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

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

Raj Mehta

Independent Researcher Columbia,

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 traditionalinternaldriversofSaaSimplementation,fostering policy synchronization, trust generation, and technical conformance.Inthecompanionstudyhere,weexplorehowAI 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 governmentinfrastructure,construction,andserviceagencies. Based on recent scholarly literature, this paper provides a frameworkforAI'sstrategicimportanceforSaaSgovernance withthefocusoninterdisciplinaryskillsetsnecessarytoclose regulatory, technical, and operational gaps. Our evidence indicates that hybrid governance coupled human intelligencewithAI isessentialforrobust,transparent,and scalableSaaSadoptioninthepublicsector

KeyWords: AI-empoweredconsultants;SaaSgovernance; domain expertise; cross-sector integration; digital transformation; algorithmic compliance; government infrastructure; construction technology; public administration;legacysystemmigration.

1.INTRODUCTION

1.1 DERCs and the Institutional Challenge

Mehta, R. (2025) presented the Domain Expert Resident Consultant(DERC)asanembeddedactorwithingovernment SaaSmigrationprojects.DERCsfacilitatealignmentbetween platform capabilities and institutional mandates by translating compliance protocols, mitigating risk, and building stakeholder trust. However, as public sector technologylandscapesbecomemoreinterdependent,datarich, and rapidly evolving, human consultants alone are insufficienttoguaranteeresilienceandpolicyalignmentat scale(Scholl&Scholl,2021;Lindgrenetal.,2019).

SC, USA

1.2 Objectives

Thispaperexpandstheanalyticalframetoconsiderhow artificial intelligence transforms the DERC model into a hybridframeworkofAI-empoweredconsultancy.Itaimsto:

 Define the functions and competencies of AIempoweredconsultantsinSaaSgovernance.

 Analyze their impact across infrastructure, construction,andagencycontexts.

 Articulate the role of cross-domain expertise in integrating AI systems with public policy, compliance,andstakeholderengagement.

1.3 Rationale for Cross-Domain Integration

As public institutions adopt AI tools for decisionmaking, compliance, and optimization, regulatory and operationalrisksincrease(Drechsler&Gregor,2021;Eaves & McGuire, 2023). Successful integration demands interdisciplinaryprofessionalswhocannotonlyinterpretAI 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 algorithmicallysupporteddecision-makingecosystems.

WhileMehta,R.(2025)demonstratedthatDomainExpert Resident Consultants (DERCs) serve as embedded facilitatorsofcompliance,institutionalcontinuity,anddigital trust in public sector SaaS adoption, the expanding complexityofmulti-stakeholderplatformsnownecessitates hybridgovernancestructures.

In the construction sector, AI-powered consultants utilize BuildingInformationModeling(BIM)coupledwithartificial intelligence in order to manage projects more efficiently. They use these technologies in conjunction to reduce conflictsrelatedtoschedulingandassessstructuralhazards in real time. Computationally empowered drones also supplementthisprocessbyperformingon-sitecompliance checks,creatingmarked-upriskscoresthataredirectlyfed

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

Withinthecontextofpublicadministration,AIsystemsare revolutionizingcomplianceprocessesingovernmentoffices. Thesesystemseffectivelydetectanomaliesincitizenservice provision, automate tax return validation, and guarantee compliancewithregulationslikeGDPR(Janssenetal.,2022).

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

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.

2. AI-Empowered Consultants: Redefining DERC Roles

As governments increasingly integrate artificial intelligence(AI)intocriticalservicedeliverysystems,anew category of consultants has emerged: AI-empowered consultants. Unlike traditional Domain Expert Resident Consultants(DERCs)whoprovidedinstitutionalcontinuity during SaaS transitions, these professionals augment their sectoral expertise with AI-driven tools such as predictive analytics,automatedregulatorycheckers,machinelearning classifiers,anddecisionoptimizationsystems(Bradshaw& Millard,2020;Farajetal.,2021).

AI-empowered consultants are not mere technical advisors but hybrid actors who co-interpret institutional goalsandmachinerecommendations.Theirdefiningattribute isthecapacitytointerfacebetweenAIsystemsanddomainspecific governance. This includes tuning data models to regulatory expectations, interpreting natural language policies through NLP tools, and simulating compliance scenariosusingautomatedreasoningengines(Drechsler& Gregor,2021).

