According to the McKinsey & Co survey on organization using a Generative AI solution, over three-quarters of companies implemented an AI strategy at least in one of their business processes. However, these are mostly large organizations with the annual revenue of $500 million and more, while smaller ones are vividly late in this race as statistics show.

In this article, we'll examine how small and mid-sized enterprises (SMEs) can improve their performance with AI development services to avoid being left behind by large corporations. We'll explore the most cost-effective and efficient AI software development services, comparing the costs of AI integration solutions versus custom AI app development. We'll begin with strategies for AI implementation.

AI deployment strategies: How to integrate AI development into the company

Here, we will examine AI integration not from the perspective of creating a separate product as part of mobile app development or web app development services, but from the standpoint of reformatting internal company processes and improving work quality through AI adoption and different AI development services. In workflows automation with artificial intelligence, companies usually follow one of three main approaches, being:

Centralized model

It is when all decisions associated with AI development, implementation and usage are made by one department or a team. This provides coordination and standardization to AI projects, leading to more efficient allocation of resources. Despite being the most popular approach within organizations, its downside is that it slows down changes in the company that need to be implemented sooner.

Decentralized model

A decentralized model is when all departments take responsibility for AI deployment within their processes. This model is less favoured by businesses as, despite fast implementation and adaptiveness to current needs, it might lead to duplications in the processes and, thus, less effectiveness due to the lack of coordination.

Hybrid model

Another favourable model, implying that centralized management takes all strategic decisions regarding AI implementation, while departments use AI to improve their specific tasks. This approach helps to both mitigate risks and quickly implement solutions unique for the team. The hybrid model requires good coordination between central management and departments.

AI models: What types are there?

Before we proceed — what are AI models? These are algorithms that can learn and make decisions based on the dataset. There are different types of models, from simple linear regressions to complex neural networks.

Complex AI product development requires lots of resources, both human and machine. It needs artificial intelligence specialists along with robust servers to develop AI models. This logically results in high costs of hiring such engineers and supporting the infrastructure. Let's look at the main AI model types and how they serve businesses in general.

Machine learning

ML is a field of AI development solutions that concentrates on building systems capable of learning from data and improving their performance on particular tasks without explicit programming. It involves creating algorithms that can recognize patterns in data, make predictions, and refine those predictions over time through experience.

Linear and logistic regression models

These models are used for prediction and data classification. Financial organizations and retail can use these algorithms to predict sales and analyze credit exposure.

Clustering

Clustering helps to collect similar data in groups. Clustering is useful in marketing campaigns to segment clients based on their behavior.

Recommendation systems

This AI system personalizes offers based on the user's interests. This type of custom AI development is popular among e-commerce platforms and streaming services like Netflix and Spotify.

Deep learning

Deep learning focuses on recognising features and patterns in data, allowing networks to perform image recognition, natural language processing, and other complex tasks, involving multiple layers.

Neural networks

These models handle image processing and speech recognition. Neural networks are used in the automotive industry for developing autonomous driving systems and in AI solutions for speech recognition.

Convolutional neural networks

These are used in computer vision technology for recognition and analysis of images and videos. This AI development is widely used in medical diagnostics and security monitoring.

Recurrent neural networks

This AI implementation is relevant for analyzing data where the order of its elements is important. For example, it's used in financial and sales forecasting.

Natural language processing

The AI development solution that focuses on understanding and processing human language.

Transformers

GPT Chat is a good example of a transformer model. Adoption of generative AI models helps to automate dialogues and create texts. Businesses can use them in client support, chatbots, and content marketing.

Voice assistants

Upgraded further into the voice assistant, the AI model can answer clients in real time without involving consultants. Such generative AI development services are useful for e-commerce and banking services.

These core AI tools and technologies are frequently employed by large businesses. However, when it comes to AI development, creating complex models can be more costly for companies than meeting business needs with existing AI models. Of course, this depends on the specific goals. If a task can be accomplished using algorithmic methods instead of AI development, then that approach should be taken. In other situations, pre-existing solutions can provide the necessary assistance.

Let's take a brief look at the current offerings from AI development companies and then explore how AI agents can serve as straightforward software solutions for startups and medium-sized enterprises.

