A Practitioners Perspective on Implementing an Agile-V Hybrid Model in Complex Engineering Systems

Project management, program management, and systems engineering are tightly linked, particularly in complex, multidisciplinary projects like those found in aerospace, defense, and software development. Although, they serve distinct purposes, where systems engineering focuses on the technical definition, integration, and verification of the product, while project management emphasizes planning, coordination, and control to deliver the product within cost and schedule constraints. Program management complements both disciplines by aligning multiple projects and systems toward broader organizational and strategic objectives.  Essentially, systems engineering provides the “how” of the technical work within the broader project management framework. Conversely, project management handles the “what, when, and who” of the overall project. The systems engineering process provides a rigorous method for large-scale systems integration product development with the intent of reducing complexity and risks (Blanchard and Fabrycky, 2011; Beal and Bonometti, 2006). Systems engineering, an interdisciplinary field of engineering and engineering management, leverages a system thinking philosophy that focuses on the problem of how to specify, design, integrate, and deliver successful complex systems over their product development life cycles (wiki, 2025). The V-Model, originated in 1991 by Forsberg and Mooz, 1991, is commonly used in modern-day systems engineering processes. The V-Model is an interdisciplinary, rigorous approach that starts from customer needs and requirements, proceeds to design synthesis, system verification and validation. Thus, going from system-level to sub-system level to component level, and then finally detail level, while managing technical and programmatic performance (Wu, 2023).

The agile methodology is a project management approach that breaks a project into sprints and emphasizes continuous collaboration and improvement throughout the project lifecycle. Teams follow a cycle of planning, execution and evaluation (Atlassian, 2024). The agile concept originated in manifesto of agile software development, developed by a consensus of 17 programmers on how to work more efficiently than traditional approaches under uncertain and dynamic conditions (Beck et al., 2001). Typical agile approaches include Lean, Extreme Programming (XP), Scrum, and Kanban; although, Scrum is the preferred methodology due to its preferred integration and alignment with hardware and physical product development (Cooper & Sommer (2016), which is normally used within the systems engineering ‘V’ construct. Lean, focuses on maximizing customer value by eliminating waste and optimizing value stream development flow. Extreme Programming (XP) focuses on development through continuous integration, test-driven development, and close customer collaboration. Scrum is a time-boxed iterative framework that places work into sprints with pre-determined durations along with ceremonies to support incremental product delivery. Finally, Kanban is a visual workflow management method that limits work-in-progress to improve product development workflow, predictability, and continuous delivery.

Modern evidence shows that Agile methods, which have typically been used primarily in software development, can be merged with traditional project management methods to improve the processes. The aerospace and defense sectors, along with advanced manufacturing industries, are now adopting hybrid project management systems that combine the adaptability of Agile with traditional project management discipline. The research community, together with practitioners have shown increasing interest in Agile Hybrid approaches during the last ten years such that the hybrid models are becoming more prevalent globally. The practical implementation of these models faces multiple challenges due to integration complexity, cultural resistance, backlog management, and constraints of physicality. Current research on hybrid frameworks focuses on software and hardware development, yet it fails to explain how these models operate in complex engineering domains, which involve hardware-software integration, long supply chains and strict regulatory frameworks. Our research investigates how subject matter experts view a proposed agile V-Model framework, which combines agile principles with the traditional systems ‘V’ lifecycle structure. The research employed a mixed-methods approach to gather interview data from 20 industry experts who work in systems engineering, program management, and academic disciplines within aerospace, military and academic fields. The research evaluates how participants understand the proposed model through its strengths and weaknesses and its potential for integration. The research uses descriptive statistics together with thematic analysis to gather evidence about how practitioners understand and would execute the agile-V model in real-world scenarios.

The agile systems engineering V-Model and agile hybrid project management processes have their own benefits and challenges. Based on the literature of each of these methods, an overview of each of the approaches with their key benefits and challenges is shown below in Figure 1.

