Introduction

Trends in applied practice come and go, but one fact we can continue to count on is that the war for talent is real and present among organizations today. While the original McKinsey research (Michaels, Handfield-Jones, & Axelrod, 2001) unsettled the business world in the late 1990s, today more than ever there are forces at work that are driving organizations to compete at record levels to attract, motivate, develop, and retain the best people. Recently we classified these into four major shifts influencing organizations and the field of organization development (OD), which have yet to be fully addressed by either researchers or practitioners in the field (Church & Burke, 2017). These consist of (1) a shift from products to platforms, that is, the rise of new, more dynamic, and fluid organizations; (2) a shift from mechanical to digital, that is, the increasing reliance on technology, data, and end-to-end design thinking (another way of thinking about organizational systems) for delivering on all aspects of business processes and performance; (3) a shift from data to insights, that is, moving beyond just the acceptance and understanding of data in its myriad of forms to advanced analyses of information and generating actionable insights that influence the business strategy in ways never dreamed of before; and finally, (4) a shift from employees to talent, that is, the focus that organizations are increasingly placing on identifying and segmenting their people into different groups, with the result being that some employees receive a greater proportion of developmental resources than others.

It is this latter shift that we are most concerned with in this chapter and one that is at the very heart of the differences between traditional models of OD and the “new” practice area of talent management (TM). Why is this shift so much more important than the others? Because it represents a fundamental tension that many organizational practitioners in the field today face between what has historically been a core value of OD, that is, implementing interventions and change efforts aimed at developing the entire employee base toward some desired goal (Burke, 1994, 2014), and an investment of resources targeted to developing a select group of employees. We have described this difference in the past as being the core difference between a focus “on the many” which is ingrained in the practice of ODand a focus “on the few” (Church, 2013) which is perhaps the core assumption of TM today (see Fig. 15.1).

Fig. 15.1
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A simple dichotomy: organization development versus talent management

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While this might not seem that divergent to some practitioners who argue that their consulting work with clients on individual coaching, specific work-group interventions, or senior leadership team effectiveness is perhaps more selective in nature than a large-scale change or whole systems approach, the consequences of this shift in mind-set are far reaching. This is because not only does the emphasis on employees differ but the outcomes from the same types of OD interventions and tools used in TM applications are very different as well. The highly popular use of 360-degree feedback, for example, a staple of OD efforts for decades (e.g., Burke & Jackson, 1991; Burke, Richley, & DeAngelis, 1985; Church, Waclawski, & Burke, 2001; Church, Walker, & Brockner, 2002), is now being deployed as the number one tool for both development and decision-making about who gets a greater bonus and merit increase in their base pay as well as who gets the next promotion (e.g., Bracken & Church, 2013; Effron & Ort, 2010; Silzer & Dowell, 2010).

We will then turn to two areas, pillars if you will, where OD and TM converge in values and application with recommendations for how practitioners can best align and influence the design and implementation. The chapter concludes with some recommendations for future research, skill building, and further exploration in the field on both sides of the OD and TM practice equation. Let us start the discussion with a short case example that shows how these two worlds of OD and TM both intersect in design and then diverge in practice using the same types of well-known tools.

Case Example

Several years ago, we were involved in the design of a new senior executive development intervention that centered on the use of data-driven feedback tools and one-on-one facilitated coaching and action planning to enhance leadership skills and capabilities. The program was grounded in the use of multiple methods and was consistent with OD efforts dating back to the 1980s with NASA and 1990s with firms like BA, SmithKline Beecham, Home Depot, Natwest, and others (e.g., Burke & Jackson, 1991; Burke & Noumair, 2002; Burke et al., 1985; Church, Shull, & Burke, 2016; Church et al., 2001, 2002). Given our firm belief in the importance of having both behaviorally based feedback from multiple sources and the use of other types of measures to get at underlying personality traits and deeper psychological drivers (e.g., derailers), we designed a new process that included a custom 360-degree feedback measure and employed the Hogan Assessment Suite as one of the core assessment suites. Although this pairing of tools is quite popular today as reported in benchmark research (Church & Rotolo, 2013; Church, Rotolo, Ginther, & Levine, 2015), it has in fact been a staple of OD practitioners’ tool kits for many years (used by about 43% of practitioners currently per the study by Shull, Church, and Burke, 2014), and reflects the same approach we used when working with senior leaders as part of NASA ’s leading-edge Candidate Development Program (Burke & Noumair, 2002). In addition, in order to ensure we would be able to provide a truly holistic view of the individual’s executive effectiveness today, as well as their strengths and opportunities for growth, we added additional tools to round out the multi-trait multi-method (MTMM) assessment process, such as observations, behavioral incidents via structured interviews, as well as various types of exercises. In the end, we had what we felt was a truly robust and incredibly valuable suite of tools for developing the senior leaders in the client organization. So, what happened?

Well, when we started the process, it was stated initially that the feedback was intended for purely leadership development. There was a clear commitment from the senior executive sponsor to the effort with a formal process, aligned timing, dedicated resources, and broader C-suite level endorsement and air cover. That was never in question. To us it sounded like a perfect OD intervention based on a new set of leadership competencies designed to develop future capability for the firm. What did emerge during the initial implementation, however, was the need for a values-based alignment up-front just before launch regarding the use of the data post the feedback process. When it was time to script the conversations with program participants, we were confronted with the tension between a classic OD approach and the emerging TM mind-set. This had happened to us on at least one other occasion in the past, where a different client organization had essentially done a “bait and switch” with us regarding the purpose of the feedback process after the data had been collected and delivered (which to us was unethical), so we always remain hypersensitive to the scenario.

