The Business Impact and Use Cases for Artificial … Content Generation ... Service providers create custom-made solutions IT vendors create APIs, ... Algorithm Market Places

CONFIDENTIAL AND PROPRIETARYThis presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other intended recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

The Business Impact and Use Cases for Artificial Intelligence

Tracy Tsai

Research Vice President

1 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

Disruption

10 years ago,we struggled to find10 ML-basedapplications ...

In 10 years, we willstruggle to find10 that don't.


1. How can we leverage AI in our business? What are the use cases ?2. What types of hardware deliver AI technologies? 3. What impact will AI have on technology providers and business users?

Key Issues


A Proliferation of Use Cases

Churn Management

Cross-Selling

CustomerSegmentation

...

B2B Propensityto Buy

Recognition

DynamicPricingRecommender

Systems

Cognitive/Smart Systems

PredictivePolicing

Smart Traffic

Fleet Optimization

QualityManagement

Drivers of Quality

Fluctuations

FailurePrediction

IT Ops. (Security)Workforce/HR

WorkflowFraud

Self-DrivingCars

& Robotics

DemandPrediction

Proxy Data

Google Flu TrendsInrix

Social Media

Automation


PredictiveClassificationExploratory

Computing VisionNatural Language Processing

Five Major Types of Deep Learning Usages


Consumers Financial Health Care Transportation Legal MarketingInternetcompanies Workplaces

Autonomous Vehicle

Investment (Robobroker)

Medical diagnosis Smart Traffic Legal robot Customers' behavior

Search Content Generation

Drone/robots Banking (credit score)

Predictive Treatment Fleet Optimization Draft document Ad targeting Ranking Virtual agent for enterprises

AR/VR/Wearable

Accounting Healthcare advisor Self-Driving Bus Crime & Fraud Productsrecommendation

Ad targeting Conversational App

Virtual Personal Assistant

Insurances (underwriting)

Monitoring Drones Predict legal outcomes

Recommended products pricing

Face/Image recognition

Securities and confidential

Smartphone/PC/ Speaker

Fraud & Crime X-rays and MRI AR/VR/Wearable E-discovery Predictive customer service

Social network AR/VR

Home Appliances Facial ID ATM/Remote

Hearing or visually impaired to hear see

Failure Prediction Screen claims for personal injury

Predict customers' needs

Languagetranslation

Wearable

IP Camera/ Surveillance

Virtual customer assistant

Service robots Surveillancesystem

Capture relevant details

Social media Virtual agent Surveillance system

ML/DL Helps Cases with Large Data in a Narrowed Domain


Five Commonalities across Vertical Use Cases

1. Tedious, repetitive, time consuming2. Difficulty processing excessive or complex data3. Performing critical and real-time decision making in a complex and fast

changing situation4. Instructional, manual-driven user interface and user experiences 5. Tasks that exceed or challenge human physical capabilities


Purposes of Using Artificial Intelligence / Machine Learning

27%

37%

49%

50%

55%

33%

46%

59%

58%

68%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Machine Automation (e.g. smartrobots,autonomous cars/equipment)

Predictive insights

Process Automation

Virtual Assistants

Smart Advisors

China WW

Base: Involved in decisions for IT Services & Sourcing. Use or plan to use AI/Machine Learning; n=703; Multiple responses allowed.A03. For what purpose(s) is your organization using or planning to use Artificial Intelligence/Machine Learning? Source: Gartner Annual Enterprise Survey, 2016

76% use or plan to use AI/Machine Learning within 12 months

8 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.8 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

By 2021, 30% of net new revenue growth from industry-specific solutions will

include artificial intelligence (AI) technology


Machine learningdepends

on

good-quality data

True story: bad data for "tank recognition"


Actionable Plans

Identify use cases or old problems that AI can fix

Identify and prioritize use cases aggressively

Examine available resources for AI

Seek C-level support

Invest in recruiting the right talent and leadership


Key Issues

1. How can we leverage AI in our business? What are the use cases? 2. What types of hardware deliver AI technologies?3. What impact will AI have on technology providers and business

users?