Intheinfrastructuresector,forinstance,AI-empowered consultants employ smart sensors and maintenance prediction algorithms to proactively prevent outages, particularly in water, energy, and transportation systems (Bounfour&Ngwenyama,2021).Theiruseofreinforcement learningmodelstooptimizeservicereliabilitywhilemeeting legal constraints (e.g., carbon targets) exemplifies this transformation.

In the construction domain, AI-enabled consultants integrate Building Information Modeling (BIM) with AI to reduceschedulingconflictsandassessstructuralriskinreal-

time. Computer vision systems attached to drones now inspect compliance on-site and feed annotated risk scores directlyintoprojectdashboards(Babajideetal.,2023).

Ingovernmentagencies,AIsystemsautomatecompliance workflows flagging inconsistencies in citizen service delivery, automating tax return validations, and ensuring GDPRalignment(Janssenetal.,2022).Here,consultantsact as auditors of algorithmic logic, ensuring that service personalization does not violate non-discrimination principles(Ghosh&Scott,2021).

Thus,AI-empoweredconsultantsarenotsimplyDERCs using new tools they are institutional actors who help organizationsredesigngovernancearchitecturestointegrate machinecognition.

Table -1: AI Tools and Functions Across Public Sector Consulting Domains

SynthesizedfromBabajideetal.(2023);Farajetal.(2021); Drechsler&Gregor(2021).

Theresultisenhanceddecision-makingspeed,reductionin manual review cycles, and improved regulatory accuracy acrosstheboard(Bradshaw&Millard,2020).

ThistablesynthesizesAItooltypes,theirprimaryfunctions, 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 constructionsettings.

ToolType Function Consulting Domain

NLP-based regulation parsing Interprets laws intorulelogic Government Agencies

Predictive analytics (ML)

Graph-based compliancemodels

Project risk forecasting and cost deviation analysis Infrastructure & Construction

Mapsinstitutional workflows to regulatory frameworks Cross-sector

ComputervisioninBIM Real-time project monitoring and safetyaudits Construction

3. AI in Government SaaS Migrations

AIplaysanincreasinglypivotalroleinmodernSaaS migration strategies for public institutions. In legacy modernization projects, where governments shift from monolithic platforms to modular, cloud-based services, AI

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augmentseverystageofmigration:fromdatacleansingand classificationtocompliancesimulationandreal-timeaudit feedback.

3.1

Infrastructure Sector

InfrastructureprojectsbenefitsignificantlyfromAIenabledplatformsthatinterpretsensordata,optimizeasset usage,andpreventfailure.Governmentsinregionssuchas SingaporeandCanadanowemploypredictivemaintenance platforms powered by neural networks to forecast road degradation,pipelineleakage,orpowergridstress(Alizadeh etal.,2021).Theconsultantsoverseeingsuchprojectsare trained in regulatory zoning laws and urban planning constraintsandactasreal-timeAIsystemcalibrators.

3.2

Construction Sector

ConstructiongovernancehasadoptedAIfortasks suchasriskmodeling,contractanalysis,andprojectlifecycle 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 convolutionalneuralnetworks(CNNs)forvisualinspection and transformer-based models for textual regulation parsing, consultants can provide near-instant recommendationsonriskmitigation(Babajideetal.,2023).

3.3 Government Agencies

Government agencies increasingly deploy AI for regulatory enforcement and decision-making. In the tax domain, natural language models are used to interpret ambiguouspolicyclauses,whileanomalydetectionmodels uncover fraudulent claims. Agencies such as the Estonian Tax Board and Australia's Digital Transformation Agency now embed AI-empowered consultants into their cloud governanceteamstobalanceefficiencywithfairness(Eaves &McGuire,2023;Janssenetal.,2022).

4.0 Cross-Domain Expertise: Bridging Technology and Context

Cross-domainexpertiseisnowthecornerstoneof successfulAIintegrationinSaaSgovernance.Itreferstothe abilityofconsultantstofluentlytranslatebetweentechnical, operational, and regulatory domains without defaulting intoanyoneofthem.Thisskillisincreasinglynecessaryas publicsectorplatformsbecomemulti-layered,incorporating legal mandates, citizen rights, and machine autonomy in equalmeasure(Saxena&Janssen,2020).