Thus, in keeping consistent with our own OD values of transparency and integrity of the process, we wanted to be sure that in this feedback implementation what we were telling people about the use of their results (i.e. who was going to see what exactly and how they might be using it) was absolutely as accurate as we could be. This came as somewhat of a surprise to the client organization as it did raise the issue of transparency to a higher level of awareness, but there had been no intent to change direction or hide anything. They simply lacked an understanding of how important it was and what it might mean to employees to know how data being collected would be used. So we raised the flag and had a robust debate (a second time) about the real purpose of the process and the results. At the end of the discussion, it was clear that the organization was interested primarily in the development of the focal senior leaders but also in using the information collected via the various assessment tools to help (a) level the playing field, (b) remove system biases that might have been present without consistent data sources, and therefore, assist them in (c) making more informed decisions about which executives might be a better fit for a given role or opportunity than others. In short, and consistent with recent benchmark research conducted with large organizations doing this same type of work (Church & Rotolo, 2013; Church et al., 2015), this organization was interested in both development (OD) and decision-making (TM) applications from the same process. As a consequence of the discussion, the internal team developed additional communications for participants as part of the orientation (which were carefully reviewed via a walk-through and again revisited during the feedback stage) to ensure the process was clear and transparent and in accordance with an OD values approach up-front. That said, one of the objectives of the program remained the differentiation of talent and the use of 360-degree feedback and other sources to both develop leaders against their strengths and opportunities and also help inform future decisions regarding succession. Fundamentally it was a TM, not an OD application.

A Brief History and Evolution of Practice

Although many definitions exist, at its core, OD is about the implementation of a process of planned change for the purpose of organizational improvement and reflects a normative or values-based approach to how organizations should function (Burke 1982, 1994, 2011; Church 2001; Cummings & Worley, 2015; Friedlander, 1976; Goodstein, 1984; McLean, 2006; Shull et al., 2014). It is grounded in the basics of social systems thinking, action learning, effective consulting and intervention skills, a robust toolkit of practices and processes, and—perhaps most important—the integral use of data, feedback, or information obtained from employees at all levels to truly drive organizational transformation (Burke, 1982, 2011; Nadler, 1977; Waclawski & Church, 2002). Grounded in psychology and the social movement in the 1960s (e.g., Bion, 1959; Lewin, 1958; Likert, 1967; McGregor, 1960), it has evolved over the years to reflect a wide range of different types of approaches to working with organizations. That evolution has seen the field overlap with practices and practitioners from other related disciplines such as organizational behavior (OB), industrial-organizational (I-O) psychology, human resource development (HRD), and diversity and inclusion (D&I). As a result, and along with new constructs such as dialogic OD (e.g., Bushe & Marshak, 2015), there remain many different definitions of OD. For the purposes of this chapter, we will adopt the one proposed by Burke (2011) for our discussion (see Table 15.1). The bottom line is OD is about development and change, and these are intended to be in a positive humanistic direction. While research with 388 practitioners in the field has continued to point to a perceived weakening of the traditional OD values of the past (Shull, Church, & Burke, 2013), those same practitioners remain highly optimistic (79% overall) about the prospects of the field going forward.

Table 15.1 Definitions of OD and TM
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Talent management, on the other hand, is not a field at all but a professional area of practice as well as a job title and/or subfunction in many organizations. Although the majority of the frameworks and tools typically associated with TM have been around for decades embedded in other disciplines, such as OD, I-O psychology, and even traditional human resources, since the war for talent phenomena started, there has been a concerted effort on the part of organizations to focus on talent over employees (our 4th shift above), which has given rise to the TM name and function. Based on a recent benchmark study of 71 large well-known organizations (Church & Levine, 2017), 94% reported having a formal enterprise or corporate TM group in place today. Interestingly, however, the construct only emerged in the mid-2000s in major conferences (e.g., Church, 2006) and in business books such as Strategy-Driven Talent Management (Silzer & Dowell, 2010), Talent on Demand (Cappelli, 2008), and One-Page Talent Management (Effron & Ort, 2010). Other authors such as Charam, Drotter, and Noel (2001), with the introduction of the leadership pipeline construct, and Boudreau and Ramstad (2007), with their notion of pivotal talent and HR as a decision science, have also been involved in shaping the thinking here in the form of business strategy and leadership progression respectively. Even concepts from popular books and movies (e.g., Moneyball ) have been leveraged into talent management parlance to promote new consulting efforts in this area. Similar to OD, there is no singular recognized definition today of TM, and recent benchmark research (Church & Levine, 2017) has shown that organizations differ dramatically in which subfunctions and practice domains they do and do not classify as TM internally (e.g., 73% of the OD groups in those same companies now report into the TM function and do not stand alone in HR, yet 51% of Diversity & Inclusion groups report in separately from TM). Generally speaking, the most commonly used definitions focus on the talent life-cycle rather than on organizational change. Table 15.1 contains the definition offered by Silzer and Dowell (2010) from one of the early and most comprehensive books on the topic.