Discover how AI solutions can improve your business — request your personalized AI consulting session

Request

Top players on the AI development scene

Before choosing custom AI development services, it is beneficial to explore ready-made solutions offered by leading AI development companies that have already embedded their actionable insights into out-of-the-box AI solutions. Here are some of the top companies currently in the field, along with some of their existing inventions.

Microsoft

Microsoft software development company is a heavy investor in artificial intelligence. They are planning to invest $80 billion in FY 2025 to AI data centers and infrastructure around the world. Their popular AI solution, helping to automate processes, is Microsoft 365 Copilot, a GPT-based AI integrated into productivity apps to automate content creation. Their Power Automate helps improve the workflow by triggering actions based on events like receiving emails or uploading files. It automates approval workflows (invoices, leave requests), data syncing between CRMs and databases, and report generation. Azure AI Services provide chatbots to handle customer inquiries and monitoring orders 24/7, eliminating manual work. Azure AI also helps with predictive analytics. In addition, Microsoft allows you to build your own custom AI solution, connected to organizational data.

Open AI

OpenAI, an AI development company, is partnering with Microsoft, among other major companies. They provide GPT AI models and generative AI applications like ChatGPT and DALL·E, and were among the first to introduce generative AI in software development tasks so widely. They focus on conversational, text generation, image generation, and enterprise AI services. OpenAI's GPT models empower companies with automation capabilities for a variety of workflows, handling operations from content creation to complex multi-step tasks. AI agents built on these platforms can autonomously handle tasks such as web searches and report generation, thus significantly reducing manual labor and speeding up project management. Custom AI agents, reducing the need for extensive coding are effective ways for SME to implement automation. We'll discuss these technologies in more detail further.

Alphabet (Google)

Alphabet is an AI developer of the Gemini platform and a rival of OpenAI's GPT. Their Gemini AI Platform is integrated into Google Workspace and helps to automate content creation, project planning, and data analysis. It also functions as a separate chatbot that can be integrated into the internal processes for improving customer service, forecasting sales, managing risks, and optimizing inventory. The software development company heavily invests in their DeepMind AI project, which, apart from building for Gemini, provides complex solutions in healthcare custom AI development and scientific applications that automate decision making in specialized sectors.

Amazon Web Services (AWS) AI

The AI software development company provides scalable artificial intelligence infrastructure for businesses of all sizes. Key business offerings from AWS include SageMaker, which helps businesses to build, train, and deploy machine learning models that automate tasks such as fraud detection, customer segmentation, and demand forecasting. Rekognition automates image and video analysis for security, quality control, and content moderation. With chatbots, an AWS AI service powers customer support automation and personalized shopping experiences, reducing manual intervention and improving customer satisfaction.

With such a wide array of automation solutions available, small and medium-sized businesses would benefit most from turning to AI agents. These tools can handle various tasks efficiently, freeing up valuable time and resources. To simplify the process without getting bogged down in technical details, it's a good idea to partner with a software company providing AI business integration services. They can smoothly integrate artificial intelligence into your business operations, allowing you to focus on what you do best while they handle the tech side. Let's first discuss what AI agents are and then what they are capable of.

AI agents as effective AI tools for SME businesses automation

AI agents are computer programs designed to perform tasks automatically by simulating human decision-making. They can process information, learn from data, and make decisions to complete tasks without needing constant human input. For example, an AI agent could help manage your calendar, respond to customer inquiries, or analyze data to provide actionable insights, all while adapting to new information and improving over time. They're like digital assistants that work on tasks to save you time and effort. To decide whether custom software development with algorithms is required or if integrating AI agents is the better option, let's answer some questions about them.

What can AI agents do?

AI agents can understand and generate human language, recognize images and videos, and learn from data to improve over time. They make informed decisions, understand context, and integrate multiple data types for better interactions. These agents personalize experiences, automate tasks, manage security, predict trends, and handle speech recognition and synthesis, freeing up human resources.

What types of AI agents exist?

AI agents come in several fundamental types, each with distinct capabilities and suited for different business applications.

Simple reflex agents

Operate on predefined condition-action rules without memory or learning. They respond immediately to current inputs.

