According to LinkedIn, there are around 63,000 open positions for Artificial Intelligence specialists in EU countries. As for the US, Indeed's open search displays more than 52,000 open vacancies for AI experts. Compared to our research in 2022, these numbers have grown significantly from 12,000 positions for AI specialists in the EU and 16,000 in the US, which makes AI development outsourcing a compelling option. According to Statista, the global Artificial Intelligence market is expected to grow at a 27.6% compound annual growth rate from 2025 to 2030.
An impressive market growth rate, combined with the already existing shortage of qualified talent in the local markets, causes AI outsourcing to become a more sought-after service. This article will explore why you should consider Artificial Intelligence outsourcing and how to benefit from it. You'll also learn tips on choosing a trusted AI development provider.
Why should you consider Artificial Intelligence outsourcing?
Outsourcing of AI and ML services continually gains more demand due to the noticeable shortage of local skilled talent in the US, UK, and EU. Finding an expertise-driven dedicated team competent in crafting the AI strategy, Deep Learning, and computer vision can be a tall order. According to Accenture, more than 75% of companies have already integrated AI into their business strategies, reinforcing its role as a key driver of innovation and competitive advantage. What’s more, there are reasons to believe that AI transformation will happen faster than a digital one.
Thus, a dedicated development partner will contribute to the AI-driven project at each stage, including research, implementation, and maintenance. Here are the main reasons why outsourcing AI is convenient:
- Easier recruitment. Hiring qualified AI specialists is a troublesome process, considering the staffing time and supplementary costs. In turn, a reliable outsourcing vendor comprises a team of competent specialists with enough resources to tackle your AI project challenges.
- Relevant tech expertise. Experts with a proven track record of delivering AI projects usually have better expertise than individual specialists. In this sense, experienced vendors, thanks to knowledge sharing and professional training, are better equipped with the necessary skills to deliver an AI project.
- Resources that ensure the AI algorithms' reliability. Compared to individual specialists, outsourcing vendors have enough capacity to work with predictive decision-making algorithms. One example is a range of libraries, data annotation tools, cloud-agnostic approaches, and pre-made testing pipelines. Such a range of tools ensures you have enough resources to cover your business request.
Generative AI boom
Generative AI is rapidly becoming a core component of AI-driven business transformation, enabling hyper-personalized customer interactions, automated content generation, and AI-powered decision-making. However, many companies face challenges in implementing and scaling these solutions due to a lack of in-house expertise, high infrastructure costs, and the complexity of model training.
According to Accenture’s Tech Vision 2025, only 36% of executives report that their organizations have successfully scaled Generative AI and just 13% say they have achieved enterprise-level impact. This highlights the widespread struggle businesses face when trying to adopt Generative AI at scale, making outsourcing AI development a strategic solution.
By partnering with an experienced outsourcing provider like N-iX, businesses can:
- Leverage specialized AI expertise to develop and fine-tune Generative AI models without building an in-house AI team.
- Reduce risks and costs by outsourcing AI model training, deployment, and maintenance instead of developing everything internally.
- Achieve faster implementation by tapping into pre-built AI frameworks and cloud-native AI tools.
A real-world example is how N-iX helped a global tech enterprise improve user experience with Generative AI. The company needed an AI-powered solution to enhance customer interactions but lacked internal resources to develop it. By outsourcing the development to N-iX’s dedicated AI team, they were able to deploy a custom Generative AI model that automated customer engagement, improved response quality, and optimized AI-driven recommendations.
Read more about the use cases and applications of Generative AI
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5 ways you will benefit from AI outsourcing
Contacting a trusted software provider is practical if you need to consult with specialists on the AI strategy, conduct PoC, or perform the infrastructure tech assessment. If you're still hesitant regarding the choice, here are the five crucial reasons to opt for outsourcing your AI project.
Optimization of development costs
From a business perspective, cost-efficiency is a critical factor before kicking off the project. This way, the decision to outsource an AI project can bring significant savings to the company. Outsourcing is advantageous if you need to quickly build up a team from scratch and scale it throughout the research and development stages.
The AI project's scaling naturally requires varied tech expertise, a large team, and thorough planning. That's why dozens of organizations seek outsourcing services to optimize development costs.
Risk reduction
The list of risks that come along with any AI project is far-reaching. Before engaging in the planning process, any organization should consider both technical and organizational risks. It is crucial to consider these aspects to avoid potential failures during the roadmap stage.
