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BUSINESS GUIDEThe CFO’s Guide to AI and
Machine Learning
These advanced technologies have possibilities
for your business. Here’s what you need to know.

Learn more at NetSuite.com

Grab a seat and enjoy.
Read Time: 11 minutes

The CFO’s Guide to AI and
Machine Learning
These advanced technologies have possibilities
for your business. Here’s what you need to know.
Introduction
After what seems like decades of promise, artificial
intelligence now presents a reality that both offers morethan we expected and yet seems more dangerous than
we foresaw. Within the short availability of ChatGPT,
it’s managed to shake up academia and intrigue andfrustrate business leaders. It’s been banned in Italy
even as its creators and competitors release new, more
powerful versions on a monthly basis.

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While ChatGPT has caught the attention of the masses,
other instances of AI and, more often, machine learning
have made businesses more productive. With thebuzz near its peak, we surveyed CFOs to learn where
they think AI fits in a finance practice. Their feedback
provided interesting ideas and laid bare misconceptions
about what the technology offers and how it works.
That inspired us to offer guidance on how various AI
and machine learning systems function and ideas toharness them for business benefit.

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Early Innovations for Business
Some of the most useful AI innovations are downrightdull. For instance, algorithmic approaches to opticalcharacter recognition peaked near 95% accuracy
on typical business documents, with handwriting
recognition far less accurate. When these systems
looked at 3,000 characters on a typical page, thealgorithms got 150 of them wrong. That left a lot forhumans to fix — so much for relieving the drudgery ofmoving data from paper into the digital domain.
Today, machine learning techniques have improved
algorithms. Better optical readers also help. With theaid of deep learning, some OCR techs now claim 99.8%
accuracy, depending on the source material.
That near-100% rate makes automating businessprocesses like bill capture a no-brainer for
many companies.
At the other end of the complexity spectrum, AI tools
aimed at enterprises with gigabytes to terabytes of data
may be enhancements to data warehouse and businessintelligence systems, or they may be standalone
products. Particularly in the latter case, adoption can bea huge commitment — one that requires a substantial
ROI promise.
No one implements these products “just to see what AI
is all about.” But for companies with enough data about
customer behavior or other key metrics, AI-assisted
systems can uncover unique insights and correlations
not easily found any other way.
For firms smaller than the Fortune 2000 or not in highly
data-intensive industries, it may seem like the basics
of reading and classifying business documents andtransactions has been the extent of AI’s benefits. And
that indeed is a huge payoff, as it digitizes data and lets
leaders start to automate rote, tedious tasks.
However, there are intriguing use cases that involve
training AI systems on huge datasets and then applying
the learnings to individual businesses. A good example:
systems that predict how changing the price of a

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product will affect retail sales and customer satisfaction.
Determining competitive pricing used to be reasonably
straightforward, even if it took some legwork. You could
go a long way just by visiting competitors’ stores andseeing what stock was being added or removed andwhen sales were run.
Now, with online marketplaces and branded ecommercesites, it’s much more difficult to determine what
consumers will do in the face of price fluctuations
across channels.

Machine learning systems can gather
competitive intelligence by scouring theinternet and use it, along with your own
data on buyer behavior, to determine
price elasticity and predict customer
trends, sometimes at a personal level.
Some machine learning systems even help retailers set
dynamic prices, maximizing the revenue from goods orservices with finite supply, like concert tickets or limitededition items. What will the market bear for a popup
performance in an intimate venue, a hand-painted
bathtub, or a 50-year-old bottle of Scotch?

Behemoths of the Cloud
Many consumer AI innovations have come from thelargest tech companies, and we’ve all watched them
improve over time. Siri debuted with the iPhone 4
in 2011, and Amazon released Alexa in 2015. Google
Assistant followed a year later. Search engines havebenefitted from AI in several ways, not the least of whichis the ability to correct our collective poor spelling. Now,
email apps are spotting grammar issues and offering
fixes or completing sentences on the fly.
These are examples of AI systems that learn and get
more accurate over time.

