Artificial Intelligence–Empowered Consultants and Cross-Domain SaaS Governance in the Public Sector

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Artificial Intelligence–Empowered Consultants and Cross-Domain SaaS Governance in the Public Sector Raj Mehta Independent Researcher Columbia, SC, USA ---------------------------------------------------------------------***--------------------------------------------------------------------1.2 Objectives Abstract - As cloud-native apps reshape the working foundation of public agencies, the strategic positioning of consultants has also changed. Domain Expert Resident Consultants (DERCs), seen in Mehta, R. (2025), have been traditional internal drivers of SaaS implementation, fostering policy synchronization, trust generation, and technical conformance. In the companion study here, we explore how AI technologies become part of these consultancy functions, giving way to the development of AI-powered consultants.

These experts leverage technologies such as predictive analytics, automated compliance verifiers, and data interpretation frameworks to enable governance in government infrastructure, construction, and service agencies.

Based on recent scholarly literature, this paper provides a framework for AI's strategic importance for SaaS governance with the focus on interdisciplinary skill sets necessary to close regulatory, technical, and operational gaps. Our evidence indicates that hybrid governance—coupled human intelligence with AI—is essential for robust, transparent, and scalable SaaS adoption in the public sector

This paper expands the analytical frame to consider how artificial intelligence transforms the DERC model into a hybrid framework of AI-empowered consultancy. It aims to:

1.1 DERCs and the Institutional Challenge

Analyze their impact across infrastructure, construction, and agency contexts.

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Articulate the role of cross-domain expertise in integrating AI systems with public policy, compliance, and stakeholder engagement.

While Mehta, R. (2025) demonstrated that Domain Expert Resident Consultants (DERCs) serve as embedded facilitators of compliance, institutional continuity, and digital trust in public sector SaaS adoption, the expanding complexity of multi-stakeholder platforms now necessitates hybrid governance structures.

Mehta, R. (2025) presented the Domain Expert Resident Consultant (DERC) as an embedded actor within government SaaS migration projects. DERCs facilitate alignment between platform capabilities and institutional mandates by translating compliance protocols, mitigating risk, and building stakeholder trust. However, as public sector technology landscapes become more interdependent, datarich, and rapidly evolving, human consultants alone are insufficient to guarantee resilience and policy alignment at scale (Scholl & Scholl, 2021; Lindgren et al., 2019).

Impact Factor value: 8.315

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As public institutions adopt AI tools for decisionmaking, compliance, and optimization, regulatory and operational risks increase (Drechsler & Gregor, 2021; Eaves & McGuire, 2023). Successful integration demands interdisciplinary professionals who can not only interpret AI outputs but align them with regulatory requirements and citizen-facing objectives. AI-empowered consultants must operate at the intersection of data science, public administration, and sector-specific operations (Goyal & Joshi, 2020). This hybrid profile reconfigures the SaaS governance landscape from human-centric facilitation to algorithmically supported decision-making ecosystems.

1.INTRODUCTION

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Define the functions and competencies of AIempowered consultants in SaaS governance.

1.3 Rationale for Cross-Domain Integration

Key Words: AI-empowered consultants; SaaS governance; domain expertise; cross-sector integration; digital transformation; algorithmic compliance; government infrastructure; construction technology; public administration; legacy system migration.

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In the construction sector, AI-powered consultants utilize Building Information Modeling (BIM) coupled with artificial intelligence in order to manage projects more efficiently.

They use these technologies in conjunction to reduce conflicts related to scheduling and assess structural hazards in real time. Computationally empowered drones also supplement this process by performing on-site compliance checks, creating marked-up risk scores that are directly fed

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into project dashboards for quick access (Babajide et al., 2023).

time. Computer vision systems attached to drones now inspect compliance on-site and feed annotated risk scores directly into project dashboards (Babajide et al., 2023).

Within the context of public administration, AI systems are revolutionizing compliance processes in government offices.

These systems effectively detect anomalies in citizen service provision, automate tax return validation, and guarantee compliance with regulations like GDPR (Janssen et al., 2022).

Consultants in this context act as top-level monitors of algorithmic operations, ensuring personalized services uphold principles of non-discrimination (Ghosh & Scott, 2021).

In government agencies, AI systems automate compliance workflows—flagging inconsistencies in citizen service delivery, automating tax return validations, and ensuring GDPR alignment (Janssen et al., 2022). Here, consultants act as auditors of algorithmic logic, ensuring that service personalization does not violate non-discrimination principles (Ghosh & Scott, 2021).

Thus, AI-empowered consultants are not simply DERCs using new tools—they are institutional actors who help organizations redesign governance architectures to integrate machine cognition.

Far from being simple technicians with sophisticated equipment, AI-facilitated consultants become key institutional players. They lead organizations through restructuring governance arrangements so they can seamlessly integrate machine cognition to drive both innovation and responsibility in sophisticated operating settings.

Table -1: AI Tools and Functions Across Public Sector Consulting Domains Synthesized from Babajide et al. (2023); Faraj et al. (2021); Drechsler & Gregor (2021).

2. AI-Empowered Consultants: Redefining DERC Roles

The result is enhanced decision-making speed, reduction in manual review cycles, and improved regulatory accuracy across the board (Bradshaw & Millard, 2020).

