DevLearn 2018 - Designing AR Experiences for Performance Support

@gowithfloat gowithfloat.com/ar ©2018 Float, LLC - all rights reserved, third party demos maintain copyright of their originators
Designing AR Experiences for
Performance Support
Session 610 - 3:00 PM Thu, October 25
Chad Udell
Managing Partner
chadu@gowithfloat.com

@gowithfloat gowithfloat.com/ar

About Me
● Managing Partner at Float, building great digital experiences for
Fortune 500 businesses and Federal Government customers
● Faculty for over 10 years at Bradley University in Interactive Media
● Awarded eLearning Guild – Guild Master in 2015
● Author of Learning Everywhere: How Mobile Content
Strategies are Transforming Training - Chad Udell
● Co-Editor and Author of Mastering Mobile Learning: Tips and
Techniques for Success - Chad Udell and Gary Woodill, Dr.
Ed.D.
● Currently preparing for publishing my next book, The Shock of
the New with Gary Woodill, planning for 2019 release
● Chronic early adopter

What are we talking about today?
● Advanced AR strategy
● What markerless AR is, and why it matters for Performance Support
● How to approach measuring AR performance support
● How businesses are solving big problems with AR solutions

We’re assuming you know about the
following…
What AR is… Augmented Reality, duh.
How AR is often being portrayed in entertainment and games… superficial,
simple. :-(
You’ve even tried creating a simple AR proof of concept, or at least dabbled a bit.
and...
How AR experiences of today seem to fall a bit short of expectations.

Maybe you’re interested in...
Understanding the intersection between AR, Machine Learning and Computer
Vision
Seeing the possibilities when these are combined for learning and performance
Talking about where all this is headed in the coming 5-10 years
Preparing yourselves and your teams for this revolution in Performance Support
Making your AR experiences more powerful and useful

AI
A Level Set on Terminology
AR
COMPUTER
VISION
AI: The field of adding cognitive services to a
computer system. There are many
subdisciplines.
Machine Learning: The process of making
an AI process “smarter” or better via statistics
and probability.
Computer Vision: The discipline of creating
image perception capabilities for a computer
program. Used as input to other processes
like Machine Learning enhanced search or
text translation.
MACHINE
LEARNING

So, let’s do
this.Let’s dig in and discover what’s going on...

So where is the disconnect?
From wow...
https://youtu.be/gefW8EC-ZCc

So where is the disconnect?
To wha!?!?
https://www.youtube.com/watch?v=Hv32V3EYauI

Why?
Tldr; Inserting digital items into reality isn’t easy. You need some help.

Marker based AR
AR markers are typically high contrast geometric,
rectangular QR-Code like badges that are printed
and then affixed to an item in order to trigger an AR
event.
Pros:
They work dependably
They anchor items to a spot
They are “old” tech
They don’t occur naturally
Cons:
They are usually pretty conspicuous
Integrating them into a workplace is time consuming
and logistically challenging
They don’t occur naturally

Markerless based AR
Without a marker, you need a smarter system.
Something with a more robust AI based approach to
Computer vision and understanding the world around
us.
Pros:
It looks great
It shows more potential for real-world interactions
It is “friction-less”
Cons:
It requires more setup, design and robust
development
It is not foolproof
It is “new”

Changing our PoV
We need better understanding of the value of markerless AR.

On Design
Why are we designing these
emergent technologies as
“training” tools?
Historical Training Observation:
Monolithic, multipurpose, product that
*might* work.
Emerging Technology Approach:
Small, sharp tools that do one thing,
very well.

On
Development
Why do we constantly look for
the easy way out?
Historical Training Observation: Is
there a Powerpoint converter for this?
Choose the right tool for the job!

On Intent
Are we here to teach or to
increase performance?
Historical Training Observation: We
need them to understand everything
about this process or tool.
Get them up and running ASAP. We
can fill them in later.

On Culture
Why provide them the cure,
when we can offer recurring
treatment?
Historical Training Observation:
They have to LEARN this tool, this
process, this thing.
Maybe they just need to learn that the
information source is there and
available for them when they need it.

An example - Retail Merchandising and Planograms
Business Problem Owner: “We have
employees that are having difficulty properly
adhering to planograms and merchandising
plans.”
Training Department Discussion: “We
need to get our employees on the same page…
some training on endcap merchandising is
needed.”
NEXT
Outcome: A 60 minute module that gives
the history of endcaps, including how
Marshall Field & Co. invented visual
merchandising, and how Dali and Warhol
both designed planograms.

