Maximizing Your Business Potential with A.I. Technology
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Chapter 1: Understanding A.I. in Business
The question on everyone’s mind is whether to integrate A.I. into business operations.
It's clear that the momentum surrounding A.I. is growing rapidly, and it may be time to embrace this technology. As more businesses adopt A.I., it raises the question: shouldn't you consider it too?
Whether you're a seasoned Generative A.I. expert, someone dabbling with GPTs, or completely new to the concept, you might be pondering if A.I. is right for your organization.
With over 13 years of experience in developing Generative A.I. solutions, I’ll provide a concise framework to help you make this decision.
Section 1.1: Asking the Right Questions
The key to unlocking the value of A.I. isn't merely about finding the right answers; it’s about formulating the right questions.
This latest iteration of A.I. is not entirely new; it’s merely a continuation of earlier developments. In 2010 and 2011, I co-created the first commercially viable Natural Language Generation (NLG) engine and platform at Automated Insights. In simpler terms, we taught machines to produce written content from data.
While our technology utilized both A.I. and machine learning (ML), it was not solely classified as either. Since then, NLG has been integrated with Natural Language Processing (NLP), a field that gained mainstream traction with tools like Alexa and Siri, evolving into what we now recognize as Generative A.I., including platforms like ChatGPT.
Back in 2010, the term NLG wasn't widely recognized, so we referred to our output as "automated content," highlighting that automation constitutes the bulk of what makes A.I. appear magical.
Therefore, the pivotal question you should ask is, “How much automation is appropriate for my business?”
Section 1.2: Differentiating Thinking and Acting
Machine learning involves "thinking," while automation refers to "acting." As technology continues to merge these concepts, we often categorize them under the umbrella of A.I.
Consider a self-driving car: the ML component determines the destination, while automation executes the navigational decisions. Though these systems typically operate independently, their synchronization is crucial.
ChatGPT exemplifies this harmony. It employs an engine that processes prompts and utilizes ML to generate coherent questions and answers. Subsequently, automation facilitates the actual creation of text, visuals, or code, making the results accessible for users.
This is where modern A.I. exhibits a level of sophistication that mimics human capabilities, as our brains also interpret questions and provide answers, which we then articulate.
Chapter 2: Leveraging A.I. for Predictive Decision-Making
The primary application of machine learning within a business context is predictive decision-making.
This practice revolves around utilizing machines to gather necessary data for making informed decisions, subsequently producing those decisions rapidly—often within microseconds. This speed allows for automation to implement those decisions efficiently.
Predictive decision-making relies on both historical data and ongoing data collection. For instance, if historical trends indicate that six trucks are needed every Thursday in a specific area, the system can predict and schedule accordingly.
By incorporating factors like seasonal changes, weather conditions, holidays, and traffic patterns, we can enhance the accuracy of these decisions, resulting in cost savings and increased revenue.
Boom. Benefit. If the advantages outweigh the costs of implementing such technology, and if the requisite data is available, then A.I. becomes a compelling option for that use case.
Incorporating A.I. can lead to innovative solutions, such as self-driving trucks or automated customer outreach to boost demand.
Section 2.1: The 80/20 Rule for Effective Automation
I advocate for the 80/20 rule regarding automation: 80% machine-driven and 20% human involvement.
This ratio should adjust based on the potential consequences of errors; the costlier the mistake, the more human oversight is necessary.
As machine learning matures and demonstrates reliable decision-making capabilities, tasks can gradually transition to full automation.
Consequently, the inquiry "Should I integrate A.I. into my business?" is complex due to the absence of a universal answer. A better approach is to evaluate all operational tasks and ask, "Which of these should be automated?" until you arrive at a definitive conclusion.
In my upcoming article, I will explore broader use cases for A.I. and provide insights based on current technological accessibility. Consider subscribing to my newsletter to stay updated on this topic and others.
This article was originally published in Inc. Magazine, where I contribute to a column on startup advice and culture. For complimentary access to my public writing, join my email list at joeprocopio.com.
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