computing capabilities to efficiently process and learn from massive amounts of data, necessitating specialized hardware such as GPUs or TPUs.
Once the model is trained, similar hardware is used for inference (e.g., to run the pre-trained models).
Working with a third-party service provider can help you achieve such computational capabilities. These services provide specialized hardware to help train and run generative AI models efficiently.
There are a few different types of service providers which allow companies to trade-off between capital expenses and operating expenses:
- AI hardware companies like NVIDIA and AMD provide powerful GPUs that are crucial for training generative AI models due to their parallel processing capabilities. These companies mainly serve data centers, enterprise clients, and consumers in industries such as computing, gaming, and professional graphics.
- Cloud GPU providers: You don’t need to own AI hardware to use it. Hyperscalers like AWS, Google and other cloud providers provides GPUs on their clouds. Some providers like Google provide their own hardware. For example, Google’s Tensor Processing Units are only available on GCP and are designed specifically for neural network machine learning.
- Serverless GPU providers: Serverless GPUs facilitate GPU scaling and management.
- Inference APIs: Instead of managing hardware, users can use APIs to receive answers from large language models or other AI systems.
4. Generative AI foundation model providers
AI foundation models are the backbone of any generative AI system. These are complex models trained on extensive datasets and can generate outputs in a range of tasks without task-specific training data.
Two notable services in this area include:
OpenAI
OpenAI is renowned for its GPT series, including GPT-3, GPT-4, and its latest o1 reasoning models. These models are powerful language processors capable of generating human-like content, answering questions, and performing various language and programming tasks. Generative AI models are commonly applied in areas such as content creation, virtual assistants, programming support, and other language-driven tasks.
Google has developed several generative AI models such as BERT, T5, and LaMDA (Language Model for Dialogue Applications). These models are used to enhance search algorithms, power conversational agents, and assist in tasks like translation and summarization.
5. Generative AI training and development services
Training a generative AI model is a challenging process that requires specialized skills because it involves:
- Understanding complex algorithms
- Optimizing neural network architectures
- Handling large datasets
- Fine-tuning models to generate high-quality outputs while avoiding pitfalls such as overfitting or mode collapse.
Third-party service providers can help streamline the whole development process.
Such services include:
H2O.ai
H2O.ai offers a machine learning platform that helps build AI models to improve business operations, including generative AI, without necessarily having an extensive background in AI.
DataRobot
DataRobot provides an enterprise AI platform that enables users to prepare data, build, train, and deploy machine learning models, including generative models.
Microsoft Azure
Azure’s Machine Learning service provides a suite of tools to build, train, and deploy machine learning models, including support for generative AI.
AWS SageMaker
Amazon SageMaker is a fully managed AWS service that enables users to build, train, and deploy machine learning models, including those for generative AI.
6. Reinforcement Learning with Human Feedback (RLHF) service providers
Reinforcement Learning with Human Feedback (RLHF) enhances traditional reinforcement learning by integrating human input to guide model training and align outputs with human expectations.
Usually, companies with a large network of contributors, like crowdsourcing platform/service providers, offer RLHF services. The services train AI models using a blend of algorithmic learning and human feedback, which refines model behavior and ensures alignment with human values.
Some of the service providers in this category include:
Clickworker
Clickworker offers RLHF-related services by leveraging its global crowdsourcing platform to provide high-quality human feedback and data labeling.
Prolific
Prolific provides AI and machine learning model training and evaluation services, using its vetted contributor network to deliver human-generated data and feedback.
7. Generative AI applications
Companies can take advantage of pre-existing generative AI applications on the market, saving the need to build custom solutions from scratch. These ready-to-use options can provide a robust, streamlined approach to harnessing the power of AI for business needs.
Here is our data-driven hub of generative AI applications, with vendor lists to compare your options and find the right fit.
FAQs
What are generative AI services?
Generative AI services leverage advanced AI models, such as foundation models and large language models, to deliver cutting-edge solutions for various business functions. These services integrate generative AI tools to automate tasks like text generation, natural language processing, and image creation.
text generation, image creation, and music production.
By using deep learning and complex models, businesses can improve productivity, reduce operating costs, and gain a competitive advantage. Generative AI solutions enable the creation of new content, fine-tuning AI models, and enhancing business operations through innovative and cost-effective technologies.
How to train a generative AI model?
To train a generative AI model, you need to follow several key steps:
Data Collection: Gather large and diverse datasets relevant to the specific generative AI solutions you aim to develop, such as text, images, or other forms of content.
Preprocessing: Clean and preprocess the data to ensure it is suitable for training. This includes tasks like normalization, tokenization for natural language processing, and augmentation for images.
Model Selection: Choose an appropriate AI model or foundation model, such as large language models or other generative AI models.
Training: Use deep learning techniques to train the model on the prepared data. This involves feeding the model large amounts of data and using machine learning algorithms to optimize the model’s parameters.
Fine-Tuning: Fine-tune the trained model on specific tasks or domains using targeted training data to improve performance and accuracy.
Evaluation: Evaluate the model using validation datasets to assess its performance and ensure it meets the desired criteria for accuracy and functionality.
Deployment: Integrate the generative AI model into your business operations, leveraging generative AI services and tools to automate tasks, generate content, and deliver cutting-edge solutions.
Monitoring and Updating: Continuously monitor the model’s performance and update it with new data and techniques to maintain its effectiveness and adapt to changing needs.
By following these steps, software developers and data scientists can create powerful generative AI solutions that enhance business value, improve productivity, and provide a competitive advantage in various business functions.
Why do you need generative AI services?
Generative AI services are essential for integrating cutting-edge AI models and generative AI tools into business operations. They enable businesses to leverage large language models and foundation models for tasks like text generation and natural language processing, improving productivity and delivering competitive advantages.
These AI-powered solutions drive business value by automating complex processes and enhancing human creativity in various business functions.
Further reading
- Generative AI Data: Importance & 7 Methods
- 5 AI Training Steps & Best Practices
- Enterprise Generative AI: 10+ Use Cases & LLM Best Practices
External resources
- 1. Global AI market size by segment 2030| Statista. Statista
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