WhereastraditionalDERCsoperatedwithvertical sectoralknowledge(e.g.,health,law,orinfrastructure),AIempowered consultants must possess an integrated understanding across data science, systems architecture,

public policy, and ethical governance. These professionals mustassessmodelbias,validatealgorithmictransparency, and align platform logic with sector-specific outcomes (Drechsler&Gregor,2021).

Table 2. Required Cross-Domain Expertise for AIEmpowered Consulting

Table2highlightshowsector-specifictechnicaltoolsrequire corresponding cross-domain expertise for effective implementation. It underscores the hybrid nature of AIempoweredconsultantswhomustbridgetechnicalsystems, policy mandates, and operational workflows across infrastructure, construction, and government agencies.

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

Sector TechnicalTool Required Cross-Domain Expertise

Infrastructure Predictive maintenance (ML)

Construction BIM + AI risk engines

Government Policy-parsing NLPmodels

Urban planning, environmentalpolicy,and sensorcalibration

Civil engineering, labor law, and automated monitoring

Administrativelaw,ethical AI, and public service workflows

Source:Babajideetal.(2023);Ghosh&Scott(2021);Janssen etal.(2022).

Suchexpertiseisnotpurelytechnical itisdeeply embeddedinpolitical andsocial context.Forexample, AIgenerated zoning recommendations in smart cities must align with not just planning codes but also neighborhood equity goals. In these settings, consultants must mediate betweenwhatthemodel“recommends”andwhatthepolicy “permits”(Desouza&Jacob,2020).

5.0 Summary of Key Themes

This paper’s investigation of AI-empowered consultantswithinpublicSaaSgovernancehighlightsthree strategicshiftsfromtheDERCmodelpresentedinMehta,R. (2025).

First, there is a paradigmatic shift in function: DERCs ensured project continuity through embedded presence, whileAI-empoweredconsultantsprovidescalabledecision support, auditability, and algorithmic transparency. Their valueliesnotjustinknowledgeretentionbutintheabilityto

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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 automatedexecution.Intaxsystems,AImodelsnowprocess returnsinreal-time,enhancingthroughputandauditability (Janssenetal.,2022).Inconstruction,real-timescheduling via generative optimization has reduced public project delaysbyover15%inpilotprograms(Babajideetal.,2023).

Third,cross-domainexpertisehasemergedasthe definitivetraitofeffectiveconsultants.Withoutthecapacity tounderstandregulation,interpretAIoutputs,andinterface withbothtechnicalteamsandpolicymakers,eventhemost powerful SaaS platforms risk failure. Institutions now require embedded consultants who combine data fluency withsocialaccountabilityandlegalliteracy(Goyal&Joshi, 2020;Eaves&McGuire,2023).

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

(Extended)

The efficiency of the adoption of software as a service (SaaS) in public sector contexts varies greatly dependingongovernancemodels,thepreparednessofthe organization, and the incorporation of expert human resources.Thissectionprovidesasystematiccomparative studyoftheoutcomesofSaaSmigrationinitiativesthatwere carried out with and without the participation of Domain Expert Resident Consultants (DERCs). To go beyond anecdotalsuccessstoriesanddiscoversystemicadvantages impartedbyDERCs,supportedbyempiricalandevaluative investigations,theobjectiveistoadvancebeyondthescope ofcurrentsuccessstories.

6.1 The Methodological Framework

Itisnecessarytohaveacomprehensiveevaluationlens when comparing the migrations of SaaS across different administrations. Such studies have been framed using a combinationofquantitativemeasures(suchasdelaylength, costoverrun,andregulatoryclearancetime)andqualitative indicators (such as internal satisfaction, institutional resilience, and user confidence) in previous comparative research(Georgeetal.,2023;Costa,2021).Inthispart,the comparisonisconductedinaccordancewiththreeprimary criteriainordertoguaranteeanalyticalcoherence:

 DERCPresenceDefinition:Theterm"DERC"inthis context refers to subject-matter specialists who havebeenembeddedwithinapublicagencyforat least one complete project cycle and have been actively involved in decision-making, compliance interpretation,ororganizationalchangefacilitation.



Temporal Scope: Projects that were started between the years 2015 and 2024, which corresponds to the decade of cloud-first policies thatarenowineffectthroughoutOECDnations.

