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Everybody is talking about AI, so much so that every executive who isn’t thinking about it may find themselves far behind the curve. But the big question is, how do you get started? It helps to break down not only what AI is but why it matters.
At the base level, it starts with data. A common value for applications using AI and ML is that they use data to automate better and more consistent outcomes. Their algorithms learn from data, detect patterns, provide answers, create predictions and make decisions without explicit human instructions. Core AI/ML technologies are advancing at a tremendous rate, providing confidence that the capability and accuracy of this technology disruption will grow—thus all of the buzz.
But will the technologies advance fast enough to make a difference in your business? That depends upon the response time required by each use case. For many uses, a response within seconds is adequate. Searching for the right health plans for your team? It’s probably fine to take a few seconds for an AI-powered technology to rate available options.
However, there is a rapidly growing set of use cases that need “real-time” speeds, generating decisions and actions at least 20 times faster than the blink of an eye. Examples include financial analytics, payment processing, call centers, fraud detection, supply chain analysis for real-time inventory, massive multiplayer gaming and others.
All About The Data
It is not AI/ML that is ushering these “real-time business” use cases into reality. They have been growing dramatically over the past few years, independent of AI/ML advances. The foundation of the real-time movement is actually digitization, which results in an ever-growing flood of real-time data.
In this context of real-time data, ML and AI can analyze incoming data streams to make instant decisions or trigger automated actions. An ML model or AI algorithm might analyze real-time sensor data from a machine to predict potential failure and automatically schedule preventative maintenance. This is especially useful in scenarios where human intervention is too slow or impractical.
When looking to deploy ML or AI in place of human-based decision-making, parse where the technologies can enhance the value derived from real-time data. By doing so, executives can be better informed as to what to invest in and when.
When getting started, consider the following factors:
Speed And Scalability
ML and AI algorithms analyze vast amounts of real-time data far quicker than humans. This rapid decision-making can keep pace with the speed at which data is generated, especially in high-velocity environments like financial markets, online applications and call centers. Speed and scalability are almost always important, but it’ll be more critical for some companies than others.
Unlike humans, ML and AI models don’t suffer from fatigue, and bias can be strategically avoided or significantly minimized if unbiased training data is used. In this way, these models can provide a consistent analysis of real-time data, requiring always-on infrastructure.
ML algorithms identify complex patterns in real-time data to make accurate predictions about future events. This level of predictive capability is often beyond human capacity, especially when dealing with large and complex datasets.
ML and AI can automate decision-making processes based on real-time data analysis. This saves time and resources and enables instant action when needed, such as triggering an alert in response to a potential cyber threat.
Continuous Learning And Improvement
ML and AI continuously learn from new data, improving performance over time. This capability allows adaptation to changing conditions and trends, which is difficult to match with human analysis.
ML and AI can handle multidimensional and multivariate data, making sense of complex relationships and interactions that would be challenging for humans to understand.
Companies will face challenges in implementing AI and ML, as they do any other new technologies. Several of the biggest that we hear about from companies day in and day out are:
One of the biggest challenges will be having clean, up-to-date data to feed the AI and ML software. If data quality is bad, the insights gleaned from it will be bad and unreliable. Software tools can improve the quality of data, but it’s important to ensure that the selected tools can seamlessly integrate with your existing data management systems and workflows.
Companies need data governance programs and processes to ensure that the data can be used to feed an AI model without running afoul of privacy or copyright rules, or to satisfy regulatory reporting requirements. Companies need to be able to integrate data from disparate sources so that the AI and ML software is looking at all relevant data.
The CIO, CTO, data privacy officers and lines of business leaders all need to be involved to ensure the right and best data is being used. Then, the data must be continually monitored as time passes to ensure the standards remain high.
The Skills Gap
The demand for professionals skilled in artificial intelligence is soaring. Successfully executing AI and ML projects hinges on assembling the right team, making it crucial to find individuals with the necessary expertise. Beyond technical skills, proficiency in areas such as data, data quality, governance and strategy is equally vital. Although AI and ML software can analyze data, it lacks the human ability to identify opportunities for growth in products or services.
While prioritizing technical skills may seem like a secure investment in navigating the growth era of AI and ML, it’s essential to recognize that these technologies lack the imaginative capabilities inherent in human intelligence. Assess skills gaps across your organization. Consult with managers to understand the specific skills required for the new era of work, and then deploy HR and other functions to address those needs.
Hand In Hand
Human expertise and judgment are still crucial, especially in interpreting and acting upon the insights generated by ML and AI. Still, when the key challenges are considered and overcome, these technologies can greatly enhance the speed, scale and effectiveness of decision-making processes based on real-time data. That’s why human design and intelligent real-time workloads often work hand in hand, and why human abdication is not an option as customers take their AI journey.
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