Path to AI Integration: Key Considerations for Businesses

There’s no need for a crystal ball to predict that the future of business is AI-powered. From optimizing financial operations to automating legislative workflows, its potential seems truly immense. Fueled by this potential, companies are actively seeking ways to incorporate AI into their operations, either through custom-built solutions or pre-built APIs. However, integrating AI models with existing systems isn’t always straightforward.

In this article, we talk about the complexities of AI integration with our team members: Ari Hershowitz, our CEO, and Andrii Kovalov, a seasoned Python Engineer. Throughout the article, you’ll gain valuable insights about the nuances of AI integration, challenges, and opportunities that arise when combining AI models with established infrastructure.

What is AI integration?

AI integration is the process of embedding artificial intelligence capabilities into existing systems or products. Essentially, it enables a seamless exchange of data between the AI component and the system, allowing the system to utilize AI functionalities such as machine learning, natural language processing, or computer vision. The technical aspect of integration often involves the use of third-party APIs or custom development to create a unified workflow that merges AI seamlessly into the existing framework.

Why integrate AI with existing systems?

In most cases, integrating AI is a strategic decision aimed at breathing new life into business operations, uncovering hidden potential, and allowing a company to better adapt to changing market demands. As our CEO, Ari, points out:

“Businesses may consider AI for many reasons: increased efficiency, lower costs, and improved customer experience. In some cases, AI can do things that were not possible before – allowing users to generate images, brainstorm ideas, or have a personalized tutor on any subject at their fingertips. However, the primary motivation behind integrating AI is to explore the wealth of possibilities it offers, potentially unveiling new avenues for business growth.”

Andrii agrees with this point, emphasizing:

“AI opened doors to previously complex logic that can now be relatively easily integrated into your system. Previously, tasks like summarization or classification required substantial time and effort; now, they’re just a request away, which eventually leads to significant enhancements in performance.”

These new possibilities provided by AI models turn into many advantages:

• Better productivity. By automating data-intensive and repetitive tasks, AI frees up time and resources for more substantial tasks that require human creativity, problem-solving, and strategic thinking.
• Cost reduction. AI algorithms automate lots of routine tasks, allowing businesses to reduce labor costs, minimize errors, and optimize resource allocation
• Personalized customer service. AI is used to personalize customer interactions, tailoring content and recommendations based on individual preferences.
• Improved decision-making. By analyzing large datasets and identifying patterns, AI provides data-driven insights and recommendations that businesses can use to make more informed decisions.
• Innovation. AI enables businesses to fulfill new ideas and develop innovative solutions whether it’s through introducing new products, optimizing processes, or identifying market opportunities.

Key factors to consider for successful integration

When evaluating compatibility between AI models and legacy systems, it’s essential to consider a variety of factors to ensure a seamless integration. Here are some key compatibility checkpoints to take into account:

• Data compatibility. AI models are data-driven, learning from the information they are trained on. If you plan to train the system with your own data, it is important to assess its quality and ensure it aligns with the AI model’s training requirements in terms of format and structure. However, some pre-trained models might not require your own data for basic functionality.

Andrii, our Python engineer, also highlights the importance of validating the AI-generated data:

“Validation of AI-generated data is essential. If the quality of the generated output (in terms of precision and absence of hallucinations) meets requirements, AI integration becomes much smoother.”

• Infrastructure compatibility. Evaluating the capabilities of your existing hardware, software, and overall infrastructure helps determine whether the current system can handle the processing power, storage demands, and potential growth of the AI model.

• Scalability. It is also crucial to assess whether the existing infrastructure can scale to accommodate the computational and storage requirements of AI models.

However, successfully integrating AI with existing systems goes beyond simply ensuring technical compatibility between systems. Our CEO, Ari emphasizes:

“It is important to consider development and maintenance costs, reliability, risks, and potential biases. As with any new technology, AI systems do not have a history of testing, so some promising results may not be sustainable when the AI system goes into production. New systems need to be tested as thoroughly as possible against the features of any legacy system they are replacing.”

Challenges of AI integration for developers

Since AI technologies are relatively new, their implementation can pose a number of challenges for developers, including:

• Data preparation. Existing systems may not have high-quality data that is required for accurate training. Developers might need to implement data pre-processing and cleaning to ensure that the data used for AI training is reliable and representative.