1. Processors (GPU, TPU, ASIC, FPGA, CPU) 2. HPC/Supercomputer infrastructure3. Communication network4. Personal devices 5. Connected home devices 6. AR/VR HMD7. Drones8. Robotics9. Automotive 10. Sensors and application components (audio, camera, LiDAR, etc)

Top 10 Types of Hardware for AI Delivery

Acceleratecomputationalperformance

AI-enabled autonomous endpoints

AI-enabled endpoints


DNNs Driving Non-Traditional Vendors to Develop their own Accelerators Not only Intel and NVIDIA, more AI solution providers are developing

customized accelerators/coprocessors to improve AI performance

Non-Chip Vendors:Microsoft's Altera FPGABaidu's Xilinx FPGAGoogle's TPUIBM TrueNorth chips

Chips Vendors:NVIDIA Drive PX, Jason, TeslaIntel Xeon Phi, Altera FPGA, Nervana, and MovidiusAMD Radeon Instinct GPU


Devices Vendors Look to AI to Differentiate Their Offerings

By 2019, 20% of all smartphone interactions will

be done via a Virtual Personal Assistant

Windows Hello Facial Recognition on Surface Pro 4

By 2020, zero-touch UI will be available on 2 billion devices and Internet of Things (IoT) endpoints

Virtual agent for health, fitness or workplace safety

and productivity.

Boltt launched an AI enabled wearable with a virtual Trainer

Alexa on Huawei Mate 9 Google Assistant on Pixel

Apple Siri on iphones


AI-enabled Connected Homes Become Truly Smart Homes

Seeing

Listening, answering, reminding

Reasoning, analyzing

Learning, adapting, personalizing

Recommending to support decisions, or decision automation

AI technologies enable intelligent capabilities

Remove challenges of connected home

Eliminate most manual operation Reduce users' learning curve Enhance system decision quality Solve the issue of silo home

systems Make sense of connected home

applications


4 Strategic Alternatives for Businesses Applying Artificial Intelligence Technologies

4

1 3

2MODE 1

Fundamental necessary to

keep business running lowest

risk, lower operational

changes

MODE 2Exploring new

business, willing to take risk,

allowing failure, bigger

operational changes

Technology Innovation Traditional Products New Product Types

New ProductsNew Business Model

e.g.New AI-solutions as a service for vertical end users (B2B) or tech

providers (B2B2C)

Current ProductsNew Business Model

e.g.VCA new direct sales, partner with AI companies for solution sales, advisory

services – shopping, fitness, healthBusiness Innovation

Current ProductsCurrent Business Model

e.g.Add AI-powered features to your

existing products – conversational AI, facial recognition, predictive service

New ProductsCurrent Business Model

e.g.Expand AI-powered apps, services,

endpoints for vertical industries, connected home, or personal devices


Key Issues

1. How can we leverage AI in our business? What are the use cases? 2. What types of hardware deliver AI technologies?3. What impact will AI have on technology providers and business

users?

18 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.18 © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the

artificial intelligence economy with disruptive business solutions.


Forces Driving the Growing Number of AI Startups

Thousands of AI startups

Plenty of VC

Talent from

academic institutes

Talent from big

companies

Governmentpolicy and

support

Market demand

for innovation

Startups more cost

competitive

Big companies acquire or

invest startup


Identify Revenue Streams - Who Will Pay for AI?

Tech Providers (B2B2C)

Consumer (B2C)

Valueof AI

Governmentand Enterprise

(B2B)


Most Important Service Provider Selection Criteria for AI/Machine Learning Technologies

13%

15%

16%

19%

19%

18%

16%

16%

15%

14%

16%

16%

14%

13%

14%

14%

16%

17%

44%

44%

46%

48%

51%

51%

0% 10% 20% 30% 40% 50% 60%

Consultants that can help us leveragethe technologies for business benefits

To help educate us on what it cando for our business

Specialists with deep skills

To help us innovate new businessmodels or explore new revenue streams

To help us explore cost efficiencies

To help us implement (e.g.,installation,integration, testing, etc.)

Most important Second Third Sum

Base: Involved in decisions for IT Services & Sourcing. Use or plan to use AI/Machine Learning; Excluding DK; n=706A04. What are the top three characteristics that your organization considers most important in its selection of external service providers for Artificial Intelligence/Machine Learning implementations? Source: Gartner Annual Enterprise Survey, 2016


Make vs. Buy vs. Outsource

Outsourceservice providers

Makevia workbenches

1. Requires in-house skills2. Analytics is differentiator3. Agility and control are key

Buypackaged apps

1. Best ease of use2. Often "good enough"3. Best time to solution4. Little differentiation

HybridDelivery

1. In-house skills not avail.2. Packaged apps not avail.3. Give up IP for sake of speed


Deep Learning Adoption Patterns

800-1000 organizations End users use tools for strategic advantage Service providers create custom-made solutions IT vendors create APIs, SaaS or packaged apps

50-100 firms Own tooling Absolute leaders

Let others come up with solutions! Evaluate solutions — understand costs Assess ongoing costs!

