VALUE CHAIN INNOVATION: THE PROMISE OF AI...of how AI can improve various stages in the value chain, provides insight on how to design business strategies that leverage AI to create

VALUE CHAIN INNOVATION:

THE PROMISE OF AIWHITE PAPER | OCTOBER 2018

STANFORD VALUE CHAIN INNOVATION INITIATIVE

2 STANFORD GRADUATE SCHOOL OF BUSINESS | WHITE PAPER: THE PROMISE OF AI

Table of Contents

I. Introduction ...................................................................................................................................... 4

What is AI? ................................................................................................................................................................. 5

Current State of AI Adoption in Value Chains .................................................................................................................. 6

II. Improvements Along the Value Chain .............................................................................................8–13

Value Chain Phases - Case Study Examples ....................................................................................................................9

III. Insights ...................................................................................................................................14–17

Forms of AI-Driven Value Creation ............................................................................................................................... 14

Strategic Considerations for Adopting AI ...................................................................................................................... 15

The Human Factor .................................................................................................................................................... 15

Technology and Data ................................................................................................................................................. 16

Business Risks of AI .................................................................................................................................................. 16

Potential Limitations .................................................................................................................................................. 17

Looking Ahead .......................................................................................................................................................... 17

IV. Conclusion ...............................................................................................................................18–32

Addendum: Case Studies

Summary of Case Studies ............................................................................................................................................ 20

Design Phase Case: Stitch Fix ....................................................................................................................................... 21

Source, Make, Store, and Delivery Phases Case: IBM ..................................................................................................... 24

Sell Phase Cases:

Adobe ..................................................................................................................................................................26

Pilot AI ..................................................................................................................................................................28

Salesforce ............................................................................................................................................................29

Use Phase Case: OhmConnect ...................................................................................................................................... 31

Citations ......................................................................................................................................33–35

AuthorsHau L. Lee, Principal InvestigatorThoma Professor of Operations, Information and TechnologyFaculty Co-Director, Value Chain Innovation InitiativeStanford Graduate School of Business

Haim Mendelson, Principal Investigator Kleiner Perkins Caufield & Byers Professor of Electronic Business and Commerce, and Management Faculty Co-Director, Value Chain Innovation Initiative Stanford Graduate School of Business

Lauren BlakeHead of Product, Shiftsmart MBA ‘18, Stanford Graduate School of Business

Sonali RammohanEvaluation and Learning Lead, Stanford SeedFormer Director, Value Chain Innovation Initiative Stanford Graduate School of Business


Executive Summary

Artificial intelligence (AI) has been touted as a technology with great promise for value chain improvements. Many papers have focused on the role of AI in manufacturing, logistics, marketing, and other value chain phases, and about organizations’ future plans to deploy the technology. Yet, survey data show that actual deployments are low, particularly in industries where information technology is not core to the business. Many papers have focused on what companies are doing with AI and the types of business benefits they expect or have realized. The focus of this paper is on the how: How companies are designing AI strategies for their value chains, how they have successfully started using AI, how they have acquired data to train models, and how they have scaled or intend to scale the technology. Our findings are based on case studies of six companies with varied backgrounds; proceedings from an executive conference at Stanford Graduate School of Business on May 31, 2018, titled Value Chain Innovation: The Promise of AI; and secondary industry research.

Our research has shed light on the questions executives are asking themselves to determine where AI will deliver value to their organizations. We have learned that, while adoption and investment vary across industries, there is potential for AI to improve many functions, including product design, manufacturing, delivery, retail, and marketing. How companies deploy AI solutions involves defining a clear strategy around a business problem or opportunity in the value chain, determining an approach to developing AI solutions, cultivating data and human capital, and carefully managing the many risks AI can pose.

By ensuring that customer value creation is the “North Star” throughout the process, companies can slowly leverage AI as a tool to generate efficiency, product/process improvement, and even product/process innovation. This paper shares our understanding of how AI can improve various stages in the value chain, provides insight on how to design business strategies that leverage AI to create value, and raises important questions about the implications of an AI-driven future value chain.


I. Introduction

There is much hype around autonomous vehicles, as consumers look toward a future where cars and trucks drive themselves. While full automation of transportation is many years away, car companies such as NIO are working toward high automation, where a car can use artificial intelligence (AI) to operate without human input or oversight but only under select conditions. NIO imagines transforming the notion of a car into a robot in which the driver manages driving on surface streets and then becomes a passenger as the car enters a highway. NIO’s in-car, AI-driven virtual assistant NOMI would manage the passenger’s needs for shopping, entertainment, and more, giving the passenger his/her time back. Padmasree Warrior, U.S. CEO and Chief Development Officer, NIO, says this freedom would allow the passenger to be more “productive, playful, and peaceful.”1 One can imagine that this type of highly autonomous car of the future could lead to the rise of new business models in which cars become extended living rooms where people engage in various activities and purchase new products and services that haven’t yet been imagined.

As seen through the NIO example, advancements in artificial intelligence and machine learning have the potential to transform the management and structure of global value chains. New technologies in critical areas such as natural language processing, sensors, robotics, edge computing, machine and deep learning, and image recognition provide significant opportunities to improve how products and services are designed, made, delivered, marketed, and used. Industry applications include targeted marketing, dynamic pricing, product design, manufacturing automation, and supply chain coordination.

Still, it is difficult to distinguish between fact and fiction when it comes to the promise of AI. Companies are reporting significant optimism about the potential for AI to transform their businesses. According to a 2017 survey of over 3,000 business leaders by MIT Sloan Management Review and Boston Consulting Group, 85 percent of companies believe AI will allow them to sustain their competitive advantage.2 Companies also expect to achieve increased revenues and decreased costs by deploying AI technologies.3 Yet, adoption rates have not kept pace with industry enthusiasm. The MIT/BCG survey found that only 23 percent of companies have adopted an AI technology while only 5 percent of companies have extensively adopted AI.4 When new AI technologies have been adopted, they have been primarily used in support functions such as customer service rather than in core functions.5

The slow pace of adoption in value chain phases such as design, manufacturing, delivery, and use is a reflection of numerous barriers. According to executives, AI adoption is most often prevented by a lack of information technology infrastructure, talent, proven technologies, and financial resources.6 AI experts also report data collection and preparation as significant challenges.7 If the required data is not collected, disaggregated, or appropriately formatted, AI models cannot be built in the first place.8

Looking ahead, AI presents organizations not only with real challenges, but also with exciting opportunities to refine marketing and pricing techniques, improve product design, production automation, supply chain coordination, and more. In this paper, we will review key AI technologies, discuss notable applications and benefits of these technologies in various phases of the value chain, and discuss their implications. Finally, we will present case studies to explore how value is being generated by AI-driven innovations. We examine companies’ approaches to data acquisition, human talent management, and potential effects across the value chain. Case study participants include Adobe, Salesforce, Pilot AI, IBM, OhmConnect, and Stitch Fix—a mix of solution providers and users, and small and more mature firms from varied industries. Unless otherwise noted, the findings from these companies come from one-on-one interviews.


What is AI?

There is no standard definition of AI, and experts even disagree on the right criteria to determine whether a technology should be considered AI. This is primarily because the relevant criteria keep evolving as machines succeed on increasingly complex tasks.9 Consider a prominent example: playing the television show game Jeopardy was considered too difficult for AI until IBM’s solution known as Watson beat human challengers in 2011.10

Without a concrete definition, it is often difficult to discern between real AI technologies and the growing AI hype. We use the following broad rule of thumb: AI can be considered any form of machine learning. Machine learning includes a wide range of models that can be trained based on data, learning its own rules instead of receiving the rules from humans. These models are used by machines to complete tasks, often making predictions, based on new data.11

Machine learning (ML) can take several forms. Two growing areas of machine learning include deep learning and reinforcement learning. Deep learning, which uses complex multi-layered models to make predictions, is inspired by the firing neurons in the human brain. Deep learning is well-suited for recognizing patterns in images, text, and sounds and is behind state-of-the-art technologies for image classification and natural language processing tasks.12 Reinforcement learning uses prediction models that are continuously retrained based on collecting and analyzing new large data sets. This retraining is based on improving the performance based on a reward function. For example, in the context of playing chess, reinforcement learning would train models to pick the optimal move, play millions of games of chess to generate data, and track its performance versus a reward function of winning each game.13

Example: Definitions of AI

• AI is a collection of advancedtechnologies that allows machinesto sense, comprehend, act, andlearn.”14

• “AI is that activity devoted tomaking machines intelligent,and intelligence is that qualitythat enables an entity to functionappropriately and with foresight inits environment.”15

• “AI is the theory and developmentof computer systems able toperform tasks normally requiringhuman intelligence, such as visualperception, speech recognition,decision-making, and translationbetween languages.”16


Current State of AI Adoption in Value Chains

FUNCTIONAL AREAS OF AI DEPLOYMENT

Companies are experimenting with and adopting AI technologies across their value chains. Most of their efforts have been invested in non-core support functions. In one survey of 3,000 professionals, over 25 percent of respondents reported having adopted AI in each of the following support functions: IT, customer service, marketing, sales, finance, and accounting.17 However, in contrast to how other new digital technologies have entered the enterprise, companies are also investing in AI for core functions.18 For example, Stitch Fix, a personal-styling service, e-commerce company, deploys deep learning to understand a user’s clothing style based on styles they like on Pinterest and also deploys machine learning to match stylists with users.

