While AI has potential, there is often a gap between expectations of what AI can do and its actual ability to produce outcomes. Causal AI aims to address this missing link by using data engineering, digital twins, and machine learning to intervene and achieve outcomes, such as growing clients or increasing revenue, rather than just observing or predicting them. This represents a fundamental shift from traditional AI approaches of collecting data, running analytics, and making predictions without accountability.

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

2. Many feel that they are unsolvable.

3. To explore (or exploit) this phenomenon, lots of companies
claim that AI is the savior of all problems.

4. They’re partly right.
But AI has had a long way to go…

5. B Over the last 5 years, there has
been a fundamental shift in
thinking about data. It has moved
from being considered the tactical
cost of doing business to a
strategic asset for revenue growth.
A…There has been a fundamental shift in thinking about data.

6. But this is only half of the equation.

7. There is a missing link…between the expectation of AI,
and its ability to produce outcomes.

8. Right now, there is a 54% gap between AI pilot and
production. 75% of companies will continue to invest in AI,
but only 21% will move out of an exploratory phase into
getting value from their AI investment. (Gartner)
…between the expectation of AI, and its ability to produce outcomes.

9. Yet, the potential is there.
And by potential we mean the redefining-entire-market-
bending-your-business-curve-not-having-to-think-about-
competition kind of potential. Early adopters of AI for driving
revenue will not just see growth. They will reshape their
market.

10. But if we’re only seeing the potential of AI, and it’s not
producing outcomes, why do we continue down the same
path? Why do we allow our AI programs to get away with
this?

11. It’s because we didn’t know how to link prediction and
production…until now.
In the past, AI was “collect data, run some analytics, and
make predictions.” Some of those predictions, themselves,
were valuable, but lacked accountability. Today, AI market
opportunity will be driven by the distance between
expectations and actual deployment.

12. If you want your AI program to be successful, address its
missing link: The capability to achieve outcomes.

13. Intervene to grow clients, intervene to get more revenue,
intervene to save lives.
It’s Time We Intervene

14. Reimagine Your Story. Reshape Your Market with
Causal AI.

15. Outcomes are changed instead of just observing or
predicting them (and creates a fundamental shift in value)
through data engineering, digital twins, and machine
learning.
Causal AI provides a path to action.

16. For AI enthusiasts and data scientists who want to see
under the hood.
That’s great, but what is it?

17. The AgileThought method: We start with a pilot, prove the
model, and integrate it with your systems – no need to build a
new AI tech stack from scratch.
Now, let’s operationalize this puppy:

18. Now, let’s operationalize this puppy:
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.
The best part? Once integrated causality requires minimal oversight
to continue to add value.

19. Causal AI 1-2-3: Move quickly from pilot to production
1-hour scoping call
2-day-long workshops including data engineering
3 weeks of pilot solution creation leading to a scaled solution
I kind of like the idea of changing the world. How can I start?