Example: automated alert systems or basic rule-based chatbots app development. They are fast and low-cost but inflexible in dynamic environments.

Model-based reflex agents

Maintain an internal model or memory of the environment to handle partially observable situations. They use recent history plus current inputs to make better decisions.

Example: e-commerce chatbots that remember previous user queries during a session. More adaptable than simple reflex agents but still limited in planning.

Goal-based agents

Act to achieve specific goals by evaluating possible future actions and selecting those that advance toward the goal. They can plan multi-step processes.

Example: AI improving management of order processing or customer onboarding workflows autonomously.

Utility-based agents

Extend goal-based agents by using a utility function to choose actions that maximize overall "happiness" or business value, balancing trade-offs.

Example: recommendation systems optimizing for customer satisfaction and profit.

Learning agents

Improve their performance over time by learning from experience and feedback. They adapt to new data and changing environments.

Example: fraud detection systems that evolve with emerging threats.

Hierarchical agents

Organize decision-making into layers, where high-level agents set strategies and lower-level agents handle specific tasks. Useful in complex business environments requiring modular automation.

Multi-agent systems

Consist of multiple interacting agents collaborating or competing to solve complex problems. Common in logistics, supply chain optimization, and distributed AI application development.

How to integrate an AI agent into a workflow?

To evaluate your internal resources in terms of technical savvy and decide whether you need to use external AI software development services, here are 4 core steps that a company needs to take in implementing this gen AI technology.

Step 1. Assess and understand your workflow

Begin by examining your current workflows to locate bottlenecks, manual tasks, and decision-making points where artificial intelligence development services could improve efficiency. Map out the sequence of tasks, interactions, and data management flows to identify areas where automation may be beneficial. This step helps to pinpoint specific tasks that can be optimized with AI software development services, making operations faster and allowing employees to focus on more strategic activities.

Step 2. Assess and prepare your data infrastructure

After understanding your workflows, focus on your data infrastructure. Identify the quality and structure of data sources, such as databases, APIs, and files, that align with your workflow needs. Use tools like Apache Kafka, AWS Kinesis, or Google Cloud Pub/Sub for automated data collection, consolidating it into a central data storage system. Clean and refine your data using Pandas or Apache Spark to resolve issues like missing values and duplicates, checking if it is ready and accurate for AI model usage.

Step 3. Implement AI agent solutions

Decide on the artificial intelligence functions you need, like language processing or decision support, and choose to develop them in-house or use third-party AI development services. Use REST API web development for communication between AI systems and your infrastructure. For in-house projects, frameworks like Flask or FastAPI can build APIs. Alternatively, platforms like OpenAI or Google AI offer pre-trained models that fit your needs. Make sure that your systems support these solutions, and consider cloud services for scalability. Focus on data security and compliance.

Step 4. Monitor performance

Set up dashboards to track metrics like accuracy, processing time, cost savings, and customer satisfaction. Use this data for ongoing model retraining and workflow adjustments. Regularly update AI agents and integration logic based on actionable insights from feedback and changing business needs to keep performance optimal.

What's about the costs?

Integrating the external solution like Open AI's might be frightening in terms of depending on the third-party software development services, while building your own AI solution might seem to be more reliable. Here is a thorough cost breakdown comparing creating a custom AI model development from scratch versus implementing an AI agent using OpenAI's pre-trained models for your business tasks:

Custom AI model development from zero

When you start using generative AI technology, a large part of your budget will go towards data acquisition and preparation. Gathering, cleaning, labeling, and structuring the data is crucial but can be both time-consuming and costly. Depending on how much data you have and its complexity, costs can range from $5,000 to well over $100,000.

Team cost

Next, you will need to assemble your software development team, which typically takes up 40-60% of the budget. This team should ideally include artificial intelligence specialists, data scientists, machine learning engineers, and software developers. In-house teams usually charge between $50 and $100+ per hour. Developing a prototype requires a minimum of 284 hours, resulting in costs of at least $14,000 to $28,000.

