The spring of AI

The spring of AI

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  The AI spring -Leveraging Collective Disruptor Insights 1 By Pari Natarajan , Sarim Hassan
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  Introduction of Turing test Convergenceof innovations has moved AI forward in the last few years 1950 Computational Power Data Platforms Better Algorithms First AI program to play Tic Tac Toe 1960 RDBMS LOGIC THEOREMS -Single layer learning, Perceptron, Adaline IBM Deep blue defeats Gary Kasparov 1997 OLAP NEURAL NETWORKS - Multilayer Back propagation 512 Core GPU 2880 Core GPU 12000 Core GPU Cost of Computing $200 Per million transistors $50 Per million transistors $0.05 Per million transistors 2011 2015 Watson became Jeopardy Champion BIGDATA PLATFORMS -HDFS DEEP LEARNING -Convoluted Neural Network 2 DeepMind’s self-taught AI can beat human players at 29 of 49 Atari games
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  And is disruptingall industry verticals 1- Analysed basis data maturity, software penetration, regulatory restrictions across the value chain representing disruption potential over next 5 years 2 – Analysed basis current investments ( talent + acquisition) for all players BFSI Healthcare PotentialtoDisruption1 AI Maturity 2 Retail Predictive diabetes management solution AI based Robo advisory service. Enterprise Software Semicon Consumer Electronics Microsoft cloud platform Azure, has NLP capabilities Nvidia’s Machine Learning Enabled Hardware - 12x faster. Recommends what styles to wear based on current customer photographs Driven 130 million miles using the autopilot feature since 2014. Auto Aerospace Consumer Software 95% of Facebook’s $17B revenue was generated through ML enabled Advertising. 3 Source: GEIP Telecom Artificial neural network to adjust helicopter rotors faster and more accurately Amazon Echo- equipped with the NLP platform Alexa.
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  Healthcare Infrastructure Tech Mafias arebuilding an AI-first future Automotive Consumer AI Talent Acquisition Patents30K $10 B 300+ Wearables for Health monitoring Project Titan Siri controlled home kit Apple Smartwatch iPhone iOS 10 image recognition Spotlight for images & text Siri AI Platforms Kinect SwiftKey Cortana for Healthcare Microsoft –Volvo Self driving Cortana Microsoft Graph – Sales lead scoring Hololens Azure ML DSSTNE Oculus Facial recognition Facebook M Facebook Deeptext FAIRWit.ai Alexa Recommender systems AWS ML CNTK 4 Source: GEIP Allo Google Home Verily –algorithms for Diagnosis Deepmind for Healthcare Google X nanoparticle research Google X –Self Driving Android Wear Smartwatch AI Robot- GoogleX Google Prediction API Google Now Tensorflow Project Jacquard Deepmind Google for Work
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  5 Sensing the opportunity,VCs have increased their investment in AI start-ups Quarterly funding trend (2013-16 YTD) Q1, 2012 Q1, 2013 Q1, 2014 Q1, 2015 Q1, 2016 $94 $137 $253 $121 $302 $552 $926 $901 $602 $1,049Raises $100M for Deep learning based ultrasound Google acquires Deepmind for $500M Raises $65M for ML based threat detection Q1, 2011 Focussing on reverse engineering the neocortex raised series A AI based unicorns have emerged since July, 20165 Billion Dollar Valuation Line Zoox Valuation - $1.85 Bn Healthcare Valuation - $1.55 Bn Automotive Valuation - $1.5 Bn Enterprise Valuation - $1 Bn Consumer Valuation - $1 Bn Healthcare Valuation Age of Start-up Zymergen
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  Healthcare Infrastructure AI start-ups areambitious and trying to disrupt various industry verticals Automotive Consumer AI Talent Funding Patents45K $15 B 200+ iCarbonX Zoox Api.ai Lumiata Butterfly Zymergen Drive.ai Affectiva H20.ai Attivio AI Platforms Imagen Technologies Nutonomy Diffbot Sentient.ai x.ai Vicarious Systems Trifacta Ayasdi 6 Source: GEIP MobilEye ZenDrive Anki Jibo Magisto Ugobe Mioji Luka Gluru EmotechSherpa SentenAI SigOpt $ 721 Mn in investments $ 1217 Mn in investments $ 365 Mn in investments $ 1032 Mn in investments $ 996 Mn in investments
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  7 Traditional methods ofanalysing individual competitors doesn’t work in analysing start-ups Porter’s Five Forces Model SWOT Analysis7S Framework
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  8 SWARM DISRUPTION FRAMEWORK Leveraging freshlenses to understand the collective impact of disruptive start-ups in highly dynamic segments like artificial intelligence to track developments in key business dimensions Zinnov’s Swarm Disruption Model provides an holistic approach to gain insights from start-ups
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  9 Uses data fromZinnov’s proprietary database on start-ups
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  10 Sample lenses usedto gain insights on the AI Swarm disruptions Value Chain Disaggregation Understand collaboration points with AI start- ups across the industry value chain Deadpool Intensity Analyse constraints for AI start-ups to scale and gain perspective into market conditions Absorption Pulse Examine the focus areas and drivers for M&A & investment strategies of incumbents Use Case Adoption Patterns on the top use cases adopted by highly scalable general purpose AI platforms Global Ecosystem Maturity Congregation of AI start-ups in enabling ecosystems across the world Skillset Transformation Adapt to the changing human capital needs to drive an AI-first business strategy
