COVID-19 has increased the need for intelligent decisioning through AI, but ROI is not guaranteed. Here's how to accelerate AI outcomes, according to our recent study.
The document provides an overview of research conducted by the London School of Economics on behalf of EY to investigate the use of artificial intelligence and machine learning in the financial services sector. It examines one use case for insurance, banking/capital markets, and wealth/asset management. The key findings are:
- Applied AI, mainly machine learning, is currently used across industries to solve isolated problems. Partnerships between large firms and startups are common.
- Prominent use cases illustrated trends in each sector, such as fraud detection in banking, predictive analytics in wealth management, and Internet of Things/home security applications in insurance.
- Both short and long term impacts are expected as machine learning capabilities advance, including changes
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Generative AI: Redefining Creativity and Transforming Corporate LandscapeOsaka University
The advent of Generative AI is redefining the boundaries of creativity and markedly transforming the corporate landscape. One of the pioneering technologies in this domain is the Reinforcement Learning from Human Feedback (RLHF). Combined with advancements in LLM (Language Model) has emerged as a notable player. LLM offers two primary interpretations: firstly, as a machine capable of generating highly plausible texts in response to specific directives, and secondly, as a multi-lingual knowledge repository that responds to diverse inquiries.
The ramifications of these technologies are widespread, with profound impacts on various industries. They are catalyzing digital transformation within enterprises, driving significant advancements in research and development, especially within the realms of drug discovery and healthcare. In countries like Japan, Generative AI is heralded for its potential to bolster creativity. The value generated by such AI-driven innovations is estimated to be several trillion dollars annually. Intriguingly, about 75% of this value, steered by creative AI applications, is predominantly concentrated within customer operations, marketing and sales, software engineering, and R&D. These applications are pivotal in enhancing customer interactions, generating innovative content for marketing campaigns, and even crafting computer code from natural language prompts. The ripple effect of these innovations is palpable in sectors like banking, high-tech, and life sciences.
However, as with every innovation, there are certain setbacks. For instance, the traditional business model of individualized instruction, as seen in the context of professors teaching basic actions, is on the brink of obsolescence.
Looking ahead, the next five years pose pertinent questions about humanity's role amidst this technological evolution. A salient skillset will encompass the adept utilization of generative AI, paired with the discernment to accept or critique AI-generated outputs. Education, as we know it, will be reimagined. The evaluative focus will transition from verifying a student's independent work to gauging their ability to produce content surpassing their AI tools. Generative AI's disruptive nature will compel us to re-evaluate human value, reshaping the paradigms of corporate management and educational methodologies
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
The document discusses the complex strategic environment businesses face due to ongoing global disruptions and economic uncertainty. It notes that navigating this environment requires leaders to develop dynamic perspectives on potential scenarios and opportunities. The best organizations turn ambiguity into opportunity by having a clear view of the challenges/opportunities in the world and an adaptable strategic plan. The document outlines some of the key disruptions, including geopolitical tensions, inflation, rising interest rates, supply chain issues, and shifts in consumer behavior. It also discusses how the economic outlook and impacts of these issues vary significantly across regions and sectors.
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
This document discusses generative AI and its potential transformations and use cases. It outlines how generative AI could enable more low-cost experimentation, blur division boundaries, and allow "talking to data" for innovation and operational excellence. The document also references responsible AI frameworks and a pattern catalogue for developing foundation model-based systems. Potential use cases discussed include automated reporting, digital twins, data integration, operation planning, communication, and innovation applications like surrogate models and cross-discipline synthesis.
The document discusses using generative AI to improve learning products by making them better, stronger, and faster. It provides examples of using generative models for game creation, runtime design, and postmortem data analysis. It also addresses ethics and copyright challenges and considers generative AI as both a tool and potential friend. The document explores what models are, how they work, examples of applications, and resources for staying up to date on generative AI advances.
The document provides an overview of the threats and opportunities of generative AI for businesses, outlining practical steps for adopting generative AI technologies including understanding the impacts on industries and business models, discovering opportunities to improve productivity and monetize assets, and starting the adoption journey with prioritized use cases and pilots.
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
Data Con LA 2020
Description
More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI?
Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services.
*List some of the sensitive use cases where AI is being applied
*Why governing AI is important and what are those principles?
*How Microsoft is approaching it?
Speaker
Suresh Paulraj, Microsoft, Principal Cloud Solution Architect Data & AI
Artificial intelligence applications are increasingly being used in the financial sector. Chatbots can help reduce costs by automating some customer service tasks, while machine learning algorithms can help make know-your-customer processes more efficient by identifying patterns in transaction data. Artificial intelligence may also allow for more accurate foreign exchange price predictions and personalized robo-advisor services. These applications demonstrate how artificial intelligence is disrupting traditional financial services.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Revolutionizing your Business with AI (AUC VLabs).pdfOmar Maher
"Revolutionizing your Business with AI" is a comprehensive yet digestible overview of Artificial Intelligence and Machine Learning. This presentation elucidates their fundamental concepts, showcases real-world applications, and equips attendees with strategic tools like the AI Idea Canvas and Evaluation Template. Whether you're a business leader or an intrigued learner, this presentation simplifies AI, aiding you in confidently navigating its transformative landscape.
Insurers expect artificial intelligence to completely transform the way they run their businesses.
Read more: https://www.accenture.com/in-en/insight-ai-redefines-insurance
Generative AI is evolving rapidly and disrupting marketing and sales in several ways:
1) It can leverage large datasets to identify new audience segments and automatically generate personalized outreach content at scale.
2) Within the sales process, it provides continuous support through tasks like hyper-personalized messaging, virtual assistance, and predictive insights.
3) It also has applications in customer onboarding, retention, and success analytics through tools like dynamic content and customer journey mapping.
