Many organizations think AI is a ‘plug and play’ technology that will deliver immediate returns to the business right from deployment. Sadl...
Many organizations think AI is a ‘plug and play’ technology that will deliver immediate returns to the business right from deployment. Sadly, it’s not as easy as this. In reality, it all depends on which type of AI your business is using, and the type of challenge you’re looking to overcome by implementing it. Take machine learning as an example. While companies may initially decide to adopt the technology to help identify patterns in their data and solve pressing business problems, this doesn’t happen overnight. IT management teams will have their work cut out before the business ever begins to see value.
Chris Stephenson, Technical Director at Sagacity.
For any kind of usable insight or data pattern to be generated from an AI machine, it must first be properly trained how to spot them. Organizations will need to feed their AI with reliable, labelled training data, most of which should be sourced internally but could also be purchased from a data marketplace or acquired via publicly available sources. The most important thing is ensuring any data fed into the AI is accurate and of high quality, and so the first step for businesses embarking on an AI strategy is operationalizing the data within their own organization.
What is the importance of feeding AI machines the right information?
It is critical to feed AI with the right information as, for example, machine learning models learn solely based on data that is presented to them. However, organizations run the risk of receiving inaccurate predictions or confusing conclusions if they improperly train their data models. It is best to use ‘representative’ data that reflects the characteristics of the real world and real people, as this gives the model the context it needs to recognize human patterns and make informed predictions.
Organizations must also remember that any changes in behavioral patterns must also be addressed, and machine learning models must be retrained with new data. For example, over the past 12 months we have seen a huge shift to remote working in the UK. Figures from the ONS published in July 2020 show that almost half (46.6%) of the entire population did some work from home because of the pandemic. Naturally, this has had an impact on how much gas and electric customers are using. Without retraining machine learning models to recognize that a change in customer behavior has taken place, they will not be aware of the shift and will continue to make inaccurate predictions on bills and usage.
How can organizations make data usable?
For data to deliver value, it needs to be usable. Quality matters. It is not enough for organizations to just collect information, they need to properly categorize, cleanse and manage it appropriately, ensuring data uniformity. The first step for organizations is to gain an understanding of exactly what data they currently hold. They should look to answer questions like: what system is the data on? How is it formatted? Which departments have access? Are there any errors or duplications? From there, they should create a common 'key' that helps to link the data across different systems or within the same systems, cleaning and enriching it to ensure it is accurate, complete and usable.
It is only then that AI and algorithms can be brought in to connect the dots between these datasets. This enables organizations to identify key insights, solve business problems and, in some cases, unlock £million data insights.
What are some of the biggest returns that companies can expect when using AI?
In today’s digital dynasty, data is queen. With that in mind, the returns from using AI and machine learning to make sense of your organization's data are limitless. This doesn’t just mean using it to help build showy front-end services to satisfy today’s constant customer demand for new products and services. AI technologies can also be used to deliver operational benefits that shine a light on inefficiencies within a business, highlighting problems they may not have even been aware existed, let alone know how to solve.
This includes breaking down silos to pull together different data sets and provide answers to questions like: who are the business’ most valuable customers? Which are the most effective sales channels? How much demand will there be for a new service/launch? Are customers paying their bills on time? Am I meeting my compliance obligations? Do I have vulnerable customers that need additional support? Only by knowing this information can organizations put plans into place to improve their service for customers and determine untapped sources of profitability. Ultimately, when AI and machine learning are deployed to answer specific questions, organizations can expect to gain the ability to identify and solve any problems within their business model, as well as uncover new opportunities for growth.
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