AI appears to be a universal solution to all business tasks and AI literacy has become a must for software development providers. However, in many cases, AI integration is unnecessary, and the goal can be easily achieved with algorithmic programming. The aim of this article is to help readers understand which approach is better in different circumstances.

The hype vs reality of AI integration

Despite offering solutions to many common challenges, artificial intelligence doesn't solve all business problems. It has become a trend to seek help in AI capabilities, even for routine tasks with clear rules, where a traditional approach is preferable. The ease of building an AI model with an AI-powered tool, requiring no code — such as AI agents by OpenAI or the Vertex AI platform by Google — only fuels interest in such digital transformation.

Thus, in app development, entrepreneurs often want to integrate AI technology into an app's functionality instead of just engineering a feature. We're not saying that AI implementation is useless — far from it — but the rush to adopt AI technology is often overrated.

One of representative examples was when Air Canada faced a legal challenge when its chatbot mistakenly advised a passenger about a bereavement fare policy, suggesting he could claim a discount after booking a full-fare ticket. This AI assistant case both underscores the importance of accurate AI interactions and sets a precedent that companies must be responsible for integrating AI into customer service. It also illustrates the broader implications for businesses that overly depend on AI technology without adequate checks and balances in place. By integrating traditional programming, Air Canada could make the chatbot cross-reference a predefined rule set. It would validate AI-generated responses against current airline policies. This approach to AI algorithms would prevent the spread of incorrect information.

However, it's just one unfortunate AI deployment case and all business goals are unique. The question is how to understand whether your product or internal system requires AI integration in the first place. Let's see how to identify the need for an artificial intelligence solution.

Identify the problem you need to solve

Before considering any solutions, it's important to clearly define the business problem. Several things are the most important, including the scope and complexity. If there's a need for complex pattern recognition, large data integration, handling ambiguous inputs, then, the AI initiative is worth it; but if the process is well-defined, rule-based, and deterministic, then a traditional approach is favorable. Here's a checklist to walk through before proceeding to AI integration:

Assess data quality and availability

An AI solution requires large volumes of high-quality, consistent data for training and operation. If your data quality is scarce, inconsistent, or sensitive with strict privacy requirements, traditional programming might be more feasible.

Evaluate business justification and ROI

Estimate expected benefits such as cost reduction, scalability, or improved customer experience. Compare these benefits against the costs, time, and expertise needed for AI integration and maintenance. If ROI is uncertain or low, traditional solutions may be preferable. Integrating AI for its own sake can lead to budget problems.

Analyze organizational readiness

Determine if your team has the skills and culture to adopt and maintain an AI system. Lack of an in-house AI expert or resistance to workflows change favors traditional approaches initially.

Review legal, ethical, and compliance constraints

An AI system can raise concerns about bias, fairness, and data privacy. If your industry has strict regulations, traditional development of a minimum viable product with transparent logic might be safer and easier to audit.

Match solution complexity with project needs

Complex projects with uncertain conditions, ambiguous requirements, or large-scale data processing benefit more from AI's adaptability and learning capabilities. Simpler, well-understood business operations with stable requirements are better suited for traditional programming.

Assess implementation speed and cost

Generative AI can be deployed rapidly through cloud APIs with lower upfront investment but comes with ongoing usage costs. Traditional programming may require longer development cycles and higher initial costs but offers stable, maintainable solutions.

So, this is the general consideration for making informed decisions towards technologies. Let's go more in depth.

When traditional solutions are enough

Tasks best addressed by traditional programming rather than AI integration are those that involve well-defined, rule-based, and deterministic logic with predictable inputs and outputs. Traditional programming excels in scenarios where the problem and solution are clearly understood upfront, and the system behavior must be consistent and explainable. Best practices include:

Rule-based systems

Traditional programming instead of AI integration suits tasks that rely on explicit “if-then” rules, such as spam filters that scan for specific keywords or tax calculation systems following fixed regulations. While agentic AI could theoretically analyze documents to deduce tax laws and calculation methodologies, a rule-based system that applies predefined tax regulations accurately calculates taxes with clarity and consistency.

Real-time control systems

Artificial intelligence might be used to predict and adjust data visualization for users' needs. Yet, traditional dashboards that refresh data at set intervals using query-based data retrieval and the data integration process offer a reliable and immediate insight without the need for AI-based prediction.

Simple calculations and deterministic processes

Integrating AI can be unnecessary for payroll processing, interest calculation in banking apps, inventory tracking in web app development, and basic arithmetic operations where logic is straightforward and outcomes must be consistent. AI-driven solutions could optimize stock levels, yet simple inventory tracking using basic addition and subtraction against stock levels is often sufficient to maintain accuracy and efficiency.

Expert systems and decision support

Applications that mimic human expert decision-making using predefined knowledge bases and inference rules, such as medical diagnostics, fraud detection based on fixed criteria, or legal research assistance don't need artificial intelligence integration. For example, AI might adapt dynamically to new fraud tactics, but traditional systems using fixed criteria like transaction limits and unusual activity alerts often provide adequate protection for existing systems without AI's complexity.

Structured data processing

Tasks involving handling and querying structured data with clear rules, such as database management and regulatory compliance monitoring, can be handled with traditional programming. For example, AI could automate complex data relationship insights, but most database operations — like executing structured queries for retrieving or updating data from a data source — are best handled by traditional, clear-cut SQL operations.

