Better customer experience, lower costs, enhanced accuracy, and new features are a few advantages of applying machine learning models to real-world applications.
According to a survey conducted by Rackspace Technology, 34% of respondents project having up to 10 artificial intelligence and machine learning projects in place within the coming two years. Meanwhile, 31% see data quality as a primary challenge to preparing actionable insights into AI and machine learning projects.
Before applying the power of machine learning to business and operations, companies must overcome various obstacles.
Let’s dive into some of the primary challenges businesses encounter while integrating AI technologies into business operations in data, skills, and strategy.
The Importance of Data Quality
Data still remains a significant barrier in various stages of planning and utilizing an AI strategy. According to the Rackspace survey, 34% of the respondents said low data quality is the foremost cause of machine learning research and development failure, and 31% stated that they lacked production-ready data.
The AI research community has access to several public datasets for practice and testing their latest machine learning technologies, but when it comes to implementing those technologies to real applications, gaining access to quality data is challenging.
To overcome the data challenges of AI strategies, businesses must fully evaluate their data infrastructure, and breaking down silos should be a top priority in all machine learning initiatives. Furthermore, organizations should also have the right methods to filter their data to boost the performance and accuracy of their machine learning models.
Soaring Demand for AI Talent
The next area of struggle for most businesses is access to machine learning and data science talent. However, with the evolution of new machine learning and data science devices, the talent problem has grown less severe.
Before starting an AI initiative, it is advised that all businesses should perform a thorough evaluation of in-house expertise, available devices, and integration opportunities. Additionally, businesses must consider if re-skilling is a logical course of action for long-term business goals. If it’s feasible for businesses to up skill their engineers to take data science and machine learning projects, they will be better off in the long run.
Outsourcing AI Talent
Another area that has seen extensive growth in recent years is the outsourcing of AI talent. According to the Rackspace survey, just 38 % of the respondents depend on in-house talent to improve AI applications. Others either completely outsource their AI projects or use a mixture of in-house and outsourced talent.
A successful strategy requires close communication between AI experts and subject matter specialists from the company executing the plan.
AI projects not only require strategy and technical expertise but also a strong partnership with the company and the leadership. Outsourcing AI talent should be done meticulously. While it can expedite the process of creating and executing an AI strategy, businesses must ensure that their experts are wholly committed to the process. Ideally, organizations should make their in-house team of data scientists and machine learning engineers work with outsourced specialists.