AI outsourcing: When and how to outsource artificial intelligence development

A company wants to introduce an AI assistant into customer service. The model is not the hardest part. The work also involves enterprise data, APIs, permissions, security, evaluation, user interfaces, cloud infrastructure, monitoring, and ongoing model changes. Building all of those skills internally takes time.

AI outsourcing gives companies another option. Instead of recruiting every specialist, a business can outsource part or all of an artificial intelligence project to an external engineering team. This approach is becoming relevant as companies move from isolated experiments toward production AI. A 2025 CEO study found that 54% of surveyed CEOs were hiring for AI roles that did not exist a year earlier, while lack of expertise remained one of the barriers to company initiatives. Outsourcing does not mean handing over responsibility for AI. The company still owns the business problem, risk decisions, data policies, and expected result. The question is what work should stay inside the organization and what work makes more sense to outsource.

Key takeaways

• AI outsourcing gives companies access to skills they may not have in-house. This can include machine learning, generative AI, AI agents, data engineering, MLOps, software engineering, and cloud infrastructure.
• Companies can outsource an entire AI project or selected technical areas. The right model depends on existing internal skills.
• External AI experts can shorten the hiring stage. This matters when an AI initiative cannot wait for a full in-house AI team.
• The lowest hourly rate is rarely the best outsourcing criterion. Architecture, security, AI evaluation, domain knowledge, and production experience matter more.
• Data and governance remain company responsibilities. An outsourcing provider should work within clear rules for privacy, regulatory compliance, security, and model access.
• AI outsourcing costs depend heavily on scope. A proof of concept and an enterprise AI system have very different engineering requirements.

Avenga provides AI services covering AI engineering, data, application development, deployment, governance, and production use.

What is AI outsourcing?

AI outsourcing means hiring an external provider to perform some or all work required to design, build, deploy, or operate an artificial intelligence system. The work can include:

• AI strategy and discovery
• Machine learning
• Generative AI
• AI agents
• Predictive analytics
• Natural language processing
• Chatbots and virtual assistants
• Data engineering
• AI application development
• AI model evaluation
• MLOps
• Cloud deployment
• AI integration

Artificial intelligence outsourcing differs from traditional business process outsourcing. Traditional outsourcing often transfers an established business process to another company. AI outsourcing more often involves engineering a new technical capability or adding AI technologies to an existing workflow. The external team may build the AI solution itself, support an internal AI team, or provide AI specialists for selected stages of the work.

Why companies outsource AI

One reason is simple: AI requires several skill sets at once. An AI project may need a machine learning engineer, data engineer, software developer, cloud specialist, security engineer, product owner, and domain expert. Recruiting that entire team in-house can take longer than building the first version of the product.

A 2025 CEO study found that 67% of surveyed CEOs believed having the right expertise in the right positions was tied to differentiation, while 31% expected part of their workforce to require retraining or reskilling within three years. Partnerships can help companies “borrow the talent” companies cannot readily build internally. That makes an external AI team useful when companies need expert AI skills for a defined period rather than a permanent internal department.

What AI services can you outsource?

Companies can outsource AI services at almost any point in the development lifecycle.

AI discovery and feasibility

An external team can assess whether a proposed use case actually needs AI. The output might include:

• Technical feasibility
• Available data assessment
• AI model options
• Risk analysis
• Architecture
• Cost assumptions
• Success metrics

This stage can prevent companies from using AI where simpler software or automation would work better.

AI development

Companies can outsource AI development for systems such as:

• Recommendation engines
• Fraud detection
• Predictive maintenance
• Document processing
• Customer support systems
• Forecasting
• AI-powered search
• Chatbots
• Internal knowledge assistants
• AI agents

The development team may combine machine learning with conventional software engineering.

Generative AI

Generative AI outsourcing often covers applications built around large language models. Examples include:

• Retrieval-augmented generation
• Enterprise assistants
• Document analysis
• Content processing
• Code assistance
• Agentic workflows

The external AI development partner may also implement model evaluation, guardrails, access controls, and monitoring.

