The Principles of Applied Artificial Intelligence

The 10 Principles of Applied Artificial Intelligence 1
The 10 Principles of
Applied Artificial Intelligence
How to Implement AI in Your Software Solution

Artificial intelligence (AI) is rapidly
moving out of the laboratory and into
business and consumer applications.

The result is a fundamental shift
in how software is built, and
what it’s capable of doing.

The 10 Principles of Applied Artificial Intelligence 4The 10 Principles of Applied Artificial Intelligence 4
Of course, the sort of AI
we mean isn’t the artificial
general intelligence
of science fiction.

Instead, we’re talking
about the specialized
uses of artificial
narrow intelligence
that are already being
used to power some
of today’s most
successful technology
businesses.

In this SlideShare, we’re going
to take a look at the principles
governing how that AI should
be applied to your business.

It’s important to point out that
applied artificial intelligence
(applied AI) has a lot in common
with applied analytics.

That’s because in some
ways applied analytics is
a subset of applied AI.

But our applied AI
principles extend the
applied analytics
model with…

ü An increased focus on
end-to-end process
automation.

ü Use of advanced machine
learning techniques.

ü Better articulated
performance objectives
and model accuracy.

ü Appropriate use of
human judgement and
automated predictions.

ü Continuous learning
and adaptation through
feedback loops.

ü Approaches to limit
the impact of model
error and bias.

But the biggest
difference of all may
be in its potential for
business impact.

While applied analytics helps optimize
processes, applied AI has the long-term
goal of total process automation.

With that context in
mind, let’s take a look
at the principles
of applied AI.

Understand all
processes.
1

As with analytics, it’s
important to start with
business processes.

That’s because applied AI has the
potential to affect every process
through augmentation or automation.

So consider customer, partner
and competitor processes, as
well as those you control today.

And even entirely
new processes that
weren’t possible before.

Document the decisions
and actions for each.

And understand which parts of the
process are driven by humans, and
which could be optimized, augmented,
automated or re-invented.

2Prioritize the most
valuable processes.

Look for opportunities within
your business to apply AI.
They could range from
improved insights through
to full automation.

Start with the desired outcomes that
would have the greatest impact
within your customers’ processes.

Recognize where in those
processes you’re short
on instrumentation or
integrations.

Then score potential opportunities
across various dimensions to
identify your top priorities.

Design frictionless
integrations.
3

Try to design solutions that
are as automated, natural
and complete as possible.

To do so, take an
outside-in approach.

Think about how your AI-enabled
process or solution will be
integrated (and used).

Then identify interdependencies
with other processes such as...

How other software
processes (or humans) will
access its capabilities.

Remember, the more integrated
your applied AI solution is,
the greater the value.

Integrate human
judgement as
required.
4

Understand the roles people
play in your solution.

They may be sources of data,
trainers, reviewers or users.

Having humans in the loop
may be a user, market or
regulatory requirement.

So even if full automation is
possible, human oversight
may be necessary.

Understand
performance
objectives.
5

Understand what model
performance levels are required
to add business value.

Model performance
includes error rates,
consistency and bias.

Determine the cost of improving
model accuracy vs. the business
impact of false predictions.

And avoid models where
the impact of false
predictions is greater than
the business can tolerate.

Match modeling
techniques to
requirements.
6

Artificial intelligence and
machine learning techniques
are changing fast.

Always aim for the simplest
model that can do the largest
part of the job…

And consider whether
explainability of the
model is a requirement.

Next, simplify processes using
decomposition as needed to
fit simpler models.

Then hire experienced
machine learning scientists
for optimal outcomes.

Test for and compare
model performance.

And have a plan for
evaluating new more
sophisticated models.

Capture relevant
data to support
your models.
7

Capture comprehensive and
diverse data from your target
business processes.

Then use this
experience data to
train your models.

Include input, context, action and
outcome data from end-to-end
processes in real time.

And derive general and
edge case experience data
using simulation techniques
if necessary.

Manage quality
for error and bias.
8

Machine learning is only fair
and objective if it’s free of bias.

Implement processes to monitor
for bias and other errors such as
inaccurate predictions.

Bias may come from
existing business
processes and their data.

And remember that any
human input into training
or evaluating AI predictions
could be biased itself!

Build fault tolerance
as part of model
integration.
9

Like all software, machine
learning models will have errors,
behave unexpectedly and
sometimes even fail.

And some machine
learning models will
change over time with
new data.

Automation will also
amplify the impact
of errors.

Traditional approaches
to error detection
and elimination are
inadequate.

So increase system
dependency through
fault tolerance.

• Rule-based circuit breakers
• Human and AI-based monitoring and audits
• Check-and-balance redundancy
• Mandatory human review
• Kill switches
• Least-harm actions
And consider techniques like:

Defend your legal
and ethical stance.
10

Understand and articulate
the overall benefits of AI
to your market.

Consider external factors
such as legislation as well
as customer sentiment.

Keep in mind that regulation
may restrict automation levels to
reduce risk and consequence.

Don’t gloss over the
replacement of human
effort with automation.

Instead, be open
and transparent.

Applied AI is already
fundamentally changing the
nature of work and life.

Understand all processes.
Prioritize the most valuable
processes.
Design frictionless integrations.
Integrate human judgement
as required.
Understand performance
objectives.
4
7
8
9
1
2
3
Match modeling techniques to
requirements.
Capture relevant data to support
your models.
Manage quality for error and bias.
Build fault tolerance as part of
model integration.
Defend your legal and ethical
stance.
Big, long-term strategic changes lie
ahead. To deal with them, remember
these principles:
6
5
10

Want to learn
more about
artificial intelligence?
Download our white paper on
the “10 Principles of Applied
Artificial Intelligence.”

The Principles of Applied Artificial Intelligence

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The Principles of Applied Artificial Intelligence