Commercial leaders anticipate moderate to significant impact from generative AI use cases and most expect to utilize such solutions extensively in the next two years. Effective companies are prioritizing technologies like generative AI to improve performance.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Companies need to complement their AI initiatives with governance that drives ethics and trust or these efforts will fall short of expectations, our latest research findings suggest.
Get Ready: AI Is Grown Up and Ready for BusinessCognizant
Despite great enthusiasm for AI, full-blown deployments remain the exception rather than the rule across businesses in the U.S. and Europe, according to our recent research. Businesses can turn the tide by honing their AI strategies, maintaining a human-centric approach, developing governance structures and ensuring AI applications are built on an ethical foundation.
The document discusses how IT infrastructure is changing to adapt to new business priorities in the digital age. It introduces the "HEROES" framework for the future of IT infrastructure, which focuses on hybrid cloud architectures, edge computing, robotic process automation, obsolescence of old IT, and enterprise security. Artificial intelligence will be integrated across all areas of the framework and fundamentally change how organizations procure and consume IT infrastructure over the next five years.
Artificial Intelligence in Financial Services: From Nice to Have to Must HaveCognizant
AI is moving beyond experimentation to become a competitive differentiator in financial services — delivering a hyper-personalized customer experience, improving decision-making and boosting operational efficiency, our recent primary research reveals. Yet, many financial services companies will need to accelerate their efforts to infuse AI across the value chain while preparing for the next generation of evolutionary neural network technologies to keep pace with more forward-thinking players.
Our latest research reveals the need for companies to complement their technology advances with a focus on governance that drives ethics and trust. Otherwise, their AI efforts will fall short of competitors’ initiatives that responsibly embrace machine intelligence.
Close the AI Action Gap in Financial ServicesCognizant
Financial institutions are making progress with AI but have been slow to scale it across their organizations, resulting in an "AI action gap". To close this gap, the article recommends four steps:
1. Identify universal use cases that are well-defined to build AI expertise.
2. Improve data management capabilities, which AI relies on, by developing intelligent data tagging strategies and integrating fragmented systems.
3. Move beyond experimentation to fully implementing more AI initiatives to realize benefits across the enterprise.
4. Mitigate unintended consequences by creating responsible AI applications.
Following these steps can help financial institutions maximize the business value and ROI of AI.
Data Modernization: Breaking the AI Vicious Cycle for Superior Decision-makingCognizant
The document discusses how most companies are not fully leveraging artificial intelligence (AI) and data for decision-making. It finds that only 20% of companies are "leaders" in using AI for decisions, while the remaining 80% are stuck in a "vicious cycle" of not understanding AI's potential, having low trust in AI, and limited adoption. Leaders use more sophisticated verification of AI decisions and a wider range of AI technologies beyond chatbots. The document provides recommendations for breaking the vicious cycle, including appointing AI champions, starting with specific high-impact decisions, and institutionalizing continuous learning about AI advances.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Artificial intelligence (AI) is a source of both huge excitement
and apprehension. What are the real opportunities and threats
for your business? Drawing on a detailed analysis of the business
impact of AI, we identify the most valuable commercial opening in
your market and how to take advantage of them.
Ibm's global ai adoption index 2021 executive summaryEmisor Digital
Almost a third of businesses surveyed in the IBM Global AI Adoption Index 2021 report that they are currently using AI, and 43% say they accelerated their AI rollout due to the COVID-19 pandemic. However, lack of AI skills and increasing data complexity were cited as top challenges. While 74% of companies are exploring or deploying AI, the most common barriers are limited AI expertise, data complexity, and lack of tools to develop AI models. Ensuring AI systems are trustworthy, fair, and can be explained is also critical for businesses.
The document discusses the potential economic impact and value of artificial intelligence (AI) technologies. Some key points:
- Global GDP could be 14% higher by 2030 due to AI, equivalent to an additional $15.7 trillion in economic value. China and North America are expected to see the largest boosts of up to 26% and 14% respectively.
- The majority of GDP gains will come from increased productivity and consumption enabled by AI. Productivity gains will be driven by automation of processes and augmentation of human workers. Consumption gains will come from personalized and higher quality AI-enhanced products and services.
- Retail, financial services, and healthcare are identified as sectors that could see the biggest gains from AI
How Companies Can Move AI from Labs to the Business CoreCognizant
APAC and Middle East organisations have big expectations from AI, but they’re only just getting started. To realise the full potential of AI-led innovation, they must rapidly, but smartly, scale their deployments and embrace a strong ethical foundation, keeping a close eye on the human implications and cultural changes required to convert machine intelligence from lofty concept to business reality.
EO Briefing 2015 is structured in three chapters. The first chapter examines the impact of digital technologies, particularly the Internet of Things (IoT) on business. The IoT presents an array of challenges and new revenue possibilities but the question is which companies will be able to capitalise on this opportunity. This an especially crucial question as C-suite executives see competition rising sharply in 2015.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
[Article] Artificial Intelligence: Changing Business Amidst COVIDBiswadeep Ghosh Hazra
An article on Artificial Intelligence: Changing Business Amidst COVID which is on the subject of AI Adoption before and during COVID-19.
The article is divided into the following sections-
1) Setting the context
2) Diving deeper
3) AI Adoption amidst COVID-19
4) References
eTailing India Launches Big Data Report - 2015 eTailing India
The document discusses the current state of big data in India and its potential impact on eCommerce growth. It notes that big data involves collecting, processing, and applying insights from large, diverse data sets. While still nascent in India, big data is projected to significantly impact eCommerce by providing deeper customer insights and more personalized experiences. Major players are adopting strategies like Hadoop to analyze customer behavior and improve conversions. Widespread adoption is expected to drive industry competition and innovation.
