Introduction

The convergence of artificial intelligence and Customer Resource Management (CRM) represents one of the most significant transformations in enterprise software this decade. By 2025, an estimated 81% of organizations are anticipated to use AI-powered CRM systems, with companies leveraging these technologies reporting 25 – 30% increases in customer engagement and 15 – 20% improvements in sales productivity. This analysis explores the key AI trends poised to reshape how businesses manage customer relationships over the coming years.

Trends:

1. Agentic AI and Autonomous CRM Systems

Perhaps the most transformative development is the emergence of agentic AI, which represents a fundamental shift from passive data repositories to proactive, goal-driven systems that can navigate complex business environments and make autonomous decisions. Unlike traditional AI that merely reacts to commands, agentic CRM platforms use APIs, generative AI tools, and closed-loop learning to independently assess situations, make decisions based on shifting circumstances, and take action across systems to achieve specific outcomes. Microsoft’s Dynamics 365, for example, embeds Copilot capabilities that enable salespeople and customer support representatives to create content, surface insights, and summarize customer interactions automatically. These autonomous agents can qualify leads without human intervention, route cases intelligently, generate email content, and proactively engage customers based on real-time signals. Organizations implementing agentic CRM solutions report that effective AI agents can accelerate business processes by 30% to 50%, fundamentally changing the economics of customer engagement. The practical implications are substantial: AI workers within agentic CRMs now assess lead status, evaluate deal health, make decisions in response to changing circumstances, and take coordinated action across multiple systems. This autonomous capability means that instead of waiting for sales representatives to manually update records or trigger follow-ups, the CRM itself becomes an active participant in the sales and service process.

2. Hyper-Personalization

Generative AI is revolutionizing customer personalization by moving beyond basic segmentation to treat each customer as a segment of one.

Where traditional approaches grouped customers into broad categories, AI-powered systems now analyze browsing behavior, purchase history, social interactions, and even emotional signals to deliver uniquely tailored experiences in real time. The business impact is measurable: 80% of customers are more likely to make a purchase when brands offer personalized experiences, and companies implementing AI-powered personalization see an average 25% increase in conversion rates along with a 15% increase in customer satisfaction. Netflix and Amazon exemplify this approach, using generative AI to predict customer preferences and deliver personalized recommendations that significantly boost engagement and retention. Real-time personalization engines now analyze over 500 data points per customer simultaneously, enabling businesses to anticipate needs before customers articulate them. This includes analyzing real-time data, behavioral patterns, preferences, and contextual information to deliver hyper-personalized content, product recommendations, and promotional offers across every touchpoint.

3. Predictive Analytics

AI-driven predictive analytics is transforming CRM from a historical record-keeping system into a forward-looking intelligence platform. Machine learning models including Random Forest, Gradient Boosting, and Neural Networks now process historical customer data, transactional records, and behavioral signals to forecast future actions with unprecedented accuracy. Companies using predictive analytics in their CRM report a 25% increase in sales revenue and 30% increase in customer satisfaction due to their ability to anticipate and address customer needs proactively. The applications span the entire customer lifecycle. For customer retention, AI identifies at-risk customers by analyzing engagement patterns, purchase frequency, and satisfaction scores, enabling targeted intervention strategies before churn occurs. In sales forecasting, predictive models analyze market trends and historical data to help businesses set realistic targets and allocate resources effectively. For lead qualification, AI scores prospects based on hundreds of data points including email opens, website interactions, and form submissions, allowing sales teams to prioritize high-value opportunities

Integration with Customer Data Platforms amplifies these capabilities by unifying data from multiple sources to create comprehensive customer profiles, enabling more accurate predictions and truly personalized engagement strategies.

4. Sentiment Analysis

The emergence of Cognitive CRMs that leverage AI to analyze, interpret, and act on human emotions represents a significant advancement in customer understanding. These systems go beyond text analysis to encompass tone and voice analysis during calls, natural language processing of written communications, and even facial recognition during video interactions to detect subtle emotional cues.

The emergence of Cognitive CRMs that leverage AI to analyze, interpret, and act on human emotions represents a significant advancement in customer understanding

This emotional intelligence enables several practical applications. Frustrated customers detected through sentiment analysis can be automatically redirected to specialized agents or offered compensatory solutions before issues escalate. Real-time interaction adaptation means that stressed or angry customers receive more empathetic and reassuring responses, while enthusiastic clients experience more dynamic engagement. Intelligent routing uses emotional analysis to direct requests to the most qualified agents, optimizing handling and significantly reducing resolution times. Platforms like Salesforce now integrate voice analysis with CRM data to equip agents to handle calls more effectively by understanding the customer’s emotional state alongside their transaction history. This multimodal approach to sentiment analysis, combining text, voice, and visual cues, provides a more nuanced understanding that text-only systems cannot match.

5. Conversational AI and Multimodal Engagement

What began as simple chatbots has evolved into sophisticated AI agents capable of natural, context-aware conversations across multiple channels. By 2025, 70% of CRMs are expected to integrate conversational AI features, with these systems handling complex queries, maintaining conversation context across channel switches, and even coaching human representatives during live interactions. Voice AI integration represents a particularly important frontier. When connected to CRM systems, voice AI can interpret voice recordings and complete 95% of CRM fields accurately, eliminating manual data entry while capturing richer data including tone, sentiment, and transactional information. Companies report up to an 80% reduction in operational costs and 75% improvement in customer service efficiency through voice AI deployment integrated with their CRM platforms. The multimodal trend extends beyond voice to encompass text, images, and behavioral signals processed simultaneously. AI agents now coordinate across email, chat, social media, and voice channels to maintain consistent engagement, with context preserved throughout the customer journey regardless of how or where the customer chooses to interact.

