Artificial Intelligence Outsourcing in 2025 – Best Practices, Cost, and Benefits

19 Feb, 2025

6 min read

How Does Artificial Intelligence Outsourcing Work?

AI outsourcing refers to partnering with external vendors to handle AI capabilities instead of building in-house teams. Specialized providers offer services like developing AI models, integrating them into business workflows, maintaining systems, and scaling solutions based on evolving needs.

Companies can choose between two main AI outsourcing models:

Project-Based Services: Outsourcing partners build tailored AI solutions for specific business requirements on a project basis. This allows testing the waters before making long-term commitments.
Managed AI Services: Providers manage the end-to-end lifecycle of a company’s AI systems including development, deployment, maintenance, and enhancements. This option offers whole-lifecycle ownership.

The right model depends on the complexity of AI needs and desired involvement levels. The core value proposition in both models lies in utilizing specialized expertise on demand without recruitment overheads for niche skills.

Stages of the Artificial Intelligence Outsourcing Process

The typical AI outsourcing process involves five stages:

Business Needs Assessment: Providers collaborate with enterprises to thoroughly understand their pain points, objectives, and data landscape. This shapes solution scoping.
Solution Design: Outsourcing partners create customized proposals covering technologies, development processes, pricing models, and milestones tailored to business requirements.
Development and Testing: Providers build, integrate, and rigorously test AI models using robust methodologies after finalizing the design. Enterprises can track progress through the project life cycle.
Deployment and Maintenance: AI outsourcing partners seamlessly integrate models within business environments and IT architectures once models are production-ready. They also manage updates as needed.
Ongoing Enhancements: Longer partnerships involve continuous improvements whether enhancing the accuracy of models or adding new capabilities to scale impact.

This process ensures just the right alignment between the custom AI solutions delivered and their usage within enterprise use cases to drive adoption and intelligent automation.

How Much Does Artificial Intelligence Outsourcing Cost?

AI outsourcing rates vary based on the scope complexity, engagement models, and provider expertise levels. In most cases, companies spend somewhere between $50,000 and $300,000 when outsourcing AI development to a trusted partner like Cubix. The three main pricing structures include:

1. Time and Material Model

This involves charging enterprises for the time and resources expended by outsourcing partners for AI capabilities. The billing is linked directly to costs like:

Monthly rates for AI experts based on experience levels
Technology infrastructure usage charges for cloud computing/storage
Licenses for commercial algorithms or frameworks

While the time & material model offers transparency into components driving overall pricing, costs can spiral if project timelines lag or requirements evolve extensively post-launch.

2. Fixed Price Per Project

As the name suggests, providers quote a flat rate encompassing all aspects like people’s effort, tech usage, and tools for delivering an AI project with predefined specs. Enterprises get cost predictability.

However, providers built buffer costs to account for uncertainties, stretching budgets. Lack of flexibility around requirement changes also challenges this model.

3. Outcome-Based Pricing

This emerging model ties AI outsourcing fees directly to the business value realized by enterprises through solutions in parameters such as:

Revenue gains post-deployment
Productivity improvements through automation
Cost savings from accelerated insights

The model incentivizes providers to maximize solution performance. However, measuring data-driven metrics can be complex for immature analytics.

Outsourcing vs. In-House AI Development

Deloitte reports that outsourcing development can slash project timelines by up to 50%. Moreover, eliminating expenses like niche skill recruitment, technology infrastructure, and continuous training opens doors to various exciting opportunities.

Small and mid-sized businesses lacking economies of scale in workloads see outsourcing as the most financially viable model for tapping AI. For enterprises, providers offer ways to stretch budgets by delivering specialized capabilities without massive capital outlays.

As algorithms continue democratizing access to emerging technologies, AI outsourcing unlocks flexibility along with the strategic benefits of automation and data-driven decision capabilities for players across the economic spectrum.

Benefits of the Artificial Intelligence Outsourcing Model

1. Faster Time-to-Market

Outsourcing partners help companies speed up ideation, prototyping, building, and deployment cycles for AI solutions through their structural processes. Their specialized teams streamline activities spanning:

Requirements analysis
Data engineering
Model development
Cloud infrastructure setup
Testing and integration

Pre-built accelerators around workflows, datasets, and algorithms also boost speed by preventing reinventing every component from scratch. Augmenting in-house efforts with outsourced development bandwidth results in faster time-to-market for minimum viable product, accelerating ROI realization.

