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Cybersecurity: A Smart City Imperative

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The COVID-19 pandemic highlighted to cities around the world the importance of smart city programs—the use of technology, data, and innovative solutions to address their social, environmental, and economic challenges. Indeed, 65% of city leaders surveyed in 2020 as part of ESI ThoughtLab’s Smart City Solutions for a Risker World reported that the top lessons learned from the pandemic was that smart city programs are crucial for their future.

However, innovation is a double-edged sword. As city leaders increase their investments in digital technologies, they also expose their cities to greater cybersecurity risks if they do not put appropriate safeguards in place up front.

The pandemic was a stress test for urban cybersecurity systems. Attacks on state and local governments went up dramatically as cybercriminals sought to take advantage of the crisis.

Many cities fell victim to ransomware and other attack vectors. For example, Knoxville, TN was hit in June of 2020 with an attack that crippled its IT systems. The disruption escalated when hackers began publishing data online in a move to extract a ransom payment. Hackers also took advantage of pandemic-related disarray by shamelessly targeting some hospitals.

The ESI ThoughtLab study shows that cities need to do more to keep their urban centers and citizens secure. Most cities, 60%, reported they are not well prepared for cyberattacks. Although small cities felt more confident about their cybersecurity systems than others, the smallest urban areas are in a more precarious situation, with only 29% believing they were well prepared. This is borne out by the incidence of attacks during the pandemic on smaller cities in the US, such as Florence, AL, and Pensacola, FL.

In fact, one sign of a smart city leader—one that is most advanced in using technology and innovative solutions—is its level of cybersecurity Ninety-five percent of cities classified as leaders in the study said they were well prepared for cyberattacks, against just 8% of beginner cities.

% of cities that are well/very well prepared for cyberattacks by size. 

Cybersecurity best practices

There are best practices that cities can follow to shore up their defenses against attacks. City managers should take a sheet from the lesson book of smart city leaders.

There are five key cybersecurity steps that leaders take far more often than other cities to address their cybersecurity vulnerabilities:

  1. Prioritize assets and create access control policies. Protecting a city’s most valuable assets is a smart first step, as is making sure the city imposes tight controls on who can access its systems.
  2. Invest in disaster recovery, response, and event management technology. No matter how strong a city’s firewalls, it only takes one bad guy to get through. So smart city leaders invest more heavily in specialized recovery and response technology to act quickly to mitigate impacts.
  3. Provide cybersecurity training to staff. This is a critical step for cities since cybercriminals often capitalize on employees’ mistakes.
  4. Protect critical infrastructure. This includes security testing of electricity grids, traffic lights, hospitals, and other urban assets. Interconnecting city assets and domains through IoT and other technologies can expose cities to a catastrophic attack if they do not adequately safeguard their infrastructure.
  5. Develop a cyber incident response and recovery plan. Smart city leaders understand they not only need to be act quickly to stop an attack, but also have processes in place to limit the aftereffects, including those related to liability and financial and reputational impacts.

Cybersecurity areas where smart city leaders invest more

One small city that learned the value of such best practices was Torrance, CA. On March 1st, 2020 Torrance experienced a cyber incident that had an adverse impact on city operations. Just two weeks later, Torrance declared a local state of emergency due to the coronavirus pandemic.

Lessons from a cyberattack

Faced with two major crises, city leaders scrambled to set up a virtual emergency operations center (EOC) through Slack, a cloud-based messaging platform hosted by Amazon Web Services. The city used Google Drive for all its forms and documents instead of email, as it was both safer and more efficient.

Within a week, the city had transitioned from brick and mortar, paper and pencil, to virtual operations. It was then able to connect area hospitals, the local school district, the Red Cross, Salvation Army, and business groups to the EOC for real-time information sharing. And when Torrance and Southern California experienced civil unrest—yet another disruption—a few months later, Slack allowed the city to flatten the information curve and share data, internally and confidentially, for increased awareness about the events.

The city was able to funnel live pictures of events directly into Slack where everyone involved in the management of the response could see and better understand the situation. “There were no longer silos that could form because the departments were all dissolved,” said Jeffrey Snoddy, emergency services manager for the city.

The experience was an eye-opener, and the city has since developed a cybersecurity plan. Reminders are sent to staff regarding practices to shore up security, such as changing passwords regularly and connecting from home with city-issued devices only. The city also does routine self-risk assessments of its vulnerabilities.

“Resilience and agility are a must to survive and to thrive,” said Torrance’s city manager, Aram Chaparyan. “Governments move at a slower pace because we have fiduciary responsibility. We have oversight by our elected officials and the public. We don’t have the luxury of time. It’s not if, it’s when we’ll have another crisis, and it’s all about creating a state of readiness.”

 

Laura Garcell Nimylowycz, Editorial Assistant | LGarcell@esithoughtlab.com 

Laura Garcell Nimylowycz is the Editorial Assistant at ESI Thoughtlab, where she supports the editorial team with project management, research, and editing. Ms. Garcell Nimylowycz attended Columbia University as a Kluge scholar and received her Bachelor of Arts in Political Science and History in 2019. While at Columbia College, she was a staff writer for the university newspaper the Columbia Daily Spectator, as well as an editor for the Columbia Journal of Politics and Society.

The Broadridge Next-Gen Technology Adoption Survey

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To understand how financial services companies are adopting AI, blockchain, the cloud, and digital technologies; where they are investing most; and the returns they are generating, ESI ThoughtLab conducted a comprehensive survey on behalf of Broadridge in late 2020 of senior executives at 1,000 companies in eight financial subsectors across 18 countries. Financial services executives agreed that use of these next-gen technologies can significantly boost business performance and profitability.

