Download The Road to Enterprise AI

Issue 2
1 The Road to
Enterprise AI 1 2084
2 Shopping for
Enterprise AI The Road to Enterprise AI
3 Automation
through AI
4 Introducing
RAGE-AITM
5 Two Key RAGE-AITM
Innovations
5 The Significance of
Context, Language,
Reasoning 6 AI Survey Results
6 AI in the Enterprise
9 From the Gartner Files:
Predicts 2017: Artificial
Intelligence
17 About RAGE
Frameworks The Road to Enterprise AI
2084
Gartner makes the following observations in
its “Predicts 2017: Artificial Intelligence”
research note.
1. By 2019, more than 10% of IT hires in
customer service will mostly write scripts for
bot interactions.
2. Through 2020, organizations using cognitive
ergonomics and system design in new artificial
intelligence projects will achieve long-term
success four times more often than others.
3. By 2020, 20% of companies will dedicate
workers to monitor and guide neural networks.
4. By 2019, startups will overtake Amazon,
Google, IBM and Microsoft in driving the
artificial intelligence economy with disruptive
business solutions.
5. By 2019, artificial intelligence platform
services will cannibalize revenues for 30% of
market-leading companies.
In the same research note Gartner also provides
a set of very helpful guidelines around navigating
these early days of the resurgence of AI, and
driving change strategically and methodically,
outside of the hype zone.
Wisely, Gartner’s predictions go out only three
years.
On 25-Jan, 2017, sixty eight years after it was
published, George Orwell’s dystopian 1984
became the number one best-selling book on
Amazon.
Orwell’s Big Brother, omnipresent government
surveillance, Ministry of Truth that dispenses lies
or “alternative facts”, society at perpetual war,
however dark, can still be conceptually grasped,
even if not understood. Extrapolating current
reality to paint a picture of the future so far
2
“How do I apply
technology to
solve my business
problem quickly,
economically and
permanently (in
a manner that is
responsive to future
change)?”
away cannot possibly be easy, but may not have
seemed impossible at the time. Couple that with
great creativity and outstanding storytelling, and
you had a bestseller then and now again, more
than half a century later albeit a few decades
behind schedule. Importantly, Orwell did not
need to involve technology to paint the 1984
he imagined and did not have to deal with the
exponential nature of the expansion and influence
of technology.
Our observations over the past couple of years,
which is also backed by several industry watchers
and analysts, is that Business and Technology
leaders tend to ask questions along the following
lines, as they explore the market to seek out AI
driven competitive advantages for their companies:
Fast forward to today and imagine writing the
sequel “2084”. Even the bravest of authors and the
farthest seeing crystal ball gazers would admit that
is simply not possible. The forces that our runaway
technological evolution will unleash by 2084 are
way beyond the predictive powers of any budding
Orwell, Huxley or Nostradamus today.
• What AI technology should I bet on?
That however is not holding back the hype
machinery. Futurists, analysts and even scientists
in small numbers, are all contributing. Anything
is plausible in our post science fiction world.
Celebrity entrepreneurs are taking pot shots with
under-researched personal views, such as declaring
with conviction that we may all be living in a
massive computer simulation! But even as the fine
line between Artificial Intelligence and the Real
Intelligence of sentient beings continues to get
blurred, how imminent is this brave new world and
what should we do in the meantime?
For those of us in the business of Business
Technology, we will have to learn to do our thing
against the backdrop of the hype and outrageous
predictions and yes, some remarkable technology
breakthroughs. How does one, distill fact from
everything else, identify and apply emerging
technology to run our Enterprises more
effectively and efficiently?
In this article we will explore a simple and
effective approach to shopping for Enterprise AI
in this tricky emerging market, by first zeroing in
on the right questions. This will help us arrive at a
set of principles and guidelines to map the Road
to Enterprise AI for the next 5-10 years. Guidelines
that we believe will become the basis of most
Enterprise AI choices and selection during this
period. We will also introduce a solution built on
these principles that is already deployed in several
Fortune 100 firms today.
Shopping for Enterprise AI
What are the characteristics of Business Technology
and AI we need to run the Enterprise today?
• How do I sift through all the emerging AI
technologies and the obtuse jargon of AI? How
do I understand my options?
• Who does AI for the Enterprise? I read about
self-driving cars, computers beating Gary
Kasparov, and see Bob Dylan vouching for
IBM’s Watson’s ability to create music, but I
don’t need that to manage internal operations
of my firm or design and manage customer
experience.
• Everyone else seems to be deploying AI. Am I
getting left behind?
All of these questions are AI centric. It presupposes
the answer. Artificial Intelligence, the ultimate magic
bullet of machine learning, deep learning, NLP, NLG,
etc. The proverbial hammer looking for a nail. These,
dear Business Leader, are “wrong questions”. You
are suffering from a case of AI and Machine Learning
bandwagon effect. And you are not alone.
The right question to ask, it seems to us, is the
one you have always asked, “How do I apply
technology to solve my business problem
quickly, economically and permanently [in a
manner that is responsive to future change]?”
Try the following 10 questions. They are designed
to help organize our technology shopping approach,
protect us from hype, and at the same time leverage
the best that technology has to offer today.
1.
What is the business opportunity or problem I
am trying to address, or how do I discover the
real problem or opportunity I should be going
after?
…Growth, cost, compliance, customer
satisfaction, responsiveness?
2.
What is the business process transformation
that will solve my business problem?
…Automation of knowledge work, straight
through processing, automation of high
variability processes, legacy modernization,
others?
3
3.
Is there any emerging technology out there
today that has solved similar problems?
…Is it proven, is it deployed, who are the
clients, what are they saying? What are
analysts saying?
4.
Can my problem be solved completely /
fundamentally?