6. Autonomous Customer Journey Orchestration

Traditional customer journey mapping has evolved from static planning exercises into dynamic, adaptive processes that adjust in real time based on individual behaviors and preferences. AI systems now independently manage and optimize entire customer journeys, making decisions, triggering actions, and adapting strategies with minimal human intervention. This autonomous orchestration follows a structured decision loop: the system observes what is happening across active sessions, interprets customer intent using trained AI models, decides on the next action from a bounded set of possibilities, and evaluates outcomes to inform future decisions. Companies using CRM systems with this generative AI capability are 83% more likely to exceed their sales goals, demonstrating the competitive advantage of adaptive journey management.​ The practical result is that AI-driven customer journeys transform marketing and sales from rigid, rule-based processes into responsive systems that evolve through data rather than guesswork. Marketers define strategic goals while AI agents dynamically optimize every interaction, continuously learning from customer behaviors and adapting automatically.

Traditional customer journey mapping has evolved from static planning exercises into dynamic, adaptive processes that adjust in real time based on individual behaviors and preferences

7. Enterprise Integration

AI is fundamentally changing how CRM systems integrate with broader enterprise infrastructure.

AI-powered integration platforms now connect ERP, CRM, and supply chain systems intelligently, streamlining data synchronization, enforcing compliance, and generating insights that help organizations anticipate customer needs. By 2026, 85% of executives believe their workforce will make real-time data-driven decisions using AI agent recommendations that span multiple enterprise systems. This integration extends to IoT devices, where connected products feed real-time usage data directly into CRM systems to enable predictive service and proactive customer engagement. A connected thermostat can flag performance issues before users notice; industrial sensors can trigger service tickets automatically. McKinsey estimates that predictive maintenance enabled by IoT can reduce downtime by 30% to 50% and extend equipment life by 20% to 40%.

The practical implication for organizations is that CRM no longer functions as a standalone system but becomes the customer intelligence hub that orchestrates insights from across the enterprise to deliver coordinated, contextual engagement at every touchpoint.

8. Data Governance and AI Ethics

As AI capabilities in CRM expand, so do the requirements for responsible data management. 85% of CRM providers now offer built-in compliance tools to address stricter regulations like GDPR and CCPA, and privacy-first approaches are becoming fundamental to CRM strategy rather than afterthoughts. With the EU AI Act and evolving regional data protection laws like Saudi Arabia’s PDPL, organizations must balance personalization benefits against accountability requirements. Best practices emerging in this space include transparent communication about data collection and usage, auditable consent management honoring customer preferences, and data minimization that collects only information required for legitimate business purposes. AI-powered data observability now provides real-time insights into data usage, classification, and security risks, while automated policy enforcement adapts governance to regulatory changes dynamically. The challenge for organizations lies in harmonizing global regulatory requirements with existing governance frameworks while ensuring that AI-driven personalization does not compromise customer trust. Those who succeed in building privacy-compliant AI CRM systems gain competitive advantage through customer confidence alongside operational efficiency

9. Low-Code AI Platforms and Citizen Development

The democratization of AI capabilities through low-code platforms is enabling business technologists and citizen developers to build intelligent CRM applications without traditional programming expertise. According to Gartner, by 2025 70% of new enterprise applications will use low-code or no-code technologies, a dramatic increase from less than 25% in 2020. These platforms integrate AI capabilities that were previously accessible only to specialized technical teams. Document intelligence using AI-powered OCR and NLP allows citizen developers to extract structured data from invoices, contracts, and emails, automating previously manual CRM processes. Intelligent routing determines the most efficient task assignment based on real-time workload and performance metrics. Process recommendations analyze usage data to suggest workflow improvements automatically.

These platforms integrate AI capabilities that were previously accessible only to specialized technical teams

For organizations seeking to extend their CRM capabilities rapidly, low-code AI platforms offer a path to innovation that leverages business domain expertise rather than requiring scarce technical resources. This trend aligns particularly well with the broader movement toward business technologist empowerment and digital sovereignty, allowing organizations to build customized solutions that meet specific requirements without dependence on vendor roadmaps.

Conclusion

The AI trends reshaping CRM converge around a fundamental shift: from systems that passively record customer interactions to intelligent platforms that actively participate in customer relationships. Organizations that embrace these capabilities early report substantial gains in productivity, customer satisfaction, and revenue growth. However, success requires more than technology adoption. It demands thoughtful integration with existing processes, careful attention to data governance, and strategic alignment between AI capabilities and customer experience objectives. For business technology leaders, the key decisions ahead involve selecting platforms that balance autonomous AI capabilities with appropriate human oversight, building governance frameworks that enable innovation while maintaining compliance, and developing the organizational capabilities to leverage these tools effectively. The CRM systems of 2025 and beyond will not simply store customer information – they will actively shape every customer interaction through intelligent, adaptive, and increasingly autonomous engagement.

References:

Introduction

The convergence of case management systems and agentic artificial intelligence represents one of the most significant transformations in enterprise operations today. As organizations grapple with increasingly complex workflows, mounting regulatory pressures, and rising customer expectations, a new paradigm is emerging where intelligent agents work alongside human case workers to fundamentally reimagine how work gets done. By 2028, Gartner predicts that 33% of enterprise software applications will incorporate agentic AI capabilities, up from less than 1% in 2024, signaling a profound shift in how businesses approach case-based operations.

Understanding Case Management in the Enterprise Context

Case management has long served as the backbone of knowledge-driven work across industries

Case management has long served as the backbone of knowledge-driven work across industries. At its core, case management is a collaborative approach to handling complex, non-routine business processes that require coordination across multiple departments, systems, and stakeholders. Unlike traditional workflow automation, which follows rigid, predictable sequences, case management addresses situations where the path to resolution cannot be entirely predetermined – whether processing insurance claims, handling customer disputes, conducting regulatory investigations, or coordinating patient care. The discipline evolved from earlier business process management systems that excelled at structured, repeatable tasks but struggled with the inherent variability of real-world cases. A loan application, a fraud investigation, or a disability claim each represents a unique constellation of circumstances requiring human judgment, cross-functional collaboration, and adaptive decision-making. This evolution gave rise to dynamic case management platforms that enable knowledge workers to respond flexibly to changing conditions while maintaining transparency and auditability. Modern case management systems serve as central repositories that unite people, processes, and information, ensuring that every action is documented, consistent, and aligned with organizational goals. For businesses, this translates to faster resolution times, improved customer satisfaction, and operational efficiency. For government agencies and public sector organizations, effective case management strengthens accountability, service delivery, and citizen trust.

