Automation is not True AI
Many companies have been touting their AI prowess for several years, offering to eliminate repetitive tasks, streamline workflows, cut costs, and improve outcomes. At first glance, everything appears to be on the up and up.
However, as you delve deeper into these purported AI solutions, it becomes evident that these magical money savers are nothing more than predefined processes performing preprogrammed, predictable tasks.
Yes, these automated solutions have value and can perform tasks more efficiently, consistently, and expeditiously than humans, but one must ask: Are they truly 'intelligent' solutions?
Can they adjust to variances in how information is presented? What if the information's location, method, or style varies from one situation to the next? What if a problem arises that has yet to be encountered before? Can these "automations" adapt, change, and learn?
The Illusion of Intelligence
Many products marketed as AI today fall short of being true artificial intelligence. They are merely advanced automation, following strict rules and operating within narrow limits. These systems can handle specific tasks efficiently but lack the ability to understand context, learn from new data, and adapt to changes.
Learning and Adaptation: Key Traits of True AI
For a solution to be truly considered AI, it requires more than just following rules. It needs to:
Learn from Experience: AI should improve its performance based on past data and experiences through machine learning, pattern identification, and prediction.
Adapt to New Situations: Unlike automated systems, AI must be flexible and capable of handling new, unforeseen situations.
Understand Unstructured Data: Real-world information often comes in messy, unstructured formats. True AI should make sense of data regardless of its format or source.
Guided by Experts
For AI to be truly intelligent, it also needs the guidance of subject matter experts (SMEs) to provide the context and training that help AI systems understand the specifics of the data and situations they are interrogating.
This involves:
Contextual Training: SMEs teach AI the intricacies of their respective fields, ensuring accurate interpretation and analysis. They rely on a lifetime of experiences and understanding that these machines lack.
Continual Education: AI systems must be continuously updated and trained to stay relevant to environmental, social, and regulatory changes. They also need constant evaluation for bias to ensure their deductions remain impartial.
Understanding Applications: SMEs provide direction and assist AI in applying its deductive and computing capabilities in practical and meaningful ways.
Identifying the Difference
Leaders today consistently face the challenge of making quick yet informed decisions regarding their operational and system needs to solve problems and improve results. These outcomes and their chosen solutions consistently measure them.
Leaders need to assess these options and identify when a solution is genuinely AI or merely a slick implementation of advanced automation, which they may quickly outgrow or find themselves constantly reinvesting in, enhancing, reprogramming, and/or reimplementing.
Critical Questions for Evaluating AI Solutions
To identify these differences, leaders should consider the following:
How often will the solution require programming updates?
How much information will they need to provide to the solution provider to make it viable?
What setup configurations, business rules, preferences, and documentation are required?
While both AI and automation need configuration, if the lead time is excessive or the level of documentation and data required is immense, then you are more likely dealing with an automation solution rather than true AI.
Moving Forward with True Intelligence
Automation and artificial intelligence are often confused, but the difference is critical. Automation makes repetitive tasks more efficient, but true AI offers the promise of learning, adapting, and demonstrating real intelligence.
By involving experts and focusing on continual learning, we can develop AI systems that truly understand and respond to the complexities of the real world. Ultimately, the goal is not just to create faster and more efficient systems but to develop intelligent solutions that can adapt, learn, and transform our interaction with technology.
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