Top 5 AI Services to Transform Your Business Operations

AI adoption is on the rise. 98% of companies are experimenting with AI, while 26% are advancing to generate tangible value.¹

Explore 5 types of AI services that can accelerate your business’s AI adoption journey.

1. AI as a Service (AIaaS)

AI as a Service (AIaaS) is a cloud-based model that enables organizations to integrate artificial intelligence capabilities into their applications without investing in extensive infrastructure or hiring dedicated data scientists.

Through AIaaS, businesses can access and deploy AI models, machine learning tools, and generative AI solutions via APIs, web interfaces, or chat interfaces, accelerating their AI journey without requiring deep machine learning expertise.

AIaaS applications

AIaaS providers offer a range of intelligent applications, from custom models for data analysis and predictive modeling to natural language processing, image recognition, and video analytics. These services help businesses extract text from unstructured data, optimize processes, and improve decision-making:

Conversational AI / Natural language processing (NLP) APIs & services, including:

• AI chatbots / conversational agents: Virtual assistants that engage with users via text or voice, automating customer support, internal queries, and workflow automation.
• Text analytics: Extracts insights from unstructured text, such as sentiment analysis, entity recognition, and topic modeling.
• Speech-to-text: Converts spoken language into text, enabling transcription, voice commands, and accessibility features.
• Text-to-speech: Generates human-like speech from text, improving accessibility and interactive voice response (IVR) systems.

Computer vision

• Emotion detection: Recognizes facial expressions to determine emotional states, useful in customer experience analysis and mental health applications.
• Image recognition: Identifies objects, people, scenes, or text within images, used in security, retail, and medical diagnostics.
• Video analysis: Processes video content to detect events, track objects, and extract meaningful insights for surveillance, sports analytics, and media indexing.

Document understanding

• Document data extraction: Uses OCR (Optical Character Recognition) and NLP to extract key information from invoices, contracts, and forms for automation and compliance.

Analytics solutions for:

• Demand forecasting: Predicts market trends, customer demand, and inventory needs using historical data and machine learning.
• Fraud detection: Identifies suspicious activities in financial transactions, insurance claims, and identity verification using AI-powered anomaly detection.
• Recommendation systems: Suggests products, content, or actions based on user preferences, optimizing engagement and conversions.

Other services include:

• Knowledge mapping: Organizes and connects information across vast datasets to enhance knowledge discovery and decision-making.
• Predictive modeling: Leverages machine learning and statistical algorithms to analyze historical data, identify patterns, and forecast future outcomes.
• Security solutions: AI-powered cybersecurity tools that detect threats, prevent breaches, and automate security responses.
• Automated Code Review: AI-assisted analysis of software code to detect vulnerabilities, inefficiencies, and compliance issues, improving development quality and security.

Top 11 AIaaS providers

Notes:

• The providers selected offer a broad range of AI services, including natural language processing (NLP), computer vision, machine learning (ML), deep learning, and speech-to-text. This ensures that they cater to various use cases, from basic automation and predictive analytics to more advanced tasks like generative AI and model training.
• All providers in the list are cloud-based, meaning they offer scalable, flexible solutions that are accessible via APIs or platform-based services.
• The providers cater to both developers (offering APIs, model training, and customization) and enterprises (with ready-made solutions, integration capabilities, and security features).

Machine learning in AIaaS

Machine learning, a core component of AI, allows models to learn from historical data, refine predictions, and adapt over time. Within the AIaaS ecosystem, companies can train and fine-tune AI models using their own data, ensuring that solutions align with their specific business context.

2. Custom AI development

Custom generative AI models, foundation models, and intelligent agents are increasingly being used to support industries ranging from enterprise applications to mobile services.

Despite the growing interest in AI adoption, the challenges still remain. ​According to the IBM report (See Figure 1), one of the challenges hindering AI adoption is insufficient proprietary data to customize models.

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.

Figure 1: Top 5 AI adoption challenges.²

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.

3.1. Consulting

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

For more on AI consulting and consultants, check out AI consulting and data science consulting.

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.

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.

For example, 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.

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.

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 one of the most widely used machine learning algorithms, but it requires a substantial amount of labeled data to effectively train an AI system. For this purpose, businesses can rely on different methods, such as:

• In-house development
• Outsourced employees
• Data labeling agencies
• Crowdsourcing

3.6. Data science competitions

Businesses 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): Initially for graphics rendering, GPUs now power AI with their parallel processing, ideal for neural network computations.
• TPUs (Tensor Processing Units): Google’s ASICs, designed for deep learning, optimize tensor operations for faster, more efficient neural network performance.
• FPGAs (Field-Programmable Gate Arrays): Reconfigurable post-manufacturing, FPGAs balance GPU flexibility and TPU specialization, supporting AI training and inference.

4.2. Infrastructure services:

• Cloud Services: These scalable, pay-as-you-go services provide AI-optimized infrastructure, letting users rent GPUs, TPUs, or FPGAs on demand.
• On-Premises Solutions: For businesses needing greater control over data due to security or regulations, on-premises hardware integrates specialized racks into company data centers.

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.

External Links

• 1. https://www.bcg.com/publications/2024/wheres-value-in-ai
• 2. https://www.ibm.com/think/insights/ai-adoption-challenges