An example - Retail Merchandising and Planograms -
Dumb AR
plans. The training didn’t work, can we use AR
to help?”
Training Department Discussion: “Great
idea, the employees will really be able to
visualize a properly merchandised endcap!
Outcome:

Better AR
plans. The training didn’t work, can we use AR
to help?”
Training Department Discussion: “Great
idea, the employees will really be able to
visualize a properly merchandised endcap in
the actual setting!”
Outcome: ¯_(ツ)_/¯

Best AR
employees that are having difficulty
properly adhering to planograms and
merchandising plans. The training didn’t
work, can we use AR to help?”
Training Department Discussion:
“Great idea, the employees will really be
able to visualize a properly merchandised
endcap in the actual setting and assess
their own work as they do it!”
Outcome:

Sci-fi?
Not necessarily! Let’s take a look at big problems getting solved with CV/AR.

Computer vision/AR
and Machine learning
REDUCES TIME ON TASK BY OVER 25%.
+ Error/Defect rate reduced by 50%
The AREA.ORG, TheVerge.com - Boeing (2016)
The fullest expression of the promise of the EPSS concept
introduced in the 1990’s by Gloria Gery.
Why? It’s instant information, in-situ, in-view, handsfree and relevant.
Every worker can be your best worker.

@gowithfloat gowithfloat.com/ar https://www.youtube.com/watch?v=TTnho9-i6dI

@gowithfloat gowithfloat.com/ar https://www.youtube.com/watch?v=XpfbGJ0pRwA

https://www.youtube.com/watch?v=4HCE1P-m1l8 https://www.youtube.com/watch?v=MtQmqaSxszU

@gowithfloat gowithfloat.com/ar https://www.youtube.com/watch?v=z5HOHNECW20

https://www.youtube.com/watch?v=qMEJnYKpwog

https://www.youtube.com/watch?v=RAJ6NqSZ31g

Float has a Machine Learning enabled Computer
Vision offering ready to add visual search to your
apps:
Key Features
1. Visual Search Based on Markerless CV
2. Analytics
3. Data Capture App / Machine Learning Training
4. Continuous Learning Model
Visual Product
Identification (VPI) Engine
Float Machine Learning Team

But how to do
this?
Breaking out of the Marker Based AR Demo World

Key Steps
Process, deliverables, activities.
KPIs and Goals
Task Analysis and Task Flow Diagrams
Gap Analysis of Task Flow
Environmental Analysis
Device Identification and Testing
IDE and Toolkit Identification
Software Design and Development
Iterative Deployment and Review

More About Task Flows
and Building Great AR
This is a vital first step.
Can you diagram the task that you
want to augment or improve?
Do you know where the task flow is
breaking down?
Where it is breaking down… Is it a
tough step? What are the constraints?
Is it time, complications, poor
person/skill match?
Understand the “Why?” of it.

Let’s Talk Tools
There are many ways to get this done.

Many many AR tools abound.
There is no single best tool.
There is only the best tool for the
job.
Even that is sometimes debatable.
Bottom line is, download demos,
attempt to build PoCs, try
alternatives, leave time for
experimentation.
Understand that the right tool now,
may not be the right one 6 months
from now.

Nearly as many AI tools are around.
This space is moving very quickly.
SaaS may be an option for you. Look
for ones that integrate with your
development tools and process.
Training and maintaining the models
are the hardest part to get your head
wrapped around.
Sometimes you may not know
“exactly” what is going on.
Opt for simple first. Extend and
enhance once you get things going.

Measuring it
Tools, approaches, DIIRDA, more...

It’s not just a technology consideration
It’s less about the “how” (which
tool?)
And more about the “what and
why” (activities and data points)
Aim for scalability, utility and
compatibility
Tie activity in your experience to
activity in other systems of record.
Eg. Don’t measure assessments
in AR – Rather, let the actual
work/task be the assessment.

What’s Next?
Build smarter AR solutions… one task at a time.

Take These
Tips
Contact me if you have
questions or comments.

q&a
Follow up with me at Booth #113.
@visualrinse • chadu@gowithfloat.com • @gowithfloat

DevLearn 2018 - Designing AR Experiences for Performance Support

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DevLearn 2018 - Designing AR Experiences for Performance Support