Business Owners Split on How They’ll be Impacted by AI and Machine Learning
In the business world, Artificial Intelligence (AI) and Machine Learning are not just the future -- they're the present. But while we are inundated with reports on large companies that are implementing these technologies to drive efficiencies and improve processes, small businesses are more of a grey area.
Recent data from the Capital One Small Business Growth Index shows that small business owners are divided on how they will be impacted by AI and Machine Learning: among business owners with $1-$10 million in revenue, about half (53 percent) believe that AI and Machine Learning is already impacting, or will impact their industry in the next five years. Business owners in the wholesale and retail industries are most likely to say that AI and Machine Learning has or will have an impact, with owners of construction and service businesses least likely to say so.
Capital One Managing Vice President and Head of the Center for Machine Learning, Dave Castillo, notes that "For companies that are just getting started with machine learning, it's important to understand the opportunities as well as the key challenges that should be taken into account prior to ramping up their machine learning program and infrastructure. In a field that is changing rapidly, the most successful companies are those that can mitigate risk and be clear-eyed about what aspects of machine learning implementation will best set up their organization for long-term success."
Following are some important best practices to keep in mind for business owners when it comes to implementing machine learning in their organization.
Be Clear About the Goal and Whether it Truly Requires Machine Learning
Is a machine learning solution the right approach to achieve the outcome you envision? Sometimes you may just need to clean up your data or solve a business intelligence problem. Among the most common reasons to apply ML to business processes is the ability of the technology to perform certain functions at scale, which would otherwise require vast amounts of time and resources.
Do Your Data Due Diligence
Data is the fuel that drives a machine learning solution, so without the right quality, diversity, and breadth of data, it's nearly impossible to get an ML solution to work properly.
Take a Holistic View
Another common pitfall in implementing ML is taking too narrow a view by creating point solutions that only work for a singular use case. As we all know, the world changes, and those changes can leave narrowly-focused solutions behind, or at least limit their value in the organization.
Understand the Risks
You can't talk about machine learning without discussing the risks involved. Do you understand what this algorithm is doing? Is there the potential for unintended consequences, like bias or unequal outcomes? It's imperative to have a well-managed infrastructure with risk mitigation solutions in place.
Recognize it's Early and Machine Learning Will Continue to Evolve
Today, we're still a long way from machine learning that's totally automated and plug-and-play. It still requires highly skilled people and humans to play a role, and a great degree of diligence for each phase of the life cycle.
As technology becomes more widely adopted and more in-demand from consumers, businesses large and small will need to adapt to keep up. AI and Machine Learning are one way that small businesses might continue to compete will they set themselves up for success in the future.