Figure 1 – Agile Hybrid Benefits and Challenges

The literature review identified three agile hybrid models that integrate agile with traditional methods for product development management, as summarized in Figure 2. The literature review was developed specifically around Agile Hyrbid models in hardware-centric development environments. Search keywords included of agile, hybrid project management, Agile–Waterfall integration, systems engineering, hardware development, & regulated environments, A broad range of scholarly sources were used during the literature review. These include scientometric databases (Scopus, EBSCO, and JSTOR), publishers’ electronic libraries (IEEE Xplore and ScienceDirect), and researchers’ social networks. The review specifically emphasized tangible hybrid models, focusing on literature that contained practitioner-centric integration of Agile and traditional methods. The literature review research methodology is depicted in figure 3. The findings show that Agile Hybrid models provide benefits with improved time-to-market metrics, team communication and cohesion, and flexibility. Conversely, showing challenges with management support, integration guidance, and lack of model validation. These concepts are excellent in theory at a conceptual level, but the proposed Agile V model dives into greater details and provides tools that can be used for project planning and execution in complex product development projects.

Figure 2 – Agile Hybrid Model Comparison

Figure 3 – PRISMA Diagram

Modern-day studies circa 2020-2025, demonstrate that hybrid implementation in complex engineering systems face challenges with theoretical models and actual deployment methods. The three models introduced in Figure 3 provide excellent and successful methods for uniting iterative and gated development, yet they remain at the theoretical stage. The models do not convey methods on how iterative sprints relate to V-Model (or Traditional/Waterfall) gates. Additionally, the constraints of physicality are often brought up ( Cooper & Furst (2023), Cooper & Sommer (2016), Salvato & Laplume (2020)), but do not offer a solution on how to manage the challenge. This research selected the Agile V-Model as the hybrid framework to implement and investigate. This model was chosen because it directly resolves the identified gaps by providing:

• A practical mapping of sprints to system lifecycle phases and milestones via the Agile V Overlay (a visual tool for planning and execution) (Berry & Allada, 2025)..
• A centralized artifact management system via the Comprehensive Adaptive Backlog (CAB), linking iterative outcomes to gated deliverables (Berry & Allada, 2025).

The Hybrid V model describes a method of evolving from digital artifacts to physical prototypes, to qualified hardware units. The current research lacks experimental evidence that demonstrates how hybrid frameworks perform in different hardware-dependent system scales.  The Agile V-Model establishes a flexible framework that merges agile development cycles with the systems engineering V-Model governance structures to resolve many currently identified implementation challenges. The Agile V Overlay serves as a visual tool to connect sprints with system lifecycle phases, while the Comprehensive Adaptive Backlog (CAB) operates as a central database that links sprint outcomes to milestone-based artifacts (Berry & Allada (2025)). The Agile V-Model enables teams to work adaptively while providing executives with program-wide visibility through its connection of sprint activities to systems engineering milestones, which resolves the communication and planning issues found in past hybrid approaches (Berry & Allada (2025). The model provides both theoretical integration of agile and V-Model principles and a functional framework for project execution and lifecycle monitoring. Our research investigated how the model worked and assessed professional feedback to establish its effectiveness for complex engineering projects under regulatory oversight.

The Agile System V-Model combines the Agile V Overlay with the CAB to create a practical framework for product development. The V Overlay shows how agile sprints integrate within the Systems-V timeline structure. The CAB serves as the core organizational unit that enables project and sprint planning activities that executives often feel are lacking in agile hybrid models. Iteration, Innovation, and Objective-Driven execution are the three main pillars of the Agile V Model. The integration of these components provides a structured method for product development. Iteration is achieved through the CAB management, Sprint Reviews, and Systems ‘V’ integration. Secondly, Innovation is achieved by identifying new methods of developing backlog artifacts, improving development efficiency, and advancing new technology to improve system development. Lastly, an Objective-Driven environment is provided stemming from the systems ‘V’ framework, which utilizes Gate Reviews to drive execution rigor, improve leadership communication, and overall Period of Performance (PoP) Management while executing in a Sprint-to-Sprint Agile framework Berry & Allada (2025). Figure 4 highlights the model’s key components, which were derived from practitioners’ experience and the literature review.