Definitions of OD and TM

If we look at the two definitions, some of the initial areas of overlap and contrast are clear even from just these statements. Both focus on processes and interventions, and both have a distinct purpose to their efforts. While business effectiveness and meeting business needs are a shared goal, in OD’s case the emphasis is on facilitating personal and organizational changes (in a positive way), whereas in TM the goal is primarily aimed at feeding the talent pipeline. In short, OD is about the system as social entity (reflecting the social psychological origins of Katz and Kahn, 1978, in many ways), and TM is about fine-tuning the machine that produces the best talent to run the organization.

Anyone who has spent time in a TM function or worked with professionals in the area knows, however, that to achieve the goals identified above requires a deeper dive into the work itself. What does it mean to attract talent to an organization? If we value inclusion in OD, as some have argued (Church, Rotolo, Shull, & Tuller, 2014; Jackson & Hardiman, 1994; Plummer & Jordan, 2007), does that mean that anyone can join the company they choose and be effective in any role that interests them? Of course not, there are elements of cultural fit, knowledge, skills, and abilities matched to requirements in certain roles, experience, and motivation, and so on. The Burke-Litwin model (Burke & Litwin, 1992) is a classic example of how these factors need to be considered in the broader context. Yet what about people development? Who should be developed and how? Does it matter if everyone is retained or only certain people? These questions are where the OD versus TM dilemmas start to emerge more clearly. Based on our combined experience in both the OD and TM practice areas across multiple consulting engagements and internal leadership roles, we see three key values dilemmas in practice that really get to the heart of the difference between these two approaches to working with organizations. They are important to understand not only because they can raise values debates in the design and implementation of work, but also because they serve as guideposts for how organizations should (or should not) be engaging with employees, in particular around data. These are described in the next section.

Three Key Values Dilemmas in Practice

As we think about the key differences between OD and TM, it is important to recognize that all of these reflect a set of assumptions about the nature of the work being done with individuals in organizations, which need to be addressed during the “contracting” phase of the consulting relationship (or at the initial design of the internal process or intervention). While we should point out that there is nothing inherently wrong in our opinion about these differences in assumptions, they do represent values dilemmas in as much as they are potential disconnects between traditional OD values and the more talent-centric goals of the TM mind-set. These disconnects, if not surfaced and addressed appropriately between stakeholders at the outset of the intervention or consulting engagement, can result in true values conflicts and even ethical breaches, so it is critical to both articulate and understand them up front in any situation where these types of methods are being employed.

1. Purpose of the Intervention (and Data Generated)

The first and simplest difference between an OD and TM approach to working with various data-based interventions concerns the purpose of the effort itself. This applies to individual measures such as 360-degree feedback, personality tools, interviews, simulations, and process observations, as well as larger-scale tools such as surveys and other forms of inter- and intragroup data collected. The key questions to consider here are: (a) Why are we collecting information, and (b) what do we believe the information should (and should not) tell us about people, groups, and organizations? For many in OD, the act of asking questions itself provides a catalyst for change; in fact, the core Lewinian (1951, 1958) model is based on this very premise. Thus, almost regardless of what is asked, there is energy created, which should be harnessed and utilized for action and development. Some of the critical outcomes of this energy might be individual behavior change, enhanced self-awareness of strengths and opportunities, personal and professional growth, improved work-unit climate, greater job-person fit, or increased productivity through engagement, participation, and commitment (Burke & Litwin, 1992; Waclawski & Church, 2002). This is one of the primary reasons why data has been at the core of many OD intervention types since inception. For OD practitioners engaging in this work, their goal is to develop and implement the best possible tools that will create positive energy for whatever change lever and follow-up is going to be put in place. Their focus is on involving as much of the organization as possible (within the scope of the consulting project or process) and ensuring active, honest, and open participation. Thus, the values of inclusion and participation are top of mind.

From the TM perspective, however, the purpose of the intervention or process using these same identical tools is entirely different. In this context, the focus is on using data-driven methods to enable the organization to segment talent (people) into different classifications or pools against which different actions can be taken. Thus 360-degree feedback, personality assessments, and interviews might be used alone or in combination (e.g., leveraging an I-O approach called a multi-trait multi-method framework) to identify those leaders with the highest potential to be successful at higher positions in the company, or perhaps to find a subgroup of senior leaders who best fit a profile for a future CFO or CMO position. Sometimes, it is simply to enable a talent review and discussion of candidates on a succession bench list based on their configuration of strengths and opportunities relative to a desired set of skills needed (Church & Waclawski, 2010). In short, the TM framework here is about differentiation among individuals intentionally to offer them different outcomes.

Often the outcomes of these segmentation processes result in the allocation of additional developmental resources (e.g., development programs, task forces, special assignments, coaches), but in other instances they can result in additional decision-based outcomes as well. All of this is typically done with an eye toward ensuring greater consistency and accuracy in how strong and weak talents are deployed in an organization (hence taking a more business process and strategic orientation toward people) and in psychometrically valid and reliable ways. While this approach is no different than traditional employee selection frameworks of course (i.e. using tests to hire people into a company), when done internally on those already with the organization, it can cause some OD practitioners and those with similar values structures significant heartburn. The core focus here is on identifying and developing the best and the brightest (and those who will benefit the organization the most) forward at the expense of those who will not. It is differentiation according to predicted and measured value for the organization.

