Is AI the Right Fit for Your Business? 5 Critical Questions Every Entrepreneur Must Ask Before Investing

AI is a buzzword now. For entrepreneurs, implementing artificial intelligence's bread-and-butter features has become a necessity. 

However, remember that integrating AI leads to sufficient financial investments. Furthermore, not all problems can be solved with artificial intelligence. It's crucial to ask the right questions and understand where and how AI can benefit your long-term vision. 

So, does your business really need AI? Let’s uncover 5 most important questions entrepreneurs should ask themselves!

1. Think: what problems in your company can be solved with the help of AI? 

Despite the high effectiveness of AI models, they can’t be a solution to everything. It all depends on your business and your specific demands. So, do you actually require AI  to achieve your goals?

For example, AI can be used to generate ideas for a marketing agency. However, creating an entire article with AI is not the best idea. Apart from the question of plagiarism, there is a high probability that this piece of writing would be superficial. It may lack depth and human creativity so important to engage the readers on a deeper level. 

AI can also be useful for data scientists who want to quickly analyze huge amounts of information to draw essential conclusions. But when we dig deeper and ask for detailed predictions, the results may not be as precise or reliable as needed. The limitations of AI models, such as bias in training data, lack of contextual understanding, or overfitting, can lead to inaccurate or overly generalized insights. 

The same can be said about:

• Detailed targeting
• Independent interpretation of research results
• Independent decision-making related to the patient’s treatment
• Complete product design without the involvement of engineers

Remember, AI can be a game-changer in two cases. The first is when repetitive tasks can be automated. The second is about opportunities where AI could create significant value, such as enhancing customer experience or improving operational efficiency. 

2. How much money do you need to integrate AI? Is your  infrastructure sufficient to use AI?

Well, let's assume that AI meets your business goals and can really be effective for your processes. But are you equipped with the right resources to implement and manage it effectively? Financial, human, technical…

This is an interesting question. One example from the company Netflix says that as the company gained popularity, the amount of data became gigantic. In Netflix's case, it consists of:

• Data centers
• Computing clouds
• Content delivery networks
• Specialized hardware

Detailed data on the value of Netflix's AI infrastructure is not available. But we can definitely say that it runs into the billions of dollars.

Let’s assume your company is somewhat smaller than Netflix, so you do not need such an extensive infrastructure. In fact, to implement AI, you need frameworks such as TensorFlow or PyTorch. You also need hardware: graphics cards, more powerful servers, and specialized ASICs. Do not forget about licenses to implement and use these frameworks effectively. 

What are these costs? Let's start in order.

• Frameworks are usually available under open-source licenses, meaning they can be used for free. However, it is worth paying attention to additional libraries or tools that may be associated with these frameworks and require a license.
   
• Graphics cards, servers, and ASICs are usually commercial products, and their use requires the purchase of appropriate licenses. In smaller projects, a graphics card such as the NVIDIA RTX 3090 or 4090 is enough. The price is $1,500-$3,000 per piece. A small team can use even 2 graphics cards. Larger teams may need 10-20 GPUs.
   
• Data. Data is one of the most important elements in terms of training AI models. If you need specific data (not publicly available), then you need to buy it from companies. Prices can range from several hundred to several thousand dollars per data set, depending on the quality and uniqueness.

Before we summarize all the costs, let's explain a few details.

1. It's hard to talk about the costs of servers. There are different types, and the costs can vary. For example, small companies can use servers with Intel Xeon or AMD EPYC processors. Small companies need somewhere about 2 servers. The price of one is $5,000.
   
   Sometimes, you can bypass the issue of owning servers and use cloud computing. Thanks to it, you can quickly start working without large investments.
    
2. You must remember about backup solutions, data archiving, and their protection. This can cost up to $1,000 per year for a small project.

So how much do we need in the end?

It is safe to say that the simplest model will cost $16,500. However, with realistic consideration of scalability, additional tool costs, data, and teamwork, the total cost of an AI project can range from $50,000 to several hundred thousand dollars.

