Author: Shima Sabbagh Tabrizi
In the past, data was not captured well because companies couldn’t do much with it. But with the emergence of artificial intelligence (AI) and machine learning (ML), the potential for data analysis and insight has become much more exciting and impactful.
The trend towards data-driven decision-making is not restricted to technology companies, either. Businesses in a wide range of industries are using data to enhance their customer experience and refine their business strategies.
In fact, any business that has been around for a year or more likely has a tonne of data ready to help them make decisions, find new customers, predict trends and failures and better plan for the future.
Use cases for AI and ML
Both AI and ML are helping deliver better business insights through data. In a nutshell, these technologies are giving businesses new opportunities to use the data that they already had but weren’t able to make sense of. They’re also unleashing a whole set of new use cases for different data types:
- Gambling is a popular activity at top hotels and casinos. In order to know the best upgrades to offer customers, gambling rooms use AI-powered analytics. The company uses collected data to predict what types of upgrades would be most effective for each guest. One particular offer doesn’t make everyone happy: a free meal could be enough for one guest to have a great experience, but someone else may require a free night’s stay. Predictive analytics helps the company know what upgrades to offer to help bring in higher revenues.
- Self-service options are empowering better customer service experience in many industries. Self-service is eliminating the need to wait on hold for the next available agent to answer a simple question on a call. AI and ML have revolutionised this area by using chatbots and virtual assistance to answer common customer service questions. AI can also automate customer-centric tasks to increase productivity and enhance the customer experience at workplaces.
- AI is enhancing the analytics world with totally new capabilities to make semi-automatic decisions based on training data. It’s not applicable in all situations, but for specific use cases, semi-automatic decision-making revolutionises the way a business creates rules, decisions and predictions without complex human know-how or manual labour.
- With the increasing prevalence of sensors in machinery, vehicles, production plants and other hard equipment spaces, physical equipment can now also be digitised and be monitored by AI. This has enormous efficiency, cost and safety implications.
- Fraud detection based on ML and AI software is used across a variety of industries, with the most popular being banking and insurance businesses. Real-time monitoring is the most effective use of these processes, as fraud can be committed in many ways and is often difficult to detect. One example of real-time detection is data analysts creating algorithms to detect patterns and anomalies.
- ML and AI can predict trends and how markets will perform in the future. Whether it be related to customer loyalty or what will happen in future, everything from what customers want to what will be big in the next year can be analysed and predicted by these new technologies.
Preparing for new technologies
Companies are always looking for ways to increase productivity, efficiency, and performance. Although new technology can accomplish that, if the implementation process is not handled correctly, it can be detrimental to success. With careful preparation, strategic implementation, and honest evaluation, new technologies can be seamlessly integrated and painlessly adopted by employees.
When implementing AI, ML or any new technology, it is best to start at the most basic level. Leaders should consider how technology can help an organisation to have a lasting, positive impact on economic output and growth. You should first be able to answer the basic questions:
- What’s not working?
- What are the expected changes that will come with the new technology?
- How will these change drive success?
A common problem faced is that businesses have massive data but they don’t have a strategy to leverage its value. Thinking cleverly about how data can bring profit to the business is one of the most important steps in successful technology implementation.
The next step is considering a full support system for migrating, and a modern approach for training. Before launching a new technology, everyone must be properly trained and comfortable with the new system. To make sure employees are committed to the transformation, it’s important to keep them in the loop and help them understand their role in the process.
Building capabilities and understanding beyond the IT department is fundamental. If everyone in the organisation has foundational knowledge of new technologies, they will be able to speak about these topics with a certain degree of confidence, which will go a long way in driving the cultural change needed within the enterprise.
Have you been a part of a transformation driven by big data, AI, ML or other technologies? I’d love to hear your thoughts – what do you see as the key factors that facilitate success?