So how do we address this dilemma in practice? It is not easy as there may not be a solution in most instances that supports both goals. Ideally, the practitioner leading the intervention or process would want to find a way to appeal to both the employee engagement side of the equation as well as collect data for whatever segmentation requirements are required by offering the process to as wide a net as possible. As long as you are transparent about the purpose of the effort and how the data will be used (as in the case earlier) then you are meeting the needs for transparency and openness while encouraging participation. This is no different than good practice guidance in OD as well when working with these same types of tools (e.g., Church & Waclawski, 2001a), but it is worth noting in this case in particular given the significance of the impact downstream.

In the case of an employee survey program, this is a relatively easy goal to achieve and one of the reasons those survey practitioners with OD backgrounds (e.g., Church & Waclawski, 2001b; Kraut, 2006) would recommend doing a census on a regular basis rather than the more popular randomized pulse methods that are in place today. On the other hand, when it comes to the cost of individual feedback assessments and complexity of providing feedback, it might prohibit the organization from offering it to all or wide ranges of employees. So it really depends on the context. One example we have seen where both goals were met, however, was at PepsiCo in their Potential Leader LeAD program (Church & Rotolo, 2016), where thousands of employees (at a specified junior level and based on specific performance and tenure criteria) were offered to participate in a feedback process, informed that they would be assessed and given a potential “LIFT” score representing their ability to perform at higher levels, as well as two strengths and two opportunities against the company’s leadership effectiveness framework regardless of how they did. In that program, the assessment process was effective in (1) meeting the TM goals of predicting future success—that is, actual performance and promotion rates one year later were significantly correlated with performance on the assessment tools; (2) living up to the OD value of transparency by telling how employees scored (their level of LIFT, a proxy for potential), which had no negative impact on satisfaction with the program (70% favorable), perceptions of organizational commitment, or actual turnover; and (3) meeting the needs of employees and the organization by providing developmental feedback to all participants with the vast majority (77% and 83% respectively) indicating that the results had helped them increase their effectiveness as a leader and showed an investment by the company in their personal growth and development. The program remains in place today after several years in running.

2. Type of Models Measured

Once the purpose has been established, it naturally leads us to the next key distinction between the OD and TM approaches to data-driven interventions, which are the types of models and associated measures that are used as part of the process. Although one might argue that they need to know the tool being examined before making the decision on what it means, it is actually the other way around. The discussion should not be about whether to use the Hogan Assessment Suite or the Myers-Briggs but rather what we are trying to achieve with the personality data we are collecting. Is this for individual self-awareness, enhancing team effectiveness, helping people see and appreciate differences in others, looking for group strengths and opportunities at the work-unit level, or making decisions based on individual capabilities? Just as structure should always follow strategy in organizational design, the type of conceptual framework and measurement that goes with it needs to flow from the content you wish to use in your intervention. In the case of OD versus TM applications, this difference cannot be clearer, and it is one of the key areas in which many OD professionals (and often HR and line leaders as well) take serious risks with their approaches. The primary topic here is one of validity of measurement and the legal ramifications of using data in ways that can influence an individual’s future in the organization.

From an OD perspective, much of the emphasis in using data-driven tools for change is just that—as a catalyst in whatever form it takes (Waclawski & Church, 2002). In the context of the classic OD consulting model (see Fig. 15.2), data is collected, analyzed, and interpreted at some level and fed back to the client and/or employees, a mutual understanding of the findings is facilitated, and ultimately a shared action plan for driving change is created.

Fig. 15.2
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Classic OD consulting process model

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This basic paradigm dates back to the early days of data-based methods in the field (e.g., Burke, 1982; Nadler, 1977) and really has not changed much in contemporary approaches, whether for interventions or for evaluating the impact of those interventions (Church, 2017). In addition, the approach taken from an OD mind-set is largely based on driving the organization forward, either individually or collectively through growth and development. Whether this means introducing a new set of core values, mission, and vision, leadership competencies, or attributes of a desired culture (e.g., following a merger or CEO change), the goal is often more about (1) communicating the desired state, (2) creating energy and momentum toward that desired state, and (3) facilitating action and tracking progress in the direction of that desired state. The best approaches here are those that are systems driven and align the interventions at multiple levels following principles and factors identified in frameworks such as the Burke-Litwin model (see, e.g., the work done at SmithKline). The values at play here are optimism, change, development, and excitement about the future which have a host of positive organizational and employee outcomes.

The TM perspective is quite different. While some approaches to what gets measured may have a future focus, the emphasis is more about the disposition, skills, and capabilities that are needed for individuals to be successful. In some ways, this implies they are not or may be less successful today in the present state. In addition, the content design tends to be less focused on an idealized future state mission and vision (which Lewin himself agrees might never be achieved) and more on the specific trait and behavioral abilities that can be either selected for or developed today. Thus, by definition, some people will not make it and no longer belong in the organization. Once again, there is a theme of differentiation running through the TM work that by design will weed people out of the process (and likely out of the organization over time). While the OD approach may yield a similar outcome by default, it is not the primary intent, and in some cases, there are active efforts to avoid this outcome. From a TM standpoint, there is a desire to segment people into those who should stay and move ahead into larger positions and others who are better served staying where they are or even leaving for better opportunities elsewhere. Thus, TM applications tend to be less focused on content such as values and aspirations and more on hard capabilities such as leadership competencies, skill sets or other attributes (e.g., experiences gained and needed) that enable better clarity regarding these types of comparisons among people. That is not to say that TM processes do not reflect future state goals but often these are expressed in more tangible, measurable ways.

