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The market meltdown in tech has left one area unscathed: while the market punishes tech stocks, AI is unabashedly on fire.

And investor interest is warranted: AI that is actually good is already here.

The question now is, When and how do businesses supplement their workflows and processes with AI? Because AI is soon going to be mission-critical to staying competitive.

AI can—today—improve or completely take over many business processes: customer service and support, employee scheduling, data analysis, expense management, legal and minor contract drafting, digital marketing, customer acquisition...

In this article (the first in a planned series), we'll explore the state of AI solutions available today and dive into how business owners are prioritizing putting the tech to work.

First, a primer. For deep adoption of new technologies to happen, it's important to understand how those technologies work. If it feels like magic instead of technology, it can be hard to understand and implement. It can also unnerve employees: Though the latest AI breakthroughs are scary good, your employees will still need to be in charge.

An Overview of AI Terms

Keeping a focus on the most promising of the business-ready AI solutions, we'll now list some of the most common and important AI terms to understand (because you'll start to see them everywhere):

  • Generative AI (or generative tech): Generative AI is the technology to create original content by using data from text, audio files, or images.
  • LLMs: Large learning models are machine-learning algorithms that are trained on large text-based data sets to generate human languages or human text. One such model is GPT-3.
  • GPT-3: Developed by OpenAI, the third version of Generative Pretrained Transformer, or GPT-3, is a language prediction model trained to receive a small input of data to generate potentially large outputs of high-quality, human-like, realistic text or copy. There are also open-source alternatives, such as GPT-J.
  • Transformers: Transformers are used in natural language generation to learn how to map input to output, converting data from one form to another that is more suitable for the task at hand. More specifically, they are a type of artificial neural network designed to process text—translating one language to another or understanding the meaning of a sentence, for example.
  • Natural language processing: Natural language processing, or NLP, is a method of teaching computers to understand human language. That includes the ability to interpret and respond to questions or commands in a way that's natural for humans. NLU, or natural language understanding, and NLG, natural language generation, are fields within NLP.
  • Model: In machine-learning (ML), a model is a program that's been trained to recognize certain patterns.
  • Model training: To "train" a machine-learning model means to fit the model to data. That is done by providing the model with a set of training data, which the model can then use to learn and improve itself.
  • Fine-tuning: Fine-tuning is the process of making small adjustments to a model that has already been trained on one task (e.g., generate photorealistic images) so that it may output a similar secondary task (e.g., generate photorealistic sneakers).

Prioritizing Areas for AI Business Investment

Incorporating AI into business operations will soon be critical to staying competitive.

The issue to tackle first is how to prioritize. There are two ways companies could go about prioritizing areas for AI investment:

  1. Which automated solutions have the potential for greatest business impact, whether in cost reduction or revenue expansion, in your business?
  2. Which team members do you desperately need to become more strategic and less focused on tactical or repetitive tasks?

AI for Digital Marketing

If your priorities are driving more revenue, or if the team members you need to make more productive are in marketing, then you'll want to prioritize AI solutions in digital marketing.

Generative AI can now be used to create more personalized and targeted marketing campaign imagery and copy. It can be used to segment customers, to understand their needs and preferences, and to create and deliver personalized content.

Writer's generative AI helped draft ideas for this article you're reading, and its NLP technology helped edit it.

Other tools, such as and Attentive Mobile, use AI to put your programmatic Web and text marketing on autopilot (both companies happen to also be Writer customers!).

AI for Customer Support

If your priorities are to reduce costs in customer support, AI can be used to provide more personalized and efficient online service.

It can be used to automate simple tasks, such as sending out automatic responses to common queries, and to provide more complex support, such as answering questions about products and services.

Using services such as Forethought is a great way to be helpful and responsive to customers 24/7—without the additional headcount.

* * *

In the words of AI researcher Rodney Brooks, "Every time we figure out a piece of [AI], it stops being magical; we say, 'Oh, that's just computation.'"

I'm looking forward to diving into each functional area in future articles with the goal of helping you incorporate generative AI into your business.

Other articles in this series: Marketing at the Speed of Thought: AI Use Cases for Four Content Types

More Resources on AI for Business

Email Marketing and Artificial Intelligence: What's Coming and What You Need to Know

Using AI to Boost Workplace Productivity [Infographic]

The Five Most Effective Applications of AI for Marketing: Christopher S. Penn on Marketing Smarts [Podcast]

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image of May Habib

May Habib is founder and CEO at Writer, an AI writing assistant built for teams. She is an expert in natural language processing and the evolving ways we use language online.

LinkedIn: May Habib