As governments increasingly integrate artificial intelligence (AI) into critical service delivery systems, a new category of consultants has emerged: AI-empowered consultants. Unlike traditional Domain Expert Resident Consultants (DERCs) who provided institutional continuity during SaaS transitions, these professionals augment their sectoral expertise with AI-driven tools such as predictive analytics, automated regulatory checkers, machine learning classifiers, and decision optimization systems (Bradshaw & Millard, 2020; Faraj et al., 2021).

This table synthesizes AI tool types, their primary functions, and the consulting domains in which they are applied. It draws from recent studies to illustrate how consultants integrate technologies like NLP, predictive analytics, and computer vision across government, infrastructure, and construction settings.

AI-empowered consultants are not mere technical advisors but hybrid actors who co-interpret institutional goals and machine recommendations. Their defining attribute is the capacity to interface between AI systems and domainspecific governance. This includes tuning data models to regulatory expectations, interpreting natural language policies through NLP tools, and simulating compliance scenarios using automated reasoning engines (Drechsler & Gregor, 2021).

In the infrastructure sector, for instance, AI-empowered consultants employ smart sensors and maintenance prediction algorithms to proactively prevent outages, particularly in water, energy, and transportation systems (Bounfour & Ngwenyama, 2021). Their use of reinforcement learning models to optimize service reliability while meeting legal constraints (e.g., carbon targets) exemplifies this transformation.

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Impact Factor value: 8.315

Function

Consulting Domain

NLP-based regulation parsing

Interprets laws into rule logic

Government Agencies

Predictive (ML)

Project risk forecasting and cost deviation analysis

Infrastructure & Construction

Graph-based compliance models

Maps institutional workflows to regulatory frameworks

Cross-sector

Computer vision in BIM

Real-time project monitoring and safety audits

Construction

analytics

3. AI in Government SaaS Migrations AI plays an increasingly pivotal role in modern SaaS migration strategies for public institutions. In legacy modernization projects, where governments shift from monolithic platforms to modular, cloud-based services, AI

In the construction domain, AI-enabled consultants integrate Building Information Modeling (BIM) with AI to reduce scheduling conflicts and assess structural risk in real-

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Tool Type

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augments every stage of migration: from data cleansing and classification to compliance simulation and real-time audit feedback.

public policy, and ethical governance. These professionals must assess model bias, validate algorithmic transparency, and align platform logic with sector-specific outcomes (Drechsler & Gregor, 2021).

3.1 Infrastructure Sector

Table 2. Required Cross-Domain Expertise for AIEmpowered Consulting

Infrastructure projects benefit significantly from AIenabled platforms that interpret sensor data, optimize asset usage, and prevent failure. Governments in regions such as Singapore and Canada now employ predictive maintenance platforms powered by neural networks to forecast road degradation, pipeline leakage, or power grid stress (Alizadeh et al., 2021). The consultants overseeing such projects are trained in regulatory zoning laws and urban planning constraints and act as real-time AI system calibrators.

Table 2 highlights how sector-specific technical tools require corresponding cross-domain expertise for effective implementation. It underscores the hybrid nature of AIempowered consultants who must bridge technical systems, policy mandates, and operational workflows across infrastructure, construction, and government agencies.

Source: Babajide et al. (2023); Ghosh & Scott (2021); Janssen et al. (2022).

3.2 Construction Sector

Sector

Construction governance has adopted AI for tasks such as risk modeling, contract analysis, and project lifecycle monitoring. Consultants in these environments now work with multi-modal datasets: combining video from construction sites, sensor data from IoT helmets, and regulatory texts. Through deep learning models, such as convolutional neural networks (CNNs) for visual inspection and transformer-based models for textual regulation parsing, consultants can provide near-instant recommendations on risk mitigation (Babajide et al., 2023).

Predictive Infrastructure maintenance (ML)

Government agencies increasingly deploy AI for regulatory enforcement and decision-making. In the tax domain, natural language models are used to interpret ambiguous policy clauses, while anomaly detection models uncover fraudulent claims. Agencies such as the Estonian Tax Board and Australia's Digital Transformation Agency now embed AI-empowered consultants into their cloud governance teams to balance efficiency with fairness (Eaves & McGuire, 2023; Janssen et al., 2022).

Civil engineering, labor BIM + AI risk law, and automated engines monitoring

Government

Policy-parsing NLP models

Administrative law, ethical AI, and public service workflows

Such expertise is not purely technical—it is deeply embedded in political and social context. For example, AIgenerated zoning recommendations in smart cities must align with not just planning codes but also neighborhood equity goals. In these settings, consultants must mediate between what the model “recommends” and what the policy “permits” (Desouza & Jacob, 2020).

Cross-domain expertise is now the cornerstone of successful AI integration in SaaS governance. It refers to the ability of consultants to fluently translate between technical, operational, and regulatory domains—without defaulting into any one of them. This skill is increasingly necessary as public sector platforms become multi-layered, incorporating legal mandates, citizen rights, and machine autonomy in equal measure (Saxena & Janssen, 2020).

5.0 Summary of Key Themes This paper’s investigation of AI-empowered consultants within public SaaS governance highlights three strategic shifts from the DERC model presented in Mehta, R. (2025).