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As an area of research, AI has been greatly aided by vastand powerful cloud computing environments. Every
major cloud provider now has infrastructure offerings
and AI software libraries that serve as the basis forcreating new AI products and facilitating AI systemtraining at a more palatable cost.
Still, most commercial AI systems require a lotof computing horsepower — so much so thatcommercially viable AI products need to provide amajor return on investment. In the voice recognition
examples we provided above, the return is millions ofpeople engaging on a regular basis and providing lots ofpersonal data. In science, the return needs to be major
discoveries that couldn’t have practically been madeotherwise or assistance with tasks that are too timeconsuming and expensive for humans to do alone.
For instance, pharmaceutical researchers now use AI
systems to simulate millions of chemical compoundinteractions in hopes of developing new drug therapies.
The AI systems involved are big, complex, andexpensive, but they can model countless interactions
in minutes and therefore are a logical alternative tothe usual lab testing of a much smaller number ofcompounds selected based on currently known science.
By quickly finding the 10 or 20 protein combinations thatwarrant human trials, AI has led to creation of drugs that
may never have been found otherwise. It’s a very big
return on a very big investment.

Machine Learning or Artificial Intelligence?
Machine learning (ML) is a huge step toward artificial
intelligence (AI), but it’s not the same thing. You canshow an ML system a few hundred thousand ofanything from X-rays to French-to-English translations;the more “good” and “bad” examples you show thesystem, the more it refines its understanding of
the subject.
A ML system uses models and probabilities that are
refined as it sees examples to become proficient ina task.

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The Origins of AI
For a solid technical look at the evolution of
artificial intelligence, check out the Wikipedia
page on AI. It does an outstanding job
documenting the history, nomenclature, anddirection of AI, a computer science disciplinethat started in the 1950s and has progressedin fits and starts, borrowing from mathematics,
statistics, economics, and even philosophy to get
where it is today.
Early attempts were disappointing for two,
related, reasons. First, the goals were lofty,
tending toward creating a system that displayed
human-like artificial general intelligence, rather
than solving specific problems. Second, thecomputers available throughout the second halfof the 20th century weren’t powerful enough to
support the goals of many projects. Even DARPA
has its patience and funding limits, so research
progressed at an uneven pace until the past 25
years or so when various practical applications
started to appear.
Through its history, the theory of AI was wellahead of the capabilities of computing hardware.
That’s been changing for the past decade or
so. Hardware capable of delivering on lofty AI
theories isn’t cheap, but it is within the grasp oflarger tech companies.

The key tenet of machine learning is that the system
adapts and improves as it’s given more examples toanalyze. Once engineers create a ML system for a given
purpose, they train it by providing examples of the sorts
of items they want the system to evaluate. Engineers
tweak and perfect the algorithm and eventually end
up with a system that’s very useful in evaluating, say,
X-rays. Providing radiologists with ML tools can lead tofaster, better, more economical diagnoses.

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In machine learning, the system produces better results
as it sees more samples, but the algorithms that do thelearning don’t change unless humans tinker with them.
The system we’ve described doesn’t know anything
about cancer research, but it does know, statistically,
what lung image aberrations look like. The algorithms
use pattern matching and probabilities to guide findings,
and they’re highly effective.
In that learning process, ML systems need a lot of data.
Problems that have many samples and classifiable
outcomes are good candidates for machine learning
to solve. Those with computer-readable examples areespecially ideal. Take spam filters. Coming up withtraining data is as easy as digging through any raw email
stream hitting a busy organization’s servers. If you havea few thousand email users, a machine learning system
could become relatively good at spotting spam bylooking through several months of messages sorted by“spam” and “not spam.”
One challenge for businesses is determining whether
the problem you’re trying to solve creates enoughdata for an AI system to adequately learn, andwhether that data accurately describes at all times thecondition you want to test. So, in the above example,
the characteristics of spam might change somewhat,
but good emails will mostly continue to look like goodemails, and bad ones will be relatively easy to spot.
Major email vendors now claim a 99.9% success rate in
identifying spam.
But let’s say you want a ML system to tell you whether
your company’s electric bill is higher than it should be.
When you start programming, perhaps the system
has access to bills from the previous 12 months tolearn from. So the system will have some idea of how
costs vary depending on seasonality. What bad data
would you give it? Perhaps bills that are 20% above or
below the previous year’s bill for each month would beconsidered bad. But then your business grows, you add
more equipment, people, and computers, and the costof electricity changes, maybe by more than 20%. The
system has no basis to understand this context, so it
flags all subsequent bills for human review.