That's when a trustworthy software vendor can help by pointing out and mitigating risks. Risk mitigation might pose a challenge for enterprises new to Artificial Intelligence projects. Such threats are complicated to tackle with scarce resources. Therefore, partnering with a reliable outsourcing vendor or hiring dedicated AI developers can ensure smooth planning and implementation.
Instant access to domain expertise
Artificial Intelligence development companies have solid expertise in researching, developing, and maintaining the project's specifications. That's why enterprises opt for outsourcing to get immediate access to the business and domain expertise.
Regarding AI nuances, outsourcing can help align the AI strategy with your business goals. On the bright side, partnering with a reliable outsourcing vendor means accessing diverse expertise in Machine Learning, computer vision, and MLOps-powered solutions. You'll hardly get the same package when hiring individual specialists in these fields.
Reducing the time to market
When it comes to project delivery, a timely release is crucial, which can be complicated to achieve with a small team. While it's becoming increasingly difficult to deploy AI systems, its main barrier is still the persistent expertise gap.
A well-rounded approach to this challenge is to opt for outsourcing AI development. It's becoming more accessible to get a wider talent pool simultaneously working on your project when partnering with a reliable vendor. This resource allocation allows ramping up the delivery speed, thus reducing the time to market.
Diverse availability of vendors
One benefit, which might not sound too obvious, is the wide availability of outsourcing vendors on the market. The global talent shortage persists, resulting in increased demand for outsourcing services.
However, before choosing an outsourcing partner, the key question is—is your business ready for AI implementation? A successful AI project requires more than just finding the right vendor; it demands a clear strategy, structured data, and the right infrastructure. Let’s evaluate your AI readiness to ensure a smooth and effective implementation.
AI readiness assessment
Before outsourcing AI development, you must assess whether your business can adopt AI effectively. Many companies face unstructured data, outdated infrastructure, unclear business objectives, or a lack of internal expertise. Conducting an AI readiness assessment helps you identify gaps, reduce risks, and maximize the value of your AI implementation.
Key areas of AI readiness assessment
1. Data maturity
- Do you have structured, high-quality data available for AI models?
- Are your data sources centralized, integrated, and easily accessible for AI training and deployment?
- Is your data secure and compliant with industry regulations like GDPR?
2. Technology infrastructure
- Does your IT environment support cloud computing, APIs, and the processing power needed for AI workloads?
- Are your existing systems AI-compatible, or do they require modernization?
- Do you have scalable infrastructure to accommodate AI expansion across different business units?
3. Business use-case clarity
- Are your AI objectives clearly defined, or do you need guidance on how AI can deliver business value?
- Do you have specific AI goals, such as cost reduction, process automation, predictive analytics, or customer experience enhancement?
- Have you tested AI feasibility through a pilot project or a PoC?
4. Scalability and operational readiness
- Can AI solutions be scaled across multiple departments, locations, or business processes?
- Does your team have the necessary AI literacy and training to use AI-driven insights?
- Have you planned for AI model lifecycle management, including monitoring, retraining, and optimization?
How N-iX helps businesses with AI readiness
N-iX provides comprehensive AI consulting services to help you assess, strategize, and implement AI solutions efficiently. Our AI consulting approach focuses on reducing risks, improving scalability, and ensuring smooth AI integration.
- Data and infrastructure audit. We assess your data quality, cloud infrastructure, and IT systems to determine their AI readiness. If necessary, we help modernize your data pipelines to support AI adoption.
- Feasibility studies and PoCs. N-iX conducts pilot projects to test AI’s business value before full-scale implementation, helping you validate AI’s impact on your operations.
- AI solution architecture and roadmap. Our team designs AI architectures, recommends the right technologies, and builds a custom AI roadmap tailored to your business needs.
- Regulatory and compliance consulting. We ensure your AI projects comply with data privacy, security, and regulatory requirements, helping you mitigate risks before AI deployment.
- AI integration strategy. If you plan to outsource AI development, we help you choose the right outsourcing model, define project scope, and select the best AI technologies to fit your business goals.
Let us build your enterprise AI strategy
How to choose a reliable AI outsourcing vendor?
Choosing a reliable outsourcing provider can be challenging, especially if you have no prior experience. Here are the three crucial criteria for choosing a reliable AI development partner for your project:
1. Tech competence and a portfolio of AI projects
When choosing an Artificial Intelligence outsourcing provider, the first thing to remember is to assess tech expertise properly. Your project needs a dedicated team of solution architects, AI experts, delivery and project managers, testers, and a maintenance crew. Regarding the particular technologies and frameworks, a dedicated team has to be competent in Python, R, Scala, TensorFlow, and C.
