When to Set Up an AI Development Team?

According to McKinsey’s State of AI global survey, nearly nine in ten specialists from companies across industries say their organizations use AI for at least one business function. However, only a few organizations have managed to reach the maturity level where artificial intelligence fundamentally changes business workflows.

This problem may have many reasons, from a lack of technical expertise to the organization’s resistance to change. But one key factor relevant to this article is that many AI projects start with a flawed assumption.

Businesses often fail to understand what level of AI integration and complexity they need and what outcomes to expect. Do they need simple, off-the-shelf AI features integrated into their software, or a more complex, AI-driven architecture? The first case can be handled by developers with basic experience in AI integration services. While the second can require a dedicated team of AI development experts.

In this article, we’ll list the use cases for basic AI integration and comprehensive development with a dedicated AI team.

Not every business needs a full-scale, custom AI solution. Basic off-the-shelf AI solutions integration can often be enough to enhance workflows, reduce manual work, and improve user experience. In such cases, you don’t need heavy customization, such as retraining the model, changing its behavior, building custom data pipelines around it, as well ongoing model tuning and monitoring.

Many applications come with one or two AI capabilities built in without heavy customization. For example, the platform remove.bg uses a basic generative AI model for a single task: removing backgrounds from images uploaded by users.

Overall, the integration of AI features without heavy customization fits the following applications:

• Chatbots and smart assistants that cover basic communication workflows with off-the-shelf generative AI models
• AI-powered optical character recognition (OCR), speech-to-text, and translation solutions where regular AI models extract text from images, transcribe meetings, or translate content automatically
• Document summarization and classification tools that rely on text processing and analysis capabilities of generative AI models

The key characteristics of this approach:

• The AI model is used solely to enhance existing software
• The model is replaceable by available market alternatives
• No long-term model learning and training is required

Integrating an uncustomized or lightly customized off-the-shelf AI model is often the right choice in the cases outlined below.

Enhancement of simple and predictable workflows

Simple and predictable processes, such as content summarization, text extraction, or email classification, typically don’t require nuanced judgment, creative problem-solving, or deep integration with proprietary systems. They can be covered by off-the-shelf AI models without extensive customization. Still, such workflows can ensure automation and reduce manual effort.

Focus on rapid delivery without significant upfront investment

If your goal is to launch an MVP with basic AI features to prove value to investors or early users, a simple AI feature integration is often the right move. Once initial traction or funding is secured, teams can shift focus toward deeper functionality. This may involve fine-tuning the existing off-the-shelf model or replacing it with a custom solution to achieve more domain-specific outputs and ensure long-term relevance.

Fast idea validation

A similar logic applies to proofs of concept (PoC) projects. When the primary goal is to validate an idea or test a specific capability, development teams typically have tight time and budget constraints. Integrating a readily available AI model allows teams to quickly assess feasibility with minimal complexity. If the PoC demonstrates clear value, you can then proceed with full-scale development, including the design and training of more specialized AI models.

In a nutshell, off-the-shelf models are relevant to cases that do not need complex AI functionality, require rapid development, or are intended for short-term use. However, as your needs and business goals evolve, your solution is likely to outgrow basic AI feature integration.

Even if you choose simple integration of off-the-shelf AI models for your solution from the project onset, you may need to consider investing in full-scale AI development. Here are several cases:

• Decisions made by your AI model directly affect revenue, safety, or compliance. For instance, using an AI off-the-shelf solution for budgeting and other critical analyses may result in inaccurate estimates because it might not understand your finance workflows or perform calculations correctly.
• You need assistance with multi-step workflows. When tasks require the AI to follow ordered steps, maintain context across actions, or coordinate with multiple systems, basic model integrations often lack the capacities. They struggle to handle orchestration, memory, or custom logic efficiently. You may consider using more complex, fully custom AI models, as well as agentic AI systems that can proactively handle sequential workflows and use autonomous reasoning.
• AI models are already showing signs of bias, and you intend to scale your model. Incomplete or biased training data, human bias baked to labels or assumptions, or overrepresentation of certain patterns in training data often make AI models produce biased outputs. In fact, these problems scale with the solution. Recent studies show that as models grow, implicit biases increase.
• Generic AI models misunderstand your domain, lacking a deep understanding of industry-specific terminology, workflows, or edge cases. This issue causes incorrect or misleading outputs that may be damaging in terms of both decision-making and user experience.

Keep in mind that deep customization of an off-the-shelf AI model or development of a custom one can be a challenging task, primarily due to common AI adoption and development challenges.

To handle such obstacles, you may need to involve an experienced AI development team.

