5 Types of AI Services to Boost Your AI Transformation in 2024
Even though AI and generative AI dominate the internet, according to new research by McKinsey and IDC, AI adoption has slightly declined (Figure 1). This could be due to various barriers businesses face while adopting the technology. Due to these challenges, business leaders look for different approaches to implement AI in their business.
In this article, we’ll explore 5 different types of AI services that enable companies to implement AI solutions in their business processes and overcome barriers.
Figure 1. AI growth1
1. AI as a Service (AIaaS)
AIaaS offers a cloud-based service that allows businesses to experiment with AI for various purposes without large initial investments. Thanks to AI services, even companies without a data science department can utilize AI to achieve benefits such as increased data-driven decision-making. These solutions can be:
1.1. Pre-trained machine learning models
Pre-trained models are offered as APIs (application programming interfaces). With pre-trained models, customers can add AI capabilities to their existing applications with minimal effort since they don’t have to collect data for model training. Adding a pre-trained intelligent chatbot to customer service software is an example.
1.2. Pre-built and customizable AI models
Pre-built and customizable models enable companies to train machine learning models with their own training data and to customize the model if necessary. These solutions can offer drag-and-drop interfaces to simplify the AI model-building process, which helps companies build AI applications without AI expertise.
1.3. AI model components
AI model components, or pre-trained models, serve as foundational building blocks for data scientists, allowing for accelerated AI development.
They reduce the need for vast amounts of training data, enhance performance by leveraging previously learned patterns, and can be fine-tuned or combined to cater to specialized tasks. These components enable data scientists to build more efficient and tailored AI models by building on top of existing knowledge.
1.4. Popular examples of AIaaS providers
Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure AI are popular examples of AI as a Service providers. Numerous startups also provide specialized services within the AIaaS framework.
Some common services that are provided by vendors are:
- Conversational AI / Natural language processing (NLP) APIs & services, including:
- Computer vision:
- Document understanding:
- Analytics solutions for:
- Other services include:
- Knowledge mapping
- Advanced search
- Personalization/ Recommendation Engines
- Security solutions
- Automated Code Review
- More areas of AI use cases are highlighted in the following research:
Feel free to check our article on AIaaS for more.
2. Custom AI development
For cases when the off-the-shelf AI solution doesn’t exist or is insufficient for your company’s needs, building a custom solution may be necessary. You can either build an in-house solution or hire outsourcing partners. The right choice depends on
- your business’ AI capabilities,
- data science knowledge of your employees,
- budget for the project
- ownership of data
- privacy requirements of your data
Gartner identifies scarce AI talent as one of the biggest barriers holding companies from adopting AI solutions (Figure 2). Hiring a partner or outsourcing is a better option if you do not have access to data or the required AI talent in your organization.
Figure 2. Barriers to AI adoption2
We provide a whitepaper that highlights all reasons, approaches, and examples for custom AI development:
And if you need to build a custom AI solution with minimal investment, let us know. We can help you with our industry experience:
3. Services for enabling internal AI development
This section highlights all the services that businesses can use to enable their development teams to build AI models in-house.
If your company is new to AI and can invest significantly in AI transformation, you can consider hiring AI consultants. Since AI projects are filled with challenges, the experience of AI consultants in the market can help you avoid common pitfalls and apply best practices such as reducing bias in the dataset.
AI consulting services include:
- Assessing the maturity of your company’s AI transformation
- Identification of areas where leveraging AI or machine learning can create value
- Formulating an AI strategy to launch new pilot products/services
- Building AI solutions
- Training your employees for upcoming AI technology implementations
3.2. AI talent recruitment
As the AI talent gap continues to expand, AI talent recruitment has become a critical business function. Therefore businesses are looking to complement full-time hiring with on-demand talent by partnering with on-demand recruiting companies that focus on AI and data science talent.
We have also written about specific types of consulting within AI, such as machine learning consulting, deep learning consulting, computer vision consulting, and healthcare AI consulting. Feel free to check.
3.3. Data collection
An accurate and unbiased AI model requires large volumes of relevant data to be trained. For instance, gathering data for large language models (LLMs) can be expensive. Businesses can work with data collection service providers that can prepare large-scale datasets for developing and improving AI and machine learning models.
Clickworker offers scalable AI datasets through a crowdsourcing platform. Its global network of over 4.5 million workers fulfilled the data needs of 4 out of 5 tech giants in the U.S., including Google, Samsung, Microsoft, and Apple.
