5 Types of AI Services to Boost Your AI Transformation in 2024

Artificial intelligence (AI) continues to transform businesses, even though adoption slowed in 2022 compared to the previous year (Figure 1). According to McKinsey, the dip is temporary as more companies plan to increase investment. However, businesses still face challenges in adopting AI, with the top barriers being lack of skills and data issues (Figure 2).

AI adoption trends

Figure 1. AI adoption trends[1]

Barriers to AI adoption

Figure 2. Barriers to AI adoption[2]

To overcome these barriers, organizations are seeking different approaches to implement AI, increasingly through external services. In this comprehensive guide, we‘ll explore the 5 main types of AI services for enabling AI transformation:

  1. AI as a Service (AIaaS)
  2. Custom AI development
  3. Services for internal AI development
  4. AI hardware and infrastructure
  5. Model monitoring and maintenance

For each type, we‘ll provide examples, benefits, and things to look out for when adopting these solutions. Let‘s get started.

1. AI as a Service (AIaaS)

AI as a Service allows organizations to leverage ready-made AI solutions without needing in-house expertise. With AIaaS, even non-technical teams can integrate AI capabilities through simple APIs. It lowers the barriers to adoption by reducing upfront investment and skills required.

According to MarketsandMarkets, the AIaaS market will grow from $2.8 billion in 2020 to $14.2 billion by 2025 as more businesses shift to cloud-based AI services.

Some common types of AIaaS solutions include:

1.1. Pre-trained machine learning models

Pre-trained models are out-of-the-box ML models offered through APIs. Users can integrate them into existing applications and start deriving insights immediately without having to train models from scratch.

For instance, Amazon Lex provides pre-built conversational interfaces to add chatbots to programs with minimal coding.

1.2. Customizable AI models

These allow users to train models on their own data and customizeprebuilt models for enhanced accuracy on specific use cases. The models come with easy-to-use drag and drop interfaces, reducing the need for coding skills.

An example isAWS SageMaker which enables building, training, and deploying ML models with flexibility to bring your own data and algorithms.

1.3. AI model components

AI model components are modular blocks that serve as building blocks for developing custom AI models. They provide pre-trained capabilities that data scientists can adapt to specialized needs.

For instance, TensorFlow Hub offers reusable ML modules for common tasks like image classification, object detection, etc.

1.4. Popular examples of AIaaS providers

Leading cloud providers dominate the AIaaS market:

  • Amazon Web Services – Amazon Lex, Amazon Polly, Amazon Rekognition
  • Microsoft Azure – Azure Cognitive Services, Azure Machine Learning
  • Google Cloud – Cloud Speech-to-Text, Cloud Natural Language API, Cloud Vision API

Numerous startups also offer AIaaS for specific applications:

  • Clarifai – Pre-trained computer vision and NLP models
  • Scale – Human-in-the-loop AI for data labeling and model training
  • Cuebiq – Location intelligence and mobility insights

Some commonly offered AIaaS services include:

  • Conversational AI / NLP APIs: Chatbots, text analytics, speech recognition
  • Computer vision: Image recognition, video analysis, emotion detection
  • Analytics: Demand forecasting, fraud detection, personalized recommendations
  • Document understanding: Data extraction from documents
  • Knowledge mapping, advanced search, personalization engines
  • Security solutions, automated code review

By leveraging readily available AI building blocks, organizations can quickly prototype and validate AI solutions before deciding to develop custom models. AIaaS allows you to start small, learn, and expand as needs grow.

However, relying solely on out-of-the-box functionality has limitations. Pretrained models may not cover specific use cases and provide less flexibility for customization. As models interact with new data, their performance can degrade over time.

Therefore, AIaaS works best when complemented with in-house capabilities to adapt and maintain models. Treat it as a stepping stone to build internal skills.

2. Custom AI development

For specialized applications or to gain competitive advantage, investing in custom AI development is warranted. According to McKinsey, nearly 50% of organizations build custom ML models in-house.

You can either build fully in-house or work with external partners to develop tailored AI solutions aligned to your business needs.

When to build custom AI?