Over time, organizations evolve from applying ready-made AI tools to developing, operating, and governing AI systems as part of their core operations. That transition almost always requires a dedicated AI development team. Here is a more detailed overview of such cases.

AI lies at the core of your product or covers multiple workflows

If you expect AI to drive the core value of your product, its quality and capabilities determine whether the product succeeds or fails. This is especially relevant to solutions like recommendation systems, predictive analytics platforms, fraud detection engines, personalization systems, or agentic solutions that handle multiple workflows and show autonomous reasoning. In particular, McKinsey’s State of AI report shows that 23% of business executives report their organizations have scaled agentic AI systems capable of handling sequential processes.

In such cases, generic, off-the-shelf models struggle due to the lack of training on proprietary data or multi-step reasoning capabilities. Training models to fit core business use cases, or building a more complex AI solution, including an agentic system, becomes a vital objective that requires the involvement of an experienced AI team.

You need domain-specific intelligence and security

Many industries operate with specialized terminology, unique edge cases, and data that cannot be shared externally. Finance, healthcare, and logistics are prime examples. In such domains, generic models frequently misunderstand context, delivering poor outputs.

Security becomes another concern, as specific domains like fintech and healthcare require an in-depth focus on security. In fact, 40% of business executives cite concerns over data privacy and confidentiality among the most significant barriers to AI adoption.

To build a domain-specific AI intelligence, you need a custom model trained on proprietary or sensitive datasets. You will also need to implement mechanisms for explainability and auditability because this transparency will help identify errors faster, address biases, and ensure compliance with regulations.

Another important objective is to establish efficient security practices to safeguard domain-specific sensitive data. Ensure your AI-powered software is secure by building reliable and observable pipelines, monitoring and limiting API usage, and protecting data at the system’s key connection points.

Delivering this level of intelligence and security typically involves a dedicated AI team.

You require high accuracy and low error tolerance

Around 45% of business executives cite model bias among the most significant AI adoption challenges. This problem becomes especially relevant in industries and workflows with low error tolerance. For instance, HR management, where 15% of AI systems do not meet fairness thresholds for all demographic groups.

To address this problem, you need to build a custom model or extensively customize an existing one to ensure consistent accuracy and minimize bias. Achieving this level of reliability requires careful data curation, continuous model testing, and close collaboration between AI specialists and domain experts. In such cases, involving an AI/ML software development team is essential to provide the necessary expertise and establish efficient collaboration and model monitoring workflows.

You need complex data integrations

Issues with data management are very common in the AI development domain. In fact, Gartner predicts that through 2026, organizations abandon 60% of AI projects unsupported by AI-ready data. To build a mature AI system capable of handling business-specific workflows and multiple steps, you need to establish efficient data management practices. This means creating a system that collects data from multiple sources, processes it in real time, and transforms it for AI consumption.

In such cases, AI development goes far beyond a simple integration of an off-the-shelf model. Even if you integrate an API that feeds necessary data to the model, data distribution can shift over time. This will eventually make your model’s performance degrade – a phenomenon known as model drift.

To manage a complex system where an AI model, whether custom or off-the-shelf, is integrated with diverse data sources, you need specialists skilled in system-level design. They will help you establish:

• Pipeline monitoring workflows
• Real-time inference infrastructure
• Drift detection practices
• Model feedback loops

Things become even more complex when you need to build agentic AI solutions where different AI agents cover varying responsibilities, each powered by a corresponding dataset. In such cases, it is no longer integration. AI development requires system design specialists capable of building a coherent and consistent data architecture.

Artificial intelligence models are not static assets. Even if you use an off-the-shelf model, its accuracy and reliability tend to degrade over time. In particular, a recent study indicates that some of the major LLMs, namely Claude 3.5 Sonnet, Claude 3.5 Haiku, GPT-4o, and GPT-4o mini, show some degree of drift.

To keep a model relevant and reliable in the long run, you should continuously monitor and train it. This can demand a lot of effort, expertise, and cost. For example, the image below illustrates the annual investment major technology providers make in their LLMs.

In such cases, you need assistance from experienced AI development specialists skilled in model prompt- or fine-tuning, as well as establishing continuous monitoring workflows.

It is worth mentioning that each update of your solution also poses a risk of unexpected model behavior. Therefore, you need to establish efficient testing through controlled experimentation. Rollbacks and versioning are also vital in cases when updates introduce unexpected behavior.

Wrapping up, the image below illustrates the key questions you should ask before committing to full-scale AI development with a team dedicated to this technology.

If you answer “yes” to more than one, you need an AI development team.

AI team structures may differ, but certain roles are essential. Let’s take a look at the key members of the AI development team.

