TM

New Generation AI for Learning

What Is Artificial Intelligence (AI) For Learning?

The continual desire to increase automation of repetitive tasks and to glean new insights from large, often diverse sets of information has fueled a surge of development around artificial intelligence (AI) across industries and disciplines. As a result, savvy marketeers have taken this opportunity to label practically any decision made by machines as “AI,” making it difficult for business professionals to discern the difference between true AI innovation and clever re-labeling of existing capabilities. In addition, many pseudo-AI solutions require massive investments of time and money behind the scenes to simulate an AI-like experience. Given these factors, it’s no wonder “AI” has become too generalized and is often confusing for those seeking solutions to real business problems.

Dictionary.com defines intelligence as “the ability to acquire and apply knowledge and skills.” Inherent in this definition are several criteria that should be considered when determining the degree to which any given solution is truly artificial intelligence versus plain machine automation. In the Learning & Development (L&D) space specifically, true AI for Learning needs to assess learners and training content virtually on its own. It should then be able to adapt the presentation of content according to that knowledge, not just navigate a pre-defined, static pathing structure. Finally, AI for Learning should deliver this personalization autonomously i.e. it shouldn’t require massive investments of human time to deliver this personalized learning experience. Instead of customers doing significant extra work to provide an AI-like experience, true AI should do the work for them.

L&D teams can benefit from true AI for Learning in several ways, such as eliminating extraneous content, ensuring content, testing, and learning objectives align with each other, and minimizing human bias in curriculum design. Learners benefit from a solution that identifies their unique learning strategy and aligns training content and modality to match, resulting in higher engagement and knowledge transfer. Businesses benefit from reduced time to proficiency, increased application of training on the job, and measurable correlations between training investments and improved business outcomes.

TM 1© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

Current L&D Landscape

Given these myriad benefits, it’s no wonder companies continue to invest so heavily in L&D. Training Industry, Inc. reports companies worldwide will spend more than $365 billion on training in 2018, up from an estimated $362.2B in 2017 and $359.3B in 20161. At an individual level, the average learning investment per employee in 2016 was $1,273, amounting to 34.1 hours, compared to $1,252 and 33.5 hours in 2015.11

In line with the continuing growth in L&D spending, C-suite executives indicate their top concern for 2018 is attracting and retaining the top talent. 80% also expect their future workforce will include an increasing use of non-traditional, contingent employees – the so-called gig economy - making investments in flexible, adaptable training programs an even greater imperative.7 At the same time, skills development must also evolve beyond basic job skills. The top skills CEOs see as a priority include “problem-solving, adaptability, collaboration, leadership, creativity and innovation.”9 As cited by CGS, “companies’ increased focus on improving employees’ soft skills and engagement indicates a greater emphasis on bridging communication skills across geographies and generations, developing talent from within and closing the skills gap.”15

With a global shortage of workers and skills, a focus on retraining existing employees is also a growing priority. Companies and employees need to anticipate and plan for automation eliminating some functions and perhaps entire job roles. Adaptable, intelligent training plans need to be delivered to retrain workers before jobs become obsolete.

TM 2

“Companies’ increased focus on improving employees’ soft skills and engagement indicates a greater emphasis on bridging communication skills across geographies and generations, developing talent from within and closing the skills gap.”15

Training Efficacy

Sundar Pichai, Google CEO, believes in a life-long approach to education: “In the past, people were educated, and learned job skills, and that was enough for a lifetime. Now, with technology changing rapidly

and new job areas emerging and transforming constantly, that’s no longer the case. We need to focus on making lightweight, continuous education widely available.”10 With consistent agreement that continual training and education is vital to a healthy career and healthy workforce, the bigger question becomes: How effective are current training programs?

According to CEB Global, now a part of Gartner, learning that is not applied on the job i.e. “scrap learning” now comprises an estimated 45% of training delivered in average companies.12 This condemning

Learning that is not applied on the job i.e. “scrap learning” now comprises an estimated 45% of training delivered in average companies.12

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 3

statistic is exacerbated by a need for lifelong learning and suggests a great deal of work still lies ahead in ensuring training delivers business value. On a positive note, organizations that made an effort to measure scrap learning and focus on continual improvement in that area saw scrap learning decrease from 45% to 33%.12 While still a high number, that 12% reduction represents a savings of over $1.5M for a 10,000-employee company.12