The survey included questions to allow ESI ThoughtLab economists to develop a maturity framework to gauge how advanced companies are in their adoption of these emerging technologies.

Firms will Invest More in AI After the Pandemic, but Delivering ROI will take Skill, Scale, and Time

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ESI ThoughtLab study of 1,200 organizations reveals that generating ROI on AI is still a work in progress that requires a focus on strategic change

September 15, 2020 (Philadelphia, PA) – Two-thirds of senior executives across industries—and nearly nine out of ten leaders from the world’s largest enterprises—believe that artificial intelligence (AI) is vitally important for the future of their businesses and will be upping their investments in the post-pandemic era. Yet their companies are now seeing an average ROI of only 1.3%, and 40% of AI projects are not yet profitable, according to Driving ROI through AI, a just-released research study conducted by research firm ESI ThoughtLab and a coalition of AI leaders, including Appen, Cognizant, Cortex, Dataiku, DataRobot, Deloitte, and Publicis Sapient.

The reason for this paradox, according to the research, is that AI initiatives require time, expertise, and scale to deliver on their promise of high returns. With the pandemic speeding up the need for quick data-driven decision-making, companies should act now to develop the skills, platforms, and processes that can enable them to achieve the full strategic, operational, and financial benefits from AI.

As part of a rigorous research program, ESI ThoughtLab economists benchmarked the AI practices, performance results, and three-year plans of 1,200 companies in 12 industries and 15 countries, which together have a combined revenue of $15.5 trillion (or about $12.9 billion per firm). Conducted during the COVID-19 outbreak, the study reveals the value that AI can bring in a socially distancing, digital-first world—including access to time-critical data, events-driven forecasts, personalized digital experiences, flexible work processes, rapid decision-making, tighter cybersecurity, and greater cost efficiencies.

But executives should not expect fast results

The research shows that delivering ROI on AI can be elusive for the uninitiated and slow going even for experienced organizations. Those in earlier stages of AI adoption often see flat results. It is not until they scale AI more widely across their enterprises and become leaders that the ROI rises to 4.3%. With frequently high upfront costs in data preparation, technology adoption, and people development, it takes an average of 17 months for a firm to reach break-even and months more to generate significant returns.

Most companies, even leaders, are still relatively early in their AI journey. Only about one-quarter of AI projects are now in widespread deployment among AI leaders. Many AI projects are still in pilot or early deployment stages. However, firms are planning to boost their AI investments by an average of 8.3% annually over the next three years, bringing their annual AI spend from $38 million currently (or 0.75% of revenue) to over $48 million.

The ROI of AI comes from strategic change

As companies progress in AI use, they often shift their focus from automating internal employee and customer processes to delivering on strategic goals. For example, 31% of AI leaders report increased revenue, 22% greater market share, 22% new products and services, 21% faster time-to-market, 21% global expansion, 19% creation of new business models, and 14% higher shareholder value. In fact, the AI-enabled functions showing the highest returns are all fundamental to rethinking business strategies for a digital-first world: strategic planning, supply chain management, product development, and distribution and logistics.

The study found that automakers are at the forefront of AI excellence, as they rush to use AI to deliver on every part of their business strategy, from upgrading production processes and improving safety features to developing self-driving cars. Of the 12 industries benchmarked in the study, automotive employs the largest AI teams (557 people on average vs. 370 for all industries) and has the largest AI budgets ($59.4 million on average vs. $38.3 for all industries). With the government actively supporting AI under its Society 5.0 program, Japanese companies lead the pack in AI adoption. Unlike in the US, where AI is viewed often as a threat to jobs, firms in Japan tend to see AI a way to fill the employment gap caused by an aging population and stringent immigration laws.

Driving high ROI from AI

To drive AI performance, executives should consider these best practices uncovered by the research:

  1. Begin with pilots, then scale AI applications across the enterprise. Companies starting out should work closely with business teams to identify use cases and demonstrate AI’s worth through pilots. But the true value of AI can materialize only with widescale deployment when firms can offset their upfront costs with substantial business gains.
  2. Lay a firm foundation. Organizations should have the proper IT and data management system in place; have a secure and sufficient budget; work through the data security, privacy, and ethical risks of AI; develop a clear vision and plan that takes into account AI-driven strategic transformation; obtain senior management support; and have a robust ecosystem of partners and suppliers.
  3. Get your data right. Nine out of ten AI leaders are advanced in data management. But ensuring your data is in good shape is not enough; organizations should bring in a diverse set of data, such as psychographic, geospatial, and real-time data. The study found that combining different types of data can create a multiplier effect on AI returns.
  4. Solve the human side of the equation. AI is as much about people as technology. AI leaders spend 27% of their AI budget on developing and hiring people, almost twice the percentage that AI beginners spend. They are also more apt to appoint specialists, such as Chief AI and Data Officers, to lead their AI initiatives. They outsource less and build internal teams more.
  5. Adopt a culture of collaboration and learning. About 85% of companies that generate large AI returns work to ensure close collaboration between AI experts and business teams. AI leaders are better at providing non-data-scientists with AI skills. They also decentralize AI authority to help ensure that AI responsibility and expertise is distributed across their organizations

“As the pandemic propels businesses into a digital-first world, AI will become a key driver of corporate growth  and competitiveness. But building proficiency in AI is not easy,” said Lou Celi, ESI ThoughtLab CEO and program director for Driving ROI through AI. “AI is not a magic bullet. It can fail to deliver results if the wrong business case is selected, the data is  prepared incorrectly, or the model is not built for scale.”