INDUSTRY ADOPTION LEVELS

As to be expected, the role AI plays in value chains today varies by industry. Figure 1 shows a snapshot of industry adoption rates and investment plans. We can contrast the high tech and telecom industry and the construction industry as one example. Over 30 percent of high tech and telecom companies have implemented at least one AI technology, whereas only 15 percent of construction companies have pursued such advancements.19 High tech and telecom companies are more likely to focus on using AI to improve their information technology while construction companies focus on using AI to improve operations and manufacturing, reflecting each industry’s focus.20

Going forward, there is a risk that differing rates of adoption could widen the AI gap between technology-focused industries and other industries. Over the next three years, AI spending at high tech and telecom companies is expected to grow over ten times faster than spending at construction companies.21 While this is a significant difference, at a recent Stanford conference on AI, Jim Sinai, VP of Product Marketing for Salesforce Einstein, remarked that because of the very fact that traditional industries such as manufacturing, retail, and healthcare have legacy systems, certain types of AI can be deployed relatively quickly and add value.22 Therefore, while technology-centric industries may be investing in AI more heavily than other industries, opportunities still exist for strong return on investment for targeted applications in more traditional industries.

One example of an industry that is in the middle range of AI adoption and investment growth is retail. Two of our case study companies are involved in retail—Stitch Fix, which uses AI for functions like marketing, product design, and stylist-customer matching, and Pilot AI, which sells visual recognition software for cameras used to track customer behavior in retail stores as one of its applications. Both are examples of how new technology can be applied to more traditional industry. Overall, AI is increasingly being used by retailers to recommend products, monitor face and hand gestures, employ virtual mirrors to track shoppers’ movements, and for overall surveillance.23

The automotive and energy industries have seen high AI adoption rates, with moderate projected investment growth. Padmasree Warrior of NIO U.S. believes that newer car companies are at an advantage with respect to AI technologies because they are not faced with transforming a combustion engine-centric engineering culture—a challenge at more mature car companies.24 She believes the biggest opportunities for innovation in the car industry are for electric and autonomous vehicles, both of which are a focus for NIO. One of our case studies focuses on OhmConnect, a clean energy solutions provider. Since the company is not an energy generator with heavy assets, it can more easily adopt AI solutions.

High tech is one of the highest adoption sectors, and we have three case studies focused on such firms: Adobe, Salesforce, and IBM. These companies offer many software-based solutions, making it easier to evolve their AI technology over time. While AI solutions may be easier to develop for such asset-light companies, other challenges such as change management may exist in even the most technology-forward firms. Adoption rates may also be influenced by varying industry regulatory structures. According to Tatiana Mejia, Head of AI Product Marketing and Strategy at Adobe, it is more challenging to deploy their AI software solutions for professionals in industries such as financial technology and healthcare due to heavy regulation,25 which may explain the moderate AI adoption in the healthcare sector.


FIGURE 1: SECTORS LEADING IN AI ADOPTION

Sectors leading in AI adoption today also tend to grow their invesment the most

Future AI Demand Trajectory*

Average estimated percent change in AI spending next three years, weighted by firm size**

* Based on the midpoint of the range selected by the survey respondent

**Results are weighted by firm size

Exhibit from “How artificial intelligence can deliver real value to companies,” June 2017, McKinsey Global Institute, www.mckinsey.com.

Copyright 2018 McKinsey & Company. All rights reserved. Reprinted by permission.

Current AI AdoptionPercent of firms adopting one or more AI technologies at scale or in a core part of their business, weighted by firm size**

13

12

11

10

9

8

7

6

5

4

3

2

1

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

Falling Behind

Leading Sectors

Financial services

High tech and telecommunications

Transportation and logistics

Automotive and assemblyEnergy and resources

Media and entertainment

Healthcare

Professional services

Retail

Consumer packaged goods

Education

Construction

Travel and tourism

http://www.mckinsey.com


II. Improvements Along the Value Chain

A value chain is structured around understanding customer demand and then supplying that demand. Efforts to deploy deep learning and machine learning technologies have begun to improve demand-and-supply management in many industries. Figure 2 highlights how various AI technologies have improved key value chain functions. We will focus on how AI is generating value in each of the following key phases: design, source and make, deliver and store, sell, and use. Note that, while “source” and “make” are distinct activities, as are “sell” and “use,” these stages have been condensed for the sake of simplicity. We will use case study examples to demonstrate how AI can be used to improve one or more value chain phases. Because AI is increasingly being deployed beyond support functions into core business processes, we have placed greater emphasis on these core functions rather than areas such as back-end IT support.

The figure below shows a simplified structure of a value chain. The case studies cited in this figure can be found in the addendum section.

FIGURE 2 – VALUE CHAINS INVOLVE MANY PHASES FROM PRODUCT/SERVICE DESIGN TO USE

VALUE CHAIN PHASES

DESIGN SOURCE/MAKE DELIVER/STORE SELL USE

Example: Stitch Fix

Example: IBM

Example: IBM

Examples: AdobePilot AI

Salesforce

Example: OhmConnect


Value Chain Phases – Case Study Examples

In this section, we will highlight examples from our case studies illustrating how AI can improve each value chain phase. Many of the companies interviewed discussed beginning with goals and a strategy, and then determining the role AI can play in meeting the goals. This process is similar to deployment approaches for other technologies. Cascading down from goals, we examine how AI solutions were deployed and the results. See the addendum section for the full case studies.

DESIGN PHASE – Stitch Fix

Customer feedback clearly has an important role in successful product and service design. In industries with strong digital relationships with their customers, feedback gained from e-commerce interactions, digital marketing campaigns, and other means can be valuable in understanding how to improve the product. Stitch Fix is a personal-styling-platform company that curates a personalized box of clothing to be delivered to customers, who then choose which items to purchase or return. The company has used AI to predict which clothing items customers will like and purchase and to match stylists with customers.

After seeing gaps in inventory and a need for better clothing choices, Stitch Fix made a strategic decision to begin designing its own clothes to complement its existing inventory. The company now uses AI to identify popular clothing features and recommend new combinations of those features to Stitch Fix’s in-house design team. This approach has led to 100 Hybrid Designs-branded products thus far. Data sources include objective data on color, pattern, etc., and subjective data such as style. Computer vision is used to analyze photos to extract additional data. From a human talent perspective, the company prides itself on using human-in-the-loop AI, where people receive input from AI models but make the final decision. For design creation, this means AI recommends potential designs, and designers edit the styles or discard them entirely. The company has a chief algorithms officer who reports directly to the CEO, highlighting the importance of data science to the company. Still, there exists a balance between data scientists and those with retail backgrounds who can do the more subjective analysis of styles. In the figure below, we highlight how Stitch Fix and other companies are using AI to improve product design.

FIGURE 2A – DESIGN PHASE: AI CAN ENABLE NEW PRODUCT DESIGNS, IMPROVE THE SPEED OF ITERATION, AND PERSONALIZE PRODUCTS

DESIGN PHASE

Goal Optimize Product Design Based on Predicted Customer Behavior

Optimize Product Design Based on Desired Criteria

Iterate Product Design Faster

Personalize Product

Company/Tech Provider

Stitch Fix / In-house26 Airbus / Autodesk27 Boeing / Citrine28 Shiseido / MatchCo29 (acquired by Shiseido in 2017)30

What Was Done Created new clothing designs that are popular with their existing customers

Improved the design of airplane partitions to be sturdy yet light

Found materials for airplane bodies that can be 3D printed

Sold makeup that matches the customer’s skin tone

How It Was Done

Used ML to analyze past customer behavior and clothing style characteristics to design new clothes

Used reinforcement learning to apply patterns for slime mold and mammal bones to make airplane partitions

Used ML to analyze Citrine’s proprietary material science database for faster material identification

Used computer vision and ML to analyze customer photos and questionnaires to customize products

Results Approximately 100 clothing styles designed with this process31

45 percent reduction in airplane partition weight

New materials identified in days instead of years

Custom foundation can be delivered to customers in under 72 hours


SOURCE AND MAKE PHASE – IBM

IBM is a technology company providing software, hardware, cloud services, and cognitive computing to clients all over the world. Watson is IBM’s AI platform, helping companies with business insights across a wide range of industries and use cases (e.g., talent acquisition, supply chain, customer engagement). In IBM’s own supply chain, Watson is now assisting in nearly every process. For example, a supply assurance manager can ask: “What are the top five components with risk of supply shortage?” and “Should we pull more of that specific part, or bring it over from another site’s inventory to mitigate the supply risk?” AI is enabling IBM to have end-to-end visibility and insights from data across systems. The company is on a journey to predict, assess and mitigate disruptions and risks, and enable a smart, resilient, and agile supply chain.

Today’s supply chains have access to internal, external, structured, and unstructured data. Supply chain professionals need real-time contextual insights to predict risks and disruptions and take action. Matthias Graefe, IBM’s Director of Digital Supply Chain Transformation, reports that Watson is now trained to provide insights in real time. This motivates professionals to interact with the platform and share with Watson the decisions they are taking and why. For example, Watson learned how to identify product changes shared by the development teams with the supply chain team via complex electronic documents. Watson identified parts that were becoming obsolete in a couple of weeks and advised planners to procure new parts. The assistant was trained within weeks based on around 300 annotated sample documents, identifying parts relationships that had not been previously known. The number of sample documents needed to train the system can be much lower if the data is more structured in nature.

IBM now has a commercial Watson Supply Chain business unit, and Graefe’s team is accelerating the rate at which Watson learns and provides deep supply chain insights. Transitioning to AI-enabled supply chain processes have yielded important lessons on human-machine interaction. Initially, users did not pay enough attention to the recommendations provided via a conversation panel. Then graphical information was coupled with the advice. With the relevant information linked to the question and answer, users could form their own assessment and decide whether or not to overrule Watson. Watson observes user decisions, learns from feedback, and provides playbooks on how to handle issues. It has started to prompt employees to look at likely follow-up questions, acting as moderator in resolution processes. Graefe envisions a future in which Watson will be a personalized advisor that learns from increasingly digitized data, interacts with users to clarify its understanding, and then engages in exponential learning. One day, he says, you may be able to ask Watson “What should I work on today?”