In-house vs outsourcing

Alternatively, outsourcing to an AI development company can be more cost-effective, with rates ranging from $25 to $50 per hour. This reduces initial prototype costs to about $7,000 to $14,000. However, developing a complete solution may take months, raising total labor expenses to between $50,000 and $300,000, depending on the project's complexity and scope.

Infrastructure cost

You'll also need to allocate 10-20% of your budget for infrastructure and computational resources. This covers the costs of cloud-based GPU or TPU resources, storage, and bandwidth necessary for training your models and running inferences, which can amount to thousands or even tens of thousands of dollars.

Integration cost

Integration with existing systems adds layers of complexity and additional costs. Providing legacy system compatibility and a smooth workflow incorporation typically constitutes 15-35% of the base development expenses.

Support and maintenance

Finally, remember to budget for maintenance and updates, which keep AI security and performance at a good level. Ongoing tasks like retraining, bug fixes, and improvements can require 15-25% of your annual budget to maintain the reliability of your AI solutions.

Typical cost ranges by AI solution complexity

Basic AI solutions ($20,000 – $80,000): chatbots, sentiment analysis, basic automation.

Advanced AI solutions ($50,000 – $150,000): fraud detection, personalized recommendations, workflow automation.

Complex AI solutions ($100,000 – $500,000+): Cutting-edge predictive maintenance, medical diagnosis, large-scale LLMs.

Timeline

Development cycles typically last 6 to 12 months or more for complex solutions.

Benefits and considerations

— Full control and customization tailored exactly to your business needs.

— High upfront investment, longer time to market, and requires ongoing expert involvement.

— Best suited for highly specialized or regulated industries where off-the-shelf models don't suffice.

Implementing an AI agent using pre-trained models

When incorporating an API-based AI system, you need to account for API usage costs. Providers like OpenAI charge based on the number of tokens or requests made, with rates for services like GPT-4 ranging from fractions of a cent to a few cents per 1,000 tokens processed. For small and medium-sized enterprises, this can lead to monthly expenses from a few hundred to several thousand dollars, depending on usage volume.

Developing the API client, integrating it with existing systems, and building necessary workflows is typically less time-intensive. However, it still requires a significant investment, often between $10,000 and $50,000, depending on the complexity and level of customization needed.

Fine-tuning pre-trained AI models on your specific data, though optional, can be valuable. This customization can improve model performance and relevance to your business needs. Costs for this process generally range from $10,000 to over $50,000, offering a more cost-effective and quicker alternative to training a model from scratch.

The API provider handles much of the maintenance and scaling responsibilities, significantly reducing your operational overhead and infrastructure costs. This allows your team to focus more on strategic implementation rather than technical upkeep.

Typical cost ranges

API usage ($100 – $5,000+ per month): Depends on query volume and model used.

Integration and setup ($10,000 – $50,000): Development and testing of workflows.

Fine-tuning ($10,000 – $50,000+): If needed for domain-specific adaptation.

Timeline

Integration and deployment can be done in weeks to a few months, providing faster time to market.

Benefits and considerations

— Significantly lower upfront costs and faster deployment.

— Access to state-of-the-art AI capabilities without managing infrastructure.

— Less control over model internals but sufficient for many common business tasks (chatbots, summarization, data extraction).

— Pay-as-you-go pricing scales with usage, reducing initial investment risk.

Conclusion

Choosing technology for app development now often involves considering AI to future-proof applications. However, the implementation of artificial intelligence technologies also holds significant potential for improving the performance and competitiveness of small and medium-sized enterprises. While larger corporations are leading the way in AI integration, SMEs can benefit by adopting cost-effective AI solutions tailored to their specific needs. Key strategies include choosing the right AI deployment model — centralized, decentralized, or hybrid — to align with their operational structure and goals.

If your business requires highly specialized AI with full control and you have the budget and timeline flexibility, custom software development of AI models is viable but costly and time-consuming. For most SMEs aiming to implement artificial intelligence quickly and cost-effectively for common business tasks, implementing an AI agent via OpenAI's APIs offers a faster, more affordable, and scalable solution with strong performance out-of-the-box.

Generative AI consulting can help efficiently integrate AI into your workflows, allowing you to focus on core business activities. If you're looking for an AI development company like this, let us know your goals in the form below, and we'll assist you.