Many managers (often the uninformed ones) assume they can skip basic analytics best practices and jump straight into adopting AI and other advanced technologies. But companies that rush into sophisticated AI before achieving a critical mass of automated processes and structured analytics can end up stuck.
These companies can be overwhelmed by startup partnerships with cutting-edge technologies, impenetrable black-box systems, compute clusters, and open-source toolkits without data scientists writing code for them. The point is simple: If your company isn’t good at analytics, it’s not ready for AI . Why do many managers struggle to understand this?
On the other hand, companies with a culture of analyzing data—such as hospital contact email database sales data and market trends—are making inroads into complex and critical areas after diving into AI . For example, a telecommunications company can predict with 75 times more accuracy whether its customers are about to cancel a plan, using machine learning . But the company can only achieve this if it has already automated the processes that make it possible to reach customers quickly and understand their preferences using more standardized analytical techniques.
Automating Basic Processes
First, managers should ask themselves whether they have automated processes in areas that are problematic, costly, and slow down operations. Companies need to automate repetitive processes that involve substantial amounts of data—especially in areas where analytical intelligence or speed would be an advantage. Without automating these data feeds first, companies will find that their new AI systems are reaching the wrong conclusions because they are analyzing outdated data.
For example, online retailers can adjust product prices daily because they have automated the collection of prices from competitors. But those who still manually check what rivals are charging can take up to a week to collect the same information and as a result, they may end up perpetually behind their competitors in price adjustments, even if they use AI, because their data is stale.
Without basic automation, strategic insights into solving complex problems at the touch of a button remain elusive. Take hedge fund managers, for example. While the profession is a prime candidate for AI automation , many managers spend weeks manually gathering data and checking for human error introduced through reams of Excel spreadsheets. This leaves them far from AI-ready to predict the next risk in client investment portfolios or model alternative scenarios in real time.