…I am not interested in temporary, partial,
band aid solutions like Robotic Process
Automation (RPA) or side-stepping the core
process challenges by simply outsourcing my
problem
5.
Can my problem be solved within 90 days, so
I can realize outcomes quickly?
…I am looking for true agility not IT
development philosophies like Agile. In my
experience Agile and ideas like fail-fast take
the exact same time as traditional methods
to get to ultimate business outcome.
6.
How do I build a perpetually flexible and
extensible solution that is responsive and can
adapt rapidly to change?
….I don’t want to revisit this again and again
as soon as something changes. Rather, I
would like to build further, go deeper, broader
or do things differently. I want to start small
and extend, expand and iterate through new
use cases at my pace. All without much fuss.
7.
I need complete auditability. A traceable
solution.
….Black boxes are not acceptable in my
business. I need a full audit trail.
8.
The solution needs to fit in seamlessly, nonintrusively, into my technology environment.
…. I need to preserve/leverage the
investments I have already made.
9.
Can a single technology platform solve my
problem end to end?
…Don’t push the burden of completing
incomplete solutions on me. I don’t want to
have to choose from an inventory of systems
and deal with integration problems. I want
everything required to solve my business
problem, like workflow systems, ETL,
analytics systems, NLP, machine learning,
etc. in the same platform?
10. How should I apply new and emerging
technologies, like Machine Learning and AI?
Explicit AI questions are at the end of the list. AI
is an enabler not the destination. “AI enabled
Automation” or “Automation through AI” puts this
is perspective, i.e. the fundamental goal is still
intelligent automation.
Automation through AI
Everything that can change will: decisions,
organization structures, regulations, business
requirements, technology standards, data
standards, IT project priorities, internal systems,
external systems. These are the new table stakes.
And complexity will continue to increase. And the
rate of change will continue to increase.
So whatever approach we adopt going forward
has to start with the idea of Flexibility or
Responsiveness. The flip side of the Flexibility
coin is Speed…slow flexibility is the same as
unresponsiveness. We need both Speed and
Flexibility.
The primary driver of inflexibility and sluggishness
in the world of Business Technology today is in the
very method we use to solve automation problems.
The software development lifecycle [SDLC] that we
have had to adopt since the beginning, still stands
in between business need and realized solutions,
separating these events by 12-24 months and
delivering rigid inflexible solutions. There are many
levels of translation from idea to solution, multiple
hand-offs to different specialists, including
programmers, elaborate testing at every step. And
the whole cycle has to be repeated with every
requirement change or fix, making the process
fundamentally slow and inflexible.
The antidote for this decades-long, fundamental
and industry wide shortcoming is a modeldriven automation framework where enterprise
applications can be assembled near real time,
where the distance from idea to realization is a
one-step journey, no translations, no programming.
No new code required or generated. All
applications, big or small, complex or simple, that
is built on a fully model driven platform will add
no lines of code. All business logic resides as
metadata.
“The flip side of
the Flexibility
coin is Speed…
slow flexibility
is the same as
unresponsiveness.
We need both
Speed and
Flexibility.”
4
The next task would be to ensure this modeldriven enterprise application development
platform, is broad enough to address and
contain most process automation activities
from data ingestion, processing, output generation
and distribution, managing work orchestration
and distribution, etc. And assembling a process
automation solution should be a simple visual
modeling exercise, requiring no specialized skills,
to ensure easy and fast development and rapid
implementation of changes.
“The RAGEAITM platform
is a pioneering
platform, driving
the emergence
of intelligent
businesses.”
Finally, we need to integrate AI and machine
learning capability, and deep learning technology
that is fully traceable. If we could pull all of this
into a single platform we would have a very strong
affirmative response to every question on the
shopping list above.
Such a platform would be ideally positioned to
enable Automation-through-AI, Analysis-andTransaction Processing, Learning-and-Execution,
Work-and-Workflow, Speed-and-Flexibility, all on
the same platform.
FIGURE 1 RAGE-AI Platform
Source: RAGE Frameworks
Introducing RAGE-AITM
RAGE-AITM, from RAGE Frameworks, was designed
and built on the principles above. It enables
real time AI powered Enterprise Application
development. It is designed to solve broad end-toend mission critical business problems quickly and
flexibly. This is accomplished via a set of twenty
building blocks or engines that intelligently
automate all/most business processes in their
entirety. Solving a process automation problem in
its entirety is the key. Solving a part of it would be
very sub-optimal, as stepping out of the solution
assembly framework would involve programming
and the traditional SDLC and the speed and
flexibility advantages of a no-code model driven
paradigm would be lost.
The RAGE-AITM platform is a pioneering platform,
driving the emergence of intelligent businesses.
It is business process centric from the ground
up. The platform fosters a business process
oriented thinking. Because processes can be
institutionalized in and executed by intelligent
machines, it makes the whole debate around
functional vs process oriented organizational
design somewhat moot. You can be both.
5
Intelligent machines will be the process glue in
the organization.
intelligence’ and integrate the insights with the rest
of the business in a systematic and meaningful way.
Two Key RAGE-AITM Innovations
Most work on Big Data analytics today however
relies on computational statistical methods
and produces ‘black box’ solutions. While such
approaches are likely to suffice for homogeneous
data sets, for many real world issues, we need
to be able to trace the reasoning that machines
find and deploy. So executives can understand
‘why’ and ‘how’. Black boxes won’t suffice. RAGE
AITM has pioneered deep learning technology
based on linguistics and is attempting to elevate
natural language processing to natural language
understanding.
The RAGE-AITM platform embodies two key
technology innovations – zero-code, model-driven
software development using highly abstract
components, and traceable machine learning.
