https://ift.tt/adp7YVc Overcoming the Hurdles to Unlock the Full Potential of Machine Learning in Business Photo by Lukas Tennie on Uns...
Overcoming the Hurdles to Unlock the Full Potential of Machine Learning in Business
According to CB Insight’s state of artificial intelligence report, AI-based startups have secured funding of $15.1 billion in the first quarter of 2022. Even though this number might seem promising to many, the amount of funding has dropped to pre-pandemic levels. Despite pouring billions of dollars into artificial intelligence, many businesses are still struggling with machine learning adoption. There are many reasons for it and that is exactly what we will try to explore in this article.
If you are interested in learning about some of the common challenges that prevent businesses from implementing machine learning systems then this article is for you. In this article, you will learn about five challenges that are holding businesses from adopting machine learning.
Here are five challenges that can prevent businesses from machine learning adoption and what you can do to overcome them.
1. Change Opposing Culture
If your business has a change in opposing culture, it will be difficult for you to implement machine learning. Opposition can come from a wide variety of different sources. It could come from top-level executives who don’t take it seriously or it could come from employees who don’t want to change because they are used to their current systems. How you handle this opposition will decide whether your machine learning implementation fails or succeeds.
If it is coming from C-suite executives, it could be because they are reluctant to take risks and invest money in machine learning projects. On the flip side, if it is coming from the employees, it could be due to the fear of job loss. Employees see automation in general and machine learning and artificial intelligence in particular as an enemy instead of a friend.
Once you have identified the source, it is time to take corrective action to change their mindset. For instance, if it is coming from the employee‘s side, you should focus on convincing them that machine learning will save their time and make their work easier. It can automate mundane and repetitive tasks they hate to do.
On the flipside, if it is coming from C-suite executives, you should make them visualize the return on their investment. Once they see that your machine learning projects can deliver exponential returns quickly, they will start supporting your machine learning adoption initiative instead of opposing it.
Instead of making tall claims and promises, start small and take it from there. This will not only give you the confidence to move forward but also show the stakeholders that machine learning adoption can really benefit their business. Focus on quick wins and target low-hanging fruit first before scaling your machine-learning projects to other parts of the organization.
2. Identifying Your Machine Learning Use Case
Let’s say, you have jumped over the first hurdle and there is no resistance to change. The next obstacle would be to find the right machine learning use case for your business. After all, you can not implement machine learning just because everyone else is doing it.
You need to identify your specific machine learning use case before implementing it. Machine learning is a vast domain and you should be crystal clear about which subdomain you need to focus on. Ask yourself, whether your machine learning initiatives leverage computer vision, natural language processing, or robotic process automation capabilities of machine learning.
Answering this question will make it easy for your business to create a clearly laid out strategy. Start from the most critical business function as it can help you prove your point. It could be computer vision-powered assembly lines or data analytics-powered marketing campaigns for your retail business.
This might vary from business to business. You can also divert all your attention toward fixing the underlying issues. This could be process-oriented issues or anything else. Machine learning can help you plug in those loopholes to enhance your business efficiency and productivity.
3. Choosing The Right Data For Training
What if your business has overcome the change resistance and use case issue? The next roadblock could be finding the right data to train your machine learning algorithms. Machine learning algorithms are as good as the data you feed to train them. It is highly recommended that you train your machine learning models with multiple data samples so it can cover all bases.
This means that the outcome of machine learning algorithms is highly influenced by the data you use to train them. That is why it is important to use high-quality data which is normalized and free from all kinds of bias to get the optimal outcome from your machine learning models.
Even if the data you are feeding to these machine learning models are of high quality, it still needs to be normalized. Data consistency across multiple data sources is critical as any inconsistency in data can negatively impact the final output.
Let’s assume that the data you are training your machine learning models on are high quality but low in volume, you won’t get the desired results. Machine learning models require a large volume of data for training. The more high quality data you can feed the machine learning model, the better will be the final output. Another benefit of the large volume of data is that it prevents machine learning models from a phenomenon called overfitting. Overfitting occurs when machine learning models master the training data to a point that it fails to generalize new data.
To sum it all up, the data you are feeding to machine learning models must be diverse, comprehensive, normalized and high quality. The volume of data should also be higher so it can deliver the right solutions for real world problems.
4. Fusing Machine Learning With Human Resource
Even if you have trained your machine learning algorithms correctly, there is still a risk that it can deliver odd outcomes. Machine learning models are complex and can sometimes produce unexpected or even counterintuitive results. For example, a predictive model might indicate that a certain candidate is the best fit for a particular job, but human recruiters might disagree based on other factors not captured in the data.
When you rely solely on machine learning models for decision-making, your decisions could have some kind of bias. As machine learning uses historical data to make decisions and that data could contain bias, it can also make your decisions biased. The worst part, these machine learning models can amplify these biases and incorporate them in their final output.
We can easily see shades of that in automated resume screening software companies use to identify the right candidate for a job. Harvard Business School found that this hiring software rejected many candidates due to strict selection criteria. Constant human supervision is necessary for machine learning models as it can sometimes give unexpected results.
To reduce the chances of unexpected results, it is imperative that you keep humans involved in the decision-making process instead of leaving it completely to machines. You can do that by including human expert reviews or allowing humans to interpret the final outcome of the machines.
5. Lack of Infrastructure
Machine learning implementation is a resource-intensive process both from a financial and human resource standpoint. You not only need skilled professionals who have hands-on experience with machine learning implementation but you also need to develop a machine learning infrastructure which can be quite a resource intensive.
Not only that, businesses must also invest in hardware and software that are capable of running machine-learning algorithms and models. Whether it is powerful computer systems, specialized software tools or high-level storage infrastructure, all of them are critical for smoother operations of machine learning models.
Not only that, businesses must also invest time money and effort in developing a machine learning pipeline that is backed up by the right skillset and expertise. Without all this, it would be impossible for you to effectively run machine learning algorithms and get the best results out of them.
To develop a machine learning infrastructure, you need capabilities such as model selection, data ingestion, data visualization, model testing, and more. In addition to this, you need a machine-learning pipeline that is automated. All of this requires a lot of investment and not every business has the type of budget to fund those activities.
Conclusion
Machine learning has the potential to revolutionize the way businesses operate, but there are several challenges that are hindering its adoption. These challenges include a lack of data quality, talent shortage, regulatory concerns, explainability, and bias. Overcoming these challenges requires a multi-faceted approach that involves investing in data quality, upskilling the workforce, ensuring compliance with regulations, using transparent and interpretable models, and mitigating bias through careful algorithmic design. By addressing these challenges, businesses can unlock the full potential of machine learning and gain a competitive edge in their respective industries.
Which is the biggest hurdle in machine learning adoption in your opinion? Share it with us in the comments section below.
5 Challenges Hampering Machine Learning Adoption was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
from Towards Data Science - Medium
https://towardsdatascience.com/5-challenges-hampering-machine-learning-adoption-1aa512b1808c?source=rss----7f60cf5620c9---4
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