Additional information on the study can be found by visiting www.esithoughtlab.com or by contacting:

Lou Celi, Program Director
ESI ThoughtLab
917-459-4614
Lceli@esithoughtlab.com   		     Mike Daly, Marketing Director
    ESI ThoughtLab
    215-717-2777
    Mdaly@esithoughtlab.com  

 

About the research coalition 

ESI ThoughtLab is an innovative thought leadership firm that creates fresh thinking and actionable insights through rigorous research and evidence-based analysis. Our firm specializes in using the latest quantitative and qualitative tools to examine the impact of technology on companies, cities, industries, and business performance. ESI ThoughtLab is the thought leadership arm of Econsult Solutions, a leading economic consultancy, with direct links to the academic community.  

Appen collects and labels images, text, speech, audio, and video used to build and continuously improve the world’s most innovative artificial intelligence systems. With expertise in more than 180 languages, a global crowd of over 1 million skilled contractors, and the industry’s most advanced AI-assisted data annotation platform, Appen solutions provide the quality, security, and speed required by leaders in technology, automotive, financial services, retail, manufacturing, and governments worldwide. Founded in 1996, Appen has customers and offices around the world. 

Cognizant is one of the world’s leading professional services companies, transforming client’s business, operating, and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 194 on the Fortune 500 and is consistently listed among the most admired companies in the world. 

Cortex is the enterprise SaaS solution for marketing executives at leading brands to predict the performance of their visuals using AI and machine learning. By leveraging Cortex’s powerful consumer insights, brands such as Marriot, Kao, Mondelez, and more are creating award-winning visual creative, at scale, that drives reliable results. 

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. More than 300 customers and 30,000 users (from data scientists to architects to analysts) across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku to massively scale AI efforts. 

DataRobot is the leader in enterprise AI, delivering trusted AI technology and ROI enablement services to global enterprises competing in today’s Intelligence Revolution. The company’s proven combination of cutting-edge software and world-class AI implementation, training, and support services empowers any organization—regardless of size, industry, or resources—to drive better business outcomes with AI. With a singular focus on AI since its inception, DataRobot has a proven track record of delivering AI with ROI. DataRobot has offices across the globe and $431 million in funding from top-tier firms, including New Enterprise Associates, Sapphire Ventures, Meritech, and DFJ Growth. For more information, please visit www.datarobot.com   

Deloitte provides industry-leading audit, consulting, tax, and advisory services to many of the world’s most admired brands, including nearly 90% of the Fortune 500® and more than 7,000 private companies. Our people work across the industry sectors that drive and shape today’s marketplace—delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthy society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them. Now celebrating 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte’s more than 312,000 people worldwide make an impact that matters at www.deloitte.com. 

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States, and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/about  to learn more about our global network of member firms. 

Publicis Sapient is a digital transformation partner helping established organizations get digitally enabled, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. As digital pioneers with 20,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting, and customer obsession—combined with our culture of curiosity and relentlessness—enables us to accelerate our clients’ businesses through designing the products and services their customers truly value. Publicis Sapient is the digital business transformation hub of Publicis Groupe.  

ServiceNow Workflow Quarterly: The Customer Issue

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The COVID-19 pandemic has exposed the need for significant shifts in nearly everything we do, as well as new business imperatives—to reduce expenses, transition workforces, and digitally transform the enterprise. Though cutting costs may have been the intuitive first response to the COVID-19 crisis, retaining customers is the existential one.

In February and March of 2020, ServiceNow and ThoughtLab surveyed 600 C-level executives to learn how companies in five major sectors are digitizing their customer experiences, and the resulting impact on their businesses. The respondents came from 12 countries and represented five industries. Job titles included CEOs, COOs, CIOs, CHROs, and board directors.

The survey, which canvassed executives in financial services, healthcare, manufacturing, telecommunications and the public sector, showed North American companies are furthest along in their digital transformation journeys. However, European and Asian enterprises are picking up the pace. The survey also suggests that the roadmap to digitizing the customer experience remains constant across industry sectors. Based on the findings, ThoughtLab and ServiceNow recommend business leaders first create a customer experience strategy aligned with business goals and install a customer management system to support that strategy. Next, identify key customer touchpoints and use them to build an immersive personalized experience. And finally, make sure your team has the skills to support a digital customer experience.

Not surprisingly, the survey also showed profitability and return on investment grow as an organization’s digital transformation matures. In addition to the overall findings, we segmented the respondents into three groups—leaders, intermediates, and beginners—based on 11 key dimensions of a digital customer experience dimensions and found:

  • Leaders are far more likely than beginners to report high or very high returns on a wide range of technology investments.
  • 37% of leaders report better profitability per customer as a result of their CX efforts, compared with just 25% of beginners.
  • 76% of current CX leaders expect to make big improvements to CX in the next three years, compared with 16% of beginners.

The study makes clear that the biggest returns from CX initiatives don’t come from initial moves, but from having the people, processes, and technologies in place to provide a consistent, superior experience over time. Nearly half of leaders report a moderate to large ROI from building an immersive, personalized customer experience, versus just 22% of beginners.

New Report Finds Cybersecurity Investment Generates Substantial ROI as Large Firms Fend off Rising Cyberattacks

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ESI ThoughtLab and group of cybersecurity advisors release findings from study of 1,009 of the world’s largest firms.  

June 18, 2020 (Philadelphia, PA): A comprehensive study conducted by ESI ThoughtLab reveals that increased investment in cybersecurity can generate a significant ROI of 179% and provide greater protection as companies cope with the fallout from COVID-19.   

ESI ThoughtLab benchmarked the cybersecurity investments, practices, and performance metrics of 1,009 firms across 13 industries and 19 countries to identify the most effective approaches for mitigating cybersecurity risks and losses. This ground-breaking research was conducted in conjunction with an advisory group of cybersecurity, cyber insurance, and technology specialists, including Arceo.ai, Check Point Software, Cowbell Cyber, Edelman, Fiserv, KnowBe4, Optiv, and Verizon Business. 