While many factors affect whether training is applied on the job, several key factors can be positively influenced by proactive L&D teams that utilize AI for Learning:

• Alignment of content, testing, learning objectives, and desired business outcomes• Content delivery modalities that align with learning strategies and job functions• Course quality improvements such as minimizing or eliminating extraneous content• Quantitative measurement of learner engagement, knowledge transfer, and course efficacy• Autonomous personalization based upon individual learner sequences of behavior

Not surprisingly, training efficacy remains a top priority for L&D teams. As shown in Figure 1, 2017 L&D team survey respondents indicated “increasing the effectiveness of training programs” was their top investment focus.14 The slight year-over-year reduction in focus on training efficiency and corresponding increase in focus on training effectiveness suggests that perhaps organizations were willing for employees to spend a little more time in training if it resulted in better knowledge transfer and application of skills on the job.

Figure 1. Top L&D investment priorities. Source: 2017 Training Magazine survey of U.S. companies of 100 employees or more.14

In evaluating training efficacy specifically, 2017 survey respondents listed employee engagement (90%), business metrics like revenue, mobility, and reduced cost (86%) and onboarding success rate/time-to-performance (82%) as the key metrics they measure.15 When asked to rank their L&D program quality overall, L&D leaders indicated a generally positive response with 62.2% reporting that their

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 4

programs were either good or excellent.15 In contrast, over 60% of Millennials surveyed indicated they don’t receive or benefit from company training, and less than 40% of Baby Boomers feel as though their management team is invested in their training and development15.

Despite a growing organizational focus on the importance of building leadership and soft skills, alarmingly 75% of employees report that their organizations do not have training programs to develop leadership skills.15 A partial explanation for this gap may be the lack of real and perceived business value delivered by many training programs. While 88% of business leaders believe that investing in employee development is critical, less than 25% believe L&D is effective at achieving business outcomes.12 Perception isn’t any better from the C-Suite, as 96% of CEOs want to see a direct business impact from their company’s training programs, but only a mere 8% report that they currently do.8

With company perceptions of L&D, workforce talent needs, and the training programs being delivered today so clearly misaligned, what is the remedy?

Learner-Centric Training Experiences

Corporate training has historically been delivered through a one-size-fits-all approach. Departments require it and employees complete it simply because it must be done. The challenge is that every person has her or his own unique learning strategy. Companies that continue to ignore the inherent need to provide personalized training designed specifically for each learner will never unlock the benefits of a learner-centric training approach.

AI for Learning delivers several key benefits in a learner-centric training approach including AI-driven content optimization, individualized learning experiences, and Predictive Learning Analytics (PLA) that can predict knowledge transfer prior to and sometimes even better than assessments. The sooner companies embrace AI for Learning, the faster they will see quantifiable results. Personalized learning will help achieve this success by prioritizing employees’ learning needs, resulting in improved knowledge transfer and faster time to proficiency which ultimately impact the bottom line.

While companies are investing in training that can be applied on the job, employees in the current talent market and knowledge economy are demanding better training and development opportunities from their employers overall. When employees feel their company is making an investment in them, they are more likely to stay and grow rather than seek opportunities elsewhere. Employees who do not feel they can achieve their career goals at their current organization are 12 times more likely to consider leaving than

Employees who do not feel they can achieve their career goals at their current organization are 12 times more likely to consider leaving than employees who do, and this number jumps to 30 times more likely for new employees.16

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

employees who do, and this number jumps to 30 times more likely for new employees.16 Given that it can take up to 6-9 months of an employee’s salary to recruit and train their replacement, preventing turnover is key.

Employees now want more personalized, on-demand training that fits the future interests of the learner, not just the specific tasks of their current job role. They want training to be linked to well-defined career paths, and they see professional development as one of the most valuable assets their company can provide. Breaking training down into micro-learning modules and offering content curation are early examples of attempts to meet this need. While a positive step forward, these solutions are still only a step on the path toward truly autonomous personalization of the learning experience.

TM 5

Early AI for Learning Offerings

As with most any new technology, the early pseudo-AI offerings in the L&D market have their share of pros and cons. A popular topic of discussion in L&D is content curation: the ability to suggest training content to learners based on past coursework, assessments, or a simple user search. The goal is to provide a more personalized learning experience tailored to the skills and preferences of the learner, which is certainly a worthy vision.

