Here are seven things I think everybody should know before making any decisions regarding AI applications of any kind. by Andy Mura ExB Group More about AI and Intelligent Document Processing - https://exb.de/intelligent-document-processing/

1. before making any decisions.
Seven things CIOs and Software
Buyers should know about
Artificial Intelligence

2. Artificial
Intelligence
AI and ML applications keep
improving rapidly. This sector is
extremely dynamic and it's
sometimes hard to keep up with
innovation.
Here are seven things everybody
needs to know before making any
decisions regarding AI applications
of any kind.

3. Artificial
Intelligence is most
of the times just a
marketing gimmick
01
AI is becoming pretty much a buzzword. And since a
lot of people out there still struggle to properly define
the boundaries of this field, companies oversell the
capabilities of their platforms by throwing in trendy
terms as "AI" and "Machine Learning".

4. A system that doesn't learn over time is
not an AI application.
A system that makes decisions based on data (either collected by the system itself or
entered in the system manually) and adapts its features to this information has nothing to
do with AI.
Conditional functions and responses rely on intrinsic rules. And any rule-based system is
per definition not AI.
Systems that leverage AI learn autonomously over time, require a specific training
infrastructure, and adopt specific ML algorithms that govern the way the system learns.
Complex, unstructured, and unpredictable applications profit from AI because there's a
direct ROI from spending time training the system.
AI is a marathon not
a sprint solution!

5. There are different
ways to evaluate AI:
And most vendors
won't tell you the truth
02
When it comes to AI applications, many vendors and
buyers rely on the F-score but Intelligent systems are
developed in stages to increase accuracy throughout
the entire process. Only focusing on a single value for
a single stage that doesn't take into account post-
processing and quality assurance is a myopic way to
evaluate a system.

6. Accuracy needs to
grow over time
It's totally okay if an AI
applications doesn't spit the
right results 98% of the times
right out of the box.
AI is a long-term investment.
And the better the system is the
faster it'll learn.
Check how fast a system can
improve its accuracy, not how
accurate it allegedly is right
out of the box.
You need to measure the
overall score of the entire chain
after validation and verification
and see how potential post-
quality gate HITL (human in the
loop) stages are designed to
increase quality.
When comparing different ai
solutions, check the overall
level of accuracy of the entire
process.

7. When testing:
Don't compare
apples and oranges
03
When comparing different solutions, it's not smart to
assign the same tasks to two completely different
systems with different approaches.
If you compare performance of a rule-based system
with an AI-driven system right out of the box, the AI
system will probably loose.

8. Carry our tests based on the
capabilities of the systems you
compare and your actual needs
If you want to compare different solutions, don't stop at the
F-score (see previous point) and don't assign trivial tasks to actual
AI systems.
And most importantly: don't judge an AI system that hasn't gone
through a PROPER training process.
If you need an AI-driven system, it doesn't
matter how poorly it performs at the
beginning compared to a rule-based system.
Think long-term!

9. It's your
responsibility to
provide the right
training data
When you deal with AI vendors,
you'll be requested to provide real
samples of data that can be used to
train the system.
If your data is either too good or
irrelevant the system will never learn
how to process information.
04

10. If you train your system
with bad or
unrealistically perfect
data you'll never reach a
satisfactory level of
accuracy.
Provide training data that reflect the
reality. If the training data is too
perfect the system will learn how to
deal with best-case scenarios but
won't be able to cope with variance.
At the same time, don't throw in
random data that can't even be
interpreted by the system as this will
affect its training speed.

11. AI transformation
is not about
one-trick ponies
05
If you plan to implement an intelligent AI infrastructure
that truly transforms business operations across the
board, scan the market for a centralized training
environment that would allow you to process data
systematically for every process in your company based
on the task you want to speed up. Don't settle for
scattered single use case AI-driven API solutions.

12. Having access to a modular system that can tackle multiple use
cases, will immediately pay off as you won't have to manage
hundreds of providers for single micro tasks.
This will reduce both costs and maintenance efforts while
guaranteeing that you have full control over the models you're
using so that each model is 100% tailored to your own needs.
An AI system needs to adapt to your
processes. you shouldn't adapt your
workflows to accommodate an intelligent
solution. Invest in scalability and modularity
to maximize your ROI.

13. Process Automation
without AI makes
little to no sense
06
Process Automation applications might able to carry
out thousands of processes a minute, but if you need
to manually feed data into such systems, the result is
that you can only leverage a tiny portion of their
capabilities.

14. Intelligent Document
Processing (IDP)
Data extraction technologies and Intelligent Document Processing
Systems automatically extract and process information to feed
Robotic Process Automation (RPA), Business Process Automation
(BPA), ERP, or Input Management Systems to match the speed of data
processing with automation.
Combine process automation
technology with centralized data
classification and extraction to achieve
the highest level of output
performance.

15. Big Data without
intelligent data
management is just
"Big Waste" of cloud space
07
70% of data contained in modern data lakes consists
of completely unstructured data.
This means that, no human, no rule-based system
and no trivial data extraction technology can actually
derive any meaningful insights from it.

16. Invest in a centralized AI infrastructure
that allows you to make sense of any
kind of unstructured data to gather the
insights you need for a digital strategy.
Investing in Big Data means leveraging the power of information to generate
actionable insights, discover patterns and dependencies, categorize
information for R&D, and increase customer intimacy with precious
analytics.
Digital transformation without data management is like planning the perfect
racing strategy for a car without an engine. The strategy will only work on
paper.

17. Thanks for your Attention!
2021
ExB Group
Intelligent Document Processing
https://exb.de
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