Talent Management: From Implementation to Redesign | Putting Human Capital Management into Practice and Using It in Business Decision-Making

This chapter examines common concerns that lead companies to consider replacing their systems. In many cases, the root causes lie not in the technology itself, but in how objectives, data, business processes, and ongoing improvement have been designed.

Companies considering the replacement of a talent management system often identify the following issues:

• Entered data is not updated
• Skills information is not kept up to date
• The system is not used sufficiently for talent reviews or succession management
• Data is not used in decisions concerning deployment, development, and promotion
• Reports and dashboards do not lead to actual discussions or decisions

When these situations are observed, system functionality or usability may appear to be the cause. In practice, there are cases in which insufficient functionality or poor usability hinders use.
Closer examination often shows that the root cause lies outside the system. If skills information is not kept current, changing the input screen alone will not solve the problem. Sustainable use requires clarity on why the information is collected, who uses it and for which decisions, who owns updates, and how the resulting value is returned to employees and the organization. The same is true of succession management. A list of candidates becomes meaningful only when it informs talent reviews, development plans, deployment decisions, stretch assignments, and regular reassessment.
Talent management often fails not because of missing system functionality, but because four elements are underdesigned: the purpose and use cases, the data to be maintained, the business processes through which the data is used, and the operating model for sustaining and improving adoption.
Therefore, when considering system replacement, it is first necessary to determine whether the system is truly the problem. Distinguishing issues that should be resolved through the system from those that should be resolved by redesigning operations, data, and operating model is the starting point for redesigning a talent management system.

This chapter sets out four requirements for making the HR Data Value Chain work in practice: clear objectives and use cases, decision-ready data, executable business processes, and continuous improvement.

The first perspective is objectives and use cases.
Talent management is the area in which the concept of human capital management is translated into actual workforce management, and its objectives and use cases differ according to each company’s management strategy and business characteristics. If system implementation proceeds while objectives remain ambiguous, the result is likely to be a framework that can register a large amount of information but does not clearly define how it will be used. Users cannot understand why they should enter information, making it difficult to sustain data updates.
For example, even within the same manufacturing industry, a core business may emphasize the transfer of technical expertise, productivity improvement, and securing key talent that supports quality, while a new business may emphasize identifying talent capable of applying experience from existing businesses to other areas and developing talent through the launch of the new business. Even within the same company, if the role and growth stage of a business differ, the required talent, the data to be reviewed, and decisions concerning deployment and development also differ. Therefore, although common principles may apply across industries and companies, specific talent management use cases cannot be designed uniformly.
Use cases must specify not merely how indicators are displayed, but also which decisions and actions the indicators will support. For example, merely visualizing the percentage of women in management does not lead to decision-making. It is necessary to identify the businesses, job categories, and levels where the pipeline is insufficient, as well as issues in the promotion process, and to review specific measures such as deployment, development, and recruiting, together with subsequent changes.
For succession management, the target positions, candidate selection criteria, readiness, risk of loss, and required experience should be defined, together with who makes which decisions in talent reviews and how those decisions are reflected in deployments and development. For skills management, the skills required in light of the current and future business portfolio should be defined, and it should be clarified which decisions related to workforce planning, deployment, development, recruiting, and reskilling will use them.
In other words, clarifying objectives and use cases means determining the decisions and actions that must change based on what the company wants to achieve "now" and in the "future," and translating them into the necessary measures, use processes, data, and system functions. This clarifies the rationale and priorities for investment in each measure and brings talent management closer to genuinely generating return on investment. Objectives and use cases are the starting point for the entire HR Data Value Chain.

The second perspective is data.
In talent management, collecting a large amount of data is not itself the objective. What is required is to work backward from decision-making use cases and prepare data that is comparable, consistently defined, and interpretable within the context of the business and management.
Specifically, companies should determine which data is mandatory, who updates it and how frequently, and how quality is verified, while also designing methods for integration with existing systems and business and financial data, as well as decision-making situations that use reports and AI.
As the use of generative AI advances, assigning meaning to data becomes increasingly important. Artificial intelligence does not automatically understand the meaning, granularity, and context of people data. For example, even when the same terms such as "international experience" and "recruiting experience" are used, business travel experience differs in its significance for decision-making from experience managing an organization at an overseas location, just as recruiting operations differ from experience designing recruiting strategy. Providing data with ambiguous meaning or context without clarification can result in recommendations that appear plausible but are unusable in practice.
What becomes important in this context is a Semantic Layer that prepares people data so that HR, management, and AI can handle it with the same meaning. Roles, responsibilities, duration, scale, difficulty, results, preferences, and constraints are extracted from narrative information such as employment history, performance management comments, career conversation records, and career aspirations, and structured in accordance with the company’s job, skill, and experience frameworks. By standardizing terminology and granularity and organizing relationships among adjacent experiences and capabilities, companies can identify as candidates not only people who have performed the same work, but also people who can apply capabilities developed in different job categories or businesses.
The role of AI is not limited to producing answers from accumulated people data. It can also contribute to the process of creating data that can be used for decision-making. It can present hypotheses regarding use cases and required data based on business and people priorities. It can also extract experiences, capabilities, roles, preferences, and constraints from narrative information and organize them into a comparable form. Furthermore, by standardizing terminology and granularity, estimating adjacent experiences, capabilities, and proficiency levels, and detecting outdated, missing, or inconsistent information, it can accelerate the construction and maintenance of the Semantic Layer.
However, HR must define what should be treated as having the same meaning, the granularity at which comparisons should be made, and to whom each type of information should be disclosed. Artificial intelligence does not replace data design; rather, it makes it possible to perform contextualization and structuring, which would be difficult for people alone, at a practical scale.
If there are too many data fields, the input burden increases and updates become less likely. Conversely, if the fields are narrowed down too much, the information required for decisions will be insufficient. Rather than assuming the use of existing data and asking "what can it be used for," it is important to backcast the required data and use of AI from "what decisions need to be made" and design the cycle of collection, use, and updating.