One of the hidden challenges with content curation is inefficiencies in the courses and assessments themselves. If the underlying content has extraneous material or the content, assessment, and learning objectives are misaligned, the inefficiencies are actually magnified with each course undertaken. With 45% of learning considered to be “scrap learning,”12 pouring more ineffective content into the mix exacerbates perhaps the greatest problem in L&D today. This point is often missed or ignored, perhaps because L&D teams feel overwhelmed with the task of fixing the content.

Given this “more isn’t necessarily better” problem, tools to measure and ensure the quality, efficacy, and efficiency of the underlying content are the remedy. If these qualities can be assured, content curation becomes a more viable tool for adding personalization into the learning journey, though it still falls short in creating individualized paths of learning. Leveraging the power of AI and machine labor, AI for Learning can optimize and align existing courses to make content curation more effective and useful.

Adaptive learning systems customize the path a learner takes through a curriculum and can remediate specialized content to personalize the learning experience. Most adaptive learning that is offered through learning management platforms today utilizes test results as the basis for dictating learning paths. Assessments, however, only tell a small part of the story, and by the time an assessment suggests poor learning results the learning experience has already failed.

Content Curation

Adaptive Learning Systems

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 6

Many adaptive learning systems are also cost and labor-intensive to configure and deploy. Most require humans to re-author existing courses in specialized tools and manually segment, index, and tag content. In addition, humans must try to anticipate and create the hard-wired "if this, then that" pathing logic, and they must pre-create all the variants of possible remediated content that would be presented for each learning path. In the end, then, is this solution an example of artificial intelligence, or is it really just machine automation after many incremental human hours? True AI for Learning ingests existing content and autonomously personalizes the learning experience based on the inferred learning strategies and knowledge states of each learner.

Zoomi - New Generation AI for Learning

Given the challenges with early pseudo-AI solutions, a new approach is needed that can provide the learning personalization that users demand, the automation and analytics L&D teams need, and the training efficacy that businesses require. New generation AI for Learning also needs to leverage smart algorithms and machine power to do most of the work, so already over-taxed L&D teams don’t have to invest 100s or 1,000s of incremental hours into existing courses. Satisfying each of these stakeholder groups requires a new approach to AI for Learning. Strategic applications of new generation AI for Learning can provide value in many areas, including:

• AI-Driven Content Analysis• Predictive and Prescriptive Learning Analytics• Learner Behavioral Modeling• Autonomous Personalization of eLearning Content• Real-time Learning Analytics Dashboard• Social Learning Networks (SLNs)

Delivering the benefits of new generation AI for Learning begins with the AI understanding what is being taught, what is being tested, and how that compares to the learning objectives. The Zoomi Content Topic Analysis (CTA) service ingests the existing course, assessments, and objectives and then scientifically discerns key topics and their context at a fine granular level. It produces a heatmap that visualizes topic progression throughout the material, and it offers several different gap analysis views to compare coverage of each topic in the testing, assessments and objectives. Noticeable misalignments then inform areas of the course or the testing that should be amended to align with each other and the overall training objectives.

Having the AI build models of course content at a fine granular level is a necessary first step in providing autonomous personalization, but Content Topic Analysis also offers numerous benefits as a standalone service. Zoomi’s AI-driven CTA can help L&D teams:

• Distinguish between Relevant and Extraneous Material• Assess Testing Efficacy versus Learning Objectives

AI-Driven Content Analysis

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 7

• Reduce Human Bias in Curriculum Design• Ensure Training Achieves Learning Objectives

CTA is a valuable tool to reduce scrap learning and reduce time to proficiency across any content type, but organizations can gain the additional benefit of reducing business risk when applying CTA to compliance courses. According to the 2018 Duff & Phelps survey of financial institutions, almost a fourth expected to “spend more than 5% of their annual revenue on compliance by 2023. In fact, a tenth (11%) felt they would spend more than 10% on it by that year.”6 With such heavy investment being made into regulatory compliance, L&D teams have to ask themselves: how well do our training and testing align with the regulatory objectives?

As shown in Figure 2, the regulatory document can be utilized as one of the comparison criteria, allowing companies to identify areas where courses and testing need to be amended. In the example shown, the course was about mortgages. The Zoomi AI-engine ingested the existing course material and identified the topics it found throughout. Many observations can be drawn from this example, but three obvious areas of concern are Topic 5, Topic 10, and Topic 7.

