Artificial intelligence (AI) has created a lot of buzz for years, but the coming years hears more about discussions centered around machine learning (ML) – an approach to AI that focuses on data sieving, data interpretation and learning and taking informed action based on the analytics using algorithms.
In other words, ML is the motor that drives data science.
Since a Machine Learning system can assess new data, its behavior and performance while operating unsupervised, enterprises across all industries are keen to experiment and implement this progressive approach for its ventures. Many businesses have built in-house data science departments or are inviting external teams to guide them in solving real business issues through the smart use of data.
But the real value of machine learning is the ability to make decisions based on what it has learned from the past, how it has been trained using datasets and intelligence and not what it is currently exploring and evaluating.
Though the anticipated commercial impact of these efforts has been discussed extensively but the realistic challenges of applying these technologies to real business can be often overlooked.
So to avoid such costly mistakes, it is necessary to ask following simple yet important questions before embarking any machine learning project-
Is there really a requirement? –
Ask yourself if you are adopting the new technology just for adoption’s sake or is there really a requirement. Often, you’ll find there isn’t any need of the same.
‘Machine learning is valuable only for use cases that benefit from dynamic learning – and there are not many of those’ – David Linthicum’
Vendors do create a need for ML cloud services, selling in the name of good fit for applications which shouldn’t use it at all. As a result, the technology seems to be misused and over-applied, diverting valuable resources from projects that can actually drive growth.
Do you need a CAIO (Chief AI officer)?
– Implementing ML requires a good understanding of the technology and a strong business vision. Though anyone from the company (CIO – Chief Information officer, CDO – Chief Data officer, and CTO- Chief Technical officer) can own the overall implementation but a CAIO is the one, who can view an organization’s potential projects that can be scaled and positioned for ML adoption and is responsible for setting a roadmap that ensures the AI integration is in line with the company’s overall strategy. He/she ensures that the right product is selected that meets the business goals and the required resources are readily available.
Are your employees scared of robot colleagues?
– The biggest challenge faced by companies is that their ‘employee’s fear change’ thinking it will make their jobs irrelevant. So the need is to empower employees and management and encourage them to engage with the machines. The hidden fact is people are unaware of the integration problems. Even if a system is developed to perform individual tasks, a person cannot be removed completely from the process – since there are interaction issues such as coordination and communication between people and AI systems. So take up a transparent approach of how machine learning will be integrated, what it means for jobs, and train people to engage with their new robot colleagues.
Is your organization mature in data capabilities?
– Machine learning requires quality data – lots of it. Organizations planning to adopt ML, need to work on their data capabilities first. Since if the data is not clean and extensive, you are not going to be successful in your machine learning efforts. Incorrect and inaccurate data results in erroneous solutions, bad decision making and potentially bad AI implementation. Apart from that, the data needs to be secure with regulations that hold back organizations from using personal/ sensitive data to train their algorithms especially in healthcare and finance industries.
Can you attract the essential Talent? –
While people can be trained on machine learning implementation, it still requires someone with a data science or machine learning background to cater to implementation hiccups. But unfortunately, trained and experience AI and ML professionals are thin on the ground. With more than 500% rise in the number of jobs in AI, less than 30% have the required experience. So, the organizations need to make themselves an attractive proposition with interesting challenges and competitive salaries to ensure more talent flows into their pipeline.
Machine learning’s potential has already been recognized and established in various industries and for many use cases. Still many projects continue to fail within businesses. The need is to focus on the initial business goals and consider the above pointers to guide in resolving the problem statement to achieve the intended results with machine learning!