Here are some signals that indicate custom development:

  • No out-of-the-box solution meets your requirements – Your application may be too industry or organization-specific.
  • Need to differentiate from competitors – A unique AI solution could be your competitive edge.
  • Require full control over IP – You may not want to share confidential data with third-party services.
  • Integration with complex IT systems – Tight integration needed between AI and existing tech stack.
  • Stringent model performance criteria – Pre-built models may not achieve your high standards of accuracy, speed, etc.

Approaches to custom AI development

  1. In-house – Develop solutions using internal skills and resources. Gives you full control and IP ownership but requires long-term investments in talent and infrastructure.
  2. Outsourcing – Partner with external specialists for all or parts of the development cycle. Flexible option but may lack context of your business.
  3. Hybrid – Combine in-house skills and outsourced support. Allows focus on core competencies while filling gaps with partners.

According to McKinsey, about half of companies take a hybrid approach. Top activities outsourced include data engineering, model building, labeling, and MLOps.

Outsourced AI activities

No matter the approach, plan for the long run. Custom AI development takes time and needs ongoing investments as models decay. Work with trusted partners who understand your domain.

3. Services for Internal AI Development

The following services enable companies to build in-house capabilities for long-term success with AI.

3.1. Consulting

AI consultants bring proven frameworks and experience for each step of your AI journey:

  • Assessing AI readiness
  • Identifying high-value AI applications
  • Formulating implementation roadmaps
  • Building, testing, and deploying AI solutions
  • Change management and capability building

According to Gartner, 75% of organizations seek external AI strategy help, underlining the value of unbiased guidance.

Look for consultants with deep expertise in your industry and technical domains. Cultural fit is also crucial for a productive long-term partnership.

3.2. AI talent recruitment

Acquiring and nurturing in-house AI talent underpins successful AI adoption. However, demand vastly exceeds supply for skilled roles like data scientists and ML engineers.

Partnering with recruitment firms can help you attract and retain suitable talent. Especially seek firms that provide:

  • Access to vetted AI expert network
  • Capability to source niche and emerging roles like MLOps engineers
  • Options for extending teams with flexible staffing
  • Assessment frameworks to effectively screen candidates
  • Market insights into competitive talent landscape

Build a blended workforce combining employees and on-demand talent to enable agility.

3.3. Data collection

"Data is the new oil" holds especially true in AI. Models are only as good as the data they are trained on.

While some open datasets are available, most businesses need custom data collection specific to their industry, use cases, and algorithms.

Specialized data partners can help with:

  • Data engineering – building data pipelines, ETL, labeling, etc.
  • Data annotation – content, images, video, audio, etc.
  • Data generation – creating synthetic but realistic datasets.
  • Data harvesting – crawling, scraping, and processing web data.
  • Ongoing data ops – managing dynamic data needs.

Look for partners with scale, quality standards, and security measures aligned to your needs.

3.4. RLHF (Reinforcement Learning from Human Feedback)

In RLHF, human input guides reinforcement learning algorithms. This is useful when:

  • Environment rewards are sparse or take too long.
  • Goals are subjective or nuanced for humans.
  • Safe exploration is crucial.

For instance, Microsoft used RLHF to train an AI chatbot to converse naturally.

Specialized providers offer RLHF services by integrating crowdsourcing platforms. This gives access to a diverse human pool for feedback at scale.

The right partner can greatly accelerate your application of RL in a cost-effective, ethical manner.

3.5. Data labeling

Supervised learning depends on quality training data. While you can label data in-house, outsourcing provides:

  • Speed and scale by distributing tasks to a larger workforce
  • Cost efficiency through specialized labeling platforms
  • Access to niche annotator skills and tooling

Weigh insourcing vs outsourcing tradeoffs based on needs for control, domain knowledge, and data sensitivity.

3.6. Data science competitions

An emerging practice is to crowdsource certain AI tasks by launching data science competitions.

External participants compete to develop algorithms for business problems based on provided datasets. The company pays only for the best performing solution.

This crowdsourced model works for focused ML tasks like classification, predictions, etc. Internal staff can focus on operationalizing the models.

4. AI Hardware and Infrastructure

The computational demands of AI necessitate specialized hardware and infrastructure support.

