Did you know 85% of AI projects will fail because of a lack of training data? Before investing time and money in machine learning, discover 4 critical requirements every company needs to employ in order to build effective machine learning applications and bring intelligence to artificial intelligence.

1. 4 Critical Requirements for
Building Truly Intelligent AI Models
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2. The market for AI is
expected to reach
$191Bby 2024*
However…
85%AI projects will fail*
*Gartner, 2019 #DATAFORAI

3. Common Challenges
Sinking Most AI Projects
• Scarcity of semantically enriched data -
structured or unstructured
• Lack of clean, accurate, quality data
• Inability to interpret the “right data” to
answer specific business questions
• Insufficient resources of subject matter
experts to interpret and annotate data
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4. Data-related challenges
are a top reason (our)
clients have halted or
canceled artificial-
intelligence projects.
Arvind Krishna
SVP, Cloud & Cognitive Software
Companies pursuing AI projects
generally lack an expert
understanding of what data is
needed for machine-learning
models and struggle with preparing
data in a way that’s beneficial to
those systems.
Michele Goetz
Principal Analyst
You can’t feed the
algorithms if you don’t have
data. Solid, clean data in
large volumes, well-tagged
and well organized is
crucial.
Michael Conlin
Chief Data Officer
CHALLENGES ECOHED ACROSS THE INDUSTRY
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5. R A H U L S I N G H A L
C H I E F P R O D U C T O F F I C E R
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“Bringing business rules into
ontologies and taxonomies are
critical to solve AI challenges.

6. The Key to AI Success:
Annotation and Labeling
• 80% of AI project time spent on data preparation*
• Companies spend 5X as much on internal data labeling than
with 3rd parties*
• Annotation and labeling is essential for training AI and machine
learning; it’s what makes them truly intelligent
• Even small errors could prove to be disastrous, therefore
human-annotated data is essential
• Humans are simply better than computers at managing
subjectivity, understanding intent, and coping with ambiguity
*Cognilytica, 2019
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7. 4WAYS TO BRING
INTELLIGENCE TO AI
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8. Having access to the right raw data set has
proven to be a critical factor in piloting an AI
project. Raw data is information that has typically
not been processed or analyzed and is routinely
considered inoperable. But deeper analysis can
yield opportunities to turn raw data into useful
insight.
01
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RAW DATA

9. Ontologies play a critical role in machine learning.
According to the Wikipedia definition, ontologies are
"formal naming and definition of the types, properties,
and interrelationships of the entities that really or
fundamentally exist for a particular domain of discourse."
In other words, ontologies give meaning to things.
Think of this as teaching your AI to communicate using a
common language. It is critical to identify the problem
statement and understand how AI can interpret data to
semantically solve a certain use case. The need for out-of-
box ontologies or availability of client ontologies that can
be used as the basis to form the data labeling is critical.
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ONTOLOGIES

10. Annotation (also known as data labeling) is quite critical to
ensuring your AI and machine learning projects can scale. It
provides that initial setup for training a machine learning model
with what it needs to understand and how to discriminate
against various inputs to come up with accurate outputs. There
are many different types of data annotation, depending on what
kind of form the data is in.
It can range from image and video annotation, text
categorization, semantic annotation, and content
categorization. Humans are needed to identify and annotate
specific data so machines can learn to identify and classy
information. Without these labels, the machine learning
algorithm will have a difficult time computing the necessary
attributes. How data is annotated and labeled brings us to our
next and most crucial requirement: subject matter expertise.
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#DATAFORAI
ANNOTATION

11. Our clients have learned how important it is to have subject
matter experts (SME) that understand their specific industry and
complex needs. This goes back to the need for annotated data. If
there are even slight errors in the data or in the training sets
used to create predictive models, the consequences can be
potentially catastrophic. That’s why the need for specific domain
expertise is so crucial, and why human knowledge still plays a
pivotal role in artificial intelligence.
For example, being able to interpret complex legal obligations
and agreements from ISDA contracts require legal specialists
that can identify and label the most appropriate information.
The same goes for other fields like science and medicine where
deep understanding and fluency of the content cannot be taken
for granted.
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#DATAFORAI
SUBJECT MATTER
EXPERTS

12. #DATAFORAI
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