Zero-code Model-driven Flexible Software
Development:
Using RAGE-AITM, the entire software application
development lifecycle can be reduced to a
modeling exercise using an extensive array of
abstract components. The platform has 20 abstract
components that together enable the rapid, data
driven implementation of end-to-end business
processes including complex, knowledge intensive
tasks. Entirely new applications can be assembled
rapidly and modified at will2.
Using the platform, we can also create flexible
software frameworks. These frameworks are a
complete skeleton of a certain set of business
processes and can be configured at any level
of granularity to suit a specific enterprise. For
example, the commercial lending framework
modeled on top of RAGE-AITM is one such
framework. It provides a default set of business
processes out of the box but enables every aspect
of those business processes to be configured by
clients without any programming. Zero-code,
model-driven software development is a key
innovation which will unleash a significant number
of application level innovation in the years to
come. The benefits accrue from the model driven
architecture that such platforms will rely on.
Traceable Machine Learning
There have been huge strides in the use of AI to
analyze the vast amounts of digital information
that has become available today. The excitement
here has to do with the ability to analyze large
data sets and let the machines discover patterns in
the entire population instead of making arbitrary
assumptions about how the data might behave in
the real world. In addition to analyzing complete
data sets instead of small samples of data, the
ability to analyze unstructured text, offers an
exciting opportunity for enterprises to become
‘intelligent’. They can systematically automate
knowledge-intensive processes like ‘competitive
Intelligent machines built on RAGE-AI™ perform
two functions: provide insight to humans to
enable the optimal design of intelligent business
processes through continuous analysis of vast
amounts of data, and automatically execute
business transactions without human assistance
based on an automated business process designed
by humans. Using AI methods, machines will
acquire knowledge from continuous analyses of
vast amounts of data. Such knowledge and insight
will be provided to humans to create/refine their
designs.
Several industry-first Intelligent Machines or
solutions built on RAGE-AITM have been deployed
in Banking, Consulting, CPG, Manufacturing, High
Tech and Insurance industries.
The tag line for RAGE Frameworks Inc., “It’s
Possible”, attempts to say in a couple of words,
what has taken years of continuous innovation to
produce.
The Significance of Context,
Language, Reasoning
Today, artificial intelligence (AI) is rapidly
emerging out of R&D labs and into the
mainstream. Smart technologies are changing
every aspect of our lives, from the way we work, to
health care, education, travel, and transportation.
One example: the self-driving cars produced by
Google and Tesla. There are also many successful
applications in the computer vision space.
But what about the non-vision applications of
AI: that is, areas including non-spatial data –
most importantly, text and numbers? Because of
AI’s revolutionary potential, its applications in
“In addition
to analyzing
complete data
sets instead of
small samples
of data, the
ability to analyze
unstructured text,
offers an exciting
opportunity
for enterprises
to become
‘intelligent’.”
6
non-vision problems have attracted tremendous
interest. There have also been attempts to
replicate what worked with spatial data and
apply it to text (and numbers)… a blind rush of
computational, statistically based approaches
to process natural language. Such approaches
attempt to turn text into data and then look for
deep patterns in that data.
“AI technologies
must overcome
three challenges
to be successful
in the non-vision
world (and
perhaps even in
the vision world):
language, context,
and reasoning.”
AI technologies must overcome three challenges
to be successful in the non-vision world (and
perhaps even in the vision world): language,
context, and reasoning.
A recent MIT Technology Review article, “AI’s
Language Problem,” eloquently points out the
first challenge. Today’s AI technologies, including
those IBM Watson and Google Alpha Go, struggle
to process language the way that humans do.
That’s because the large majority of the current
implementations approach text as data, not as
language. They apply the same techniques that
worked on spatial data to text.
FIGURE 2 AI in the Enterprise
The second challenge—understanding context—is
related to the language problem, but is sufficiently
significant that it should be thought of as an
independent issue. Natural language text needs to
be processed in the right context. The right context
can only be developed if the technology focuses
on the language structure, not just on the words
in the text, as most current technologies seem
to be doing, according to a 2014 article in IEEE
Computational Intelligence Magazine. Then there’s
the third challenge: the traceability of reasoning
that the solution deploys to reach its conclusion.
Various technologies are attempting to address
all three challenges today. Several successful
enterprise AI solutions deal with language,
context, and reasoning transparency effectively.
To address the language challenge in AI, we have
to understand the language by using its linguistic
structure and the principles we have learned to
express our thoughts. A deep understanding of the
linguistics structure in text would involve applying
several principles from computational linguistics
to decompose the text back into the concepts
and verbiage used to connect them in the text in
context. This is essentially reverse-engineering the
text back to its fundamental ideas to understand
how those ideas were connected together to form
sentences and paragraphs.
RAGE-AITM applies deep linguistic learning and
natural language understanding to interpret text
in its context and keeping the reasoning visible
at all times. With the adoption of deep linguistic
learning, we can maintain full and complete
visibility to the reasoning.
AI Survey Results
RAGE conducted a survey with a targeted group to
get to the heart of what senior leaders would value
most in the AI solutions that they are looking
for. The survey included 132 senior business
executives, 67% of whom were C-level execs,
including CEOs, COOs, CIOs, and CTOs. 80% of the
respondents were from companies with greater
than $1 billion in annual revenues.
The survey topic was “Can artificial intelligence
deliver for today’s enterprise”.
Source: RAGE Frameworks
The response to the question “What capabilities
are important to the AI solutions you would invest
in?” are shown below in Fig 2. Not surprisingly
7
Context, Language and Reasoning, bubbled up
to the top.
The AI shopping list and RAGE-AITM
1.
What is the business opportunity or problem I
am trying to address, or how do I discover the
real problem or opportunity I should be going
after?
…Run AI enabled rapid diagnostics to zero in
on areas of biggest opportunity, where they
are not already obvious.