FIGURE 2B – SOURCE AND MAKE PHASE: AI CAN IMPROVE OPERATIONAL EFFICIENCY, INCREASE ASSET UPTIME AND MINIMIZE INPUT PRICE FLUCTUATIONS

SOURCE AND MAKE PHASE

Goal Optimize inventory levels, reduce surplus, excess, scrap

Increase Machine Uptime Minimize Input Price Risk


IBM / In-house (Watson) MidAmerican Energy Company / Uptake32

Big River Steel (steel manufacturing startup) / Noodle.ai33

What Was Done Provided alerts and recommendations to supply chain professionals on obsolete parts resulting from upcoming product changes

Anticipated wind turbine breakdowns and minimized their occurrence

Hedged the difference between scrap steel input and finished-steel output pricing

How It Was Done

Built an AI-powered assistant tool for the supply chain team. The tool analyzes structured and unstructured data to answer a wide range of employee questions. Example question: Should we order more of a specific part?

Used ML on data collected from wind turbine sensors to predict near-term machine failures

Used ML to estimate prices of scrap steel and finished steel from historical prices, projected demand, and estimated wear and tear on their factory (captured by 50,000 sensors)

Results Lower inventory levels, reduction of scrapping cost

Realized $250,000 in savings in first week; estimates $3.3 million savings per year

Increased overall profitability of the mill


STORE AND DELIVER – IBM

To capture the short-term impact of AI investments, Graefe’s group keeps track of the number of questions posted and answered, questions that receive a “thumbs up” rating, and the net promoter score (NPS) among employees.

Graefe see IBM’s Supply Chain team just reaching the starting point of exponential learning. Strong conversational speech interfaces for the assistant tool encourage more employee interaction with the tool and, as a result, capture better training data. It makes it easier for employees to provide the “why” behind decisions, which is so critical for Watson to learn with the team.

In Figure 2c, we highlight how IBM and other companies are using AI to improve storage and delivery.

FIGURE 2C – STORE AND DELIVER PHASE: AI CAN REDUCE DELIVERY TIMES, IMPROVE FUEL EFFICIENCY OF DELIVERY VEHICLES, INCREASE WAREHOUSE EFFICIENCY, AND REDUCE INVENTORY

STORE AND DELIVER PHASE

Goal Avoid Weather-Related Delays

Reduce Delivery Times Improve Fuel Efficiency

Increase Warehouse Efficiency

Reduce Inventory


IBM / Watson (in-house)34

DoorDash (food delivery startup) / Starship35

Vale (Brazilian mining company with railroad network) / GE Digital36

Gap / Kindred AI37 Otto (German grocery delivery service) / Blue Yonder38

What Was Done

Provided an early warning for weather events (e.g., hurricanes) and recommend alternative plans

Avoided traffic by using robots that drive on sidewalks

Automated train braking and accelerating

Sped up assembly of e-commerce orders with robots

Decreased inventory by improving forecasts and coordinating automated ordering

How It Was Done

Used ML to anticipate timing of the weather events impacting their factories and warehouses based on Weather Company data (acquired by IBM in 2016)

Used computer vision and AI to navigate robots through an urban environment relying on data from cameras and ultrasonic sensors

Used computer vision and machine learning to optimize train maneuvering based on sensor data, weather, and railroad routes

Used reinforcement learning and deep learning to transition robots from being human-operated to working autonomously

Used deep learning to extrapolate trends from historical transaction data

Results Saved money and “continued to delight customers”

15-30 minute delivery times

4 percent fuel savings

Plans to expand its six robot pilot program

20 percent reduction in surplus stock


SELL PHASE – Pilot AI, Salesforce, and Adobe

With the growth of omni-channel and online retail, and the emergence of digitized physical retail shopping through stores like Amazon Go, we have seen a large increase in data available to strengthen insights into customers. One example is Pilot AI, which offers computer deep-learning vision software for compute-constrained cameras. One solution they offer is to analyze video footage in retail stores to count and identify customers’ demographic characteristics and create heat maps of customer movement. Given its low cost per store, investment in the Pilot AI solution can be recovered by small increases in in-store sales.

Salesforce is an $88 billion industry leader with an AI solution called Einstein to inform recommendations such as prioritizing sales leads and estimating marketing performance. Jim Sinai, VP of Product Marketing for Einstein, emphasizes that successful AI enterprise implementations require new business processes that provide the right data to their software instead of repurposing existing data. He says “[Most companies are] thinking about it 100 percent the wrong way. What you should think about is how you architect a workflow where executing that workflow trains the model.” The company foresees that advanced voice capabilities will make the user experience more conversational, enabling access to data insights in an informal and intuitive way. The combination of a clear AI strategy, a process-based approach, and a human-centered model is critical to the company’s vision.

Adobe is another high-tech leader using AI to improve the customer experience throughout its software offerings. Adobe Sensei uses AI and machine learning to power the company’s solutions across its Creative Cloud (e.g., eliminating manual, time consuming, and repetitive tasks for creative professionals, such as photo and video editing), Experience Cloud (e.g., helping marketers and advertisers deliver relevant, personalized experiences in real time and make content recommendations to reach the right customer at the right time), and Document Cloud (e.g., searching and understanding large amounts of content at a deep level, like the sentiment behind documents, and producing clean, secure, sharable PDFs from paper). According to Tatiana Mejia, Head of Product Marketing and Strategy, seamlessly integrating AI into Adobe’s solutions is a key factor in Sensei’s success. Mejia notes, “Most of our customers aren’t aware that they’re leveraging AI.” The company conducts deep AI research in-house, conducts extensive online training, and has learned that AI talent must be dispersed through the organization due to AI’s importance and evolution. Success is evaluated based on the extent to which Adobe is addressing its customer pain points.

Figure 2d highlights how Pilot AI, Salesforce, Adobe, and other companies are using AI to improve selling.

FIGURE 2D – SELL PHASE: AI CAN ANALYZE CONSUMER BEHAVIOR, IMPROVE LEAD CONVERSION, PERSONALIZE MARKETING, AND INCREASE UPSELLING

SELL PHASE

Goal Analyze Store Traffic Improve Conversion Improve Efficiency of Ad Development

Personalize Marketing

Increase Upsell


Large retailers / Pilot AI

U.S. Bank / Salesforce39

Adobe / In-house Starbucks / In-house40 Square / In-house41

What Was Done

Tracked in-store customer behavior for specific demographic profiles

Provided wealth managers with product suggestions for their clients

Eliminated manual photo editing work

Optimized food, beverage offers in their mobile app

Sold payroll tools and loans to existing small and medium business customers

How It Was Done

Used computer vision and deep learning to recognize people in video footage from security cameras

Used ML to identify clients likely to convert based on their unified customer database and historical data

New AI-powered feature assigned metadata word labels to photos

Used AI to customize the app based on customer’s order history, along with current weather, date, and time

Used ML to predict new purchases based on customer’s purchase history and product usage

Results Increased sales 2x increase in conversion of top-ranked leads

Reduced photo search time from 10 minutes to approximately 30 seconds

3x increase in response rate with individually personalized offers

Increased sales of higher margin products


USE PHASE – OhmConnect

OhmConnect, founded in 2013, works with energy providers to incentivize consumers to save energy. The company obtains information on customer energy use from online utilities accounts, pays consumers to reduce electricity usage during peak periods when more “dirty” energy is used, and sells the energy back to utility companies, thereby reducing overall pollution. Using proven machine learning, OhmConnect predicts how customers will respond to requests to save energy and thus estimates the energy supply needed. Going forward, the company plans to transition to deep learning to gain a more in-depth understanding of relevant energy demand and supply. The company recruits software engineering generalists, with an eye on hiring specialists as it grows. Given the unpredictability of AI advancements and business applications, the company has diversified its analytics efforts so it can select elements of various strategies as needed over time.

Figure 2e highlights how OhmConnect and other companies are using AI to improve the ways in which customers use products and services.

FIGURE 2E - USE PHASE: AI CAN INCREASE EFFICIENCY OF CONSUMER PRODUCTS/SERVICES, AUTOMATE CUSTOMER SERVICE, AND PREDICT MAINTENANCE

USE PHASE

Goal Increase Efficiency Automate Customer Service Anticipate Maintenance


OhmConnect / In-house UPS / Microsoft42 U.S. Department of Defense / C3 IoT43

What Was Done Improved the efficacy of their energy saving campaigns

Provided detailed delivery updates through their chatbot

Received an early warning for fighter jet maintenance

How It Was Done

Used ML to optimize consumer segmentation for campaigns based on household-level energy usage data, aggregated demographics data and weather forecasts

Used the natural language processing and ML capabilities embedded in the Microsoft Bot Framework to manage conversations

Used ML to anticipate engine problems based on sensor data and maintenance logs

Results Increased user participation in campaigns

Managed 200,000 customer conversations in first eight months

Improved asset availability; reduced maintenance costs


II. Insights

It is clear from our research that, like the Internet of Things, 3D printing, and automated delivery vehicles, AI can be a powerful tool. If strategically deployed, it has the potential to drive many forms of value creation. In this section, we share the strategies and key factors to consider in order to assist AI-driven value creation, and we look ahead to the future implications of AI-powered value chains.

Forms of AI-Driven Value Creation

We have observed examples of AI improving precision and speed in many value chain phases such as marketing, customer insights, design, manufacturing, delivery, and retail. By improving the precision and speed of these functions, we find that the following key improvements are possible.

• Process efficiency: AI automation often improves repetitive processes that are not enjoyable or challenging for humans. Forexample, Abundant Robotics44 has designed AI powered automated machines to harvest apples, reducing the amount of laborneeded. Since manual apple picking is physically demanding and labor supply is low, farms have an incentive to improve thisprocess.

• Process enhancement: AI can also enhance existing processes, leading to better outcomes for users. For example, SalesforceEinstein can prioritize leads for a salesperson and hide those that have a very low likelihood of converting to a sale. Thisenhances the sales process, allowing executives to devote high-quality time to high-value leads.

• Product or service innovation: AI can enable the creation of new products and services. As seen with Stitch Fix, AI can powernew product design, using data from multiple sources to predict styles that will resonate with customers.

