How to Use AI to Your Benefit#
Are you looking to use Artificial Intelligence (AI) to benefit your organization, but feel unsure of how to get started? Do you feel worried about how to use AI securely? If so, reading this article will provide you with insight towards what AI is, how it can work for you and your company, and debunk a few myths surrounding its use.
Artificial Intelligence#
While some may interact with AI on a day-to-day basis, most people have been introduced to the concept of a computer becoming sentient through the lens of the media and entertainment industry, which hasn't always demonstrated it in the best light (I'm looking at you, I, Robot). But what exactly is AI?
At its core, AI is about creating systems that can perform tasks that typically require human intelligence. Think of AI as a set of powerful algorithms—essentially, step-by-step instructions—that are designed to learn from data. Like a recipe, an algorithm guides a computer through the steps to solve a problem. Training these algorithms involves feeding them vast amounts of data so they can learn from examples.
So, while the term "Artificial Intelligence" might evoke images of futuristic robots, it's really about leveraging advanced algorithms and extensive training data to create systems that can assist with a wide range of tasks, varying from understanding natural language to making complex decisions.
Generative AI vs. Machine Learning#
Generative AI and Machine Learning (ML) are both subsets of AI. They can be used separately, or in conjunction to enhance organizational performance. The differences between both are outlined in further detail below:
Machine Learning#
Machine Learning involves training algorithms to make predictions or decisions without being explicitly programmed to perform the task. These algorithms learn patterns from data and improve their performance over time.
For instance, if you want an AI to recognize images of cats, you show it thousands (or even millions) of pictures labeled as "cat" or "not cat." The AI studies these images and starts to understand the patterns and features that make a cat, a cat—such as the shape of the ears, the texture of the fur, the typical body structure, etc. Over time, the AI becomes adept at identifying cats in new, unseen images by recognizing these learned patterns.
Because of functionalities like these, and the efficiencies they create, a multitude of companies use Machine Learning as a way to improve and quicken various processes. For example, have you ever noticed your streaming service recommending shows based on your previous viewing habits? That's Machine Learning in action. Financial institutions use it to detect anomalies and prevent fraud, while retailers leverage it to predict optimal inventory levels based on past sales data.
Generative AI#
Generative AI refers to Models that create new content, such as text, images, music, or even code. Generative AI works with Large Language Models (LLMs) by leveraging their ability to understand and produce human-like text. LLMs, such as GPT-4, are trained on vast datasets containing diverse language patterns, which enable them to generate coherent and contextually relevant content based on given prompts. This ability to understand prompts that are formed as if in a human conversation is called Natural Language Processing (NLP).
Have you ever interacted with a chatbot that provides detailed and accurate responses? That's Generative AI in action. Creative tools like Adobe's suite use it to automate repetitive design tasks and suggest new creative elements, while content creators leverage it to produce blog posts, marketing copy, and product descriptions effortlessly. These are just a few examples of how generative AI is being applied to enhance various processes across industries.
When you envision Artificial Intelligence, it's most likely Generative AI. Generative AI is used frequently by the general public to write content, create images, and more. It’s even possible to ask an LLM to help write a blog post on how to use AI to your benefit... 😊
Use It#
So, now that we know the differences between Machine Learning and Generative AI, how can they be used to improve your business? Here are 5 simple steps:
1. Identify Pain Points#
The first step to using AI is to know your business. Look for pain points in your processes. Start by mapping out your business processes and identifying areas where inefficiencies, bottlenecks, or repetitive tasks exist. Are there reports that need to be written? Customer service chats that need to be answered? Opportunities for automation are everywhere once you start to understand the capabilities of AI.
2. Use Good Data#
As any good data scientist will tell you, your results are only as good as your data. If you input garbage data, it’s safe to assume that you can expect poor results. When planning a use case for AI in your business, it's crucial to evaluate the data resources you have available.
Start by collecting and analyzing data related to the pain points you've identified. This data might include performance metrics, customer feedback, and operational data specific to your company.
