As today’s business world becomes more complex, so do the problems that must be solved. Thus, a company’s collection and interpretation process of data must evolve if they wish to succeed in the marketplace. Gartner predicts that by 2023, artificial intelligence and deep-learning techniques will be the most common approaches for new applications of data science.
While traditionally used for products such as Amazon’s Alexa and self-driving cars, artificial intelligence has been making its way into the data and analytics space. The IDC predicts that global spending on AI and machine learning will rise roughly 380% between 2017 and 2021, going from $12 billion to nearly $58 billion.
This growth is spurred by rising adoption within organizations as they seek to leverage AI to increase productivity and reduce decision times. By utilizing the extreme amounts of data collected through the internet of things, companies have the ability to train their analytics models through machine learning, enabling the model to adapt itself and become “smarter.” The potential for AI to increase accuracy in decision making—particularly among customers highly dependent on automation—is a key selling point as they look to integrate AI-based data analysis into the workplace.
Artificial intelligence will allow automated processes to be in place to deliver pre-built reports based upon what it thinks is the most relevant information. The user will be able to spend less time preparing reports and more time using them to better their decision making. By 2021, Gartner predicts that 75% of all prebuilt reports will be delivered in this manner. Additionally, 61% of organizations said AI and machine learning are their most significant data initiatives to pursue next year. This global rise in AI-related investments directly correlates with customers’ improved knowledge of its capabilities and ways that it can improve their organization.
While the capabilities of AI and deep-learning are vast, they present their own set of challenges that must be overcome if their benefits are to be fully realized. Compared to artificial intelligence, traditional analytic techniques are much easier to conceptualize, as the user can more easily envision the process that led to the result. The idea of being able to understand the how of the data analysis process generates trust in the accuracy of the results. It’s human nature not to trust something we don’t understand, especially if commission is part of the equation. When one is unable to picture how a specific number was created, it can be much harder to leverage the result and have the confidence you are making the right business decision.
How to Navigate and Take Full Advantage of the Growing AI Presence in the Marketplace
Emphasize data relevancy
Automated reporting can only be achieved successfully if the system knows what data it should show for a specific business situation. Too much data can be overwhelming for even the most experienced user. To combat this, when speaking with customers, emphasis should be placed on showing how the AI-based analytics system can determine what data is most relevant. This will be a key factor for customers and should be heavily touched on.
Build trust in your models
Getting decision makers to embrace AI-generated analytics will be a critical barrier to overcome. Implementing an AI-dominant analysis program where the user can examine how the model operates—even at a basic level—will do wonders in building trust from its users if they are skeptical of how it’s performing.
The other side to building trust in AI analysis isn’t understanding, but proven success. A lot of users will care less about understanding the model, and more about the benefits it can provide. If you can track a model’s performance, you can work to improve it. Having processes in place to measure the impact AI-led analytics can have will positively impact whether users buy in to the system.
Educate customers yourself
A great way to help potential customers will be to educate them on the capabilities and benefits of AI-based analytics. Not every lead is going to take the initiative to ask about the details of AI analytics, so you must be proactive in teaching them. Hold learning sessions and walk customers through the platform in a simple, straightforward manner and encourage users and decision makers to participate. If customers can be shown what the platform can do for them, their interest is much more likely to turn into a purchase.
AI analytics represent the future of business analysis. Every year, more organizations are putting aside their skepticism and hopping aboard. It won’t be long before this technology is no longer seen as unfamiliar to most, but rather the new normal.
By Bryant Brewster
Market Intelligence Analyst
Last modified: May 2, 2019