FIGURE 3 – SUMMARY OF VALUE DRIVERS: AI CREATES VALUE BY DRIVING PROCESS EFFICIENCY, PROCESS ENHANCEMENT, OR PRODUCT/PROCESS INNOVATION

SUMMARY OF VALUE DRIVERS

Design Source/Make Deliver/Store Sell Use

Optimize product design basd on predicted customer behavior

Increase efficiency Reduce delivery times Analyze store foot traffic Increase efficiency

Optimize product design based on desired criteria

Increase machine uptime

Improve fuel efficiency Improve conversion Automate customer service

Iterate product design faster

Minimize input price risk Increase warehouse efficiency

Improve conversion Anticipate maintenance

Personalize product Reduce delivery times Reduce inventory Personalize marketing

Optimize input purchase timing

Avoid weather-related delays

Process Efficiency Process Enhancement Product or Service Innovation


Strategic Considerations for Adopting AI

Organizations must consider many factors when designing an AI strategy: identifying problems and opportunities, what type of AI to deploy, and how to introduce and eventually scale the application by developing human talent and a strong execution strategy. In this section we discuss some key questions organizations should consider when designing their AI strategy and determining how to execute it.

What problem/opportunity have we identified? In order to generate value creation from AI, those interviewed stressed the importance of first having a clear value chain strategy. Organizations must be very clear about the value chain problem they need to solve or the opportunity they want to pursue. Once the area of focus is well understood, according to those we interviewed, organizations should first design ideal workflows and then gradually deploy AI to effectively support and improve these workflows.

“People are solving one global problem–you can take AI to any workflow.”—Jim Sinai, VP of Marketing, Salesforce Einstein

How should we develop our AI capabilities? AI solutions can be achieved through building proprietary technology or through leveraging external technology. Some companies rely on a mix of both approaches.45 Salesforce has designed much of its AI technology in-house, but also augments it with acquisitions. Clean energy broker OhmConnect is designing machine learning algorithms in-house, but also relies on third party plug-in software and Amazon Web Services.

Should we diversify our technology? Because the pace of technological advancement and research breakthroughs in the field of AI is brisk, it is not always clear which technology will become widespread. As a result, organizations may consider deploying multiple AI strategies and models to be prepared for a few to ultimately prove useful.

Should AI be front and center, or invisible to the user? Several of the software providers we interviewed have chosen to make AI invisible to their customers. Adobe, for example, has dozens of AI models behind its solutions, but doesn’t directly brand its AI technology within its product-user interface. Adobe’s goal is to provide intelligence assistance and make solutions frictionless to adopt. Salesforce uses a similar approach and considers AI just another type of software embedded in its solutions. Providers may need to consider whether branding their AI tools offers specific value in customers’ minds, or whether their customers value the solution regardless of the technology used.

“We want to make good AI invisible...There are dozens of features powered by Adobe Sensei inside solutions.”

—Tatiana Mejia, Head of AI Product Marketing and Strategy, Adobe

The Human Factor

Acquiring AI talent

Whether an organization decides to primarily outsource AI solutions or develop them in-house, significant challenges exist around building talent. Many companies do not have sufficient AI expertise, and hiring top AI talent is extremely competitive. A report by analytics provider Teradata found that 34 percent of companies surveyed cited a lack of in-house AI expertise as a roadblock for AI adoption.46 Even if a firm has chosen to outsource AI, it can be difficult to find the right vendors. Twenty-six percent of companies surveyed by the McKinsey Global Institute believed the market lacked AI products that were relevant for their business.47


Other domain experts matter

Regardless of the amount and level of AI talent in-house, those interviewed expressed the importance of including domain experts not necessarily trained in AI on AI-related teams. For example, at Stitch Fix, retail experts are involved in classifying clothing, which is considered a subjective exercise. This information is then converted into objective data which can be used in algorithms. While many companies are more focused on deploying AI to determine how much inventory to carry, Stitch Fix’s approach ensures that human input and subjective data help determine what inventory to carry. Padmasree Warrior of NIO U.S. also stresses the importance of having non-AI staff with deep subject matter knowledge in a variety of areas and an affinity for learning about other subjects.

“My biggest challenge as CEO is to have machine learning data scientists, supply chain, user interface designers and people from every discipline working together...This keeps me up at night.” 48

—Padmasree Warrior, U.S. CEO and Chief Development Officer, NIO

Human-machine collaboration

Regardless of the type of AI solution deployed, ensuring that human judgment is always involved is critical. Problems can arise if AI algorithms make strictly profit-maximizing decisions. For example, unless a human intervenes, consumers who fall outside of average foot sizes may be overlooked by shoe retailers using AI to optimize inventory. In applications where work safety is critical, such as self-driving cars and in the defense industry, experts argue that human judgment should always be closely coupled with AI. For example, in a future where self-driving cars become commonplace, in certain dangerous conditions it may be important for the car to warn owners to take over and if they don’t, pull off the road and stop.49

Technology and Data

Strong AI ecosystems are needed to scale: To successfully scale AI, many elements of the AI ecosystem need to be in place, such as a strong Internet of Things (IoT) infrastructure, back-end IT expertise, top AI talent, and knowledgeable non-technical employees who embrace AI opportunities and work alongside AI experts. For example, in the home energy market, providers may envision the real-time matching of supply and demand via AI to achieve an optimally efficient market. This type of real-time matching may only scale once home IoT devices become more dependable and accurate and can seamlessly report energy usage and demand.

Data and model training: A significant amount of high quality data is imperative for training various AI models. For many companies, the necessary data is currently out of reach. Jonathan Su, CEO of Pilot AI, has observed that companies often have more data than they can actually use. This can be because companies do not have the right data infrastructure or usage rights. As suggested by Jim Sinai, one solution is to establish business processes to collect this data. For example, IBM captures important data on supply chain best practices by having their top employees train AI tools directly and by encouraging decision-making conversations to be recorded to further train models.

Business Risks of AI

Managers are concerned about many risks involved in adopting AI solutions, including regulations, counterfeiting, lack of transparency, and privacy. Companies applying AI in heavily regulated industries such as healthcare and banking may encounter roadblocks in accessing data protected under strong privacy rules. Counterfeiting is a concern for companies using AI to design digital solutions such as advertisements and marketing campaigns. Tatiana Mejia notes that AI technology such as Adobe Sensei can incorporate digital signatures or watermarking to show a photo’s authenticity. “It’s a responsibility for the companies that create these [technologies] to think about different ways they can be used.”50


Machine learning solutions provided by some companies can offer explanations for why a solution was determined to be optimal. Yet AI solutions providers are concerned that model decisions made by deep learning and reinforcement learning systems cannot always be understood and justified, making decision-making and accountability more opaque to the user. There is the risk that error rates in AI models may have harmful consequences. If an AI model targets the wrong customer for a marketing campaign 5 percent of the time, there are minimal harmful effects. Yet, if a defense contractor uses AI visual recognition systems to identify a noncombatant in a battle, being wrong 5 percent of the time is a risk the contractor may not want to take. All of these risks can be amplified deeper in the supply chain, since supplier networks may not be as well-equipped to manage them compared to better-resourced buyer firms.51

Companies must also consider the comfort level of consumers for new AI advancements. Even if a store can use facial recognition software to identify customers as they approach, retailers believe they don’t want to be greeted by name as they walk in. As Jon Su of Pilot AI notes, “The consumer values privacy, and we as an analytics provider want to make sure we treat the consumer’s privacy with the utmost care and consideration alongside our customers.”52

Potential Limitations

Questions arise as to whether companies with a heavy physical asset infrastructure or physical products that take time to produce are at a disadvantagein adopting AI compared to asset-light or virtual product companies. At Stitch Fix, AI is used to gain insights into demand, make sophisticated product recommendations, and even design clothes. However, the company still has a long production cycle relative to a software company, and there may be limits to the gains in efficiency or product/process enhancements generated by AI. This raises interesting questions. Can future developments in AI help address long production cycles? How can technologies such as 3D printing be combined with AI to enable faster supply chain cycle time?

As with other technologies, change management within an organization introducing AI can be quite difficult. Even with a targeted strategy and a clear approach to acquiring or developing AI tools and data, if leaders do not manage the transition to AI well, value creation may never be achieved.

Looking Ahead

Going forward, digitization of the modern economy will cause dramatic shifts in how customers use products and services, how businesses manage their value chains, and the role of workers. With respect to customers, Padmasree Warrior of NIO U.S. has already observed a digital transformation driven by the internet and an “app economy.” Warrior now expects that industries will shift to a physical transformation—a post-app world where physical objects such as cars, drones, and other robots will have virtual assistants that engage with the user.53 Stanford conference speakers discussed a future where customers enjoy more natural interactions due to advancements in language processing. Combining AI with IoT technology shows great potential for fixed physical objects, such as refrigerators, to interact with users, and for moving such objects autonomously. In both cases, the increasingly rich supply of data will be useful to train models to improve over time.

For businesses managing evolving value chains, there is an expectation that more processes will take place in real time. Bo Zhai leads emerging technology investments at U.S. Alibaba, the Chinese e-commerce and internet conglomerate. He believes that if e-commerce firms can use AI to help them find more efficiencies in the back office and supply chain, ordering merchandise maybecome even easier for consumers and delivery times will decrease. “It all comes down to shortening the lead times at reducedcost,” Zhai said.54 Through process transformation, AI has the potential to upend entire industries. Russ Altman, who is professorof bioengineering, genetics, medicine, and biomedical data science and of computer science at Stanford University and a memberof the standing committee of the Stanford One Hundred Year Study on AI, believes that future AI-based systems will lead to areinvention of medicine. AI systems will be able to analyze information collected from sensors placed on individual patients, socialmedia, medical records, and genomic data to help doctors design health policy and prevention strategies, diagnose illness, plantreatments, and determine prognoses.55


There also continues the age-old debate of automation as job creator or job destroyer. While it is unclear what the net effect of AI on jobs will be, it is clear that AI will lead to new types of human-machine interactions. Completely automating some worksites, such as a brick-and-mortar store, would require such a huge redesign and financial investment that companies may be better off using automation for only select functions and retaining humans to perform the rest.