2.
What is the business process transformation
that will solve my business problem?
….Once you have a response to question #1,
run further analysis on the process, using
RAGE-AITM to determine how best to solve the
process problem
3.
Is there any emerging technology out there
today that has solved similar problems?
….Yes. With broad deployment dealing with
leading edge AI challenges.
4.
Can my problem be solved completely /
fundamentally?
AI in the Enterprise
RAGE has a set of widely deployed and ready-todeploy intelligent machines built on RAGE-AITM,
providing Automation through AI in the Banking,
Consulting, CPG, Manufacturing, High Tech and
Insurance industries. See Fig 3. In addition, a
broad range of custom solutions have also been
developed on RAGE-AITM.
As an example, RAGE’s Wealth Management
solution, LiveWealthTM, is currently being used
by 3 of the top 5 global wire-houses for servicing
their high net worth and institutional clients. This
Intelligent Machine has three core modules. One that
aggregates data for assets held away with hundreds of
custodians, automatically dealing differences in form
and format in which the data is sent, often on paper.
The second is a performance analytics and flexible
reporting module. The third module, Active Advising,
has five intelligent agents that support the advisor
and enable them to be more effective at their job. One
of these intelligent agents for example continuously
monitors portfolio performance and alerts the advisor
on specific actions. FIGURE 3 RAGE Intelligent Machines
Source: RAGE Frameworks
“RAGE-AI applies
deep linguistic
learning and
natural language
understanding
to interpret text
in its context
and keeping the
reasoning visible
at all times.”
8
….Yes. AI driven intelligent and deep
automation, work and workflow automation.
10. How should I apply new and emerging
technologies, like Machine Learning and AI?
5.
Can my problem be solved within 90 days, so
I can realize outcomes quickly?
….Yes. Enterprise scale mission critical
solutions have been built on RAGE-AITM and
deployed in less than 90 days.
6.
“RAGE-AITM has
industry leading
AI technology
built in. It
addresses most
of the challenges
of prevalent AI
technologies
today in the
context of
business
processes.“
How do I build a perpetually flexible solution
that is responsive and that can adapt rapidly
to external and internal changes?
….All solutions built on RAGE-AITM are
fundamentally flexible. All process
configuration and behavior are stored as data,
easy to set up and easy to change, at any
time.
7.
I need complete auditability. A traceable
solution.
….All analysis RAGE-AITM is fully traceable
to source and all solutions come with a
complete audit trail.
8.
The solution needs to fit in seamlessly, nonintrusively, into my Technology environment…
…RAGE-AITM solutions can be introduced
non-intrusively. For a legacy modernization
exercise for example, RAGE-AITM solutions
can be introduced in the cloud, or inside the
client’s firewall, while retaining all systems of
record, which can be gradually integrated into
and/or replaced by the RAGE solution at the
pace desired by the client.
9.
Can a single technology platform solve my
problem end to end?
….Yes. The twenty engines of RAGE-AITM
is broad enough to address most business
process automation problems.
….RAGE-AITM has industry leading AI technology
built in. It addresses most of the challenges of
prevalent AI technologies today in the context
of business processes.
The technology, the approach and the proof points
above are a good representation of the state-ofthe-art “AI in the Enterprise”
While perhaps less exciting than drones delivering
Amazon packages and driverless cars delivering
take-out, the state-of-the-art AI in the Enterprise
is now enabling us to automate things that were
previously written off as automation proof.
So if one were to write a futuristic book today, not
as a sequel to Orwell’s 1984, but as a tentative
peek into the future of Business Technology, and
if it were titled 2025, then we could hazard a
guess that it will include several of the elements
described here.
By 2025 the execution of processes and
enterprises would be largely automated. They
would be run on AI technology that simulates the
nuances of human behavior [the sledgehammer
of statistical algorithms would be relegated to
lesser problems], that can understand language
and context, a framework that can solve big
problems in their entirety. As several Fortune
100 companies using RAGE-AITM are already
experiencing.
Source: RAGE Frameworks
9
From the Gartner Files:
Predicts 2017: Artificial Intelligence
Artificial intelligence is changing the way in
which organizations innovate and communicate
their processes, products and services. Practical
strategies for employing AI and choosing the right
vendors are available to data and analytics leaders
right now.
Through 2020, organizations using cognitive
ergonomics and system design in new artificial
intelligence projects will achieve long-term
success four times more often than others.
By 2020, 20% of companies will dedicate workers
to monitor and guide neural networks.
Key Findings
• Chatbots driven by artificial intelligence (AI)
will play important roles in interactions with
consumers, within the enterprise, and in
business-to-business situations.
By 2019, startups will overtake Amazon, Google,
IBM and Microsoft in driving the artificial
intelligence economy with disruptive business
solutions.
• Smart machines need to be properly set up,
maintained and continuously governed if they
are to be of maximum benefit to the enterprise.
By 2019, artificial intelligence platform services
will cannibalize revenues for 30% of marketleading companies.
• Smaller “boutique” vendors are offering
chatbots targeted at specific industries and can
perform niche tasks that the big-name players
— like Amazon, Google, IBM and Microsoft —
are not equipped to provide.
Analysis
• Large AI vendors must adjust their strategies
to compete with the smaller, more-nimble
competitors that are threatening to dominate
the market.
Recommendations
Application leaders, data and analytics leaders and
strategists should:
• Find workers who excel at internal
communications and articulating processes to
lead bot scripting and development.
• Seek out proposals from smaller AI vendors for
specific project needs.
• Establish skills programs for developers in
algorithm testing, content acquisition and data
employment in artificial intelligence projects.
Strategic Planning Assumptions
By 2019, more than 10% of IT hires in customer
service will mostly write scripts for bot
interactions.
