Accelirate Inc. has created a guide for getting started with artificial intelligence. From enterprise use cases, to the technology involved, and even how to build a world class RPA and AI team, everyone can benefit from this all-inclusive guide to AI.

1. APracticalGuidetoGetting
StartedwithArtificial
IntelligenceandAutomation
Accelirate Inc.

2. Table of Contents
Introduction to Artificial Intelligence……………………………………………………3
Machine Learning Use Cases……………………………………………………………….8
Where and How do I use AI?...................................................................10
AI Training (Packaged Machine Learning)………………………….13
AI Implementation……………………………………………………………………………..15
Enterprise Use Cases for AI………………………………………………………………..17
Chat Bots………………………………………………………………………….19
Natural Language Processing……………………………………………………………..20
Analytics & Prediction………………………………………………………………………..22
Robotic Process Automation………………………………………………………………24
AI Consumption Model and AI Toolsets………………………………………........26
Building World Class AI and RPA Teams………………………………………………29
Data Scientists………………………………………………………………....31
Machine Learning Engineers…………………………………………….33
RPA Architects and Engineers………………………………………….……...34
Developers……………………………………………………………………….35
Business Analysts & Business Intelligence Developers……..36
Managers, Project Managers, and Executives…………………..37
Conclusion…………………………………………………………………………………………38 2

3. Introductionto
Artificial
Intelligence
3

4. Introduction to Artificial Intelligence
4
Not a day goes by that you don’t hear of the dire predictions about Artificial Intelligence taking most of the human jobs
away. When you look around and hear about self-driving cars, Alexa, Siri, etc., it feels like humans may not have much to do
in a few years. The reality however is not that bleak…
AI can be taught to drive cars, but the same AI can’t be used to clean the tables. AI can beat humans on the “Go” board
game (AlphaGo) but the same AI program does not know how to play chess. So, today’s AI and Machine Learning can
perform incredibly well at tasks that we train the computers on, but without the proper “labeling” or training of the
algorithms, it’s still a garbage in and garbage out scenario. Today’s AI technology is fundamentally great at processing huge
amounts of data and can use supervised, semi-supervised, and unsupervised AI techniques to solve a narrow set of
problems that its trained on.
The term “Narrow AI” describes the state of AI Technology today as compared to human-like “True AI” which still may be a
few decades away.
So why use AI?

5. Introduction to Artificial Intelligence
5
Businesses can use AI to solve a lot of prediction problems using their own internal data, as well as, combine their data
with publicly available external datasets. For example, if a financial institution is trying to predict sales for the next few
quarters, the accuracy of the prediction may be much better if they use not only their own existing datasets but also utilize
macro-economic data, such as interest rates, to better align their sales forecast with broader market factors. So, Business
Intelligence (BI) is probably one of the first places where AI and Machine Learning technologies can have a huge impact as
it allows BI groups to go beyond their traditional retrospective and predictive analytics and add a prescriptive element to
their analytics.
Business Process Automation is another area where there are tremendous applications for AI. For example, many
businesses have large document management and OCR system deployments but they still have a lot of manual Business
Processes around such implementations. AI and ML can potentially lead businesses towards autonomous execution of such
processes using AI technologies like Natural Language Processing, Robotic Process Automation, etc.
Some organizations have large deployments around Business Process Management (BPM) systems which use Workflow and
rules-based software to “assemble” business applications. AI and RPA technologies can complement such systems by
enabling significant additional end point automations by utilizing UI based surface automation (RPA) as well as AI
algorithms for decision making beyond the scope of rules engines.

6. Introduction to Artificial Intelligence
6
Marketing can use AI to create better customer engagement through the company’s existing customer facing channels such
as their Websites, Mobile Apps, and so on. For example, AI technologies such as sentiment analysis can help in real time
social brand management. Chatbots and Customer Service Rep Assistive Bots can help to create better overall customer
engagement for customers. The list of business use cases goes on and on…
In summary, AI is NOT magic (at least for now). But the ability of machines to learn and program themselves, even within
the bound of “Narrow AI”, is an incredibly powerful evolution of computing which has been termed as the 4th Industrial
Revolution and rightfully so. In this white paper, we will go into a bit more detail on the who, what, when, where, and why
questions around Artificial Intelligence. The technical definition of AI can be found all over the Internet; it is essentially
defined as a technology that is designed for computers to mimic intelligent human behavior. Computers were originally
designed to solve data processing problems where humans were just not as fast or efficient and could not handle large
amounts of data. Traditional computing has come a long way since the early days but still primarily solves the data
processing, workflow, and rules problems.