IV. ConclusionGiven the speed of technological advancements and continued breakthroughs in AI research, it is unclear which technologies will generate the most value and encounter widespread adoption in the near term. Yet, from our case study explorations and conference discussions, it is clear that AI can deliver process efficiencies, process enhancements, and product/service innovation, leading to significant transformations. Some estimate that the technology already exists to automate nearly half of all global work activity.59 Process efficiencies from automation alone will influence job functions, departmental structures, supplier relationships, and much more. As technologies evolve, organizations should keep a constant focus on how to create value for customers, with AI being one tool to deploy toward that goal.

Thoughts on the Future of AI

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

—Andrew Ng, Stanford Adjunct Professor, Coursera Co-founder, AI Fund Founder56

“The demand for machines to explain and justify answers in terms humans can understand will grow with our desire to leverage computers in the process of human decision making, especially in areas where the reasons matter...The future of AI will need to focus on making sense of the world and being able to answer the Why?”

—Dave Ferrucci, Creator of IBM’s Watson, Founder and CEO, Elemental Cognition57

“The future is bright. People can act more like people.”

—Daragh Sibley, Director of Data Science, Stitch Fix58


ADDENDUM – CASE STUDIESWe selected a broad range of companies that highlight the use of AI within one or more phases of the value chain. The following section

summarizes what we learned about how each company has deployed AI, either within the firm, for customers, or both.


FIGURE 4 - SUMMARY OF CASE STUDIES

SUMMARY OF “PROMISE OF AI” WHITE PAPER CASE STUDIES

Design Source, Make, Store, and Deliver

Sell Use

Stich Fix IBM Adobe Pilot AI Salesforce OhmConnect

AI Application Strategy

AI helps Stitch Fix understand its customers, make product recommendations, and purchase inventory. Stitch Fix uses “humans-in-the-loop” to audit AI’s recommendations

Watson, IBM’s AI platform, helps companies make sense of their data to make better decisions.IBM’s internal supply chain team built an AI-powered “assistant” with Watson’s APIs that answers employee questions

Adobe Sensei accelerates the adoption of AI across their software products, addressing challenges for Adobe’s domain expertise areas: content, creative, and marketing

Pilot AI offers a computer vision software platform with proprietary technology to make AI model training more efficient. In retail use cases, the platform can track in-store activity

Salesforce’s Einstein creates custom AI models for each customer. Most users access the insights through various features in the existing software

OhmConnect uses ML technologies to increase the amount of energy saved through segmenting its users and personalizing requests to use less energy

Data Analysis

In addition to traditional metrics, data is gathered from fashion experts and computer vision analysis of clothing images

The assistant tool relies on domain expertise along with data from the ERP system and Watson. Top employees invested significant time showing Watson how to answer questions

Sensei utilizes trillions of content and data assets, from high-resolution images to customer clicks that Adobe already has in its domain expertise areas

Pilot AI’s platform analyzes video camera footage, which often comes from existing security cameras

Einstein takes advantage of all of a customer’s data in Salesforce and eliminates a significant amount of manual data prep

Because users connect their online utilities accounts to the platform, OhmConnect collects detailed user behavior and household electricity consumption data

Human Talent

Stitch Fix’s large data science team is encouraged to work on novel problems across the business

All supply chain professionals are encouraged to help maintain the assistant tool by grading its answers and recording their conversations

Adobe Research develops new technologies and publishes academic papers. Adobe has online training to teach other technical employees about AI

Pilot AI has PhDs from math, computer science and other disciplines developing leading- edge AI solutions

Salesforce helps clients with change management as they adopt new processes, which can be easier to manage with smaller groups

Software engineering generalists work on various AI projects

Longer-term Implications

AI can enable new product designs and a more efficient design cycle. With growing opportunities to use sensor and other data on how customers use products, this information can be fed into product design, creating a more effective design cycle

Solutions that improve supply chain precision and the speed of decision making can shorten production cycles, improve product quality, and reduce costs

AI solutions to improve customer insights, targeted marketing, and creative ads can uncover new product ideas and new customer segments, improve customer loyalty, and strengthen the overall customer relationship

Growing insights on how customers use products and services can lead to new products better targeted to customer needs, improving customer satisfaction


DESIGN PHASE CASE: Stitch Fix

Business

Founded in 2011, Stitch Fix is a personal styling platform that uses a unique e-commerce model; stylists curate a personalized box of clothing items to be delivered to each customer. The customer chooses which items to purchase and which to send back to Stitch Fix. The success of this publicly traded company, valued at approximately $1.5 billion at the time of their IPO, depends on predicting which clothing items customers will like and ultimately decide to purchase. AI plays a critical role in making these predictions, helping the company determine which clothes to keep in inventory and prioritizing clothing items that a stylist should select for a particular customer.

“We leverage data science to deliver personalization at scale, which we don’t see anywhere else in retail. Data science is not just part of our culture —it is our culture and it’s woven into every aspect of our business.”

— Cathy Polinsky, Chief Technical Officer, Stitch Fix60

To fill gaps in inventory and produce better clothing, Stitch Fix made the strategic decision to design its own clothes in addition to purchasing clothes from existing vendors. The company uses AI to identify popular features of existing clothing items and recommend new combinations of those features to Stitch Fix’s in-house design team. The company has already created approximately 100 new clothing items using this approach.

The company also anticipates that AI will become involved in more far-reaching areas of their business over time. Stitch Fix has already seen success using AI to match stylists to each client and believes more successes are yet to come.

AI technology

Stitch Fix is a sophisticated user of many AI technologies and applies them throughout their value chain. Below are some prominent examples.

FIGURE 5 – STITCH FIX AI APPLICATIONS

AI Technology Deep Learning Machine Learning Machine Learning

In-house application at Stitch Fix

Describing the user’s style by measuring the similarity between his or her favorite Pinterest photos and pictures of items in inventory

Matching stylists and users based on any prior interactions, stated preferences, and inferred characteristics

Identifying common characteristics across popular clothing items and recombining these characteristics to create new items

Source: http://algorithms-tour.stitchfix.com (accessed May 17, 2018)

http://algorithms-tour.stitchfix.com


Much of Stitch Fix’s AI success has been attributed to “human-in-the-loop AI,” where humans receive inputs from AI models but ultimately make the final decision. The company first used human-in-the-loop AI for curating items based upon AI recommendations. This approach is also used in creating designs; AI recommends potential designs, and designers edit the designs or discard them entirely.

Stitch Fix believes they receive the best of both machine and human intelligence with human-in-the-loop AI. With this approach, computers can excel at running large, complex numerical analyses while humans can excel at evaluating aesthetics and answering innate questions. The human factor also allows AI to make more risky recommendations since any outrageous recommendations will be intercepted before they impact customers.While Stitch Fix expects human-in-the-loop AI to evolve over the next five to ten years, the company expects to maintain sizable roles for both humans and computers.

“Machines and humans have very different talents when it comes to processing information. This shouldn’t be surprising given their very different DNA makeup. The abilities of each needs to be appreciated in order for us to evolve the way we work together.”

— Eric Colson, Chief Algorithms Officer, Stitch Fix61

Data analysis

Stitch Fix brings together diverse data sources for AI, which extend beyond the typical financial metrics and customer feedback. For example, fashion experts generate objective data (e.g., color, pattern) and subjective data (e.g., style) for each clothing item. Computer vision analyzes photos of each item to extract additional data. This often includes data that describes the item’s color (e.g., hue, saturation) and cut (e.g., sleeve length).

With all of this data, the challenge then becomes identifying what data has predictive power. Stitch Fix relies on their data scientists and fashion experts to identify new variables and then tests whether these variables improve their predictions.

“You’ll find people doing operations research in AI where they’re figuring out how we decide how much inventory to carry. I think there are comparatively few of us who are now trying to use it to figure out what content we should have.”

—Daragh Sibley, Director of Data Science, Stitch Fix

Human talent

Data science is heavily emphasized at Stitch Fix. The algorithms team, which is responsible for AI, has more than 100 employees and is led by Chief Algorithms Officer, Eric Colson, who reports directly to the CEO.62 The company believes this structure encourages data scientists to develop novel ideas and experiment. When looking for new hires, the company focuses on the “scientist” part of the data scientist role. These employees draw upon a variety of hard science disciplines and bring new approaches and algorithms to existing retail problems. At the same time, the company relies heavily on people who have retail backgrounds to guide how to classify clothing, a more subjective exercise, which can then be converted into objective data.


Results

Stitch Fix is focused on building “long-term enduring” customer relationships by sending items that make customers extremely satisfied. As a result, the company favors measuring business impact not only through business metrics such as revenue or gross margin, but also through customer satisfaction. The company collects this feedback through a survey that customers are encouraged to fill out after receiving a shipment. The survey asks the customer to rank each item for style, fit, quality, and price.

The company observes that traditional business metrics, particularly revenue, are not necessarily aligned with customer satisfaction. For example, Stitch Fix may send a customer the perfect blouse but not sell the item because the customer already owns a similar blouse. Customer satisfaction is high because the customer liked the item and sees that Stitch Fix understands his or her personal style; however, short-term incremental revenue is zero. Over time, Stitch Fix expects that customer satisfaction will drive higher lifetime value as customers continue requesting Stitch Fix shipments, leading to revenue growth.

