Developing and Implementing AI Solutions in Business: A Practical Guide for 2025

In this guide, we’ll help you understand whether your business really needs AI or if it’s just the effect of hype. Based on our experience in artificial intelligence integration for our clients, we’ll explain how to move from an idea to a real, well-working solution that can become an integral part of your product.

Do You Really Need AI — or Just Want It?

Before we move to the development part, it’s important to ask a simple but important question: Does your business truly need AI?

There’s no doubt AI is powerful. But not every problem or business task needs a machine learning model. Besides, we are more than sure that not every product becomes better with artificial intelligence. We see that companies often consider AI to be the next step because competitors do so.

Instead, it’s better to focus on the problem you need to solve. Here are a few cases when your business may benefit from AI:

• You have more data than your team can process
• You need to detect patterns or predict outcomes
• You want to improve the user experience with the help of personalization

And here are some signs you don’t need AI, at least now:

• You don’t have a clear use case or goal
• Your business doesn’t have structured data to work with

AI is not a panacea. It’s just a practical tool. It can increase the effectiveness of your business, fasten operational processes, or improve customer experience when you use it correctly.

How Companies Can Use AI for Business Growth

To help you understand how artificial intelligence can help grow your business, let’s view several examples of how global companies integrated artificial intelligence into different processes.

Customer Preference Prediction

Spotify has launched graph neural networks to model user preferences for music, podcasts, and audiobooks. As a result, it improved new audiobook start rates by 46% and increased streaming by 23%. That’s why we can assume that AI-based recommendation engines strengthen user engagement and can make content recommendations very relevant.

Recruitment Process Automation

Unilever launched AI recruitment tools that allowed it to screen more than 250,000 applications automatically. It decreased the average hiring cycle from 4 months to just 4 weeks.

How Unilever Is Using Artificial Intelligence And Machine Learning In Their Recruitment (VIDEO LINK)

On the contrary, the employed had to spend more than 70,000 hours to make it work. Consequently, you can delegate repetitive, high-volume administrative tasks to AI, which saves money and time.

Sales Performance Improvement and Insights

Empat developed BigSister.AI, an AI-powered system that monitors companies’ sales processes in real time, provides predictive insights, and helps managers identify areas that need improvement. It’s like a sales operations assistant that never sleeps or makes mistakes. It analyzes patterns, outlines inefficiencies, and helps teams read settled sales goals. As a result, such a tool allows sales managers to save time on manual analysis and get insights for actionable decisions.

Create an AI-Based Product

Unlike previous cases, when businesses integrate AI into some of their business processes or tasks, your choice may be to add an AI-based product to your portfolio. That’s exactly what we did with PixShare.

Empat AI engineers developed PixShare, a mobile app that uses AI to recognize faces in photos and automatically share them with the right people. It saves time and efforts for customers when people need to send photos to friends and family after any event or vacation. In such a way, our AI development allowed us to create a new product on the market and solve the pain point of millions of people. 

Step-by-Step Guide: From Idea to AI Solution Launched

Assume that you really need AI to become part of your business, if you still keep reading.

So, let’s discuss your next steps to turning an AI idea into a working solution for your company.

1. Determine Business Needs

First, analyse your business and identify real gaps, problems, or opportunities where AI can create value. Find spheres where you can automate repetitive processes, improve customer experience, or optimize internal workflows.

2. Choose the Right Problem

Remember that not every problem can be solved with AI. You need to focus on spheres where machine learning can perform better than traditional solutions. It can be pattern recognition, prediction, or intelligent automation.

We recommend starting small and integrating AI into only one process at a time.

3. Build the Right Team or Choose a Partner

Such projects are rather complex and require hiring a skilled AI development team. Probably, you’ll need:

• A data scientist to design and train models
• An AI/ML engineer to build and improve systems
• A product owner to align the AI solution with the settled business goals
• A domain expert to validate relevance and accuracy

You can build an AI team inside your company or outsource development to a partner.

4. Data Readiness

To develop an AI solution, you need clean, relevant, and well-structured data. This data is the basis for training accurate models.

If you feel that it’s a bottleneck in your operational processes, consider investing in data infrastructure, cleaning pipelines, or even data collection strategies. It’s an important stage before moving to building AI.

5. Develop & Iterate

Thinking of AI implementation as a one-time project may lead to failure. We recommend approaching it like a product. Try using Agile methods to build, test, and improve your AI solution in short cycles.

During this phase:

• Select and train appropriate models
• Continuously test for accuracy and performance
• Keep user experience and business goals at the center

It’s better not to aim for perfection upfront but for something useful, usable, and improvable.

6. Deploy & Integrate

Once your AI model performs well, integrate it into your existing systems or products. This includes:

• Ensuring API or infrastructure compatibility
• Monitoring real-time performance
• Preparing for improvement and maintenance

A successful deployment doesn’t refer only to AI working. It’s about it working well inside your business environment.

5 Tips for Successful AI Implementation

Even if you follow all the steps precisely, your AI project may still fail. The reasons can relate to technical, organizational, or human factors. Take into account further recommendations based on our experience, which we've learned while helping companies launch AI solutions.

1. Start with a Narrow Use Case

Don’t try to launch AI everywhere at once. It’s better to choose one clear problem where AI can create a considerable impact. It will be easier to see whether the improvement is really working.

2. Don’t Wait for Perfect Data

Just trust —  your data will never be perfectly clean and complete. When you try to reach it, you are just wasting your time. Try to work with what you have and improve it continuously. The key to success is to start small, validate fast, and improve your model and your data pipeline iteratively.

3. Focus on Your Customer

Remember that your business works for people. Make AI an invisible helper that improves your efficiency. Avoid situations when you complicate your user experience or customer journey with AI.

4. Involve Business Stakeholders

Don’t let AI be just an additional project for the tech team. Try to involve decision-makers from the beginning. Their input will help the project meet business goals, which is one of the main success points of any activity.

5. Constant Control is a Must

We know that the launching process is complicated. Bad news - it’s just the beginning. After implementation, don’t forget to track performance metrics, gather user feedback, and make improvements often. AI models may degrade rather quickly without updates. That’s why continuous improvement is important to remain effective.

AI is a Must for Businesses in 2025

AI is no longer a privilege for tech giants. It’s an effective tool that helps even small businesses grow. But success doesn’t come from a single idea. It is based on understanding your business needs, starting with clear goals, and building with simplicity and purpose.

No matter whether you’re integrating AI into your business operations or developing a new AI product, focus on:

• Solving one well-defined problem
• Improving data and user experience
• Staying flexible and results-oriented

Remember, AI doesn’t have to be complex. It just has to be useful and help your business reach settled goals.  

About the Author

Kateryna Stankova

Kateryna Stankova is a Business Development Manager at Empat. They lead strategic growth initiatives, foster long-term client partnerships, and identify new market opportunities. With a strong understanding of the tech ecosystem and an emphasis on human-centered communication, they help bridge visionary ideas with scalable, high-impact software solutions.

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