7. Introduction to Artificial Intelligence
7
On the other hand, AI takes datasets as the input, processes the data using Machine Learning Algorithms and Models, then
outputs patterns found in the input datasets. The process can run cyclically as the AI models are fed more data they
become better at detecting patterns. Hence the AI systems are fundamentally designed to learn through feedback loops
and additional datasets. The algorithms can be supervised, unsupervised and semi-supervised. Supervised algorithms use
both sets of input and output data to predict an outcome (for e.g. estimating the value of a house by using past price
history of similar homes). Unsupervised algorithms, on the other hand, use only input data to detect patterns in the data
(for e.g. if a classroom has male and female students and the only data provided is the weight and or physical attributes of
students, the algorithm can classify the gender of the student).
There are other types of Algorithms such as reinforcement learning and you can find more information out there on the
internet but our point here is to differentiate between AI and traditional computing. The image below outlines the types of
problems that can be solved by these algorithms. This of course by no means is reflective of all AI algorithms as the field of
AI is very wide.

8. 8
Machine
LearningUse
Cases

9. Machine Learning Use Cases
9

10. 10
WhereandHow
doIuseAI?

11. Where and How do I use AI?
11
AI, at a high level, has 3 main branches that are relevant for business (there may be others)
1. Machine Learning
2. Natural Language Processing
3. Robotics
These are very wide topics and they are relevant to business; but where do we apply them? Finance, HR, Operations, IT, the
Executive Branch? The answer is simple; all of the above. Next question, how do we apply them?
Well, most businesses fundamentally have processes, Manufacturing Processes and/or Business Processes, and they also
have Managers and Executives who make the management decisions. More and more Executives are making these
decisions based on data that is generated by the business and the operations. So, at a high level, AI and its branches can be
used for the following:
1. Prediction & Forecasting Problems
2. Process Automation Problems

12. Where and How do I use AI?
12
Prediction problems are usually addressed by advanced business intelligence and analytics groups within enterprises. Machine
Learning and Data Science expands the horizons of current BI systems by bringing in the ability to analyze vast amounts of external
and internal structured and unstructured data and by applying the “learning” concept that is inherent to Machine Learning. In a
nut shell, by applying Machine Learning and Data Science you can probably increase the accuracy of the prediction and forecasts
that you are able achieve today using existing BI technologies. This sounds simple but this is a huge competitive advantage to
many businesses. For example, loss prediction and mitigation in Insurance and fraud prediction and prevention in Banking. As you
can imagine, this list goes on and on, however, the skill sets required to solve such prediction problems is highly technical.
Process Automation problems are usually addressed by all sorts of enterprise software. ERP, CRM, BPM, etc. Enterprise Software
today is pretty good at Workflow and Rules based process problems but is still reliant on humans to make inputs into the systems
and interpret the output of the systems. This is where manual Business Processes start to pile up. The field of Robotics (Robotic
Process Automation) is one such solution to these problems, but RPA inherently can only “automate” tasks in between various
systems without any “judgement”. In other words, RPA, to a certain extent, is “dumb” automation.
This is where the evolution of “smart” process automation comes in, using technologies such as NLP Data Extraction, Text
Analytics, Sentiment Analysis, etc. If you are already evaluating such technologies, it’s important that you separate out the actual
AI implementation problems from AI training problems because the skill sets required for these problems are different. AI Training
requires a much lower level of skills as compared to AI implementation. AI Training is essentially the “labeling” of data to describe
the data.
Let’s clarify the AI implementation and training problems further.

13. AITraining (Packaged Machine Learning)
13
Many AI enabled software platforms already solve some of the common business problems, however, almost all of them
require training for specific business use cases. These use cases require subject matter expert Business and Technical
Analysts to train these systems, however, they do not necessarily require Data Scientists or Machine Learning Engineers.
Consider a few examples below:
OCR is one such area where there are plenty of existing OCR platforms that focus on solving data extraction problems using
Computer Vision and Machine Learning. Most of this software allows the users to “teach” their document structures to the
system. So, this job does not require highly technical skill sets. However, if the objective of the process automation is not
just to extract name/value data but to also contextually classify blob data (legal contracts, etc.) then you either need
additional packaged text analytics software or Machine Learning engineers who are experts in text analytics. The skill sets
are dramatically different for data extraction vs. text extraction and analytics.