Example costs:

• Frameworks: Free
• Graphics cards: $6,000+
• Servers: $10,000+
• Data: $500+

Note: Here, we don't mention your AI team's salaries. For example, let's say you have ML engineers, data scientists, and analysts. Remember that AI development is a separate, very specific skill.  Be sure to factor in the salaries of the people with the right skills when calculating your final costs.

3. How are you going to integrate AI into current systems?

Think about the most important data that needs to be integrated. How will this happen? 

Imagine that you have an online store. You want to integrate AI with:

• CRM to personalize product recommendations
• PIM with a helpdesk chatbot to answer the most common customer questions

You don't have to integrate AI with all systems at once. It's better to do it one by one. For example, start by working on personalizing products.

1. Start with a data set. In this case, you may need purchase history, information on average customer spending, and what questions they ask most often.
2. Think about whether the new system will be able to handle advanced algorithms? It is possible that you may require a server modernization or migration to the cloud.
3.  Now, let’s return to data again for a moment. Is your data correct and up-to-date?
4. After ensuring everything is fine with your data, you can implement algorithms that analyze customer data and personalized recommendations.
5. Then, it’s high time to analyze your system.
6. Apply the AI ​​to the user interface.

4. What return on investment (ROI) can I expect from this AI implementation?

First, let’s explore what AI ROI is? 

The ROI of AI refers to the measurable benefits that a company gains from its AI initiatives compared to the total investment required to implement and maintain those AI systems. 

Unlike traditional investments, AI ROI can be more complex to calculate because it involves both tangible (monetary) and intangible (non-monetary) factors.

Tangible factors:

1. Increased revenue
2. Cost reduction

Intangible benefits: 

1. Faster decision-making processes
2. Enhanced customer experience

The use case you choose for AI directly affects ROI. AI is most effective when applied to well-defined problems with significant potential for improvement. Begin with projects with clear, measurable benefits and low implementation risk. Target problems where automation, prediction, or optimization can make a significant difference. 

To get the most out of AI implementation, try regularly monitoring, retraining, and optimizing existing AI models. 

5. How will AI adoption affect my team and their roles?

As it was mentioned above, developing an AI model requires specific skills and domain expertise. To tackle this issue, you need to either retrain existing employees or hire new ones. 

What are the main skills required to build AI models? 

• Programming: Python, R, libraries like TensorFlow, PyTorch
• Statistics and Math: Probability, Linear Algebra, Calculus
• Data Handling: SQL, NoSQL, Cloud databases
• Machine Learning Models: Supervised, unsupervised, deep learning
• Data Engineering: ETL pipelines, data cleaning, infrastructure

To streamline AI development, your team will need tools that enhance collaboration and productivity, such as:

• Version Control Systems for tracking changes in code
• Data Management Tools like Apache Kafka for real-time data processing
• AI Development Platforms like Google AI and AWS SageMaker for model deployment and scaling

Remember, the AI model is not a set-and-forget project. The real work begins after the model's initial deployment. As an entrepreneur, you can offer training programs to help your team use AI-powered tools effectively. This is a more cost-effective approach than immediately hiring AI specialists.  

However, this works only in case your employees already have an understanding of programming languages like Python, math, stats, and data literacy. In other cases, you may find it difficult to close the gap with training alone. 

Summing up…

In this article we’ve covered 5 main questions entrepreneurs should ask themselves before implementing AI models in their business processes. We have a hope now you are better positioned to determine whether AI is an option for you. 

Remember, the success of your case depends on clearly defined objectives, sufficient resources, and a well-thought-out strategy. Do not make a costly experiment using AI unless you are sure it could help you. Approach it as a calculated investment that aligns with your business goals.

Start small, monitor your progress, and adjust as needed. When done right, AI can be a powerful tool to drive innovation, efficiency, and growth in your business.

So, are you ready to implement AI and achieve remarkable results? Sign up to Devler.io 

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