Rule-Based Vs. Machine Learning AI: Which Produces Better Results?

There’s a reason (or three) why business leaders choose machine learning over rule-based AI. Learn the strengths and weaknesses of both.

In a nutshell:

  • Rule-based systems rely on predefined business rules, while AI-powered machine learning learns continuously from data.
  • Rule-based systems are simple and cost-efficient, but lack adaptability and struggle with ambiguity and bias.
  • Machine learning systems are dynamic, adaptable, and can handle complex situations better.
  • Machine learning systems outperform rule-based AI in lead scoring, predicting inventory surges, and retargeting mobile game players.
  • Businesses can easily harness the power of machine learning and AI with tools like Pecan.

Did you know that the world generates trillions of bytes of data daily — more than 2.5 quintillion bytes? Yes, that’s 18 zeroes worth of bytes. It's an overwhelming reality, even for tech-savviest among us. That’s precisely why we have built an ally to make sense of all those ones and zeroes: machine learning and artificial intelligence (AI).

Even though it can be overwhelming, data is a goldmine for your business. Think of every online interaction, personal preference, and purchase history — it’s all teeming with insights and opportunities. Accessing our treasure troves of data is like turning on the lights, so why would any business leader choose to make decisions in the dark?

That’s a fundamental contrast between the approaches represented by rule-based vs. machine learning AI. The biggest difference between rule-based systems and self-learning systems is that humans manually program rule-based systems, whereas machines automatically train self-learning systems.

One relies on predefined business rules, while the other unlocks data-driven decision-making and AI-powered predictions.

Ready? It’s time to flick the lights on.

What does it mean to use business rules and rule-based AI? 

Rule-based AI relies on predefined business rules and business logic, often crafted by human experts who understand specific situations. Businesses refer to these rules as "business rules," guiding decisions in areas like pricing, inventory, and compliance.

Business rules and rule-based AI have diverse applications, from streamlining customer support with chatbots to targeting customers based on demographics. In straightforward tasks, business rules make sense. For example, rule-based techniques are used with chatbots' "if-then" instructions. If a customer asks that, the chatbot says this. Easy peasy.

But consider how banks traditionally used business rules for fraud detection, where a simple "if-then" scenario would flag overseas card transactions. (So convenient to be cut off from your bank in a foreign country, right?)

Evolving customer expectations push businesses to provide personalized experiences — and rule-based systems can struggle to meet these demands, leading to missed opportunities for customers and companies alike. 

Advantages of rule-based AI:

  • Simplicity and cost-efficiency: Rule-based AI is not very resource-intensive to develop since it does not require extensive data collection, cleaning, or training. Human experts can define business rules based on existing knowledge, reducing implementation complexity.
  • Transparency and explainability: Rule-based AI offers high transparency as its decision-making process relies on explicit "if-then" predefined rules. This transparency allows business leaders to easily understand and interpret the system's decisions, fostering trust and facilitating compliance.

Disadvantages and limitations of rule-based AI:

  • Lack of adaptability: Rule-based AI systems are inherently static and inflexible. They can only adapt to changes or evolving conditions with manual adjustments to the predefined rules, which means the process is not scalable.
  • Difficulty with ambiguity: Rule-based systems may struggle when faced with ambiguous or uncertain situations where predefined rules do not offer clear guidance.
  • Riddled with bias: Reliance on predefined criteria may not account for individuals' nuanced and evolving behaviors, potentially leading to unfair or inaccurate assessments.

In a word, the major disadvantage of rule-based AI? It’s static.

comparison of rule based system and machine learning AI system
A comparison of rule-based vs. AI-based machine learning systems

What is machine learning (and how is it different)?

Machine learning systems stand in stark contrast; they are dynamic, agile, and adaptable. The AI learns patterns from large datasets over time, absorbing knowledge with each new data point, customer interaction, product sale, or website click. That's the essence of machine learning.

Machine learning models represent a subset of AI that enhances performance in a specific task by learning from data rather than relying on explicit programming. Developers construct these intelligent systems using algorithms and statistical models, enabling them to automatically identify patterns, make predictions, simulate intelligence, and enhance decision-making capabilities as they analyze new data inputs.

