Artificial Intelligence (AI) has been offering opportunities for innovation and efficiency for quite a while now. However, the actual journey from conceptualizing an AI solution to its successful deployment in business is filled with challenges that could be seen from the fact that only 35% of global companies use AI (Cardillo, 2024). Businesses often grapple with questions about which AI model to choose, where to deploy it, and how to manage time, costs and resources effectively. In this blog, we’ll discuss these critical decision points, providing insights and examples to guide SMBs owners through the complexities of AI implementation.

Choosing the Right AI Model

One of the initial and most critical steps in any AI project is selecting the appropriate model. This decision solely depends upon the specific problem you aim to solve. For instance, if you’re looking to enhance customer service with conversational chatbots, you might consider models like OpenAI’s GPT-4 or Google’s BERT. These models excel in natural language processing (NLP) tasks but differ in architecture and performance characteristics.

For image recognition tasks, convolutional neural networks (CNNs) such as ResNet or Inception are popular choices. In more specialized scenarios, such as fraud detection in financial transactions, ensemble methods like XGBoost or deep learning models specifically made for anomaly detection may be more suitable.

Example: A retail company aiming to implement a chatbot for customer support might lean towards GPT-4, whereas a healthcare provider looking to automate the analysis of medical images might prefer a CNN-based model like ResNet.

Model Deployment: Cloud vs. On-Premises

Deciding where to deploy your AI model — on the cloud or on-premises — is another critical decision. Each option has its own set of advantages and challenges. For SMBs looking for cost-effective AI model deployment, the cloud is generally the more attractive option. Here’s a breakdown of why:

Cloud Advantages

• Cloud has lower upfront costs because it eliminates the need for expensive hardware purchases and IT staff for maintenance. You only pay for the resources you use.
• Cloud resources can be easily scaled up or down based on your needs, avoiding overspending on underutilized hardware.
• Cloud providers offer a wide range of pre-configured solutions for AI development and deployment, allowing you to choose the ones that fit your specific needs.

On-Premises Considerations

• It has high upfront costs because it requires investment in hardware, software licenses, and IT staff for maintenance.
• Scaling up resources can be expensive and time-consuming.
• On-premises solutions may not offer the same range of pre-configured AI services as cloud providers.

Exceptions

• If your AI model handles highly sensitive data, you might prefer the control offered by an on-premises solution.
• A consistent and high-bandwidth internet connection is crucial for cloud deployments.

Finding the best option

• Consider factors like budget, technical expertise, data security requirements, and the complexity of your AI model.
• Major cloud providers like Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure offer various AI-specific services with different pricing models. Look for free trials or pay-as-you-go options to test the waters.

By carefully considering these factors, SMBs can leverage the cloud’s cost-effectiveness and scalability for their AI deployments.

Choosing the Right Resources

The financial aspect of AI projects is a little complicated. Beyond the cost of model development and deployment, businesses must consider the expense of skilled personnel. Finding and retaining a talent who’s expert in AI and machine learning is a well-known challenge.

Using pre-trained models and APIs can be a cost-effective strategy. For example, IBM’s Watson offers a range of pre-built AI services that can be integrated into applications without requiring extensive AI expertise. Additionally, outsourcing development to specialized firms like BlocBelt or utilizing freelance platforms can provide access to skilled professionals without the long-term financial commitment of full-time hires.

Example: A mid-sized e-commerce company may not have the budget to hire a full-time data scientist team. Instead, they could use IBM Watson’s pre-trained models to implement AI-driven product recommendations and employ a single resource from specialized firms or platforms to customize the solution to their specific needs.

Challenges in AI Project Delivery

Beyond the selection and deployment of AI models, several other challenges can impact the successful delivery of AI projects:

• High-quality, relevant data is the backbone of any AI project. Making sure that data is clean and well-labeled can be a significant hurdle when it comes to training the model for more accurate results.
• AI solutions must seamlessly integrate with current business processes and systems. This often requires additional development and customization that requires people with related expertise. Partnering with complete IT solutions providers like BlocBelt can help SMBs manage the project under a single umbrella, allowing them to have access to all the required resources in a cost effective manner.
• Implementing AI solutions can fundamentally change workflows and job roles. Effective change management strategies are essential to ensure smooth adoption and minimize resistance.

Example: A logistics company implementing predictive analytics to optimize delivery routes needs to ensure that the AI system integrates with their existing GPS and fleet management systems. They must also manage the changeover for employees who are used to manual route planning.

Conclusion

The path to successful AI implementation is complex, involving strategic decisions about model selection, deployment, cost management, and more. By carefully considering each of these aspects and using appropriate tools and resources, businesses can implement AI to drive innovation and efficiency in their current processes. Real-world examples highlight different approaches and solutions available, which explains that while challenges are inherent in AI, they can be addressed with the right strategies.