Cloud-based AI/Machine Learning Workflows and Hyperautomation: Tech Tools to Accelerate Business Innovation

Within the next year, leading organizations are looking to double their number of Artificial Intelligence (AI) projects, with nearly 40% of them looking to deploy AI solutions by the end of this year alone[1]. We specifically consider two trends that are slated to gain prominence in the coming years – cloud-based AI and ML workflows, and Hyper-automation. While many companies are looking to invest in AI, many others struggle with leveraging the same to derive optimal benefits that enable thorough digital transformation. Read on to find out how your firm can employ AI/ML workflows and Hyper automation to accelerate their business innovation.

Cloud-based AI Customized Solutioning and Simplification

Most cloud service providers have created tools that enable seamless scaling, deployment and solutioning, and integration with other parts of the cloud platform. This implementation of data science will reportedly grow five times in the coming years. According to a recent study by Gartner, “Strategic use of cloud technologies like cognitive APIs, containers and serverless computing can help simplify the complicated process of deploying AI. By 2023, cloud-based AI will increase 5X from 2019, making AI one of the top cloud services.” [2] While many businesses are under the impression that making this shift can burn a hole in the company’s pocket, the transformation costs actually remain largely similar to existing operational costs, and the move adds value to firms in the long run.

Implementing AI/ML Workflows

One of the largest airlines in South-East Asia was looking to deploy a series of cascading Big Data Recommender Systems to predict preferred destinations, ancillary services, and customizations for their entire customer base. Using an AI/ML automation tool called Amazon SageMaker, along with PySpark for ETL, MathCo. deployed a deep learning model capable of scaling and without significant engineering requirements. The tool also enabled automated iteration for training and simplified meta-analysis of the training process. Data Scientists were able to train multiple models all the while monitoring metrics and performance through AWS reporting mechanisms.

Tools that facilitate workflows such as these reduce the requirement for engineering resources. In an orthodox structure, a data scientist would oversee exploratory analysis and Machine Learning engineers would ensure scalability and robustness of the solution. But with tools that help build cloud-based workflows, Data Scientists can play a hybrid role. They may focus on building the solution, reducing their time spent optimizing and deploying. This saves many resources that would have otherwise been employed and can help speed up the solutioning process to twice the normal turnaround time.

Deriving Maximum Use from Workflows

There are a few factors one must consider before implementing these cloud-based workflow solutions, to derive the maximum benefit from them: 

Picking the best fit: Choosing the right place to disrupt existing set-ups and enhance operational efficiency is vital. Identify silos where people have non-transferrable skills, such as a surplus of Machine Learning engineers or Data Engineers. These siloes operate as both cash and time sinks and can be easily optimized. Hence, identifying disruption/optimization potential using cloud-based AI and ML workflows, is pivotal.

Striking the balance of human involvement: When building solutions, if human innovation is needed in every variant of the problem solution, then it is an indication that disparate solutions are being built. Developing solutions that are uniform or rather, developing a logic for solutions that are commonplace, is needed.

Security concerns: Wherever possible, alternative private clouds or private cloud clusters within public clouds can be set up to resolve common security concerns with the cloud.

Integration Guidelines: There is no existing ideal architecture or plan that tells you exactly how to integrate or pick the tools to be used. As there is no established practice of integration, the task of finding the tools best-suited for an organization can prove to be time-consuming, and is an area that still needs further innovation. For now, this process follows a trial and error format.

Hyper-automation: Digital Transformation on Steroids

Firms are attempting to tap into AI to automate repetitive tasks and reduce the time spent on tasks that are ancillary to the regular work of an employee, world over. The global hyper-automation market, which was valued at USD 4.2 Billion in 2017, is expected to surpass USD 23.7 Billion by the end of 2027[3].

Due to the very deep integration of data science, automation technologies and other pertinent practices; hyper-automation, in many cases, results in the creation of a digital twin of an organization. While the primary goal is always improvement and augmentation, the creation of the digital twin is an ancillary effect which takes advantage of the goldmine of data that is collated. Essentially, collating information on day-to-day processes helps run simulations of the workings of the organization.

For instance, an organization that MathCo. collaborated with had a high employee attrition rate and wanted to identify the primary movers of employee attrition. In the quest for the same, supplementary data about the employees’ salaries, leaves, breaks, and other data, was collated and directed to the right data pipeline. Also, other voluntary information shared by employees, such as their family members’ details and health consultations, was also collated. While this was leveraged to determine the factors of attrition, we were also able to forecast leave application patterns. In this pattern determinants like national holidays and seasonal factors like monsoon flooding were also accounted for. The data allowed us to predict upticks in leave-availing.

Therefore, while the primary goal was to analyze employee attrition, it also resulted in the creation of a digital twin, which subsequently enabled employee absence prediction.

The key to implementing hyper-automation

Digital transformation heads strive to leverage technology in ways that can enable swift skill augmentation in the organization. One important challenge that might crop up is the justification of budgets for hyper-automation. Most companies might prefer solutions that will quickly resolve short-term issues. However, it is key to get a buy-in from C-Suite leaders, by focusing on the long-term impact that can be brought about through hyper-automation. Once achieved, it will not only help to cut down the time spent on ancillary tasks, but also “make visible the previously unseen interactions between processes, functions, and key performance indicators.”[4]

Therefore, by aptly leveraging these two upcoming tech tools, organizations can innovate existing business processes by identifying gaps in operational activities and bridging them with AI.