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It’s Not AI Just Because Someone Says
It Is
Because of the current fascination with AI
systems, there’s a temptation to label smart
algorithms as AI. This is a problem if ROI
depends on the system scaling in a certain way
or improving as it’s given new data. Asking
questions about what data was used to train thesystem and how it will learn from your data willoften help identify whether you’re dealing with atrue ML/AI system.
Media outlets bear some responsibility for calling
technology AI when it isn’t, and then others
repeat those claims. An example is technology
from Hawk-Eye Innovations used by theprofessional tennis circuit to determine whether
a ball is within the lines and by Minor League
Baseball to call balls and strikes. Many articles
have labeled the technology as AI because it
uses a series of cameras and some very goodcomputer programming to do what line judges
and umpires used to do.
One place that doesn’t call the system AI is the
Hawk-Eye website. In both use cases, humanjudgment is what you want to eliminate. A tennis
ball is either in or it’s out. Strikes are strikes andballs are balls. Calling both is a perfect use of anon-learning system. That the technology cando the job better than humans doesn’t make it
AI. By that standard, robot welders, painters, andeven car washes would be AI, since they can all
perform jobs once done by humans and do them
faster and more efficiently.

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Your monthly electricity bill doesn’t generate nearlyenough data for a machine learning algorithm toprovide insights. You’d be far better off with an ERP
system that could automatically look at last year’s billand see if this year’s bill differs. You could set a businessrule that says to notify facilities if a bill varies by morethan 20% from the previous year. It’s still going to flag
the bill after you’ve added new equipment and people,
but you’ve only invested 10 minutes to set a businessrule versus spending significantly on a ML system thatwas never going to yield insights you didn’t alreadyhave. And, you can easily adjust the business rule toreflect expected growth.

AI and ML in Finance
As AI and ML technology matures, it’s more commonly
being embedded in business applications. As we’ve
discussed, for CFOs, one of the first benefits is the abilityof systems to recognize, interpret, and classify businessdocuments, storing their contents as data. Accounts
receivable and accounts payable automation systems
use these technologies to digitize and classify paperand digital invoices. The process is considered robotic
process automation (RPA) because once documents are
read, they are further processed according to businessrules you set.

The diagram below makes a distinction between
process-driven automation, which can include
everything from spelling and grammar checkers on thedesktop to those image recognition/optical character
recognition systems we mentioned earlier, and datadriven automation, which depends on ML and AI tooffer insights and guide decisions.
Robotic process automation happens when financeteam members use a system that knows and follows
business rules to accomplish tasks. For expense reports,
if you’re using a system that captures receipt images,
classifies expenses, and enforces reimbursement rules,
you’ve got a good example of RPA.
In our findings from CFOs, many want to leapfrog
from limited automation to artificial intelligence. But
you can’t skip the process automation phase because
this is where you teach systems the business rules tofollow with the data they take in. Without automation,
there’s no way for the AI system to know what to do —it needs both digitized data and knowledge of yourprocesses to accomplish a desired job on its own.

Intelligent Automation
Artificial
Intelligence
Machine Learning
Robotic Desktop
Automation
Robotic Desktop
Automation
Process Driven© Oracle | Terms of Use and Privacy

Data Driven

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Many teams, for example, spend a
lot of time executing repetitive tasks.
Whether it’s managing accounts
receivables or payables, three-way
matching expenses, running payroll,closing the books, or any of many othermonthly functions, automation is anaffordable way to save tremendous
resources while enforcing yourbusiness processes. Not only is that
helpful for finance efficiency, it’s
essential for AI, since AI systems canwork only with digital data.
Right now, automation is the place to start if you’relooking to channel finance team resources to morestrategic tasks, like scenario or demand planning, FP&A,
and other data analysis.
The quickest way to reduce days sales outstanding (DSO)
is to automate most of the work that goes into accounts
receivable. Billing and collection actions happen more
quickly and predictably, the system generates the data
you need to get constant updates on how your DSO is
tracking, and you can get early warnings on accounts
that are pushing DSO in the wrong direction so you candeal with problems early on.
Once a finance team has defined processes anddigitized business data, automated tasks can follow thebusiness rules required to complete them. Now it makes
sense to start considering opportunities to use datadriven machine learning and AI to further improve oreven automate operational decisions.
But before diving into data-driven intelligent automation,
pause to take stock and avoid mistakes you may havemade in the past.

