AI Implementation Checklist: Things You Must Know

Most people have already met with artificial intelligence (AI) in one way or another. Some did it in their daily lives, some found it useful for professional situations, and some simply read some news. It’s obvious that AI is drastically increasing its influence in all areas of human life. Therefore, it’s not surprising that businesses from many industries want to get their hands on this technology and use it to their advantage. But is it that easy?

Using AI to optimize business processes seems very simple, but in fact, this approach has a lot of pitfalls. In this article, we will discuss how to integrate AI into your business without struggles, what benefits it can bring you, and what challenges you may encounter along the way.

When AI makes sense (and when it doesn't)

Before diving into the details of introducing AI to your business, let’s see if you really need it. Many businesses jump into action right away and want to add AI everywhere, but oftentimes it leads to a total disaster. To avoid that, here’s a short list of situations where you need AI and where it’s not necessary.

When AI is good for your business

1. Automating repetitive tasks: This is the number one area for implementing AI. If your business has many routine, repetitive tasks that your team spends too much time on, AI can easily get them done. This way, your team will have more time to complete higher-value tasks that are truly important for your business. Plus, your team members will be able to show more initiative and come up with new ideas for the company’s development.

2. Personalizing customer experiences: The second best option for AI to take over. Personalization is no longer just a trend in customer service, it’s a total must-have. However, human efforts may not be enough to personalize the experience of every single customer. AI can be truly invaluable for this process.

3. Enhancing decision-making: AI-driven analytics can uncover insights from vast datasets to help businesses make informed choices. Of course, humans should have the final say, but AI can help you see other perspectives and facts you might not have even considered before.

When AI is not needed

1. For small-scale, low-volume work: If your team is small and processes aren’t yet well-established, or if the task itself is occasional or low-volume, AI could only complicate the situation.

2. When creativity and empathy are key: AI can assist with plenty of tasks for sure, but it struggles with human-like creativity, emotional intelligence, and nuanced decision-making, so if your business is built on these concepts, AI is not for you.

3. If ROI isn’t clear: AI implementation can be expensive. If the return on investment isn’t strong, simpler solutions might fit better.

AI implementation costs

The next step in finding out whether you should go through with AI implementation is learning about its costs. Some may think that getting yourself a ChatGPT account is easy and, well, free, but this is just a tiny portion of what AI integration is. Here is a short breakdown of what you will have to spend your money on.

Required infrastructure and data preparation

So, you have the right reasons—and the budget—to implement AI. Great! Now it’s time to set up the necessary groundwork to ensure AI runs smoothly. This involves several key parts:

• Infrastructure: It consists of computational resources (on-premise servers, cloud-based AI services, edge computing), storage solutions (data lakes and warehouses, high-speed storage solutions), and network and connectivity elements (high-bandwidth Internet, cloud connectivity, edge networking).

• Security and compliance: Handling AI data requires strict security measures. You need to have strong encryption and access control to protect data at rest and in transit. Also, it’s an absolute must to adhere to GDPR, CCPA, HIPAA, and any industry-specific regulation.

• Data preparation: AI is only as good as the data it’s trained on. You must aggregate data from multiple sources like CRM, ERP, and transaction logs and ensure data diversity to avoid bias.

• Data cleaning and preprocessing: Raw data is often messy and needs refining. Your team should remove any inconsistencies and handle missing values. And don’t miss ensuring data consistency in formats, units, and structures.

• Data labeling and annotation: For supervised learning models, labeled data is a must. Your team can do it either automatically or manually, but your data should be properly labeled.

• Data management: Finally, you need to keep track of data origins and usage and ensure AI models use the latest and most relevant data.

How and why AI projects fail

AI has the potential to bring almost any business to new heights, but many projects fail before ever delivering real value. Here are some of the most common reasons why AI initiatives fall apart—and how to avoid them.

Lack of clear objectives

It happens pretty often that businesses jump into AI because it’s shiny, new, and trendy, without defining clear business goals. They have no measurable success criteria so tracking ROI becomes impossible.

