Making AI a Part of your Company Culture
With the level of disruption that the Covid-19 pandemic has had on businesses, it's no surprise that one of the most significant changes companies face is the shift to a more automated business model. Supply chains have shifted and adapted. Traditional storefronts have moved online, and software as a service or SaaS providers have popped up seemingly overnight to support these global changes. Companies that resisted pre-Covid technological business advantages have suffered or failed altogether. Despite a little shaken, those left standing are in a race to AI and the most efficient ways to manage the large amounts of data that the shift to digital business provides.
Access to large amounts of unprocessed business data, such as demographics, sales information, and CRM software, has rapidly created the need for consolidated data services. Without the ability to translate the raw data collected into actionable information, once again, businesses run the risk of failing to keep up with competitors who squeeze every ounce of efficiency from the data they collect.
The answer to drowning in our disjointed data and the catalyst for the business landscape shift is AI. And while it may be technically simple enough to adopt a new AI software for your company to streamline your sales efforts, this doesn't mean that your company culture is ready to use the information well.
Monolithic corporate structures that once stood tall thrived in a world where buying something required a trip to the store or sending a message needed one or several pieces of paper. If the message was for more than one person... let's say it wasn't very efficient. This is an extreme example, of course. Most businesses have fully adopted email, at least at this point. However, the same business structures that courted this antiquated way of doing business still have lasting effects that business leaders must acknowledge if they plan to shake off any dead weight and become as adaptable as possible in a time of rapid growth.
One of the most extensive considerations that businesses have felt is the newest generation to enter the workforce. Many companies have struggled to keep up with the labor force shift to ensure their companies present GenZ workers opportunities to thrive.
Luckily, this ties hand in hand with technological development. While older, more experienced business leaders might juggle the different technical aspects of AI software to change a company's operational system, digital natives and younger generational employees have a much easier time adopting and adapting the technology. In this sense, any software choices should increase business efficiency on the back end and for new generations of workers who will use this technology from now on.
Will you have to rebuild your IT infrastructure every time your company decides to use new software? Does your framework allow for the integration of several services, all designed to collect critical business data? Working towards this goal will provide peace of mind and flexibility to corporate leaders adapting to rapid technological changes. According to the report by Cohesity, The State of Data Management, more than 40 % of IT professionals spend too much time managing data infrastructure instead of working on more critical projects. All things that data management as a service or DMaaS coupled with AI intend to remedy.
With increased efficiency on the business end and useful data in hand, this leaves the more human dynamic of a corporate structure that needs to be reevaluated. What does your vertical communication structure look like? Is there a culture gap that separates management staff from an employee in a very post-industrial age fashion, or is communication and approachability fluid and open?
Especially for the younger side of the workforce, understanding the work's impact can be a huge factor in employee satisfaction and employee retention rates. This can also easily be accomplished if management teams' goals and expectations are integrated to work towards a singular purpose. All employees from CEO to entry-level positions should hold a level of personal responsibility. Employers will need to trust and share plans and information with their teams. Executive employees must also be willing to take personal responsibility for projects at a "lower" level of the company so that a sense of overall unity and cohesive effort becomes the norm.
Stretching the chain of command out in this horizontal structure versus a strict vertical system isn't just for making everyone feel more comfortable and friendly either. Leadership hierarchies that tend to be more horizontally structured allow for team creativity and efficiency to flourish in ways that vertical structures don’t by nature. Companies that have a more evenly spread leadership structure also share and manage data more efficiently, which is the goal of integrating AI. Having a business culture to match these needs is critical in AI integration.