The third perspective is executable business processes.
Talent management does not work simply because data has been entered into a system. It becomes useful only when embedded in workable HR and management processes, including talent reviews, succession planning, deployment decisions, development planning, performance management, career conversations, and workforce planning.
Here, business process design means more than defining who enters data and who approves it. Employees and managers must understand why the process matters, provide and review the right information at the right time, and use it in meaningful dialogue and decisions.
Consider, for example, the career planning process. Many companies design a flow in which employees are asked to enter career goals and career plans, which their managers then review and comment on. However, not all employees can complete this process as expected. Situations may arise in which employees do not know what to write, have never considered their careers systematically, or, even if they have, feel psychological resistance to candidly communicating their transfer preferences or future aspirations to their managers.
Even if the system merely sends an input request under such conditions, entries are likely to remain perfunctory. As a result, career plan data may accumulate but is unlikely to be used in decisions concerning development, deployment, career conversations, succession management, and other areas. In other words, even when a process exists, talent management will not function unless feasibility has been sufficiently designed.
Therefore, for career planning to function as a business process, it must be designed to include measures beyond the system and input flow. Examples include sharing basic concepts related to career development, providing opportunities for career consultation, conducting workshops that enable employees to organize their strengths and preferences, providing career discussion training for managers, and offering career paths and development opportunities by job category that can be presented to employees.
Managers also require preparation. If managers only review the career plans entered by employees and cannot engage in dialogue based on the individual’s preferences, current skills, future growth opportunities, and the role expected by the organization, the career plan becomes merely an input field. It is therefore also important to prepare conversation guides, discussion prompts, feedback methods, and methods for reflecting the discussion in development plans for managers.
Designing executable business processes therefore goes beyond configuring system workflows. It means creating the conditions in which employees can provide meaningful information, managers can review it and hold productive conversations, and HR can use it in decisions and actions related to deployment, development, and promotion.
Talent management involves stakeholders with different roles and needs. Management, HR, business units, managers, and employees themselves each engage with people data for different purposes and at different times. In redesign, it is important not to begin by considering who should be encouraged to use the system broadly, but to specify within the business process which decisions will use which information and by whom.
Accordingly, in addition to system operation design, it is necessary to design executable business processes that encompass committees, decision-making authority, update timing, allocation of roles, training, communication, and responses to psychological barriers.

The fourth perspective is continuous improvement.
In HR and workforce management, talent management differs in nature from time and attendance and payroll. In time and attendance and payroll, there is value in continuously operating processes accurately and stably while complying with laws and systems. Therefore, maintaining accuracy and stability through maintenance and operations preserves value.
By contrast, it is difficult to preserve the value of talent management merely by maintaining and operating the framework designed at implementation. This is because talent management is a core area for connecting people data to decision-making in the practice of human capital management. When management strategy, workforce strategy, and the business portfolio change, the organizational structure, required skills and experience, and critical positions also change. Accordingly, the required people data, business processes, and indicators to be reviewed also change.
The value of talent management therefore lies not in preserving the original design, but in continually revisiting its use cases, data, processes, and KPIs as business and people priorities change.
In practice, however, improvement activities may be scaled back after the completion of a system implementation project, and frameworks for confirming the implementation status and effectiveness of measures may not be sufficiently established. As a result, the frequency of data updates and use declines over time, ultimately creating an "unused framework."

To ensure continuous improvement, a framework for periodically confirming the implementation status and results of measures and linking the findings to improvement is essential. For example, companies must confirm whether business processes such as talent reviews, deployments, development, and succession planning are actually functioning, while continuously evaluating alignment with business and people priorities and the results of measures using key performance indicators. It is also important to identify user issues and improvement requests and determine the next improvement themes.

Furthermore, the environment surrounding talent management continues to change. New workforce management concepts are spreading, including skills-based workforce and organizational management, dynamic workforce and capability planning including Strategic Workforce Planning, internal talent marketplaces, development through experience, and the design of work and roles in the era of AI. Technologies and analytical methods supporting HR and workforce management are also evolving, including generative AI, AI agents, People Analytics, Semantic Layers, and process mining. To ensure continuous improvement, it is necessary to evolve workforce management itself while appropriately incorporating these new issues and technologies.

Achieving continuous improvement also requires an organization that implements improvement measures and a framework for continuous investment decisions. It is effective to combine change management that promotes understanding and behavioral change in the workplace, a Digital Adoption Platform that supports sustained adoption, and Business Process Outsourcing and Business Process as a Service that optimize the burden of HR operations. These should be positioned not merely as measures to promote use or improve operational efficiency, but as an implementation platform for continuing to connect people data to decision-making.

If continuous improvement can be ensured, companies can continue to flexibly review the required talent, skills, and workforce initiatives in response to changes in management strategy and the business environment. Talent management is not a framework that ends once it has been implemented; it is important to continuously evolve it as a human capital platform that connects management and the workplace.