We’ve dedicated this article to the benefits of AI and machine learning (ML) for business. All of them boil down to one thing: making better decisions.
Often, these decisions are related to product or customer-facing problems, such as reducing churn, launching new product lines, or improving marketing strategies. Other times, bringing ML and AI into the organization can improve efficiency by replacing human-intensive and repetitive tasks.
If you’re already convinced that ML and AI are more than just buzzwords, you may wonder how you can launch a successful artificial intelligence and machine learning project in your company. It’s not an easy task, but we’ve put together a few steps to help you reduce the risk of wasted resources and generate more value for your business.
This step is the cornerstone for developing a machine learning strategy for your business. It determines how you approach every following step — from the types of data you collect to the metrics you measure.
You need to know what your pain points are before you try to fix them. Many small businesses have a long list of problems they’d love to solve with AI, but trying to fix everything at once isn’t realistic. Start small — with a simplified version of the most pressing issue — and expand later.
If your business challenge seems too big, break it down into manageable parts. This approach will help you analyze different aspects of the problem and find areas where machine learning can help.
You can also look at what other companies in your industry are doing with ML. While you don’t want to copy their strategies, this research can provide inspiration.
Once you’ve chosen a problem to solve, take time to understand the scope of AI and ML. It’s crucial to know what modern machine learning tools can actually do — especially in 2025, with so many new developments in generative AI, predictive analytics, and real-time decision-making.
This understanding is especially important for business leaders and managers who will be working alongside data science teams. Even a basic understanding of supervised learning, natural language processing (NLP), computer vision, and neural networks can go a long way in aligning business goals with technical solutions.
There are many excellent resources available, including introductory courses on Coursera, edX, or Udemy, as well as free guides from OpenAI and Hugging Face.
The third step is to collect high-quality data. The type and amount of data you need will depend on your use case and the algorithm you plan to implement.
Data is the raw material for your ML model. If your data is poor or incomplete, the model will underperform, no matter how good the algorithm is.
Be thoughtful about the data you gather. Include control factors and noise to improve the robustness of your model. Don’t assume real-time data is always better — use what best reflects the problem you’re solving.
For example, if you’re trying to predict customer churn, your client’s purchasing history might be more useful than their geographic location.
Also, don’t overlook the data you already have. Your business likely generates a significant amount of useful data through point-of-sale systems, CRM software, website analytics, or customer support tickets.
Before jumping into data preparation, start with exploratory data analysis (EDA). This step helps you identify outliers, trends, missing values, and other inconsistencies.
The purpose is to spot potential biases or patterns that could skew your results. For instance, if you’re building an algorithm for equitable hiring, your dataset needs to represent a balanced view of all candidates. Otherwise, your model could perpetuate or amplify existing biases.
Things to evaluate during data exploration include:
Use data visualization tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn to identify trends. This stage is also a good time to start thinking about which model might suit your data structure.
Data preparation is one of the most time-consuming parts of any ML project — and one of the most important. Studies estimate it can take up to 70–80% of the total project time.
Your goal here is to clean and standardize your data. Tasks may include:
Feature extraction may also come into play. This involves reducing the complexity of your data without losing key information. For instance, you might combine several variables into a single feature that captures the same meaning with less computation.
This step sets the foundation for your model to succeed.
Now it’s time to choose your model and train it. You may use regression models, decision trees, neural networks, or transformers, depending on the complexity and type of your data.
Start by splitting your dataset into training and validation subsets. The model uses the training data to “learn” patterns and relationships and then tests that learning against new data.
You likely won’t get it right the first time — and that’s okay. Iteration is part of the process. Run different models, compare results, and fine-tune your hyperparameters.
Key performance metrics may include:
You can also consider using AutoML tools like Google Vertex AI, Amazon SageMaker, or Microsoft Azure ML Studio to automate parts of this process.
Once you’ve trained and tested your model, take a step back and evaluate the end-to-end process.
Was the model useful? Did it solve the problem you set out to fix? If not, don’t be afraid to go back to earlier steps — maybe you need better data, a different model, or a different approach entirely.
Remember, the goal isn’t to build the perfect algorithm — it’s to solve a business problem efficiently and effectively.
Start small and run A/B tests or pilot implementations before scaling to your entire business. Keep monitoring your model’s performance in the real world and retrain it periodically with fresh data.
ML and AI don’t stop at your product or service — they can also optimize internal operations. Start by identifying manual, repeatable processes where automation can help.
For example:
AI and ML are evolving fast. You don’t need to be an expert — just be curious, experiment responsibly, and focus on solving problems that matter to your business.
Whether you’re predicting customer churn, personalizing marketing emails, or forecasting demand, machine learning offers enormous potential to drive growth and efficiency.
Start small, learn as you go, and scale what works.
Disclaimer: The content on this page is for information purposes only and does not constitute legal, tax, or accounting advice. If you have specific questions about any of these topics, seek the counsel of a licensed professional.
Share
Related Articles
Start Your LLC Today