The document discusses how IT will change between now and 2020 based on a study of over 150 chief information officers. It finds that demand for IT will significantly increase as digital technologies become more prevalent and disruptive. Companies will invest more in customer-facing IT systems and applications to drive sales. To meet this rising demand, IT organizations will need to focus on simplifying complex systems, increasing outsourcing, and addressing skills shortages. Forward-thinking companies will balance IT costs with business value to ensure they have the capabilities required for the digital future.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is becoming a key strategy for technology companies as they shift to cloud-based subscription models. This requires building an "experience ecosystem" that breaks down silos and involves partners. Building such an ecosystem involves adopting a cross-functional approach to experience, making experience data-driven to generate insights, and creating platforms to enable connected selling between companies and partners.
Intuition is not a mystery but rather a mechanistic process based on accumulated experience. Leading businesses are engineering intuition into their organizations by harnessing machine learning software, massive cloud processing power, huge amounts of data, and design thinking in experiences. This allows them to anticipate and act with speed and insight, improving decision making through data-driven insights and acting as if on intuition.
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Enhancing Desirability: Five Considerations for Winning Digital InitiativesCognizant
To be a modern digital business in the post-COVID era, organizations must be fanatical about the experiences they deliver to an increasingly savvy and expectant user community. Getting there requires a mastery of human-design thinking, compelling user interface and interaction design, and a focus on functional and nonfunctional capabilities that drive business differentiation and results.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
Manufacturers are ahead of other industries in IoT deployments but lag in investments in analytics and AI needed to maximize IoT's benefits. While many have IoT pilots, few have implemented machine learning at scale to analyze sensor data and optimize processes. To fully digitize manufacturing, investments in automation, analytics, and AI must increase from the current 5.5% of revenue to over 11% to integrate IT, OT, and PT across the value chain.
The Work Ahead in Higher Education: Repaving the Road for the Employees of To...Cognizant
Higher-ed institutions expect pandemic-driven disruption to continue, especially as hyperconnectivity, analytics and AI drive personalized education models over the lifetime of the learner, according to our recent research.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
The document discusses potential future states for the claims organization of Australian general insurers. It notes that gradual changes like increasing climate volatility, new technologies, and changing customer demographics will reshape the insurance industry and claims processes. Five potential end states for claims organizations are described: 1) traditional claims will demand faster processing; 2) a larger percentage of claims will come from new digital risks; 3) claims processes may become "Uberized" through partnerships; 4) claims organizations will face challenges in risk management propositions; 5) humans and machines will work together to adjudicate claims using large data and computing power. The document argues that insurers must transform claims through digital technologies to concurrently improve customer experience, operational effectiveness, and efficiencies
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
Green Rush: The Economic Imperative for SustainabilityCognizant
Green business is good business, according to our recent research, whether for companies monetizing tech tools used for sustainability or for those that see the impact of these initiatives on business goals.
Policy Administration Modernization: Four Paths for InsurersCognizant
The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
As #WorkFromAnywhere becomes the rule rather than the exception, organizations face an important question: How can they increase their digital quotient to engage and enable a remote operations workforce to work collaboratively to deliver onclient requirements and contractual commitments?
Five Priorities for Quality Engineering When Taking Banking to the CloudCognizant
As banks move to cloud-based banking platforms for lower costs and greater agility, they must seamlessly integrate technologies and workflows while ensuring security, performance and an enhanced user experience. Here are five ways cloud-focused quality assurance helps banks maximize the benefits.
Getting Ahead With AI: How APAC Companies Replicate Success by Remaining FocusedCognizant
Changing market dynamics are propelling Asia-Pacific businesses to take a highly disciplined and focused approach to ensuring that their AI initiatives rapidly scale and quickly generate heightened business impact.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Realising Digital’s Full Potential in the Value ChainCognizant
When we spoke with executives across Europe who lead digitising efforts, they described a diverse range of deployments, but digital can, and must, deliver far more than it has so far. In this ebook, we explore how businesses can explore digital's full potential across their value chain.
Realising Digital’s Full Potential in the Value Chain
AI: From Data to ROI
1. Special Report
September 2020
Produced in partnership with
AI: From Data to ROI
If the COVID-19 crisis has revealed anything to business
leaders, it’s the dire need for intelligent decisioning. But even
as businesses embrace AI, high ROI is not guaranteed. Here’s
what works for accelerating AI outcomes, from where to invest
and how much to spend, to the returns you can expect,
according to our recent research.
2. < Back to Contents2 / AI: from data to ROI < Back to Contents
Contents
Click a link below to jump to that section.
3 Introduction: AI Meets Its Moment
5 All Eyes on AI
9 It’s All About the Data (But Not Just Any Data)
16 What and Where to Spend
20 What to Expect in Return
25 How to Accelerate the ROI of Intelligent
Decisioning
28 Methodology
In our global study,
almost two-thirds of
senior executives –
regardless of industry
or region – see AI as
highly important to
the future of their
businesses.
3. 3 / AI: from data to ROI Back to Contents
Introduction: AI Meets Its Moment
So far this year, the world has been faced with the COVID-19 pandemic, social unrest, economic upheaval and
vast uncertainty. Amid the chaos, individuals and organizations have desperately sought anything that provides a
glimmer of insight into an unknowable future. That’s why 2020 will also be known as the tipping point for artificial
intelligence (AI).
Our groundbreaking research, conducted during the outbreak, reveals
executives are turning en masse to AI to make better, more intelligent
decisions, especially when much of the information and decision models
needed are fast-changing or unknown. In our global study of 1,200
companies, conducted in conjunction with ESI ThoughtLab, almost two-
thirds of senior executives – regardless of industry or region – see AI as highly
important to the future of their businesses (see methodology, page 28).
From the early days of the pandemic, it became painfully clear to most
businesses that they didn’t have the data they needed to make intelligent
decisions in the face of chaos. Even now, their data isn’t always current,
accurate or relevant enough to be of use to them, and it’s hard to interpret.
Their forecast models, which were previously “good enough” are now way
off-target. Many realize they can no longer trust their old decision systems.