4.1. Types of AI infrastructure

  • GPUs – Graphics processing units efficiently power the math-intensive computations required for deep learning algorithms. Nvidia leads in providing GPU hardware and cloud services for AI workloads.
  • TPUs – Google‘s custom-built tensor processing units are optimized specifically for ML model training and inference. TPUs are available via Google Cloud.
  • FPGAs – Field programmable gate arrays bridge the flexibility of GPUs and specialization of TPUs. FPGAs are used for AI across training, inference and cloud platforms.
  • Cloud vs On-premise – Cloud platforms like AWS, GCP and Azure allow convenient access to AI infrastructure with flexible scaling. But highly regulated industries often favor on-premise solutions for tighter control.

Choosing the right infrastructure depends on your training volumes, performance needs, and security requirements.

4.2. Specialized hardware providers

Leading technology vendors offer a range of infrastructure solutions:

  • Nvidia – GPUs, DGX systems for enterprise AI, cuQuantum for quantum computing
  • Habana – Gaudi AI processors for training and inference workloads
  • Cerebras – Wafer-scale AI compute platforms with extreme parallelization
  • Graphcore – IPU (Intelligence Processing Unit) designed for neural network training

Evaluate options based on compute power, accelerators, ease of programming and scaling needs.

For small projects, cloud services allow convenient experimentation. For large-scale production workloads, on-premise solutions may be more cost-effective.

5. Model Monitoring and Maintenance

The job doesn‘t end once AI models are deployed. Like any business asset, they require ongoing management to sustain value.

Models interact with dynamic real-world data. If new data diverges significantly from the training data, performance can degrade through concepts like drift and skew:

  • Drift – Predictive accuracy drops over time as input data distributions change.
  • Skew – Model makes less accurate predictions for certain data segments.

Continuously monitoring metrics like accuracy, latency and bias is crucial. When issues are detected, models need retraining, tuning or reigning.

This process of monitoring, maintenance and governance is called MLOps. According to Gartner, about 50% of models developed will never make it into production because of deficiencies in MLOps.

Therefore specialized MLOps platforms and services are gaining popularity:

MLOps platforms make deploying and managing models easier with features like:

  • Automated model testing and monitoring
  • Analytics dashboard of model metrics
  • Workflow automation for retraining and updates
  • Integration with data engineering pipelines

Leading platforms include DataRobot, H20.ai, Weights & Biases and Valohai.

MLOps-as-a-service provides expert support for:

  • Audit of model risks – accuracy, bias, security etc.
  • Ongoing model maintenance and enhancement
  • Technical integration with IT systems
  • Building internal MLOps capabilities

Top AI service firms like Accenture, Infosys and Genpact offer these services.

The right MLOps approach depends on your team‘s skills and existing infrastructure. Investing in MLOps pays off by maximizing returns from AI investments.

  1. Adopt a portfolio approach across AIaaS solutions, custom development, and services to balance business needs and capabilities.
  2. Leverage partnerships strategically – focus internal efforts on differentiating capabilities while filling gaps via external services.
  3. Take a long-term view – sustained success requires building in-house skills, data assets, and MLOps muscle.
  4. Choose partners with deep expertise in your industry domain and use cases.
  5. Start small, fail fast and scale – adopt an agile, iterative mindset to maximize learning.

This guide provides a starting point to identify and evaluate AI services for your needs. Here are suggested next steps:

  • Conduct an AI readiness assessment – Review data, talent, and infrastructure readiness. Identify quick wins and long-term priorities.
  • Develop an AI adoption roadmap – Outline phased deployment of solutions balancing business impact, cost and risk.
  • Evaluate partners – Shortlist service providers aligned to your strategy and assess their capabilities.
  • Launch pilots – Start with limited-scope projects to test solutions and partners before expanding.
  • Monitor and refine – Review progress at regular intervals and adapt plans based on learnings.

At each step, don‘t hesitate to tap external experts to guide and accelerate your AI journey.

For personalized recommendations on AI services for your organization, contact us. Leverage our experience of partnering with Fortune 500 companies on AI transformation initiatives.

References

[1] McKinsey, IDC. (2023). Artificial Intelligence: in-depth market analysis 2023. Statista.

[2] Laurence Goasduff. (2019). 3 Barriers to AI Adoption. Gartner.

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