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The market for data-driven AI is shaping up much
like the business application market has over the past
30-plus years. Vendor A might excel at AI-assisted
supply chain management. Vendor B is great at
customer sentiment analysis. Vendor C can apply AI tofinance and accounting. Vendor D has a great solution
for AI-assisted inventory management. You get theidea. Engaging with all those vendors leads to lots ofstandalone systems that each optimize exactly one
piece of the business management puzzle.

Enter the world of deep learning.

Going challenge by challenge to find ML systems that
can help is daunting and probably no wiser than it wasto go problem by problem to find applications to help
solve business challenges.

In a neural network, the idea is to create layers ofartificial neurons that solve a problem by successively
getting into more detail — pretty much how the humanbrain works.

But don’t discount obvious wins, particularly if yourbusiness is slowed by one intractable problem. Just bearin mind that data-driven intelligence is often best servedby collecting many data streams into a single data
warehouse that’s capable of complicated “what-if” data
analysis and that offers techniques like data visualization
along with ML tools. Combining finance data with
operational data, web analytics, lead-generation data,store or warehouse traffic information, customer
satisfaction metrics, and other business insights lets
analysts uncover trends unique to your company thatwouldn’t otherwise be discoverable.

Think of it as each layer of the network analyzing one
feature of the data it’s learning. If it’s looking at picturesof humans, the first layer might determine edges andlines within the picture, while the next layer understands
more about contours. Eventually you get far enoughinto the layers of the network that it can identify eyes,
noses, and ears. If the goal is facial recognition, thealgorithm refines itself so that it can identify individuals
after seeing many millions of varied images of millions
of people.

The key is to keep the advantages ofprocess automation as you move into
data-driven analysis.
Ideally, specialty systems that help with a particular
aspect of business can easily — that is, without a lot ofcustom code or ETL work — use the data store in which
you’ve chosen to keep your business data. Look for
those that do.
Here’s where ChatGPT stirs the imagination. It’s expert
in a lot of things, maybe enough so that it or technology
like it could subvert this whole evolutionary journey frompaper to digital to process automation to data-driven
AI nirvana.

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Go Deep
In ML, the algorithm remains constant unless a
programmer intervenes while the system improves as it
is shown more examples through guided learning. The
next step is to let the algorithm develop as it learns. Thisis the domain of artificial neural networks that seek touse huge arrays of computers to mimic the function of
the human brain in a process known as “deep learning.”

Just like our own brains, the facial recognition processneeds to work on massive amounts of data in parallelto get results quickly enough to be useful. The CPU of atypical computer, like your laptop or phone, might haveeight cores, meaning it can process eight streams or run
eight algorithms simultaneously. It does so very quickly,
but in a task like image processing, data needs to beprocessed in parallel — not eight streams, but 100,000.
In a human brain, millions of neurons may be working
at the same time to allow us to instantly recognize thethings we see.
A revelation in 2012 was that we could use graphicalprocessing units (GPUs) rather than general-purpose
CPUs to analyze pictures. That technology, combinedwith very large data sets and some algorithmic
advances, particularly in the learning phase, led to vastly
improved accuracy in identifying images after training.