The analysis found that, last year, firms surveyed spent $9.6 million on average on cybersecurity ($515 per employee), and 97% of those expect to increase their spending by an average of 14% this year (pre-COVID–19 estimates). Companies are investing in three areas: people, process, and technology. While the average ROI is 179%, it ranges from 271% for investments in people, 156% for process, and 129% for technology. According to the research, on average, investments in people result in a 46% decline in the probability of a breach vs. 30% for process and 37% for technology.   

“These cybersecurity investments can generate enormous ROI for companies, particularly for those in earlier stages of cybersecurity maturity,” said Lou Celi, CEO of ESI ThoughtLab and the program director of the research. “The reliance on digital technology during the pandemic, together with the rise of remote working, shopping, and healthcare, have served as a stress test for corporate cybersecurity systems. Our CISO interviews have revealed that companies with advanced protection, detection, and response frameworks, backed up by strong cybersecurity hygiene and governance, have fared well during the crisis.”  

Companies still need to do more to combat rising threats 

According to the surveyed companies, one in three attack attempts over the last year resulted in a successful breach. While most cybersecurity breaches are minor, affecting only a small number of people or machines, the average price tag per breach is around US$330,000. However, for firms that are in the top 10% in terms of breach costs, the average cost per breach is over $1.8 million. Adding to the complexity, companies may be underestimating their exposure to a potential breach and overestimating the protection offered by their cybersecurity systems. While the average company assigns a 45% probability to a moderate or material breach, the research shows that the probability is much higher, ranging from 62% to 86%.  

The research shows that companies need to go well beyond compliance with cybersecurity frameworks, such as NIST or ISO, to be effective in reducing risks. For example, only 64 of 151 companies (42%) classified as leaders in NIST compliance are advanced in cybersecurity effectiveness, according to the study’s rankings. Rather than applying the NIST framework as a box-ticking exercise, the most cyber-secure companies adapt this framework to their business goals, strategies, and individual risk profiles. Cybersecurity leaders also combine analysis from advanced quantitative tools and input from internal business partners and third-party experts to make the best decisions. 

Even before COVID-19 hit, companies reported the largest losses from malware (66% of survey respondents), phishing (60%), and password reuse (49%), with cyber criminals cited as the biggest threat actors. As business goes digital over the next two years, executives also expect an increase in attacks through artificial intelligence (38%), denial of service (34%), and web applications (29%). With geopolitical and social unrest growing, and greater economic volatility ahead, CISOs in the financial, energy, automotive, retail, and telecom sectors are bracing for a jump in cyber terrorism and activism, along with greater risks from nation-states.  

The most successful approaches of companies advanced in cybersecurity 

The study identifies the practices of cybersecurity leaders that are most effective in mitigating cybersecurity risks and losses. Leaders commonly do six things that keep them well prepared for today’s high-risk environment: 

  1. Invest more in cybersecurity. Leaders spend about 25% more than others on cybersecurity per employee, increase those investments each year more than the average, and invest more than others in recruiting specialists, working with external consultants, and training, such as end-user security awareness training with simulated phishing. 
  2. Make cybersecurity hygiene a top priority. Leaders have the lowest percentage of “critical” unpatched or “high” vulnerabilities based on CVSS scores (18% for leaders vs. 28% for others). They also do more frequent backup restoration drills (5.6 times a year vs. 4.3 for non-leaders), IT infrastructure scans (4.9 vs 3.4), and phishing tests (5.1 vs. 4.4).  
  3. Keep management teams focused and aligned. Cybersecurity heads typically report into the CEO, COO, or the Board in leader companies. CISOs at these firms focus more on security than IT (75% of leaders) and play a bigger role in managing data privacy (54%), digital transformation (57%), and operational resiliency (49%). Leaders are also more likely to make cybersecurity a shared responsibility of two executives, such as the CIO and CISO, or the CISO and CSO.   
  4. Rely heavily on advanced analytics and specialized teams. More than 8 out of 10 leaders conduct cyber-risk scenario analysis, assess the financial impact of risk events, and measure the effects of mechanisms to mitigate cyber risks. Leaders also outsource incident response, red team, risk management, and security ops more often than others.   
  5. Extract greater value from cybersecurity tools. Leaders invest more heavily in—and achieve greater effectiveness from—key cybersecurity technologies, including cloud workload security, endpoint detection, mobile device management, deception technology, email filtering, multi-factor authentication, and firewalls and web filtering.  
  6. Make more use of cybersecurity insurance. Since it is impossible to mitigate all risk, leaders rely more on insurance to transfer it: 57% of leaders have cyber insurance coverage over $10 million, compared with 30% of non-leaders. Overall, six out of 10 firms plan to spend more on cybersecurity insurance over the next two years.  

“Companies across the board are improving their cybersecurity practices and reducing their losses thanks to smart investments in people, process, and technology,” said Celi. “While these steps have helped contain cyberattacks during the pandemic, today’s turbulent environment has underscored the value of business continuity and resilience, as well as using advanced analytics to assess cyber risks in an interconnected world.”   

The full findings of the study can be found at https://thoughtlabgroup.com/esi-thoughtlab/driving-cybersecurity-performance/ 

For media inquiries, please contact: 

Lou Celi, Program Director
ESI ThoughtLab
917-459-4614
Lceli@esithoughtlab.com  		     Mike Daly, Marketing Director
    ESI ThoughtLab
    215-717-2777
    Mdaly@esithoughtlab.com   

About ESI ThoughtLab: ESI ThoughtLab is the thought leadership arm of Econsult Solutions Inc., a leading economic consultancy. The innovative think tank offers fresh ideas and evidence-based analysis to help business and government leaders understand and respond to economic, industry and technological shifts around the world. Its team of top economists and thought leaders excel at creating valuable decision support that combines visionary thinking, analytical excellence, and multi-format content.   