First, there is a paradigmatic shift in function: DERCs ensured project continuity through embedded presence, while AI-empowered consultants provide scalable decision support, auditability, and algorithmic transparency. Their value lies not just in knowledge retention but in the ability to

Whereas traditional DERCs operated with vertical sectoral knowledge (e.g., health, law, or infrastructure), AIempowered consultants must possess an integrated understanding across data science, systems architecture,

Impact Factor value: 8.315

Urban planning, environmental policy, and sensor calibration

Source: Babajide et al. (2023); Ghosh & Scott (2021); Janssen et al. (2022).

4.0 Cross-Domain Expertise: Bridging Technology and Context

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Required Cross-Domain Expertise

Construction

3.3 Government Agencies

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monitor and recalibrate system performance at runtime (Bradshaw & Millard, 2020).

Second, AI-empowered consultants enhance governance efficiency by aligning policy logic with automated execution. In tax systems, AI models now process returns in real-time, enhancing throughput and auditability (Janssen et al., 2022). In construction, real-time scheduling via generative optimization has reduced public project delays by over 15% in pilot programs (Babajide et al., 2023).

6.0 Comparative Analysis Regarding the Presence and Absence of Resident Domain Experts (Extended)

DERC-Present DERC-Absent Projects Projects

Source

Average Delay 2.1 months in Migration

7.4 months

UK NAO (2022); GAO (2022)

User Adoption Rate at 6 81% Months

53%

Salesforce (2021)

46%

Cordella & Paletti (2019); GovTech SG (2021)

Projects with Technical Debt 18% Backlog

It is necessary to have a comprehensive evaluation lens when comparing the migrations of SaaS across different administrations. Such studies have been framed using a combination of quantitative measures (such as delay length, cost overrun, and regulatory clearance time) and qualitative indicators (such as internal satisfaction, institutional resilience, and user confidence) in previous comparative research (George et al., 2023; Costa, 2021). In this part, the comparison is conducted in accordance with three primary criteria in order to guarantee analytical coherence:

George et al.

(2023); Australian Department of Human Services, 2021

Compliance Workaround Frequency

Rare (in <10% Frequent of (spreadsheet deployments) use in ~38%)

Stakeholder Satisfaction

Costa (2021); UK “High” in 74% “High” in only Home Office of projects 38% Report (2022)

Table 3. Comparative Metrics – SaaS Projects With and Without DERCs This comparative analysis table presents quantitative performance metrics of SaaS migration projects that included Domain Expert Resident Consultants (DERCs) versus those that did not. Metrics include migration delays, adoption rates, technical debt, compliance workarounds, and stakeholder satisfaction.

DERC Presence Definition: The term "DERC" in this context refers to subject-matter specialists who have been embedded within a public agency for at least one complete project cycle and have been actively involved in decision-making, compliance interpretation, or organizational change facilitation.

Impact Factor value: 8.315

Sectoral Scope: Sectors analyzed include justice, taxation, social services, healthcare, and administrative services—all of which demand complex regulatory and operational alignment.

Metric

6.1 The Methodological Framework

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6.2 Project Performance Metrics

The efficiency of the adoption of software as a service (SaaS) in public sector contexts varies greatly depending on governance models, the preparedness of the organization, and the incorporation of expert human resources. This section provides a systematic comparative study of the outcomes of SaaS migration initiatives that were carried out with and without the participation of Domain Expert Resident Consultants (DERCs). To go beyond anecdotal success stories and discover systemic advantages imparted by DERCs, supported by empirical and evaluative investigations, the objective is to advance beyond the scope of current success stories.

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Temporal Scope: Projects that were started between the years 2015 and 2024, which corresponds to the decade of cloud-first policies that are now in effect throughout OECD nations.

A variety of sources, including comparative surveys (George et al., 2023), audit reports (UK NAO, 2022), case study research (Saberg, 2023), and independent reviews (Costa, 2021) have been utilized to collect the necessary data. Although the table that came before it in section 7.2 provided a summary of numerical deltas, the subsections that followed elaborated on the qualitative patterns and strategic ramifications that were seen across all of the examples.

Third, cross-domain expertise has emerged as the definitive trait of effective consultants. Without the capacity to understand regulation, interpret AI outputs, and interface with both technical teams and policymakers, even the most powerful SaaS platforms risk failure. Institutions now require embedded consultants who combine data fluency with social accountability and legal literacy (Goyal & Joshi, 2020; Eaves & McGuire, 2023).

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The data shows that the inclusion of DERCs correlates strongly with faster delivery, fewer disruptions, and improved user trust. These findings hold across domains—justice, health, taxation, and human services— suggesting that domain expertise offers systemic value beyond sector-specific knowledge.

embedded throughout the migration cycle, they provide institutional continuity, familiarity with compliance policies, and interpersonal credibility — all of which significantly improve internal acceptance of new systems.

A recent study by George et al. (2023) found that public agencies with embedded consultants were significantly more likely to achieve executive alignment and cross-functional participation in digital initiatives. These consultants often act as institutional memory, linking historical system behaviors to modern SaaS capabilities, thereby reducing fear among legacy users and mid-tier managers.