Data and machine learning outsourcing

Many AI projects become data projects before model development begins. Teams may outsource:

• Data pipelines
• Data preparation
• Feature engineering
• Predictive analytics
• Model training
• Data processing
• MLOps

Avenga’s data services cover the data engineering and governance work required around AI systems.

AI implementation

Companies can also outsource the work required to integrate AI into existing systems. This may involve APIs, databases, enterprise applications, cloud infrastructure, identity systems, or customer-facing software. Integration is often where an AI prototype becomes an operational AI service.

When should you outsource AI development?

It makes sense to outsource when external AI expertise removes a specific constraint.

You need skills faster than you can hire them

Recruitment may not match the timetable of the AI initiative. An external team can provide specialized AI engineers without waiting for a full hiring cycle.

Your existing team lacks specialized AI knowledge

An experienced software team may still need help with model evaluation, MLOps, retrieval systems, machine learning, or AI agents. In that case, companies can outsource selected areas rather than the whole AI project.

The AI use case is still being tested

Building an in-house department before knowing whether the idea works creates unnecessary fixed cost. Companies can outsource an early proof of concept, test the business case, and then decide what knowledge should move in-house.

You need to connect AI with existing software

AI rarely operates alone. External specialists can work alongside the existing team to integrate AI with business applications, data, security controls, and development processes.

Need AI engineering skills without assembling every role internally?

Learn more

When should AI stay in-house?

Not every AI capability should be outsourced. Keeping work in-house can make more sense when:

• AI is central to the company’s intellectual property
• The product requires constant model experimentation
• Sensitive data cannot leave tightly controlled environments
• The company already has mature AI capabilities
• AI engineering is expected to become a permanent major function

A hybrid approach is common. Internal teams retain product ownership, domain knowledge, and governance. The outsourcing partner contributes specific engineering skills or extra capacity. This model can also help companies integrate AI knowledge into the existing engineering organization instead of creating a separate technical silo.

Benefits of outsourcing AI

Faster access to specialists

The company can use experienced AI engineers, architects, and data professionals without recruiting every role.

Flexible team size

AI needs often change during a project. Early discovery may need an architect and AI specialist. Development may require more software engineers. Deployment adds MLOps and cloud work. Outsourcing can allow the team composition to change with the project.

Access to practical AI experience

AI tools are easy to demonstrate. Production systems are harder. An experienced AI outsourcing partner should understand model evaluation, security, integration, monitoring, cloud cost, deployment, and failure handling.

Focus for the internal team

The company’s employees can remain focused on domain knowledge, customers, product direction, and business processes while the external team handles selected technical work.

Risks of artificial intelligence outsourcing

Outsourcing artificial intelligence also creates risks.

Loss of internal knowledge

If the outsourcing company owns all architecture and technical decisions, the client can become dependent on that provider. Documentation, shared repositories, architecture reviews, and internal technical ownership reduce this risk.

Data exposure

External AI systems may process customer records, financial information, source code, or proprietary knowledge. Contracts alone are not enough. Technical controls should define data access, environments, retention, logging, and model usage. Avenga’s cybersecurity services address security requirements around applications, infrastructure, and AI environments.

Poor AI output

An AI model can produce incorrect or unsafe results regardless of who builds it. Testing should include accuracy, hallucination, reliability, security, bias where relevant, latency, and failure behavior.

Vendor lock-in

An AI implementation can become tied to one model, cloud provider, or proprietary component. Architecture decisions should make these dependencies explicit before deployment.

How to choose the right AI outsourcing partner

A good AI outsourcing engagement begins with technical questions, not sales claims. Ask prospective outsourcing companies:

1. What production AI systems have you built?
2. Who will actually work on the project?
3. How do you evaluate AI output?
4. How do you handle customer data?
5. How do you approach model and cloud selection?
6. What happens if the selected AI model changes?
7. Who owns the source code and intellectual property?
8. How is knowledge transferred to our internal team?
9. How do you measure the AI project’s result?
10. Can the architecture support another provider later?

