Machine Learning (ML) and Artificial Intelligence (AI) are no longer aspirational technologies. With the tech giants - Amazon, Netflix, and Google - adopting it holistically, it is interwoven into our lives as consumers, more than we can ever imagine. With the latest advances in computing power, a decrease in storage costs, and migration to cloud computing, these technologies have expanded their presence beyond the major tech companies and hardcore academia. While machine learning has been around for decades, it was only recently recognized as a tool to transform businesses. There is no doubt that machine learning and artificial intelligence can help companies achieve more by increasing productivity and efficiency. However, due to the lack of a proven path to success, it is still a daunting journey for any company to undertake.
According to Forrester, 53% of the global data and analytics decision-makers say they are in some stage of this journey - either in the process of implementing or have already implemented machine learning in their workflow. It also forecasts that by 2024, AI/ML will be integral to every aspect of a business.
How do you do it?
There are a couple of key areas that a company needs to look into while thinking of adopting AI/ML. First, it needs to undergo a cultural change where all departments and groups understand the importance of data and how it can help them. The company needs to develop a data strategy so it can ensure that data is managed like an asset. It needs to identify, standardize, and regulate the data sources so employees understand what data they possess and what “source of truth” exists for their business. Knowing what they have will help them understand what they need to address via their business objectives. Employees should increase their data literacy and some will need to enhance their current skill set so they are prepared to work directly on ML technology.
Second, companies need to consider the instances where AI/ML can add the most value. With every aspect of business being qualified for a potential change, it is a challenge to identify the right business use case. The most popular use cases often revolve around prediction, classification, and decision making with a higher level of certainty. With ML, we are trying to achieve these objectives faster and with greater accuracy. This requires data to be clean and accurate, and it implicitly points towards planning a comprehensive data strategy. This not only helps to make informed decisions more quickly but also helps in recognizing opportunities faster so companies can act on them to get better results.
Finally, it’s important to look out for organizational fatigue in such a big undertaking. Many times, a company gets stagnant - between identifying the right use case, creating an AI/ML model, promoting it to production, and scale beyond pilot needs. The whole process can take months, if not years in some cases. One of the ways to break out of stagnation and avoid being overwhelmed is to try to keep projects small and keep iterating.
Picking the right platform
It’s also important to select the platform partner that best aligns with your objectives. Amazon Web Services(AWS) provides the broadest set of machine learning products and services. These address the critical use cases in almost all industries, including retail, healthcare, and financial services, etc. AWS is continuously improving its services, keeping up with the market needs, providing two unique and different approaches to solve the business use cases in terms of Amazon Machine Learning Services and Amazon Sagemaker. They both belong to the “Machine Learning as Service” category, but are different and tuned to different audience needs. With Amazon doing the heavy lifting in the case of the Amazon Machine Learning Service, companies have a unique opportunity to try out the business use cases, learn, and iterate quickly.
So, where do you start?
With machine learning, businesses can leverage data to develop innovative ideas and introduce new products to the market. It can enrich existing products and services, improve customer engagement, and attract new users through deeper experiences.
But don’t panic if you aren’t well-versed in AI/ML or don’t have access to resources to help you get this kind of optimization in place. Working with an established digital transformation partner can provide you with the ability to be thorough, strategic, and ongoing in your approach.
Mobiquity is well established in artificial intelligence/machine learning (AI/ML), omnichannel experiences, such as mobile, web, and voice, and can help you unearth new digital solutions to elevate your digital transformation efforts.
Ready to chat with a partner that knows a thing or two about AI? Let’s talk.
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