6.3 Technical Debt and the Capability of the System to Adapt Transitions to SaaS in public institutions frequently inherit or increase technical debt, which is defined as the accumulation of rework that is necessary as a result of fast repairs or settings that are not aligned properly. In projects where DERCs were not present, technical teams frequently relied on vendor defaults, which led to the creation of systems that were unable to enforce legally needed controls or procedures. During the CRM migration that Ireland's Revenue Commissioners undertook in the years 2020–2021, for example, the absence of embedded tax policy advisors resulted in the following: 

Misinterpretation of evaluation timeframes and automated correspondence that is prone to incorrect understanding;

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There were violations of the GDPR's logging standards, which resulted in the complete revalidation of audit trails after the launch;

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In the event of an emergency, the application programming interface (API) of the system will be modified to conform to the procedures for data subject access requests.

An illustrative case is the Australian Department of Human Services, which faced sluggish adoption of its CRM system during the first six months of implementation.

According to a 2021 departmental audit, introducing embedded analysts with policy and platform familiarity helped increase onboarding module usage by 22% and reduced manual form submissions by 35%. These outcomes were driven not only by technical fixes but by increased trust in the system’s governance — a direct result of having internal champions with hybrid domain expertise.

Similarly, George et al. (2023) report that organizations lacking DERCs often face higher rates of “informal system bypassing,” with spreadsheet workarounds and unauthorized data silos emerging in nearly 38% of vendor-led projects. This behavior stems from the absence of credible, internally trusted figures who can address both business process change and technical escalation in real time.

In effect, DERCs act as internal ambassadors of the SaaS platform. Their trust capital enables them to explain policy changes in plain language, justify updates with compliance logic, and secure buy-in from departments that would otherwise resist change due to uncertainty or lack of clarity.

The correlation between trust and DERC presence is not incidental — it is causal, repeatable, and now documented across multiple jurisdictions.

The "Workpal" human resource management platform in Singapore, on the other hand, was developed under the direction of resident civil service subject specialists. It utilized AWS Lambda and AWS Config to construct modular policy engines, which were then used to handle leave policies, rotations, and contract variations across more than one hundred agencies. Not only did this get rid of hardcoded assumptions, but it also made policy modifications easier to implement by using infrastructure-as-code models. This made it possible to update legal compliance without having to rebuild the software.

6.5 Long-Term Knowledge Retention and Cost Savings In traditional SaaS deployments, much of the architectural and process knowledge resides with external vendors, often leading to institutional memory loss once the transition team is offboarded. Domain Expert Resident Consultants (DERCs), by contrast, operate as internal stewards of platform logic, compliance configuration, and policy customization — enabling sustainable, long-term operational continuity.

This difference emphasizes how DERCs decrease architectural stiffness and prevent misalignment between legal reasoning and software design, which is a frequent cause of costly rework and failure in public-sector information technology.

6.4 The Trust of Stakeholders and the Buy-In of Internal

A study by Costa (2021) investigating SaaS transitions in European ministries found that knowledge retention was 42% higher in organizations that deployed

Trust and internal buy-in are two of the most intangible yet mission-critical factors in public-sector technology transformations. When resident consultants are

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hybrid in-house–consultant teams. These teams reported reduced re-training cycles and greater consistency in crossdepartmental workflows.

In Ireland, the Revenue Commissioners' CRM rollout faced severe stakeholder disengagement when project leaders failed to integrate tax domain specialists into sprint teams. A post-implementation review by the Irish Comptroller & Auditor General (2021) found that over 22% of helpdesk tickets were related to policy misinterpretation, suggesting a gap between configuration teams and institutional rule knowledge.

In Canada, the Treasury Board Secretariat (TBS) 2022 Digital Operations Strategic Plan highlighted that agencies that internalized technical governance functions (via either permanent staff or embedded consultants) experienced up to 16% reduction in dependency on Tier-3 vendor support after the first year. This translated into faster internal troubleshooting cycles and significantly reduced external billing for advisory support.

Similarly, Zambia’s Public Procurement Authority attempted a complete e-tendering SaaS migration in 2020. A case study published by Transparency International (2022) indicated that the system was eventually suspended due to irregular vendor payments and black-box integrations — both traceable to the absence of internal technical validation capacity.

Additionally, an evaluation by George et al. (2023) found that legacy agencies who onboarded DERCs during their SaaS transition retained over 65% of internal workflows in documentation platforms like Confluence or internal wikis, compared to under 30% in vendor-led transitions. This institutionalization of knowledge prevented duplication of effort and enabled smoother onboarding of new staff.

These cases are not outliers. They illustrate a systemic risk: when technical platforms evolve faster than institutional adaptation, failures are not just technical — they’re policy, audit, and reputational in nature.

From a cost-efficiency perspective, retaining platform and process expertise internally ensures that recurring vendor engagement — for tasks like data schema updates or SLA adjustment — can be minimized. More importantly, it enables public agencies to control their tech stack without relying on external consultants for every system evolution.

To mitigate such risks, several agencies (e.g., UK GDS, US OMB, and Canada TBS) have now begun embedding full-time compliance architects or resident technologists to serve as internal QA and policy interpreters throughout their modernization lifecycles.