Page 8ChatGPT-3 Training Data
DatasetNumber of Tokens

Proportion Within Training

Common Crawl

410 billion

60%

WebText2

19 billion

22%

Books1

12 billion

8%

Books255 billion

8%

Wikipedia

3 billion

3%

Data: Wikipedia

GPUs have hundreds to thousands of cores and canaccess memory very fast, meaning they can efficiently
process lots of data in parallel. In many ways, they’re
ideally suited to neural net designs. In particular, they’re
far better suited to analyzing the very large training sets
than are CPUs. Datasets that can be analyzed in days byGPUs would take years with the same number of CPUs.
Over the past decade, Nvidia and other vendors haverefined their architectures to create chips specificallydesigned for deep-learning applications. Now, deeplearning is leading to the sorts of mind-blowing results
we see from ChatGPT. The generative AI system canpass medical and bar exams because everything itneeds to know to do so can be found on the internet
and in other learning datasets. It’s also a very goodcoder and writes in clear English with few errors.

Reduce to Practice for Business
ChatGPT works on a huge scale. Microsoft reportedly
spent hundreds of millions just to create the computing
infrastructure where the system was trained and now© Oracle | Terms of Use and Privacy

lives. So far, details about GPT Version 4 have not beenreleased, but the Version 3 training dataset was roughly
3 terabytes of text, as shown above.
To put this in context, the King James version of the
Bible runs 4.13 megabytes, meaning the algorithm
studied text amounting to more than 800,000 KingJames Bibles. In that training set listed above are
medical and legal texts along with the many, many
programming language examples and explanations on
the web plus a lot of general-knowledge content.
And while V3 was trained only with text, V4’s training will
include images.
You can have a conversation with ChatGPT-3. It
will answer questions and, beyond that, build on
previous responses. For purposes of answering basic
questions and writing content for business or otherpurposes, it’s fairly good. If you need a refresher onbusiness practices — say, the pitfalls of intercompany
transactions — it can provide that.

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We asked ChatGPT a question. Here’s its response.
Q: Can you tell me 5 things to watch out
for when accounting for intercompany
transactions?
A: Sure, here are 5 things to watch out for when
accounting for intercompany transactions:
1. Proper documentation: All intercompany
transactions should be properly documented
and recorded. This documentation should
include invoices, purchase orders, andpayment receipts.
2. Elimination of intercompany transactions:Intercompany transactions should be eliminatedfrom consolidated financial statements to avoid
double-counting or misrepresentation of thecompany’s financial position.
3. Intercompany pricing: The appropriate pricing forintercompany transactions should be determinedbased on market rates or other industry
standards. This helps to ensure accurate financial
reporting and avoid tax implications.
4. Intercompany loans: Intercompany loans should
be properly structured and documented, withclear repayment terms and interest rates.
5. Legal and regulatory compliance:
All intercompany transactions should comply
with applicable legal and regulatory requirements,
including tax laws, accounting standards, andtransfer pricing regulations. Failure to comply
with these regulations can result in penalties andlegal issues for the company.

That’s a pretty good list of concerns, and of course you
can dig further. But this version of ChatGPT can’t audit
your books and flag where intercompany transactions
haven’t been handled properly because it doesn’t haveaccess to your books and wouldn’t inherently know how
to read them.
Finance and other business leaders would love an
AI system that checks things like intercompany© Oracle | Terms of Use and Privacytransactions or, better yet, one that checks byfollowing the FASB and GAAP rules that ChatGPT
seems to understand.
Asking ChatGPT to describe how to comply withaccounting rules is one thing. Having a system likeit that applies those rules to your books is,unfortunately, another.
Here’s what ChatGPT has to say about ASC 842:
Q: What steps should I take to ensure mycompany is ASC 842 compliant?
A: ASC 842 is an accounting standard that affects
the way companies report leases in their financial
statements. To ensure your company is ASC 842
compliant, you should take the following steps:
1. Proper documentation: All intercompany
transactions should be properly documented
and recorded. This documentation should
include invoices, purchase orders, andpayment receipts.
2. Elimination of intercompany transactions:Intercompany transactions should be eliminatedfrom consolidated financial statements to avoid
double-counting or misrepresentation of thecompany’s financial position.
3. Intercompany pricing: The appropriate pricing forintercompany transactions should be determinedbased on market rates or other industry
standards. This helps to ensure accurate financial
reporting and avoid tax implications.
4. Intercompany loans: Intercompany loans should
be properly structured and documented, withclear repayment terms and interest rates.
5. Legal and regulatory compliance:
All intercompany transactions should comply
with applicable legal and regulatory requirements,
including tax laws, accounting standards, andtransfer pricing regulations. Failure to comply
with these regulations can result in penalties andlegal issues for the company.

