Companies today are looking towards machine learning to drive value for customers and generate growth. However, while certain industries have benefited greatly from machine learning automation - for instance, highly-relevant product recommendations in e-commerce — the fact of the matter is that many machine learning programs never get deployed.
According to a recent Gartner report, only 53% of machine learning projects make it from prototype to production. This statistic refers to companies that already have a certain level of experience with using AI. For organizations new to machine learning and artificial intelligence, that number is probably a lot higher; some estimates put failure rates at 90%!
It's easy for executives to get blinded by the massive popularity of machine learning seen in countless conferences, venture investments, and news stories. But, it's also true that many companies struggle to identify how exactly machine learning can optimize their business operations.
The focus of this article is to get past all the noise and hype surrounding machine learning and help business leaders make the right decision for their companies. The excitement surrounding machine learning is widespread, and justifiably so, due to the huge potential of this technology.
However, for most companies, using machine learning algorithms to optimize their processes is not only unnecessary but can even get them into serious problems.
Machine learning tools are built to handle complicated and time-consuming tasks under fast-paced conditions. However, these capabilities come at a cost.
Businesses that decide to invest in machine learning should be prepared for a long-term commitment given that it takes a lot of resources to properly train a machine-learning algorithm to make accurate decisions and predictions.
Why is Machine Learning Still Problematic for Companies?
The feature that differentiates machine learning algorithms from other technologies is their ability to autonomously make increasingly complex decisions — such as which stocks to buy, making a medical diagnosis, or recommending products to online shoppers — and constantly change in light of new data.
However, things don't always go seamlessly. Machine learning algorithms don’t always make correct decisions.
Discrepancy Between Business Needs and Machine Learning Capabilities
As businesses scramble to incorporate machine learning into their operations, they will need to build a team of data scientists and computer science experts to automate or optimize processes.
Before embarking on any project, business leaders need to ask themselves whether machine learning is the best way to solve a particular business problem.
Companies need to make sure that substantial investment in machine learning actually makes business sense and is not done for the sake of ticking machine learning off a list.
Implementation Hurdles
Rolling out machine learning projects is never easy. Netflix is a good case in point.
Netflix never deployed its award-winning recommendation algorithm due to the model’s intricacy — instead, the streaming giant opted for another far simpler solution that was easier to integrate.
Many machine learning implementations require infrastructure, alerting, maintenance, and much more in order to be successful.
Lack of Quality Data
The success of machine learning software rests on the quality of data used to train the algorithms. This is the most glaring shortcoming. If your company lacks high-quality and relevant data, then your machine learning algorithm will perform poorly.
Businesses need to perform data preprocessing which involves getting rid of outliers, choosing the right historical data, removing unwanted features and noisy data, and adding missing data points.
And these strenuous tasks must be done without even the slightest mistake.
Market and Environmental Changes
What's more, businesses need to bear in mind that a machine learning model that was trained on past data is likely to make erroneous decisions in fast-changing industries. So, just because you spent all these resources to train your machine learning models, market changes or new business cycles might render your algorithms ineffective.
For example, the door delivery and e-commerce industries are some of the industries where business processes drastically change on an annual basis. Therefore, training data from a few years ago most likely will be useless today for many e-commerce businesses.
Large Amounts of Data
The ability of machine learning technology to identify hidden links between various data points is unparalleled. It's able to identify sophisticated patterns that would be practically impossible for a human to spot.
However, most machine learning algorithms need copious amounts of data (for complex issues, it may require millions of data records) before they can produce valuable results.
Companies in fast-moving industries face serious problems with machine learning algorithms.
For instance, pricing and credit scoring systems have to deal with changing market conditions whenever business cycles change. The objective is to make sure that the machine learning platform and the environment change in the same direction that enables companies to make proper decisions.
To do so, businesses need to perform regular monitoring and maintenance of the machine learning algorithms.
The Black Box Problem
Put simply, machine learning algorithms suffer from a lack of explainability. This means that as time goes by, the results are virtually impossible to understand. The fact that it's almost impossible to reverse engineer a machine learning algorithm after some time, reducing its validity.
Obviously, companies need to understand how their software systems are making decisions. Unfortunately, complex machine learning systems don't provide the necessary clarity or transparency.
For instance, most insurance carriers will have a very serious problem with machine learning solutions because they must have a transparent overview of all of their decisions.
Let's take a closer look at the risks involved in machine learning's lack of transparency.
Understanding What Went Wrong
When an accident or a mistake occurs, executives need to be able to get some helpful indication of the extent of the organization’s potential liability.
And due to machine learning's black box problem, it will remain uncertain what was the cause of the malfunction. In other words, was it a system developer who made an error, or was the data quality used to train the algorithm compromised? These are some of the questions that an organization needs to know.
Slow Implementation
Lengthy implementation is a very common problem with which machine learning experts have to deal. It takes a tremendous amount of time for machine learning algorithms to start producing highly efficient results.
And again, this requires continual monitoring and maintenance, which is a time-consuming and strenuous process that doesn't allow for any incomplete data or mistakes.
Lack of Talent
Despite the great interest in machine learning among software developers and data scientists, there is still a lack of experts who can effectively manage such a sophisticated piece of technology.
Training the machine learning algorithm, finding useful data, and monitoring it merits an experienced data science team and properly trained employees. This is a huge investment on the part of any company.
Companies today are still; struggling to hire qualified experts who are capable of deploying a reliable machine learning solution.
Conclusion: Rules Engines Are Often the Better Option
Machine learning is designed to usher in a big transformation in software technology. It is one of the most exciting technologies that is delivering impressive results in medical diagnosis, speech recognition, natural language processing, product recommendations to only name a few.
However, for the foreseeable future, business rules engines, which have a proven track record of driving value for organizations of all sizes across industries will continue to sit at the forefront of the digital processes of most companies.
While machine learning solutions are more sophisticated than their business rules-based digital counterparts, they require such a large investment which makes them unfeasible for most companies.
On the other hand, business rules engines are easy to set up and are user-friendly, which means that even non-technical employees can create and manage rules-based algorithms.
And importantly, rules engines aren't susceptible to the black box problem.
Take a look at our article on rules engines to learn more about the transformational capabilities of these powerful solutions.