It’s no wonder, then, that businesses have little interest in returning to the old
ways of working. Over the next three years, twice as many businesses expect
to be in the advanced stages of AI maturity vs. today, and annual spending
increases will nearly double from 4.6% to 8.3%.
However, while AI ultimately offers significant ROI, it can be difficult to
achieve and does not come overnight. While currently more than half
of businesses are seeing positive returns on their AI investments, the
average ROI is just 1.3%. Further, with potentially high upfront costs in data
modernization, technology adoption and people development, it can take 17
months on average to realize positive payback.
4. 4 / AI: from data to ROI Back to Contents
To make it a game-changer and generate value, businesses must
have the right data, plan, applications, skills and use cases, and
they must focus on real business objectives and problems to solve.
For beginners, the challenges can include limited AI skills and
inflexible IT infrastructures; as companies scale AI across their
organizations, other hurdles appear, including managing risks and
ethics, and embedding AI into day-to-day business processes. Data
modernization is a continual stumbling block; in fact, businesses
spend about 35% of their AI budgets on data modernization,
according to our study.
Ultimately, though, even organizations in the early stages of AI
adoption are achieving a variety of business goals, including
improved productivity, profitability, employee engagement and
customer satisfaction. More mature AI adopters are achieving even
more growth-oriented benefits, such as increased revenue, improved
decision-making, greater market share and enhanced innovation.
In the following pages of our ebook, executives will find an evidence-
based roadmap for supercharging their business performance with
AI. It’s our hope that these insights will light a path through the chaos
of today and help businesses prepare for a better tomorrow.
Assessing leaders vs. laggards
A prime objective of our research was to
determine what constitutes an AI leader.
To answer this, we assessed respondents
along two key dimensions: level of AI
implementation and the benefits from AI
investments.
Just 15% of businesses are at the highest
stage of AI maturity (what we call leaders),
while about one-third are just behind them
(advancers).Just over half are in the early
stages of AI development (a beginner or
implementer). As we see in the pages that
follow, these percentages will radically shift
in three years’ time.
20%
32%
33%
15%
Beginner
Developing
plans and
building internal
support for AI
Leader
Widely using AI
to generate
many benefits
and transform
business
Implementer
Starting to pilot
AI and use a
few simple
applications
Advancer
Using AI in key
parts of the
business and
seeing gains
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 1
Organizations by AI maturity
5. 5 / AI: from data to ROI Back to Contents
All Eyes on AI
6. Back to Contents6 / AI: from data to ROI
Seeking order in chaos
AI is crucial across industries
Percent of respondents rating AI as having high importance.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 2
The pandemic cast a spotlight on the need for AI.
A decisive majority – 64% – of executives in our study
believe AI is considerably or very important for the future
of their business (see Figure 2). That figure jumps to 98% for
respondents at the highest levels of AI maturity, and to 85% for
the largest organizations in our study (revenue over $20 billion).
As the world went online, many businesses had difficulty grasping
the continuously shifting dynamics that ensued. Predictive
models failed to account for the sudden and ongoing changes
to customer, market and supply chain behaviors. The experience
shifted AI into high gear.
0 10 20 30 40 50 60 70 80
Investment
Media
Energy
Consumer/retail
Insurance
All industries
Manufacturing
Telecom
Life sciences
Healthcare
Technology
Banks
Automotive
64%
77%
74%
66%
66%
64%
63%
60%
59%
52%
42%
75%
75%
7. 7 / AI: from data to ROI Back to Contents
A surge in AI maturity
Two-thirds of businesses in most industries will reach
advanced levels of AI maturity
Percent of respondents at a mature or advanced stage of AI.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 3
Most companies, however, are in the early stages of AI adoption, with
just 29% of respondents across industries at a maturing or advanced
level in implementing AI (see Figure 3). Most AI projects are in pilot or
early deployment stages, and even among AI leaders, just about one-
quarter of AI projects are now in widespread deployment.
This will change dramatically, however, in the next three years, when the
percent of businesses that expect to be at a maturing or advanced stage
of AI adoption will more than double to 63%. In industries that are in the
earlier stages of their AI journey – such as insurance, wealth and asset
management, and media and entertainment – the increase will be fourfold.
Currently, the sectors with the highest percentage of AI leaders are the
automotive, healthcare and banking industries. While the auto industry
isn’t often the first sector that comes to mind when it comes to AI (beyond
self-driving cars), automakers’ use of AI is far-ranging, including driver-assist
features, connected vehicles, manufacturing, quality control and product
design. General Motors, for example, is using AI-driven “generative design”
to shave unnecessary weight from car parts, while Volkswagen is increasing
the precision of its market forecasts with AI analytics, pulling in data on
household income and customer preferences.
0 20 40 60 80 100
Automotive
Healthcare
Banks
Telecoms
Manufacturing
All industries
Life sciences
Technology
Energy and utilities
Consumer and retail
Media and entertainment
Insurance
Investment management 327%
306%
210%
136%
119%
117%
97%
84%
57%
52%
50%
273%
235%
% Change
Now
In three years
8. 8 / AI: from data to ROI Back to Contents
AI across industries
We’re working with businesses across industries to drive intelligent decisioning.
Financial services: When a global
financial services organization wanted
to reduce its fraud risk, we worked
with the organization to develop a
machine-learning system that flags
potential fraud in near-real-time.
The technology compares scanned
images of handwritten checks
against a growing database of checks
previously identified as fraudulent. It
then flags potential counterfeits while
deposit transactions are in process.
The system has demonstrated
50% savings on fraud losses and is
forecast to reduce annual fraud losses
by $20 million.
Insurance: We worked with an
industry-leading PC insurer that
wanted to improve the quality of its
call-center customer interactions.
