Artificial Intelligence (AI) and Machine Learning (ML) are two technologies commonly referred to as disruptive technologies, but many people are still unaware of what they actually do. A recent CompTIA report found that only 29% of US companies said they regularly use AI. The lack of exposure to AI solutions has led to many misconceptions about its capabilities.
Among marketers, a limited awareness of AI and ML has led to unclear and unrealistic expectations about these technologies’ capabilities. It has also led to concerns among employees that their positions will be taken over by machines. These misconceptions are preventing many companies from automating manual processes that cost time and money.
At their core, both AI and ML are about automating manual processes and working alongside human users. Understanding what these technologies’ are and the differences between the two is critical to eliminating unrealistic expectations, reducing anxiety over automation, and maximizing the business results of AI or ML deployment.
What’s the Difference Between AI and Machine Learning?
The truth is, what constitutes AI and what doesn’t is a sliding scale. Broadly, AI is any computer or system that mimics human cognitive functions like learning or problem-solving. The term encompasses a range of AI-driven technologies, including natural language processing, problem-solving, autonomous vehicles, intelligent routing, image recognition, and machine learning.
ML is a subcategory of AI where a computer uses algorithms and statistical models to learn how to perform specific tasks without the need for instructions from a human-user. ML uses patterns and inference to complete tasks. Each time an ML process runs, the system can use the results to measure the algorithms’ accuracy and make improvements automatically.
The key difference between AI and ML is that AI refers to an intelligent machine that thinks independently like a person, and ML is a single application of AI. In other words, AI is a broad term that refers to systems that imitate human thinking. Current AI applications include converting speech to text, converting handwritten text to machine text with OCR, or classifying content.
AI and ML each provide a way to automate repetitive manual tasks in the workplace. These technologies augment the capabilities of human workers, rather than replacing them.
Why ML is Essential for Marketers: Managing Creative Assets
For marketers, leveraging AI through ML is useful for managing creative content. In recent years, there has been an explosion in content types for marketers to distribute across an increasing number of marketing channels, which each have their own unique formatting requirements.
While many companies use Digital Asset Management (DAM) solutions to centralize content management and search for content, these solutions have been held back by poor metatagging. In non-automated DAM solutions, human users have to go through thousands of images and creative assets in disparate locations to add metatags, which results in inaccurate or incomplete information.
The process isn’t scalable. As a consequence, ML is an ideal solution for managing creative assets because it can automate the process of asset recognition and metadata application. It can add tags to images automatically so that human users can focus on more important tasks.
Forrester Research shows that a single AI bot can do the work of 3-4 full-time employees. It’s important to note that ML isn’t intended to replace employees but to work alongside those people and augment their capabilities. By deploying AI, you can increase the scale, speed, personalization, division of labor, quality, and security of operations.
Refining the Digital Supply Chain with AI
AI and ML are key technologies for increasing the efficiency of the digital supply chain. For most companies, the struggle to manage creative assets is a consistent pain point, with countless hours wasted on ineffective manual tagging. Deploying a next-generation DAM platform alongside automated ML models enables a company to automate these inefficient processes so that employees can focus on other tasks.
For the best results, marketers should develop ML models based on their own data rather than relying on generic AI services. Using custom ML models increases the accuracy of your metatagging, resulting in more in-depth insights for your business.
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