Sectoral Scope: Sectors analyzed include justice, taxation, social services, healthcare, and administrative services all of which demand complexregulatoryandoperationalalignment.

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.

6.2 Project Performance Metrics

Metric DERC-Present Projects DERC-Absent Projects Source

Average Delay inMigration 2.1months 7.4months UKNAO(2022); GAO(2022)

UserAdoption Rate at 6 Months 81% 53% Salesforce (2021)

Projects with TechnicalDebt Backlog 18% 46%

Compliance Workaround Frequency Rare(in<10% of deployments) Frequent (spreadsheet usein~38%)

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

George et al. (2023); Australian Department of HumanServices, 2021

Stakeholder Satisfaction “High” in 74% ofprojects “High” in only 38% Costa(2021);UK Home Office 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) versusthosethatdidnot.Metricsincludemigrationdelays, adoptionrates,technicaldebt,complianceworkarounds,and stakeholdersatisfaction.

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The data shows that the inclusion of DERCs correlatesstronglywithfasterdelivery,fewerdisruptions, and improved user trust. These findings hold across domains justice, health, taxation, and human services suggesting that domain expertise offers systemic value beyondsector-specificknowledge.

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 accumulationofreworkthatisnecessaryasaresultoffast repairsorsettingsthatarenotalignedproperly.Inprojects whereDERCswerenotpresent,technicalteamsfrequently relied on vendor defaults, which led to the creation of systemsthatwereunabletoenforcelegallyneededcontrols or procedures. During the CRM migration that Ireland's RevenueCommissionersundertookintheyears2020–2021, for example, the absenceof embeddedtax policyadvisors resultedinthefollowing:

 Misinterpretation of evaluation timeframes and automated correspondence that is prone to incorrectunderstanding;

 There were violations of the GDPR's logging standards, which resulted in the complete revalidationofaudittrailsafterthelaunch;

 In the event of an emergency, the application programminginterface(API)ofthesystemwillbe modified to conform to the procedures for data subjectaccessrequests.

The"Workpal"humanresourcemanagementplatform inSingapore,ontheotherhand,wasdevelopedunderthe direction of resident civil service subject specialists. It utilizedAWSLambdaandAWSConfigtoconstructmodular policy engines, which were then used to handle leave policies,rotations,andcontractvariationsacrossmorethan onehundredagencies.Notonlydidthisgetridofhardcoded assumptions,butitalsomadepolicymodificationseasierto implement by using infrastructure-as-code models. This madeitpossibletoupdatelegalcompliancewithouthaving torebuildthesoftware.

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 informationtechnology.

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

Trust and internal buy-in are two of the most intangible yet mission-critical factors in public-sector technologytransformations.Whenresidentconsultantsare

embedded throughout the migration cycle, they provide institutionalcontinuity,familiaritywithcompliancepolicies, and interpersonal credibility all of which significantly improveinternalacceptanceofnewsystems.

A recent study by George et al. (2023) found that public agencies with embedded consultants were significantlymorelikelytoachieveexecutivealignmentand 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.

AnillustrativecaseistheAustralianDepartmentof HumanServices,whichfacedsluggishadoptionofitsCRM 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 reducedmanualformsubmissionsby35%.Theseoutcomes weredrivennotonlybytechnicalfixesbutbyincreasedtrust in the system’s governance a direct result of having internalchampionswithhybriddomainexpertise.

Similarly, George et al. (2023) report that organizations lacking DERCs often face higher rates of “informalsystembypassing,”withspreadsheetworkarounds and unauthorized data silos emerging in nearly 38% of vendor-ledprojects.Thisbehaviorstemsfromtheabsenceof 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.Theirtrustcapitalenablesthemtoexplainpolicy changesinplainlanguage,justifyupdateswithcompliance logic, and secure buy-in from departments that would otherwiseresistchangeduetouncertaintyorlackofclarity. The correlation between trust and DERC presence is not incidental itiscausal,repeatable,andnowdocumented acrossmultiplejurisdictions.

6.5 Long-Term Knowledge Retention and Cost Savings

In traditional SaaS deployments, much of the architecturalandprocessknowledgeresideswithexternal vendors,oftenleadingtoinstitutionalmemorylossoncethe 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 operationalcontinuity.