Figure 2. Zoomi Content Topic Analysis comparing course content and assessments to regulatory document.

In the first case, the coverage of Topic 5 in the assessment is woefully underrepresented. Unless this is tested elsewhere perhaps in another course in the curriculum, this represents a potential business exposure and the testing may need to be amended. Topic 10 has the opposite problem, where the topic is lightly covered in the regulations and the course but is a significant portion of the testing. Unless there is a reasonable business explanation for this, the testing should be examined to reduce the questions centered around that topic. Finally, Topic 7 is a substantial percentage of the regulatory document, but it is only lightly taught and moderately tested. Unless this topic is covered more thoroughly in other courses in the curriculum, this is another area of business risk that should be remedied.

How do L&D teams currently measure success? One common practice is conducting learner surveys at the end of training. Unless the surveys are mandatory, however, typically only the happiest and the unhappiest learners respond. This leaves L&D teams in the dark about what the bulk of the “middle” section of learners thought of their training experience. Even when they are mandatory, surveys are often a crude measure of how much knowledge transfer actually took place. Most

Predictive and Prescriptive Learning Analytics

Descriptive analytics that only measure courses completed, time spent, and scores achieved do not accurately illustrate the real success of a training course.

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 8

companies also report on training results such as courses completed, time spent, and scores from end-of-training tests or quizzes. However, these forms of measurement do not accurately illustrate the real success of a training course either, especially since time spent in training is in many cases not positively correlated to knowledge transfer.

To improve the efficacy of training, it is necessary to shift to predictive and prescriptive analytics that move from reporting the past to predicting the future. Access to additional forms of learning data—behavioral analytics data— also helps learning leaders better predict their employees’ comprehension, retention and application of the material. Based on behavior and performance metrics captured in-course, predictive learning analytics (PLA) provide insight into the efficacy of a learner’s current path and changes that should be made to achieve improved results.

Predictive learning analytics provide a methodology for behavior-based prediction of a learner’s future behavior as they interact with training content3. PLA can help instructors improve course quality, predict student drop-off rates, and predict quiz and exam performance.2 To capture learner behavioral data, the Zoomi Player is integrated as an IFrame snap-in with the organization’s existing learning management system, which allows the capture of numerous learner behavioral measurements as course content is consumed.

Based upon peer-reviewed data science collected over hundreds of thousands of online learners, Zoomi’s AI algorithms can identify key behavioral traits and patterns that correlate to success or failure in a course by studying each learner and each piece of content. These sequences of behavioral indicators, or behavioral motifs, can be correlated to knowledge transfer, before assessments are given. With predictive and prescriptive analytics determined from this learning data, Zoomi’s AI can then predict employee success, their likelihood to pass a course and their future

engagement levels with training content, all based on their interactions with content. Even when no quizzes are given, Zoomi’s artificial intelligence has predicted with over 90% accuracy whether a learner will pass or not by the midpoint of a course, solely from analyzing his/her behavioral data.

Most importantly, Zoomi predictive learning analytics allow L&D to get ahead of employee training by understanding what learners already know, what still needs to be taught, and how to best teach it so courses can be focused on each individual employee’s needs. All this information allows L&D teams to develop new strategies to improve future performance of both learners and content and to better align training initiatives with business objectives. In doing so, businesses will help employees to learn more effectively, making them better prepared for their jobs, which will translate to increased overall ROI for the company.

Even when no quizzes are given, Zoomi’s AI has predicted with over 90% accuracy whether a learner will pass or not by the midpoint of a course, solely from analyzing his/her behavioral data.

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 9

Armed with a fine granular understanding of course content and assessments from CTA plus the behavioral data captured by the Zoomi Player through PLA, Zoomi achieves autonomous personalization of eLearning content with No-Touch Individualization (NTI). Leveraging the power of AI and over 250 proprietary algorithms, Zoomi AI for Learning can:

• Identify a learner’s unique learning strategy from their behavioral sequences as they interact with training content• Correlate and predict knowledge transfer from these sequences of behavioral indicators with 90+% accuracy• Autonomously generate remediated content from existing courses to align with the learner’s unique learning strategy i.e. L&D teams don’t have to pre-author this remediated content• Quantify learner engagement with course material in real-time (no surveys needed)• Identify preferred content delivery modalities and personalize to each learner’s preferences

Zoomi NTI can predict the outcome of an employee’s training far before they finish the course and can pivot the training so learners don’t have to take the same lessons over and over until they understand the content and pass the tests. This reduces wasted time, alleviates learner frustration, and improves knowledge transfer through a personalized learner experience.