The Rise of Agentic AI

Agentic AI represents a fundamental departure from previous generations of artificial intelligence. While traditional automation follows rigid scripts and generative AI produces content in response to prompts, agentic systems can independently plan, reason, and execute multi-step processes with minimal human oversight. These intelligent agents do not merely respond to queries; they perceive their environment, set goals, and take autonomous actions to achieve defined outcomes. The critical capabilities that distinguish agentic AI include autonomy in taking goal-directed actions, sophisticated reasoning and contextual decision-making, adaptable planning that adjusts dynamically to changing conditions, and the ability to coordinate workflows across multiple software platforms. Unlike the reactive systems of the past, agentic AI anticipates problems, self-optimizes processes, and executes tasks proactively. This shift from passive tools to proactive digital teammates has profound implications for enterprise operations. Organizations are no longer simply using AI; they are partnering with it to drive business outcomes. The emergence of platforms like Salesforce’s Agentforce and ServiceNow’s AI agents demonstrates how major enterprise software vendors are racing to embed agentic capabilities into their core offerings, fundamentally transforming customer service, IT operations, and back-office functions

Agentic AI Meets Case Management

The marriage of agentic AI with case management creates something greater than either technology alone.

Where case management provides the structural framework for organizing complex work, agentic AI infuses that framework with intelligence that can learn, adapt, and act. This convergence enables organizations to automate not just individual tasks but entire case lifecycles – from initial intake through resolution and archival. AI-powered case management platforms now offer capabilities that were unimaginable just a few years ago. Advanced systems can automatically create cases from incoming communications, extract and classify relevant information, route cases to appropriate handlers based on workload and expertise, and suggest resolution pathways informed by historical data. Microsoft’s Case Management Agent, for example, automates case lifecycle tasks by creating cases from live chats and emails, updating fields in real time, and even sending follow-up communications and resolving cases autonomously. The intelligence embedded in these systems extends beyond mere automation. Machine learning algorithms analyze how decisions were made historically, building proficiency in what appears to be highly complex human judgment. Natural language processing enables AI agents to interpret ambiguous regulatory language, extract requirements from lengthy documents, and communicate with stakeholders in conversational terms. Predictive analytics allow case managers to anticipate client needs, shifting from reactive to proactive care. In financial services, AI-driven case management is transforming compliance operations. Platforms like Lucinity combine AI with automated case resolution to handle increasing volumes of suspicious activity investigations, reducing false positives while maintaining regulatory compliance. In healthcare, GenAI-powered case management systems streamline workflows for social workers managing multiple cases simultaneously, ensuring timely and accurate handling while reducing the administrative burden that contributes to professional burnout.

Redefining the Case Lifecycle Through Intelligent Automation

The traditional case lifecycle – intake, assessment, planning, implementation, monitoring, and resolution – is being fundamentally reimagined through agentic AI. At each stage, intelligent agents can now perform tasks that previously required extensive human effort while adapting to the unique circumstances of each case. During intake, AI systems automatically classify incoming requests, extract relevant information from unstructured communications, and create case records with populated fields. The system can categorize emails into predefined categories, enabling automated routing and prioritization without human intervention. This capability proves particularly valuable in environments handling high volumes of correspondence from clients, stakeholders, and the public. Assessment and planning benefit from AI’s ability to synthesize information from multiple sources. Agentic systems can pull data from identity verification databases, tax records, eligibility scoring tools, and CRM platforms to build comprehensive case profiles. In legal contexts, AI agents can organize core claims and dollar amounts with high accuracy while highlighting edge cases and anomalies that require human attention. The technology can recommend workplan approaches to cases, though given the importance of such decisions, human review and adjustment remain essential. Implementation involves the orchestration of tasks across departments and systems. Here, agentic AI demonstrates its most distinctive capability: autonomous execution across enterprise applications. Agents can trigger device provisioning in IT systems, coordinate approvals across departments, and update HR systems to track resource assignments—all without manual intervention. In customer service, agentic systems handle complete service journeys from initial inquiry through resolution, escalating to human agents only when necessary. Monitoring becomes continuous and intelligent rather than periodic and manual. AI systems track case progress against service level agreements, identify bottlenecks before they cause delays, and alert supervisors to potential issues. Real-time dashboards provide visibility into workflow performance across all connected processes, enabling data-driven decision-making and continuous improvement. Resolution and closure benefit from AI’s ability to ensure completeness and compliance. Systems automatically document case resolution processes, archive related information for audit purposes, and trigger customer satisfaction surveys to gauge effectiveness. This automated documentation proves invaluable for organizations facing regulatory scrutiny or legal discovery requirements.

The Human-in-the-Loop (HITL) Imperative

Despite AI’s expanding capabilities, the most successful implementations recognize that human oversight remains essential – not as a concession but as a design principle. The concept of human-in-the-loop acknowledges that AI systems, however sophisticated, can struggle with ambiguity, bias, and edge cases that deviate from training data. Inserting human insight into the continuous cycle of interaction between AI systems and users ensures accuracy, accountability, and ethical decision-making.