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

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hybrid in-house–consultant teams. These teams reported reducedre-trainingcyclesandgreaterconsistencyincrossdepartmentalworkflows.

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) experiencedupto16%reductionindependencyonTier-3 vendorsupportafterthefirstyear.Thistranslatedintofaster internal troubleshooting cycles and significantly reduced externalbillingforadvisorysupport.

Additionally,anevaluationbyGeorgeetal.(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.Thisinstitutionalizationofknowledgeprevented duplication of effort and enabled smoother onboarding of newstaff.

From a cost-efficiency perspective, retaining platform and process expertise internally ensures that recurringvendorengagement fortaskslikedataschema updates or SLA adjustment can be minimized. More importantly,itenablespublicagenciestocontroltheirtech stack without relying on external consultants for every systemevolution.

Insummary,DERCshelpbridgethedangerouspostdeployment knowledge gap. Their presence mitigates knowledgeattritionandofferslong-termoperationalsavings not through temporary cost-cutting, but by enabling sustainableself-sufficiencyintechnicalgovernance.

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 knowledgesilos.WhileDERCsarenotaguaranteedsolution, their absence is consistently correlated with project fragmentationandescalatedinstitutionalrisk.

Oneofthemostwidelycitedbreakdownsoccurredduring the New Zealand Ministry of Health’s Health IT platform migrationin2021,wheretheimplementationteamlacked embedded privacy specialists. According to a 2022 NZ Auditor-General’sReport,thisledto“fourseparateGDPRequivalent data breaches, affecting over 100,000 health records.”Thereporthighlightedtheabsenceofcontinuity betweenpolicyadvisorsandsoftwarevendorsasaprimary failurepoint.

InIreland,theRevenueCommissioners'CRMrollout faced severe stakeholder disengagement when project leadersfailedtointegratetaxdomainspecialistsintosprint teams. A post-implementation review by the Irish Comptroller&AuditorGeneral(2021)foundthatover22% ofhelpdeskticketswererelatedtopolicymisinterpretation, suggesting a gap between configuration teams and institutionalruleknowledge.

Similarly, Zambia’s Public Procurement Authority attemptedacompletee-tenderingSaaSmigrationin2020.A casestudypublishedbyTransparencyInternational(2022) indicatedthatthesystemwaseventuallysuspendeddueto irregular vendor payments and black-box integrations bothtraceabletotheabsenceofinternaltechnicalvalidation capacity.

These cases are not outliers. They illustrate a systemicrisk:whentechnicalplatformsevolvefasterthan institutional adaptation, failures are not just technical they’repolicy,audit,andreputationalinnature.

To mitigate such risks, several agencies (e.g., UK GDS,USOMB,andCanadaTBS)havenowbegunembedding full-timecompliancearchitectsorresidenttechnologiststo serveasinternalQAandpolicyinterpretersthroughouttheir modernizationlifecycles.

While not every project without a DERC fails, the statisticalandnarrativeevidenceincreasinglysupportsthe argument that the absence of embedded, cross-functional consultantsisaleadingindicatorofSaaSunderperformance.

6.7 Summary of Comparative Value

TheinclusionofDERCsappearstoconsiderably boostthe chanceofsuccessfulSaaSmigrationingovernmentcontexts, accordingtotheresearchthathasbeencomparedacrossa varietyofjurisdictions,platforms,andindustries.Notonly doestheircontributionextendbeyondspecificactivities,but it also encompasses a more comprehensive function in enabling:

 The translation of policy into code in a holistic manner;

 TheestablishmentofculturaltrustbetweentheIT andoperationalteams;etc.

 Memoryretentionstrategiesandtheinternalization ofdigitalabilitiesarebothimportant.

Although there are certain organizations that have been successfulwithoutDERCs typicallythosethathavemature enterprise architecture offices or restricted compliance exposure the general trend is that the DERC model minimizesinstitutionalrisk,promoteslong-termvalue,and protectspublictrustindigitaltransformation.

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

Domain Expert Resident Consultants (DERCs) continuetoexhibitstrategicvalueinthemigrationofpublicsector software as a service (SaaS), but there is still a significantobstacletoovercome:howtoproperlyidentify, attract,andintegratethesespecialists.Detailedinformation about the needed competences, selection filters, and integrationmethodologiesforDERCsingovernmentcloud projects is provided in this part, which is a synthesis of results from current public-sector transformation frameworks, academic literature, and comparative assessments.

