The                              in Artificial Intelligence

Missing Link

How to build an outcome-based intelligent business  

Outcomes > Promises

Most organizations are anxious over the fact that they have no idea how to start to solve their biggest challenges.

Many feel that they are unsolvable.

Companies are turning to AI to take on their challenges

Unfortunately,
AI, as it stands today, is flawed.

(Gartner)

And by potential we mean the redefining-entire-market-bending-your-business-curve-not-having-to-think-about-competition kind of potential. 

Yet, the potential is there.

Source: McKinsey Global Institute analysis

Front-runner breakdown, % change per cohort

Economy-wide output gains

Output gain/ loss from/
to peers

Transaction
costs

Capital expenditure

Total

82

122

-77

-18

135

Early adopters of AI will not just drive revenue. They will reshape their market. Competitors will not be able to keep up.

Early adopters of AI will not just drive revenue.

They will reshape their market.

Competitors will not be able to keep up.

Economy-wide output gains

Output gain/ loss from/
to peers

Transaction
costs

Capital expenditure

Total

Laggard breakdown, % change per cohort

11

-22

19

-4

-49

For laggards, this can cause an extinction event.

 

Followers will survive but fall behind.

Source: McKinsey Global Institute analysis

Front-runner breakdown, % change per cohort

Economy-wide output gains

Output gain/ loss from/
to peers

Transaction
costs

Capital expenditure

Total

82

122

-77

-18

135

Source: McKinsey Global Institute analysis

For laggards, this can cause an extinction event .
Followers will survive but fall behind.

Economy-wide output gains

Output gain/ loss from/
to peers

Transaction
costs

Capital expenditure

Total

Laggard breakdown, % change per cohort

11

-22

19

-4

-49

Why do we continue to allow AI programs to fail if they do not produce outcomes.

How are we allowing ourselves to get away with this?

production

…until now.

It’s because we didn’t know how to link

and

prediction

If you want your AI program to be successful, first address its missing link:

the ability to achieve outcomes.

Change the world,

don't just observe it.

Reshape Your Market...

...with Causal AI.

Causal AI is architected as a modern SaaS platform. It is delivered as a cloud service that is easy to deploy and get started with.

Reimagine Your Story.

Causal AI is architected as a modern SaaS platform. It is delivered as a cloud service that is easy to deploy and get started with.

Causal AI is architected as a modern SaaS platform. It is delivered as a cloud service that is easy to deploy and get started with.

Data

Data is the debris
of human activity

Cognitive

Behavior insights from the data to enhance the causal and predictive analysis

Data is the debris
of human activity

Data

Data is the debris
of human activity

Within the artificial world, grow prescriptive solutions designed to optimize outcomes.

Optimization-Based Prescriptions

 

Using causal data, we build digital twins of the problem, creating artificial model of the world

Digital Surrogate

That’s great, but what is it?

For AI enthusiasts and data scientists who want to see under the hood, causal AI is a key part of an AI-led digital transformation lifecycle.

Causality-Based Predictions

For AI enthusiasts and data scientists who want to see under the hood, causal AI is a key part of an AI-led digital transformation lifecycle.

Data

Data is the debris
of human activity

Cognitive

Behavior insights from the data to enhance the causal and predictive analysis

Causality-Based Predictions

Determine what data is causal to the business problem

Field Implementation

Implement prescriptive solutions, and collect new data

Within the artificial world, grow prescriptive solutions designed to optimize outcomes.

Optimization-Based Prescriptions

 

Using causal data, we build digital twins of the problem, creating artificial model of the world

Digital Surrogate

How is it different from other Data Science and AI platforms?

?

That’s great, but what is it?

AgileThought Causal AI 

How is it different from other Data Science and AI platforms?

Extensive data preparation and transformation

Manual build of functions and models 

Focuses on impact of individual variables

Higher structural complexity and time-consuming

Poor accuracy with high interpretability (linear regression, decision tree) OR High accuracy with poor interpretability (deep learning, neural nets)

Additional data / variables means back to drawing board

Other Data Science & AI Techniques and Platforms

Black box AI

Weeks and months to implement

Assumption free / unbiased modeling

Uses data in raw / as-is form

Custom model every time based on evidence in data

Analyzes group effect of variables (mutual information)

Significantly simpler and orders of magnitude faster than technologies like TensorFlow

Human-centric = high accuracy with high interpretability & actionability

AgileThought’s Causal AI Transformation

Adaptive and self-learning using feedback and new data / variable

Every single prediction can be traced and reasoned

Implementation in hours and days 

Build upon hypothesis / biases

?

AgileThought Causal AI 

How is it different from other Data Science and AI platforms?

Extensive data preparation and transformation

Manual build of functions and models 

Focuses on impact of individual variables

Higher structural complexity and time-consuming

Poor accuracy with high interpretability (linear regression, decision tree) OR High accuracy with poor interpretability (deep learning, neural nets)

Additional data / variables means back to drawing board

Other Data Science & AI Techniques and Platforms

Black box AI

Weeks and months to implement

  • Assumption free / unbiased modeling
  • Uses data in raw / as-is form
  • Custom model every time based on evidence in data
  • Analyzes group effect of variables (mutual information)
  • Significantly simpler and orders of magnitude faster than technologies like TensorFlow
  • Human-centric = high accuracy with high interpretability & actionability

AgileThought’s Causal AI Transformation

  • Adaptive and self-learning using feedback and new data / variable
  • Every single prediction can be traced and reasoned
  • Implementation in hours and days 

Build upon hypothesis / biases

?

Pilot and scale on your terms.

In other words, we…

start with outcomes

optimize the activities that achieve outcomes

Are always causally driven

Because it doesn't make sense to optimize correlations

Pilot and scale on your terms.

Because outcomes trump promises

Your pilot program will do most of the validation work. Once it reaches the level of proof of concept, we move to adoption, repeating the process in a growth capacity, and scaling, integrating into systems and processes to continue to work for you.

 

Once integrated causality requires minimal oversight to continue to add value.

 

Outcomes > Promises

Your pilot program will do most of the validation work. Once it reaches the level of proof of concept, we move to adoption, repeating the process in a growth capacity, and scaling, integrating into systems and processes to continue to work for you.

Once integrated causality requires minimal oversight to continue to add value.

 

I like the idea of changing the world. How can I start?

Causal AI 1-2-3: Move quickly from pilot to production

1 hour scoping

2 days of workshops including data engineering

3 weeks of pilot creation leading to a scaled solution