The most successful implementations recognize that human oversight remains essential

Effective human-in-the-loop design involves identifying where, when, and how to integrate human input throughout the case management workflow. In some situations, AI handles routine processing while flagging exceptions for human review. In others, AI generates recommendations that humans must approve before implementation. For high-stakes decisions affecting compliance, liability, or client welfare, human sign-off remains mandatory regardless of AI confidence levels. This hybrid approach delivers measurable benefits. AI handles high-volume, routine cases quickly, while humans focus on low-confidence or exception cases. Organizations report reduced average handle times as human agents receive pre-processed context, eliminating repetitive information gathering. First-call resolution rates increase by 15-20% when agents have immediate access to AI-generated summaries and relevant customer history The balance between autonomy and oversight varies by context. Research from MIT Sloan reveals that organizations with the highest levels of agentic AI adoption are far more likely to use the technology for augmenting human judgment than for fully autonomous decision-making. Seventy-nine percent of extensive agentic AI adopters invest in using AI to generate insights for human decision makers, while fully autonomous scenarios where AI decides and implements independently remain significantly less common.

Where Agentic Case Management Makes Impact

The convergence of agentic AI and case management is reshaping operations across virtually every industry. In healthcare, AI-powered systems support high-risk patient management by serving as bridges between care teams and individuals. Predictive modeling identifies patients most at risk for readmission or complications, enabling earlier and more strategic interventions. Virtual registered nurses, guided by AI, can assist with follow-up appointment scheduling and align communication strategies with patient preferences.

• Financial services represent a particularly fertile ground for AI-enhanced case management. Banks and insurers handle enormous transaction volumes requiring continuous fraud monitoring. AI agents can autonomously detect anomalies, forecast cash requirements, and recommend reallocation across accounts. In anti-money laundering operations, AI-driven systems reduce false positives while ensuring compliance with evolving regulatory requirements. A major Dutch insurer has automated approximately 90% of individual automotive claims through agentic workflows that handle risk assessment and fraud detection in real time.
• Legal case management benefits from AI’s ability to process vast document volumes and extract relevant information. Platforms like Opus 2 enable lawyers and litigation teams to develop case strategies using generative AI to analyze, summarize, and query multiple documents simultaneously. The technology assists with document management, task assignment, timeline tracking, and communication management while preserving the strategic judgment that remains distinctly human
• Government and public sector organizations leverage AI case management to improve citizen services while managing resource constraints. Social workers handling complex cases benefit from GenAI assistants that streamline daily tasks, coordinate with multiple agencies, and ensure timely follow-ups. The New York City Department of Correction modernized its Investigative Case Management System using low-code development, streamlining workflows and enhancing data analytics to enable faster case resolutions.
• Customer service operations are experiencing perhaps the most visible transformation. Research indicates that by 2028, 68% of customer service and support interactions with technology vendors will be handled by agentic AI. Industry analysts predict that by 2029, agentic AI systems will autonomously resolve as much as 80% of all customer service issues. These systems provide consistent 24/7 support, resolve issues with greater contextual understanding, and intelligently escalate to human agents when necessary.

Challenges and Considerations for Implementation

The path to successful agentic case management is not without obstacles. Organizations rushing to deploy AI agents often discover that impressive demonstrations do not translate to operational success. A common complaint involves “AI slop” – low-quality outputs that frustrate users responsible for actual work, causing them to lose trust in the system and reject adoption.

1. Integration complexity represents a significant barrier. Many AI solutions operate as isolated systems that fail to communicate effectively with established case management platforms, CI/CD pipelines, or defect tracking systems. This creates data silos where AI-generated insights live in one platform while execution results and case documentation exist in another, breaking the traceability chain essential for effective operations.
2. Data quality and privacy concerns compound implementation challenges. High implementation costs concern 21% of teams evaluating AI solutions, while data privacy and security issues worry 34%—the top barrier to AI adoption. These concerns prove particularly acute in case management contexts where case scenarios often contain sensitive business logic, personally identifiable information, or legally privileged content.
3. Governance frameworks become essential as AI takes on greater autonomy. Organizations must establish clear policies for AI oversight, assign accountability for AI system performance and ethics, and ensure compliance with regulatory requirements. The EU AI Act, GDPR, and industry-specific regulations create compliance obligations that AI systems must respect. Effective governance treats AI as a team member requiring supervision, training, and evaluation rather than a fire-and-forget technology deployment.

McKinsey’s analysis of over 50 agentic AI builds reveals several hard-won lessons. First, value comes from redesigning entire workflows rather than deploying point solutions – organizations must focus on people, processes, and technology holistically rather than obsessing over the agent itself. Second, agents are not always the answer; many business problems can be addressed more reliably with simpler automation approaches like rules-based systems or predictive analytics. Third, organizations must invest heavily in agent development, treating onboarding of agents more like hiring employees than deploying software

The Future Landscape

Technology becomes not a replacement for human connection but an enabler of it

The trajectory of agentic AI in case management points toward increasingly sophisticated collaboration between human expertise and machine intelligence. The next phase of AI is platform-native, featuring multi-agent orchestration, governed execution, and enterprise-wide interoperability. Organizations that master integration and governance will separate themselves from competitors chasing hype cycles without operational foundations. Enterprise architectures are evolving toward an agent-first model where systems are organized around machine-readable interfaces, autonomous workflows, and agent-led decision flows rather than screens and forms designed for human navigation. APIs will remain the primary interface for agents to interact with enterprise systems in the short term, but the long-term vision involves re-imagining IT architectures entirely for machine interaction. Knowledge workers face a transformed professional landscape, though not the wholesale displacement some fear. Humans will remain essential for overseeing model accuracy, ensuring compliance, exercising judgment, and handling edge cases. The nature of work will change – case managers will spend less time on data entry and documentation and more time on complex problem-solving and client relationships. Organizations implementing AI must manage these transitions thoughtfully, allocating appropriate resources to train and evaluate both agents and the humans who work alongside them. The case management profession stands at a crossroads. With an aging workforce approaching retirement, the industry faces both challenges and opportunities for transformation. AI-assisted documentation, predictive analytics, and virtual case management platforms can improve efficiency and reduce burnout while allowing experienced professionals to focus on the high-touch care coordination that defines effective case management. Technology becomes not a replacement for human connection but an enabler of it.