This is even more the case when the processes are used for decision-making purposes. Here the values dilemma becomes one of tool kit content and measurement properties. Just because a vision is exciting or a tool is engaging does not mean it will meet the rigor of being a valid assessment for other outcomes. In TM applications where the data has more value to the organization than just individual growth and development, the importance of having targeted and predictive frameworks and measures becomes paramount. After all, you are making decisions on people based on their results, so the data generated needs to predict what it purports to. In these situations, the TM professional must consider alternate types of measures that may be more intrusive, lengthy, complex, or otherwise less “positive” in tone at times in order to meet the criteria of having predictive properties. It also means that some tools which people can find intimidating if shared (e.g., cognitive tests of intellectual skills, deeper personality assessments which highlight derailers or other significant flaws) are in fact those that are more commonly used. Similar assessment centers and simulations that test for responses under stress are far more daunting than a workgroup climate tool used for team effectiveness and collaboration.

Recognizing that many of these more “aggressive” types of assessments produce the least developable types of feedback (Church, 2014), it becomes even more important that practitioners using them know how to design the process to meet the demands of a rigorous validation approach, interpret the feedback appropriately, and ensure participants understand the full implications. While practitioners must be careful to adhere to legal standards set for the use of decision-making from assessment data, validation is generally not a requirement for enhancing self-awareness for development purposes only. However, both OD and TM practitioners must adhere carefully to the Uniform Guidelines when data could be used for selection, promotion, retention, performance decisions, and so on (Equal Employment Opportunity Commission, 1978). When beginning to look at relationships between certain factors and performance outcomes, or for certain types of decision-making, validation becomes especially important. Conducting statistical analyses to make predictions among variables measured in a feedback tool is when validation becomes critical to ensure the measures being used are sound. Therefore, the intent of the survey can, and will, dictate whether or not validation is of importance.

This is where both OD and TM practitioners can face challenges on the values front as well as on the pure capabilities side. If OD professionals are not familiar with validation methodology and are engaged in designing TM processes with their tools, they may put the organization at serious risk of adverse impact and other negative consequences. TM professionals, on the other hand, may or may not understand the psychological and interpersonal dynamics involved in coaching against these types of tools (Church, Del Giudice, Margulies, 2017). The Leadership Potential BluePrint (Church & Silzer, 2014; Silzer & Church 2009) is one such framework in TM that outlines the six key factors required to maximally understand and predict future potential in organizations (see Fig. 15.3). Knowing which tools will work best and which will not in each aspect of the BluePrint is required to ensure a robust and defensible measure. It takes a combination of skills on the part of the consultant/practitioner to make these types of efforts effective.

Fig. 15.3
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The Leadership Potential BluePrint

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Our recommendations to practitioners (whether in OD, TM, or any other related discipline) is to familiarize themselves with the types of tools and frameworks that are available and give more specific attention at the design and contracting phase to what types of measures and resources will be needed to ensure the right level of content will be assessed and what degree of measurement rigor will be required. Moreover, just listening to the client may not be enough. As with any good OD consulting, you will need to test for underlying questions and assumptions about talent and people—are they looking to use the data in ways they are not articulating (or do not want to tell you)? What would they say if you told them they cannot have access to the individual-level data even if they asked for it? How about testing the idea of risk of legal action if the design or output of an intervention or process was ever misused for other purposes? These are the kinds of areas that need testing.

3. Use and Transparency of Data for Decision-making

The third key area in which OD and TM differ and we see a key values dilemma concerns the expressed use of data obtained from the same types of interventions and measures for development-only versus decision-making purposes. As we have discussed above, OD has its roots in the social sciences, and although data has held an integral place in the expansion of the field since its inception, it has largely been in the role of a facilitative and developmental tool rather than a decision-making one. This is clear in the consulting model noted earlier as well as in the core writings around the use of data-driven methods (Burke, 1982, 1994; Nadler, 1977; Waclawski & Church, 2002). The role of data in OD is both diagnostic, in that it can be used to identify trends and insights, and catalytic, in that it enables the client organization or sponsor to reach a shared understanding and thus work toward a compelling solution. While that solution certainly results in decisions being made about the organization, for example, processes to change or modify, such as performance management, structures, mission, and vision, it is not the data itself that is driving the decision. Moreover, the data is typically not being acted upon (or reported) at the individual level in OD interventions.

TM applications, in contrast, are almost exclusively aimed at assessing and differentiating talent into groups of those with more or less capability (and/or potential) for decisions to be made following completion of a given process. While development is almost always a key component as well (e.g., only 8% of those top development companies in the benchmark study report using assessment data for only decision-making and not development among their executives), it is often a shared outcome at best. For example, at senior levels in an organization, the emphasis is more likely to be on development as well as assessment given the level of success those individuals have already achieved, while at more junior levels the process is more likely to have been designed to segment talent quite aggressively into those with high potential and those with less potential. As a consequence, the processes and tools from a TM standpoint must be designed with a level of rigor and care that goes beyond the OD approach (not that OD efforts cannot leverage those same higher rigor measures). In addition, there is enhanced pressure on the design of the tools to ensure that what is being identified and measured will have a predictive capability for the organization; that is, it will tell the executive sponsors, senior leaders, and HR professionals who the best and brightest individuals are, which ones will fit the key roles in the succession plan, and who might not ever be ready for promotion in the company and therefore really should not be part of the ongoing leadership development agenda. These are much harder decisions to make, and the data plays a key role in removing biases and ensuring a standard playing field for everyone (and protects the organization if designed with no adverse impact). The decisions themselves are still never easy. As we have written about in other contexts, applying a TM framework to OD practitioners ourselves can be challenging as we become the recipient of our assessment outcomes (Happich & Church, 2016). The bottom line, however, is that TM applications simply do not get designed and funded if they do not yield some level of data that can be used by the organization, or worse, the data that is generated is garbage and leads to poor decision-making.