Let's explore a few everyday scenarios where you come across machine learning, both in your personal life and at work:

  1. Binge-watching Netflix: Machine learning powers your show recommendations, which dynamically evolve with your viewing history and preferences. After watching a wildlife documentary, for example, the AI engine considers a broader range of factors, including your past viewing history, genre preferences, and even what other viewers with similar tastes have enjoyed. As a result, you might see a diverse selection of content, such as nature documentaries, travel shows, or even a highly-rated drama series set in the wild. (Rule-based systems may only come up with predictable and limiting recommendations, like a similar wildlife documentary that was preset to populate if someone watched a particular show.)
  2. Fraud protection by banks: Machine learning detects unusual transactions based on your holistic behavior, reducing false alarms and ensuring a smoother banking experience. It can recognize that while overseas transactions may seem unusual, they're not inherently fraudulent. For example, a machine-learning model might notice that you regularly travel and shop overseas. Still, a large purchase from an unusual store might raise a red flag, depending on your average transaction amounts and frequency. (Rule-based systems may trigger an account freeze if you don’t alert your bank of travel plans ahead.)
  3. Customer personalization: Predictive modeling with machine learning helps brands serve shoppers with highly-targeted ads and suggested products for personalized customer experiences by analyzing your browsing history, purchase behavior, product preferences, and real-time interactions. For example, if you recently searched for hiking gear and browsed hiking-related products, machine learning algorithms might suggest items representing cross-selling and upselling, such as specific hiking equipment, trail guides, or even nearby outdoor events. (Rule-based systems may only target you based on demographic data, like if you’re a 45-year-old woman living in Chicago.)

Why machine learning AI is a better approach to decision-making

When it comes to data-driven decisions and predictions, machine learning AI outshines traditional rule-based systems. Its agility and power to harness comprehensive data make it the superior choice when making decisions for your business.

A key difference is that machine learning is more adaptable and can handle complex situations better than rule-based AI, guiding business decisions capably.

Let’s see how machine learning outperforms rule-based AI in a variety of real business examples:

Lead scoring example

Take the process of scoring marketing and sales leads. Say a potential customer exhibits unusual behavior for the first time: They initially interact with the website but then go quiet for a while, only to return and engage more actively, showing a solid intent to make a purchase.

This behavior change would not be accounted for in a rule-based AI setup. The traditional scoring method might not recognize evolving interests, resulting in a lower score and, consequently, being deprioritized. This oversight could cause the company to miss out on a valuable customer on the verge of making a significant purchase.

In contrast, predictive lead scoring with AI considers every interaction and data point, identifying the shifting patterns of every customer’s behavior. This allows for a more accurate assessment of their conversion potential, ensuring they receive the attention and engagement they deserve. The company increases sales productivity and gains a valuable customer — and demonstrates the agility and responsiveness modern consumers appreciate.

An example of predicting inventory surges

Imagine a small business owner grappling with inventory management. Striking the right balance between meeting customer demand and avoiding waste is a perpetual challenge. With rule-based AI, relying on static guidelines like "Reorder the pink T-shirt when stock falls below 20 units" seems logical. But when unforeseen events disrupt these rigid rules, like the highly-anticipated premiere of the Barbie movie, a pink T-shirt shortage becomes a real issue.

Now, picture the same owner using a machine-learning platform. The owner can anticipate trends and manage inventory using predictive AI by analyzing data like sales patterns, customer behavior, and external factors like seasons and events. Yes, like a box office hit drawing enthusiastic pink T-shirt buyers. 

Envision this scenario on a larger scale — think not just one store but hundreds, managing inventory for millions of products. Business leaders must also navigate the complexity of understanding diverse customer preferences. For example, global retailers need to predict the number of Barbie-loving, pink T-shirt enthusiasts in Los Angeles compared to, say, Sitka, Alaska. A set of rules with pre-defined outcomes would struggle with these complex tasks.

An example of retargeting mobile game players

Deciphering the puzzle of player retention — how to draw them back in again (and again and again) — is a challenge for most mobile gaming companies. Traditionally, companies in the mobile gaming industry used a set of business rules to guide their retargeting efforts. For example, "If a user has reached a specific level in the game but hasn't played for seven days, send them a push notification."

Leading mobile entertainment provider SciPlay found the traditional approach to retargeting inefficient. It became evident that casting a wide net and retargeting every player didn't yield the desired results. So, instead, SciPlay adopted a more refined strategy by targeting a select group, improving their experience with exciting bonuses and personalized features that aligned with their likelihood to return.

SciPlay implemented Pecan to rethink its marketing approach, using unique models for each game and saving millions in marketing expenses. That’s the power of predictive analytics and machine learning.

Try out machine learning and predictive AI for yourself

In today's data-centric world, machine learning is the clear choice for businesses seeking to harness data for informed decision-making and growth. 

You know where we stand in the rule-based vs. machine learning AI debate. But you don't need to be a tech wizard or have a data science team to harness the power of machine learning techniques. We've simplified the process for you. Unlock the potential of AI for your business in just 5 minutes — sign up for a free trial of Pecan AI.

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