Tangible Ways to Increase Operational Efficiency with AI in 2024

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The greatest promise of machine learning systems at their current capacity is to automate large parts of what is currently white collar labor. The impact over the next decade is going to be monumental. The transformation is well on its way.

So, what are some tangible ways you can cut costs with AI-powered systems today?

Customer Service

ChatGPT was the first application that passed the Turing test. In other words it was the first time a computer was able to dynamically imitate human conversation in indistinguishable fashion.

This is great news for the customer service functions trying to automate thier efforts, but never quite getting there. We've all been stuck listening to an endless loop of preprogrammed responses.

Well, pre-program no more! Large language models (LLMs) are now capable of keeping track of a dynamic conversation, while having access to business specific data and functions the same way your customer service associate would. It can be designed in such a fashion to triage the ~20% of requests that require executive evaluation to a human operator.

There are a range of different companies trying to create interfaces for customer service agents (check out Parabolic and Markprompt), but frankly, I there is no need to reinvent the wheel. The quickest win lies in setting up your custom trained LLM agent with an email client that customers can send requests to. No need for fancy integrations or onboarding - everyone knows how to use email (Semantic Life is building this technology open-source).

Your customer service operation can be reduced upwards of 80% once a bespoke AI agent is ensuring customer satisfaction..

Data Processing

Machine learning models are statistical models. Guess what they are really good at modeling? You got it - statistics.

The lowest hanging fruit when using AI for data processing is letting it take care of data entry.

Real Estate

By strapping together the web scraping with an LLM you could retrieve all the prices of the three bedroom apartments in lower Manhattan in seconds. The LLM can dynamically interpret website architecture. You no longer have to understand the HTML to set up your data entry automation, nor modify it when the structure of the scraped site changes.

Accounting

Current applications already support uploading documents to complement your prompts. No need to scour through your income statements to put together a tax report anymore, a LLM can take all your inputs into account, output a spreadsheet with the relevant data points, and give you the relevant aggregate values for your return.

Beyond data entry, machine learning is a great way to find patterns and model future outcomes. These functions are still fairly reliant on human supervision, but will grow in self-sufficiency as models and applications mature.

Content Generation

Content is the essence of marketing. And now you can keep an abundant stream of engaging content flowing with less operational overhead.

Effective writers, that have adapted to the rise of generative AI technology, can now produce articles at 10 times the rate they did a couple of years ago.

Graphic designers can now brainstorm, draft and process their visuals in a fraction of the time.

It is important to note here that the cooperation between generative AI and their human operators is symbiotic. There remains a fundamental need for human curation of the generative content in order to ensure the highest quality.

After a production session, the curated generative content can then be scheduled out for consistent automatic publishing in the future.

There is no longer a need to maintain gigantic writing and publishing operations.

We are at the cusp of a transformation akin to the early days of the internet in 1998. A decade from now, AI's influence will be profound, embedding itself into every aspect of life - much like mobile and cloud computing have today.

Might as well get acquainted with the technology and be the Netflix of tomorrow!