This document discusses opportunities to leverage artificial intelligence (AI) technologies to transform existing business services. It identifies several business services sectors that are well-suited for AI enablement, including accounting services, third-party logistics, contact centers, property management, travel agencies, and insurance agencies/brokers. These sectors are analyzed based on their market size, growth rates, level of existing technology adoption, and potential high-impact AI use cases. The document argues that early adopters of AI in business services will gain competitive advantages over peers through improved services, automated tasks, and increased productivity.

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AI Enablement of Business Services
August 2019
Kirk Mahoney, Jackson Evans

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AI Enablement of Business Services Overview
▪ Market for Artificial Intelligence
• The businesses that move first to embed AI technologies will have a pronounced competitive advantage over
their competitors, improving their services (taking market share and increasing revenue) and automating tasks
(reducing costs).
• AI will contribute up to 15.7tn to global GDP by 2030, 55% of which will come from labor productivity[1].
• 51% of time spent in US occupations, representing $2.7tn in wages, is spent on highly automatable tasks
(collecting and processing data & performing manual tasks in a predictable environment)[2].
▪ Opportunity / Framework
▪ Generation defining technology – AI will ultimately have an impact on productivity on the magnitude of steam
power, electrification, computing, etc.
▪ Core tech done by others – the frameworks (e.g. Google Tensorflow), core AI services (e.g. Amazon AI
Services), and foundational applications (e.g. Microsoft LUIS) will either be open source & collaborative or
otherwise require immense amounts of capital and data to develop and therefore are better left to the
Internet Giants and Silicon Valley-based investors.
[1] PWC: Sizing the prize: What’s the real value of AI for your business and how can you capitalise?, 2017
[2] McKinsey Global Institute: A Future that Works, 2017
Catalyst’s Investment Thesis: license third party applications and services to transform an existing business service

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$ in millions Legacy Business Services AI-Enabled Business Services
Year 0 1 2 3 4 5 0 1 2 3 4 5
Revenue $25.0 $26.3 $27.6 $28.9 $30.4 $31.9 $25.0 $27.5 $30.9 $35.6 $40.9 $47.1
Revenue Growth % 5% 5% 5% 5% 5% 5% 5% 10% 13% 15% 15% 15%
Gross Profit $6.3 $6.6 $6.9 $7.2 $7.6 $8.0 $6.3 $7.6 $9.3 $12.5 $16.4 $21.2
Gross Margin % 25% 25% 25% 25% 25% 25% 25% 28% 30% 35% 40% 45%
Sales & Marketing $1.3 $1.3 $1.4 $1.4 $1.5 $1.6 $1.3 $3.4 $4.6 $4.6 $4.7 $4.7
Research & Development 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 3.1 3.6 4.1 4.7
General & Administrative 2.5 2.6 2.8 2.9 3.0 3.2 2.5 2.8 3.1 3.6 4.1 4.7
EBITDA $2.5 $2.6 $2.8 $2.9 $3.0 $3.2 $2.5 $0.0 ($1.5) $0.7 $3.5 $7.1
EBITDA Margin % 10% 10% 10% 10% 10% 10% 10% 0% (5%) 2% 9% 15%
Note: figures above are illustrative
The Opportunity for AI Transforming Business Models
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4
5
Growth accelerates as the business takes market share from competitors stemming from superior service
Margins expand as tasks are automated
Increase in customer retention drives a shift from “contribution” mindset to “LTV / CAC” mindset; S&M becomes a
source of operating leverage
Research & development investments pay dividends in the form of higher growth and margin
Earnings are initially plowed back into the business before economies of scale drive higher operating margins
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4. 4
AI Framework
Defining what constitutes “AI” presents a challenge. AI includes any tech that allows machines to simulate the cognitive capabilities of a human.
However, consensus has changed over time; as AI technologies go from leading edge to commercially accepted to mundane, those technologies
are often dismissed as “not real AI”. For the purposes of this paper, we define AI to be the generation of technologies that have been enabled by
advances in machine learning (“ML”). ML adds adaptability to computer algorithms, thus allowing machines to continuously improve their
performance on tasks. ML has become commercially viable due to continued decline computing costs and democratization of access to parallel
computing via the cloud.
Natural Language Processing Robotic Process Automation Advanced Analytics Computer Vision
Definition: Enabling machines to
understand and generate human
language, both textual and auditory
Key Applications:
• Virtual assistants
• Interactive voice response
• Speech to text
• Chatbots
• Entity extraction
• Text mining
• Language translation
• Grammar checking
Definition: Enabling software to emulate
human tasks within digital environments
Key Applications:
• Form automation
• Data extraction
• Web scraping
• Data validation
• Anomaly detection
Definition: Enabling time series
algorithms to optimize over time in order
to more accurately predict future events
Key Applications:
• Price & marketing optimization
• Customer segmentation
• Route optimization
• Predictive maintenance
• Financial & risk modeling
• Sentiment analysis
Definition: Enabling machines to
recognize and apply context to images
Key Applications:
• Optical character recognition
• Robotic guidance
• Facial recognition
• Process control
• Autonomous vehicles
• Image search