“[Machine learning] hits the top and bottom lines in very real ways.” —Eric Colson, Chief Algorithms Officer, Stitch Fix63

Challenges with expanding AI

Designing new clothing items, such as blouses, is fundamentally a challenging problem. First, there are nearly infinite potential blouses. Only a small fraction of all potential blouses have been made across all possible configurations of blouses. Stitch Fix has data on an even smaller fraction of potential blouses. The company doesn’t know which popular blouses might exist but have not yet been discovered. Second, there is a slower feedback loop with physical products like clothes. The company must wait for the items to be manufactured, sent out to customers, and rated, which can take up to six months. This severely restricts the number of designs that Stitch Fix can test, compared to a company with only digital products. The company mitigates this second challenge by simulating customer responses to new clothing items with backtesting. In backtesting, Stitch Fix uses historical data to estimate whether a customer would have purchased a new item if it had been available at that point in time.

“When you’re in the world of software, you can move really quickly. When you’re in the world of physical products, it is fundamentally slower to experiment and learn about AI.”

— Daragh Sibley, Director of Data Science, Stitch Fix

Insights

Stitch Fix is an example of a company that uses AI to enable machines to perform tasks they are strong in, and humans to perform roles that involve creativity, subjectivity, real-world experience, or judgment. According to Daragh Sibley, “Humans are really good at places where we just don’t have very much data.” In particular, humans are needed to understand how AI-generated designs might combine with one another to contextualize recommendations and create items customers will enjoy.

The company has challenges given the physical nature of the apparel industry compared to the focus of a services company. The timescale for experimentation and learning is fundamentally slower than at a services company. Another main challenge is that data is sparse and variable, which underscores the importance of having humans continually involved in evaluating styling options.


SOURCE, MAKE, STORE, AND DELIVERY PHASES CASE: IBM

Business

IBM has been an AI pioneer, pushing forward fundamental AI technologies with its tool Watson. Mentioned earlier for winning the game Jeopardy, Watson has evolved into an AI platform that helps companies find and use business insights across a wide range of use cases, such as talent acquisition, supply chain, customer engagement, and industries like healthcare and financial services.64 As described by IBM’s CEO, Ginni Rometty, IBM hopes to augment human intelligence with machine intelligence and “help you and me make better decisions amid cognitive overload.”65

“Guess what percentage of the world’s data is searchable? The answer is 20 percent. The other 80 percent lives with all of us who’ve established businesses—and my view is that data has got a lot of gold in it.”

— Ginni Rometty, CEO, IBM66

While IBM offers Watson to clients looking to improve business insights, the company is also using the platform to improve its own internal operations. This case study focuses on Watson applications within IBM’s own supply chain, which manages $30 billion worth of parts each year.67 The company is managing a digital transformation of its supply chain, incorporating more analytics and AI into daily business practices.

Watson now acts as an assistant to IBM’s supply chain professionals. Employees can send the assistant questions and receive answers informed by the company’s latest data and domain expertise. Sample questions for the assistant are:

• What are the top five components of supply shortage issues?

• Should we order more of a specific part or bring it over from another site that has it in inventory?

Although the assistant tool provides advice and data, supply chain professionals can override suggestions and remain the final decision makers. Watson incorporates these human decisions into its learning over time to improve its functionality.

AI technology

IBM’s supply chain team uses Watson as external customers do, building the assistant tool based on existing APIs and training the underlying models for the nuances of IBM’s supply chain. Matthias Graefe, director of Digital Supply Chain Transformation, reports that Watson is sophisticated enough to “provide fast data and enhance human machine interaction.” For example, the assistant learned how to identify product changes relevant to the supply chain team (e.g., new parts that need to be purchased, existing parts that are now obsolete) from thousand-page specification documents. The assistant was trained based on 300 annotated sample documents, although fewer documents can be used if the data is more structured in nature.

The machine assistant has now started to prompt employees to look at likely follow-up questions, taking more of a moderator role in resolution processes. Graefe envisions a future in which the way supply chain professionals work will change completely. Watson will serve in a personalized advisory role in which it learns from more and more digitized data, interacts back and forth with users to clarify its understanding of problems, and then engages in exponential learning. One day, he says, you may be able to ask Watson “What should I work on today?”

Data analysis

The supply chain team embraces the domain expertise of its employees when training Watson.

For several months, top employees from various supply chain management areas (e.g., inventory management, quality engineering) spent 5 to 10 hours a week demonstrating for Watson how to answer different types of questions. These employees welcomed the opportunity to share their knowledge with the broader team.


Domain expertise is complemented by other data sources. The assistant tool is connected to IBM’s Enterprise Resource Planning (ERP) system. Besides the data sources already available in Watson, it also draws upon news, social media, and weather information.

The role of human talent

Software engineering generalists are responsible for building and maintaining the assistant tool.

These engineers receive informal coaching and advice from AI experts at IBM but operate largely independently. Matthias Graefe believes that the focus on engineering generalists rather than AI experts on his team exemplifies the point that Watson’s models are easy to train.

The entire supply chain team plays a role in training Watson by providing feedback. Employees grade each answer with a thumbs up or thumbs down, triggering Watson to adjusts its models. In addition, there is a push for the team to use Watson Workspace, a collaboration tool similar to Slack, instead of meetings or conference calls to discuss supply chain issues. These conversations are recorded as text, creating training data for Watson that may be helpful in the future.

Results

To capture the short-term impact of AI investments, the group keeps track of the number of questions answered, the number of questions that receive a thumbs-up rating, and the net promoter score (NPS) among employees.

There are promising early proof points. Taking advantage of the weather data in Watson, the assistant tool anticipated a hurricane heading toward an important hardware manufacturing plant in Guadalajara, Mexico. The team adjusted their plans accordingly. This insight saved the company money and kept clients satisfied.68

Insights

There is room for AI tools to take on more advanced tasks and become truly transformational for IBM’s supply chain team. As Watson is trained with information relevant for all supply chain teams, such as customs rules for different countries, benefits can grow. The human interface for the assistant tool will continue to encourage more employee interaction, and, as a result, capture better training data. This would make it easier for employees to provide the “why” behind decisions, which is critical for Watson to fully understand the decision made.

As AI tools take on more advanced tasks, IBM expects that the work of a supply chain professional will be divided between machines and humans. The machines will take care of common activities (e.g., ordering parts) while humans will handle collaboration (e.g., negotiations with partner suppliers), which requires their nuanced understanding of culture, politics, and risks.


SELL PHASE CASE: Adobe

Business

Adobe has made a strong push with its unified AI platform Adobe Sensei, which makes it easier for Adobe customers to create, deliver, and optimize digital experiences using its software products. Sensei offers numerous services in the form of pre-trained AI models that make predictions from the software user’s data, which are relevant for Adobe’s customers–primarily marketers and designers. These services are embedded in Adobe’s products, powering new software features, and can be accessed by external developers through an API. Adobe is also starting to open up the Sensei platform, which will allow external data scientists to create their own models.

Example: AI-powered features across Adobe’s software offerings

• Creative Cloud: Eliminates manual work to edit an object out of a photo

• Analytics Cloud: Sends an alert when an item on a retailer’s website is selling above or below expectations

• Advertising Cloud: Offers more granular audience targeting across channels (i.e., search, display, video marketing)

• Marketing Cloud: Automatically crops an image to show off the advertised item depending on screen size

“Most of our customers aren’t aware that they’re leveraging AI. They just know that they press this button and they get what they need.”

—Tatiana Mejia, Head of AI Product Marketing and Strategy, Adobe

Technology

Adobe’s domain expertise has shaped the Sensei platform and brought AI to address new problems. The company has chosen to invest in AI especially relevant to its business—content intelligence, creative intelligence, and experience intelligence—instead of general purpose AI. For example, it has partnered with Microsoft to create Experienced Data Models, an industry-standard open-source database schema for digital marketing data. Given Adobe’s focus on applied AI, Sensei leverages open-source tools and uses proprietary code only when necessary. It is built to accommodate a variety of data types, ranging from high-quality images for designers to purchase-history data for marketers.

“Sensei for us is a common approach in platform and framework with all three domains — creative, document, and customer analytics — because at the end of the day, customers use all these tools in concert. They use Creative Cloud to create the content, then they use Marketing Cloud to deliver the content, and Analytics to measure how it’s performing. Having the full journey allows us to do a Sensei stack that’s very unique because it goes all the way.”

—Abhay Parasnis, EVP and CTO, Adobe69


Human talent

With over 10 years of experience with AI technologies, Adobe Research serves as an AI center of excellence within the company. Adobe Research is on the forefront of AI technology, often partnering with academic researchers and publishing scientific papers. In 2017, 66 of Adobe’s papers were featured at preeminent computer vision and computer graphics conferences.70

Once AI technologies are fully functional, they are transferred from Adobe Research to the Sensei platform and then to the company’s software products. According to Tatiana Mejia, Sensei’s role is “to accelerate the speed with which our product teams are able to adopt AI into our software.”

Adobe has learned that AI talent needs to be dispersed throughout the organization due to AI’s importance and rapid evolution. To fill gaps in AI talent present today, the company has released an extensive online training program for engineers and product managers. The online training was internally developed with Adobe-specific content and is taken on a part-time basis. In response to strong interests from other employees, Adobe is exploring another AI online training program for more general audiences next year.

Results

Adobe evaluates Sensei’s effectiveness by how much the company’s products are addressing customer pain points. This is informed by both qualitative and quantitative feedback from customers. Exemplifying a clear success, a new AI-powered feature that assigns metadata word labels to photos reduced the time for a particular digital creative agency to search for relevant photos from over 10 minutes to approximately 30 seconds.

Insights

There is good potential for AI technologies like Sensei to enhance human creativity and intelligence, as opposed to replacing it. Given the ease of using Sensei-powered capabilities within Adobe’s products, job satisfaction for entry-level jobs can be improved due to the reduction in mundane or repetitive work. Adobe’s current offerings are considered a small step toward what the company believes is ultimately possible. In the future, an intelligent virtual assistant may sense what the user is trying to achieve and offer suggestions to streamline the process or improve the outcome.