14. AITraining (Packaged Machine Learning)
14
There is plenty of Chatbot software out there that uses some sort of an AI Engine from either IBM, Microsoft, Google or
Amazon to figure out user intent from a chat question and then match that to an answer. Once again, in most cases this is a
training problem where the Chatbot needs to be “trained” on a set of questions and configured as per the business’s
requirements. There are plenty of Chatbot software and platforms (ChatFuel, Microsoft Bot Framework, etc.) which
essentially “package” the AI required to develop the Chatbots. Therefore, the training of Chatbots does not require
technical skills but if you are embedding Chatbots within your applications you need Developers who are familiar with the
APIs of these Chatbot platforms and these developers do not necessarily have to be Machine Learning Experts.
Splunk (www.splunk.com) is used widely in the Enterprise to analyze infrastructure and software log data which is used for
various purposes including investigating infrastructure issues, etc. Splunk uses Data Science and Machine Learning
techniques to analyze the information but the Technical and Business Analysts do not need to know the underlying details
of algorithms utilized. This is also referred to as “packaged machine learning”.

15. 15
AIImplementation

16. AI Implementation
This is where you are trying to solve a problem which is not
already solved by an existing off the shelf software. For
example, you want to create a system which automatically
analyzes the chat or blog content on your website. This
problem requires an in-depth problem definition, analyzing
existing datasets, algorithm selection, and implementation.
Such problems require your teams to have Data Science
and Machine Learning skills which are much higher-level
skill sets than those of folks who are just training the AI
systems.
In summation, it’s important to differentiate between the
Operator, Modeler, Consumer, and Developer roles that
exist within the AI and Machine Learning world.
16

17. 17
EnterpriseUse
CasesforAI

18. Enterprise Use Cases for AI
18
Let’s take some high-level examples of AI Technology Use Cases within Customer Engagement, or Customer Service and
Customer Acquisition:
AI Technologies can also be used across Enterprise Operations. A few examples are provided below:
AI is not just limited to the Technology Applications above; the list of AI Applications is endless. Let’s go into a bit more
detail about the AI Applications identified above.

19. Chat Bots
19
Customer Engagement includes Customer acquisition, Customer service, and other front office related functions. Many
firms have self-service apps including: web, web mobile apps, and IVR systems to allow customers to manage their
accounts. However, customers rely on call center agents if the apps are not able to service their requests. Live chat has also
been around a while, allowing Customer Service Reps to potentially handle multiple customers at a time without getting on
the phone (convenient for the customer).
AI enabled Chatbots are an emerging customer engagement channel where chat responses can be handled by “Bot” agents
which are capable of parsing customer questions and customer intent, providing them with an appropriate response. These
Bots have to be trained on variations of incoming questions and just like other Machine Learning applications, they get
better over a period of time as they are trained. We also see many product vendors focusing on Chatbot niche areas where
they train their Bots for a specific industry or sub-industry (e.g. https://kasisto.com/ bots are purely focused on retail
banking solutions)
However, there are risks associated with deploying Chatbots too quickly and without the proper training. In addition, the
Chatbot workflows must be designed in such a way that a human CSR can take over if a Chatbot is leading the customer to
confusion. AI is not designed to be an alternative to humans at this stage but is more of an assistive technology.

20. 20
Natural
Language
Processing

21. Natural Language Processing (NLP)
21
Businesses of all size deal with text. In most cases, text has to be extracted, interpreted, and utilized for business processes
and workflows. For example, after collecting loan documents, the Loan Processors have to extract the key information from
those documents, interpret it, and process it based on the appropriate workflows and rules. NLP is a broad term used to
define the AI driven technology that is used to process different types of text.
For example, NLP can be used to extract relevant information from Legal Contracts (the text within contracts has to be
“labeled” first over a period of time to train the AI algorithms). NLP can also be used to determine the sentiment polarity of
text. For example, analyzing the sentiment of tweets and social posts which is an essential part of “dynamic brand
management”. NLP can also be used to summarize text as well; it can even be used to “generate” text from data. There are
many websites that use NLG (Natural Language Generation) to generate content by feeding data into NLP templates. Like
sports sites such as ESPN using NLG to generate the summary of games and other sporting events.

