8 Workflow Benefits of an AI/ML Infrastructure You Should Know About
Technology is advancing at a rapid pace. If they want to remain competitive in any niche, businesses have to face tough decisions. Should you adopt the new tech or stay true to your legacy solutions? One of the most sensitive business aspects of technology is workflow. That’s precisely where new technology implementations have the biggest impact.
When it comes to new technologies, their implementations, and the benefits/downsides they have on a workflow, AI/ML is definitely a tech that disrupts processes across industries. Should you implement it and use AI/ML infrastructure to improve your workflow? What are the potential risks? Who will be impacted the most?
Stay with us as we answer these questions and go through all the workflow benefits of an AI/ML infrastructure.
What is AI/ML Infrastructure?
You are probably already familiar with the term IT infrastructure. It encompasses all the devices connected via the internal company network you and your employees use daily to complete basic tasks. AI/ML infrastructure refers to the same concept. The only difference is that this infrastructure is enabled by cutting-edge technologies such as Artificial Intelligence and Machine Learning.
AI is a huge field, and ML is one of its most important subfields. ML enables computers to learn. Thanks to ML, computers can not only process data but learn from it, and build new programs, so-called trained models. However, let’s not dive into AI/ML specifics.
Now that you know what AI/ML infrastructure is, let’s see how it benefits your workflow.
1. Upscaling and Downscaling on Demand
Every organization has a unique workflow, even if it is inspired by the best industry practices. However, the volume of information and work encompassed by a workflow is never the same. The small fluctuations are easy to manage, but what happens when you need to significantly upscale or downscale your operation?
Do you hire more people? Do you invest in IT infrastructure upgrades? What will you do with all that processing power and storage space once your workflow requirements fall back to average?
AI/ML infrastructure delivers unparalleled scalability, especially if you use cloud-based AI/ML solutions. AI/ML cloud service providers provide access to all the processing power and storage capacity you’ll need.
On top of that, the latest ML algorithms know how to use the resources available to them effectively. With an AI/ML infrastructure, you’ll be able to upscale or downscale on the go and keep up with your workflow requirements at all times.
2. Better Data Utilization
Your company, partners, and customers/clients generate massive quantities of data. You can use this data to facilitate the business decision process, improve your processes and services, and automate a lot of repetitive tasks.
However, legacy solutions used to help you streamline your workflow have no capacity to help you track, record, and store this data, let alone process and analyze it. It translates to gigabytes of data and potential improvements lost.
However, if you use ML/AI infrastructure to support your workflow, you’ll be able to start leveraging the data your workflow generates. No matter how massive it is, you’ll be able to ingest it, process, and analyze it. AI/ML infrastructure enables you to turn your workflow into a number one resource when it comes to identifying potential upgrades, fine-tunes, and minimizing risks.
3. Workflows Generate Valuable Insights
When it comes to identifying bottlenecks, process optimization, and driving progress in terms of efficiency and productivity, nothing can offer better insights than the workflow data. However, many companies use several different software tools to complete their day-to-day tasks.
Capturing all that data, storing it, and analyzing it is borderline impossible, especially in organizations with complex workflows. Simply put, legacy solutions don’t pack this sort of power. If you wanted to use this data, it will require a great deal of time and effort to generate the reports, go through them, and identify patterns.
AI/ML infrastructure makes it all possible. Thanks to ML, all the data that goes in and out of a workflow can be processed and analyzed. More importantly, ML can crunch copious amounts of data and only produce what is most relevant for a specific business and given workflow.
It can produce actionable insights in a matter of seconds. You don’t need a data scientist to interpret the reports – anyone in your team can read and understand them.
Repetitive work generates a lot of human errors, which can affect deliverables’ turn around time and cost a business client, revenue, or reputation. That’s why one of the ongoing efforts across industries is workflow automation. Companies want to automate as much repetitive work as possible and have their employees focused on tasks that require a human touch.
All these efforts, however, face the same challenges. Identifying repetitive tasks in complex workflows is tedious and leveraging automation when a repetitive task is at the same time task dependency is also a problem. Finally, there are no software solutions that can automate all repetitive tasks in the workflows. The current situation forces companies to use different tools.
ML/AI infrastructure delivers companies automation opportunities no other solution ever did. Don’t forget ML algorithms are capable of learning from the data you feed into them. Not only can they identify and help automate current repetitive tasks in your workflow, but do it in the future, too, as your workflow evolves.
5. Ongoing Quality Assurance
What system do you have in place to ensure the quality of the deliverables in your workflow? Do you use software or human agents to do it, or the combination of these two? Whatever you do, the chances are that your efforts are focused on the later phases of your workflow. It means that the processes at the start and middle of the workflow are left unattended.
Given that workflows are often complex and they involve several departments, it becomes increasingly hard to keep an eye on every process and its outcome. In the end, even a small mistake can affect the quality of your products or services.
ML infrastructure can tap into your workflow and process the data in real-time. ML doesn’t sleep, nor does it need breaks. It allows you to turn quality assurance into a routine throughout your entire workflow structure.
6. Better Access to Information
As we’ve earlier stated, a workflow constantly generates data. This data can provide valuable and actionable insights. However, to be useful, data has to be properly cataloged. Only then you can efficiently use it to find exactly what you are looking for.
In the past, the solutions that enabled the research were only accessible to enterprises with large budgets. Without the proper tool at your disposal, you practically have to do all the heavy lifting – research, analyze, and report.
AI/ML infrastructure enables businesses to efficiently compile the data and pull together relevant findings. Thanks to natural language processing (NLP), research is simplified and doesn’t require excessive technical knowledge. NLP streamlines text analysis at scale on different types of files.
7. Cut Down Expenses
The ultimate goal for many organizations is to optimize the operational expenses in order to maximize profits. It is often achieved through workflow optimization. Getting high-quality results with minimal effort means fewer work hours, employees, and teams working on one task.
AI/ML infrastructure delivers plenty of opportunities to cut down expenses related to workflow processes. First, there is automation, which enables employees to stay focused on more pressing matters. Then we have the upscaling and downscaling on demand and the ability to gain access to actionable insights in a matter of minutes.
Such an infrastructure immediately cuts down operational expenses and provides additional opportunities to achieve better cost efficiency down the line.
8. The Potential Risks of Implementing AI/ML Infrastructure
There are a couple of risks you should be aware of before implementing AI/ML infrastructure in your company. Knowing the risks will help you prevent your ML initiative from failing.
The most noteworthy one is skill erosion. What does it mean? Since the AI/ML will take over some of the processes and automate them, your workforce won’t be able to play an active role in certain tasks, which can lead to losing some of the skills.
Then, there’s the risk of poor data training. ML is as efficient as the volume, quantity, and relevance of data you ingest into it. Without proper data training, ML won’t be able to produce high-quality results.
We also have to talk about the risk of unethical practices that often come as a result of poor AI/ML monitoring practices. An organization has to come up with specific policies and guardrails to ensure the AI/ML systems operate within ethical boundaries.
AI/ML infrastructure has a lot to offer to organizations looking for a way to make their workflows more relevant, optimized, and drive efficiency and productivity. As you can see, both AI and ML have a profound impact on how the business is done, especially in the data collection, cataloging, and use departments.
Given the number of benefits, it is safe to assume that we will see more and more organizations from various sectors transitioning away from legacy systems with a renewed focus on AI and ML.