We helped this carrier develop an
AI-driven analytics platform that
automates call monitoring and
enables agents to respond more
empathetically and effectively to
customers during calls. The system
was taught to recognize agents’
progress through a 40-point checklist
for each call, as well as improve
agents’ real-time response to
customers through speech analytics,
which interprets word choice, diction
and tone. Agents have improved
the customer experience through
personality profiling and conversation
cues.
Healthcare: A leading healthcare
services provider wanted to
proactively identify potential drug-
seeking behavior in order to reduce
addiction among its patients. We
developed an AI-driven machine-
learning solution that analyzes data
from three sources: physicians’ notes
from patient visits recording their
impressions of a patient’s behavior,
appearance and diagnoses; the
drugs the patient had previously
been prescribed; and the behaviors
and symptoms caused by each
drug. The system uses text analytics
and advanced machine learning
to generate system alerts when
a pattern of at-risk behavior is
identified. This enables caregivers to
intercede with patients in real time
and take corrective actions. Using
the system, the health provider has
identified 85,000 at-risk patients and
anticipates a potential $60 million
reduction in care costs.
Life sciences: A biotech company
wanted to improve patient
adherence to medication regimes.
We developed a solution that
uses AI, machine learning and
natural language processing to
pull insights from case notes that
reveal what motivates patients to
start, discontinue and switch use of
medications. Using these insights,
the company was able to identify
adherence roadblocks and improve
patient support through more
effective KPIs, recommendations and
documentation.
9. Back to Contents9 / AI: from data to ROI
It’s All About the Data (But Not Just Any Data)
10. 10 / AI: from data to ROI Back to Contents
Nearly all AI leaders have mature data
modernization practices
Percent of respondents who say they’re maturing or advanced in data management.
It’s clear from our study that there’s an inextricable link between AI
maturity and data management (what we call “data modernization”):
the work involved with ensuring the accessibility, reliability and
timeliness of data for AI and analytics. Nine out of 10 AI leaders say
they’re in the maturing or advanced stages of data management,
while literally none of the AI beginners rate themselves that way (see
Figure 4). At the same time, having a proper IT architecture and data
modernization processes in place was the most important lesson
learned for beginners (60%).
What’s more, more than half of healthcare, banking and auto manufacturing
businesses – the industries with the highest percentage of leaders in
them – have already made significant progress in modernizing their data,
and an overwhelming majority expect to by 2023.
Modernizing data = AI maturity
0
20
40
60
80
100
BeginnerImplementerAdvancerLeader
Now
In three years
91%
97%
53%
85%
20%
74%
0%
35%
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 4
11. Back to Contents11 / AI: from data to ROI
Further, the same industries
expecting particularly high growth
in AI maturity are the same ones that
expect the greatest gains in data
modernization (see Figure 5). In all,
businesses spend about 35% of their
AI budgets on data modernization,
or about $13.3 million per company.
Beginners spend even more, or 44%.
Industries expecting to accelerate their AI maturity will also
surge in data modernization
Percent of respondents who say they’re maturing or advanced in data management now
and in three years.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 5
0 20 40 60 80 100
Healthcare
Automotive
Banks
Telecoms
All industries
Technology
Insurance
Manufacturing
Consumer/retail
Life sciences
Investment
Energy
Media 212%
192%
126%
117%
111%
105%
97%
56%
55%
55%
44%
167%
145%
% Change
Now
In three years
12. 12 / AI: from data to ROI Back to Contents
Data sources will expand greatly in three yearsIncreasingly, businesses are finding the most easily
accessible data sets aren’t enough to make the most
intelligent decisions. By 2023, we’ll see businesses pulling
from wider and more diverse data sets for AI-driven
insights (see Figure 6).
Today, IoT, customer and internal information are the main
types of data integrated into AI applications. In many cases,
this is simply because of the sheer volume of accessible
data generated by sensors and customer interactions. But
other forms of data are where the greatest insights often lie,
particularly when such data is combined.
For example, we worked with a home goods store that wanted
to know where people went when they left the store. By
combining geospatial data with other data, we discovered that
30% of people go to McDonald’s. That insight became very
valuable for cross promotions.
Data that matters
0 10 20 30 40 50 60
Data used for AI today
Percent of respondents integrating each type of data
into their AI applications.
Geospatial
Psychographic
Local
Macro
Competitive
Supply chain
Manufacturing
Real-time
Social media
Internal
Customer
IoT
0 20 40 60 80 100 120
Fastest growing over three years
Percent growth in the use of each type of data.
Internal
IoT
Local
Manufacturing
Customer
Supply chain
Macro
Social media
Real-time
Geospatial
Competitive
Psychographic 109%
76%
76%
75%
60%
41%
35%
31%
24%
15%
14%
13%
52%
46%
43%
21%
20%
18%
18%
17%
16%
15%
8%
7%
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 6
13. 13 / AI: from data to ROI Back to Contents
Further, during the pandemic, global brands will
see different regions of the country and the world
open for business at different paces. For one of
our clients, that has meant continually adjusting
its product mix, product placement and product
sourcing as supply and demand conditions
change unpredictably, region by region.
We’re using machine learning to refine our
analytic models to predict the effects of
everything from ongoing infection rates to
regional weather conditions on future sales
and demand trends. This data includes medical
information from leading healthcare providers, as
well as historical internal data, such as same-store
sales. At each step, we’re using our agile analytics
methods to make sure we deliver the analytics
the business needs most as the recovery from the
pandemic unfolds.
Over the next three years, companies will double
their use of psychographic data and ratchet up
their reliance on competitive, geospatial and
real-time data by about 75%. No AI program is
complete without “voice of the customer data”
gleaned from sources such as social media and
call center analytics. While much of that data is
generally lost or overly summarized, it’s essential
to extract insights from that data and get them to
relevant business teams.
14. 14 / AI: from data to ROI Back to Contents
Top 10 data challenges
Percent of respondents naming each as a top challenge.
