Uncover why machine learning fails and learn how to avoid them for successful implementation.
We want to share a secret.
The truth is that although machine learning and AI are very buzzy words these days, and nearly every tech company’s product and solution is “AI-enabled,” the harsh reality is that most of the customers we encounter have failed mainly in implementing ML inside their own companies.
Some of them give up after failing. Others try repeatedly, grinding through highly expensive trial-and-error situations until they eventually figure out how to make it successful in their organization.
From our perspective, neither of these are viable business options. Today’s business pace is just moving too fast, and to not just survive but to thrive and compete with the industry disruptors, you need to get machine learning right, get it right quickly, and figure out how to make it work and scale to grow your business.
Five Reasons Why You Experience Machine Learning Fails
Let’s take a look at machine learning fails. There are many reasons for this… but the five most recurring reasons have to do with too many opportunities and ideas, a fear of failure, a lack of solid leadership, poor motivation in management, and trying to do too much too soon.
#1 Too Many Opportunities/Ideas
Perhaps an embarrassment of riches is plaguing your organization. Your team is fired up about pursuing a machine learning project, but where will you put your resources?
When there are too many ideas or opportunities for you to consider, it’s not uncommon for a company to keep spinning its wheels. This may be motivated by a fear of missing out, not wanting to commit to one idea because a more suitable ML project may materialize.
You can address this obstacle by prioritizing what you want to get out of ML for your first project. Give your team a time limit to decide and rank ideas in order of importance and support to help you better gauge what path you will take to machine learning.
Towards Data Science suggests, “Before starting any project, ask your team or stakeholders: What business problem are we trying to solve? Why do we believe that ML is the right path?” You’ll need to ask how valuable the project would be and how to measure that, as well as define what “good enough” results will be.
#2 Fear of Failure
Just as fear can paralyze an animal confronted by a predator, fear can also paralyze companies, keeping them from truly moving forward.
Fortune reported on Athena Reilly, a managing director at Accenture, who noted that “In certain cases, corporate data management problems have ballooned to the point where some executives feel overwhelmed, and they use those problems ‘as an excuse’ to avoid A.I. entirely.” Reilly noted that these wary executives tell themselves, “I have bad data. Therefore, I can’t do things like A.I.”
Instead of making fear-based excuses, a company can recognize the opportunity that AI/ML represents to find new business opportunities and provide more valuable services, both to customers outside and to employees within.
#3 Lack of Strong Leadership to Secures Resources for the ML Effort
Without leadership backing a machine learning project, failure is a real possibility. Management may be hampered by data being guarded jealously by stakeholders, keeping ML from going forward. VentureBeat reported on Deborah Leff, CTO of AI and data science at IBM, who noted that “most organizations are highly siloed, with owners who are simply not collaborating and leaders who are not facilitating communication.”
Leff noted that “I’ve had data scientists look me in the face and say we could do that project, but we can’t get access to the data,” Leff says. “And I say, your management allows that to go on?” People in leadership positions in your organization will need to not only be on board but supportive, or the ML experiment may die before it gets underway.
#4 Lack of a Well-motivated Delivery Manager to Shepherd the Project
You can’t expect a project to give you good results if you don’t have an individual supporting it from day one. Find out what’s hindering a manager in your IT department from feeling committed to the project.
Perhaps he or she is skeptical that management will follow through on its support and now fears funding will be subject to being cut or pulled without warning. Or, the manager detects there isn’t much support at the outset and doesn’t want to put a career on the line because a project already looks doomed. For best results, involve the delivery manager from the beginning to shape the scope and quality of the first machine learning project.
#5 Trying to Eat the Whole Elephant at Once
In large institutional dining areas, it’s not uncommon to see a sign posted reading something to the effect of, “Take all you want but eat all that you take.” This take on the idea of not biting off more than you can chew applies to the business world, such as when launching a machine learning initiative in your organization.
It’s a positive step to deploy ML, given its many advantages. But if you try to do too much with machine learning at the outset, you may grow discouraged because of poor results due to a project being too complicated or poorly designed.
Be patient and clear-eyed about your goals and how they match up with your current capabilities before you start with artificial intelligence and machine learning. Chances are you could benefit by consulting with experts, such as the professionals at F33.
When You Launch a Machine Learning Project, Select a Fundamental Problem to Solve
If your organization does decide to pull the trigger and go with machine learning, you’ll get the best results if you act as if there is no way back.
That is why you should select a fundamental problem to be solved. Doing so will help motivate everyone in your company to support the ML effort. If you have questions about deploying machine learning in your organization, our experts will help you. Please get in touch with us for a consultation today.