SELL PHASE CASE: Pilot AI

Business

Pilot AI, founded in 2015, offers a computer vision software platform for compute-constrained camera devices. The platform’s proprietary technology makes AI model training more efficient. Insights are extracted from the platform and entered into a database that customers can access with a straightforward API. Among the many potential use cases for their platform, Pilot AI is currently focusing on applications that analyze video footage in retail and smart home settings. Customers are primarily device manufacturers and end users are the consumers or companies benefiting from the analytics solutions.

• Retail analytics: Analyzes in-store activity based on video footage and data collected from existing security cameras. As one example, Pilot AI can track how many customers with a certain demographic profile are in a particular section of a store.

• Smart home devices: Provides consumers with real-time alerts on what is happening at their home (e.g., a package arrived) from a connected camera or doorbell video footage.

Technology

Deep learning and other AI technologies are used for computer vision applications, enabling many forms of analysis of objects and people. The company’s platform accelerates the training of deep learning models, reducing the time and memory required for processing. This allows for model training to occur on compute-constrained commodity hardware, such as security cameras, instead of on more expensive hardware such as desktop computers, or in the cloud. Pilot AI’s software can be installed remotely. This allows customers to rapidly scale deployments from an initial pilot to every camera used for monitoring.

Data analysis

The Pilot AI platform analyzes video footage from a single camera. CEO Jonathan Su has found that customers often believe they have more video data than they can actually use. This is often due to the company not having easy access to the video data in their systems, not having legal rights to use the video data, or the video data being too sensitive.

Results

The value derived from the Pilot AI platform varies by application and customer. For the typical large national retailer, minimal additional sales from the platform’s insights are required to offset the low per-store cost of the solution. According to Su, if a retailer “sells an extra set of headphones, then they’ve recouped their cost for the month.”

Future vision

Pilot AI believes that “intelligence platforms” for speech, natural language processing, and vision will emerge. These platforms will grow symbiotically with other companies in the IoT value chain. The company aspires to be one of the leading intelligence platforms with widespread use in all IoT devices. In the company’s future vision, IoT device manufacturers will promote “Pilot inside,” similar to the way PC manufacturers promote “Intel inside” today.

Insights

Pilot AI exemplifies how intelligence platforms can bring powerful yet cost-effective AI technology to their enterprise customers. This allows enterprises to gain insights from new data sources without having to create innovative AI themselves or, in Pilot AI’s case, without building the underlying efficient computer vision technology. Questions remain on whether the accessibility of relevant data will limit the adoption of intelligence platforms. However, this challenge may be mitigated by intelligence platforms being distributed on IoT devices that both collect the necessary data and run AI models.


SELL PHASE CASE: Salesforce

Business

Salesforce is an $88 billion company71 aiming to democratize access to AI in order to enhance productivity of sales, service, and marketing professionals. The company’s AI efforts center around the Salesforce Einstein platform, which is embedded in their software products as well as sold to consumers directly. Einstein provides customers with over one billion AI-driven predictions each day.72

In the core software product, AI works in the background to inform recommendations, such as prioritizing sales leads or estimating email marketing performance. The company tries to present these recommendations where they are most actionable and provide some rationale for the recommendation.

Salesforce emphasizes that successful enterprise AI implementations require new business processes that provide the right data to their software instead of repurposing existing data. According to Jim Sinai, VP of Marketing for Salesforce Einstein, “[Most companies are] thinking about it 100 percent the wrong way. What you should think about is how you architect a workflow where executing that workflow trains the model.”

Technology

With Einstein, Salesforce has built AI that can create custom AI models for each of their customers. These models reflect the differences in each customer’s data and business model and are updated and retested when customers start keeping track of new data variables.

“You don’t need a PhD to use Einstein— employees of all skill levels can easily leverage it to connect with their customers, and they can also customize it to fit their unique needs with just clicks.”

—Richard Socher, Chief Scientist, Salesforce73

The AI models used by Einstein vary by the type of data. Machine learning is used for structured data (e.g., statistics) while deep learning is used for unstructured data (e.g., text, photos, videos, audio). Unlike other AI tools that require “big data,” many of Einstein’s AI models can accommodate a small number of data points and still make useful predictions.

Data analysis

Einstein benefits from all the data in Salesforce’s CRM and other software modules. This data describes the customer journey through both standard variables and custom user-defined variables. Approximately 80 percent of the data stored is for custom variables. The different types of data are labeled with tags, called metadata, which allows the Einstein platform to use the data in models without knowing the actual data contents.74 Einstein prepares this data to be used in AI models, replicating much of the manual work traditionally done by data scientists.

Results

The results are highly customer dependent. Salesforce Einstein has seen their customers succeed at enhancing the user experience in order to both increase revenue and decrease costs. Often, these benefits come from optimizing one small part of a broader business process (e.g., improving email marketing open rates).

Change management often slows or prevents large companies from obtaining these results. Extended discussions among a large number of stakeholders draws out the planning process. Smaller organization size and institutional learning, especially that gained from the first AI project, can accelerate this process.


Future vision

Looking forward, Salesforce Einstein will continue to work toward their goal of democratizing AI, but the exact shape of that future is unclear. Sinai believes that technical progress in one year is overestimated, yet technical progress in 10 years is underestimated.

Salesforce is excited about improving the user experience through adding advanced voice capabilities to its software. Also, the company plans to continue to make its software more “conversational,” allowing users to access data-informed insights in a more informal and intuitive way.

Insights

Salesforce has deployed AI as an engine behind its key marketing, sales, and service offerings. The company emphasizes process flow design as a key step in designing effective AI models that can be retested over time, enabling the company to rely on process rather than voluminous data. As important as the technology is in automating routine micro-processes and generating better customer insights, human factors play a large role in the success of AI deployment for Salesforce. Within the company, multiple departments collaborate to understand user needs and develop relevant AI solutions. Sinai believes that managing change while redesigning processes is very important to Salesforce clients. Users also play a role in teaching AI models over time with interfaces that ask for feedback. The combination of a clear AI strategy and an approach that is both client-based and human-centered is critical to Salesforce’s vision for expanding the use of AI over time.

“You need to have an AI strategy now. You don’t need to have it implemented, but you need to have a strategy and engineer your data flows and business pipelines, being mindful of how AI will improve them.”

—Jim Sinai, VP of Marketing, Salesforce Einstein

“AI is the next platform—all future applications, all future capabilities for all companies will be built on AI.”

—Marc Benioff, CEO and Co-founder, Salesforce75

Customer results with Salesforce Einstein

LIDS The sports merchandise retailer had a more than four-times increase in email open rates for new products (71 percent with Einstein Engagement Scoring compared to 8-15 percent previously).

U.S. BANKThe bank more than doubled their conversion of top-ranked leads with Sales Cloud Einstein, Einstein Analytics, and Einstein Discovery.


SELL PHASE CASE: OhmConnect

Business

Founded in 2013, OhmConnect works with energy providers to encourage consumers to save energy. The company obtains information on customer demand from online utilities accounts, pays consumers to reduce electricity usage during peak periods, and then sells the electricity savings back to utility companies. This reduces the amount of energy sourced from polluting, or “dirty,” power plants that are activated during peak periods. OhmConnect has 300,000 users across the U.S. and Canada and has saved the energy equivalent of removing from the road over 341,000 cars (CO2 equivalent).76 Users typically receive payments of $70-$150 per year.77 The company has been using machine learning to estimate energy savings opportunities and establish performance targets.

OhmConnect is investigating how it can deploy AI to more effectively segment its users and identify patterns in their electricity usage. Building on this understanding, the company hopes to personalize requests for consumers to use less electricity by optimizing times of power usage, targeted messaging, and financial incentives. Planned next steps are to develop and test early versions of AI models.

AI technology

Carson Moore, senior software engineer at OhmConnect, emphasizes the importance of understanding the business goal before entering into an AI strategy: “You have to be very upfront about what you’re optimizing for.” In the case of OhmConnect, the company is trying to optimize individual users’ ability to reduce energy consumption.

OhmConnect uses machine learning technologies to predict how customers will respond to the company’s requests to save energy and thus estimate the energy supply needed. Going forward, the company plans to transition to AI technologies such as deep learning to gain a more in-depth understanding of relevant energy demand and supply.

The company builds its own models and internal tools for testing and deployment, and is supported by third party plug-in software and Amazon Web Services. The company has systems in place to test early models, receiving initial results in under two weeks.

“We’ve built out a number of internal tools to be able to put AI models into practice without committing lots of engineering time to implementing the models at full scale. We’re able to get feedback pretty quickly on whether or not this was a good idea or...whether we should go back to the drawing board.”

—Carson Moore, Senior Software Engineer, OhmConnect

Data analysis

The company obtains comprehensive data about its users, directly collecting information on user behavior and household electricity consumption data through online utilities accounts connected to the platform. OhmConnect relies on external load forecasts to understand grid electricity requirements. The company is working to examine other signals from the data they collect in order to group users together and better predict their behavior. For example, some customers may be more responsive to the price offered for an hour of electricity saved, while others may be more responsive to special deals offering a small chance at a receiving a large sum of money if they turn off their power.


Over time, OhmConnect is gaining a better understanding of usage patterns. The company plans to use data such as weather forecasts, percentage of homes with air conditioning, and other targeted information to better group users and assess their electricity needs. In San Francisco, for example, most homes do not have air conditioning due to a mild climate. As a result, electricity usage is negatively correlated with hot weather in the city—a counterintuitive phenomenon.

The role of human talent

Artificial intelligence at OhmConnect is executed by software engineering generalists. In the future, the company may hire data science or AI specialists as it becomes harder to increase OhmHours savings with current AI technologies, or as future technologies become more relevant to the business. Given the unpredictability of AI advancements and business applications for the company, OhmConnect has diversified its analytics efforts so that one or two people are focused on three or four different strategies. This way, the team can pick and choose elements of various strategies as needed over time.

