Top 5 AI Services to Enhance Business Efficiency
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
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.
For more on AI consulting and consultants, check out AI consulting.
3.2. 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.
3.3. 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.
3.4. 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 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.
3.5. 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.
3.6. 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.
3.7. 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. 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
AI models require continuous attention after deployment to ensure they keep delivering accurate and reliable results. Over time, 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.
💡Conclusion
Businesses aiming to expand their AI adoption should approach it as a structured, continuous process. Start by identifying where AI can create measurable value, then select the right combination of services to support that goal:
- Use AIaaS to begin experimentation and reduce entry barriers.
- Invest in custom AI development when off-the-shelf tools do not meet specific needs.
- Strengthen internal capabilities through consulting, data services, and MLOps.
- Ensure reliable performance with appropriate hardware, infrastructure, and monitoring.
Treating AI as an ongoing capability rather than a one-time project allows organizations to integrate it into core operations, maintain consistent results, and adapt to new opportunities as technology and data evolve.
FAQ
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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