7.1 Required Skill Sets for SaaS Migration Contexts

Atthenexusofpolicy,process,andplatform,DERCs thatareeffectiveruntheirdepartments.Thepositionsthey play need them to have a fluency in several disciplines, integratingin-depthtopicknowledgewithpracticaldigital transformationexpertise.Inaccordancewithacompilation ofresearchconductedbyTovar(2024),Georgeetal.(2023), and the Government Digital Service (GDS) of the United Kingdom,thefollowingtechnicalandnon-technicalabilities areconsideredtobeindispensable:

A. Expertise in both Fields and Policies

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

 The capability to convert policy restrictions into logicalrules,workflows,orconfigurationtemplates (forexample,eligibilitycriteriaandaudittrails).

B. Digital Literacy and SaaS Awareness

 Anunderstandingofcloud-nativedesigns,suchas REST APIs, microservices, and data lakes, among otherthings.

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

 OAuth2, SAML, and role-based access models are examples of identification and access control systemsthatthisindividualmaybeexposedto.

C. Management of Risk and Compliance Measures

 Interpretation of compliance regimes that are specifictoacertainindustry,suchasGDPR,HIPAA, CJIS,andISO27001?

 The ability to demonstrate familiarity with data retentionrules,metadatagovernance,andsecurity logging.

D. Communication with the Stakeholders

 Acapacitytoactasamediatorbetweenend-users, policyunits,procurementofficials,andinformation technologyteams.

 Having the ability to drive change preparedness, turningtechnicaloutputsintopolicynarratives,and leading workshops are all examples of competencies.

E.

Participation in Agile Delivery

 The ability to demonstrate familiarity with DevSecOps methods, product backlogs, sprint cycles,andstorypoints.

 Experience with user acceptance testing (UAT), business process mapping (BPMN), and iterative feedbackloops,eitherasaparticipantorasaleader.

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 andleadershipindigitaltransformation.

7.2 The selection criteria and filtering strategies are as follows:

DERCs are selected through a process that incorporatesbothtechnicalvettingandculturalalignment, which is especially important in public situations that are hierarchical and sensitive. According to Costa (2021) and Saberg(2023),thefollowingstructuralcriteriahaveevolved asaresultoftheGlobalDigitalService(GDS),Digital9(D9+) alliances,andacademicevaluations:

A. Experience and Track Record

 Experience working within the domain or regulatory sector (for example, healthcare informaticsorfinanceregulation)foraminimumof fivetosevenyears.

 Prior experience in enterprise information technologyinstallations,digitaltransformation,or policyimplementationisstronglyencouraged.

B. Becoming Familiar with Institutions

 Sincecandidatesfromwithinthepublicsectorare better able to handle the complexities of bureaucratic complexity and legacy procedures,

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theyfrequentlyoutperformthosefromoutsidethe field.

C. Capability to Work Across Disciplines

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

 Forinstance,contributionstointer-agencyprojects ordata governancecouncilsareexamplesofsuch efforts.

D. Ethical Integrity and Public Service Orientation

 Sensitivity to public value, citizen trust, and the ethicalimplicationsofdigitaltechnologies.

 Training in public ethics, administrative law, or digitalrightsmayberequiredpriortoemployment.

E. Flexibility and the capacity to think in systems

 Thecapacitytohandleuncertainty,complexity,and shiftingprojectscopesorrequirements.

 Itisimportantforcandidatestodemonstratethat they have successfully solved problems in highpressure,multi-stakeholdersettings.

Screeningforthesecompetencesisincreasinglybeingdone throughtheuseofcompetency-basedinterviews,scenariobased issue walkthroughs, and stakeholder simulation exercises.

7.3 Combining

with

Existing Organizational Structures

Followingtheselectionprocess,DERCsarerequired tobeincludedintotheinstitutionalstructureinawaythat ensures their autonomy, authority, and continuity are maintained.Inmostcases,integrationismadeeasierbythe utilizationofthreestrategicmodels:

1. Model of the Embedded Cell

 In addition to being allocated to transformation teams or IT program boards, DERCs also report laterallytoseveralfunctionalunits,suchashuman resources,compliance,andoperationsandsoon.