Aside from the issues of the purpose of the intervention and types of tools, the real values dilemma here is not so much the use of the data itself (after all, effective OD survey interventions are predicated on sharing results and taking action from them, Church et al., 2012), but rather the degree of visibility and transparency associated with that practice. While some practitioners may balk at the idea of making decisions based on data, the reality is that I-O psychologists have been doing this for years. But from a values standpoint, are we telling the participants in these programs exactly what the data measures, who sees it, and how it will be used to impact decisions about their future career prospects or performance? These are the key questions of transparency as discussed in the initial case, and these are the ones that are often at odds with OD and TM practice.

When a tool is designed for development-only purposes, it is important to limit who has access to that information; for example, details of the feedback data are often shared only with the feedback recipient. This is thought by many to facilitate greater internalization and ownership of the development agenda. As an OD professional, you would actually find yourself fighting to protect the confidentiality of the assessment feedback from the client organization. We spent many years doing this in purely developmental OD interventions aimed at culture change over time. In some cases, broad themes may be shared with the consulting team and senior management; however, this is generally not at a level of detail where those parties can influence individual behavior change. Further, when a tool is designed for development purposes, action planning is typically an expectation of the feedback recipient alone for individual development planning rather than action planning taking place at multiple levels and by multiple stakeholders, as is typically the case when the intent of the feedback is for team or organization effectiveness and decision-making. So, accountability for follow-up is thought to be stronger yet can also be more diffuse at the same time. In many ways, the actions that come out of development-only processes are directly proportional to the energy the individual has to develop themselves in the first place. This has been called the Achilles’ heel of 360-degree feedback (London, Smither, & Adsit, 1997), and it has been a real concern for some practitioners in the field, who have called for more formal mechanisms of accountability for change (e.g., Bracken & Church, 2013).

Transparency also reflects who gets access to the data. For example, when data is collected for development-only purposes, there are ethical concerns with sharing feedback with a recipient’s boss or other career decision-maker, such as HR Business Partner. The main intent of development feedback is to create self-awareness for the recipient, with research done years ago demonstrating that higher self-awareness leads to a host of positive developmental and performance outcomes (Atwater, Roush, & Fischthal, 1995; Church, 1997; Reilly, Smither, & Vasilopoulos, 1996). That dynamic shifts, however, with an emphasis on TM and decision-making. When the purpose of the feedback is first and foremost for talent segmentation and decision-making rather than individual development, the argument is made that the data belong to the organizational members (i.e., leaders and employees), and that not only is there an expectation that results be shared with them, there is also an expectation that those individuals are involved in taking action with the results in some way. Therefore, not only is it the responsibility of the recipient as an organizational member to share their feedback, it is also a responsibility that they participate in identifying a solution, implementing that solution, and being a part of the change. Thus the accountability is solved. PepsiCo ’s implementation of the Manager Quality Performance Index (MQPI) as an annual upward feedback tool (distinct from their 360-degree feedback measure) designed for direct reports to assess their managers on People Results is an example of such an intervention aimed at driving accountability through data-based methods. Self-ratings were not part of the process by design because that tool was not meant to be a measure of self-awareness but rather a behavioral scorecard and part of the performance management system. But, and this is important, managers were given a “free ride” for the first year of administration to test the tool, set their own baselines, and understand what the data would look like for them before the first wave of results actually counted for or against their performance.

The final area of transparency, of course, is what practitioners and managers tell participants about the process. In more development-oriented OD efforts, it is far easier to tell employees you are focused on driving a large-scale organizational change effort than it is in a TM process where the focus is on identifying the highest potential individuals so you can give them more resources and developmental support. The latter situation if done poorly can cause anxiety and stress, as well as negatively impact engagement and other behaviors in the workplace. If done well, however, you can energize people who want to do well and achieve. This is part of the reason that TM processes work well in many larger organizations, where people are drawn to them because of their career advancement opportunities (which takes us back to the war for talent), compared to others where the work and employment proposition is more stable and emphasizes additional factors such as tenure.

Openness and transparency though appear to be challenging values in the context of TM, particularly for leaders and managers as well. While research indicates that most large companies have formal talent review processes, and 70% of top development firms use formal assessment methods to identify and develop their highest potential future leaders, only 34% are transparent about the process and formally tell their people where they stand (Church et al., 2015). Why? Because there is a real concern among many senior leaders and HR professionals that transparency will lead to negative outcomes for the company, including decreased engagement, poor performance, and increases in turnover among the approximately 85% of employees who are not deemed to be high potentials. Since this vast majority of individuals deliver results every day, telling them (or having them figure out) that they are part of a program to make promotion decisions (and then telling them how they did) represents a real or perceived concern. This once again raises a values dilemma between OD and TM. While no self-respecting OD practitioner would enable such a process, there are some practitioners in TM without the same social science backgrounds who might not share these same values. It is imperative then to ensure that the purpose and intent are aligned up front, including what is shared with employees, managers, HR, and why. This takes us back to the case study at the very beginning.