While not every project without a DERC fails, the statistical and narrative evidence increasingly supports the argument that the absence of embedded, cross-functional consultants is a leading indicator of SaaS underperformance.

In summary, DERCs help bridge the dangerous postdeployment knowledge gap. Their presence mitigates knowledge attrition and offers long-term operational savings — not through temporary cost-cutting, but by enabling sustainable self-sufficiency in technical governance.

6.7 Summary of Comparative Value The inclusion of DERCs appears to considerably boost the chance of successful SaaS migration in government contexts, according to the research that has been compared across a variety of jurisdictions, platforms, and industries. Not only does their contribution extend beyond specific activities, but it also encompasses a more comprehensive function in enabling:

6.6 Failure Modes in DERC-Absence Scenarios SaaS migrations that lack embedded technical or domain expertise often face recurring patterns of failure.

These include incomplete compliance alignment, poor change management execution, and post-deployment knowledge silos. While DERCs are not a guaranteed solution, their absence is consistently correlated with project fragmentation and escalated institutional risk.

One of the most widely cited breakdowns occurred during the New Zealand Ministry of Health’s Health IT platform migration in 2021, where the implementation team lacked embedded privacy specialists. According to a 2022 NZ Auditor-General’s Report, this led to “four separate GDPRequivalent data breaches, affecting over 100,000 health records.” The report highlighted the absence of continuity between policy advisors and software vendors as a primary failure point.

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Impact Factor value: 8.315

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The translation of policy into code in a holistic manner;

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The establishment of cultural trust between the IT and operational teams; etc.

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Memory retention strategies and the internalization of digital abilities are both important.

Although there are certain organizations that have been successful without DERCs—typically those that have mature enterprise architecture offices or restricted compliance exposure—the general trend is that the DERC model minimizes institutional risk, promotes long-term value, and protects public trust in digital transformation.

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7. Competencies, Criteria for Selection, and Integration Strategies for Consultants

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Domain Expert Resident Consultants (DERCs) continue to exhibit strategic value in the migration of publicsector software as a service (SaaS), but there is still a significant obstacle to overcome: how to properly identify, attract, and integrate these specialists. Detailed information about the needed competences, selection filters, and integration methodologies for DERCs in government cloud projects is provided in this part, which is a synthesis of results from current public-sector transformation frameworks, academic literature, and comparative assessments.

D. Communication with the Stakeholders

At the nexus of policy, process, and platform, DERCs that are effective run their departments. The positions they play need them to have a fluency in several disciplines, integrating in-depth topic knowledge with practical digital transformation expertise. In accordance with a compilation of research conducted by Tovar (2024), George et al. (2023), and the Government Digital Service (GDS) of the United Kingdom, the following technical and non-technical abilities are considered to be indispensable:

Demonstrated grasp of the laws, regulations, and operational processes that are peculiar to the agency (for example, public health, taxes, and criminal justice procedures).

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The capability to convert policy restrictions into logical rules, workflows, or configuration templates (for example, eligibility criteria and audit trails).

An understanding of cloud-native designs, such as REST APIs, microservices, and data lakes, among other things.

OAuth2, SAML, and role-based access models are examples of identification and access control systems that this individual may be exposed to.

Impact Factor value: 8.315

The ability to demonstrate familiarity with DevSecOps methods, product backlogs, sprint cycles, and story points.

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Experience with user acceptance testing (UAT), business process mapping (BPMN), and iterative feedback loops, either as a participant or as a leader.

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Experience working within the domain or regulatory sector (for example, healthcare informatics or finance regulation) for a minimum of five to seven years.

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Prior experience in enterprise information technology installations, digital transformation, or policy implementation is strongly encouraged.

B. Becoming Familiar with Institutions 

Interpretation of compliance regimes that are specific to a certain industry, such as GDPR, HIPAA, CJIS, and ISO 27001?

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A. Experience and Track Record

Possession of knowledge regarding software as a service (SaaS) configuration frameworks, such as Workday Studio, Salesforce Lightning, and Dynamics Logic Apps.

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Having the ability to drive change preparedness, turning technical outputs into policy narratives, and leading workshops are all examples of competencies.

DERCs are selected through a process that incorporates both technical vetting and cultural alignment, which is especially important in public situations that are hierarchical and sensitive. According to Costa (2021) and Saberg (2023), the following structural criteria have evolved as a result of the Global Digital Service (GDS), Digital 9 (D9+) alliances, and academic evaluations:

C. Management of Risk and Compliance Measures 

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7.2 The selection criteria and filtering strategies are as follows:

B. Digital Literacy and SaaS Awareness 

A capacity to act as a mediator between end-users, policy units, procurement officials, and information technology teams.

These talents are not only additive; rather, they are indicative of a hybrid professional identity, which is frequently developed at the intersection of public service and leadership in digital transformation.

A. Expertise in both Fields and Policies

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E. Participation in Agile Delivery

7.1 Required Skill Sets for SaaS Migration Contexts

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The ability to demonstrate familiarity with data retention rules, metadata governance, and security logging.

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Since candidates from within the public sector are better able to handle the complexities of bureaucratic complexity and legacy procedures,

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they frequently outperform those from outside the field.

digital lead, and second, to a director in their respective area (for example, the Director of Welfare Policy).