About Arceo.ai: Arceo.ai enables cyber resilience by combining smarter insurance products with dynamic security solutions. Headquartered in San Francisco, Arceo empowers insurers and brokers to better assess, underwrite, and manage cyber risks through a patented methodology called Cyber Meteorology. Arceo’s holistic risk analytics and insurance platform enables enterprises to better identify, respond to, and recover from cyber risks using AI to drive advanced risk assessment and proactive security services. For more information, visit www.arceo.ai and stay up to date on our blog  Twitter and LinkedIn. 

About Cowbell™ Cyber: Cowbell Cyber maps insurable threats and risk exposures using artificial intelligence to determine the probability of threats and impact on coverage types. In its unique approach to risk selection and pricing, Cowbell compiles Cowbell Factors™, a set of risk-rating factors, that enable continuous underwriting and expedite quoting and binding for brokers. Cowbell Prime™, Cowbell’s standalone, admitted, and individualized cyber coverage is available to small and mid-size businesses (SMBs) through a network of independent insurance agencies and brokers.  

About Edelman: Edelman is a global communications firm that partners with businesses and organizations to evolve, promote and protect their brands and reputations. Our 6,000 people in more than 60 offices deliver communications strategies that give our clients the confidence to lead, act with certainty and earn the lasting trust of their stakeholders. We develop powerful ideas and tell magnetic stories that move at the speed of news, make an immediate impact, transform culture, and spark movements. 

About Fiserv: Fiserv, Inc. (NASDAQ: FISV) aspires to move money and information in a way that moves the world. As a global leader in payments and financial technology, the company helps clients achieve best-in-class results through a commitment to innovation and excellence in areas including account processing and digital banking solutions; card issuer processing and network services; payments; e-commerce; merchant acquiring and processing; and the Clover® cloud-based point-of-sale solution. Fiserv is a member of the S&P 500® Index and the FORTUNE® 500 and is among FORTUNE World’s Most Admired Companies®. Visit Fiserv.com and follow us on social media for more information and the latest company news. 

About KnowBe4: KnowBe4 is the world’s largest security awareness training and simulated phishing platform that helps you manage the ongoing problem of social engineering. The KnowBe4 platform is user-friendly and intuitive. It was built to scale for busy security leaders and IT pros that have 16 other fires to put out. Our goal was to design the most powerful, cost effective and easy-to-use platform available.  

About Optiv Security: Optiv is a security solutions integrator – a “one-stop” trusted partner with a singular focus on cybersecurity. Our end-to-end cybersecurity capabilities span risk management and transformation, cyber digital transformation, threat management, security operations, identity and data management, and integration and innovation, helping organizations realize stronger, simpler, more cost-efficient cybersecurity programs that support business requirements and outcomes. At Optiv, we are leading a completely new approach to cybersecurity that enables clients to innovate their consumption models, integrate infrastructure and technology to maximize value, achieve measurable outcomes, and realize complete solutions and business alignment. For more information about Optiv, please visit us at www.optiv.com  

About Verizon: Verizon Communications Inc. was formed on June 30, 2000 and is celebrating its 20th year as one of the world’s leading providers of technology, communications, information and entertainment products and services. Headquartered in New York City and with a presence around the world, Verizon generated revenues of $131.9 billion in 2019.  The company offers voice, data and video services and solutions on its award–winning networks and platforms, delivering on customers’ demand for mobility, reliable network connectivity, security, and control.

A Deep Dive Review of the Future of Artificial Intelligence

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For this Present Value post, ESI’s thought leadership team sat down with experts participating in our  Driving ROI Through AI research program to gain deeper insight into how companies around the world are currently using artificial intelligence, where the technology is headed, and how it will impact the future of business.

Driving ROI Through AI is a multi-client initiative launched by ESI ThoughtLab aimed at providing executives with the market intelligence, strategic insights, and data needed to help them use AI to drive corporate performance.

 

In your opinion, what are some of the most challenging issues organizations currently face in adopting artificial intelligence?  

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: One of the biggest challenges is articulating the business problem they want to solve. Often, companies start with the technology, saying “I want to use AI” and then look for the business problem. Even after defining the problem and how to solve it, they need to understand the data they have access to, as AI systems are only as good as the training data we put into them.

Kurt Muehmel, Chief Customer Officer, Dataiku: At Dataiku, one of the biggest things we see organizations struggling with today is really scaling AI; that is, going from executing on one or a handful of use cases successfully to tens or hundreds. More and more, companies are discovering that leveraging AI at scale is the only way to make it profitable in the long run.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: More often the biggest obstacle is the inescapable fact that stakeholders are not ready for a new way of thinking. Without a cultural transformation preceding the newly executed digital strategy, the “AI is a new technology, and let’s adopt it like any other new technology deck” approach does not work. This costs companies precious resources without clear ROI, causing some of the justified skepticism.

Peter V. Henstock, Machine Learning & AI Lead, Pfizer: Adopting AI is a major transformation for companies. It’s asking corporations to make their data not only accessible but also findable and connected, perhaps for the first time. The mere processing of this data may require specialized hardware that may not even be used in all companies but may be available through the cloud, so the infrastructure must change. It then calls for a group with a different set of skills to find patterns, predict outcomes, and help make decisions from this data.

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient: There is a general scarcity of data scientists within the market. Firms are facing a challenge in understanding how they can restructure and transform their business to meet the needs of a digital world. There are very few employees within firms today who understand the possibilities of leveraging AI to transform their business.

 

How can these challenges be addressed?  