Two Pillars of Values Alignments

Now that we have discussed the three areas where values dilemmas emerge in OD versus TM work, let us turn to two areas where values align in these practices areas. The good news is that these two pillars can form the basis of a partnership between approaches if considered together and in the context of having clarity and setting appropriate objectives up front.

1. Commitment to Participant Feedback and Development

In OD and TM, there is almost always a belief that feedback should be used to drive improvement and growth even if that leads to less desirable individual outcomes in the short term. Despite differences in approach for OD and TM practitioners, a focus on participant development through the use of individual feedback is a key area of overlap between the two areas of practice. Similar to the importance of accountability for following up and sharing results with people who have provided feedback being important in OD and TM, there is a shared belief in both approaches to working with data that feedback should be used for growth, development, and continuous improvement. In other words, despite concerns over how talent reviews work in organizations (e.g., see Church & Waclawski, 2010; Silzer & Dowell, 2010), very few approaches would see data collected for its own secret (“black box”) purposes. In OD, it would be a pure ethical issue not to share results back with employees and offer them feedback as it violates the implicit (or explicit in many cases) data collection-feedback contract. In TM, it would be a business issue (and poor financial decision) to not share results back because you would diminish the value of the data which should be used to maximum impact for both the organization (for decision-making) and employees (for enhancing their development and increasing readiness for larger roles).

Further, research has demonstrated the importance of action planning and the effects of taking action versus sharing results alone (Church et al., 2012), so there is evidence it works. With the current corporate landscape and the continuing need for HR to demonstrate its ROI, it is unlikely that data for data’s sake, even for the purposes of providing valuable feedback, would be enough. Business leaders are demanding to see results of their efforts, and we would argue they should be. Both OD and TM believe that the leader plays a pivotal role in successful behavior change. Therefore, whether it is action planning from an engagement survey, an upward or 360-degree development assessment, or some other type of feedback, both TM and OD hold a commitment to doing something with the results, usually in the form of facilitating a feedback debrief and action planning process on behalf of the organization in which leaders are involved and engaged along the way.

Finally, a specific type of action planning having to do with individual growth and development appears to be a commonly held value among OD and TM practitioners. Providing feedback data is generally thought of as the best way to promote self-awareness, which can lead to individual growth and development. This has implications though, and sometimes data do not lead to the outcomes intended. For example, for TM telling a leader how poorly they did on their 360-degree feedback or an assessment suite that is used for decision-making could result in significant angst, particularly if that data also means the employee will no longer be on the high-potential list. Being transparent with the results may make them disengage and even leave the company. While this might be desirable for those who were not seen as high-caliber talent before, what happens if a high-potential leader whom everyone loves fails the assessment suite? Are they no longer a high-potential? Once again this raises the question of transparency: Do you tell them how they did but not what it means? Do you tell them if their status changes? These are some of the reasons companies choose not to divulge talent management information such as high-potential status even if they do share feedback results openly. All these are tricky values questions that need to be addressed in a company-by-company context. While there are no right answers, our guidance here is to be consistent within the context of the same culture and setting. Moreover, research has shown that transparency is preferred over secrecy by employees even if the results are not as positive as they would like them to be (Church & Rotolo, 2016).

Similarly, for OD, an unintended consequence could be survey results leading to decisions around how to structure an organization that will certainly affect the people in that organization but is designed to, and will ideally lead to, an intended outcome of enhancing the organization’s effectiveness in the longer term. How much of that short-term versus long-term plan can and should be shared? Moreover, when cultural or performance data are poor, what is the best way to share these (i.e., in the spirit of transparency) without disengaging those with whom you are sharing the information? Imagine telling 20,000 employees in a company town hall that faith in senior leadership is only at 24% favorable? It is clearly important information, but the best delivery and action planning mechanisms need to be well thought through. These are some of the key issues involved when it comes to feedback and development.

The bottom line is that both TM and OD value doing something with data, turning feedback into action, and promoting growth and development for individual leaders and the organizations within which they work. While there may be a difference in the initial lens that TM and OD take (individual leader focused for TM and organization focused for OD), people make up organizations and ultimately drive organizational effectiveness. Therefore, we would argue the two go hand in hand, and neither TM nor OD is likely to be successful in their efforts if they work in isolation.

2. Commitment to Organizational Insights and Capability

A common distinction between OD and TM as we have discussed earlier is that OD tends to focus on the team and organization (or groups of people), and TM tends to focus on the individual leader. Beyond this initial lens, however, another area that both OD and TM share is the recognition and importance of looking to the systems level to make connections, draw conclusions and insights, and take action. The environment that people experience day to day is made up of the work that both OD and TM focus on whether that is team effectiveness, leadership effectiveness, growth and development, talent and succession planning, performance management, or engagement feedback (Burke, 1987; Effron & Ort, 2010; Shull et al., 2014; Silzer & Dowell, 2010). All of these elements ultimately contribute to the culture of an organization and its resulting level of effectiveness. On the OD side, this is often expressed in terms of the cultural impact that various facets have on company performance (Burke & Litwin, 1992), and on the TM side, it is more about identifying and predicting which individuals will reach the seniormost leadership levels to have the most impact there (Church & Silzer, 2014; Silzer & Church, 2009).