Figure 4 – Agile V Core Concepts

The Agile V Overlay Model presents a visual representation that merges the Systems V Model with the Sprint cycles throughout the product development lifecycle. This tool allows for improved strategic planning and resource management, along with risk and opportunity management (Berry and Allada, 2025). The extended planning cycle enables agile planning of work execution to align with traditional systems engineering milestones that are seen downstream of the product development lifecycle. The hybrid approach demonstrates potential to improve systems engineering concepts through the combination of adaptable methods with structured milestone-based approaches. Additionally, the overlay model adds value to test and verification teams by specifically aligning development sprints with downstream verification and validation activities. By identifying visibility of test readiness relative to Systems V milestones, the approach supports earlier test planning, improved traceability between requirements, design, and verification artifacts, along with reducing late-stage integration and test risk. Prior literature emphasizes that early and continuous test involvement is critical to controlling cost, schedule, and defect escape in complex systems (Forsberg et al., 2005). Within the Systems Engineering V model, test teams are integrated into sprint planning and milestone alignment, allowing verification considerations for maintaining the structure of downstream test gates. In summary, the overlay model is used to lay out the engineering lifecyle while tying Systems V gates to Sprints for planning purposes. This provides a mechanism to track progress and as the project progresses. Sometimes, project progress gets lost in sprints while losing sight of the big picture (Cooper (2016). This overlay picture is helpful to the executive and development teams. The Agile Overlay is depicted in Figure 5.

Abbreviations

ATP: Authorization to Proceed
CDR: Critical Design Review
LLPL: Long Lead Parts Procurement Start
MRR: Manufacturing Readiness Review
PDR: Preliminary Design Review
PIC: Program Integration Cycles
POD: Parts on Dock
SRR: System Requirements Review
SVR: System Verification Review
TRR: Test Readiness Review

Figure 5 – Agile V Overlay

The Comprehensive Adaptive Backlog (CAB) functions as a key integration tool between agile and traditional systems engineering practices in the Agile V Hybrid Model to deliver a flexible yet organized project management system. The CAB defines V milestones that consist of Authorization to Proceed, System Readiness Review (SRR), Preliminary Design Review (PDR), Critical Design Review (CDR), Manufacturing Readiness Review, Test Readiness Review (TRR), Systems Verification Review, and Delivery within the agile framework. The tool develops and monitors all backlog artifacts which fulfill compliance and regulatory needs throughout the Performance of Performance (PoP) and groups them according to sprint target dates, which align to gate reviews. The CAB serves as the core element of the Agile V-Model by creating a flexible database which expands the standard agile backlog to support hardware-based and regulatory requirements. The system captures all engineering artifact deliverables which include digital models, engineering drawings, design information, test plans, test reports, compliance documentation, and certification evidence. A brief overview of the CAB is depicted in Figure 6.

Figure 6 – Comprehensive Adaptive Backlog (CAB) Overview

5.1 Research Design

The research design used a sequential explanatory mixed-methods approach to study practitioner views about the Agile V-Model framework through both quantitative and qualitative data collection methods. The research design started with quantitative data collected to understand participant familiarity and perspectives at a general level before moving to qualitative interviews that explained observed patterns. (Dawadi et al., 2021) states that a mixed-methods approach is valuable because it enables researchers to address complex social phenomena by integrating both quantitative and qualitative data, thereby providing a more comprehensive understanding. Additionally, the approach allows for simultaneous confirmation and explanation of findings, facilitating the construction, confirmation, and theorizing of results through integrated data analysis (Dawadi et al., 2021). Mixed methods also help explain contradictory outcomes by combining diverse data sources, which enhances the credibility of the findings (Dawadi et al., 2021). Quantitative data contributes to the narrative by enabling generalizations to larger populations, while qualitative data provides depth through detailed understanding of participants’ experiences (Dawadi et al., 2021). Moreover, triangulation of data sources enhances the validity and reliability of results, as the strengths of one method compensate for the weaknesses of the other, ultimately leading to more credible and robust conclusions (Dawadi et al., 2021). The practitioner interviews were conducted following an introductory briefing on the Agile V model. In some cases, the respondents completed the interview questions during the briefing session, while others reviewed the questions and submitted their responses at a later date. A total of 20 practitioner interviews were conducted.

The Likert grading scale was used as the measurement tool for this study since it offers several key benefits for quantitative research analysis. The Likert scale allow for the measurement of underlying factors by aggregating responses across multiple individual questions, enhancing reliability and validity of the measurement compared to single-item measure. By averaging these item responses, Likert scales approximate interval-level data, allowing for the application of parametric statistical tests that require quantitative data (Harpe, 2015). Additionally, they provide adaptability to various response formats beyond agreement, such as frequency or importance, while still maintaining the advantages of aggregation of standardized response categories (Harpe, 2015). This response technique facilitates ease of interpretation for both respondents and researchers. Lastly, Likert scales are broadly acceptance for their understanding and long-standing use, contributing to standardized data collection methods across studies (Harpe, 2015). It should be noted that the survey and rating data used to support this analysis are non-parametric in nature, reflecting ordinal response scales versus interval measurements. As a result, interpretation of the data should require more careful statistical inferencing. Avoidance of assumptions associated with parametric distributions should be considered. Research shows that non-parametric data are well suited for exploratory and perceptional studies, but should be recognized with caution regarding generalizability and statistical power (Conover, 1999).