C. Capability to Work Across Disciplines 

A history of cooperating across legal, information technology, and operational departments is demonstrated.

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For instance, contributions to inter-agency projects or data governance councils are examples of such efforts.

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3. A Model of the Transformation Pod 

During sprint cycles, DERCs work in close proximity with product managers, user experience designers, and cloud architects.

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When quick prototyping or user-centric development is favored, such as in the MoJ Common Platform, there is the greatest potential for success.

D. Ethical Integrity and Public Service Orientation 

Sensitivity to public value, citizen trust, and the ethical implications of digital technologies.

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Training in public ethics, administrative law, or digital rights may be required prior to employment.

Each model ought to incorporate the following:

E. Flexibility and the capacity to think in systems 

The capacity to handle uncertainty, complexity, and shifting project scopes or requirements.

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It is important for candidates to demonstrate that they have successfully solved problems in highpressure, multi-stakeholder settings.

Screening for these competences is increasingly being done through the use of competency-based interviews, scenariobased issue walkthroughs, and stakeholder simulation exercises.

Following the selection process, DERCs are required to be included into the institutional structure in a way that ensures their autonomy, authority, and continuity are maintained. In most cases, integration is made easier by the utilization of three strategic models:

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This paradigm, which was utilized in the transfer of the Salesforce CRM system in Australia (Australian Department of Human Services, 2021), encourages cross-silo visibility while also preserving domain loyalty.

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Impact Factor value: 8.315

The incorporation of change control boards and user acceptance testing cycles;

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Documentation and knowledge transfer duties that are required to be completed.

8.1 Account the Value of DERCs Over the Long Term Quantitative longitudinal studies are still in short supply, despite the fact that anecdotal and case-based data demonstrates that DERCs contribute to cost savings, risk reduction, and user satisfaction. Among the most important questions are:

DERCs continue to report to two different people: first, to the chief information officer (CIO) or the

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

The strategic significance of Domain Expert Resident Consultants (DERCs) in SaaS migrations is becoming more and more apparent as government agencies all around the world deepen their transition toward cloud-native service delivery models. In spite of the theory, methodology, and policy guidelines that have been covered, there are still significant gaps in the practical and empirical data that has been presented. The following part provides an overview of the most important research goals and open topics that will be addressed in subsequent work by academics, policymakers, and practitioners.

2. Matrix Accountability Model 

Clarity on the scope of authority (for example, the capacity to veto configurations and the rights to interpret policies);

8. Future Research Directions and Open Questions

1. Model of the Embedded Cell In addition to being allocated to transformation teams or IT program boards, DERCs also report laterally to several functional units, such as human resources, compliance, and operations and so on.

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Importantly, design evaluation and review committees (DERCs) should not be limited to advising responsibilities alone; rather, they should be granted decision-making authority in order to guarantee that compliance, usability, and domain relevance are structurally included into platform design.

7.3 Combining with Existing Organizational Structures

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When it came to the "Workpal" project in Singapore, this structure proved to be useful, as it successfully balanced agility with domain specialization.

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After taking into account the downstream savings in terms of reliance on external vendors and compliance rework, what is the return on investment (ROI) of integrating DERCs over a period of five years?

What kind of impact do DERCs have on the flexibility of systems and how can they improve resilience after migration Do you think it would be possible to create a standardized DERC value index, which would be comparable to DevOps performance indicators such as DORA?

What are some ways that governments might institutionalize the DERC role within the frames of public service career opportunities?

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What kind of governance structure would make it possible for DERC frameworks all throughout federated states or municipal networks to be compatible with one another?

8.3 DERCs and AI-Augmented Software as a Service Platforms

Impact Factor value: 8.315

What kinds of supervision procedures ought to be developed so that DERCs can identify and rectify algorithmic drift or policy misalignment at the appropriate time?

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In order to incorporate DERCs into the process of anticipatory policy creation, what arrangements may be made for policy laboratories or agile regulatory teams?

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What kinds of design approaches, such as double diamond and design justice, are most effective in supporting digital policymaking that is headed by DERC?

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Co-designed regulatory mechanisms are compared to standard legislative channels in terms of speed, accuracy, and equity. How do these mechanisms compare?

In order to provide answers to these problems, a more indepth interaction across the disciplines of public administration, legal informatics, and service construction is required.

The complexity of domain-policy-model alignment is rising as a result of the increasing proportion of artificial intelligence (AI) components that are being integrated into SaaS systems. These AI components include predictive analytics, citizen chatbots, and automated case triage. New questions include the following:

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The following are important research avenues:

Experiments in organizational design might be part of the research process. These experiments could take inspiration from shared services models or agile transformation offices located in countries such as Estonia, Singapore, and the United Kingdom.

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What are the ways in which domain experts might contribute in the selection of training datasets and the validation of AI models?

Both the deployment of technology and the interpretation of policy are inextricably linked inside the DERC paradigm. However, is it possible to reverse this relationship? To put it another way, are domain experts capable of not just translating current policy into systems but also co-designing new policy frameworks that are optimized for being delivered digitally?

The deployment models that are currently being used by DERC are still mostly ad hoc and project-specific. There is a requirement to investigate:

Is it possible to establish centralized DERC pools, distribute them across other ministries, or allocate them through agile task forces?

