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Businesses should be making more use of artificial intelligence to draw out predictions. This centres on predictive analytics, where AI is used to makes prediction about unknown future using data mining and predictive modelling. While predictions can address short term market fluctuations or be geared around a new product launch, organizations can also leverage AI to predict their own "deaths" and evade business doom, according to Justin Richie, data science director at Nerdery. He explains how to Digital Journal.
Justin Richie：While the use of artificial intelligence in the enterprise was once seen as a mere theory, it has now become a vital business solution that many companies see as the key to success. Its ability to predict outcomes and boost efficiency is unparalleled to any other technology the industry has introduced, and the companies who fail to consider adoption, risk falling behind their competitors choosing to capitalize on AI's potential.
Richie：Artificial intelligence is a key driver of many digital transformation strategies as companies across a wide range of sectors develop an "AI-first" approach. Nearly every department within a company stands to benefit from AI adoption, but in order for the technology to work across an organization, the department heads and team members using the AI solutions need to fully understand how it fits into their processes. Rather than solely relying on the IT department to handle new AI systems, non-technical employees and managers are responsible for understanding the role AI plays in their day-to-day work. As digital solutions move away from single projects to being a part of the core business value, the level of technological sophistication across whole companies grows, making digital transformation more widespread.
Richie：AI's capability to draw valuable insights from data can help companies achieve several business goals. Through machine learning and natural language processing technology, companies can better evaluate their internal processes, as well as gain a stronger understanding of their customers. For example, sales teams can analyze internal content management to improve new business proposals and better serve existing customers, and marketing teams can evaluate social posts in real time to determine which posts have the most potential to drive engagement Various sectors can also use AI to their advantage: retailers can analyze customer behaviors to provide a better shopping experience, financial institutions can discover inconsistencies to detect fraud, and automakers can leverage reinforcement learning to better navigate 3D environments for autonomous driving.
Richie：Change management is how companies avoid conflict in the midst of internal restructuring or updating processes. Similarly, using AI to collect data and build predictive models has the potential to predict a major crisis, like a systems malfunction, allowing teams to quickly address and keep it from becoming catastrophic. Companies can also leverage AI to predict factors that can affect the organization, allowing for a proactive approach to change management that gives employees room to adapt to gradual changes. AI can also help enable employees to work together more efficiently by analyzing team dynamics and providing insights on how to improve communication, which is essential to any change management strategy.
Richie：Machine learning is the driving force behind predictive models and it starts with a core data set to train the model to produce good outputs. The type of predictions AI can make all depends on the data they have access to. The more high-quality data you have, the more accurate AI's predictions will be. The problem is that most of the time, the data you are starting with is either insufficient or not structured appropriately to be able to answer the questions you are posing, creating biased and incorrect results. If you want accurate predictions for the future, you need to be able to identify and understand which factors of an AI algorithm are driving the output of the model. It's a lot easier to remove entry points for bias and bad data upfront than it is to anonymize and remove the features that drive false predictions. Depending on the data businesses have access to, AI can be used to predict various outcomes, including customer behavior, system malfunctions and project results.
Richie：While using AI on a micro-level for individual firm success or failure is difficult, you can make predictions on macro shifts in an industry that could impact a firm. Technology shifts and consumer preferences are items that can be modeled to diagnose industry-wide shifts, and then closely monitor which firm adapts. One of the most famous examples of this is Blockbuster and Netflix — Blockbuster had multiple chances to acquire Netflix but waited until it was too late.
Richie：Although AI is a technology at its core, culture plays a major role in the solution's ability to make accurate predictions. As bias in results is one of the biggest controversies surrounding AI, businesses need to make sure their company culture reflects an inclusive environment that prioritizes objective decision-making. Part of being able to remove biased sets of data is being able to identify the individual decisions that are driving recommendations and ensuring they accurately represent our current society and history.