The best outsourcing provider for one company may be wrong for another. A bank may prioritize auditability, security, and model governance. A retailer may care more about recommendation performance and customer experience. A manufacturer may focus on predictive systems, automation, and integration with operational technology.

How much does AI outsourcing cost?

There is no useful universal rate for AI development outsourcing. Cost depends on:

• Project scope
• Team size
• Location
• Seniority
• AI model requirements
• Data readiness
• Cloud infrastructure
• Integration work
• Security requirements
• Regulatory requirements
• Production support

A short AI discovery engagement costs far less than building and operating an enterprise AI platform. Companies should also compare outsourcing cost with the full cost of building an in-house AI team. That calculation includes recruiting, salaries, management, cloud costs, data infrastructure, AI tools, training, and the time required to assemble the team. The cheaper proposal is not automatically the cheaper project.

How AI is changing outsourcing itself

AI is affecting both what companies outsource and how providers perform the work. A February 2026 report found that AI technologies and productivity tools are forcing organizations to reassess conventional outsourcing models, while agentic AI is pushing IT services toward more outcome-based approaches.

The change is already visible in customer support outsourcing.’s 2026 market guide states that organizations use outsourcing to expand capabilities, lower costs, and speed automation and AI adoption. This means software outsourcing providers themselves increasingly use AI for coding, testing, documentation, analytics, support, and other development work. The question for clients becomes more specific: what can the provider accomplish with its engineers and AI tools, rather than how many people can it assign?

Which industries use AI outsourcing?

AI outsourcing can apply across most sectors, but the use cases differ.

Industry	Common outsourced AI work
Banking	Fraud, document processing, customer service, analytics
Retail	Recommendations, forecasting, customer behavior
Healthcare	Document workflows, analytics, assistants
Manufacturing	Predictive maintenance, computer vision, automation
Telecommunications	Support automation, network analytics
Media	Recommendations, content processing
Transportation	Forecasting, routing, operational analytics

Healthcare provides one current example of the partnership model. A 2025 healthcare survey found that among surveyed healthcare organizations implementing generative AI, 61% planned to use third-party partnerships for customized systems, compared with 20% planning primarily to build in-house.

Companies should outsource AI engineering when the external team fills a real skills or capacity gap, not because ownership of AI can be delegated. The business still needs to own the problem, the data rules, the risk decisions, and the measure of success.

Petyo Dimitrov, Director of Data and AI at Avenga

FAQ

What is AI outsourcing?

AI outsourcing is the use of an external engineering or consulting provider to design, build, integrate, deploy, or operate artificial intelligence systems. Companies can outsource an entire AI project or individual areas such as machine learning, data engineering, generative AI, MLOps, or application development.

Is outsourcing AI cheaper than building an in-house team?

Outsourcing AI can cost less when a company needs specialized skills for a limited project or needs a team quickly. Building in-house can make more economic sense when AI is a permanent core capability with a steady pipeline of work.

Is AI outsourcing secure?

AI outsourcing can be secure when the engagement defines data access, infrastructure, model use, identity controls, logging, retention, intellectual property, and regulatory requirements. Security needs to be part of the technical architecture rather than handled only through contracts.

What should you look for in an AI outsourcing company?

Look for production AI experience, strong software and data engineering, clear evaluation methods, security discipline, transparent ownership terms, and evidence that the provider can connect AI with existing business systems.

Conclusion: Outsource the missing capability, not accountability

Companies do not need every AI specialist on payroll before starting an AI project. They can outsource discovery, data engineering, machine learning, generative AI, AI agents, software development, deployment, or selected pieces of the work. The boundary matters. Keep business ownership, risk decisions, product direction, and data policy inside the organization. Use an external team where specialist engineering knowledge, additional capacity, or faster execution makes the business case stronger. A good outsourcing partner should leave the company with more technical understanding, not greater dependency.

If you are deciding whether to build in-house, outsource AI services, or combine both models, contact Avenga to discuss the technical scope and team structure.