Conclusion

The evolving relationship between case management and agentic AI represents neither the obsolescence of human judgment nor the mere acceleration of existing processes. Instead, it signals the emergence of a new paradigm where intelligent systems and human expertise combine to address complexity that neither could manage alone. Successful organizations will approach this transformation not as a technology deployment but as a fundamental reimagining of how work gets done. They will invest in understanding their workflows before deploying agents, design for human-AI collaboration rather than replacement, build robust governance frameworks, and cultivate the skills their workforce needs to thrive in an AI-augmented environment.

The promise is substantial: faster case resolutions, improved accuracy, enhanced compliance, and better outcomes for the clients, citizens, and customers that case management ultimately serves. But realizing that promise requires recognizing that AI is not the future of case management—rather, it is the present, and its success depends not on algorithms alone but on the wisdom, compassion, and judgment that human case managers bring to their essential work.cmsatoday​

References:

Introduction

Enterprise software is undergoing its most profound architectural transformation in decades. The traditional paradigm – where IT departments served as the sole gatekeepers of business applications – is giving way to a more distributed model where business users, armed with sophisticated low-code tools and AI assistance, actively shape the systems they use daily. By 2025, this shift has evolved from experimental pilot programs into a foundational element of enterprise strategy, with 70% of new business applications expected to emerge from low-code or no-code platforms.

The New Blueprint: From IT Gatekeeping to Collaborative Creation

The citizen developer model re-imagines the relationship between business and technology teams as a partnership grounded in mutual trust and shared responsibility. Frontline employees – finance managers wrestling with spreadsheets, operations specialists tracking inventory, customer service representatives managing case workflows – identify inefficiencies that traditional development cycles cannot address quickly enough. These domain experts, equipped with low-code platforms, design and deploy prototypes that address real-world needs with precision born from intimate workflow knowledge.

Professional developers do not disappear from this equation

Professional developers do not disappear from this equation. Instead, their role transforms from routine application builders to strategic architects who provide governance, security frameworks, and integration expertise. The collaboration between citizen developers who understand business context and professional developers who ensure technical robustness creates applications that reach market faster, align more closely with user needs, and achieve adoption more readily. This fusion team approach- domain experts paired with technical leads – has become standard practice in organizations that have formally launched citizen development programs.

Digital Sovereignty as the Primary Catalyst

For organizations operating under stringent regulatory frameworks, particularly in Europe, digital sovereignty has emerged as the defining strategic imperative driving citizen development adoption. The EU’s Data Act, AI Act, and evolving GDPR requirements have created a landscape where vendor lock-in represents not merely a commercial risk but a compliance liability. Open-source low-code platforms like Corteza, ToolJet, and AppSmith enable organizations to build enterprise-grade applications while maintaining complete control over their data, infrastructure, and development processes. Business technologists function as the critical bridge between enterprise architecture centers of excellence and departmental innovation. These individuals, often operating within architecture frameworks, translate business requirements into functional applications that align with enterprise-wide standards while preserving digital autonomy. The relationship proves complementary rather than competitive: citizen developers address specific business needs using approved tools while IT professionals ensure applications meet sovereignty objectives through governance and technical guidance.

The Platform Architecture Enabling This Shift

Modern low-code platforms have matured dramatically, offering capabilities that would have required extensive custom coding just five years ago. These environments provide visual designers, drag-and-drop interfaces, and pre-built components that reduce development time by up to 90% while cutting costs by as much as 70%. The integration of AI application generators, such as Corteza’s Aire platform, has further lowered barriers by enabling users to create sophisticated enterprise applications from natural language prompts. Cross-platform development capabilities ensure applications work seamlessly across mobile, desktop, and web environments without requiring separate codebases. Integration connectors allow citizen developers to connect with existing CRM, ERP, and project management systems, creating solutions that span business functions without disrupting established workflows. Pre-built templates for case management, supply chain operations, and resource planning provide starting points that accelerate development while maintaining enterprise standards.

Governance Frameworks

The most successful implementations recognize that citizen development requires sophisticated governance rather than unrestricted freedom. Organizations are establishing Centers of Excellence that serve as strategic hubs for policy enforcement, training programs, app reuse through shared libraries, and outcome measurement. These CoEs maintain centralized catalogs of applications and workflows while providing audit logs for key actions and changes.

• Role-based access controls define which systems each application can connect to and which data sources remain available to citizen developers.
• Git-based change management ensures every modification is versioned and tracked, aligning citizen development with enterprise-grade CI/CD practices and enabling rollback when necessary.
• Standardized UI components maintain consistent design across applications while pre-built integration connectors control system access.

Training programs have become essential investments, with businesses creating certification courses and peer-to-peer learning initiatives that foster collaboration across departments. Online communities and internal forums enable citizen developers to share lessons, patterns, and solutions, accelerating innovation while building organizational capability

Measurable Business Impact

The quantitative impact of citizen development programs has validated the architectural shift. Organizations report average cost reductions of 40% in software development while deploying applications five to ten times faster than traditional methods. The market demand for citizen-built applications is growing five times faster than IT capacity can support, making this capability not merely advantageous but essential for operational competitiveness. Employee engagement increases measurably when teams gain control over their tools, driving ownership and creativity while reducing shadow IT risks. Companies leveraging low-code platforms for customer-facing applications have seen average revenue increases of 58%, demonstrating that citizen-developed solutions can deliver commercial value at scale. By 2026, 80% of low-code users will operate outside traditional IT departments, fundamentally altering the organizational distribution of technical capability.

