AI has existed for several decades yet it’s more recent appearance, at the top of the infamous buzz words list, has not resulted in widespread adoption across all enterprises. There are instances of use and even a few companies who have reaped the benefits of widespread use but why, when technologies usually are at the end of their life cycle or in the laggard stage after twenty years, is AI still in the early adopter stage?
The issue or obstacle is not the technology. It is neither ahead of its time nor unproven. Artificial intelligence is also well supported and capable of transforming businesses. Data is the feed or fuel of AI and each year the amount of data we produce doubles and it is predicted that well before 2030 the number of networked sensors will reach 150 billion or more (more than 20 times the people on earth). This is a significant aspect of the value of AI can deliver. The more data that becomes available, the higher the benefit. The more information there is to process, the more data the system is given, the more it learns and ultimately the more accurate it becomes. From the standpoint of competitiveness and maintaining or gaining market share, not implementing AI solutions now equates to competing in a race with a delayed start.
Here are few examples of where organizations have effectively used AI/Machine learning to improve their businesses:
- American Express processes $1 trillion in transaction and has 110 million AmEx cards in operation. They rely heavily on data analytics and machine learning algorithms to help detect fraud in near real time, therefore saving millions in losses. Additionally, AmEx is leveraging its data flows to develop apps that can connect a cardholder with products or services and special offers. They are also giving merchants online business trend analysis and industry peer bench marking.
- Large Manufacturing companies like Boeing, BMW and others are increasingly using AI and Data Analytics to manage their complex supply chains (their productions schedules and hence millions of dollars are dependent upon timely availability of thousands of parts). These companies are also using AI to predict potential failure of parts (such as engine parts) to proactively help their clients.
- Companies like Amazon, Netflix and even Walmart are using AI/Machine learning to provide better options/suggestions to their online customers thereby boosting sales (and consumption of more media in case of Netflix)
The data is present and the capacity to leverage this data is a proven technology. AI provides a competitive advantage and can improve key performance measurements of the organization. Possibly more important, it increases the connection and service provided to customers and stakeholders. So, why the hesitation?
As it is with many buzz words, there is often a connotation of a future state. Although virtually every corporate and technology leader gives the nod to investigating AI, there appears to be an undercurrent of ‘wait and see.’ This viewpoint is supported by numerous analysts and prognosticators but does this viewpoint support some extremely basic covenants of improving margins, market share, competitiveness, etc.
Vu360 has listened to the analysts and understand the obstacle but we have given more weight to the feedback provided by our customers. The desire for utilizing rich data sources exists and the objective of leveraging same for organizational improvement is clear. It is only the unknown or even assumed obstacles that have prevented full scale adoption. Corporate leaders and visionaries know that a wait and see approach, particularly in this ultra-fast paced environment, is not the path to continued success. Vu360 has taken the approach of removing obstacles starting with the most common hesitation starter – the replacement or even adjustment of currently installed and proven systems.
Correctly, businesses are reluctant to replace or even augment systems that are working effectively. If it’s not broken, don’t fix it is the often-unspoken reason for hesitation. Our solutions negate this concern.
Vu360 Solutions provides a technology that compliments current infrastructure, utilizes existing data and associated analysis to identify opportunities for improvement. The solution does not introduce a highly customized model whereby the number of data scientist required is a tenfold increase over current personnel. This is a process and solution that leverages the infrastructure, data and talents already in place. In so doing, it becomes an addition – versus replacement – to the investment or foundation that is already in place.
Vu360 Solutions’ Real time Data Pipeline can connect to a variety of different sources such IoT/Sensor Data, ERP Systems and even Web data to seamlessly ingest existing business and other relevant data. This way data can then be transformed, analyzed and processed in a separate layer without effecting the current business processes or flow of information.
The Vu360 Data pipeline has been designed to run autonomously and in real time with a number of pre-built AI/machine learning libraries (such as Demand Forecasting) thus providing benefit where it is needed without having to deploy and army of consultants and data scientists.
What opportunities and advantages already exist within your data sets and operations? How will this information grow and change in the immediate future? How can we assist in developing a non-disruptive and non-intrusive road map to enhanced usage of information and improved outcomes with your customers?
Contact us today or send us a question via this form.