Businesses across multiple industries are going through digital transformation with artificial intelligence (AI) and machine learning (ML) playing a significant role ensuring the success of organizations’ digital transformation. Whether it’s healthcare, finance, automotive, or retail and e-commerce, machine learning and artificial intelligence technologies are being used by companies of all size across those industries.
In this digital age, organizations are being swamped with business data and the challenge is to transform that data into business insights, providing competitive advantage and success for companies. According to Gartner, in 2019, Artificial Intelligence is among the top strategic technologies that will drive disruption and new business models.
AI and Machine Learning applying in business can be in many different kinds: from chatbots to fraud detection to voice-powered virtual assistant and others. The application of AI and Machine Learning is quickly growing, offering endless capacities for business to provide personalized experiences to their customers with increasing demand. As mentioned before, we are in the midst of digital transformation, with business data is being generated at an exponential rate from different sources, including: users’ mobile devices, smart home appliances, and the Internet of Things (IoT). In order to derive business intelligence from such huge amount of data, it’s required of business to integrate big data analytics into their business process for real time insights in a systematic way, gradually eliminating manual works which is inefficient and resource intense.
Through the insights produced by AI and Machine Learning, not only business can discover more about their customers, competitors, markets and other metrics, but they can also predict customers’ behavior. Consequently, companies can take measures to improve the outcome for better results. In our previous blog post, we mentioned one of the prominent examples of predictive analytics is to help predict and reduce mobile app users’ churn rate for marketing campaign.
In order to stay ahead of the competition and thrive in the era of digital transformation, businesses need to formulate a strategy across Artificial Intelligence & Machine Learning technologies, big data and analytics – together with modern infrastructure that are driving digital transformation.
Traditional Business Analytics vs. Digital Transformation
Traditional businesses usually operate on legacy or on-premise systems that are difficult for users to access data from, especially while being away from the office premises. Particularly, data analytics on traditional IT environment heavily involves manual process with intense IT resources and also generate many data points, which is inconvenient for business users to navigate or contextualize. With Machine Learning-driven analytics, businesses can automate their analytics process and find out hidden patterns through smart data discovery. Moreover, predictive analytics allow business users to forecast the outcomes in a systematic way, letting them to proactively taking action instead of having to wait and react in a passive way.
With advanced AI and Machine Learning capabilities, it is expected that a majority of analytics queries will be automated in 2020. The end result will be a more simplified and personalized insights which predict changes and make recommendation using predictive analytics.
Applying AI and Machine Learning in business
In a modern IT environment, Many IT tasks such as email response, data back-ups, and passwords reset, and more are now all being automated. AI and Machine Learning technologies can actually help determine which business tasks can be automated, ultimately boosting productivity and digital efforts. The increase in automation is beneficial to businesses as it enables people to do more, result in an increase of productivity.
Machine learning (ML) is a sub-field of artificial intelligence where Machine Learning algorithm can “learn” over time through the data feed into it, increasingly develop its capacity to discover new patterns and make informed decisions. Thus, businesses can leverage ML and AI technologies, especially Machine Learning algorithms, to quickly produce insights anywhere and anytime. AI and ML enable systems to learn instead of being program explicitly by human; ML algorithms are trained with approriate datasets that is intended to pair inputs for the expected outputs.
ML allows companies to use software to solve many business challenges which tradional methods could not. Instead of having to manually analyze data to support an assumption or hypothesis, machine learning helps companies discover what the data is actually telling, revealing new insights and patterns. The use of machine learning in multiple industries is becoming increasingly prevalent.
For example, many start-up whose revenue generated from SAAS models offer trial services to potential customers have a very low conversion rate after the trial period. Using machine learning algorithm together with the data collected regarding customers’ behavior (such as usage frequency, support request history), such start-ups can predict which customers are more likely to convert and focus more on them. Similarly, start-ups can also find out which customers are more likely to churn and take preventative actions. Financial firms also leverage AI and Machine Learning for real-time fraud detection enabling thousands of credit card transactions per seconds securely. Automotive industry also witnessed a leap in transformation with the introduction of self-driving cars, helping businesses to save bulk of time, especially in transportation and delivery. Healthcare companies also receive many benefits in research and improved operations in providing patients care. All these examples demonstrate the fact that developing a ML-driven analytics system would provide business organizations a competitive advantage. Instead of taking manual actions and react after events, businesses can now move ahead by predicting events and take preventative, forward-looking actions, thanks to the availability of data and ML/AI based technologies.
AI & ML for Digital Transformation Success in 2019
The above examples have stated the powerful capacities of AI and ML can provide. However, even after a company decides to adopt Machine Learning for their businesses, there are still a number of factors that can make things go wrong. For example, a general lack of understanding around Machine Learning and Data Science means organizations might not have sufficient data or that data may not be suitable for the ML projects at hand. And if data quality is not good enough, the algorithms might not get trained or tested correctly. All result in a poorly developed project.
Moreover, it could be quite a challenge to find qualified ML engineers and AI experts who can help implement those AI projects. The talents who possess the knowledge and skills are in very high demand and can command high range salaries.
Another significant problem when companies carrying out their digital transformation is that many of them still depend heavily on legacy applications for daily operations. Additionally, silos model and a mismatch approach to analytics may introduce blockade, preventing organizations to maximize and utilize the power of AI and Machine Learning capacities. The decision of replacing legacy applications is not an easy one. It’s a challenge for business to compete based on outdated IT infrastructure
Thus, for a successful digital transformation, business need to formulate a plan optimizing their technologies stack from hardware to software and applications that can fully take advantage of machine learning technologies.
Conclusion
In the coming years, Artificial Intelligence and Machine Learning technology will be one of the critical factors contributing to business success; those organizations with a clear vision for intelligent, autonomous analytics will have a greater access to information with a predictive nature and will be able to take forward-looking action, along with an improvement and innovation in their business process, providing competitive business advantages to thrive in the new digital economy.
Machine Learning and AI development in Vietnam
As a leading market for software development outsourcing, Vietnam is on its way to research, develop, and apply AI & ML technologies into their economy. The country has a large pool of talents and also attracting highly qualified intellectual (e.g. Scientists, PH.D students etc.) into the development of AI & ML in the country. The number of start-ups with a focus on AI and ML is also gradually increasing over time. In the coming years, Vietnam could prove itself to be one of the emerging pioneers in AI & ML research and development in the region.