Autonomous Personalization

One of the goals of learner course surveys is to collect quantifiable feedback around the learning experience for each individual. In theory, this would be used to continually improve course content and delivery methods to optimize the learning experience. As previously stated, the survey data collection process is often imperfect due to the surveys being voluntary. They are also available only at long timescales i.e. after the course has finished, which is at best only sufficient for making changes to the next set of learners. In addition, processing qualitative feedback about what learners liked or didn’t like about their experience can prove particularly daunting when the number of surveys gets large.

Because learner behaviors are captured by the Zoomi Player during training, real-time readouts are possible, which may obviate the need for surveys, or at least reduce their frequency. For example, Zoomi’s AI captures over a dozen behavioral factors during learner interactions with course content and scientifically determines an “engagement” score. This score is then provided in real-time in the Zoomi Dashboard at the course, module, and segment levels. This information can be used by instructional designers to tailor future courses and amend existing ones to optimize the learning experience.

Figure 3 provides an example of the engagement dashboard for a course. In addition to the engagement scores themselves, learner preferences for different content types are calculated and shown. At the course level, we see learners found videos the least engaging content type. This is contrary to the widespread belief that all new learners prefer multimedia content. It further underscores that not all learners have the same learning strategy, including how they prefer information to be presented. At the module level, the content types preferred by the learners are shown for the modules where different types

The End of Course Surveys?

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 10

were presented. This provides invaluable feedback to curriculum designers, enabling continual design improvement without needing learner surveys and offering a much more granular view into learner experiences and preferences.

Figure 3. Sample Zoomi Learner Engagement Dashboard at Course and Module Level

Networks are everywhere in our lives, from social networks on interactive digital forums to communication networks of cell phones. Our world is increasingly interconnected, with social connections forming at a more rapid pace due to digital technologies like social media platforms. In 2008, the degree of separation between Facebook users was 5.28, while by 2016 the degree of separation was only 3.57.13 Credited with what is now often cited as the 70-20-10 rule for how people learn, Lombardo and Eichinger suggest that 20% of learning comes through social interactions5. While it’s debatable exactly what percentage of learning is attributed to interacting with others, it remains a key component of everyone’s learning journey.

A manifestation of this social component of learning is the Social Learning Network. “Social Learning Networks (SLN), are a type of social network that forms when people learn from one another through structured interactions”4. Online discussion forums and enterprise social platforms like Slack™, Yammer™, and Microsoft Teams™ provide a digital trail of knowledge sharing between users that can be analyzed to model SLNs in an organization. By ingesting the learning discussions that occur on these digital platforms, Zoomi’s AI provides insights into how social learning is occurring within an organization, predicts learning outcomes, and makes recommendations on how the SLN can be optimized to improve outcomes.

As shown in Figure 4, modeling SLN interactions in an organization can provide insight into which employees are frequent knowledge seekers, and which are frequent knowledge disseminators. This information could be used in many ways. For example, frequent knowledge disseminators could be included in L&D feedback and planning sessions, as they are already viewed by their peers as a trusted source of information and they already serve a valuable role in the social learning process.

Social Learning Networks (SLNs)

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 11

Insights into instructor efficacy could also potentially be gleaned through SLN analysis. If two instructors, A and B, are teaching the same course to different classes, and students from Class A are frequently answering questions from students in Class B, this could suggest knowledge transfer is occurring more successfully for the Class A students. Further investigation could reveal insights in environment, material, or teaching approach in Class A that could be replicated for Class B.

Through AI-driven analysis of an SLN, Zoomi can also predict future interactions and make recommendations such as “encouraging early formation of groups of learners who are expected to frequently communicate or recommending that learners respond to newly-posted questions that they are expected to answer/contribute to later.”4 Teaming recommendations can be based upon several factors, but connections based upon topic are common. Consider the benefits from knowing that learners in Bangladesh, Rio, London, and Dallas are all discussing the same topic, so perhaps they should all be included on the new team being formed around that subject. In a very large organization, this insight would likely have been completely missed without AI assistance.