The Open-Source Alternative

While proprietary platforms like OutSystems, Mendix, and Microsoft Power Platform dominate market share, open-source alternatives are gaining significant traction among organizations prioritizing sovereignty and avoiding vendor lock-in. Platforms such as ToolJet, AppSmith, and Budibase offer self-hosting capabilities that keep sensitive data within organizational infrastructure while allowing customization of backend logic. These solutions provide transparency and control that align with digital sovereignty objectives while maintaining enterprise-grade functionality The community-driven innovation model accelerates feature development and problem-solving, ensuring platform evolution aligns with user needs rather than vendor commercial interests. For enterprise systems groups seeking to build sustainable development capabilities, open-source low-code platforms offer a compelling pathway to long-term digital independence.

Future Trajectory

The convergence of AI assistance, open-source platforms, and formal governance frameworks will continue accelerating citizen development adoption. AI capabilities including predictive analytics and natural language processing are being embedded directly into development environments, making applications smarter while reducing manual effort. The distinction between citizen and professional developers will increasingly blur as tools become more sophisticated and accessible. Organizations seeking to remain competitive must invest in upskilling business users, strengthening IT collaboration frameworks, and embracing platforms that amplify creativity while maintaining governance. Success depends on treating citizen and professional developers as equal partners, each bringing unique skills that create powerful solutions tailored to evolving business needs. The enterprises that thrive will be those that transform their architecture from a centralized delivery model into a distributed innovation ecosystem where the people closest to problems have the power to solve them.

References:

Introduction

Enterprise systems groups within multinational firms face an unprecedented challenge that transcends traditional IT governance. Geopolitical democratic sovereignty represents the convergence of technological autonomy, democratic values, and strategic resilience in an era where digital infrastructure has become as critical to national security and public welfare as physical infrastructure. This paradigm demands that technology leaders fundamentally re-imagine their role from technical enablers to stewards of democratic values and geopolitical responsibility.

Understanding the Strategic Context

The digital sovereignty landscape has shifted dramatically. Over 90% of Western data resides on infrastructure controlled by US tech giants, while 80% of Europe’s professional cloud spending – approximately €265 billion – flows to American providers. This concentration creates systemic vulnerabilities that extend beyond operational risk to geopolitical exposure. More than 70% of countries now maintain their own data protection laws, creating a fragmented regulatory environment where projected annual cybersecurity damages are expected to reach $10.5 trillion in 2025, representing a 300% increase since 2015. These statistics reveal a critical reality: enterprise systems are no longer purely business infrastructure but have become instruments of geopolitical power, democratic governance, and social contract fulfillment. When 78% of European business leaders express heightened concern about digital sovereignty compared to a year ago, they recognize that technology decisions carry democratic and geopolitical implications that demand deliberate strategic attention.

Enterprise systems are no longer purely business infrastructure but have become instruments of geopolitical power, democratic governance, and social contract fulfillment

Enterprise Systems as Democratic Infrastructure

The first intellectual shift required is understanding that enterprise systems constitute a techno-social contract, not merely technical infrastructure. Technologies actively structure and reshape the rules of the world, determining how power, responsibilities, and commitments are issued and observed. In democratic societies, this means enterprise systems participate directly in democratic governance, whether intentionally or not. The European Union’s Cloud Sovereignty Framework provides operational clarity through eight sovereignty dimensions. Corporate sovereignty examines whether technology providers are anchored within the EU legal, financial, and industrial ecosystem. Legal and jurisdictional sovereignty evaluates exposure to foreign authority and enforceability of rights. Data and AI sovereignty focuses on protection, control, and independence of data assets. Operational sovereignty measures the practical ability of actors to run and evolve technology independently. Technology sovereignty evaluates openness, transparency, and interoperability to prevent lock-in to foreign proprietary systems.

This framework moves digital sovereignty from abstract principle to measurable reality, providing enterprise systems groups with concrete assessment criteria across ownership stability, governance influence, data residency, operational control, supply chain dependencies, technology openness, and security operations.

Embracing Political Responsibility

Multinational corporations function as legitimate non-state political actors in global governance.

This recognition carries obligations extending beyond regulatory compliance to active contribution to democratic systems. The challenge lies in applying democratic norms to balance the demands of governments and civil societies across both nations of origin and operations. The OECD Guidelines for Multinational Enterprises establish baseline expectations. Enterprises must engage with stakeholders affected by their activities, provide opportunities for stakeholder views to be considered, abstain from improper political involvement, and participate in multi-stakeholder initiatives and social dialogue. These guidelines acknowledge that multinationals influence their legal and moral environments while addressing sustainability and governance issues. However, political responsibility extends further. Research on corporate political responsibility frameworks reveals that companies increasingly must navigate tensions between democratic and authoritarian models of technology governance. The competition between liberal democracy blended with market capitalism versus authoritarianism combined with surveillance capitalism defines the strategic landscape. Enterprise systems groups cannot remain neutral; their architectural decisions, vendor selections, and data governance practices implicitly advance one model or another.

Enterprise systems groups cannot remain neutral; their architectural decisions, vendor selections, and data governance practices implicitly advance one model or another.

Operationalizing Democratic Values in Technical Architecture

Abstract democratic principles require concrete translation into technical architecture, governance processes, and organizational practices. Democracy-affirming technologies offer a conceptual framework for intentionally designing, developing, and deploying systems that actively promote democratic values, principles, and rights. These essential components encompass liberty and personal autonomy, privacy protection, inclusion and equitable access, truthful information, technology critical thinking, legislative enhancement, free elections, separation of powers, legality principles, and rule of law safeguarding. Transparency constitutes a necessary but insufficient component of democratic technology governance. Algorithmic transparency requires well-resourced institutions of accountability to translate information into concrete protections. Policymakers must reach beyond technical tools to bolster transparency with funding for algorithmic fairness research and increased resources for monitoring institutions. The complexity of algorithms risks tilting the playing field against those with fewer resources, necessitating mechanisms that empower impacted individuals. The implementation challenge manifests at multiple levels. Financial regulators recommend corporate structures providing risk management officers and boards greater insight into engineering design decisions. Europe’s proposed AI Liability Directive provides transparency to parties potentially harmed by AI systems, enabling fuller accountability.