2. Access to Diverse Software and AI Development Expertise

Niche AI skills remain scarce despite rising demand. Outsourced partners mitigate talent gaps for enterprises by cultivating diverse competencies needed to translate business challenges into tailored solutions utilizing approaches like:

Machine learning (supervised, unsupervised, and reinforcement)
Natural language processing
Computer vision
Predictive analytics
Expert systems

The multitude of programming languages and frameworks inherent to AI development requires expansive capabilities. Partners accelerate expert matching to problem statements and augment capacities along the delivery lifecycle.

Smooth integration between software and AI skill sets ensures optimal user experience and adoption through intuitive interfaces, smart workflows, and customized controls. Partners investing continuously to nurture talent diversity and technology breadth can catalyze innovation.

3. Improved Product Innovation

Embedding intelligence within existing products or creating net-new AI-powered offerings can seem intimidating without internal capabilities. Outsourcing specialists apply cross-domain expertise to simplify the journey through co-innovation partnerships.

They facilitate idea-generation workshops tapping business context and technology possibilities. This shapes high-level solution scoping centered on key parameters like consumer pain points, feasibility, commercial viability, and differentiating factors versus market alternatives.

Equipped with clearly defined product specs and user flows, outsourcing teams assemble robust prototypes utilizing leading tools and cloud-hosted lab environments without enterprises investing heavily upfront. Partners also handle activities from UX polish to scalability enhancements pre-launch.

4. Customized AI Solutions

Every business faces unique challenges and objectives that necessitate tailored AI solutions. Outsourcing specialists collaborate with enterprises across requirements analysis, design, and configuration stages to nurture understanding and ensure customization.

Key areas they customize include:

Dataset Curation: Partners source, cleanse, and label relevant datasets for scenario contexts. This trains algorithms to handle niche vocabularies or formats.
Model Optimization: The approaches are continuously fine-tuned to maximize precision for specialized use cases through retraining approaches.
Solution Integration: Seamless embedding within existing IT ecosystems using APIs and microservices architectures prevents disjointed systems.
Deployment Mode: Fitting solution packaging like on-premise servers, private cloud, hybrid, or SaaS to enterprise contexts.

The depth of customization spans across the AI lifecycle to address the uniqueness of each business scenario by optimizing solutions for the purpose.

5. Reduced, Shared Project Risks

Entrusting crucial AI projects solely to internal teams heightens risks around misdirected investments. Outsourced engagements allow enterprises to reduce project risks by capitalizing on partners’ proven methodologies.

Providers combine learnings from multitudes of deployments across clients to enhance the reliability of outcomes. Shared best practices mitigate risks around data management, model bias, explainability constraints, or edge case handling.

Piloting outsourced projects focused on high-ROI applications minimizes risk exposure before larger commitments. Partners also structure pricing models based on milestones achieved, ensuring fee alignment with value delivery.

The collective mitigation and accountability with outsourcing smooth the road to AI adoption.

6. Continuous Support and Maintenance

Post-launch, AI solutions require continuous enhancements whether to improve model accuracy, accommodate new datasets, or address evolving business requirements. Outsourcing provides long-term assistance through technical support and maintenance services.

Dedicated partner resources can implement changes faster through existent solution familiarity. Enterprises also avoid AI skill gaps associated with nurturing niche custodian teams for existing systems. Partners offer optimized capacity allocation across clients to balance optimization requests.

The long-term support alleviates the pains of legacy AI system ownership.

7. Unmatched Flexibility and Scalability

Outsourced models allow enterprises to calibrate teams and tools based on evolving AI project needs through on-demand access instead of fixed overheads with in-house budgets. Companies can thus explore more initiatives faster by tapping specialized skills and cloud infrastructure when required rather than recruiting niche profiles prematurely.

Partners structure teams utilizing combinations of technical talent, subject matter experts, and business analysts to align with project complexity and organization skill profiles. Companies can also scale compute resources without procuring expensive hardware on-premise.

The built-in flexibility and savings with outsourcing fuel enterprise agility to pursue ambitious AI roadmaps.

Choose Cubix As Your Artificial Intelligence Outsourcing Partner

AI outsourcing enables businesses to accelerate ideation, amplify innovation, and customize solutions for amplified outcomes, tapping into AI’s true potential.

If you’re looking for a trusted AI outsourcing partner for achieving your product innovation goals – choose Cubix!

Having delivered over 1300 custom software solutions and specializing in numerous enterprising technologies, including AI, Cubix has the resources, tools, expertise, and teams to make things happen for your company in the AI landscape.

Our agile methodologies for streamlined AI development and deployment accelerate time-to-market so you remain competitive.

Whether you plan to optimize operations with computer vision or create next-gen experiences with machine learning, trust Cubix to strategically unleash AI’s potential for your organization via flexible engagement models.

Read More: How to Build an LLM Like DeepSeek?

Let’s discuss how Cubix can help you drive AI innovation for your specific use cases.