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Erik Trautman

Technical entrepreneurship, business strategy and product development

A Practical Framework for Identifying Opportunities in Artificial Intelligence

Despite being years deep into the new Age of AI, there is still surprisingly little clarity around how to systematically approach and model the space in order to find opportunities for investing and building new businesses.

My intention here is to create a simple top-down process for identifying opportunities by analyzing the core components that are critical in any viable AI-driven product or business.

As an entrepreneur, these are the questions I ask myself when deciding which areas look most fertile to pursue. As a VC, you might use this to find green fields for investment or as a tool to evaluate businesses that cross your radar. As a researcher, this may provide new areas to explore and as an industry expert this should help you think about triangulating in on a good opportunity within your domain space.

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What it Means to be AI-Driven

AI Assisted, Enabled or Driven stick figures by Erik Trautman

The conversation about Artificial Intelligence has become so muddy lately that it is important to be very clear about what we mean when we say an organization is "using AI" or is "AI driven". This post will clear up those definitions and some of the implications of each classification.

There are three levels at which an organization can adopt artificial intelligence:

  1. AI-Assisted: AI is a technical bolt-on to existing processes, usually through the use of AI-created tools, for example AI assisted sales or team management tools.
  2. AI-Enabled / AI-Augmented: AI is applied to existing processes or product data to make the product or service better or more useful, for example the recommendation engines behind Netflix, Spotify, Amazon, etc. This is often in the form of one or more AI-driven features or products.
  3. AI-Driven: AI is literally the lifesblood of the company or initiative, for example self-driving technologies and the companies providing tools to AI-Assisted companies. This is truly an AI-Driven company.

Each of these cases has different implications from the perspective of the implementing organization.

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How Artificial Intelligence is Closing the Loop with Better Predictions

Much of the hype around Artificial Intelligence centers on some vague sense that it continuously learns from the world around it so it gets ever-better at performing tasks. In reality, it's important to understand that the core truth underlying this is much simpler and more powerful: AI technologies allow us to make better predictions than we could before.

Despite the simplicity of that fact, it is an enormously powerful building block which enables automation on a scale we've never seen before by disrupting task loops across the information and physical worlds. Understanding it will allow you to better appreciate the kinds of change it can and will drive.

The semiconductor represents a useful analogy for this reduction. Semiconductor technology obviously changed the world but these empires were built on top of a single simple truth: it reduced the cost of arithmetic. That most fundamental use case forms the basis of computing and, at sufficient scale, allows us to do everything we can today.

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The Virtuous Cycle of AI Products

The Virtuous Cycle of AI Products, also called the "AI Flywheel Effect," is one of the most exciting ideas in Artificial Intelligence and it's also incredibly simple. Essentially, when AI technologies are integrated with a product properly, they create a feedback loop where the product continuously improves with use, generating more usage and a better competitive position relative to other products.

It looks like this:

  1. Product gets used, generating data
  2. Data from usage is fed into machine learning (or similar) models
  3. Models improve the product, generating more usage

Context

Any product tends to improve with usage regardless of its underlying technology because a good team will use qualitative feedback and analytics data to bring it closer in line with user needs. This improvement, though, tends to reach an asymptote where additional usage and data no longer provide much marginal insight to the product.

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