Ensure that your data is clean and normalized. Clean data is free from errors, duplicates, and inconsistencies. Normalized data is organized in a structured format that makes it easy to analyze. This might involve removing irrelevant information, correcting errors, and ensuring consistent formatting across your datasets. High-quality data is essential for generating accurate and reliable AI-driven insights, so take the time to assess and prepare your data carefully.
3. Evaluate AI Solutions#
There are three primary Cloud Service Providers (CSPs) when it comes to AI solutions: Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Each CSP offers a suite of tools and services to help you harness the power of AI for your business or agency.
Azure: Microsoft's Azure AI offers powerful tools like Azure Machine Learning, OpenAI, Cognitive Services, and Bot Services to integrate AI into your operations.
AWS: Amazon Web Services provides a robust set of AI tools including Amazon SageMaker, Amazon Bedrock, AWS Rekognition, AWS Lex, and Amazon Polly to meet diverse needs.
GCP: Google Cloud Platform offers a comprehensive suite of AI services such as Google AI Platform, VertexAI, AutoML, Dialogflow, and Vision AI.
All three platforms—Azure, AWS, and GCP—provide extensive resources and training to help you get started. Their documentation, tutorials, and support services are designed to make the integration of AI tools smooth and efficient. If you need more hands-on help, you can also consult with an AI Strategist or Expert.
4. Create a Pilot Project#
You have to walk before you can run. Start with a small-scale pilot project to test the AI solution in a controlled environment. Monitor its performance and gather feedback. Analyze the results of the pilot project against your defined objectives. Look for improvements in efficiency, cost savings, or customer satisfaction. If the pilot project is successful, plan for a broader rollout of the AI solution across the organization. Ensure you have the necessary infrastructure and training in place.
5. Continually Improve#
AI is constantly evolving. It’s important to stay informed on what products and improvements are available. Continuously monitor the performance of the AI solution you’ve deployed and make adjustments as needed. Stay updated with the latest AI advancements to keep improving your processes.
Concerns#
LLMs Learning from Private Data#
"But wait! Won't the LLM keep all my data and get smarter?", you may ask. The answer to that question is: not necessarily. If you are using a free generative AI service, like ChatGPT, chances are they can use your data to make their LLM smarter. But, for a business use case with a subscription making API (Application Programming Interface) calls to an LLM like Azure’s OpenAI, the answer is a resounding NO. When you make an API call to an LLM, you are essentially using the Natural Language functionality of that LLM; think of it as a big brain that has studied vigorously and is adept at both talking and googling. All of the data the “brain” has consumed up to this point is what made it skilled at talking and googling, much like a brain’s long-term memory. Requests made via an API call are like short-term memory; they are only around long enough for the brain to use its Natural Language capabilities and answer the question. You can even determine if the AI gets its information from the web or from your specific data. In short, LLMs like OpenAI are not learning from your requests.
Company Reviewing Prompts#
You may have seen some fine print that companies like Microsoft reserve the right to review your prompts to the LLM and the corresponding responses. Initially, this may seem strange or even cause concern. However, let's think more critically about the reasoning surrounding this request. Think about it: what if someone was asking AI questions to facilitate violence or terrorism (e.g., How to make a bomb)? It’s important for companies to monitor interactions with LLMs to ensure that they are used appropriately. However, if your business has sensitive data, there are options, such as Microsoft’s Modified Abuse Monitoring Waiver, that you can request to be exempt from their review process.
Conclusion#
Whether you intend to use Machine Learning or Generative AI capabilities, integrating AI into your business is within your reach. Start by identifying pain points in your processes, then determine applicable data. Once you have a target process and the data to back it up, explore which Cloud Provider best suits your needs, and design a pilot project. As you see success with your pilot, continue to monitor, scale, and keep informed of possible improvements. Don’t hesitate to seek expert advice if needed. With careful planning and execution, AI can transform your business processes, drive innovation, and give you a competitive edge.