22. 22
Analytics&
Prediction

23. Analytics & Prediction
Many firms utilize advanced business intelligence and data
visualization platforms to analyze the vast amount of
information they collect so they can understand business
trends and forecast their sales, understand their risk
profiles, and so on. Machine Learning takes these
capabilities a step further, it makes analytic models more
accurate by training the models with past and present
internal data as well as external datasets.
23

24. 24
RoboticProcess
Automation

25. Robotic Process Automation
(RPA)
RPA can be used to automate business processes that are
repetitive in nature and, as mentioned earlier, can also
evolve by having RPA Bots use Machine Learning
algorithms to take those automations further.
Let’s look at an example:
Automatic Reconciliation has been a goal for Corporate
Accounting for a long time. Today’s G/L systems are quite
powerful in matching and reconciling transactions based on
primary and secondary keys and as well as a set of rules.
However, they still leave many exceptions which have to be
sorted out by humans. Using RPA and Machine Learning,
one can train these G/L systems to handle these exceptions
and over time these systems can achieve up to 99%
accuracy. See the SAP example here.
25

26. 26
AIConsumption
ModelandAI
Toolsets

27. AI Consumption Model
AI Tools are evolving fast but you have several options to evaluate from.
You can use a combination of the options as well if you want to build AI
capabilities within your Enterprise Applications; some of the options you
have include:
• OpenSource tools such as Appache PredictionIO
• Custom code with Python and readily available algorithm libraries
with public/ private datasets
• AI Platforms such as Azure Machine Learning, IBM Watson, and
Google TensorFlow
• Get algorithms and code from sites like Algorithmia or put up a Data
Science challenge on Kaggle
• Depending on what problem you are trying to solve, you can create
your own internal datasets or procure publicly available data sets
We discussed earlier the about concept of “packaged machine
learning”. Many Enterprise Software Vendors are adding Machine
Learning capabilities to their software where they already have
implemented a lot of algorithms within their products and part of that
implementation is to train those algorithms. 27
AI Consumption Model

28. AITool Sets
28
Earlier we discussed the concept of “packaged machine learning”. Many Enterprise Software Vendors are adding
Machine Learning capabilities to their software where they already have implemented a lot of algorithms into
their products; part of that implementation is to train those algorithms.

29. 29
BuildingWorld
ClassAIandRPA
Teams

30. BuildingWorld Class AI and RPATeams
30
We have talked about different types of AI Technologies and how/where they can be used in the enterprise. It is also
important to understand the different types of skill sets and organizational structures that can support businesses with
these types of initiatives.
A typical lean organizational structure we see emerging is one in which independent Enterprise Technology Optimization
groups are established and aligned with business but are governed by IT. We also see some organizations aligning AI and
RPA initiatives within their broader shared service organizations. Although both models have their pros and cons, the
choice depends on the organization and its culture. Larger organizations may even have multiple such groups in different
silos.
We will now go into a bit more detail about the different types of roles that exist within AI.

31. Data Scientists
31
There are lots of non-qualified “data scientist” profiles on job websites. Just because someone has been writing SQL queries
and working on BI platforms does not necessarily make them a Data Scientist. Even folks with experience in Data Warehousing
Technologies or Big Data platforms such as Hadoop still does not necessarily make them a Data Scientist. It is important to
understand the distinction between a Database Developer and/or Data Engineer vs. a Data Scientist.
In our opinion, to qualify as a Data Scientist, one needs to have the necessary academic or proven background in advanced
statistics, applied mathematics, as well as, computer science. We will spare you from the technical Wikipedia definitions of
Data Scientists, however, it’s important for you to understand the types of things a Data Scientist is expected to do:
• Create and execute strategies for analyzing and extracting insights from large structured and unstructured data. The skills
required to do so are the ability to query using traditional SQL as well as query big data sources such as Hadoop, etc. using
Appache Hive, Stinger, etc.
• Create and execute strategies around the ETL transformation of traditional, as well as, big data. Familiarity with ETL
techniques and tools for data migration, cleansing, and transformation is a must
• Create and execute strategies around statistical data modeling and machine learning. Expert knowledge in breadth of
machine learning algorithms and the ability to find the best approach to a specific problem. Familiarity with several
supervised and unsupervised learning algorithms such as Ensemble Methods (Random forests), Logistic Regression,
Regularized Linear Regression, SVMs, Deep Neural Networks, Extreme Gradient Boosting, Decision Trees, KMeans, Gaussian
Mixture Models, Hierarchical models, and time series models (ARIMA, GARCH, VARCH, etc.)