This is one of the reasons why key data-driven processes such as 360-degree feedback and other assessments are so important. These types of tools (and surveys as well) help to provide individuals with information needed to change their behaviors to improve their own skills and capabilities, but they also can and should be aligned to the broader cultural goals of the organization. By aligning these tools and ensuring that the content being measured and developed meets both sets of needs, we are ensuring that the organization as a whole is being served in the best possible manner. At PepsiCo, for example, the behaviors created to drive manager quality and inclusive behavior at the individual level via the MQPI were directly aligned to the cultural indicators measured by the organization’s organizational health survey, and the talent practices ensured these data-based inputs were integrated at higher levels of analysis (Church et al. 2014). These were not just nice-to-do practices, however, but linked to the business imperatives as outlined by the CEO and required for the future success of the business (Thomas & Creary, 2009).

Just as important as contributing to individual growth and development is, it is as important to ensure a focus on organizational insights and capability. Within TM, maximizing one’s leadership potential is often discussed as the most important outcome (Effron, 2017; Lombardo & Eichinger, 2002; Silzer & Dowell, 2010). Ultimately, TM is in the business of maximizing potential in order to increase business performance outcomes. We would argue that in OD, it is the same thing but through a different lens. Whether through employee engagement, team effectiveness, organization design or culture, all of these are ultimately done with the goal of enhancing organization effectiveness and performance (i.e., business outcomes) at the highest order. Both approaches are grounded in wanting to develop people and their capabilities. OD emphasizes wanting to help people through maximizing human potential and performance, and in doing so will result in making organizations more effective and better performing. TM, on the other hand, is focused on ensuring the best and most talented individuals are developed at the fastest possible speed to get them ready to take on key leadership roles with the same outcome being that the organization is more successful in the short and long term. Thus, these two practice areas do share a common ground when it comes to building capability and leveraging insights through data. Practitioners from both approaches ultimately want to ensure that they are providing data-driven insights that are of value to business leaders to support them in making decisions for the organization. One way of doing so that has been described in detail elsewhere is by analyzing data collected at the individual level (e.g., 360-degree feedback, personality, work-group climate) at higher levels of analysis to generate unique insights and connections across the organization (e.g., Church, 2017; Church et al., 2002, 2015). For example, it might be the case that although the leader of a given marketing function might have the needed creative and innovative skills to develop new strategies for driving market share, the team itself is comprised primarily of individuals low on inquisitive (or creative thinking) capabilities. This can suggest a host of actions both developmental in the form of training and decision-making with respect to team composition in the future.

Unfortunately, these data-based insights skills do not appear to be a natural strength today of practitioners from either approach. We have raised the red flag on this skill gap in OD practitioners before (Church & Burke, 2017; Church & Dutta, 2013; Church et al., 2016). There is a critical need on the part of current practitioners to be able to analyze large sets of data, find the relevant and actionable insights, and weave them into a compelling story for the organization about where they are today and where they need to be going in the future. Today this is simply not likely to be the case with your average consultant. On the TM side, the gap is just as large, and as a result, we have seen the rise of dedicated “talent analytics” functions and subfunctions for this very reason. The benchmark study by Church and Levine (2017) reported that 91% of top development companies today have a formal analytics function, though interestingly enough only 47% of those report directly into the TM function. So there continues to be a disconnect on both sides of the insights equation in this area. Still, the importance of insights for driving the organization forward is a key area where OD and TM do overlap even if both areas lack the requisite skills needed to do this well today.

Conclusion

Based on the discussion above, it should be clear that while the practice of OD and TM share a common set of goals, tools, and practices in application, there are some key differences in the values structures that underlie the two types of work. Both approaches value the individual (and the organization overall) and emphasize growth and development as a core component of the work, but how individuals are identified and for what purposes differ dramatically (see Fig. 15.4).

Fig. 15.4
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Summary of the differences in perspective between OD and TM

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As a result, the values dilemmas that can unfold when work collides between the two areas can be significant. Here are some examples where differences in an OD and TM mind-set become most challenging:

  • The purpose of a given intervention, process, or implementation that collects data on individuals (development and individual growth only vs a combination of development and decision-making)

  • How that purpose is expressed and articulated to senior leaders, human resources, and employees (transparency vs selective messaging; an emphasis on driving culture change vs building future leaders, etc.)

  • What content will be measured and what tools will be used (future focused vs competency based, an emphasis on identifying high-potentials or focusing on role fit, development-only measures or fully validated assessments, etc.)

  • Who will be identified to participate in the effort (emphasizing a highly participative and inclusive approach vs a differentiated talent segmentation model)

  • How the data collected will be used by the organization (at what level of aggregation and with what access)

  • What type of feedback and action planning process will be deployed and at what levels (e.g., individual and/or group vs integrated with other talent management processes such as succession planning or performance management)

In the final analysis, the answer to the question whether OD and TM are at odds with one another is it depends. From a pure values standpoint, there are key differences which do not align. From a practice perspective, however, the real decisions to be made are those by practitioners operating in the lines between and ensuring that both OD and TM efforts are designed and executed with the right level of emphasis on clarity of purpose, rigor in approach, transparency wherever possible, and above all else consistency in the manner in which all of the work is applied to individuals in organizations. Both sides of the equation are surely needed—an emphasis on broad-based development and a focus on identifying and developing future leaders who can move the organization forward. The key is ensuring both sets of practitioners have the requisite skills in systems thinking, data-driven tools for change, insights capabilities, feedback facilitation and development planning, and cultural sensitivities to ensure a smooth and fully integrated set of processes are in place to meet both sets of needs.