Figure 4. Sample Zoomi Social Learning Network (SLN) Visualization

Using AI and Data-Driven Learning to Get a Seat at the Table

Despite the recognition that being an integral part of the executive team is crucial, only 24% of executives surveyed indicate that L&D has a seat at the executive table17. The remedy is a focus on linking learning to business outcomes and helping the company overcome key business obstacles.17 Executives indicate that the “success of L&D can best be demonstrated by the impact of learning on [employee] retention and performance metrics.”17

“The success of L&D can best be demonstrated by the impact of learning on [employee] retention and performance metrics.”17

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

TM 12

Only 32% of executive survey respondents thought employee feedback about training experiences was useful in determining the success of learning programs compared with “retaining top talent” (68%) and “increase in performance metrics” (65%).17 These findings strongly suggest that the statistics L&D teams have historically measured like test scores and qualitative feedback about the learner training experience are viewed as really only useful to the L&D teams themselves. If L&D teams want a more strategic relationship with their executive teams, they must learn to speak their language.

Aligning training with business outcomes begins with understanding the strategic business objectives and key performance indicators (KPIs) of your organization. One straightforward way for L&D leaders to understand the metrics that matter to their business leaders is to spend time with those leaders reviewing what they track on their daily, weekly, and monthly dashboards.18 Common KPIs include:

- New Hire Time-to-Performance- Employee Turnover Rates- Customer Satisfaction Scores- Sales Volume- Customer Retention

L&D leaders must also be proactive in reaching out to the key business leaders to review employee performance metrics before and after training, so the L&D impact can be isolated and the return on learning (ROL) can be quantified. While linking learning to business KPIs requires incremental time, these proof points will speak to results-driven leadership.

Zoomi AI for Learning delivers immediate improvements on training investments by providing actionable intelligence on employee performance, engagement levels, learning preferences, and social learning networks. Through AI-driven modeling of training content, assessments, learner behaviors, and social network exchanges, Zoomi delivers predictive and prescriptive learning analytics that drive improvements in business outcomes. From productivity increases and efficiency gains to risk mitigation and higher employee engagement, Zoomi AI for Learning allows L&D teams to move beyond the reactive, historical statistics that describe the past - into proactive, prescriptive analytics that influence the future.

Zoomi AI for Learning is Here to Help

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

www.zoomi.ai

TM 13

1 Training Industry, Inc., Training Industry Conference and Expo 2018

2 W. Chen, C. Brinton, D. Cao, A. Mason-Singh, C. Lu, M. Chiang. ‘Early Detection Prediction of Learning

Outcomes in Online Short-Courses via Learning Behaviors’. IEEE Transactions on Learning Technologies,

2018.

3 A. Lan, C. Brinton, T. Yang, M. Chiang. 'Behavior-Based Latent Variable Model for Learner Engagement'.

International Conference on Educational Data Mining (EDM), Jun. 2017.

4 T. Yang, C. Brinton, C. Joe-Wong. ‘Predicting Learner Interactions in Social Learning Networks’. IEEE

Conference on Computer Communications (INFOCOM), Apr. 2018.

5 Lombardo, Michael M; Eichinger, Robert W (1996). The Career Architect Development Planner (1st ed.).

Minneapolis: Lominger. p. iv. ISBN 0-9655712-1-1.

6 Duff & Phelps, Global Regulatory Outlook 2018, p.4

7 The Conference Board, C-Suite Challenge 2018™, January 2018

8 The Conference Board, CEO Challenge 2014: People and Performance

9 PwC, Workforce of the Future: the Competing Forces Shaping 2030

10 NBC News, “Google CEO Sundar Pichai: Digital technology must empower workers, not alienate them,”

January 28, 2018

11 Association for Talent Development, 2017 State of the Industry, December, 2017

12 CEB Global, Confronting Scrap Learning, 2014

13 Brinton, C. and Chiang, M., “The Power of Networks: Six Principles that Connect Our Lives,” Princeton

University Press, 2017

14 Training Magazine, November/December 2017, p. 26

15 CGS, Inc., Enterprise Learning 2018 Annual Report, Corporate Learning Predictions, Observations, and

Trends, p. 5, 15, 18

16 IBM, The Value of Training, 2014

17 LinkedIn Learning, 2018 Workplace Learning Report, p. 35

18 Sequira, Gerard, 5 Learning & Development Metrics that Actually Matter to the C-Suite, Human Capital

Institute, June, 2017

© 2018 ZOOMI, INC. ALL RIGHTS RESERVED.

Footnotes