Kurt Muehmel, Chief Customer Officer, Dataiku: There are two components to addressing AI scalability. One is organizational; it’s critical to have the right setup that both standardizes processes and enables people across the business to leverage data for more and more diverse use cases. For a lot of companies, that means having a center of excellence combined with robust programs for training, gamification, or whatever it takes to ensure data initiatives don’t stay siloed in this central team. The other component is reusability. Common sense and economics tell us not to start from scratch every time, and that is exactly the principal behind reducing costs associated with data cleaning, preparation, operationalizing, model maintenance, and even hiring woes.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: Many of these challenges can be addressed by creating a cross-functional team—a product owner who is leading the direction of the investment paired with team members from product, engineering, and data science, who can collectively focus on a business problem and attempt to discover a solution. It may be that the business problem does not need AI as the solution, but leaders need to give the team the chance to solve for a narrow, specific, and important business challenge. In the case that the business problem warrants an AI solution, having gone through this exercise enables the team to have plans in place to solve for the data challenges, or look for partners that can help them launch world-class AI.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: Culturally evolving, continual re-skilling—starting at the very top—and redefining the “job description” of all roles across the organization.

Peter V. Henstock, Machine Learning & AI Lead, Pfizer: The challenges exist for companies of all sizes but the volume of data of large companies represents a larger stumbling block. Newer and smaller companies can transform their data much faster than the larger companies. For companies with a legacy record, the approach is either incremental or may require a larger digital transformation.

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient:  Senior leaders within the firm need to agree on a clear AI and quantitative strategy with focus towards improving data quality. The key lessons learned from our experience is that there must be a healthy balance between AI engineers focused on evaluating the latest academic research and ML engineers adopting and applying it to use cases to showcase incremental value.

 

For organizations just beginning to consider the use of AI, what advice would you offer to help them succeed in their efforts? 

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: Hire AI domain experts who have demonstrated impact in multiple domains. Do not limit your leadership search to senior experts who have decades of expertise in that specific domain. Disruption and transformation can happen with a fresh perspective.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: Focus on the business problem you want to solve and narrow down the first use case for AI. Make sure the team working on your first AI application is empowered to justify why AI is well suited to solve the problem at hand, and articulate what data will be used in service of solving the problem.

Kurt Muehmel, Chief Customer Officer, Dataiku: We always recommend carefully choosing not one but several use cases with which to start. With dozens of potential use cases but limited resources, that means prioritizing projects that have both high business value and a high likelihood of success. And why several use cases? Well, the reality is that AI isn’t always the answer; that is, applying machine learning to a problem won’t always provide results that are more effective than what the business was doing before. But one failure doesn’t mean that the organization should abandon AI efforts entirely, it just means that they haven’t found the right use case.

 

AI covers a range of technologies that allow machines to imitate human behavior (robotic process automation, machine learning, deep learning, computer vision, natural language processing, etc.) Of these technologies, where do you see the most investment currently being made and what is the underlying reason for that investment?

David Donovan, Executive Vice President, Financial Services Lead, North America, Publicis Sapient: The answer to this question is really dependent on where firms are with their maturity and adoption of AI within their processes and their business strategy and transformation. We are seeing investments made on all of the above. Most investment by financial firms now is on NLP and RPA focused mainly on reducing operational cost, while there is some experimentation towards alpha generation as well, but not comparable to the focus towards the former. We are also seeing big investments in customer centricity and targeting when it comes to sales and marketing.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: I see the most investment currently being made in tools to make AI more approachable and easier to use and deploy. There is a high demand for data science talent that has the training, experience, and sophistication to build and deploy world-class AI with confidence. There is always a journey from pilot to production, and smart tools can help shorten the time to market.

 

Where are you seeing organizations place responsibility for AI management and implementation? Which executives are in charge? Is AI management handled in a centralized or decentralized manner in organizations? 

Peter V. Henstock, Machine Learning & AI Lead, Pfizer: The success of AI is governed by two factors:  the amount of clean data readily available, and the maturity of the industry in its acceptance of data-driven analyses. Older companies can have a wealth of data but may also have a disadvantage in organizing that data compared with younger companies like Uber that have created their data platforms with analytics in mind. The healthcare field has stricter rules for leveraging data and making decisions from them.

Kurt Muehmel, Chief Customer Officer, Dataiku: I don’t think there is just one answer to this; we’ve found that it depends a lot on the size of the company, what industry they’re in, and how far along they are on their path to Enterprise AI. However, we do see in very large organizations that AI management handled centrally with a center-of-excellence model combined with initiatives to ensure infiltration throughout the lines of business is the most common recipe for success.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: The size of the organization, and culture, play a role in making that decision. With decentralization comes a lot of waste and misalignment. Centralization is difficult. Each strategy requires a specific type of leader in the driver’s seat.

David Donovan, Executive Vice President, Financial Services Lead, North America, Publicis Sapient: In some cases, there is a data organization with a softly appointed Enterprise CDO and a separate Enterprise analytics/AI function, which is obviously heavily data driven. Clarity of responsibilities across both functions is so important to avoid constant friction over who is accountable for what.

 

What industries will see the most benefits from AI adoption and why? 

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: Industries that are more standardized and rely on repetitive work will see the most benefit. The impact of AI is industry agnostic, but it tends to benefit back-office operations first, often not directly interacting with customers. Use cases range from business process automation (invoicing, for example), automation of inventory management, or claims processing in the insurance industry.

Kurt Muehmel, Chief Customer Officer, Dataiku: I’m of the belief that when it comes to seeing returns from AI, it matters less what the organization’s industry is and more what the predominant mindset is on AI from both the top down (leadership) as well as the bottom up (i.e., are individual contributors empowered to use data, and are they excited about it?).

 

What are some of the most important use cases for AI across enterprises?