What You Need to Know
This year’s Predicts are gathered under the topic
of “artificial intelligence.” While we continue to
use the term and notion of “smart machines,”
vendors and end-user organizations are familiar
with the term “artificial intelligence,” so we will be
increasingly applying that term in this research area.
Smart machines won’t run themselves, no matter
what the movies and TV have shown since Fritz
Lang’s Metropolis. AI continues to drive change
in how businesses and governments interact
with customers and constituents. And our 2017
predictions show that the humans — as is always
the case in computing change — are the pivot on
which AI can turn.
Organizations must plan through their adoption
of AI because such fundamental changes always
require agility.
Organizations must concentrate on:
• Identifying key roles, the workers that will fill
them and the metrics they will have to meet.
• Considering smaller, disruptive vendors and
service providers.
10
• Implementing suitable methodologies for
execution.
• Planning tactical and strategic targets for
projects.
• Recognizing that AI may threaten core
assumptions about revenue sources and
volume.
Excitement about artificial intelligence is growing
rapidly. Gartner analysts’ conversations with
clients have grown in number exponentially in the
last two years. As can be seen in Table 1, there
were 14 inquiries with Gartner clients that used
the term “Artificial Intelligence in 2014.” In 2015,
there were 89, an increase of more than 500%.
As of the end of the third quarter of 2016, Gartner
had fielded more than 290 queries about artificial
intelligence since January, representing a greater
than 200% increase only partway through the year.
Implementing chatbots, for example, is a business
imperative for organizations with extensive
customer service needs. Chatbots will soon deliver
customer satisfaction at significantly lower cost
than human customer service agents. But building
and maintaining the neural networks required will
take dedicated workers with specific skills.
FIGURE 1
Chatbots are growing in popularity because
AI systems can manage customer and worker
progress through a decision tree more effectively
than human direction can. The next generation
of customer interaction chatbots will cause less
customer frustration. For example, machine
learning could effectively employ useful contextual
data to skip a concern several levels in the service
hierarchy more reliably than a worker could.
Chatbots are just one category of AI applications
that will drive disruption in many businesses. To
make AI opportunities a large-scale proposition
rather than isolated curiosities, companies will
need to invest in AI programs.
Agile enterprises will seek out AI vendors with
the best solutions. Large vendors like Google
and Amazon dominate, but smaller vendors
offering more-targeted AI products are on the rise.
Enterprises should make sure they seek out these
smaller vendors when searching for AI solutions.
Employing AI offers enterprises the opportunity
to give customers an improved experience at
every point of interaction, but without human
governance, the opportunity will be squandered.
Inquiries to Gartner About Artificial Intelligence (AI)
Record Count
2,500
2,000
1,500
1,000
500
0
2014
Source: Gartner (November 2016)
2015
2016
11
Strategic Planning Assumptions
Strategic Planning Assumption: By 2019, more
than 10% of IT hires in customer service will
mostly write scripts for bot interactions.
Analysis by: Martin Reynolds
Key Findings:
• Chatbots generate the best returns when
backed by well-scripted decision trees written
by dedicated staff.
• The best scripters understand the business and
how customers interact with it.
• Programmers are not the best choice to design
customer service interfaces.
Market Implications:
Chatbots hold the potential — through naturallanguage processing — to make the customer
experience easier, faster and more satisfying while
furthering business goals. Chatbots have access
to available knowledge about the customer and
how similar customers responded. But they need a
great “navigator” to deliver the best results.
The fundamental technology that underpins
chatbots is not changing. It is a decision tree
that leads the customer to the right result.
The tree structure gives good answers with
correct grammar. One AI transformation is the
introduction of highly reliable speech to text. The
text tags result in better navigation of the tree.
Every enterprise has the opportunity to improve
its business incrementally by tapping into the
potential of chatbots. For the enterprise, investing
now to make such communication models work
effectively will pay dividends for years.
The key is hiring the right people to shape the
knowledge tree to customer needs, and identifying
the customer keywords that drive the correct
moves within the tree. Programmers are typically
not well suited to this task, as it requires both a
customer service touch, and in-depth knowledge of
the business.
Instead, dedicated bot scripters are the answer. Call
center workers often have the needed skills to be
effective bot scripters. They understand the business
as well as how customers think when shopping
for a product, making them effective at choosing
keywords and linking nodes in the right way.
These bot scripters will also be able to handle
exceptions (where the bot cannot identify the
correct next step and has to hand the task to a
human operator).
As chatbots get better at helping customers,
more calls will be handled automatically —
and with higher satisfaction. Customer service
representatives will have time to deal with tougher
questions… and the tougher customers.
Over time, AI may lead us to new ways to support
customers. However, the decision tree, because
of its high-quality results when it successfully
completes an interaction, will underpin bot
services for some time to come.
By way of evidence, we have companies such
as IPsoft, Nuance and x.ai building bot systems
that use the “human augmented” approach.
Sometimes, bot agents open up with the question
“tell me, in a few words, what you want to
do.” This opening allows a smarter bot to start
interacting with a customer at a deeper location in
the tree.
It is also worth noting that, in China, WeChat
public account chatbots are becoming the new
face of commerce. It is critically important to build
a customer-facing bot strategy now.
Recommendations:
• Hire dedicated people to write bot scripts.
• Interview the best employees in your call
center as potential bot scripters.
• Recognize that good quality chatbots may
significantly improve some informational
aspects of operations.
Strategic Planning Assumption: Through
2020, organizations using cognitive ergonomics
and system design in new artificial intelligence
projects will achieve long-term success four times
more often than others.
Analysis by: Kenneth F. Brant
Key Findings:
• Gartner’s inquiries indicate that organizations
are not approaching AI and smart machines
holistically or as systems with implementation
challenges different from traditional IT.
