AI Enablement of Business Services
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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 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](https://image.slidesharecdn.com/aibusinessservicesv108-190909143713/85/AI-Enablement-of-Business-Services-2-320.jpg)
AI Enablement of Business Services
- 1. 1 AI Enablement of Business Services August 2019 Kirk Mahoney, Jackson Evans
- 2. 2 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
- 3. 3 $ 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 1 2 3 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 1 2 3 4 5
- 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
- 5. 5 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.
- 6. 6 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.
- 7. 7 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
- 8. 8 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
- 9. 9 Please send any inquiries to jackson@catalyst.com