Top 5 AI Services to Enhance Business Efficiency in 2026

AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.¹

Explore the top 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 delivers artificial intelligence capabilities to organizations without requiring dedicated infrastructure or hiring data scientists. It enables integration of AI systems and models through APIs, web interfaces, and chat interfaces. This simplifies the AI journey and supports scalable adoption of machine learning and generative AI solutions.

AIaaS allows businesses to leverage a comprehensive platform to build and deploy AI applications aligned with business objectives, offering faster time-to-value with lower barriers to entry.

Conversational AI / Natural language processing (NLP)

These services apply natural language technologies to improve communication and automate workflows:

• AI chatbots / conversational agents: Digital assistants that handle customer support, internal inquiries, and task automation through text or voice.
• Text analytics: Extracts insights from unstructured text using sentiment analysis, topic modeling, and entity recognition.
• Speech-to-text: Converts spoken language into text for transcription, command execution, and accessibility.
• Text-to-speech: Transforms text into human-like speech, supporting IVR systems and improved accessibility.

Computer vision

Computer vision capabilities enhance the interpretation of visual data in business contexts:

• Emotion detection: Analyzes facial expressions to determine emotional states, relevant for customer experience and mental health applications.
• Image recognition: Detects and classifies objects, scenes, or text in images, used in retail, security, and diagnostics.
• Video analysis: Monitors and interprets video content to track events or behaviors in surveillance, media, and sports.

Document understanding

These tools improve productivity and accuracy in document processing:

• Document data extraction: Uses Optical Character Recognition (OCR) and NLP to extract text and key fields from documents such as invoices and contracts, enabling automation and compliance.

Analytics solutions

AI systems applied to business data enable forecasting and anomaly detection:

• Demand forecasting: Uses machine learning on historical data to predict customer demand and optimize inventory.
• Fraud detection: Identifies irregular patterns in financial data to detect fraudulent activity.
• Recommendation systems: Suggest content or products based on user behavior and their own data to increase engagement.

Other services

Additional AI capabilities that support broader enterprise use cases:

• Knowledge mapping: Organizes data across systems to enhance discoverability and support decision-making.
• Predictive modeling: Analyzes historical patterns using AI models to forecast business outcomes.
• Security solutions: AI-powered tools that detect threats, automate responses, and safeguard digital assets.
• Automated code review: Evaluates software code for vulnerabilities, inefficiencies, and standards compliance to improve quality and security.

AIaaS enables organizations to explore, develop, and scale AI systems across multiple domains. By offering access to generative AI models, foundation models, and task-specific solutions, these platforms support real results in customer experiences, operational efficiency, and business outcomes.

Top 11 AIaaS providers

Notes:

• The selected providers offer a wide range of AI services, including NLP, computer vision, machine learning, deep learning, and speech-to-text. This allows them to support use cases from simple automation and predictive analytics to advanced applications like generative AI and model training.
• All providers are cloud-based, offering scalable and flexible solutions accessible through APIs or platforms. They serve both developers, with tools for model training and customization, and enterprises, with ready-to-use solutions, integration options, and advanced 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, challenges remain. According to the IBM report (See Figure 1), one challenge hindering AI adoption is insufficient proprietary data to customize models.

Building a custom solution may be necessary for cases when the off-the-shelf AI system doesn’t exist or is insufficient for your company’s needs. You can either make an in-house solution or hire outsourcing partners. 

The right choice depends on:

• Your business’s AI capabilities.
• Data science knowledge of your employees.
• Budget for the project.
• Ownership of data.
• Privacy requirements for your data.

Figure 1: Top 5 AI adoption challenges.²

2.1 Agentic AI services

Custom AI development increasingly extends beyond building standalone models toward systems that can operate autonomously within business processes. Agentic AI systems enable AI to interpret user intent, select appropriate tools, and execute multi-step actions with limited human intervention.

Depending on implementation, these systems can be delivered through different service approaches. Simpler agentic setups rely on configurable, workflow-based agents that follow predefined sequences, while more advanced architectures dynamically access tools, maintain context across interactions, and revise outputs based on feedback.

More autonomous agentic systems incorporate control mechanisms such as feedback loops, tool discovery, and human-in-the-loop approvals to support adaptability and self-correction, particularly in high-impact or uncertain tasks.

In practice, agentic AI services are applied to productivity automation, scheduling, communication handling, and knowledge organization. These use cases show a shift in custom AI development from automating individual tasks toward building systems that coordinate actions across applications and data sources.

Read personal AI agents to learn how to build and use these tools.

3. Services for enabling internal AI development

Organizations aiming to advance their AI journey require supporting services that facilitate the development, deployment, and management of AI models.

These services help integrate AI capabilities internally, optimize the AI lifecycle, and align AI efforts with broader business objectives.