A qualitative descriptive approach was used to analyze feedback from 20 Subject Matter Experts (SMEs) regarding the proposed Agile V hybrid model. Responses were coded and interpreted using thematic analysis, following the six phases outlined by Naeem et al. (2023). The step-by-step systematic thematic analysis method presented by Naeem et al. (2023) is a structured, six-stage approach designed to enhance rigor and depth in qualitative research by providing instruction and guidance from raw data to the development of a conceptual model. The method consists of transcription and familiarization, keyword selection, data coding, theme development, conceptualization through interpretation, and finally, the creation of a conceptual framework. The interview results were captured and organized in a framework matrix structured around the interview questions to compare patterns of agreement and divergence between the 20 participants. Themes were inductively developed from the data, ensuring that the findings were based on participant perspectives rather than pre-defined categories as typically completed when deductively developed. The Qualitative Research Analysis process used is identified in Figure 7.The interview assessment of the Agile–V Hybrid Model used a qualitative thematic analysis approach following Braun and Clarke’s (2008) and Naeem et al. (2023) six-phase framework for identifying, analyzing, and reporting patterns. Braun and Clarke (2008) and Naeem et al. (2023) have a similar core foundation in thematic analysis as an iterative, flexible method for identifying themes in qualitative data. Both provide a structured six-step process that begins with data familiarization and immersion, progress through coding and theme development, and concludes with refinement and reporting or synthesis. Naeem et al. explicitly draws from Braun and Clarke’s foundational framework, adapting their six phases as a starting point but extending it into a more prescriptive, outcome-driven protocol for theory generation. The data was analyzed using a multi-level thematic analysis approach (Braun & Clarke, 2008), which was organized into themes, sub-themes, and conceptual categories. Secondly, a structured thematic matrix was used to align themes with the research questions, similar to the hierarchical coding method outlined by Naeem et al. (2024).

Figure 7 – Qualitative Research Analysis Process

5.2 Research Setting and Participants

Participants were drawn from engineering and management professionals working within aerospace, defense, and academic sectors—domains where structured systems engineering processes are prevalent. The sample was selected using a purposive sampling strategy to ensure representation across disciplines familiar with both agile and traditional gated methodologies. (Patton, 2015) states that purposive sampling is key in qualitative research strategies. It highlights that purposive sampling focuses on selecting small, information-rich cases for in-depth understanding rather than aiming for statistical generalization, which contrasts with quantitative probability sampling (Patton, 2015). Overall, purposive sampling is portrayed as essential to qualitative inquiry, emphasizing depth and relevance over representativeness (Patton, 2015). A total of 20 practitioners participated in the study. All participants had at least 2 years of professional experience and direct involvement in systems development and/or project management, with prior experience using Agile methods. The diversity of participants allowed for cross-functional insights from software, hardware, and systems integration perspectives. Participants were identified through professional networks, onsite work solicitations, and general networking.

5.3 Overview of Interview Questions

The Introductory questions (one through –four) collected data about participants’ professional background and work experience through questions about their job role and years of experience in systems engineering or project/program management with Agile, and their understanding of Agile Hybrid models and their current industry sector. The Research Alignment section (five through–nine) evaluated how well the model defines a “Done Sprint” and how it unifies executive management with product development, and merges Agile and Gated approaches, explains backlog content and structure, and enables traditional gated planning to work with Agile sprint planning. The Relevance section (questions 10 through 13) evaluated how well the model explains its concepts, whether it solves typical problems in participants’ work, and how well a development backlog functions throughout the entire developmental period. The Feasibility section (questions 14 through 17) evaluated the model’s ability to simplify operations and its implementation feasibility, system integration capabilities, and its potential to reduce costs and resources. The Impact section (questions 18 through 21) evaluated the expected results of successful implementation and the positive effects on stakeholders, and the potential obstacles and disruptions that could occur. The Perceptions and Experiences section (questions 22 through 24) gathered participants’ general opinions about the model, their Agile-related challenges, their knowledge of model deployment scenarios, and their assessment of both beneficial and detrimental aspects of the framework. A total of 24 semi-structured interview questions were asked of all 20 participants; however, only a subset of these questions was used in the results assessment. The down-selected questions were ones that provided the most relevant insights aligned with the study’s research objectives and allowed for consistent themes across multiple participants.   The questionnaire used to collect data on the Agile V-Model’s application and effectiveness is provided in Annex A. It includes items evaluating perceived benefits, implementation challenges, generalizability across project scales, and alignment with regulatory oversight in complex engineering systems.