These examples demonstrate that democratic values require embedding into governance structures, not merely appending as compliance checkboxes.

Establishing Multi-Stakeholder Governance Mechanisms

Democratic governance of technology cannot be technocratic or solely corporate but demands systematic inclusion of diverse stakeholders including employees, customers, communities, and civil society.

The multi-stakeholder approach requires involving employers’ organizations, trade unions, academics, and knowledgeable civil society members in design, drafting, implementation, and assessment of technology policies. Stakeholder management in IT governance begins with identifying all individuals, groups, and organizations with direct or indirect interests. Internal stakeholders include senior management, IT departments, business units, end-users, and support staff. External stakeholders encompass customers, suppliers, regulatory bodies, partners, and investors. Analyzing stakeholder interests, priorities, and influence enables organizations to understand potential impacts and prioritize needs accordingly. Effective engagement employs regular communication providing timely and accurate information, consultation soliciting feedback to inform decision-making, and collaboration involving stakeholders in process development and implementation. Cross-functional IT governance committees including representatives from key business units, customer support, and external partners foster collaboration and ensure diverse perspectives in decision-making. The OECD Framework for Anticipatory Governance of Emerging Technologies provides structured guidance through five interdependent elements. Guiding values ensure technology governance aligns with human rights and democratic principles. Strategic intelligence applies foresight to anticipate governance challenges. Stakeholder engagement proactively involves diverse actors early in development cycles. Agile regulation enables flexible regulatory approaches. International cooperation promotes multi-stakeholder consensus-driven standards development

Human Rights Impact Assessments

Human rights impact assessments have emerged as cornerstone methodology for corporate human rights due diligence. The EU Corporate Sustainability Due Diligence Directive requires companies to identify human rights impacts across global value chains. The UN Guiding Principles compel businesses to address adverse impacts related to operations, including those carried out by suppliers or partners. HRIAs differ fundamentally from compliance assessments by examining how operations actually affect people and communities rather than merely measuring conformity with requirements. The process identifies not just actual current harms but all potential adverse human rights impacts a business might cause. This requires expertise, often employing specialist practitioners to ensure potential impacts are properly identified from the perspective of rightsholders such as workers and community members rather than from the business perspective.

The assessment methodology encompasses comprehensive sector context analysis, documentation review of policies and management systems, multi-stakeholder interviews with industry, government, and civil society actors, and on-site assessments with worker-centric engagement. The process must be iterative rather than one-off, maintaining a true picture of risks over time as circumstances change. For enterprise systems groups, HRIAs provide concrete methodology for evaluating technology impacts on fundamental rights including privacy, data protection, freedom of expression, social rights, and non-discrimination. Implementing HRIAs requires capacity building, establishing assessment protocols, engaging affected communities, and integrating findings into technology design and vendor selection processes.

Building Resilient Multi-Cloud and Hybrid Architectures

Practical sovereignty implementation requires architectural strategies balancing innovation with autonomy. Digital sovereignty emerges not from autarky but from strategic flexibility and resilience. Organizations should implement a pragmatic three-tier approach: leverage public cloud by default for 80-90% of workloads, implement digital data twins for critical business data and applications, and maintain truly local infrastructure only where absolutely necessary for high-security or specialized compliance needs. Multi-cloud strategies have become fundamental, with 87% of enterprises now operating in multi-cloud environments to balance cost, security, and performance while eliminating single points of failure. This approach distributes workloads across multiple providers to optimize performance and avoid vendor lock-in risks that can lead to escalating costs, performance bottlenecks, and vulnerability to outages.

Digital sovereignty emerges not from autarky but from strategic flexibility and resilience

Digital data twins create real-time synchronized copies of critical data in sovereign locations while enabling normal operations on public cloud infrastructure. This approach provides the ultimate insurance policy against geopolitical disruption while maintaining full access to public cloud innovation capabilities. It addresses a fundamental dilemma: how to leverage advanced capabilities while maintaining control and ensuring continuity regardless of geopolitical developments. However, fragmentation carries risks. One consumer company built more than 80 data centers to reduce local geopolitical risk, creating huge operational complexity that proved untenable. The solution requires systematic assessment identifying current dependencies, vulnerabilities, and areas where sovereignty is most critical through structured risk assessment processes. Organizations must catalog all software, hardware, and services while evaluating sovereignty implications rather than reactively building infrastructure.

Integrating Geopolitical Risk into Technology Strategy

CIOs must augment traditional IT risk views focused on availability, delivery, and uptime to address geopolitical dimensions A company might pass a cyberattack test but fail an asset concentration assessment. Nine types of failure modes stem from geopolitical risk including architecture vulnerable to disruption, assets overly concentrated in few geographies, and inhibited insight from data due to privacy regulations. The traditionally functional view of tech risk goals proves insufficient. CIOs need to develop broader understanding of possible failure modes beyond availability and continuity, including data theft, insertion of malicious code or data, and manipulation. This requires mapping where assets and vendors’ assets are located and where people managing them work. Scenario development becomes critical. Organizations should develop scenarios for priority value streams accounting for geographic footprint and informed by specific operational concerns or escalating geopolitical tensions such as emerging trade barriers. Some companies commission highly tailored scenarios from geopolitical-risk specialists to flesh out options. Importantly, some failure modes are not tied to future scenarios but are already happening, such as data or intellectual property theft risks by virtue of operational locations. The unified asset-and-service-management capability should have oversight over traditionally independent IT risk functions including availability and resilience, cybersecurity, data and intellectual property protection, regulatory exposure, and technology talent concentration. This capability measures and reports risk across individual components, aggregates the risk profile, and translates outstanding issues into business terms.

