Whether businesses have a specific aversion to change or they simply find change too difficult to manage, the bottom line is that it holds companies back. Yet in the age of machine learning and AI, change is the new normal that the average enterprise will have to embrace. Here are the top ways that new technologies will impact your organization (whether you plan for it or not).
Probably one of the most important ways in which AI will change organizations worldwide is on the human level, starting with hiring. Not only will data skills become more important for young job seekers, but also companies that are progressive when it comes to using data in general (or data science, machine learning, and AI more specifically) will have a leg up for attracting talent.
Data talent especially (like data scientists or engineers) will seek organizations that are on the cutting-edge, using technologies that are open source and that they already know (like R and Python) rather than having to learn new, outdated systems that require special knowledge. Bonus: adapting to change here also means it takes less time to get new people on board.
Data talent especially will seek organizations that are on the cutting-edge.
But on top of hiring new people, the AI transformation also means turning everyone - whether (s)he has a “traditional” data role, like analyst, data scientist, etc., or not - into a data person. That is, providing the tools that allow people at all levels of the organization to use data to make decisions.
Turning everyone into a data person sounds well and good, but practically, how can businesses make it happen? That’s where the process comes in.
Today’s most advanced data transformations happen because the company is able to expand data use throughout the organization using a self-service data/analytics program. That involves easy (but controlled) connection directly to data sources - no more back-and-forth asking data for IT and sending around spreadsheets! - as well as simple ways to share projects between employees for validation and cross-checking.
Of course, changing entire processes isn’t as simple as just snapping one’s fingers. This white paper on developing AI services through a self-service data platform can help with the details. Or, read about how a specific company - GE Aviation - was able to execute in this free 40-page, detailed white paper.
It’s also worth mentioning data governance when talking about processes in AI. What was something previously only handled by an IT team now becomes a process that everyone at the company needs to be concerned with under a data democratization model. So solid data governance processes don’t get thrown by the wayside with these changes; on the contrary - they’re more important than ever.
The last bit of the AI transformation puzzle is technology, which is - in many ways - intertwined with both the people and the process element. The idea of making your business more cutting-edge and appealing through technology choices is important, but the critical idea is making data technology available to everyone (to enable the processes described above).
That means not only going with open source, but also considering an AI platform that provides a simple user experience no matter what level of technological skill the person has.
Clearly, the combination of enablement of people, processes, and technology required to enable AI projects necessarily means pretty significant organizational change. But that’s no reason to shy away. Machine learning and AI will change your organization whether you’re prepared or not, so better to be prepared, right? Brush up on the basics of machine learning or the basics of deep learning if you’re a beginner. Or check out some of the top trends regarding AI and data teams in 2019.