With data modernization comes challenges – most of which don’t ease
up to any great degree as maturity increases (see Figure 7). This makes
sense, given that advanced AI maturity also means more scaling of
pilot solutions, more use of diverse data sets and a greater shift toward
modernizing data (i.e., through the use of data lakes and the cloud) vs.
simply managing data (i.e., with databases and storage).
Compliance, for example, becomes more difficult as organizations scale
their AI solutions around the world, and cleaning and normalizing data
becomes twice as difficult for leaders compared with non-leaders as
they leverage richer data sets. Identifying trusted data continues to be a
challenge as businesses turn to third-parties for external data, as does
ensuring data integrity as businesses work to ensure ethical algorithmic
decisions. In short, as AI gets used for more powerful business outcomes,
the responsibility grows to meet ever higher standards.
Perishability and other data challenges
0 5 10 15 20 25 30 35 40
Cleaning and normalization
Data silos in organization
Availability of data
Size/frequency of data
Identifying corrupt records
Identifying trusted data
Integrating data
Data security
Governance/compliance
Data integrity/quality 38% -5%
5%
-1%
-10%
-2%
-6%
0%
-9%
-3%
10%
36%
35%
25%
22%
16%
17%
10%
36%
36%
% gap between
'leaders' and
'nonleaders'
Note: Multiple responses permitted.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 7
15. Back to Contents15 / AI: from data to ROI
As businesses embrace a wider selection of data types, a
particular challenge is managing data perishability – ensuring
the data used for insights is current, accurate and relevant.
Unlike ERP and other structured data, more dynamic
data types – such as IoT, social, real-time, geospatial and
psychographic – come with a shorter expiration period. By
using machine learning, businesses can continually assess the
timeliness, accuracy and relevance of their data and analytic
models, testing millions of new models against real-world data
to continuously provide new scenarios and ranges of forecasts
for changing conditions.
As businesses embrace a wider selection of data types, a
particular challenge is managing data perishability – ensuring
the data used for insights is current, accurate and relevant.
16. 16 / AI: from data to ROI Back to Contents
What and Where to Spend
17. 17 / AI: from data to ROI Back to Contents
On average, companies expect to increase their spending on AI by a factor of two.
While companies increased their AI investments by 4.6% on average over the last
year, that will accelerate to an 8.3% increase in annual AI spending over the next
three years (see Figure 8).
A closer look reveals that it’s the non-leaders accounting for the spending increases.
While non-leaders expect to double the rate of their AI investment – from a 4.4% increase
over the last year to 9% annually over the next three years – AI leaders expect to trim their
spending growth, from 6% over the last year to 4.5% over the next three years. Yet since
leaders say they are spending 2.6 times others in absolute dollars, they still expect to
outspend their non-leader counterparts.
Some sectors plan to ramp up spending at more than twice the rate of last year: media,
investment management, and consumer and retail.
While spending expectations may be moderated due to budgetary pressures during
the pandemic, it’s very likely that businesses will feel similarly pressured to invest in AI to
navigate the COVID crisis with intelligent decisioning.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 8
The rise and fall in AI spending rates
Percent increase in AI spending, last year and in three years.
AI spending is on the rise
0
2
4
6
8
10
All respondentsLeaderNon-leader
Last year In three years
9%
4.4%
6%
4.5% 4.6%
8.3%
18. 18 / AI: from data to ROI Back to Contents
When companies start out in AI, they spend over half of
their AI budgets on technology and only 15% on people.
But as they mature, a greater share of their spending goes
to training and hiring people to achieve their goals (see
Figure 9).
In fact, AI leaders invest almost twice as much of their budget
in people as AI beginners. They know that AI excellence
goes beyond using the latest AI technology. It requires hiring
the best talent, training staff on AI, investing in external
partnerships, and building a culture of collaboration between
analytics teams and business units.
While many people assume that technology spend will be high,
the technologies themselves are commoditizing reasonably
quickly. Companies that invest in the education of their own
teams will go further.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 9
With AI maturity comes a shift in spending toward people
Percent of budget invested today in people, process and technology.
Where to spend: tech vs. people
TechnologyProcessPeople
30%55%
15%
Beginner Implementer Advancer Leader
31%54%
15%
30%
46%
23%
34%
39%
27%
19. 19 / AI: from data to ROI Back to Contents
Leaders in our study reveal the path ahead for where to spend on
AI technologies. Already, leaders spend 40% of their AI budget on
advanced AI technologies, such as machine learning, deep learning,
computer vision and natural language processing, whereas non-
leaders are more focused on basic AI technologies, such as data
management, digital assistants and robotic process automation. In
the next three years, leaders will increase that to 43% – more than
double what their non-leader counterparts plan to spend on these
technologies (see Figure 10).
Deep learning will be particularly valuable as AI adoption expands, since
it will provide businesses with the ability to find meaning in diverse sets of
unstructured data. NLP will be a game-changer for businesses. Over the last
few years, there have been huge advances in voice recognition, whether it is
to capture different accents or build capabilities into more devices.
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 10
Spending will shift to advanced AI
Percent of budget allocated to advanced AI technologies vs. basic AI.
Which technologies to master
0
10
20
30
40
50
Non-leadersLeaders
Today In three years
43%
40%
15%
19%
20. 20 / AI: from data to ROI Back to Contents
What to Expect in Return
21. 21 / AI: from data to ROI Back to Contents
Identifying the right use cases is critical for maximizing ROI. In fact,
77% of companies generating the highest returns from AI do this one
thing well. Use cases vary by industry and are best selected in close
conjunction with business teams.
Leaders are well ahead of non-leaders in the number of use cases
implemented and scaled across their enterprise. Nine out of 10 report
having largely or fully implemented AI in the 19 functional areas included in
the study (see Figure 11).