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8.4 Participatory Migration and the Co-Design of National Policies

8.2 Expanding the DERC Model to Include Multiple Agencies

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What are some new skills that DERCs will need to acquire in order to evaluate the fairness, explainability, and compliance of algorithms?

It is possible for academics to investigate frameworks that span many disciplines, such as AI ethics, administrative law, and algorithmic auditing, in order to provide direction for the development of the DERC position in AI-infused software as a service models.

The estimation of long-term economic and operational implications might be accomplished by the use of techniques such as comparative time-series analysis, control-group assessments, or Monte Carlo simulations in the direction of future study.

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8.5 Harmonization on an International Scale and the Portability of DERC Many software as a service (SaaS) companies operate on a worldwide scale, such as Microsoft, Oracle, and Salesforce.

Additionally, the legal environment is becoming increasingly

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global, such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act. 

In what ways are DERCs able to transfer their skills from one jurisdiction to another?

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Would it be possible to create a uniform certification or accreditation structure in order to acknowledge DERC expertise on a worldwide scale?

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Important ethical and accountability problems are raised as a result of the responsibilities that DERCs play, which are becoming increasingly influential in terms of the configuration and control of digital systems in the public sector.

What kinds of internal measures are there that can protect neutrality and integrity, especially in organizations that are politically sensitive?

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Who conducts audits of the DERC's interpretations when there is uncertainty in the regulatory framework?

Crucially, the successful deployment of AI in public SaaS environments demands more than technical implementation; it requires consultants who embody crossdomain literacy—capable of mediating between policy, platform, and predictive logic. From predictive maintenance in infrastructure to automated scheduling in construction and compliance automation in agencies, the value of AIempowered consultancy lies in its ability to enhance decision-making transparency, reduce technical debt, and fortify stakeholder trust.

This continuation of the DERC framework proposes that the institutionalization of AI-augmented consultancy roles is essential not only for system scalability and accountability, but for upholding the legitimacy of digital government. As governments evolve into data-centric ecosystems, the human-AI consultant hybrid becomes not merely a facilitator, but a steward of public value.

In light of this, it is necessary to do research on institutional ethical models, drawing analogies to the duties of legal counsel or internal auditors, in order to establish professional limits specifically for DERCs. 8.7 Open Research Infrastructure and Analyzing Research Performance

REFERENCES

For the purpose of facilitating benchmarking across several projects, there is an immediate requirement for an open research infrastructure. This includes the following: 

Alizadeh, T., Farid, R., & Sarkar, S. (2021). Towards understanding the role of artificial intelligence in smart city governance. Government Information Quarterly, 38(3), Article 101582. https://doi.org/10.1016/j.giq.2021.101582

Case study setups, compliance practices, and migration logs that are stored in public repositories (with some information removed for privacy purposes);

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Impact Factor value: 8.315

Knowledge bases that are created through collaboration and document both lessons learned and configuration templates.

This paper has advanced the foundational DERC model introduced in Mehta, R. (2025) by integrating the transformative role of artificial intelligence in public SaaS governance. We defined and examined the emergence of AIempowered consultants—professionals who augment domain expertise with algorithmic tools to support compliance, operational efficiency, and institutional resilience. Across infrastructure, construction, and governmental agencies, these hybrid actors increasingly serve as both interpreters and co-designers of machineguided governance systems.

8.6 Accountability of the DERC and Ethical Governance Structures

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9.0 Conclusion

Comparison studies that involve digital government units from the OECD, eGov projects from the United Nations, or cross-border digital public services from the European Union can provide essential insights into this topic.

In the same way that procurement officials are subject to conflict-of-interest disclosure regulations, should DERCs get the same treatment?

collections of data for assessing the involvement in the DERC and the outcomes of the organization;

This kind of infrastructure would make it possible to conduct research that is repeatable, speed up the spread of best practices, and cut down on errors that are replicated across projects. It may be possible to achieve this goal through the formation of partnerships between governmental agencies, cloud providers, and educational institutions.

What kinds of dangers are associated with the movement of DERCs across different national regulatory environments, notably in federated states or transnational policy zones specifically?

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Australian Government Digital Transformation Agency.

(2021). Case study: Salesforce implementation for citizen

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engagement. https://www.dta.gov.au/our-projects/casestudies/salesforce-implementation-2021

Public Utility Information Review, 12(1), https://doi.org/10.1016/j.puir.2023.101038

Babajide, B., Olawale, O., & Afolabi, A. (2023). AI-driven project management in construction: A review of current trends. Automation in Construction, 147, Article 104741. https://doi.org/10.1016/j.autcon.2023.104741

Ghosh, R., & Scott, J. (2021). AI and regulatory compliance in government systems. AI & Society, 36(4), 1217–1230. https://doi.org/10.1007/s00146-020-01065-6 Goyal, N., & Joshi, A. (2020). Interdisciplinary approaches to AI governance in public sector systems. Technology in Society, 63, Article 101410. https://doi.org/10.1016/j.techsoc.2020.101410

Bounfour, A., & Ngwenyama, O. (2021). Digital transformation and public sector performance: A comparative analysis. Telecommunications Policy, 45(7), Article 102201. https://doi.org/10.1016/j.telpol.2021.102201