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  Value Chain Disaggregation 11 Start-ups areleveraging deep learning techniques to build solutions for autonomous cars and infotainment systems ADAS Driver Safety Autonomou s cars Image Processing Connected Car/ Infotainment Machine Learning NLP Computer Vision Deep Leaning $24M $10M $80M $18M $337M TotalFundingofValueChainSegment VALUE CHAIN SEGMENT AITECHNOLOGY Key Trends in Auto Start-up LandscapeImpact Assessment of AI start-ups in the automotive value chain HighLow AI Tech Adoption Tier 1 players on acquisition spree Interest from Non Automotive Companies OEMs creating innovation labs in Bay Area Emergence of New Tier 1 players
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  12 AI start-ups areleveraged across the retail value chain by large retail enterprises Security& Surveillance Supply Chain Management In-Store Analytics Customer Engagement Multi-Channel Marketing Subscription Model Licensing Model One Time Payment Affiliate Fees $24M $ 90M $ 159M $ 100M $105M TotalFundingofValueChainSegment Acquired predictive Intelligence Platform to reduce fraud and improve customer targetingCustomer Engagement Supply Chain Management Acquired Machine learning platform that solves out-of-stock and overstocking problems Top retailers are partnering with AI start-ups to bolster their value chain Impact Assessment of AI start-ups in the retail value chain Lowe’s set up an innovation lab in Bangalore for AR/VR tools on demand manufacturing and robotics Value Chain Disaggregation BUSINESSMODELS VALUE CHAIN SEGMENT HighLow AI Tech Adoption
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  13 AI platform companiesare prioritizing use cases in Finance, Marketing and Healthcare Total Funding Patents Vicarious Systems DataRobo t $135.8 Mn $97.9M $75.6 Mn $67M $57.4 Mn 8 16 9 - 3 Total Funding Patents $48M $37.4 Mn $34.2 M $30.6 Mn $30M 4 29 42 - 2 Sentiment Analysis Personal- ization Customer Engagement Digital Marketing Marketing Fraud Detection Insurance claims Algorithmic Trading Risk Modelling Finance Patient Monitoring Drug Delivery Population Health Precision Medicine Clinical Variance Healthcare Use Case Adoption
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  14 US is thedominant innovation hotbed for AI start-ups led by the Bay Area Ecosystem 2322 Start-ups $14.74 Billion Global AI start-up distribution 86 Canada Netherlands 25 26 Australia Brazil 18 France 43 25 Singapore Hong Kong12 Total FundingNo. of Start-ups Global AI start-up distribution $11.5 B $0.6B $0.03 B $0.5 B $0.6 B $0.1 B $0.1 B $0.1 B APPLICATIONS-FINTECH, HEALTHCARE APPLICATIONS –HRTECH, HEALTHCAREAPPLICATIONS – AUTO, FINTECH, RETAIL PLATFORMS- DEEPLEARNING, VISION ENABLERS –BIGDATA PLATFORMS Vision based advanced assistance system AI based consumer robotics start-up Massively scaled deep learning ML based threat detection ML based recruitment solution ML for retail ML for personalised healthcare NLP API Data cataloguing and cleaning PLATFORMS- DEEPLEARNING, NLP Global Ecosystem Maturity $0.1B
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  6 deals 15 Corporate acquisitions andinvestment strategies indicate the direction the industry is focussed on Build New Products Bolster Technology Stack 23 deals $625M Google Deepmind Enter New Markets 10 deals Not disclosed Amazon Snaptell Facebook Wit.ai Not Disclosed Acquihire Talent 6 deals Intel Indisys Not Disclosed Technology and market expansion are primary drivers for M&A Amazon Apple Facebook MicrosoftGoogle Acquisition Year AcquireeMaturity Dot Com Era Smartphone Era Cognitive Era 2004 2010 2016 1 2 3 4 5 Bulk of the acquisitions by Microsoft and Google to boost their Search Tech. MS and Apple begin work on Gesture Control devices. The Tech Mafia investing heavily in AI enablement platforms Google begin work on Maps NLP Vision ML NLP Vision ML NLP Vision Robotics Indicates Average Salesforce Intel Oracle IBM GE Absorption Pulse
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  16 Legend Application Companies Platform Companies InfrastructureCompanies Consumer Enterprise Industry ML NLP Vision Data Platform Hardware Age of Start-up 0 2 4 6 8 10 12 Deadpool Scale(BasedonInvestments,HeadcountGrowthandCustomerTraction) Major factors that have prevented AI start- ups from crossing the value chasm Regulatory Restrictions Business Model Product Market Fit Regulatory restriction and lack of product/market fit dominates the reason for start-up failures Series A Seed. $3.1M Series C Comma.ai *700 Start-ups plotted Deadpool Intensity
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  17 AI start-up engineeringfootprint distantly different from incumbent competitors R&D IT Sales & BD Product Management Others 29.7% 33% 21.5% 7% 30% 38% 5.8% 4% 13% 17% HC 10+ yrs exp Hardware Software Architect Analytics UI/UX 24% 23% 5% 6% 14% 73% 50% 92% 55% 52% HC 10+ yrs exp 12% 18% 4% 12% 10% 80% 30% 90% 36% 40% ML/NLP Release QA 11% 8% 11% 42% 27% 73% 43% - 2% 26% - - Engineering Talent Hired From Skillset Transformation
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  18 Swarm Disruption Modelwill allow enterprise leadership teams to answer several strategic questions CTO HR Product Manager Strategy and M&A “ “ What are the emerging technology paradigms that disruptors are leveraging to get ahead ? What is the nature of talent I need to recruit to make my business AI – ready ? “ “ What are the top locations where my organization can hire new age skills ? “ “ How mature are start-ups operating in aligned areas and what synergies can they bring ? Should I invest, acquire or partner with emerging disruptors in my space ? “ “ What value adding features can be incorporated into my products to enhance customer satisfaction ? What new business models are gaining traction in my industry ?
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