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Who Will Succeed
Visionary Technologist: Choosing which investments to make and over what time frame will require an executive with a
unique mix of creativity, technical insight and experience. Businesses that are serious about getting ahead of their
competitors will empower this individual by placing them in the C Suite.
Digital Foundation: The companies that will be best positioned to adapt AI will be ahead of the curve of their peers in
terms of digitization of existing processes. At least over the intermediate term, the majority of AI investments will
automate digital, as opposed to physical, processes. Companies that have gone through significant digital investments
will have learned lessons in terms of implementing complex technology which can apply to implementing AI.
Strong Data Strategy: Businesses that are able to harness proprietary data assets to train the algorithms at the core of
the AI systems they build will benefit from their technology optimizing to the unique elements of their operations and
customers. Doing so will require in house expertise around data governance and analytics.
Early Mover: As is often the case with technology, there are clear advantages for early adopters of AI including brand
recognition as a technology leader and greater lead time to achieve economies of scale.

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Who Will Succeed (Cont.)
IT Capacity: Implementing AI technologies will often require investments into the underlying IT infrastructure. As such,
companies that have a disciplined approach to managing IT resources and a capable staff of IT professionals will be best
able to make the parallel investments in IT infrastructure.
Project Prioritization: While we are bullish on the opportunity to leverage AI technology to improve business services
companies, we do not think that an “all of the above, right away” strategy makes sense. Rather, we believe that it is crucial
that companies ensure that their roadmap of projects aligns with their strategy and that they do not “bite off more than
they can chew”, so to speak.
Project Management: Making the most of AI investments will require exceptional project management. Oftentimes
implementing an AI technology will require training entire departments how to use complex systems and change their
existing workflows. Other times, systems may require an entire reorganization human resources.
Senior Buy In: Perhaps most important of all, we think the companies that succeed will be the ones that have buy in
from all major shareholders and operators.