When it comes to AI and ML tech competence, a trusted vendor should be proficient in:
- DL/ML frameworks/libraries: Tensorflow, Keras, PyTorch;
- Hyperparameters tuning: Optuna, Ray Tune, Hyperopt;
- Data annotation tools: Labelbox, CVAT, VoTT;
- ML platforms: Kubeflow, Amazon SageMaker;
- Orchestration tools: Kubeflow Pipelines, Metaflow, Apache Airflow;
- ML deployment and serving: Kubeflow, BentoML.
Once you have such a company in mind, check its portfolio of AI/ML projects. Ensure that the vendor has relevant experience in your field and a proven track record of delivering such projects over the last years.
2. AI and ML expertise
Besides expertise in technologies, frameworks, and applications, a trusted vendor must be competent in how specific AI and ML solutions are applied. In this sense, AI and ML expertise When it comes to AI and ML tech expertise, look for:
- ML models development;
- Data annotation;
- Deep Learning;
- Computer vision;
- Natural language processing;
- Service personalization;
- Chatbots and conversational AI;
- Reinforcement learning;
- Time-series modeling;
- AutoML.
The combination of these AI/ML technologies guarantees that the outsourcing vendor has sufficient skills to tackle any technical challenges. After all, it’s always best to partner with a service provider that uses the end-to-end approach in designing, building, deploying, integrating, and maintaining custom AI/ML solutions.
3. One-stop shop service
One typical problem enterprises of all sizes face is the lack of proper planning when partnering with an outsourcing provider. In some business scenarios, a client might need not only the work of specialists to design algorithms or create a chatbot but also thorough planning and strategy design.
Regarding strategy, a reliable outsourcing partner has to be proficient in Product Discovery. It usually includes use-case definitions, conducting PoCs, delivering the AI/ML architecture design, establishing model governance, and estimating the costs. In turn, the strategy assessment options capitalize on architecture review, roadmap analysis, and estimation of AI maturity levels.
Look for an outsourcing partner that can deliver strategy, design, implementation, service personalization, and post-release maintenance. When partnering with such a vendor, you'll know it's a one-stop shop for your AI project and your current business needs.
Featured success story: AI-powered vehicle identification
Redflex, a global leader in intelligent transport solutions, aimed to enhance road safety enforcement and expand its market presence by leveraging AI-powered automation. The company required a highly accurate computer vision system capable of identifying vehicles, analyzing traffic violations, and detecting distracted driving and seat belt violations in real time. However, developing such a solution presented significant technical challenges, including the need for high-accuracy AI models, real-time data processing, and seamless scalability to handle large volumes of visual data.
N-iX provided a dedicated AI team to develop a real-time AI-powered traffic monitoring system. The solution incorporated advanced computer vision models to recognize vehicles, detect key attributes such as speed, plate numbers, and colors, and identify violations with deep learning algorithms. The system enabled automated detection and reporting, allowing law enforcement to process violations efficiently while minimizing manual intervention. To ensure accuracy and scalability, N-iX built two PoCs before full-scale deployment, integrating the solution into Redflex’s existing infrastructure and optimizing its performance.
With an AI-powered detection accuracy of 88%, Redflex significantly improved the precision of traffic law enforcement and reduced reliance on manual monitoring. The real-time processing capabilities allowed for instant anomaly detection, strengthening compliance and enhancing road safety. By outsourcing AI development to N-iX, the company accelerated its AI-driven transformation, optimizing operational costs while creating a scalable solution that could be expanded to new markets.
Why should you choose N-iX for your next AI project?
- N-iX is a reliable global vendor with 22 years of experience and a proven record in Data Science, AI, and ML projects;
- The company's team consists of 2,200 software engineers and IT experts, with 200 data and AI specialists;
- N-iX's portfolio includes 60 data projects delivered for global clients, including Gogo, Redflex, Ringier, Cleverbridge, and Fortune 500 enterprises;
- The company adheres to international industry standards, such as ISO 27001, ISO 9001, ISO 27001:2013, GDPR, and PCI/DSS;
- N-iX has a reputation as a trusted software development vendor and ISG-recognized as a rising star in Data Engineering. The company's credibility is proven by numerous awards and industry ratings, such as IAOP, GSA, CRN Solution Provider 500, and ISG.
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