Podcast: What Is AI?
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Artificial intelligence (AI) is one of the most talked about topics in technology today, but many organizations struggle to define what AI actually means for them. In this episode of Weaver: Beyond the Numbers, Morgan Page explores the core components of AI, how it differs from machine learning (ML) and robotic process automation (RPA) and why defining AI properly is critical for successful adoption.
Key Points:
- AI is an automated process that uses historical data and learns heuristically or through trial-and-error over time.
- Many companies confuse AI with ML or RPA and how each works.
- Clearly defining what AI means within an organization helps streamline implementation and reduce risk.
In spite of its widespread application, the concept of AI remains largely misunderstood. AI is characterized by three crucial elements: it’s an automated technology that utilizes historical data and has a heuristic function that enables learning. In contrast, RPA solely performs automated transactions and doesn’t leverage historical data or learning capabilities.
While ML also uses historical data to predict outcomes, it lacks the heuristic function that distinguishes AI. AI bridges the gap between ML and human heuristic processing, evolving over time with the introduction of new data. Hence, it’s vital for organizations to understand these differences when investing in AI solutions. “Not everything is AI. Defining AI within your organization is the first step toward a successful digital transformation,” noted Morgan.
Many organizations often mistake RPA or ML for AI, reflecting a significant need for an internal understanding of what AI means specifically for a company. Miscommunication can lead to unnecessary roadblocks or inefficiencies. Therefore, it’s essential to align organizational communication and establish what AI signifies for the company before embarking on its implementation journey.
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