Advanced

Skilled End Users

Inexperienced End Users

Tools and APIs Only APIs

APIs, SaaS, packaged apps and

custom-made solutions

3-5K firms Have data scientists

APIs, SaaS, packaged apps and

custom-made solutionsTools and APIs

Most Gartner clients Have no data scientists on staff


Conversational User InterfaceAmazon; Baidu; Facebook; Google: IBM;

IPsoft; Microsoft; NextIT; Salesforce

Virtual Personal AssistantApple; Google; IBM; Microsoft;

Nuance; Xaioi

Virtual Customer Assistants24/7; Artificial Solutions; Creative Virtual; eGain; IBM

Watson; Ipsoft; Microsoft; NextIT; Nuance: Xiaoi

AI-enabled Virtual Agent

Algorithm Market PlacesAlgorithmia; Apervita; FICO; IBM:

Microsoft(Azure); Teradata

Deep ReinforcementLearning

Google, NVIDIA, OpenAI

Cognitive ComputingAccenture; CognitiveScale; Deloitte; Digital

Reasoning; Google; IBM; Ipsoft; KPMG; Microsoft; Saffron (an Intel company)

Proactive SearchApple; Coveo; Google; HPE;

IBM Watson; Sinequa

General Purposes AI Solutions and Platform

DNNs Solutions

Natural Language ProcessingBrainspace;Clarabridge; CognitiveScale; Digital Reasoning; Microsoft; Narrative Science; SAS;

Yseop; Ipsoft, Nuance, Microsoft, Amazon, Apple, Baidu, Xiaoi, AiSpeech, VoiceBox, Unisound,

Natural Language Generation

Arria NLG; Automated Insights; BeyondCore;

Narrative Science: Yseop

Compute VisionAmazon; Apple; Baidu; Facebook;

Google; Herta Security; Luxoft; MathWorks; Microsoft, Megvii,

DeepGlint, Sensetime

Speech RecognitionNuance, ThinkIT, iFlyTek,

AiSpeech, Mobvoi, Unisound, SinoVoice,

AI-enabled Endpoints

Commercial UAVAeryon Labs: Amazon; Boeing: DJI; Facebook:

google (Titan Aerospace): Parrot: precisionHawk;

Skycatch: Trimble

Smart RobotsAethon; Amazon

Robotic;ARxIUM; Google; iRobot; Panasonic;

Rethink Robotics; SoftBankRobotics; Symbotic

Autonomous VehiclesContinental; Delphi

Automotive; General Motors; Google;

Mobileye; Nokia; Nvidia: RobertBosch; Uber; ZMP

Augmented RealityBlippar; Catchoom;

Daqri;Google;Kudan; Microsoft; Wikitude

Virtual RealityHTC, Mechdyne;

NextVR; Oculus VR; Samsung; Sony; Valve;

Virtual Heroes; WorldViz

DronesAIBOTIX;

AeroVironment; Measure; SenseFLy; Cyberhawek; AirShi;

Trimble; Kespry;

A Long List of AI Companies Reveals the New Era of AI in Next 10 Years


Reinvent existing products with new value Answer questions - why do users need this; what issues are solved;

how easy it can be used Outsource solutions or partner with 3rd parties; if you lack required

skills or it takes time to build Provide incentives based on the impact of AI or IoT on the business Invest in recruiting the right talent and leadership Change from transaction sales to reoccurring revenue from buyers

or 3rd parties by offering add-on value services enabled by AI or IoT

Recommendations


Recommended Gartner Research

Conversational AI to Shake Up Your Technical and Business Worlds

Hype Cycle for Smart Machines, 2016

Hype Cycle for Emerging Technologies, 2016

Predicts 2017: Artificial Intelligence

Innovation Insight for Deep Learning

Machine-Learning and Data Science Solutions – Build, Buy or Outsource?

Market Guide: Virtual Customer Assistants

CONFIDENTIAL AND PROPRIETARYThis presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other intended recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. © 2017 Gartner, Inc. and/or its affiliates. All rights reserved.

The Business Impact and Use Cases for Artificial Intelligence

Tracy Tsai

Research Vice President

Documents

The Business Impact and Use Cases for Artificial … Content Generation ... Service providers create custom-made solutions IT vendors create APIs, ... Algorithm Market Places