Why Enterprises Are Building Custom AI Models

Every enterprise is racing to gain a competitive advantage with AI. But if everyone is using the same off-the-shelf models, are you gaining a real edge? 

Companies that stand out aren’t just using AI, they’re building their own.

Many organizations were initially exposed to the benefits of AI through off-the-shelf AI solutions (often due to their employees engaging in Shadow AI). Now that the advantages are clear, enterprises are increasingly investing in custom AI models vs off-the-shelf models to gain ownership, protect their data, and secure a greater competitive advantage compared to companies solely using generic models such as OpenAI GPT, Google Gemini, and Claude.

Let’s explore the strategic advantages and practical benefits enterprises are gaining through AI model customization and why this trend has accelerated.

Why Enterprises Are Building Custom AI Models

Enterprises currently face intense competition, increased security risks, and a critical demand to differentiate themselves. These needs are driving organizations toward custom AI models, which offer:

Ownership and Control

Complete ownership over a custom AI model allows the enterprise to tailor it to their needs and continuously improve and adapt. Proprietary AI models allow organizations to precisely tailor the model to their data and unique operational needs. Third-party AI models will most likely improve at a good pace, but the companies using these models have no say on the direction of these advancements. While the model is well-suited to their business need now, this isn’t a guarantee in the future. 

Custom AI model development ensures that enterprises can continuously refine, enhance, and adapt their models as their business evolves, free from the dependency on third-party vendors.

Model ownership also means enterprises aren’t limited by licensing agreements or usage restrictions. Companies can use their AI models for multiple use-cases internally, freely expanding their capabilities and scaling across different business functions. This additional autonomy and adaptability are critical for enterprises whose needs frequently change and evolve.

Data Security

The need to keep data secure and on-premises makes third-party AI services less attractive. Proprietary AI models allow companies to maintain complete control over their data while hosting their models and AI solutions securely. This significantly reduces the risks associated with exposing sensitive data to external vendors.

This concern is especially relevant for enterprises in the finance, healthcare, and government spaces, where data privacy is critical. With tailored AI models, enterprises manage security protocols internally, greatly reducing compliance risks, cyber threats, and breaches in confidentiality.

Greater data security confidence means enterprises can leverage data more aggressively within their custom AI models. The more proprietary company data these models are trained on, the more they are differentiated from generic AI models and the greater competitive advantage they can create.

Competitive Advantage

At this point, every enterprise has access to the same off-the-shelf AI tools and APIs. While AI offers massive business advantages, generic models don’t offer much differentiation. Building custom AI models is a strategic differentiator that allows enterprises to develop unique features, deeper insights, and superior capabilities.

This competitive advantage can translate into increased customer loyalty, improved brand positioning, and distinct market advantages. 

Benefits of Developing Custom AI Models

Custom AI model development offers several advantages over generic AI tools. Organizations investing in tailored AI solutions are positioning themselves for long-term innovation and competitive advantage across all business functions.

Tailored Solutions and Superior Performance

Generic AI models are built to serve a broad user base and a range of applications. This means they usually perform well across a variety of general tasks but may fall short for specialized applications. Tailored AI solutions can be specifically optimized for specific workflows, processes, and industries, offering superior performance.

Data Privacy & Compliance

Data privacy and compliance are key considerations when implementing any enterprise technology. Third-party models open up sensitive data to breaches, regulatory fines, and reputation damage. Custom AI models allow the enterprise to keep all data secure, adhering to regulatory guidelines and enabling secure handling of sensitive data.

Cost Efficiency at Scale

Generic AI models are typically priced based on usage. These costs can balloon dramatically when dealing with high volumes. Proprietary models, after the initial development costs, incur much lower usage expenses, especially at scale. The cost-per-token decreases as an enterprise AI model achieves cost efficiency through the internal management of infrastructure, resources, and model updates.

Continuous Improvement & Adaptability

While a generic model may suit the organization’s existing needs, the future growth of that model may not match the enterprise's growth. Custom AI models are dynamic assets that enterprises continually improve over time. This ongoing AI model customization allows businesses to adapt their models quickly to changing business priorities, customer demands, or market conditions. Unlike off-the-shelf models, proprietary solutions can easily be retrained or fine-tuned on internal data, leading to improved performance year over year.

Talent & Intellectual Growth

Organizations such as Google, Meta, and OpenAI attract top talent because they are doing cutting-edge work. Proprietary AI model development is the perfect way to attract leading AI talent, researchers, and engineers. This positions the enterprise as industry leaders, driving further innovation and creating a culture of technological excellence and creation.

Best Custom AI Model Use Cases

While the benefits of custom AI models are clear, they may not be right for every enterprise AI use case. How do enterprises decide between custom AI models and off-the-shelf models? 

Situations involving sensitive data, unique business processes, proprietary data, and scalability challenges are ideal custom AI model use cases. Being able to recognize these scenarios helps enterprises identify where custom AI solutions can deliver the most immediate and impactful value.

• Highly Regulated or Sensitive Data Environments: Enterprises in highly regulated sectors, such as healthcare, finance, and government agencies, are building proprietary AI models to adhere to strict compliance and data security requirements. For example, healthcare organizations have access to extremely valuable patient data that is too sensitive to be used by generic AI models. Building a custom AI model could allow for this data to be used for much more specialized model training and result in high accuracy rates compared to generic models.
• Domain-Specific or Unique Use Cases: Generic models are built to fit a variety of use cases. This makes them ill-suited to industry-specific use cases, especially in specialized industries. Custom-built AI solutions allow organizations to address unique industry scenarios and workflows to create models that are highly specialized.
• Core Strategic Differentiator: Many enterprises are currently “doing AI” regardless of whether it has the potential to be a major strategic differentiator. This should be an even greater factor when considering custom AI models. Unless the specific capabilities of a custom model are integral to the company’s positioning or can create a major competitive advantage compared to generic models, the opportunity is not an ideal custom AI use case.
• Strong Proprietary Data: One way enterprises can use custom AI models as a strategic differentiator is through rich, proprietary data. If the custom AI model can leverage data that competitors lack, this results in uniquely accurate and valuable AI applications. Without strong data, custom AI development will struggle to outperform generic models.
• Need to Scale: Organizations planning to scale their AI usage significantly should consider proprietary AI solutions. Custom AI models are easier and more cost-effective to scale once the initial model is trained and integrated, especially for high-volume scenarios like customer support automation, transaction processing, or large-scale analytics.
• Ability to Execute: The biggest consideration for whether to build a custom AI model is whether the enterprise has the ability to successfully execute the project. This ability is determined by the organization’s AI readiness, data maturity, and access to AI talent and resources. Organizations in the early preparation and exploration stages of AI readiness shouldn’t begin their journey with a custom AI model. Enterprises also shouldn’t consider proprietary AI models if they don’t have the resources to build and maintain the model.

How to Build Custom AI Models

The decision to develop custom AI models is becoming a strategic imperative for forward-thinking enterprises. Tailored AI solutions offer unparalleled ownership, security, competitive differentiation, and cost-efficiency, making them the clear choice for enterprises aiming to harness AI strategically.

Access to skilled AI professionals, such as data scientists, machine learning engineers, and infrastructure specialists, is critical for custom AI model development. Enterprises facing AI talent shortages can leverage fully managed development teams or staff augmentation through Gigster. Working with Gigster helps enterprises quickly access specialized expertise, reducing project risk and accelerating time-to-market.

Looking to build your own custom AI models? Gigster’s AI development services provide world-class AI talent, enabling you to rapidly scale your AI initiatives without compromising quality. Contact Gigster today to explore custom AI solutions tailored specifically for enterprise needs.