6.1 Demographics

6.1.1 Economic Sectors

In this mixed-methods study, qualitative data was gathered through semi-structured interviews with 20 domain experts to complement the interview component, ensuring a robust triangulation of perspectives on the proposed Agile V framework. Participants were purposively sampled from relevant sectors to capture diverse insights into model applicability, challenges, and outcomes. The sectoral distribution, reflecting the interdisciplinary nature of aerospace engineering, is detailed below. This composition (70% from core aerospace and defense sectors) underscores the sample’s alignment with the research focus, enabling nuanced exploration of efficiency gains and implementation barriers in high-stakes engineering environments. All participants held mid- to senior-level roles (e.g., systems engineers, program managers) with 10+ years of experience, enhancing the credibility of thematic findings.

Figure 8 – Economic Sectors

6.1.2 Years of Agile Hybrid Experience

The interview results showed that participants had between one and 16 years of work experience in either systems engineering, project/program management, and/or academia. This dataset consists of 20 responses to the question: “How many years of experience do you have in systems engineering and/or project/program management using agile?” Each value represents the self-reported years of experience for an individual respondent. The data ranges from two to 16 years, indicating a group with generally moderate experience levels, but with notable variation.

Figure 9 – Years of Experience

Statistical analysis on experience levels with the 20 respondents reveals a moderately experienced workforce, with a mean of 8.45 years and median of 7 years, indicative of mid-career practitioners. The distribution showed multimodal peaks at 3, 5, 7, and 10 years, suggesting clustered entry indicating early agile adopters and recent converts. While a pronounced right skew (standard deviation 7.34 years) is driven by high-experience outliers (12 to 16 years, comprising 20% of the sample), elevating the central tendency; conversely, 30% reported equal to or less than3 years, highlighting a novice subgroup. A normality test was completed using the Shapiro–Wilk test with n= 20, which showed a rather large deviation from normal distribution (W = 0.72, p < 0.001).

6.1.3 Familiarity of Agile Hybrid Models

Interview results regarding self-rated familiarity with agile hybrid models among 20 professionals in aystems engineering and project/program management indicated moderate overall familiarity, with a mean score of 3.1, a median and mode of 3 (Moderately Familiar, reported by 50% of respondents), and a standard deviation of 0.89, reflecting low variability. Only 30% (6 individuals) rated themselves as Mostly Familiar (level 4; n=5) or Very Familiar (level 5; n=1), underscoring limited expertise despite broad exposure, potentially signaling a transitional phase in hybrid model adoption, where the majority (80%) exhibited at least some familiarity (levels 2 through 5). This distribution, although skewed slightly rightward, highlights most of the recipients were moderately or very familiar with the hybrid model. It should be noted that additional subjects were screened out of interview process due to their inexperience with hybrid models. This study was directed around those who have direct experience with the hybrid philosophy, which appears to still be a novelle concept. A normality was completed using the Shapiro–Wilk test with n = 20, which indicates a deviation from normal distribution (W ≈ 0.90, p < 0.05); therefore, familiarity ratings were treated as ordinal data and interpreted using medians and response distributions.

Figure 10 – Familiarity with Hybrid Models

6.2 Quantitative Results

The quantitative feedback from the 20 practitioner interviews indicated generally positive perceptions of the Agile–V Hybrid Model; although, there are some areas that could benefit from further refinement. First, the overall impression of the model was strongly favorable, with 14 participants rating the model effective and 3 highly effective, signaling that practitioners see substantial value in the framework, regardless of some areas requiring additional refinement. It should be noted that none of the 20 practitioners had direct experience applying the Agile–V Hybrid Model in real world scenarios. They evaluated the framework using expert professional judgment, based on prior experience with Agile, gated, and hybrid product development processes. When asked about the practicality of implementation, responses were mixed as six participants rated it as highly effective and six as satisfactory, but six also noted improvement was needed, highlighting organizational and cultural factors as potential barriers to adoption. Responses on the CAB showed a more balanced view with eight participants rating it as satisfactory and another eight as effective, three rated it highly effective and one noted improvement needed, indicating that while the concept is appreciated, practical implementation details may require refinement. The most favorable feedback was synchronizing executive management with product development and team alignment. The majority (12 effective, five highly effective) felt the model successfully aligns cross-functional teams and leadership, which reflected strong approval of its visual overlays and CAB structure.