When you examine the areas of greatest difference between leaders and
non-leaders, it becomes clear that the trajectory moves from improving
internal functionalities to more outward-facing endeavors as maturity
grows. For instance, in addition to applying AI to connected devices and
customer service (34%), non-leaders are mainly focused on improving
internal functionalities such as IT operations (26%), data security (23%) and
customer analysis (24%).
Meanwhile, the gap between leaders and non-leaders is high in outward-
facing functions, such as distribution and logistics, supply chain, business
development and marketing. Leaders will reap the benefits of driving
growth as they’re well ahead in applying AI to RD and innovation.
Start with internal use cases and move the
focus outward
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 11
Choosing use cases
Percent of respondents who have largely or fully implemented AI in each function.
0 20 40 60 80 100
Connected devices, products, and services
IT operations and IT infrastructure
Strategic planning and decision making
Brand management and reputation
Product development, RD, innovation
Supply chain, procurement
Distribution and logistics
Customer service and experience
Market and customer analysis
E-commerce and customer facing platforms
Customer onboarding and administration
Marketing, promotion, and channel management
Pricing and business models
Sales and business development
Data security and privacy
Legal and compliance
Fraud detection and mitigation
Finance and auditing
Risk management 5.7
5.5
5.2
4.7
4.1
5.5
5.2
4.8
4.7
4.7
3.7
2.7
6.4
5.6
5.4
5.0
5.0
3.5
2.8
Leader
multiplier
Non-leader Leader
StrategyOperationsMarketsCustomersFinanceRisk
22. 22 / AI: from data to ROI Back to Contents
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 12
Top five benefits of AI
Percent of non-leaders realizing each benefit of AI.
Because non-leaders are more focused on internal operations, it stands
to reason that they’re also more intent on looking for value in those areas.
That’s why we see non-leaders reporting value creation in areas such as
increased productivity, profitability, employee engagement and customer
retention (see Figure 12).
Meanwhile, as businesses grow in maturity, the focus shifts toward driving
a growth strategy. As such, leaders are seeking – and often finding – value
in the areas of increased revenue, greater market share, and new products
and services. Importantly, one-quarter of leaders (vs. just 16% of non-
leaders) are focusing their AI efforts on intelligent decisioning, which will be
vital for navigating the pandemic and beyond. As we see on the next page,
intelligent decisioning is also an area of high ROI.
Outcomes shift as maturity grows
0 10 20 30 40 50
New products/services
Improved profitability
Improved employee engagement
Increased customer satisfaction
Higher productivity 49%
45%
38%
32%
19%
Percent of leaders realizing each benefit of AI.
0 10 20 30 40 50
New products or services/greater market share
Improved profitability
Improved planning and decision making
Decreased costs/greater efficiencies
Higher productivity/increased revenue 31%
29%
25%
23%
22%
23. 23 / AI: from data to ROI Back to Contents
Most businesses are seeing positive returns on the 19 AI areas included in
our study. The area generating a positive ROI for the largest percentage of
companies is customer service and experience, followed by IT operations
and planning/decision making (see Figure 13).
Other activities to note are pricing and business models, brand
management and reputation, and distribution and logistics. The percentage
of businesses with positive returns in these areas is particularly high given
that fewer are focusing their efforts there.
Underperforming areas include sales and business development, finance
and auditing, fraud detection, and marketing and promotions.
Customer experience is a high-return
place to start
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 13
The ROI of AI across functions
Percent of respondents seeing positive returns in each area.
0 10 20 30 40 50 60 70 80
Sales and business development
Finance and auditing
Fraud detection and mitigation
Marketing, promotion, channels
Market and customer analysis
E-commerce/customer platforms
Legal and compliance
Distribution and logistics
Customer onboarding/admin
Brand management and reputation
Data security and privacy
Average across all functions
Pricing and business models
Connected devices and products
Supply chain, procurement
RD and innovation
Risk management
Planning and decision making
IT operations and IT infrastructure
Customer service and experience 74%
69%
66%
62%
62%
62%
61%
61%
60%
60%
60%
59%
59%
58%
58%
57%
57%
57%
53%
51%
24. 24 / AI: from data to ROI Back to Contents
So much hope has been invested in AI to pull businesses through
today’s chaotic environment that it’s easy for expectations to exceed
reality when it comes to ROI. As our study shows, achieving ROI on AI
initiatives takes time, smart deployment and the ability to scale.
On average, businesses have seen an ROI of just 1.3% from their AI
investments. However, that varies considerably based on AI maturity (see
Figure 14). Businesses in the first half of their AI journey hardly break even. It
is only when companies are advanced in AI and more widely implementing
it across their organizations that they start to see the fruits of their labor.
Leaders report an average ROI of over 4.3%, with nearly 40% reporting an
ROI of over 5%. All leaders report positive returns, while no beginners or
implementers post returns over 5%.
Generating ROI from AI is a slow-burning process. With the average
payback period at 17 months (see Figure 15), it clearly takes time to identify
the appropriate business case, acquire and prepare the right data, and then
build, test, refine and deploy working models. Management teams will want
to set realistic targets that consider not just the short-term financial gains
from AI, but also the longer-term strategic benefits.
ROI increases with AI maturity
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 14
Average ROI by maturity level
0
1
2
3
4
5
LeaderAdvancerImplementerBeginner
4.3%
1.5%
0.2% 0.4%
Response base: 1,200
Source: ESI ThoughtLab/Cognizant
Figure 15
Being realistic about payback times
Typical payback period Percent of respondents
Less than six months 5%
Six months to less than one year 38%
One year to less than two years 37%
Two years to less than three years 17%
Three years or more 3%
25. 25 / AI: from data to ROI Back to Contents
How to Accelerate Intelligent Decisioning
26. 26 / AI: from data to ROI Back to Contents
Begin with pilots, but then scale AI applications across
the enterprise. Companies starting out should focus on
working closely with business teams to identify use cases
and demonstrate their value through pilots. It’s important to
identify multiple use cases, since some AI initiatives will fail.