Data scientists

Data is fundamental to AI systems; therefore, data scientists form the backbone of AI engineering teams. They gather and analyze datasets, design models, and validate outputs, ensuring the quality of data on which your AI solution depends.

Machine learning engineers

These are the specialists responsible for developing your AI model and making it work. ML engineers handle model deployment, optimize its performance, and build pipelines that feed models with clean, structured data.

Data engineers

Around 84% of businesses report significant efficiency gains from better access to data. Building and managing a solid data infrastructure where the necessary data is accessible is the responsibility of data engineers. These specialists take responsibility for extracting, transforming, and loading data that feeds AI models, while keeping the architecture high-performing and compliant.

AI software engineers

AI models don’t exist in isolation. To build a product around your AI model, you need software engineers capable of integrating AI features into existing systems or building AI-ready solutions from scratch. Such specialists use technologies like .NET or Python to build app back-end, create UI/UX design with frameworks like of Angular or Blazor, and design API and services around models. Their goal is to create a high-performing, secure, and user-friendly app that utilizes AI capabilities efficiently.

DevOps and MLOps specialists

AI-powered apps are dynamic, and you may need DevOps and MLOps specialists in your team to handle continuous integration, versioning, and monitoring. MLOps experts are also responsible for retraining the pipelines to keep models healthy over time.

Domain experts

To make sure that AI models understand context and are based on curated proprietary data, you need domain experts. They help technical specialists interpret outputs, define edge cases, and ensure that AI aligns with business goals.

The first and most important thing that an AI development team brings you is expertise, a factor that 42% of business executives mention among the biggest obstacles to AI adoption. Specialists in custom AI development can help you ensure that the solution remains unbiased, accurate, and high-performing in the long run. Skilled specialists ensure quality outputs by providing support across the following stages of AI development:

• Data preparation and labeling. With the help of data scientists, you can ensure that models learn from accurate, relevant, and unbiased input.
• Model selection and training. AI developers help you choose the right algorithms, architectures, and model training strategies that are tailored to your business goals.
• MLOps and infrastructure development. You get professional assistance with building a reliable infrastructure. With efficient deployment workflows, reliable pipelines, and scalability options, you ensure that your model runs securely in the long run.
• Monitoring, retraining, and governance. As has already been mentioned, an AI engineering team helps you with long-term system maintenance by handling model monitoring, retraining, and optimization workflows.

With such benefits, you get an adequate response to common AI adoption challenges. The involvement of an experienced AI development team ensures that you get a sustainable software ecosystem instead of a disorganized software project with unclear value from AI implementation.

Despite the growing adoption of artificial intelligence, the talent shortage remains a significant challenge in the AI development domain. In fact, 55% of organizations report a shortage of skilled professionals capable of developing and maintaining agentic AI software.

Outsourcing becomes an option because it allows organizations to find specialists with strong AI development expertise in the short term. That’s when Leobit is ready to help. We are a company with strong AI development and adoption experience and are ready to help fill the skill gap. As a Microsoft Solutions Partner for Data and AI, as well as Digital and App Innovation, we have significant experience in deploying, customizing, and configuring diverse AI models. Our team also involves certified Azure AI engineers who can help you leverage Azure AI services.

Our AI developers are skilled in working with:

• Public AI services and models like ChatGPT or OpenAI API, Google Gemini​, and GitHub Copilot
• Privately hosted AI models and services like Llama 3, Mistral 7BGPT-J,​ GPT-NeoXPhi-2​ NVIDIA NeMo

Our portfolio includes numerous AI development projects, and we are actively using custom AI agents to support our internal processes. For example, our sales team uses Leona, an AI-powered RFP scoring and proposal generation solution, and Leo, an email auto-response solution, to facilitate lead management and communication. We also use Leonardo, an AI-powered workplace assistant implemented as a Slack bot, to enhance HR workflows.

To enhance our expertise with artificial intelligence, our team actively experiments with AI-powered PoCs that cover multiple tasks, from icon generation to voice transcription.

Whether you need to set up a dedicated AI development team from scratch or expand your existing team with skilled AI experts, we are ready to help.

In sum, not all projects involving AI functionality require an artificial intelligence software development team. However, we suggest you rely on dedicated AI developers in the following cases:

• AI lies at the core of your product
• You need domain-specific intelligence and security
• Your error tolerance is low
• Complex data integrations are required for your model
• You want to use and expand your AI-powered product in the long run

An AI development team can help you avoid common mistakes and cover the skill gap across multiple project stages, from data preparation and labeling to model monitoring, retraining, and governance.

Leobit, a team with significant expertise in AI development services, is ready to provide you with such a team. Contact us to discuss your needs and see how we can help you build AI-powered products that bring value to your business.