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.
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
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?
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
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
AgileThought’s Causal AI Transformation
Build upon hypothesis / biases
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
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