Once pilots succeed, it’s essential to follow through. The real
value of AI is not in the models themselves, but in a company’s
ability to scale them across their organizations. It’s telling that
75% of organizations with high ROI have scaled AI across
businesses units.
Use a hybrid organizational structure to scale AI
initiatives. Beginners often start out with a centralized
approach to AI, with a core of data scientists. But these efforts
struggle because the teams are often not sponsored by the
business lines, which are the ones with many of the ideas.
These central service teams are slow and ultimately collapse
under their own weight. Business people, on the other hand,
tend to work in a decentralized way. AI teams need to be close
to them, as well as the HR leader, the marketing leader, the
supply chain leader, the ops leaders. AI should be seen as a
service to them, not something that’s centrally controlled.
For example, we recently worked with a company that realized
its supply chain predictive models didn’t work anymore due
to COVID-19. They immediately put data scientists in with
the supply chain team and deployed new models in just two
weeks. The models went into production quickly because they
were tied to a business outcome and the people responsible
for those outcomes.
Once the organization grows its AI maturity, it can start
establishing standards. How do you know when you’re using
responsible AI? How do you eliminate bias? What tool sets are
appropriate? How do you integrate third-party data? Which
partners do you need? These types of decisions are better
served centrally but executed locally as you scale.
Driving ROI from AI
To succeed at using AI to drive intelligent decisioning, executives should consider the
following best practices uncovered by our research:
5
2
3
1
5
2
3
1
27. 27 / AI: from data to ROI Back to Contents
Get your data right. Nine out of 10 AI leaders are advanced in
data modernization. That’s why 35% of beginners and 74% of
implementers plan to have sophisticated data modernization
systems in place by 2023. Ensuring your data is in good
shape isn’t enough; businesses should also bring in a richer
set of data, such as psychographic, geospatial and real-time
data, which drives higher AI performance. At the same time,
organizations should integrate fast-growing data formats into
their AI applications, such as high-dimensional, video, audio
and image.
Solve the human side of the equation. AI is not just about
technology; it’s also about people. Tellingly, AI leaders spend
27% of their AI budget on people, almost twice the percentage
that AI beginners and implementers spend. It’s critical to
hire AI talent that can understand business needs and create
solutions, not just build models. Eighty-three percent of
businesses in our study with high ROI have been successful at
developing and acquiring the right people. It’s also important
to consider other people issues when adopting AI. Before
scaling projects, businesses should put an HR plan in place to
address jobs that may be disrupted.
Adopt a culture of collaboration and learning. About 85%
of businesses that generate large AI returns ensure close
collaboration between AI experts and business teams. Also,
83% of high performers are advanced at developing and
acquiring AI talent, and nearly nine out of 10 excel at providing
non-data scientists with the skills and tools to use AI on their
own. AI leaders are also more likely to have chief AI and
analytics officers in place and multiple executives working
together to lead AI initiatives.
5
2
3
55
28. 28 / AI: from data to ROI Back to Contents
Methodology
ESI ThoughtLab conducted a comprehensive
benchmarking survey of executives at 1,200
companies across 12 industries and 15 countries.
It was carried out over the phone in March
and April 2020. Respondents had superior or
excellent knowledge of the use of AI within their
organizations. A full 85% were C-level executives,
and the rest report directly into the C-suite.
The study examined AI investments, plans,
practices and performance results at
respondents’ organizations. It included
quantitative questions to allow ESI ThoughtLab
economists to develop a rigorous AI maturity
framework, analyze performance results,
benchmark practices, and measure the ROI on AI
investments.
Less than $1 billion
$1–$5 billion
$5-$10 billion
$10–$20 billion
$20–$50 billion
Over $50 billion
19%
12%
25%
25%
13%
6%
Respondents by industry
Automotive 8%
Energy/utilities 8%
Investment 8%
Life science 8%
Banks 8%
Healthcare 8%
Manufacturing 8%
Technology 8%
Consumer 8%
Insurance 8%
Media 8%
Telecom 8%
Respondents by function
Technology CIO, CTO, CDO 24%
Finance CFO, CRO and investment 17%
Management CEO, DOO 15%
Marketing CMO, CPO, CCO 13%
Direct report 8%
Strategy innovation CSO, CIO 7%
Other C-level CHRO, etc 7%
Business head business units, divisions 7%
Respondents by geographic region
Asia Pacific 31%
Australia
China/Hong Kong SAR
India
Japan
Singapore
Europe 33%
France
Germany
Netherlands
Nordics*
Switzerland
UK
Latin America 8%
Brazil
Mexico
North America 27%
Canada
U.S.
Percent of respondents by revenue
(Percentages don’t always add to 100% due to rounding.)
29. Back to Contents29 / AI: from data to ROI
About the author
Bret Greenstein
Global Vice-President and Head of Digital Business AI Analytics Practice, Cognizant
Bret Greenstein is Global Vice-President and Head
of Cognizant Digital Business’s AI Analytics
Practice, focusing on technology and business
strategy, go-to-market and innovation, helping
clients realize their potential through digital
transformation. Prior to Cognizant, Bret led IBM
Watson’s Internet of Things offerings, establishing
new IoT products and services for the Industrial
Internet of Things. He built his career in technology
and business leadership across a range of roles
throughout IBM in software, services, consulting,
strategy and marketing, and served as IBM’s CIO
for Asia-Pacific. He has worked globally in these
roles, including living in China for five years,
working with clients and transforming IBM’s IT
environment. Bret holds patents in the area of
collaboration systems. He holds a bachelor’s degree
in electrical engineering and a master’s degree
in manufacturing systems engineering from
Rensselaer Polytechnic Institute. He can be reached
at Bret.Greenstein@cognizant.com |
www.linkedin.com/in/bretgreenstein/.