AI Empowered Workflows

By the team at Foom

This is a series of posts on how to build, create, and augment AI-empowered workflows. In this first post, we outline the how and why of AI.

How much of your work could be put on autopilot?

Consider everything you do in your workday: How much of it can be automated?

For most of us, the answer is a lot.

• 30% of business tasks done by 60% of occupations are fully automatable with artificial intelligence tools according to McKinsey.

• US businesses spend over $5.28 billion on data entry every year. 76% say they spend 1-3 hours a day simply moving data from one place to another. Additionally, 73% of workers spend 1-3 hours just trying to find information or a particular document.

• 83% of workers said they spend 1-3 hours a day fixing data entry errors.

• 81% of workers say they spend less than 3 hours a day on creative work.

  

It’s time for an upgrade

Our world is too important to spend more time on mundane work

At Foom, our mission is to upgrade every human in the world with AI superpowers. By subscribing to our newsletter, you’ll learn about:

1. Converting unstructured data to structured data

   We’ll share the latest and greatest tools and skills needed to begin data automation for your business. Optimize your storage, information retrieval, and analysis capabilities.

2. Measuring the ROI of automation investments

   Not every AI automation accomplishes the same work. We’ll outline a framework of analysis to decide which decisions and processes will benefit the most from AI investments.

3. Bringing structure to unstructured data

   Unstructured data refers to information that is not organized in a pre-defined manner and is difficult to process using traditional methods. Think about PDFs, emails, spreadsheets, Word docs, photos, videos, and the web. We’ll show you the best practices for managing your information, extracting key insights, and building structure for your data workflows.

4. Actionable solutions

   A lot of excellent tools already exist to enhance your workflows, but many of them fly under the radar. We’ve created a huge database of workflow automations, AI plugins, and optimizers that you can implement in as little as 5 minutes.

Building your own AI workers

Data is the new oil. And AI runs on data

How do you get started with creating new AI employees?

1. Feeding your robots

   Building automations first means managing your data. IBM implemented a data governance program that reduced its mailing costs by $10 million annually. Structured or unstructured, we’ll show you how to put it all together.

2. Defining outcomes for success

   AI is simple by nature: It’s designed to optimize some kind of outcome. Are you struggling with human error in data entry? Is speed the most important factor? How much visibility do you need for your workflow? We’ll provide a framework for answering these questions

3. Managing AI’s limitations

   Manually processing unstructured data is time-consuming, error-prone, and not scalable. But AI isn’t perfect either. This is especially challenging for businesses that deal with large volumes of data on a daily basis. We’ll manage and weigh the pros and cons of both.

4. Making your first AI Automation

   We’ll show you the easiest ways to get started plus provide the exact tools needed. For instance, Foom browser extension users perform data entry over 80% faster.

   

Learning the landscape

Augmenting yourself with AI superpowers

The automation landscape is huge, so it’s easy to get lost. We’ll cover several core technologies that make AI automation enablement easier. These include:

1. LLMs (Large Language Models)

   Have you ever tried ChatGPT? Congratulations: You’ve interacted with a Large Language Model. There are countless plugins and applications that use LLMs. We’ll show you how to master them.

2. OCR (Optical Character Recognition)

   OCR technology allows for the automatic extraction of text from images and other unstructured data formats. This technology has revolutionized converting unstructured data to structured data and made it more accessible and efficient. One popular approach is to convert PDFs into a format that machine learning models can analyze. Once the PDF has been converted to text, you can use NLP techniques to extract structured data from the text. You can try the Foom browser extension to get started.

3. Popular frameworks for data processing:

   Programming languages are the de facto tools for data processing. But different tools solve different problems. For example, Scala is used by Netflix to process millions of transactions per second. Whereas Python can run on your local laptop. We’ll lay out the right tools for the right job.

4. No code tools

   There are hundreds of apps and workflow tools that require no knowledge of programming whatsoever. We’ll show you the best ones around.

Best Practices and Tips for Success

1. The impact of data automation on businesses and individuals:

   Automating the process of converting unstructured data to structured data can have a significant impact on businesses and individuals. It can lead to time and cost savings, improved accuracy and insights, and support better decision-making. Foom users have seen productivity improvements of over 80%. To ensure the success of the automation process, it is important to follow best practices and use high-quality tools and technologies. This includes having a clear understanding of the data being processed and the goals of the analysis, using high-quality OCR software, and combining LLMs with knowledge bases

Are you ready for a revolution?

Subscribe to this newsletter for deep dives into the latest and greatest in AI automation