12
• AI and smart machines are in the early stages
of innovation and commercialization; vendors
report they are subject to much greater “churn”
in the marketplace than traditional IT offerings
and that end-user IT organizations struggle with
new system requirements.
• Organizations investigating AI and smart
machines have focused prematurely on
evaluating technology categories and/or
vendor evaluation selection before acquiring
the skills and methodologies for successful
implementation.
• Success in defining the “art of the possible”
and in tech-focused proofs of concept (POCs)
for AI and smart machines is often followed by
difficulties in scaling and maintaining system
performance.
Market Implications:
CIOs must evolve their workforce — and strategic
partnerships with service providers — if they are to
achieve sustainable change in the disruptive era
of smart machines. CIOs must reallocate budgets
and resources while acquiring new competencies
within their organizations.
Deploying smart machines means that CIOs must
take into account several factors that affect the
enterprise’s ability to both prove and sustain value
in smart machines:
• Cognitive Ergonomics: As defined by the
International Ergonomics Association, cognitive
ergonomics is concerned with “mental
processes, such as perception, memory,
reasoning, and motor response, as they affect
interactions among humans and other elements
of a system.” This is a subset of the larger field
of human factors and ergonomics.
Part of the human factors field, employing
cognitive ergonomics ensures that the mix of
smart machines and human workers is effective
and augments the intelligence of both in
problem solving and decision making.
• System Design for Maintainability: System
design is the transformation of an idea into a
system that meets the designer’s requirements
and the end user’s needs. Maintainability is the
degree to which the design can be easily and
economically repaired.
This concept is important with respect to the
ease of adding and updating content in smart
machines, the ease of tuning the various
elements of smart machines (such as the
optimum combination of content, algorithm(s)
and user interface), the ease of appending,
reformatting and expanding content, plus the
ease of retraining the neural network.
Recommendations:
CIOs and enterprise architects must:
• Balance their organization’s current focus
on the “what” and the “who” of artificial
intelligence to include more attention to the
“why” and the “how” of implementations in
order to drive long-term value through human
factors and design principles.
• Reskill their organizations to include holistic,
related competencies in content acquisition,
preparation, updates, algorithm testing, training
and retraining of neural networks, design for
human acceptance and effectiveness, and
design for high-reliability systems.
• Conduct POCs with the actual professionals
who will use the smart machine in order to gain
insights into the human factors that will drive
usage and effective outcomes. The success
of these POCs should not be based on the
conclusions of data scientists in a laboratory
environment.
• Build the proven benefits of cognitive
ergonomics and design for maintainability
observed in human-centered POCs into their
change management programs for smart
machines.
Strategic Planning Assumption: By 2020, 20%
of companies will dedicate workers to monitor and
guide neural networks.
Analysis by: Magnus Revang
Key Findings:
• Many enterprises view smart machines as
magical investments that need neither tending
nor updating once they are deployed.
• While many enterprises have data scientists
doing dedicated work on advanced analytics
13
and machine learning in other parts of the
company, there is little collaboration with IT
and these systems are traditionally maintained
outside of the IT application portfolio.
• Many CIOs have not prioritized the skills
needed to implement, maintain and update
neural networks, leading them to either play
catch-up or cede that responsibility to other
parts of the enterprise.
• Much of the hype around smart machines
focuses on the relatively few places they are
employed in public and fails to consider the
thousands of places artificial intelligence is
already used in the background systems of
enterprises.
Market Implications:
The assumption that smart machines with neural
networks can be deployed as a finished project
— with no consideration to the challenges of
continuously maintaining and monitoring the
implementation — will lead to failure for many
enterprises. In reality, neural networks need to be
retrained constantly as data is collected. No longer
can models stand the test of time for two or three
years like they did in the 1980s. Today’s models
are only good until new information becomes
available.
An example of the retraining needed to keep
neural networks working at their best can be seen
in the way modern automobiles adapt to their
drivers. A trip to the mechanic that results in an
adjustment to the gearbox causes the car’s “brain”
to restart the process of learning the driver’s
habits. It’s the same for any neural network that
receives new data.
CIOs need to understand that workers with new
skill and a new way of looking at problems
are needed for successful retraining of smart
machines. Developers, with their typical binary
approach (it works or doesn’t) have a difficult
time working with neural networks. People with
different backgrounds — in design, data science
and logic, for example — have the mindset to work
with neural networks.
Separation of responsibilities for neural networks
will be split among different parts of the enterprise,
making it essential for CIOs to remain relevant by
ensuring IT owns the platform that is running the
neural network. Instituting a robust governance
structure establishes how responsibility is divided
among areas of the organization.
Smart machines hold the potential to further
business goals through the use of neural networks,
which are becoming more adept at creating
models based on datasets than are their human
counterparts. At the same time, effective use of
neural networks requires a change in thinking
and redeployment of IT staff. Resources currently
used for development will be needed to feed the
endless retraining loop that allows neural networks
to maintain their value to the enterprise.
Recommendations:
• Completing POC exercises is imperative for
CIOs to show the value in the smart-machine
era.
• CIOs must look outside of programmers to
hire data scientists and other staff members
with the skills to create and maintain neural
networks.
• CIOs need to make the business case for neural
networks to ensure the project is properly
funded.
Strategic Planning Assumption: By 2019,
startups will overtake Amazon, Google, IBM and
Microsoft in driving innovation in the AI economy
with disruptive business solutions.
Analysis by: Jim Hare
Key Findings:
• Every software application (and business
process) is likely to become more intelligent as
more vendors embed AI capabilities into their
solutions.
• CIOs must find the AI vendors that offer
domain-specific solutions that best align with
their businesses to help automate and improve
decision making. The number of startups
offering AI solutions is accelerating. Research
firm CB Insights reported 140 AI startups in the
first quarter of 2016, up from 70 in all of 2011.