GovTech Singapore. (2021). Workpal implementation case study: Enabling agile governance. Government Technology Agency of Singapore. https://www.tech.gov.sg/files/casestudies/Workpal-Implementation-2021.pdf

Bradshaw, K., & Millard, C. (2020). Ethical dimensions of AI in public sector applications. International Journal of Law and Information Technology, 28(2), 123–145. https://doi.org/10.1093/ijlit/eaaa007

Janssen, M., Hart, P., & van Veenstra, A. F. (2022). AI-driven governance: Opportunities and challenges in the public sector. Government Information Quarterly, 39(1), Article 101666. https://doi.org/10.1016/j.giq.2022.101666

Comptroller and Auditor General of Ireland. (2021). Review of revenue CRM modernization. Government of Ireland. https://www.audit.gov.ie/en/FindReport/Publications/2021/Review-of-Revenue-CRMModernization.pdf

Lindgren, I., Madsen, C. Ø., Hofmann, S., & Melin, U. (2019).

Close encounters of the digital kind: A research agenda for digital transformation in the public sector. Government Information Quarterly, 36(3), 427–436. https://doi.org/10.1016/j.giq.2019.03.002

Cordella, A., & Paletti, A. (2019). Government as a platform: Orchestrating digital transformation through resident expertise. Government Information Quarterly, 36(3), 415– 426. https://doi.org/10.1016/j.giq.2019.01.003 Costa, J. (2021). Evaluating the impact of resident domain experts in public sector digital transformation [Master’s thesis, Universidade Nova de Lisboa]. RUN Repository. https://run.unl.pt/bitstream/10362/128906/1/TGI0452.pd f Desouza, K. C., & Jacob, B. (2020). Big data and urban governance: Lessons from smart city initiatives. Public Administration Review, 80(4), 567–578. https://doi.org/10.1111/puar.13189

Salesforce. (2021). Public sector digital transformation: Case studies in cloud adoption.

Salesforce. https://www.salesforce.com/resources/casestudies/public-sector-transformation-2021

Faraj, S., Pachidi, S., & Sayegh, K. (2021). Working and organizing in the age of AI. Information and Organization, 31(2), Article 100339. https://doi.org/10.1016/j.infoandorg.2021.100339

Scholl, H. J., & Scholl, M. C. (2021). Smart governance: A roadmap for digital transformation in the public sector.

Government Information Quarterly, 38(2), Article 101568. https://doi.org/10.1016/j.giq.2021.101568

George, B., Paul, J., & Mishra, S. (2023). Measuring the impact of resident expertise on public sector digital transformation.

Impact Factor value: 8.315

Office of the Auditor-General New Zealand. (2022). Review of health sector digital transformation. New Zealand Government. https://oag.parliament.nz/2022/healthdigital-transformation

Saberg, P. (2023). The role of resident expertise in public sector transformation: A case study approach. Public Management Review, 25(6), 1123–1145. https://doi.org/10.1080/14719037.2023.112032

Eaves, D., & McGuire, B. (2023). Digital governance in the public sector: Lessons from global case studies. Policy & Internet, 15(2), 239–263. https://doi.org/10.1002/poi3.310

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Mehta, R. (2025). Strategic integration of domain expert resident consultants in government SaaS migration: Bridging policy, technology, and transformation. International Research Journal of Engineering and Technology (IRJET), 12(7), 22-127

Petrov, A., & Others. (2022). Digital transformation and user adoption in public sector SaaS platforms. Journal of Public Sector Technology, 10(2), 123–145. https://doi.org/10.1000/xyz123

Drechsler, K., & Gregor, S. (2021). Ethical frameworks for AI in public sector decision-making. European Journal of Information Systems, 30(3), 220–237. https://doi.org/10.1080/0960085X.2021.1912597

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45–67.

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Saxena, D., #xAI, & Janssen, M. (2020). Cross-domain expertise in digital government: A framework for analysis.

Government Information Quarterly, 37(2), Article 101465. https://doi.org/10.1016/j.giq.2020.101465 Tovar, M. (2024). Skill requirements for AI-driven public sector transformations.

ResearchGate. https://www.researchgate.net/publication/382367453 Transparency International. (2022). Corruption risks in public sector digital procurement. Transparency International. https://www.transparency.org/en/publications/corruptionrisks-digital-procurement-2022 Treasury Board Secretariat of Canada. (2022). Digital transformation in federal agencies: A review. Government of Canada. https://www.canada.ca/en/government/system/digitaltransformation-review-2022.html UK Government Digital Service. (2021). Government transformation strategy 2021.

UK Government. https://www.gov.uk/government/publications/government -transformation-strategy-2021 UK Home Office. (2022). Digital transformation in immigration services: A review. UK Government. https://www.gov.uk/government/publications/digitaltransformation-immigration-2022 UK National Audit Office. (2022). Digital justice: Common platform implementation. National Audit Office. https://www.nao.org.uk/reports/digital-justice-commonplatform/ United States Government Accountability Office. (2022).

Federal IT modernization: Challenges and opportunities. U.S. Government Publishing Office. https://www.gao.gov/assets/gao-22-105493.pdf

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