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 systems 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. Public sector AI services

Public sector organizations increasingly use AI services to modernize operations and improve service delivery, while operating under stricter regulatory and accountability requirements than private enterprises. As a result, AI adoption in government settings typically begins with advisory and consulting services that establish governance frameworks, ethical guidelines, and implementation roadmaps.

Public agencies also apply AI services to document processing, case prioritization, citizen interaction, and internal decision support, with a focus on transparency and regulatory compliance.

3.3. AI talent recruitment

As the demand for AI expertise grows, recruiting AI talent has become essential to maintaining competitiveness. Businesses face challenges in sourcing skilled data scientists and AI engineers due to a limited supply of professionals.

• Partnering with on-demand recruiting services: Companies engage with specialized recruiting firms to access pre-vetted AI and data science professionals.
• Flexible hiring models: Includes a mix of full-time hires and contract-based experts to meet dynamic project needs.

This approach supports rapid scaling of AI capabilities while controlling costs and increasing access to specialized skills.

3.4. Data collection

High-quality data is critical for training effective AI models. Developing datasets for generative AI models and large-scale machine learning applications often requires extensive effort.

• Working with data collection providers: Businesses collaborate with vendors that curate domain-specific and task-specific datasets.
• Ensuring relevance and scale: Services are designed to match the business context and provide the volume and diversity required for reliable model performance.

These services are especially valuable in developing LLMs, where training data impacts model accuracy and fairness.

3.5. 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 impractical or too expensive.

Working with an RLHF partner offers businesses standardized workflows for training models with human feedback. An RLHF partner brings expertise in integrating human insights with machine learning, ensuring that AI systems are trained more safely, ethically, and aligned 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 an extensive network of workers conducts RLHF in the form of micro-tasks.

3.6. Data labeling

Supervised learning, a core component of many AI systems, relies on accurately labeled data for training. Multiple approaches are used to generate labeled datasets:

• In-house development: Internal teams handle data annotation using business-specific standards.
• Outsourced employees: External contractors label data based on detailed guidelines.
• Data labeling agencies: Specialized firms offer scalable annotation services with domain expertise.
• Crowdsourcing: A distributed workforce provides annotations at scale, suitable for less specialized tasks.

Each method supports different stages of the AI lifecycle and varies in terms of quality control, scalability, and cost.

3.7. Data science competitions

Organizations can use data science competitions to enhance model development:

• Crowdsourced model building: Competitions attract developers and data scientists to solve defined AI problems.
• Operational focus for internal teams: Internal teams can concentrate on deploying and maintaining models rather than building them from scratch.

This model supports innovation, reduces deployment time, and expands access to external expertise.

3.8. AI / MLOps platforms

AI and MLOps platforms manage the development, deployment, and management of AI applications:

• Model building and deployment at scale: These platforms automate workflows from data preparation to model monitoring.
• Integration with existing systems: Supports faster transition from experimental models to production-ready AI products.
• Support for responsible AI: Provides tools for bias detection, auditability, and performance tracking.

AI/MLOps platforms enable real results by operationalizing AI models, reducing latency, and improving productivity in AI initiatives.

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 significantly increased. 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. AI-native infrastructure and managed AI compute

As AI workloads increase in scale and operational complexity, traditional infrastructure models centered on provisioning individual hardware resources become difficult to manage efficiently. In response, infrastructure services increasingly emphasize AI-native delivery models designed specifically for training, deploying, and operating AI systems.

These services typically provide managed access to accelerators, optimized inference environments, and AI-optimized cloud regions, shifting responsibility for hardware orchestration, scaling, and availability to the service provider.

By shifting responsibility for infrastructure management to the service provider, organizations can focus on developing, testing, and deploying AI models instead of managing hardware capacity and low-level system operations. This makes it easier to scale AI workloads from experimentation to production.

5. Model monitoring and maintenance

AI models require continuous attention after deployment to ensure they keep delivering accurate and reliable results. The data that models process can change, a situation known as data drift. If not managed, data drift can lead to declining model performance and poor decision-making.

Model monitoring focuses on tracking the behavior and performance of models in real-world conditions. It involves observing metrics such as:

• Prediction accuracy and error rates.
• Response time and latency.
• Data quality and input consistency.
• Fairness and bias in model outputs.

Monitoring allows teams to detect when a model’s performance decreases or when it starts producing inconsistent results.

Model maintenance includes the activities required to keep models relevant. This may involve:

• Retraining models with updated or additional data.
• Adjusting parameters to reflect new business conditions.
• Validating models to ensure compliance and reliability.
• Redeploying improved versions after testing.

Partnering with model monitoring and maintenance service providers can help organizations maintain consistent performance and manage operational risks. Regular updates and evaluations allow AI systems to remain aligned with current data patterns, business needs, and regulatory requirements.

FAQ

Reference Links

1.

Where’s the Value in AI?

BCG Global

2.

AI Adoption Challenges | IBM

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% 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 and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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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Sıla Ermut

Industry Analyst

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Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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