Regarding the concept of a “Done Sprint”, most respondents rated the model as Satisfactory (six) or Effective (ten), suggesting that the framework provides a reasonable understanding of how completed work should be evaluated in a hybrid environment, though some perceived/anticipated ambiguity remains around detailed documentation and backlog management philosophies.

In summary, these responses align with qualitative interview insights, showing that the Agile V Model is generally well-received for clarity, alignment, and adaptability, while further model usage guidance, examples, and training could improve overall model execution of complex engineering projects.

Figure 11- Quantitative Results

6.3 Qualitative Results

Across all questions, participants highlighted key advantages including enhanced visibility, stakeholder communication, milestone readiness, and integration of hardware/software workflows. While conversely, participants also pointed out applicable concerns with the model—most notably unclear definitions of “Done Sprints”, limited real-world examples, process complexity, cultural adoption, and the need for training. Figures 13 through 19 provide the Thematic analysis based on each question, while providing a general description of some of the key responses from the participants. Although a total of 24 interview questions are listed in Annex 1, only seven were included in the qualitative results presented in this study. These seven questions were selected based on their relevance to the research objectives and their contribution to the validation of the proposed model.

Q1 – Are any components of the model unclear or difficult to understand? If so, please describe

Figure 12 – Question 1 Thematic Analysis of Practitioner Feedback for Model Validation

Q3 – Can this model streamline existing processes or improve efficiency? If so, please describe

Figure 13 – Q3: Thematic Analysis of Practitioner Feedback for Model Validation

Q5 – What outcomes do you think this model could achieve if implemented effectively?

Figure 14 – Q5: Thematic Analysis of Practitioner Feedback for Model Validation

Q6 – Would this model impact stakeholders positively, and if so, how?

Figure 15- Q6: Thematic Analysis of Practitioner Feedback for Model Validation

Q9 – Is there anything else you would like to share about the model or its potential applications?

Figure 16 – Q9: Thematic Analysis of Practitioner Feedback for Model Validation

Q10 – Can you provide some good aspects of the model/framework?

Figure 17 – Q 10: Thematic Analysis of Practitioner Feedback for Model Validation

Q11 – Can you provide some not-so-good aspects of the model/framework?

Figure 18 – Q11: Thematic Analysis of Practitioner Feedback for Model Validation

Breaking down the thematic assessment further into a Framework Matrix, the respondents viewed the Agile–V Hybrid Model as a promising framework that bridges agile and systems engineering philosophies. Some key attributes identified consist of promising gains in vertical and horizontal team communication, visibility, and milestone readiness. Participants felt the strengths consisted of their clear visualization, iterative milestone support, and ability to align technical and managerial functions with respective teams and leaders. Conversely, several respondents emphasized the need for greater operational detail, real-world examples, and training to ensure consistent understanding. Mixed Opinions were identified regarding process streamlining and integration, as efficiency benefits appear dependent on regulatory and contractual constraints and requirements. Meaning, organizations with less regulatory requirements could benefit more positively versus highly regulated industries while using the model. Furthermore, respondents had strong optimism that leadership support and policy flexibility could potentially improve key performance metrics, such as schedule, cost, and alignment. To ensure successful implementation it was noted that addressing cultural readiness, training needs, descriptive process definition, and scalability challenges will all need to be solidified. In summary, the model was reflected as a solid foundation merging agile adaptability with V-Model rigor, but it needs continued refinement, empirical validation, and tailored guidance for true effectiveness in engineering and program management environments. A summary can be reviewed in the Framework Matrix identified in Figure 20 below.

Figure 19 – Framework Matrix

Concluding with the philosophy of Naeem et al. (2023), Figure 14 provides a Hierarchical Taxonomy of the Thematic Analysis assessment.