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It understands the regulation, so creating a systemthat does the work seems well within reach. ChatGPT
is already advising you not to try it without software.
Tech companies are scrambling to release products thatleverage ChatGPT by building systems that access thetechnology through APIs or that use similar technology.
Those systems could read and interpret your books
and apply an AI-driven accounting tool to comply withFASB rules and your business practices, though thetechnology used probably won’t be generative AI.
Your AI accountant is somewhere on the horizon.
The question will then be: How much do you trust
your AI accountant, and how will you review its work toensure the rules you’ve set for managing your books
are followed?
CFOs and controllers will start out with caution, so
understanding how to monitor work AI does for you will
be a big part of adoption.

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Ethical and Practical Concerns
Ethical concerns abound, particularly as AI systems start
making decisions that affect lives in substantial ways orcreate output that encroaches on the copyrights andintellectual property ownership of humans.
Discussions of these issues are important, complicated,and nuanced. Ethicists, lawmakers, and technologists
are raising warnings about potential issues that could
arise from a headlong rush toward widespread use of AI
in all sorts of applications. An open letter encouraging apause in development of “Giant AI Experiments” starts
out with these two sentences: AI systems with humancompetitive intelligence can pose profound risks tosociety and humanity, as shown by extensive research
and acknowledged by top AI labs. As stated in the
widely-endorsed Asilomar AI Principles, Advanced AI
could represent a profound change in the history of lifeon Earth, and should be planned for and managed withcommensurate care and resources.

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The letter has been signed by more than 28,000 people
so far, including many leading thinkers in technology,
business, and academic research. Many of the signers
have pointed to the unintended negative consequenceson society of unregulated social media and postulatethat unregulated AI could have a much greater effect.
How will this affect your industry? Here’s a short list of
papers to consider:• AI in healthcare: As in many areas, some of the leadconcerns in healthcare revolve around whether
existing legal structures will be sufficient to assess
responsibility for the actions of AI systems.
• AI in law enforcement: Two questions tend to
dominate law enforcement concerns. The first isaround privacy. AI has the potential to use hugeamounts of data to let law enforcement track what
citizens are up to. There’s an obvious potential forabuse of such power. The second concern revolves
around implicit bias. Training sets may not include
enough data about minority groups. As a result, false
positives, including in facial recognition, are more
likely in those groups, possibly leading to improper
actions by law enforcement.
• AI ethics in the legal profession: Lawyers have aprofessional obligation to fully understand and agreewith and stand behind the output of their practice,whether it’s creating contracts, offering opinions on
disputes, or citing case law that would guide actions
on behalf of a client. AI offers a way to much more
quickly reach legal opinions. However, particularly in
deep-learning systems, the tools operate as a “blackbox,” which is to say the algorithm used to create
output is purely mathematical and therefore notintelligible to humans in a way such that we can know
if the output was derived “in the right way.” As a result,lawyers can’t just trust what AI tells them. Vetting
is required.

© Oracle | Terms of Use and Privacy• AI ethics in banking: Here again, the black box natureof systems leads to concerns. AI will advise bankers
on issues like whether clients are suitable for loans,or whether the bank’s risk portfolio is well managed.
But without understanding how conclusions werereached, it’s impossible to know whether implicit
bias played a part. In more complex matters of risk
management, AI systems could propose portfolio
structures that are so complex that bankers andauditors cannot assess whether they’ll meet the riskgoals of the institution.
This very short list provides a sampling of the questions
that must be addressed industry by industry and use
case by use case. Stanford University offers a paper
on AI ethics that explores the general topic, andHarvard Business Review offers another on how AI
might be regulated.

The Bottom Line
We’re only beginning to understand the ramifications ofAI, both good and bad. There are many, many questions
to be answered about its use, but the promise is alluring.
Vendors won’t necessarily have all the answers, so
smart decision-makers will carefully consider theirchoices and watch efforts to restrict what AI is allowed
to do.

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