Process automation. Fraud detection. Personalized recommendations. Voice search. Chatbots. Smart cars. The use cases of Machine Learning in the enterprise are countless. IDC predicts that spending on AI and Machine Learning will grow $57.6 billion by 2021. But despite all the chatter and the steady rise in adoption, many organizations still lack understanding of the foundational practices to derive value from Machine Learning at scale.
Why is machine learning adoption important?
Machine learning adoption refers to the process by which organizations integrate machine learning techniques and technologies into their operations and decision-making processes to improve efficiency, accuracy, and insights.
Machine learning adoption is important because it enables organizations to leverage data-driven insights to make better decisions, improve processes, enhance customer experiences, and gain a competitive edge in their respective industries.
Understanding how Machine Learning can aid business, where the opportunities lie, where to look for skilled resources, and how to implement the concept across the enterprise has proven to be a significant challenge.
Common Challenges in Adopting Machine Learning
While there is no doubt that Machine Learning can help your organization to automate tasks, improve process efficiency, and accelerate time to market, there are some adoption challenges you must be aware of:
Lack of a clear strategy
The biggest challenge organizations face when it comes to embracing Machine Learning is the lack of a clear strategy. If you cannot get an enterprise-wide view of compelling opportunities, how can you expect to leverage the full benefits?
What you need is a robust strategy that clearly defines the need for Machine Learning adoption in your organization and the benefits that can accrue to employees-across teams and departments. The strategy will also need to mention how you will capture the data required to feed Machine Learning algorithms, how you will train employees on the new technology, as well as the governance processes and digital infrastructure needed to drive real value from your efforts (and investment).
Employee resistance
A major advantage of Machine Learning is the automation of some of the most mundane, error-prone everyday tasks. This potential for large-scale automation actually poses a significant challenge in Machine Learning adoption–that of resistance from the workforce. With the fear of losing jobs and the fear of becoming inconsequential or irrelevant with bots looming large, employee resistance can lead to poor outcomes. According to Gartner, although
AI will eliminate 1.8 million jobs by 2020, it will also create 2.3 million new jobs.
Training the workforce and educating them about the benefits of Machine Learning is crucial for success. It will be crucial to show how employees will be reskilled and upskilled so they can drive higher levels of productivity. Plan on putting a robust change management process in place, so you can skillfully manage resistance–especially because starting on the wrong foot can risk damaging the culture of your organization. Celebrate successes, but also work with your employees to overcome the challenges.
Unavailability of the right technological infrastructure
According to McKinsey, organizations that are winning the digital transformation race are also the ones successfully adopting Machine Learning and AI solutions. So, if your IT infrastructure is outdated, and if you’re still using chunky legacy systems to operate your business, the pace at which you will adopt Machine Learning will be a lot slower. The lack of the right technological infrastructure poses a big hurdle in successful adoption, but here’s what you can do.
Since good data leads to good insights, start by having the right data management tools and practices in place. Implement systems that can efficiently aggregate, prepare, and transform data. Use the right analysis methods along with robust data-processing, security, and governance techniques. Invest in high-performance servers, processors, storage systems, and networks and consider leveraging the benefits of the cloud to minimize interruptions and delay in results. Make sure the data engineering tech stack is the best for your needs.
Handling unstructured data
The success of Machine Learning adoption will depend largely on the data that is fed into the algorithms. However, if you have too much-unstructured data, and don’t know how to structure, consolidate, and access it, what you will be left with is irrelevant, inaccurate, and in all likelihood, biased or skewed output.
Since Machine Learning requires large amounts of comprehensive training data for supervised learning, you need to collect data from different business functions and store it in a unified repository. Ensuring context and categorization is extremely important; as is getting rid of data that is irrelevant, to yield relevant results.
Dearth of skilled talent
No matter how advanced your Machine Learning adoption strategy is, the lack of skilled talent can make it fail. Since the technology is fairly new, it is likely that your current employees will not have the skills and capabilities needed to drive maximum value from the investment.
When planning for Machine Learning implementation, pay close attention to how you source people and skills: you can either hire skilled candidates from the market or develop capabilities in-house. Since much of the construction and optimization of deep neural networks requires specific expertise, you need to make sure you can attract and retain workers with these specialized skills.
Are you ready to kickstart your digital journey?
With digital transformation becoming a critical enabler for businesses, deriving value from technologies like Machine Learning has become urgent. While the benefits of Machine Learning are significant, for many, the implementation challenges create a deterrent.
Achieving Machine Learning success involves making the relevant changes to key business processes that lie at the core of your organization. So, have a clear strategy in place, overcome employee resistance, build the right infrastructure, get the right people on board, consolidate and transform your data, and then kickstart your ML journey today!
FAQ
What are the benefits of adopting machine learning?
Adopting machine learning can lead to various benefits, including improved predictive capabilities, enhanced automation of repetitive tasks, better personalized recommendations, increased operational efficiency, and reduced costs.
How can organizations overcome challenges in adopting machine learning
Organizations can overcome challenges in adopting machine learning by investing in data quality management, providing training and upskilling opportunities for employees, collaborating with experienced partners or consultants, ensuring compliance with regulations and ethical guidelines, and starting with small-scale pilot projects before scaling up.
What are some real-world examples of successful machine learning adoption?
Real-world examples of successful machine learning adoption include recommendation systems used by streaming services like Netflix and e-commerce platforms like Amazon, fraud detection algorithms employed by financial institutions, predictive maintenance models in manufacturing, and personalized healthcare diagnostics.
How can organizations measure the success of machine learning adoption?
Organizations can measure the success of machine learning adoption by defining clear objectives and key performance indicators (KPIs) aligned with their business goals, tracking metrics such as accuracy, efficiency gains, cost savings, and customer satisfaction, and regularly evaluating the impact of machine learning initiatives on overall organizational performance.