 Thisparadigm,whichwasutilizedinthetransferof theSalesforceCRMsysteminAustralia(Australian DepartmentofHumanServices,2021),encourages cross-silo visibility while also preserving domain loyalty.

2. Matrix Accountability Model

 DERCscontinuetoreporttotwodifferentpeople: first, to the chief information officer (CIO) or the

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

 Whenitcametothe"Workpal"projectinSingapore, thisstructureprovedtobeuseful,asitsuccessfully balancedagilitywithdomainspecialization.

3. A Model of the Transformation Pod

 Duringsprintcycles,DERCsworkincloseproximity withproductmanagers,userexperiencedesigners, andcloudarchitects.

 When quick prototyping or user-centric developmentisfavored,suchasintheMoJCommon Platform,thereisthegreatestpotentialforsuccess.

Eachmodeloughttoincorporatethefollowing:

 Clarityonthescopeofauthority(forexample,the capacity to veto configurations and the rights to interpretpolicies);

 The incorporation of change control boards and useracceptancetestingcycles;

 Documentationandknowledgetransferdutiesthat arerequiredtobecompleted.

Importantly, design evaluation and review committees (DERCs)shouldnotbelimitedtoadvising responsibilities alone; rather, they should be granted decision-making authorityinordertoguaranteethatcompliance,usability, anddomainrelevancearestructurallyincludedintoplatform design.

8. Future Research Directions and Open Questions

The strategic significance of Domain Expert Resident Consultants(DERCs)inSaaSmigrationsisbecomingmore andmoreapparentasgovernmentagenciesallaroundthe worlddeepentheirtransitiontowardcloud-nativeservice delivery models. In spite of the theory, methodology, and policy guidelines that have been covered, there are still significantgapsinthepracticalandempiricaldatathathas beenpresented.Thefollowingpartprovidesanoverviewof themostimportantresearchgoalsandopentopicsthatwill be addressed in subsequent work by academics, policymakers,andpractitioners.

8.1 Account the Value of DERCs Over the Long Term

Quantitativelongitudinalstudiesarestillinshortsupply, despite the fact that anecdotal and case-based data demonstrates that DERCs contribute to cost savings, risk reduction,andusersatisfaction.Amongthemostimportant questionsare:

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 Aftertakingintoaccountthedownstreamsavingsin terms of reliance on external vendors and compliance rework, what is the return on investment (ROI) of integrating DERCs over a periodoffiveyears?

 What kind of impact do DERCs have on the flexibility of systems and how can they improve resilienceaftermigration

 Do you think it would be possible to create a standardized DERC value index, which would be comparabletoDevOpsperformanceindicatorssuch asDORA?

The estimation of long-term economic and operational implicationsmightbeaccomplishedbytheuseoftechniques such as comparative time-series analysis, control-group assessments,orMonteCarlosimulationsinthedirectionof futurestudy.

8.2 Expanding the DERC Model to Include Multiple Agencies

Thedeploymentmodelsthatarecurrentlybeingusedby DERCarestillmostlyadhocandproject-specific.Thereisa requirementtoinvestigate:

 What are some ways that governments might institutionalizetheDERCrolewithintheframesof publicservicecareeropportunities?

 Is it possible to establish centralized DERC pools, distributethemacrossotherministries,orallocate themthroughagiletaskforces?

 Whatkindofgovernancestructurewouldmakeit possible for DERC frameworks all throughout federated states or municipal networks to be compatiblewithoneanother?

Experimentsinorganizational designmightbepartofthe researchprocess.Theseexperimentscouldtakeinspiration fromsharedservicesmodelsoragiletransformationoffices located in countries such as Estonia, Singapore, and the UnitedKingdom.

8.3 DERCs and AI-Augmented Software as a Service Platforms

The complexity of domain-policy-model alignment is rising as a result of the increasing proportion of artificial intelligence(AI)componentsthatarebeingintegratedinto SaaS systems. These AI components include predictive analytics,citizenchatbots,andautomatedcasetriage.New questionsincludethefollowing:



WhataresomenewskillsthatDERCswillneedto acquire in order to evaluate the fairness, explainability,andcomplianceofalgorithms?

 Whatarethewaysinwhichdomainexpertsmight contributeintheselectionoftrainingdatasetsand thevalidationofAImodels?