You can also check our data-driven list of data collection / harvesting companies to find the best option for your AI project.
3.4. RLHF (Reinforcement Learning from Human Feedback) services
RLHF is an approach within the broader spectrum of reinforcement learning (RL). In RLHF, the usual rewards coming from the environment are combined with or replaced by feedback derived from humans. This becomes especially useful when obtaining real-world rewards is either impractical or too expensive.
3.4.1. Why work with an RLHF service provider?
Working with an RLHF partner offers businesses a streamlined approach to developing advanced AI models. An RLHF partner brings expertise in integrating human insights with machine learning, ensuring that AI systems are trained more safely, ethically, and in alignment with nuanced human values.
By collaborating with a specialized partner, businesses can leverage this hybrid training approach without the steep learning curve, accelerating AI project timelines and achieving more reliable and human-centric outcomes.
Since RLHF requires a high level of human intervention, service providers usually offer it through a crowdsourcing platform where a large network of workers conducts RLHF in the form of micro-tasks.
3.5. Data labeling / annotation
Supervised learning is the most common learning algorithm for machine learning. Yet, you need a large volume of labeled data to train an AI system. For this purpose, businesses can rely on different methods such as:
- In-house development
- Outsourced employees
- Data labeling agencies
Each method contains the pros and cons for businesses, and you can check our data labeling article to learn the advantages of each approach.
Crowdsourcing platforms can offer all types of data services, including data generation, collection, annotation, processing, etc. Check out this guide to finding the right crowdsourcing platform for your data needs.
3.6. Data science competitions
You can crowdsource the machine learning lifecycle, for example, by launching data science competitions to handle algorithm building. This can allow your team to focus on operationalizing machine learning models within your company which is harder to outsource.
3.7. AI / MLOps platforms
There are AI platforms that enable businesses to deploy machine learning models for applications at scale. These platforms ease the process of machine learning model building and productization of ML models.
4. AI hardware and infrastructure services
As AI and machine learning models grow in complexity and size, the demand for specialized hardware and infrastructure has seen a significant surge. The computational requirements of training deep neural networks, running simulations for reinforcement learning, or serving millions of predictions in real-time have transcended the capabilities of conventional hardware.
4.1. Types of specialized hardware:
- GPUs (Graphics Processing Units): Originally designed for rendering graphics, GPUs have become a mainstay in the AI community. Their parallel processing capabilities make them well-suited for the matrix operations common in neural network computations. Companies like NVIDIA and AMD are at the forefront of this shift.
- TPUs (Tensor Processing Units): Designed by Google specifically for deep learning tasks, TPUs are application-specific integrated circuits (ASICs) that optimize the speed and efficiency of tensor operations, crucial for neural network computations.
- FPGAs (Field-Programmable Gate Arrays): FPGAs are integrated circuits that can be reconfigured post-manufacturing to fit specific needs. They offer a middle ground between the flexibility of GPUs and the specialization of TPUs, being used in AI for both training and inference tasks.
4.2. Infrastructure services:
- Cloud Services: Major cloud providers like AWS, Google Cloud, and Microsoft Azure offer AI-optimized infrastructure services. These platforms allow users to rent GPU, TPU, or FPGA computational time, scale resources as needed, and only pay for what they use. These services are bundled with a suite of tools to make the development, training, and deployment of AI/ML models seamless.
- On-Premises Solutions: For businesses that require more control over their data due to security, regulatory, or operational reasons, on-premises hardware solutions are available. These often come in the form of specialized hardware racks that can be integrated into a company’s data center. Companies like NVIDIA, with their DGX systems, offer AI-optimized on-premises hardware solutions.
5. Model monitoring and maintenance
Once AI models are transitioned from the development stage to production, the journey doesn’t end there. These models interact with real-world data, which is dynamic and can change over time. Such changes necessitate regular monitoring and maintenance of these models to ensure consistent performance.
As organizations struggle to fill the AI talent gap, working with model monitoring and maintenance partners can help business leaders sustain the performance of their AI solutions.
Some popular companies that offer these services include:
For more on AI, feel free to check our recommended list of articles:
- Bias in AI: What it is, Types & Examples, How & Tools to fix it
- Explainable AI (XAI): Guide to enterprise-ready AI
- AI in analytics: How AI is shaping analytics
You can also check out our list of AI tools and services:
And if you still have questions about AI services, don’t hesitate to ask:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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