Andrii shares his experience:

“Ensuring data quality for AI integration can be a significant challenge. Existing systems often lack the clean and structured data needed to train AI models effectively. Many projects demand high precision, and when integrating AI as a subsystem, the primary challenge becomes thorough validation and checking to maintain accuracy and reliability.”

• Explainability and reproducibility. Our CEO, Ari, points out a significant challenge: the lack of explainability and reproducibility in AI systems:

“AI systems are built as massive statistical systems that can produce remarkable results. While they can deliver impressive results, understanding how they reach those conclusions can be difficult. This lack of transparency makes customization a challenge, often resorting to trial and error. Additionally, for Large Language Models (LLMs) accessed through third-party APIs, there’s a risk that changes to the model or its output can cause the entire integrated system to malfunction.”

Off-the-shelf vs Custom AI: what’s better?

Once the decision to implement AI is made, a crucial question arises: is it better to use off-the-shelf AI APIs (like OpenAI) or build custom AI solutions? Both approaches offer distinct advantages and disadvantages, but the optimal choice depends on a number of factors such as budget, timeline, customization requirements, and strategic objectives.

Off-the-shelf AI refers to pre-built AI models offered by various vendors. These models can be used for a wide range of applications and often come with user-friendly integration tools. However, customization options might be limited, and they may not perfectly align with your specific data or business processes.

Andrii, our Python engineer, shares his perspective:

“Pre-built solutions like closed-source AI APIs (for example, OpenAI) definitely have their pros. They are easy to use and cost-effective for small data sets. However, they can get expensive for large data sets, and you may face potential issues such as censorship and lack of privacy, as your private data could be exposed to the AI provider.”

Custom AI, on the other hand, refers to solutions built from scratch to address the company’s unique needs. While offering a high degree of customization and control, custom AI solutions require significant investment in time, resources, and expertise.

Andrii highlights a crucial point,

“The primary reason for building custom AI is privacy. If the thought of sending a request with custom private data to an API is immediately dismissed, then the only option is to establish a local private infrastructure to fulfill your specific privacy needs.”

For his part, Ari emphasizes the importance of cost-benefit analysis before embarking on custom AI development:

“Custom AI development can be expensive, and the development process can be unpredictable. While it offers high customization, achieving the desired results might involve extensive experimentation. However, there are situations where custom development is the best option:

• Data Availability. When a large amount of labeled data is readily available specific to your needs.
• Privacy and Security. If privacy or security concerns make it undesirable to use third-party APIs and your data needs to remain confidential.
• Competitive Differentiation. When the AI model itself is a key differentiator for your business, offering a unique advantage over competitors.
• Market Potential. If you plan to license the custom model or provide your own AI API as a service, the long-term market potential might justify the investment.”

AI Integration for Businesses: Expert Recommendations

To help businesses navigate the exciting world of AI integration, we asked our team members for their insights. Here are three key recommendations they offered:

• Consider existing models first Building your own foundational AI models, like those from OpenAI or Google, requires significant investment. Our CEO, Ari suggests considering readily available options first:

“With the rapid development of large general models, it is usually worth testing off-the-shelf solutions before trying to build a custom system from scratch. Model training can take a great deal of data and computational resources, and building a custom model can be a significant investment. It may be worth doing so if third-party open source or commercial models don’t meet your requirements, and there is a sufficient market for the specialized system you plan to build.”

• Focus on appropriate use cases While AI has great potential, it’s crucial to recognize that it’s not a magic bullet that solves every problem. Some tasks are still better suited for human expertise. As Ari points out:

“LLMs are good at generating and processing information, but they do not have true judgment. They will perform best at tasks that involve information extraction and transformation, and will be less reliable on tasks that require judgment about facts in the world.”

• Implement rigorous validation AI is a powerful tool, but remember it’s still a tool whose work has to be checked for accuracy and potential biases. Andrii emphasizes the importance of robust data validation:

“Never rely solely on AI output. Implement multi-level checks, testing, and validation systems to ensure the data produced by the AI is accurate and usable for your specific purposes.”

Wrapping up

Integrating AI into existing systems can significantly enhance capabilities and efficiency, but it requires careful planning. The choice between off-the-shelf and custom AI depends on your specific business needs, the importance of data privacy, the availability of resources, and your long-term strategic goals.