How to avoid it:

• Define specific problems AI will solve (“use AI in marketing” is bad, “reduce customer churn by 20%” is good).

• Set KPIs to track performance and impact.

Poor data quality

The second most common reason for an AI initiative to fail. AI models depend on clean, structured, and unbiased data, so any incompleteness or inconsistency will lead to biased, unfair, and inaccurate results.

How to avoid it:

• Invest in data cleaning before training models.

• Use diverse datasets to reduce bias.

• Continuously update data sources.

Lack of AI expertise

All AI projects require skilled professionals (like data scientists or ML engineers) to work properly. Companies without any in-house expertise can struggle with model development and deployment. And if they decide to outsource it, they can easily start over-relying on vendors without getting any internal knowledge.

How to avoid it:

• Hire AI experts/upskill existing employees/collaborate with AI consultants.

• Start with small, manageable AI projects before scaling.

Unrealistic expectations

Many people treat AI as some magic potion that will instantly transform any process and give their businesses unbelievable results. Executives often expect immediate ROI, which can easily lead to disappointment since model training, fine-tuning, and real-world validation can take months to complete.

How to avoid it:

• Set realistic timelines.

• Educate stakeholders on AI’s limitations.

• Focus on step-by-step improvements.

Resistance to change

Even though the fear of AI replacing employees is exaggerated, some employees can still resist adding AI to their toolkit. This usually stems from a lack of AI literacy among staff. Also, AI still disrupts existing workflows, which can cause some friction during adoption.

How to avoid it:

• Communicate AI’s role in augmenting, not replacing, human work.

• Provide training for employees to work alongside AI tools.

AI project planning framework

If you want your AI project to succeed, you should create a solid plan to help you navigate the new approach. Use this checklist to guide your AI implementation from start to finish.

1. Define business objectives

• ✅ Identify the specific problem AI will solve.

• ✅ Set measurable goals and KPIs.

• ✅ Align AI initiatives with your overall business strategy.

2. Assess feasibility and resources

• ✅ Evaluate whether AI is the right solution.

• ✅ Determine the required budget and timeline.

• ✅ Assess in-house AI expertise/hire or partner with AI specialists.

3. Data preparation

• ✅ Identify and collect relevant data sources.

• ✅ Clean, label, and preprocess data.

• ✅ Ensure compliance with data privacy laws (GDPR, CCPA, HIPAA).

4. Choose the right AI approach

• ✅ Decide between off-the-shelf AI tools or custom-built models.

• ✅ Select the appropriate AI technique (machine learning, deep learning, NLP, etc.).

• ✅ Evaluate existing AI frameworks (TensorFlow, PyTorch, etc.).

5. Build and train the AI model

• ✅ Split data into training, validation, and test sets.

• ✅ Train the model using appropriate algorithms.

• ✅ Evaluate its performance and optimize it for scalability and efficiency.

6. Test and validate

• ✅ Conduct real-world testing with a sample group.

• ✅ Identify biases and errors.

• ✅ Adjust and retrain the model based on feedback.

7. Deploy and integrate

• ✅ Ensure AI integrates smoothly with existing systems.

• ✅ Set up monitoring tools to track model performance post-launch.

• ✅ Establish security protocols to protect AI-generated data.

8. Train employees

• ✅ Educate employees on how to work with AI tools.

• ✅ Address concerns about AI replacing jobs.

• ✅ Provide ongoing support and AI literacy training.

9. Monitor, maintain, and improve

• ✅ Continuously track AI performance against KPIs.

• ✅ Regularly update and retrain models with new data.

• ✅ Create a long-term AI maintenance plan.

To conclude

Now you have a guide on how to properly integrate AI into your business. Of course, no one is immune from risks, but if you follow these tips and recommendations, the process of implementing AI solutions will be much smoother and more enjoyable for all parties involved.

And if you need any help with harnessing the potential of artificial intelligence, Yellow is here to help you! We are specializing in developing and optimizing AI solutions that will make your business more agile and drive better results. Feel free to contact us and discuss your idea.