Figure 20 – Conceptual Model

7.1 Summary limitations and assumptions

This study has several limitations and assumptions that should be considered when evaluating the results. First, the study is based on a purposive sample of 20 SMEs drawn primarily from aerospace, defense, and academic environments which constrain statistical generalizability to similar regulated domains. Second, it should be noted that none of the participants had direct experience implementing the proposed Agile–V Hybrid Model. The quantitative and qualitative results reflect expert professional judgment and perceived effectiveness based on conceptual review and prior experience with agile, gated, and hybrid development methods. Several assumptions also apply, such as Likert-scale responses were treated as ordinal and non-parametric, and results are interpreted as perceptional indicators versus precise interval measurements. Additionally, the experience with agile dataset includes an outlier of 35 years, which impacts the mean and variability and contributes to non-normality. Lastly, the research assumes that participants’ experience with agile hybrid concepts allows value added evaluation of the Systems-V model. Respondents’ responses may still be influenced by sector-specific norms, organizational culture, and their preferred project management process with Gate management, which can introduce bias.

7.2 Significance

The significance of this work contributes meaningfully to the hybrid agile literature by moving beyond conceptual integration and providing practitioner-centric planning and execution tools in complex engineering systems. Prior hybrid approaches, such as Agile–Stage-Gate and other gated approaches have demonstrated value; however, practitioner feedback continues to highlight persistent issues including integration complexity, lack of detailed guidance for execution, ambiguity in backlog philosophy, and uncertainty in sprint to milestone correlation in regulated environments. This study advances the engineering discipline via evaluation of a hybrid framework that addresses these gaps through two model components: (1) the Agile V Overlay, which visually maps sprints to Systems Engineering lifecycle phases, and systems engineering milestones, and (2) the Comprehensive Adaptive Backlog (CAB), which manages backlog development to include compliance and gate deliverables typically required in highly regulated environments. The study also contributes new evidence by offering qualitative themes and quantitative perception ratings from SMEs who work in environments where Agile hybrid models could be used. Collectively, the results provide practitioner-centric response to refining the Agile–V Hybrid Model.

7.3 Implications for Industry

For industry implications, the findings suggest that the Agile–V Hybrid Model has strong potential to improve cross-functional integration, executive visibility and insight, and backlog progress tracking. In regulated environments, hybrid approaches contain implementation challenges due to unclear (or confusing) relationships between sprint competitions and gate input/output criteria. Practitioner/SME feedback shows that the Agile V Overlay and CAB have potential to reduce this ambiguity by clarifying how sprint goals translate into gate artifacts. This can improve governance and decision making quality at gate reviews. As a result, organizations seeking agile benefits without compromising compliance may use the model as a planning, execution, and communication tool to align teams around milestones and required artifact maturity. Additionally, the implications for test organizations are particularly pertinent. The Systems Engineering V model inherently focuses on verification and validation, yet agile implementations frequently struggle to integrate test planning early enough to avoid late-stage integration risk. By mapping sprint cycles to V-model phases and gate readiness (e.g., TRR/SVR), the Agile V Overlay supports earlier test team planning & involvement. Also allows for improved scheduling of test-centric deliverables (E.G. test plans, procedures, trace matrices, verification evidence, and readiness criteria). The CAB further strengthens test execution by providing a tool to manage test-centric artifacts as backlog items rather than downstream paperwork/bureaucracy. This allows improved requirements traceability, clearer verification evidence, earlier identification of test dependencies (equipment, facilities, test articles), and reduction of “test surprises” that can cause schedule slips. In short, the model allows test and verification teams early involvement as contributors throughout the lifecycle versus downstream recipients of inadequate designs.

7.4 How to raise awareness

Raising awareness of the Agile–V Hybrid Model can be achieved beyond publication by contacting and targeting the organizations that typically guide lifecycle governance, systems engineering practice, and test culture. First, additional dissemination beyond publication could identify and contact systems engineering and/or program management audiences through venues where Agile hybrid models are taught/discussed and possibly adopted. Some of which could be professional societies, practitioner-based conferences, and industry working or focus groups. Additional venues for awareness could include invited talks and workshops at systems engineering forums, tutorials aligned with program management and systems engineering training tracks; and practitioner-focused articles or webinars that translate the model into repeatable adoption steps. Additionally, awareness could be distributed through education and training channels. The model could be incorporated into graduate-level engineering management and systems engineering curricula as a case-based module covering sprint-to-gate planning, CAB artifact management, and milestone readiness. In some graduate level classes, instructors provide invite practitioners to provide short lectures on their expertise from industry. This model could be presented in that environment as a short lecture in the fields of project management and/or systems engineering. Short-course training at local community colleges targeted at industry could also be an option targeting engineers, program managers, systems engineers, and testing leaders.