Traditional programming is preferred when transparency, reliability, and explainability are critical, especially in regulated industries like healthcare, finance, and aerospace. It is less suited for tasks with high variability, ambiguity, or requiring adaptation to new patterns, where generative AI or machine learning would be more appropriate.

In summary, traditional programming is ideal for predictable, rule-driven, and well-specified tasks that demand consistent and explainable results, which you can develop with reliable backend development services. In contrast, AI shines in creative, adaptive, and data-driven problem domains.

When AI integration makes sense

Despite some ambiguities described in the previous section, there are clear scenarios where AI application adds efficiency:

Complex pattern recognition

Artificial intelligence, especially machine learning models, is highly effective at finding complex patterns in data that are hard to handle with rule-based systems. This is particularly helpful when workflows deal with unstructured data or when there are subtle correlations to uncover. In the financial sector, AI can analyze transaction data to spot suspicious activities by detecting patterns that might indicate fraud. Unlike traditional systems that depend on set rules, AI can learn from past data, adjust to new fraud tactics, and lower the number of false alarms.

An AI tool can also process medical images like X-rays, MRIs, or CT scans to detect anomalies such as tumors or fractures that might be missed by human radiologists. The ability to recognize complex patterns in images makes diagnosis faster.

Natural language understanding

AI's ability to understand and generate human language is extremely useful for tasks involving text and speech interpretation. AI-powered chatbots can interact with customers in a human-like manner, understanding and responding to their questions. Unlike simple bots that follow basic rules, these AI systems use natural language processing to handle complex queries, providing more accurate and satisfying answers.

Companies also use large language models to analyze social media posts, reviews, and feedback. This analysis helps them understand how customers feel about their brand and adjust their marketing strategies based on public perception. Our backend AI integration services can help implement artificial intelligence in marketing campaigns and automate various tasks.

Predictive analytics with large, dynamic datasets

Artificial intelligence is great at handling large amounts of data and making predictions based on past trends and current conditions. This is particularly useful in fast-changing environments. For example, retailers and manufacturers use AI to forecast future product demand by analyzing past sales, market trends, and factors such as seasonal changes. This helps them manage inventories better, cut costs, and automate tasks. In industries like manufacturing and utilities, AI predicts equipment failures by looking at data from sensors and operational logs. This helps companies to conduct maintenance before failures happen, minimizing downtime and reducing maintenance expenses.

In these cases, AI provides advantages by embracing complexity, absorbing multifaceted data inputs, and adapting to dynamic environments. Successful AI integration helps systems to go beyond the rigid frameworks of traditional programming, offering actionable insights, improving user interactions, and optimizing operational efficiency. When AI is thoughtfully integrated into systems that benefit from these capabilities, it can unlock significant value across various industries.

Contact us for AI integration services or traditional programming — we do both and will help you to decide.

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Decision framework: AI or not?

Let's sum it up with a simple decision tree to guide the choice between AI and traditional solutions.

Step 1: Problem complexity

Is the problem complex and non-deterministic?

Yes: Consider AI solutions as they are often better suited for complex, non-linear problems where traditional rule-based systems may fall short.

No: If you're developing a minimum viable product with just enough functionality, the urge to integrate AI is not as high.

Step 2: Data quality and availability

Is there enough quality data available?

Yes: Data availability supports the seamless integration of artificial intelligence workflows, as AI systems need large amounts of data to learn and provide accurate predictions. Data quality should be consistent and exhaustive.

No: Traditional solutions may be more appropriate since they often require less data to be effective.

Step 3: Cost and resources

Do you have the resources and budget to implement AI?

Yes: This supports pursuing an AI solution, understanding that AI can have higher upfront costs related to data acquisition, processing, and the development of models.

No: Traditional solutions might be a better fit, given their generally lower short-term costs.

Step 4: Benefit evaluation

Are there clear benefits to using AI over existing methods?

Yes: If AI could provide significant advantages, such as improved accuracy, speed, scalability, or uncovering insights that are impossible through traditional means, it may be the preferable choice.

No: Consider whether traditional methods can sufficiently solve the problem at hand.

Step 5: Maintenance and scalability

Is the solution expected to scale or change over time?

Yes: AI solutions often offer better scalability and adaptability to new data.

No: If the problem domain is stable and well understood, traditional solutions may be more beneficial.

Step 6: Regulatory and ethical considerations

Are there regulatory or ethical constraints that may affect responsible AI adoption?

Yes: Carefully evaluate if the potential use of AI aligns with regulatory and ethical guidelines, and check if you have mechanisms to address these concerns.

No: If there are no constraints, AI adoption might be straightforward from a compliance perspective.

Final decision

If the majority of answers point towards AI: Proceed with exploring AI solutions, keeping in mind the necessary infrastructure, expertise, and ongoing costs.

If the majority of answers point towards traditional solutions: Stick with traditional methods unless further analysis suggests that AI could still offer additional value or insights.

Summing up

AI is a powerful tool, but it is not always the right one. Therefore, it is important to approach technology decisions with clarity, pragmatism, and a focus on real business value. Even though many may follow the hype and implement generative AI into their processes and products, it doesn't guarantee success with consumers. Ultimately, success depends on matching technology choices with real business needs. Describe your request in the form below, and we'll find the right solutions that suit your goals.