 Whatkindsofsupervisionproceduresoughttobe developed so that DERCs can identify and rectify algorithmic drift or policy misalignment at the appropriatetime?

Itispossibleforacademicstoinvestigateframeworksthat spanmanydisciplines,suchasAIethics,administrativelaw, andalgorithmicauditing,inordertoprovidedirectionfor thedevelopmentoftheDERCpositioninAI-infusedsoftware asaservicemodels.

8.4 Participatory Migration and the Co-Design of National Policies

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 optimizedforbeingdelivereddigitally?

Thefollowingareimportantresearchavenues:

 InordertoincorporateDERCsintotheprocessof anticipatory policy creation, what arrangements may be made for policy laboratories or agile regulatoryteams?

 What kinds of design approaches, such as double diamond and design justice, are most effective in supportingdigital policymakingthatisheadedby DERC?

 Co-designedregulatorymechanismsarecompared tostandardlegislativechannelsintermsofspeed, accuracy, and equity. How do these mechanisms compare?

Inordertoprovideanswerstotheseproblems,amoreindepth interaction across the disciplines of public administration,legalinformatics,andserviceconstructionis required.

8.5 Harmonization on an International Scale and the Portability of DERC

Manysoftwareasaservice(SaaS)companiesoperateona worldwidescale,suchasMicrosoft,Oracle,andSalesforce. Additionally,thelegalenvironmentisbecomingincreasingly

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global, such as the General Data Protection Regulation (GDPR)andtheArtificialIntelligenceAct.

 InwhatwaysareDERCsabletotransfertheirskills fromonejurisdictiontoanother?

 Would it be possible to create a uniform certificationoraccreditationstructureinorderto acknowledgeDERCexpertiseonaworldwidescale?

 What kinds of dangers are associated with the movement of DERCs across different national regulatory environments, notably in federated statesortransnationalpolicyzonesspecifically?

Comparison studies that involve digital government units from the OECD,eGovprojectsfrom theUnitedNations, or cross-border digital public services from the European Unioncanprovideessentialinsightsintothistopic.

8.6 Accountability of the DERC and Ethical Governance Structures

Importantethicalandaccountabilityproblemsareraisedas 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.

 In the same way that procurement officials are subjecttoconflict-of-interestdisclosureregulations, shouldDERCsgetthesametreatment?

 Whatkindsofinternalmeasuresaretherethatcan protect neutrality and integrity, especially in organizationsthatarepoliticallysensitive?

 WhoconductsauditsoftheDERC'sinterpretations when there is uncertainty in the regulatory framework?

Inlightofthis,itisnecessarytodoresearchoninstitutional ethical models, drawing analogies to the duties of legal counsel or internal auditors, in order to establish professionallimitsspecificallyforDERCs.

8.7 Open Research Infrastructure and Analyzing Research Performance

For the purpose of facilitating benchmarking across severalprojects,thereisanimmediaterequirementforan openresearchinfrastructure.Thisincludesthefollowing:

 Case study setups, compliance practices, and migrationlogsthatarestoredinpublicrepositories (with some information removed for privacy purposes);

 collectionsofdataforassessingtheinvolvementin theDERCandtheoutcomesoftheorganization;

 Knowledge bases that are created through collaboration and document both lessons learned andconfigurationtemplates.

Thiskindofinfrastructurewouldmakeitpossibletoconduct research that is repeatable, speed up the spread of best practices,andcutdownonerrorsthatarereplicatedacross projects.Itmaybepossibletoachievethisgoalthroughthe formationofpartnershipsbetweengovernmentalagencies, cloudproviders,andeducationalinstitutions.

9.0 Conclusion

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.WedefinedandexaminedtheemergenceofAIempowered 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 machineguidedgovernancesystems.

Crucially, the successful deployment of AI in public SaaS environments demands more than technical implementation;itrequiresconsultantswhoembodycrossdomain literacy capable of mediating between policy, platform,andpredictivelogic.Frompredictivemaintenance 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 fortifystakeholdertrust.

ThiscontinuationoftheDERCframeworkproposesthatthe institutionalization of AI-augmented consultancy roles is essentialnotonlyforsystemscalabilityandaccountability, butforupholdingthelegitimacyofdigital government. As governments evolve into data-centric ecosystems, the human-AI consultant hybrid becomes not merely a facilitator,butastewardofpublicvalue.

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