Results

OhmConnect focuses on a few key metrics. One is the percentage of normal energy usage that is avoided due to OhmConnect’s offering. Another is incremental OhmHours—additional hours of electricity that are not consumed by OhmConnect’s users. To date, hours saved have equated to 100 megawatts of energy. As additional AI technologies are deployed, the incremental impact will be identified by comparing individual user behavior before and after the AI technologies are introduced.

With the next stage of AI technologies, OhmConnect hopes to respond to real-time electricity requirements. For example, if there were a sudden increase in electricity demand, the company could automatically turn off all unused appliances in users’ homes. These plans will heavily depend on the adoption of IoT technology for household appliances and improved reliability of IoT-connected device data. The fact that future advancements in AI at OhmConnect may depend on improvements in IoT-enabled devices is a good example of interdependencies within the AI ecosystem.

Ultimately, Moore believes that connecting demand and supply information will add the most value. “What could we do if we had the most optimal machine learning algorithm that perfectly predicted when people are going to do everything they’re going to do? And what would happen if users knew exactly what they needed to know and were able to act on it every time?...It’s the combination of both that actually gets you into a place where we feel like we’re really providing the value we need to be providing.”

Insights

OhmConnect demonstrates that AI can create new business opportunities. To date, the company has deployed machine learning to predict customer usage. Going forward, AI can help the company understand nuanced consumer behavior at scale, which can lead to increased efficacy at reducing electricity consumption and thus the potential for broader geographic reach. The company’s vision includes leveraging AI to connect demand and supply in real time for optimal consumer savings and pollution reduction. By leveraging developing multiple AI strategies today, OhmConnect is also positioning itself to take advantage of new AI technologies as they become relevant for their current operations or inspire new approaches.


Citations

1 May 31, 2018 conference at Stanford Graduate School of Business, “Value Chain Innovation: The Promise of AI,” Padmasree Warrior.

2 Sam Ransbotham, David Kiron, Philipp Gerbert, and Martin Reeves, “Reshaping Business With Artificial Intelligence,” MIT Sloan Management Review and The Boston Consulting Group, September 2017, p. 1.

3 “State of Artificial Intelligence for Enterprises,” Teradata, 2017, p. 14.

4 Op. Cit., “Reshaping Business With Artificial Intelligence,” p. 5.

5 “Artificial Intelligence: The Next Digital Frontier,” McKinsey Global Institute, June 2017, p. 17.

6 Op. Cit., “State of Artificial Intelligence for Enterprises,” p. 18.

7 Op. Cit., “Reshaping Business With Artificial Intelligence,” p. 8-9.

8 Ibid.

9 Peter Stone et al, “Artificial Intelligence and Life in 2030.” One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. http://ai100.stanford.edu/2016-report (accessed September 6, 2016).

10 John Markoff, “Computer Wins on ‘Jeopardy!’: Trivial, It’s Not,” New York Times, February 16, 2011.

11 “Accenture Technology Vision 2018: Intelligence Enterprise Unleashed,” Accenture, 2018, p. 25.

12 Ibid.

13 Ibid.

14 Ibid.

15 Nils J. Nillson, “The Quest for Artificial Intelligence: A History of Ideas and Achievements,” Cambridge University Press, 2010.

16 “Artificial Intelligence,” OED Online, Oxford University Press. https://en.oxforddictionaries.com/definition/artificial_intelligence (accessed September 17, 2018).

17 “Getting Smarter by the Day: How AI Is Elevating the Performance of Global Companies,” Tata Consultancy Services, 2017, p. 31.

18 Op. Cit., “Artificial Intelligence: The Next Digital Frontier,” p. 17.

19 Ibid, p. 19.

20 Op. Cit., “Reshaping Business With Artificial Intelligence,” p. 4.


22 Op. Cit., “Value Chain Innovation: The Promise of AI,” Jim Sinai.

23 Karl Utermohlen, “Four Cases of AI in Retail,” Towards Data Science, April 10, 2018.

24 Op. Cit., “Value Chain Innovation: The Promise of AI,” Padmasree Warrior.

25 Op. Cit., “Value Chain Innovation: The Promise of AI,” Tatiana Mejia.

26 “Algorithms Tour,” Stitch Fix, http://algorithms-tour.stitchfix.com (accessed February 19, 2018).

27 “Airbus: Reimagining the Future of Air Travel,” Autodesk, www.autodesk.com/customer-stories/airbus (accessed February 19, 2018).

28 Sophia Chen, “The AI Company that Helps Boeing Cook New Metals for Jets,” Wired, December 6, 2017.

29 Celia Shatzman, “bareMinerals’s New App Is A Game-Changer For Makeup,” Forbes, June 20, 2017.

30 Polina Marinova, “Shiseido Just Bought a Makeup App That Scans Your Skin and Creates Custom Foundation,” Fortune, January 18, 2017.

31 Diana Bubbs, “How Stitch Fix Is Using Algorithmic Design To Become The Netflix Of Fashion,” Fast Company, June 8, 2017.

32 https://www.uptake.com (accessed May 25, 2018).

33 Rachelle Blair-Frasier, “Q+A: Inside a Smart Steel Mill,” Manufacturing.Net, January 4, 2018.

http://ai100.stanford.edu/2016-report

https://en.oxforddictionaries.com/definition/artificial_intelligence

http://algorithms-tour.stitchfix.com

http://www.autodesk.com/customer-stories/airbus

https://www.uptake.com


34 https://www.ibm.com/watson/commerce/resources/order-optimizer-demo/fulfillment.html (accessed June 18, 2018).

35 Melia Robinson, “Tiny self-driving robots have started delivering food on-demand in Silicon Valley — take a look,” Business Insider, March 24, 2017.

36 “Fasten Your Seatbelts: GE And Vale Are Making The Brazilian Railway More Connected And Smarter!”, GE Reports Brazil, April 22, 2017.

37 Jonathan Vanian, “Futuristic Robots Are Lending Their Hands in Gap’s Warehouse,” Fortune, October 24, 2017.

38 “How Germany’s Otto uses artificial intelligence,” The Economist, April 12, 2017.

39 https://www.salesforce.com/customer-success-stories/us-bank (acessed May 17, 2018).

40 Madeleine Johnson, “Starbucks’ Digital Flywheel Program Will Use Artificial Intelligence,” NASDAQ, July 31, 2017.

41 “Square Has a Profitable New Talent: Reading Your Mind,” Barron’s.

42 https://customers.microsoft.com/en-us/story/ups (accessed May 25, 2018).

43 “DIUx Selects C3 IoT as Strategic AI Software Platform for U.S. Air Force,” C3 IoT, November 1, 2017.

44 https://www.abundantrobotics.com (accessed June 26, 2018).

45 Op. Cit., “State of Artificial Intelligence for Enterprises,” p. 19.

46 Ibid, p. 18.


48 Op. Cit., “Value Chain Innovation: The Promise of AI,” Padmasree Warrior.

49 Ibid.

50 Op. Cit., “Value Chain Innovation: The Promise of AI.”

51 Ibid.

52 Ibid.

53 Ibid.

54 Ibid.

55 Ibid.

56 Shana Lynch, “Andrew Ng: Why AI is the New Electricity,” Insights by Stanford Business, March 11, 2017.

57 Nick Hastreiter, “What Impact Will AI Have In The Next 20 Years?,” Huffington Post, September 14, 2017.

58 Op. Cit., “Value Chain Innovation: The Promise of AI,” Daragh Sibley.

59 “A Future that Works: Automation, Employment, and Productivity,” McKinsey Global Institute, January 2017, p. 5.

60 Natalie Gagliordi, “How Stitch Fix Uses Machine Learning to Master the Science of Styling,” ZD Net, May 23, 2018.

61 Eric Colson, “More Human Humans: One Way in Which Our Lives Can Be Made Better By Ceding Tasks to Machines,” MultiThread, June 16, 2016.

62 https://multithreaded.stitchfix.com/algorithms (accessed May 12, 2018).

63 Op. Cit., “How Stitch Fix Uses Machine Learning.”

64 https://www.ibm.com/watson/about (accessed June 12, 2018).

65 Megan Murphy, “Ginni Rometty on the End of Programming,” Bloomberg Business Week, September 25, 2017.

66 Ibid.

67 Peter Buxbaum, “IBM and the Cognitive Supply Chain,” Global Trade, November 28, 2016.

68 Ibid.

https://www.ibm.com/watson/commerce/resources/order-optimizer-demo/fulfillment.html

https://www.salesforce.com/customer-success-stories/us-bank

https://customers.microsoft.com/en-us/story/ups

https://www.abundantrobotics.com

https://multithreaded.stitchfix.com/algorithms

https://www.ibm.com/watson/about


69 Khari Johnson, “How Adobe Used Its Huge Data Banks to Build Sensei, an AI Tool for Creatives,” Venture Beat, March 17, 2017.

70 https://research.adobe.com/adobe-research-papers-at-top-visual-computing-conferences (accessed May 17, 2018).

71 https://finance.yahoo.com/quote/CRM (accessed April 27, 2018).

72 Blair Hanley Frank, ISG, “Salesforce Einstein Now Powers Over 1 Billion AI Predictions Per Day,” Venture Beat, February 28, 2018.

73 Srinivas Tallapragada, “How WEF Young Global Leader and Salesforce Chief Scientist Dr. Richard Socher Views the Future of AI,” Salesforce Blog, March 23, 2017.

74 Scott Rosenberg, “Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone,” Wired, August 2, 2017.

75 Ibid.

76 https://www.ohmconnect.com/about-us (accessed May 10, 2018).

77 Op. Cit., “Californians Collect Cash for Saving.”

https://research.adobe.com/adobe-research-papers-at-top-visual-computing-conferences

https://finance.yahoo.com/quote/CRM

https://www.ohmconnect.com/about-us

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VALUE CHAIN INNOVATION: THE PROMISE OF AI...of how AI can improve various stages in the value chain, provides insight on how to design business strategies that leverage AI to create