“Data is becoming the new raw material of business.” – Craig Mundie, Senior Advisor to the CEO at Microsoft
The 3 most common question that has been trending among CPG enterprise and their Sales Leaders were;
1. How does AI/ML differentiate itself from conventional rule-based systems?
2. How can AI/ML solve their business problems while ensuring model accuracy on a near real-time basis?
3. Can these AI/ML platforms be integrated into the company’s existing systems?
Many enterprises are considering shifting from preventive analytics to predictive analytics, so there are questions on when and for who such analytical technique suits best. In this market, enterprises are inundated with the amount of data poured into the business. Therefore, there is a need for a solution that addresses these data-capturing challenges while also ensuring increased productivity, low costs, and interoperability.
Many people use ML/AI & Rule-based Systems interchangeably, but they have distinct features and are suitable in unique situations. This article will dive deep into some exciting characteristics of autonomous AI and rule-based systems and explore which suits you for the business.
1. Dynamic in Nature – Today’s business landscape is seeing a massive transformation with hyper-personalized communications and omnipresent approaches. These inundated business requirements need a technology that complements the complexity and potential to process vast amounts of data seamlessly. As machine learning AI software is run on the algorithm, it allows itself room to extract insights and useful data in real-time dynamically.
2. Regular Experimentation – Rule-based systems are human-built logic that has pre-defined rules and logic to produce the desired result. This does not allow the enterprise to experiment with the software as it takes more time and resources to deploy additional inputs in the system quickly. However, ML AI evolves through learning from past data and behavior; this makes it easy for the solution to act on the data hidden from human knowledge and produce actionable insights.
3. Enabling Discovery-oriented Journey – Rule-based system is deterministic, and thus it is difficult to generate new insights. Machine Learning requires lots of training data and works on complex algorithms that help produce insights and predict future patterns.
4. Scalability – Thanks to graphics processing units (GPUs) and cloud computing, machine learning now has the ability to deliver a large number of computational capabilities. At the same time, cloud computing can deliver exceptional ML capabilities and allow enterprises to experiment with it at a reasonable cost.
5. Real-time Actionable Insights Delivery – Rule-based Software produces output based on human-coded rules, and the computer does not understand the human language, making it time-consuming to interpret data. NLP (Natural Language Processing) is one of the branches of AI which helps in leveraging texts to gain usable data and insights.
Imagine a company wanting to add a new rule to its rule-based system; here, it is difficult to include new rules without introducing countering rules. These processes are also expensive and time-consuming to maintain.
To choose between them, organizations need to consider their near-term goal and long-term strategies to ensure a productive and cost-effective solution. In this unpredictable world, businesses cannot rely on pre-defined rules setting or logic but need dynamic, self-learning solutions to forecast future patterns. AI and ML pave the way to discovering new emerging patterns, finding gaps, and, most importantly, enabling cross-functional teams to work together.
EmlyLabs is a no-code-based ML platform for business experts to explore the world of predictive analytics and insights with a pre-built auto-ML framework that encourages regular experimentation.