David Donovan, Executive Vice President, Financial Services Lead, North America, Publicis Sapient: This world is already seeing a massive disruption, with companies not adopting digital business transformation either being already extinct or running the risk of not being relevant 5-10 years from now. Consumers and the enterprise are demanding a much more immersive experience when dealing with a company. AI helps to create a deeper understanding of and more personal relationship with the end client.

Kurt Muehmel, Chief Customer Officer, Dataiku: Every industry has its own unique use cases, so it’s very difficult to generalize across all enterprises. Of course, some examples of common use cases are marketing or finance related, such as churn or attribution and forecasting, respectively. But those aren’t necessarily the most interesting use cases or the ones that will really give organizations a leg up in the race to AI. In fact, at Dataiku, we believe that applying machine learning in these use cases will become status quo in the very near future. It’s the use cases that are very specific to the business’s operations that will become the most important and bring the most value.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: While companies are beginning to transform, they are looking for short-term ROI. Marketing is one of these areas where AI brings in measurable ROI. This is the obvious. Companies that will differentiate themselves in the long run will be the ones that can plan for the long term.

 

In general, how knowledgeable do you feel the general public is about AI? 

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient: In our opinion, the general public is extremely green field with very limited knowledge of AI, although they do consume AI on a daily basis over their phones, or through augmentation of AI within their day-to-day activities through AI-powered companies, appliances, etc.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: While the general public interacts with AI every day through their search engines, social networks, virtual assistants, maps, and other smartphone apps, they are not that knowledgeable about how it works. Often, they don’t know they are interacting with a product or service that has AI embedded in it, nor that those technologies use training data curated by people to make that AI work in the real world.

Kurt Muehmel, Chief Customer Officer, Dataiku: Unfortunately, much of the media coverage around AI doesn’t have anything to do with Enterprise AI–it’s much more focused around consumer-facing AI (e.g., self-driving cars, smart home systems, etc.). In some ways, that makes sense, because those are the devices that are tangible manifestations of AI, so they’re somewhat easier to understand. But it’s naïve to think that Enterprise AI doesn’t affect the general public. Think about algorithms banks are developing to determine whether a client is eligible for a loan, or even AI systems determining pricing used by e-commerce systems. I think people are much less educated about how these systems are developed, how they work, and how they affect their everyday lives. Again, instead of focusing on these things, unfortunately, much of the media coverage around Enterprise AI has been more focused on fearmongering around AI taking peoples’ jobs. This is unproductive and prevents people from really taking the time to understand Enterprise AI at a deeper level.

Peter V. Henstock, Machine Learning & AI Lead, Pfizer: AI has been lurking in the background for generations and the public has many misconceptions.  In addition, the recent wave of AI has caused major transformation within just a few years.  A related question is who has the knowledge within corporations and who is driving the strategy?  It requires a different skillset and a different vision.  Finding the experts who also understand the opportunities and space is very challenging.

 

Do you feel there is any current or foreseeable backlash/fears on the use of AI? 

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: There has been a lot of conversation about jobs and how AI will impact them, as well as the “black box” decision making around deep learning technologies.

Kurt Muehmel, Chief Customer Officer, Dataiku: I do think there are fears and that perhaps that will lead to backlash, but ultimately at Dataiku, we believe in human-centered AI. That means not sitting back and letting AI systems make decisions for us, but instead using AI to enhance—not replace—people. If everyone follows this principal, I think people will see the positive and embrace instead of condemn AI.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: It’s inescapable to hit a point when the market behavior will push companies to prioritize for short-term ROI. AI operations will be affected. But I do not see any evidence for a global backlash. We will manage to regulate and learn as we reap the benefits along the way.

Peter V. Henstock, Machine Learning & AI Lead, Pfizer: There is a buzz and excitement about using AI. It offers an opportunity to leverage data more fully than it has been utilized before and in new ways. We hear about backlash in areas such as Robotic Process Automation, but it mostly offers opportunities to do better science, better analysis, and achieve better results.

David Donovan, Executive Vice President, Financial Services Lead, North America, Publicis Sapient: There has been fear around cannibalization of jobs, but over time as AI tech matures it will open up greater opportunity for human involvement, as in order for AI to be most successful it has to co-exist with humans.

In the spirit of Building Ethical, Responsible, Reproducible and Explainable AI models, model governance is going to be in the forefront with regulations mandating the same sooner than later. Bias detection and fairness are among the examples that are to be injected through the modelling process, which is going to be absolutely key.

  

More broadly, how important will AI be in the future for both business and society? 

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient: Absolutely crucial without an ounce of doubt. Our lives have already seen AI starting to play a huge role in our day to day, be it our phones in using speech to text, or using Alexa/Google Home and other AI-powered devices, to autonomous and driverless cars coming in near future, to AI-enabled home appliances, and the list can go on and on. Because of big tech like Apple, and Amazon, consumers’ demand bigger experience, and AI will help create more personalized, real-time experiences which can unlock unique information and lead to a great experience with consumers.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: AI is just a collection of technologies—nothing more, nothing less. It will be important to use them responsibly and effectively. The technologies have already permeated everyday life and disrupted most industries—I think this trend will continue. AI is becoming less optional for companies. Statistics suggest that it can improve customer experience, productivity, etc. making it a smart business decision when done correctly.

Kurt Muehmel, Chief Customer Officer, Dataiku: It will be such an important part of life in the relatively near future that, like electricity, most of us will start to take it for granted and get to a place where we don’t even realize it’s there.

Dr. Bulent Kiziltan, Chief Data and Analytics Officer: Anything I say will be an understatement!

 

In what ways are you seeing AI being implemented to combat the outbreak of COVID-19?

