3 Things You Need to Know (and Do) Before Adopting AI

3 Things You Need to Know (and Do) Before Adopting AI
Author

Ronald Van Loon

Last updated March 8, 2018


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Artificial Intelligence (AI) has enjoyed some giant strides forward during the last couple of years. With advancements in deep machine learning, predictive forecasting, Internet of Things (IoT), smart personal assistants and intelligent business processes, it is evident that AI is moving into the future at an ever-increasing rate. 

AI enables machines to learn and act, either in place of humans or to supplement the work of humans. We’re already seeing widespread use of AI in our daily lives, such as when brands like Netflix and Amazon present us with options based on our buying behaviors, or when chat bots respond to our queries. AI is used to pilot airplanes and even streamline our traffic lights.

And, that’s just the beginning as we enter the age of AI and machine learning, with these technologies replacing traditional manufacturing as drivers of economic growth. A McKinsey Global Institute study found that technology giants Baidu and Google spent up to $30 billion on AI in 2016, with 90 percent of those funds spent on research and development, and deployment and 10 percent on AI acquisitions. In 2018, AI adoption is expected to jump from 13 percent to 30 percent, according to Spiceworks' 2018 State of IT report. 

As AI moves from something futuristic to something practical, organizations are beginning to think through how AI can benefit their businesses. If your organization is considering the use of AI, there are three aspects that you should understand before integrating AI into your strategic plans: compliant data collection, clean data, and relevant data. 

1. Compliant Data Collection

AI is fueled by data. That is how it learns and adapts. Data collection is, therefore, the first step in any AI plan. As an organization that relies heavily on the use of data, the CIA has 137 AI projects underway to enhance their ability to collect data. 

There are numerous data collection methods that promise the transfer of high-quality data, deemed sufficient for fueling your AI system. Data can be collected from just about anywhere: customer transactions, geographical information, buying behaviors, social media posts, traffic conditions, weather forecasts and countless other sources. However, it must be compliant data collection. Every organization should comply with ethical standards and should consider the best interest of the users as well. Your data collection procedures should be beneficial, progressive, sustainable, respectful and fair.   

2. Clean Data 

The data collected must also be clean. You know the saying, “garbage in, garbage out.” That’s particularly true of AI. If you commit the mistake of feeding bad data to AI, your system will not learn to make good decisions because the data does not align well with what you want the system to show. Machine learning algorithms are the backbone of AI and require significant amounts of accurate data to learn from. 

The conundrum we face today lies in organizations producing way more data than they can use within their realm of control. That data often ends up being unstructured and difficult for the system to comprehend. AI analyzes the chunks of data that are present to find patterns that indicate some predictability. You’ll notice problems when the system develops a pattern based on flawed data. Considering that the system processes terabytes of data in a short span of time, even the minutest deficiencies in data can result in a loss of unimaginable magnitude.  

It’s not that we’re not aware of the flawed data. According to research, more than 30 percent of all data professionals spend half their time cleansing data. Although AI-powered tools do help in cleansing through numerous methods, human help is still needed for labeling the data. Thus, humans are still a part of AI as both augment each other. To ensure that AI is beneficial, you need to get a proper handle on the data that you collect, store and use.

3. Relevant Data 

In addition to data being both compliant and clean, it must be relevant to your AI intentions and goals. You don’t need all the data, just the data that is necessary. You must know what to look for so that your AI knows what to look for, which means knowing the problem you’re trying to solve and the key pieces of data needed to solve that problem. In addition, you must be able to integrate disparate data sources. The integration of relevant data is a vital step for successful AI integration. Businesses that fail to connect critical data sources are losing out on huge opportunities. More unified data can power better analytics that is vital for creating better-rounded AI. 

Despite the threat of potential job losses that AI might bring about, AI also has the potential to change our world for the better. AI can reduce human bias and error in analyzing data, streamline efficiencies, free people from repetitive tasks and more at the enterprise level. But it can bring about even greater good on the global level. 

Max Tegmark, President of the Future of Life Institute, said, “Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before—as long as we manage to keep the technology beneficial."

And keeping that technology beneficial for your business starts with compliant, clean and relevant data. 

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About the Author

Ronald is named one of the 3 most influential people in Big Data by Onalytica. He is also an author for a number of leading big data & data science websites, including Datafloq, Data Science Central, and The Guardian, and he regularly speaks at renowned events.


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