Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal

Consumer data is extremely valuable because it gives prediction machines data about these preferences.

As long as enough people keep their sexual activity, financial situation, mental health status, and repugnant thoughts to themselves, the prediction machines will have insufficient data to predict many types of behavior.

“known unknowns,” rare events that are difficult to predict due to lack of data,

“unknown unknowns” are, by definition, difficult to predict or respond to.

“unknown knowns.” For example, we discussed the challenges of deciding whether to recommend this book to your friend, even if you become fabulously successful at managing AI in the future.

First, it needed to figure out what “best” meant: short-term revenue or something longer term?

Second, it needed to choose a specific price.

KEY POINTS

KEY POINTS

because prediction is a key input to decision making.

no manager is going to achieve large gains in productivity by just “throwing some AI” at a problem or into an existing process.

AI is the type of technology that requires rethinking processes in the same way th...

The actual implementation of AI is through the development of tools.

“the task.”

Tasks are collections of decisions

Decisions are based on prediction and judgment and ...

FIGURE 12-1

Sometimes we can automate all the decisions within a task. Or we can now automate the last remaining decision that has not yet been automated because of enhanced prediction.

AI tools can change work flows in two ways.

First, they can render tasks obsolete and therefore remove them from work flows. Second, they can add new tasks. This may be different for every business and every work flow.

KEY POINTS

In deciding how to implement AI, companies will break their work flows down into tasks, estimate the ROI for building or buying an AI to perform each task, rank-order the AIs in terms of ROI, and then start from the top of the list and begin working downward.

(often referred to as “artificial general intelligence” or AGI, or “strong AI”).

But how should you decide whether you should use an AI tool for a particular task in your business?

prediction, input, judgment, training, action, outcome, and feedback.

The AI canvas

ACTION: What are you trying to do?

PREDICTION: What do you need to know to make the decision?

JUDGMENT: How do you value different outcomes and errors?

OUTCOME: What are your metrics for task success?

INPUT: What data do you need to run the predictive algorithm?

TRAINING: What data do you need to train the predictive algorithm?

FEEDBACK: How can you use the outcomes to improve the algorithm?

FIGURE 13-2

The AI canvas for Atomwise

KEY POINTS

Tasks need to be decomposed in order to see where prediction machines can be inserted.

The AI canvas is an aid to help with the decomposition process.

Fill out the AI canvas for every decision or task.

training, input, and feedback.

At the center of the AI canvas is prediction.

You need to identify the core prediction at the heart of the task, and this can require AI insight.

“What is our real objective, anyhow?”

Prediction requires a specificity not often found in mission statements.

we need to specify what “best” means

evaluating entire work flows,

breaking down the work flow into constituent tasks and seeing whether you can fruitfully employ a prediction machine in those tasks. Then, you must reconstitute tasks into jobs.

automation that eliminates a human from a task does not necessarily eliminate them from a job.

KEY POINTS

job is a collection of tasks.