Bill Hobbib, Senior Vice President, Marketing, DataRobot: There are a tremendous number of AI use cases to try and combat the outbreak of COVID-19.  In healthcare, life sciences, and public health, the categories and use cases include the following:

  • Detection, pandemic trends, and analysis: early predicting; herd immunity forecasting; alerting of towns/counties of spread and expected extent of outbreaks; actively predicting deaths, peaks, and diminishing of pandemic, to better predict medical resource needs and safe reopening timeframes
  • Virus containment: predictive enforcement, behavior analytics, detection and correction of “fake news”
  • Hospital/healthcare operations: AI-assisted patient chatbots and virtual assistants, hospital staffing, augmented triage, image recognition, pneumonia detection and diagnosis, hospital supplies and equipment forecasting and supply chain, patient throughput and capacity management, remote patient monitoring and alerting
  • Life sciences vaccine or treatment research and development: information retrieval, predictive analytics, R&D coordination & collaboration

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: Technology platforms, government, academic research institutions and healthcare organizations are partnering together to try to diagnose COVID-19 more accurately, predict and model the spread and impact of the disease, and find solutions for medical treatment as well as economic impact. As an example, there is a project using computer vision to diagnosing CT scans from patients suspected of having COVID-19. Another example, AWS used its extensive modeling platform to help scale and improve the models that were predicting how the disease was going to spread in communities. The project successfully reduced the model run-time from a week to 12 hours with many variations.

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient: Publicis Sapient is currently engaged with a large global investment management firm helping them build out their AI & Investment Research platform. Specifically, in response to COVID-19 we have enabled access to varied sets of alternate data as part of the AI platform, for the macroeconomists and front-office investment professionals to be able to consume. This includes COVID-19 tracking (Recovered vs. Confirmed vs. Deaths by County/Country/Region/State), testing results, travel datasets (Canceled flights, Traffic Congestion levels, Airbnb, Hotel datasets), hospital beds, ICU availability, and more.

 

Are you seeing any new trends in the adoption of AI use given that many businesses are closed, remote, or socially distancing their interactions with clients and customers?

Ari Kaplan, Director, Industry Marketing, DataRobot: Across the board, companies need to rapidly find signals in the unprecedented noise of the changing world. Models used for predictions and other business decisions are based on data that is limited or no longer applicable. Businesses are looking to AI now to more rapidly gain insights based on the changing economy, shutdown of physical businesses, the move to online purchasing, and changing interactions with their clients and customers.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: Yes, we are seeing new trends in adoption and addition of AI given the shift to remote work and disruption to the normal economy. Those trends include the increased demand for remote work—on our platform alone we saw a 31% increase in user activity in March. Additionally, we are seeing our customers double down on their investment in AI and invest further in use cases such as combatting disinformation, combatting fraud using, and content moderation using AI. Customers are looking for ways to automate processes and reduce cost in their businesses or respond to changing demand needs. One social media company is looking to further personalize content. Advertisers are looking to understand the content they are promoting to users, so they don’t strike the wrong tone given the pandemic.

Mihir Sharma, Head of AI & Cognitive Initiatives, Financial Services, North America, Publicis Sapient:  Yes definitely. We are seeing added leveraging of call center AI solutions, using virtual assistants powered by NLP and voice to help manage the sudden increase in call volumes that most firms are facing, and better manage workforce planning.

 

What role will AI play in preventing and combatting future natural disasters, pandemics, and global crises?

David Donovan, Executive Vice President, Financial Services Lead, North America, Publicis Sapient: AI will be instrumental in future prevention and combatting of natural disaster, pandemics, and global crises. There will be huge advancement and focus on medical research in order to prevent this kind of an occurrence in future, and AI is supposed at the core of that research.

Alyssa Simpson Rochwerger, VP, AI and Data, Appen: AI can play a role by modeling future crisis and recommending mitigation strategies such as predicting the impact of climate change on food crops and recommending different seeds or agricultural strategies which use less resources and provide greater output. Furthermore, AI applications can calculate the data around the number of damaged homes and structures or high flood levels to provide that information for first responders to make quicker, more accurate decisions to coordinate response and recovery efforts.

Ari Kaplan, Director, Industry Marketing, DataRobot: A bright spot in the COVID-19 pandemic has been how people and companies across the AI spectrum have come together to collaborate with healthcare, government, and academia. As a result, mankind has been better equipped to understand and minimize the crisis, with AI as a centerpiece of this effort.

Global crises come in many forms, and AI can help predict when and how they arise, and once they do, how to minimize their harm. These crises can come in many forms: financial collapse, political instability, biological/chemical/electronic/terrorist or conventional warfare, famine and water shortages caused by global climate change. AI can help model scenarios so that governments, businesses, and populations can make more informed decisions to mitigate the risks. And if a global crisis does indeed arise, AI can help inform with supply chain for supply distributions, optimizing staffing levels, and running scenarios for appropriate financial assistance for optimal resolutions.

 

Successful thought leadership is a team sport, requiring close collaboration with our clients and the right blend of analytical, editorial, and marketing skills. Econsult Solutions and ESI ThoughtLab would like to give special thanks to our research coalition and participating cities committed to Driving ROI Through AI.

 

During his more than 35 years of research, marketing and publishing work, Lou Celi has helped top organizations build their businesses by engaging corporate and government decision makers. Prior to setting up ESI ThoughtLab, Mr. Celi was board director and president of Oxford Economics, where he built the firm’s successful business in the Americas and set up its global thought leadership practice.

 

Daniel Miles is a vice president and associate principal at Econsult Solutions. He leads economic analysis projects across a variety of sectors and industries. Additionally, Dr. Miles is the Chief Economist for ESI ThoughtLab, the firm’s thought leadership arm.