Prioritizing Artificial Intelligence and Machine Learning in a Pandemic

Scalability is something that any business craves. Artificial Intelligence (AI) and Machine Learning (ML) has the ability and the means to scale any business. We, humans, are bound to our respective timeframes. We have allocated time for family, friends and ourselves. In a way, we could say that a business's scale is limited and subject to human ability. However, we can also say that AI and ML can continue to work without stopping, resting or taking breaks. With the sole purpose to complete the project assigned. With the pandemic at our doorsteps, the future success of any business relies on its remote working capabilities, efficiency and scalability.

Some Interesting Stats

43% of businesses say that ML and AI initiatives are more effective than expected. One out of four businesses says that they should have adopted ML and AI earlier in the journey.

There are around 56% of businesses that rank security, governance and auditability issues as their highest priority.

Businesses plan to spend 50% more on ML and AI this year, with around 20% saying that they will surely increase their budget to get ML and AI implemented.

In a survey, it was found that in around 38% of the businesses, data scientists spend more than 50% of their official time on model deployment.

Challenges With ML and AI

Nowadays many companies are opting for AI solutions development. There are many benefits of using AI and ML for your business. However, it goes without saying that the implementation has its own set of challenges. One of the top challenges with ML and AI is its lack of skill and the time for proper implementation. In a report by Deloitte, it was found that around 69% of the executives surveyed say that the skill gap for AI implementation varied from moderate - major - extreme. On the other hand, even the companies overlook the investment it takes to build an efficient infrastructure and process needed to successfully test, train, deploy and maintain ML and AI in their enterprise.

Since the pandemic, imbalanced processes and administrations in most of the businesses, the above-mentioned challenge led companies to deprioritize ML and AL. However, most of the organizations have fought back and are now putting their efforts into ML and AI to support crucial business processes. This is true keeping in mind the lockdown norms effective in countries where employees are working remotely.

No matter how challenging implementing ML and AI could be, it is still just a hurdle not a roadblock. To streamline critical business processes despite risk and limitation of resources and the prevailing influence of the pandemic on the business landscape, four key steps are helping in the effective implementation of ML and AI.

Identification Of The Problem

Many companies think of ML and AI as if they were a magic potion for all their problems. This leads to false expectations, unsatisfactory results and unfocused approaches. Instead, the business should chalk out the specific problems faced by the organization and evaluate if the implementation of ML and AI would solve the said problem or not.

Data Selection

The next step in creating a strong algorithm for ML and AI is to the selection of the source data that your algorithm would be trained on. The training allows two options, first, training performed on your accumulated data and second, training on a large scale data set. However, it is pointed out that training an algorithm on your own data set could put you at a disadvantage. On the other hand, if you train on a large data set the chances of much more as the data would be varied and representative.

Companies can also opt to use advanced concepts such as transfer learning, where businesses use semi-trained models based on larger data sets and then train the rest of the algorithm using their own specific data set which is unique to their business.

Right Data

The most basic rule for data management is garbage in and garbage out. What matters the most is the accuracy and quality of the data which would be fed. ML and AI when fed with good and right data can streamline processes and enhance benefits for an organization.

When using ML and AI for your project, just remember that it is critical to clean up the data that you are going to train your algorithm with, especially if you are using your own accumulated data or models.

Training

A properly cleaned and selected data can train your ML and AI easily and act as the last step of the entire process. It also allows you to go back to the first two steps and further refines them based on your training.

The most time-consuming training is the training of the front end of an ML and AI algorithm. However, when we follow these four steps it is efficient and easier to receive significant outcomes. Across industries, ML and AI can rapidly contribute to ROI. Take the example of the insurance industry where ML and AI can help the staff to quickly search contracts, hence reducing the burden from the staff who has to look through the contract repository, to answer a simple question.

It is always suggested to work with a SaaS provider. This is cost-effective and easier to handle. SaaS platforms allow businesses to leverage the infrastructure, security, and pre-trained models which would be already in place to decrease the overall time and effort to value. Many platforms enable the users to up train the predefined models with unique data sets, which in turns reduces the training efforts needed for the model creation. Businesses can focus on their core business processes rather than model creation itself.

Conclusion

There is no doubt that AI and ML can decrease the impact of the COVID impact on businesses. The implementation of the two can increase business productivity during these chaotic times. As we are on the path of recovery we need to realize that AI and ML are tools that could bring success to your business and streamline the process. If you are looking for an AI solutions development, do visit us.

Author Bio - Nora is a copywriter and content writer for Daffodil Software. She specializes in ghost blogging, email marketing campaigns and content for sales pages. She works closely with B2C and B2B businesses providing digital marketing content that gains social media attention and increases your search engine visibility.