Other sources suggest there are now 2,000 to
3,000 AI-related firms (see “Entering the SmartMachine Age”).
14
• Many startups are led by employees who often
previously worked on AI projects at big vendors
like Amazon, Google, IBM and Microsoft.
Market Implications:
The explosion in the number of vendors offering
AI and machine-learning solutions has created a
gold rush with small startups threatening to grab
most of the nuggets from the big vendors that get
most of the hype. The robustness of the AI economy
is evident in the $1.5 billion in equity capital raised
by 200 startups, as reported by CB Insights. These
startups are focused on building businesses to solve
difficult real-world problems with AI at the center.
Employees who learned the basics, and even built
the AI technologies at the big vendors, have moved
on to start their own firms. These small vendors offer
products tailored to specific industries or lines of
work. Of course, many of these startups will likely
be acquired or try to stay independent to become
giants themselves. According to CB Insights, over
140 private companies working to advance AI
capabilities have been acquired since 2011, with
more than 40 acquisitions taking place in 2016.
While many startups are focusing on industries
ripe for disruption like healthcare — for example,
to help discover drugs and offering care — any
industry that collects data that can be analyzed can
benefit from the power of AI. The use of AI in retail,
financial services, investment advising and call
centers, among others, has proven its ability to help
companies make smarter decisions in less time with
more context, and to get to the next best action
decision quicker than possible by a human alone.
For example, in healthcare, “computer assisted
diagnosis” has been used to review the early
mammography scans of women who later
developed breast cancer, and the computer spotted
52% of the cancers as much as a year before the
women were officially diagnosed. Many online
news publishers like AP, Fox, and Yahoo use AI
to write simple stories like financial summaries,
sports recaps and fantasy sports reports. AI isn’t yet
writing in-depth investigative articles, but it has no
problem with basic reports that don’t require a lot
of synthesis.
With data collection rising, the places where
humans can match the ability of machines to
make real-time decisions will be precious few.
Solutions to help lines of business (such as sales
and marketing departments) to improve decision
making are also popular. Smart machines take
data and send salespeople off to focus on the most
promising opportunities. CRM vendor Salesforce
has acquired a number of smaller AI-savvy
companies to take advantage of the possibility of
more-effectively supporting its clients.
The proliferation of industry- and domain-specific
applications from specialist firms is an opportunity
for enterprises to use packaged “smart analytics”
solutions to leverage increasing levels of data
and more-sophisticated analytics to improve
and augment their decision-making processes —
without the need to hire teams of data scientists.
Still, end-user organizations should be mindful of
avoiding the risk of standardizing on just one or
two AI specialists given the acquisition euphoria
underway and how fast the technologies are
evolving.
Recommendations:
• CIOs should analyze key business processes
to locate the areas where AI could be applied
to augment and improve human decision
making, especially in underserved areas of the
organization that lack access to analytics.
• Data and analytics leaders need to understand
both the benefits and limits of AI. The goal
is to find the right way to blend humans
and machines. For example, AI capabilities
such as deep neural networks (DNNs) can
perceive patterns that humans can’t detect.
They can also “program” a model to classify
from large bodies of data and, under certain
circumstances, be more effective and efficient
than other approaches. But if the available data
falls below a threshold, no amount of artificial
intelligence can solve the problem.
• CIOs should investigate and evaluate packaged
AI solutions in the market before considering
building a custom AI solution from scratch.
There are new startups emerging almost every
day focused on solving different business
problems. Packaged AI solutions can usually
be deployed faster and require less technical
resources to support.
Strategic Planning Assumption: By 2019,
artificial intelligence platform services will
cannibalize revenues for 30% of market leading
companies.
15
Analysis by: Frances Karamouzis
Key Findings:
• From 2010 through 2015, funding in the AI
sector has multiplied nearly sevenfold.
including business intelligence, e-commerce, and
healthcare.4 Above and beyond these investments,
Baidu, the Chinese internet search giant, has
created a $200 million venture capital unit to
invest in artificial intelligence projects.5
Market Implications:
These venture capital investments into startups
and R&D initiatives within existing firms will
certainly lead to a huge proliferation of new
business ideas that will cause significant
disruption to conventional products and services
offered by the current market leaders. These new
AI platform services will cannibalize revenues of
the largest companies as they will be focused on
tightly coupling products and services together to
create a customer experience that permeates for
a longer time, and is defined by new and different
commercial structures.
There is a series of studies that all reach similar
conclusions — market-leading companies that get
disrupted or disintermediated face their demise.
A landmark study showcased that, over the last
35 years, the rate of failure for leading global
companies is accelerating.1 The same study found
that more than half of these companies have gone
bankrupt, have been acquired or have ceased to
exist.
The next big shift is convergence of technology
products and services to create next-generation
service offerings that will include AI platforms.
More specifically, Gartner defines these nextgeneration service offerings as “intelligent
automation” services that use one or more AI
technologies (such as a cognitive-computing
technology platform) as the basis of an offering’s
core value proposition.
A study by Standard & Poor’s found that the average
life span of a company listed in the financial
company’s index of leading U.S. companies has
decreased by more than 50 years in the last century,
from 67 years in the 1920s to 15 years today,
according to Professor Richard Foster of Yale.2
These AI platform services offerings include AI
(or AI-related) technologies as part of their core
platform or underpinning. Such inclusion often
requires R&D investment to achieve an aggregated
(tightly bundled) solution that ensures predictable,
reliable outcomes. This often results in a relatively
large portion of value being derived from IP,
accelerators and verticalization, as opposed to pure
labor or licensing.
• Since 2000, 52% of large global companies
have gone bankrupt, have been acquired or
have ceased to exist.