32. Data Scientists
As you can see, the talent pool with such skill sets is a very
limited one and reskilling an existing Data Engineer into a
Data Scientist is not an easy, if not impossible, task.
Most of the talented Data Scientists usually find jobs in
large companies such as Google, Facebook, Amazon, etc.
So, one strategy, in addition to looking for lateral hires, is to
look for Masters or PHD graduates from Universities that
have very strong programs around Data Science. As an
evolving, but very important discipline, we feel that
investing in the right early stage talent can pay big
dividends over a period of time.
32

33. Machine Learning Engineers
33
A Machine Learning Engineer shares some skills as a Data Scientist but, nevertheless, they have an important skill set which
makes them not as readily available in the market. A Machine Learning Engineer needs to have the following skill sets:
• Familiarity with the traditional Software Development Lifecycle (SDLC) and Programing using ML friendly languages like
Python, R, etc.
• Familiarity with Probability and Statistics and an understanding of some of the Algorithms. (These folks may be tasked to
select appropriate algorithms for specific problems but may not need to understand the inner workings of the
algorithms in depth)
We believe that while some of the traditional Developers can reskill themselves to become Machine Learning Engineers,
but not all of them are capable of doing so. To be a Machine Learning Engineer, the skills require the relevant academic
background, aptitude, and talent.
Unfortunately, technology evolves quite fast and the skills of yesterday, although helpful, may not necessarily translate into
the experience required to move into these newly evolving engineering disciplines.

34. RPA Architects and Engineers
RPA Architects and Engineers usually come from either a
QA automation background or a traditional development
background. RPA is a fairly advanced area with plenty of
packaged enterprise software offerings like UIPath,
BluePrism, AutomationAnywhere, and so on. RPA Engineers
need to be familiar with not only RPA software but they
must also be well versed in automation strategies and the
infrastructure related issues that come up with any RPA
program designed to scale. The role of RPA Architect is an
advanced role for someone who has an extensive technical
architecture background and has a thorough understanding
of how to set up Centers of Excellence for RPA Programs.
34

35. Developers
35
A lot of traditional Developers have updated their LinkedIn profile to say Machine Learning Developer. Although it’s
certainly admirable that folks are upskilling themselves, we advise that if you are looking to hire Machine Learning
Engineers, you go through a thorough vetting process to qualify them.
If history is any sort of guide, some COBOL developers did not make a successful transition to GUI based application
development and many GUI based Application Developers could not make a successful transition to mobile and social
application development, not because they were not smart, but because they were stuck in maintaining the legacy
codebases.
So, just because a Developer can train and consume a Chatbot in their application with a simple API, it does not mean that
they are a Machine Learning Engineer.

36. Business Analysts & Business Intelligence Developers
36
Business Analysts
Traditional Business Analysts have always been more successful when using their business subject matter expertise along
with their data analysis skills. We recommend that Business Analysts take basic trainings on AI and Machine Learning
technologies to discover what they can do for business. This will allow them to adapt quickly to the endless applications of
AI and ML technologies.
Business Intelligence Developers
We see a huge opportunity for BI Developers who are able to upskill their strong data analysis skills using ML and AI. BI
Developers, Data Scientists, and Machine Learning Engineers are the ones at the core of being able to solve some of the
fundamental prediction and forecasting problems for businesses.

37. Managers, Project Managers, and Executives
We believe that Executive Project Managers, Program
Managers, and the Executive Suite folks should take
strategy courses in Artificial Intelligence, Machine Learning,
and Robotic Process Automation. Only by taking the time to
understand these technologies can they be ready for the
hyper completive business environment that is rapidly
evolving in front of us. There is a reason it is being called
the 4th Industrial Revolution.
37

38. Conclusion
AI implementations require a fundamentally different
mindset than that of traditional IT Operating models.
Therefore, it is critical that the Executive Management
takes the time to train themselves on how these
technologies work so that they can think through operating
and resource models that are optimal in taking advantage
of these technologies.
38

39. WanttoLearn
More?
info@accelirate.com
www.accelirate.com