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Target Business Services Sectors
Accounting Services
Industry Metrics:
• $114bn revenue
• Growing 3.8%
• 95k firms
• 568k employed
Industry Dynamics:
• SMB and mid market sized businesses require
outside expertise for bookkeeping and tax
services
• Business of all sizes require third party audits
• Self-employed market growth (“gig economy”)
driving robust demand
Existing Technology Enablement:
• Accounting firms leverage third party general
ledger software (e.g. Quickbooks, Sage, etc.), tax
preparation software (e.g. ProConnect), and
other accounting software (e.g. Expensify,
Bill.com) for their clients
• Internal tech stack includes CRM, ERP, project
management and digital marketing tools
AI Use Cases:
• Machine vision powered OCR technology to
digitize and automate the processing of
invoices, receipts and other financial data
• RPA to automate transactional processes such as
data extraction and validation
Industry Metrics:
• $194bn revenue
• Growing 3.0%
• 21k firms
• 425k employed
Industry Dynamics:
• Commerce and manufacturing businesses
frequently rely on outside parties to streamline
or optimize warehousing and transporting their
goods
• Ecommerce, omnichannel commerce and
globalization are making supply chains more
complex, in turn driving demand for outsourced
logistics
Existing Technology Enablement:
• Warehouse Management Systems (“WMS”) serve
as the transactional system of record
• Warehouse Control System (“WCS”) directs real
time activity within a warehouse
AI Use Cases:
• Machine vision enabled robots to pick, transport
and sort goods
• Leverage RPA for transaction logging including
invoice processing
Industry Metrics:
• $28bn in revenue
• Growing 4.7%
• 28k firms
• 541k employed
Industry Dynamics:
• Firms of all size rely on contact centers both for
telemarketing services as well as providing
omnichannel customer support
• A shift in priority from cost to quality has driven
contact centers from offshore countries back to
the US
Existing Technology Enablement:
• CRM systems serving as knowledge
management and system of record
• Telecommunications systems enabling and
optimizing voice and SMS interactions
• More tech forward centers will leverage
workflow automation tools such as interactive
voice response (“IVR”), predictive dialing and
voice annotation tools
AI Use Cases:
• Apply natural language processing to move
beyond speech-to-text to seamless, automated
interactions
• Forecast capacity requirements and staff
turnover using predictive analytics
Third Party Logistics Contact Centers
Note: Industry metrics per IBIS, reflect US market in 2019

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Target Business Services Sectors (Cont.)
Industry Metrics:
• $76bn revenue
• Growing 0.8%
• 281k firms
• 844k employed
Industry Dynamics:
• Both institutional and mom and pop real estate
owners often elect to outsource property
management for convenience or economics
• 25 year low rental vacancies driving strong
demand for property management services
Existing Technology Enablement:
• Property management software (e.g. MRI
Software, Buildium, etc.) provide business
management features including accounting,
property websites, customer portals and tenant
applications
• IOT & smart building systems enable property
management companies to automate building
operations including temperature and security
AI Use Cases:
• Embedding predictive analytics to optimize
building operations
• Leveraging chatbots to automate
communications related to maintenance
requests and other tenant interactions
Industry Metrics:
• $41bn revenue
• Growing 3.3%
• 62k firms
• 238k employed
Industry Dynamics:
• Travel agencies largely exist to lessen the friction
for businesses that frequently book travel last
minute
• US business travel outlook remains strong
Existing Technology Enablement:
• Online travel agencies provide a digitized form
of self-served travel booking, most suitable for
consumer travel
• Online booking systems provide business
management features including trip building
tools and online management
• Other internal tech stack incudes CRM and
digital marketing tools.
AI Use Cases:
• Providing chatbots to automate response to
customer inquiries
• Forecasting flight and hotel prices using
predictive modeling
Property Management Travel Agencies
Note: Industry metrics per IBIS, reflect US market in 2019
Industry Metrics:
• $167bn in revenue
• Growing 2.4%
• 425k firms
• 1,027k employed
Industry Dynamics:
• Insurance agents and brokers serve as a crucial
conduit between insurance carriers and
policyholders (both businesses and consumers)
• Agencies and brokers typically receive sales
commissions based on premium streams,
creating a recurring revenue base
Existing Technology Enablement:
• CRM and customer engagement systems
including CPQ and eSignature capabilities
• Integrations with carrier partners core insurance
system for underwriting, policy and claims
management
AI Use Cases:
• Leveraging RPA to automate internal processes
such as inputting data from forms
• Using alternative data generated by computer
vision to inform underwriting decisions
• Automating policyholder engagements with
natural language processing enabled technology
including chatbots and virtual assistants
Insurance Agencies & Brokers

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Please send any inquiries to jackson@catalyst.com