While advanced artificial intelligence (AI) and machine learning have been around since the 1990s, embedding AI into business operations to run the business more efficiently and effectively is a relatively new concept. And like many new concepts, this has led to uncertainty of its definition, hesitance toward its adoption, and even skepticism of its value.
Before we take a deep dive into how AI analytics can bring more value to enterprise productivity, let’s first clear up its definition, and the stigma behind AI’s adoption.
When people think of AI they often think of machine learning, which is the study of computer algorithms that learn and improve automatically through experience. Machine learning is, however, a subset of AI. That is, all machine learning is related to AI, but not all AI relates to machine learning.
In the same vein, AI analytics is a subset of business intelligence (BI). AI analytics involves using machine learning to analyze large sets of data in the same way a data scientist would without human limitations.
Given that the traditional approach to data analytics has been viewed as needing a human touch to decipher big data, many organizations have been hesitant to adopt AI analytics. Our data has shown that this is partly due to the fear that automation will lead to job loss. However, the majority of business leaders say that their buy-in is impacted by doubts in the value and reliability of AI data. .
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While automation will disrupt nearly every sector of the workforce — and the types of new jobs that will be created as a result of automation still remains a mystery — AI offers many benefits to organizations looking to automate and optimize routine tasks.
One thing is clear: If the goal is to understand data more accurately to better meet business goals and objectives, AI analytics can measure millions of data points with a speed and accuracy unparalleled by even the brightest team of data scientists.
This does not mean the occupation will become obsolete. In fact, we are witnessing the exact opposite. In 2017, LinkedIn named “data scientist” as the fastest-growing career and, in 2018, Glassdoor ranked it the best job in the US.
Despite the rapid development of automation, the Bureau of Labor Statistics predicts the data scientist job market to grow by more than 30% in the next decade. That’s because AI analytics is a powerful tool that data scientists can leverage to deliver more accurate data insights, untainted by human assumptions.
For example, traditional data scientists spend a great deal of time collecting data, building worksheets to structure the data, and creating reports to distribute the data. With AI analytics, we can now automate each of these actions, allowing analysts to focus solely on creating actionable insights and strategies to further sales and company success.
In other words, AI analytics helps automate data drudgework and channel resources toward concentrating on sustainable development.
In this sense, adopting AI will not only help data scientists to pursue more meaningful, problem-solving business objectives, but it will also create opportunities and insights that may have been previously unforeseen due to factors such as human bias.
The growth of automation is not something to be feared, but should be embraced as the ability to automate repeatable processes not only brings incremental efficiencies but redirects the workforce’s efforts towards more productive and fulfilling pursuit.
While the accuracy of data produced by AI is based on the singularity of the problem it is attempting to solve, AI programs cannot determine whether the data being analyzed is accurate. It takes a keen understanding of machine learning algorithms to evaluate the data. Many organizations fail to recognize this or think AI technologies will act as a plug-and-play application.
Business leaders, IT departments, and data scientists need to be on the same page to clearly and succinctly formulate data analysis into strategic recommendations. We have found that many enterprises fail to align these sectors at the beginning of AI adoption, and therefore do not reap AI’s full potential.
Furthermore, without a keen understanding of AI and workforce balance, the insights gathered may be flawed and risk the potential of negatively affecting business productivity and outcomes.
What is often misunderstood or miscalculated is the level of data governance required to produce accurate data from new-age technologies. Building accurate data requires an enterprise-wide commitment to producing new insights that can take the business to new levels. The key to AI analytics is effectively managing that asset at all levels of data entry and management to gain the greatest possible insights from AI.
In summation, while AI analytic technology is highly accurate, the insights gathered from AI are no better than the team being used to analyze the data.
By integrating AI across the entire business enterprise, numerous applications of AI technology can generate a competitive advantage. One recent example relates to a Trianz client in the coal-mining industry.
This enterprise leveraged artificial intelligence to analyze land surveys, resulting in the identification of mining deposits that would be previously missed with traditional methods. In addition, the mining operation was able to streamline its supply chain with predictive AI analytics, increasing truck capacity utilization and reducing fuel consumption.
Another significant benefit was coal-mining material asset management. With AI-powered analytics, our client reduced their supply shortages and maintained constant output generation, meaning logistical planning was improved as the enterprise could reliably predict when storage capacity would be full.
By implementing AI predictive technology, they improved the utilization of large-capacity shipping vehicles, reduced their fuel consumption, and increased energy efficiency — enhancing their reputation as an eco-conscious business.
You can read more about how Trianz helped this mining multinational in this Mining Global news article.
In another project, Trianz was able to leverage AI analytics to improve customer service provision. AI analytics was useful for driving the customer experience by “massaging the sales pipeline” with intelligent algorithms that monitor keystrokes and webpage behavior.
Imagine that a user is interacting with a web chatbot. With live AI analytics in the background, an enterprise could detect if a user is about to leave the website and transfer them to a human customer support agent. This will result in the AI technology helping to retain prospective customer interest, giving the business another vital opportunity to gain a new customer.
With the scale of big data increasing every year, the need for enterprise-wide adoption of AI analytics has taken center stage. What we are already seeing is that those who fail to adopt AI are missing out on a major competitive advantage.
Though it is tough to say where this technology will be in ten to twenty years from now, it is clear those who choose not to leverage the skills of AI to automate workflow will risk obsolescence.
At Trianz, we believe our people are the key to helping you meet the challenges of your evolving business environment. We understand the state of cloud security, investment priorities, the best practices followed by the most successful companies, as well as the latest technologies that will help our clients gain a competitive edge.
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