• Many company failures have resulted from
companies that did not see the disruptive
forces in their respective industries coming,
and/or failed to adapt and shift once a big
disruption was upon them.
John Chambers, former CEO of Cisco, was quoted
as saying “More than one-third of businesses today
will not survive the next 10 years. Companies
should not miss the market transition or business
model nor underestimate your competitor of the
future — not your competitor of the past.”3
One of the leading causes of disruption is a variety
of artificial intelligence technologies. Some do not
fit the exact definition of AI, but employ various
technologies such as deep neural networks,
machine learning or some type of cognitionbased functionality. In fact, the investment
community has recognized this and responded
with record level investment. AI startups have
raised an aggregate $967 million in funding since
2010, with investments going to companies
in 13 countries and 10 industry categories,
This notion of combining technology and services
marks the key shift from labor-driven to IP-driven
offerings, and results in different commercial
terms, which are based on outcomes. In this
structure, commercial terms are directly tethered
to distinctive business results (outcomes in the
form of completed transactions or fully enabled
processes) where payment is triggered by delivery
of the AI platform service. This is juxtaposed
against more-conventional commercial terms,
which are defined by licensing of software or
services that are priced by labor hour, which places
the focus on effort (input) rather than output.
16
Recommendations:
CTOs and CIOs should:
• Take a strong lead in your industry by
developing multidisciplinary, cross-functional
teams in order to track, adopt and execute
POCs for AI platform services.
• Focus on multiple ideation workshops per
year and many POCs to ensure you are part of
the successful companies that will engage in
disrupting others rather than being disrupted.
• Be dynamic and vigilant about iteratively
exploring the changing landscape of market
offerings, provider investments, competitor
adoption, and related regulatory, legal, ethical
and societal shifts.
• Ensure this is an enterprisewide initiative
with CEO sponsorship and funding. There will
be lots of small passionate teams across the
organization that are exploring and trying
new options. It’s important that everyone
is connected, coordinating and sharing.
While each team may be going at a different
cadence and velocity, there should be periodic
knowledge sharing and IP exchange.
A Look Back
In response to your requests, we are taking a look
back at some key predictions from previous years.
We have intentionally selected predictions from
opposite ends of the scale — one where we were
wholly or largely on target, as well as one we
missed.
On Target: 2013 Prediction — By 2016,
Microsoft will offer virtual personal assistants in
Microsoft Office 365.
Analysis By: Whit Andrews
We predicted in 2013 that by 2016 Microsoft
would offer virtual personal assistants (VPAs) in
Microsoft Office 365. We were correct, in that the
Cortana (which had not yet been announced or
described at the time) now integrates to Office
365. The product is not exclusive to or embedded
in Office 365, but it is key to Microsoft’s strategy
and is a prominent aspect of the Windows 10
operating system, as well as being part of a range
of consumer applications. Cortana was announced
in 2014, and its integration to Office 365 was
announced and previewed in 2015.
Missed: 2012 Prediction — By year-end 2017,
25% of workers will engage search in business
applications through natural expression at least
five times daily.
Analysis By: Whit Andrews
We predicted in 2012 that by year-end 2017,
25% of workers would engage search in business
applications through natural expression at least
five times daily. At the time, Siri’s effectiveness
was improving and IBM Watson was impressing
organizations with its effective deconstructions
of natural expression in popular demonstrations.
Google Now also grew in prominence as Android
incorporated it more broadly. However, APIs to the
VPAs did not emerge as swiftly as we expected
them to do (more have become available in the
recent past, but notably, they are more accessible
from consumer-only specialists such as Facebook,
Alexa and WhatsApp). And only two in five of the
references for the 2015 Enterprise Search Magic
Quadrant indicated that their search vendor
supports natural-language question answering. We
still expect this model to become stronger, largely
through improved “autosuggest,” as APIs continue
to mature.
Evidence
“Startup Survival, Failure and Growth,” Kauffman
Report.
1
2
“Can a Company Live Forever,” BBC News.
“Retiring Cisco CEO Delivers Dire Prediction: 40%
of Companies Will Be Dead in 10 Years,” Business
Insider UK.
3
“Baidu Launches $200 Million Venture Capital
Unit Focused on Artificial Intelligence,” South
China Morning Post.
4
“Deep Interest in AI: New High in Deals to
Artificial Intelligence Startups In Q415,” CB
Insights.
5
17
About RAGE Frameworks
RAGE Frameworks Inc. is a leader in knowledgebased automation technology and services
providing AI for the Enterprise. RAGE-AI™ is a nocode patented platform for end-to-end automation
of knowledge-based processes. RAGE-AI™ is
currently used by some of the largest banks,
manufacturers, consulting companies, high tech
firms, and logistics companies. Headquartered in
Dedham, Massachusetts with global operations
centers in Pune and Belgaum, India, RAGE offers
unprecedented speed, flexibility and insight in
solving today’s most complex, critical business
problems. Visit us at www.rageframeworks.com
Connect with RAGE Frameworks:
• Website: www.rageframeworks.com
• Twitter: www.twitter.com/rage_frameworks
• Facebook: www.facebook.com/rageframeworks
• LinkedIn: www.linkedin.com/company/
rageframeworks
• 3 Allied Drive, Suite 230, Dedham, MA 02026,
U.S.A. Phone: 800-203-4399 / 781-493-6900
The Road to Enterprise AI is published by RAGE Frameworks. Editorial content supplied by RAGE Frameworks is independent of Gartner analysis. All Gartner research is used
with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2017 Gartner, Inc. and/
or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of RAGE Frameworks’ products and/or strategies.
Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from
sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to
change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not
be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research.
Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or
influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and
Objectivity” on its website, http://www.gartner.com/technology/about/ombudsman/omb_guide2.jsp.