7.5 Future Work

The areas for improvement on the model were in the areas of , adding operational detail (e.g., clear “Definition of Done,” backlog structure, sprint-to-gate linkage), developing training materials to ensure consistent understanding, clarifying CAB prioritization and entry/exit criteria, and enhancing scalability for long, multi-phase projects. The Agile V hybrid model shows strong positive results for integrating agile methodologies with the traditional Systems Engineering V-Model, but stakeholder feedback indicates several areas for refinement. Key suggestions for improvements include elaborating on “Definition of Done,” with backlog items, and milestone artifacts; providing practical examples that map sprints to V-Model phases and Program Integration Cycles; enhancing process by defining CAB priorities, entry/exit criteria, and sprint guidelines; and strengthening integration to show how iterative sprints can support multiple gate objectives. Additional refinements involve developing comprehensive training and stakeholder coaching, addressing scalability for long-duration projects and emergent work, refining visuals and documentation for clarity, and establishing empirical metrics to validate effectiveness. Implementing these refinements is expected to improve model clarity, usability, stakeholder engagement, cross-functional alignment, and measurable efficiency, schedule, and cost outcomes. Lastly, pilot programs and iterative feedback will be critical for validating these improvements and ensuring the model’s practical applicability.

Based on the feedback from the 20 practitioner interviews, the Agile–V Hybrid Model provides a practical approach to address key challenges identified during the interviews. Participants indicated that the model offers clarity in backlog management, improving understanding of what constitutes a “Done Sprint” and how work items are tracked, which directly addresses challenges related to backlog management philosophy. The integration of agile sprints with the V-Model overlay diagram helps resolve integration complexity by explicitly linking iterative development cycles with traditional gated processes, providing a clear roadmap for aligning cross-functional teams. Several respondents also highlighted that the model facilitates cultural adaptation, as the visual overlay and CAB artifacts offer transparency and alignment that can reduce resistance to adopting hybrid models. Additionally, the model accounts for constraints of physicality by providing structured guidance for evolving digital artifacts into physical prototypes and final products, thereby bridging conceptual design with tangible system outcomes. Overall, practitioners noted that, unlike prior high-level conceptual frameworks, the Agile–V Model delivers actionable details and tools that support planning, execution, and governance of complex new product development initiatives, improving both adaptability and predictability in practice. In summary, findings show strong potential to improve efficiency, communication, and alignment in complex engineering and product development environments. From the 20 participants, it was highlighted that the model’s strengths were, visually linking sprints to gated milestones, encouraging communication across technical and management levels and providing structure while retaining adaptability in regulated settings.

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Survey and Interview Instrument

Participant Background

1.What is your current job title/role?
2.How many years of experience do you have in Systems Engineering and/or Project/Program Management using Agile?
3.How familiar are you with Agile Hybrid Models?
4.What industry or sector do you primarily work in?

Research Alignment

5.In your view, does the model identify what a “Done Sprint” is in a hybrid methodology?
6.Do you find the Agile V model’s approach to synchronizing executive management with product development effective in ensuring alignment across teams?
7.Does this model help integrate the interactions of the two philosophies (Agile vs Gated)?
8.Does this model answer the questions: What should be in a backlog? What does a backlog look like in the hybrid model?
9.Does this answer how one integrates the planning process of a traditional gating model with the sprint planning model of Agile (plan on the fly)?

Relevance

10.Are any components of the model unclear or difficult to understand? If so, please describe
11.Does this model address a problem or gap you frequently encounter in your work? If so, please describe
12.How effective do you think a development backlog for the entire developmental phase, as proposed in the Sprint V model, would be in your environment?

Feasibility

13.Can this model streamline existing processes or improve efficiency? If so, please describe
14.How practical is the implementation of this model in your organization/field?
15.How practical is it to integrate this model with current systems or workflows?
16.Do you see potential for cost or resource savings with this approach? If so, how? If not, why not?

Impact

17.What outcomes do you think this model could achieve if implemented effectively?
18.Would this model impact stakeholders positively, and if so, how?
19.Could the model unintentionally cause challenges or disruptions?

Perceptions and Experiences

20.What is your overall impression of the model/framework?
21.What are some pain points that you experience using Agile?
22.Is there anything else you would like to share about the model or its potential applications?
23.Can you provide some good aspects of the model/framework?
24.Can you provide some not-so-good aspects of the model/framework?