From global corporations to domestic government agencies, organizations are quickly identifying the capabilities of artificial intelligence (AI) or more specifically, machine learning (ML), in order to operate leaner and more effectively. Whether you’re looking to identify a problem within your supply chain, or create a program for better customer service, ML can get you there faster and more economically.
Machine learning leverages algorithms that gather data sets and provide information based on patterns or nuances of that data. That data identifies trends or patterns within data sets that indicate inaccuracies and potential issues. Those patterns are then analyzed and addressed depending on the need.
In a nutshell, machine learning does what humans can’t do: solve problems faster and more accurately.
It’s human nature to possess bias, and it’s common for bias to seep into decision making. Machine learning removes the opportunity for bias to impact the information, and thus, provides a more accurate set of data.
But it’s not just about doing things faster that makes ML so appealing, it’s about transitioning from human-led, intuitive decision making to data-driven decision making. By removing inaccuracies, organizations, whether private or public, can save man hours and money by letting machines do the work.
Automation Can Achieve What No Human Can
While ML captures the information needed for better decision making, automation is the game-changing strategy. This is where companies can demonstrate the efficacy and the value of leveraging ML. The value that comes from better decisions and processes in an organization can be profound and can positively impact resource allocation, efficiency, priorities, and workflows.
The word automation can conjure up images of desolate lands in an apocalyptic state ravaged by giant, metal machines. It has wedged its way into the human psyche as a love-hate relationship. But there is power in automation, from operating leaner to providing better customer service, one could argue that automating will one day be the only way to surpass the competition.
Just like previously mentioned, human bias can creep into analyzing data. The same thing can happen when attempting to manually construct a machine learning model. This step requires knowledge and expertise across myriad disciplines: computer science and mathematics to name a few. Additionally, as humans are not perfect, errors can occur that can taint the model. When this process is automated, accuracy is increased and value captured.
Automated workflows are based on machine learning models. These workflows can help organizations determine where to start first when implementing new processes. For government agencies, it’s vital to continue to seek ways to save taxpayer dollars, which is why automating certain processes is imperative.
Today, more and more agencies are championing ML due to an increase in expertise within the ML community, case studies of effectiveness, and better access to tools. And although the idea of automation was once deemed the precursor to the end of the world, organizations are finding more and more value within automation.
Identify Issues Before They Happen Through Healing Systems
AI-enabled self-healing platforms don’t require human intervention and are able to identify and fix problems before those problems begin to impact business. The ability to predict issues allows for a more accurate and timely response. This could potentially save lives, money, and other crucial resources.
Natural Language Processing (NLP) deals with how humans communicate with computers. Its practical implementation can span across platforms, from curating sentiment across digital channels to identifying the pain points of customers through call center interaction. Although not a new concept, when paired with other ML practices, NLP can have an enormous impact on an organization.
As imperative as automation is, coupling that with technology that can diagnose its own issues, fix those issues, and keep going demonstrates a holistic approach to AI.
Leveraging Analysis to Create Game-Changing Organizations
What if you could predict an event that could bring in millions of dollars to your agency, or predict an event that could cost you millions?
Through predictive analysis, it’s possible. Looking at data such as statistics and modeling, we are able to identify patterns allowing decision makers to adjust their actions accordingly. By leveraging modeling, data mining, machine learning, and artificial intelligence to name a few, your organization can predict future events that could impact your organization.
While predictive analysis focuses on determining possible outcomes, prescriptive analysis determines the best course of action. Prescriptive analysis takes the guesswork out of decision making and better decisions mean better bottom lines.
AI/ML will one day very soon be the differentiator in whether your agency is able to execute its mission — helping American citizens — or fail.
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