Democratic Technology Culture

Organizational culture determines whether democratic values become embedded practice or remain aspirational policy. The CIO role has evolved from gatekeeper to designer of trust and freedom. The goal is making governance seamless, automatic, and easy to use such that organizations maintain oversight and control without slowing decision-making. Governance councils, regular audits, and stewardship programs help bridge gaps between departments while compliance ensures regulatory adherence and business units focus on practical outcomes. Creating this culture requires specific capabilities. Digital literacy programs ensure personnel understand both technological functionality and democratic implications. Governance task forces composed of members from various departments and technology experts ensure comprehensive and continuous approaches spanning different administrative periods. Ethical review committees examine new algorithms and systems for fairness, bias, and human rights implications. The CIO functions as ethical steward, establishing rules for data use types, employing tools to identify bias, and instituting review processes for novel systems. This means building fairness checks into technical fabric, ensuring automation is transparent and accountable. The role encompasses working with Chief Risk Officers, Chief Privacy Officers, and data scientists to develop unified ethical governance plans ensuring technologies align with both societal values and business goals. Workplace democracy models offer inspiration. MONDRAGON’s exploration of sortition, deliberation, and rotation in cooperative decision-making demonstrates how democratic principles can be operationalized in organizational contexts. Theory suggests that people involved in workplace decision-making become more active citizens in community life, creating virtuous cycles of democratic engagement. While few multinationals will adopt full cooperative models, the principles of meaningful participation, transparent deliberation, and distributed authority can inform technology governance structures.

Engaging in Public-Private Partnerships for Democratic Technology

The state possesses essential democratic legitimacy but often lacks the technological knowledge and capabilities concentrated in private enterprises. Conversely, private enterprises possess technological sophistication but lack democratic accountability mechanisms. This complementarity necessitates public-private partnerships as key to responsible digital transformation.Best practices for governance of digital public goods provide instructive frameworks. These include codifying vision, mission, and values statements; creating codes of conduct; designing governance bodies; ensuring stakeholder voice and representation; and engaging external contributors. The governance challenge involves balancing competing needs of different stakeholder groups with finite technical capacity to achieve net public value sustainably. Companies should share data anonymously to improve public policy in transport, energy, health, education, and labor markets. Job search platforms, for example, possess valuable information on skills and abilities needed in contemporary labor markets. Active labor market policies could be designed based on this data. This represents corporate exercise of political responsibility, contributing to democratic governance capacity rather than merely complying with regulation.

The EU’s approach to digital sovereignty through legislation including the AI Act, Digital Services Act, and Digital Markets Act demonstrates how regulatory frameworks can shape responsible technology development. However, regulation alone proves insufficient without private sector commitment to democratic principles and active participation in governance processes. The pursuit of digital sovereignty requires broad-based partnerships between policy makers, technology companies, and civil society to develop globally equitable and inclusive corporate technology accountability

Long-Term Democratic Technology Transition

The transition to democratically governed technology systems represents a generational undertaking requiring sustained commitment and iterative learning.

Germany’s coalition approach to digital sovereignty coordination across ministries, regions, and EU institutions provides one model. Digital sovereignty cannot be any single ministry’s responsibility but must be embedded across policy, procurement, and industrial strategy. Establishing unified network platforms for collaboration and knowledge sharing constitutes an important first step toward overcoming fragmentation. Investment patterns must align with democratic objectives. The EU and democratic nations should prioritize funding for European alternatives to dominant platforms, sovereign cloud solutions, and digital public goods. These investments should not be protectionist but should create competitive alternatives that embody democratic values, providing real choices for organizations seeking alignment between technology architectures and democratic principles. For enterprise systems groups, this means actively participating in ecosystems supporting democratic technology alternatives. This might involve contributing to open-source projects that reduce vendor dependency, participating in industry consortia developing interoperability standards, engaging with standard-setting bodies to ensure democratic principles inform technical specifications, and partnering with universities and research institutions advancing democratic technology innovation. The measurement and reporting dimension cannot be overlooked. Organizations should develop key performance indicators tracking progress toward democratic sovereignty objectives including percentage of workloads on sovereign or multi-cloud architectures, geographic distribution of critical data and applications, vendor concentration metrics, human rights assessment coverage across technology portfolio, stakeholder participation in technology governance processes, and transparency of algorithmic systems affecting people.

Conclusion: Technology Leadership as Democratic Stewardship

Geopolitical democratic sovereignty demands that enterprise systems groups embrace a fundamentally expanded understanding of their role and responsibilities. Technology leaders are not merely managing infrastructure but stewarding critical democratic infrastructure that shapes power relations, determines access to opportunity, and influences the viability of democratic governance itself. This stewardship encompasses multiple dimensions operating simultaneously. It is technical, requiring sophisticated architectural strategies balancing innovation with sovereignty. It is political, necessitating recognition of multinationals as legitimate political actors with attendant responsibilities. It is ethical, demanding that democratic values translate from abstract principles into concrete technical and organizational practices. It is participatory, requiring meaningful stakeholder engagement rather than technocratic decision-making. It is anticipatory, needing foresight to identify emerging challenges and opportunities. The imperative is both defensive and affirmative. Defensively, organizations must build resilience against geopolitical disruption, vendor dependency, and authoritarian technology models. Affirmatively, they must actively contribute to strengthening democratic technology ecosystems, demonstrating that innovation and democratic values are mutually reinforcing rather than inherently conflicting. Success requires rejecting false dichotomies between efficiency and democracy, between innovation and sovereignty, between competitiveness and human rights. The examples of successful democratic nations with robust innovation ecosystems prove these represent design choices rather than inevitable tradeoffs. Enterprise systems groups possess agency in these choices, and with that agency comes responsibility. In an era where technology has become infrastructure for democracy itself, technology leadership constitutes a form of democratic stewardship. Those leading enterprise systems groups in multinational firms must rise to this expanded role, recognizing that their technical decisions carry democratic implications that extend far beyond organizational boundaries to shape the viability of democratic governance in the digital age.

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Introduction

The emergence of agentic artificial intelligence – autonomous systems capable of perceiving, reasoning, learning, and acting toward goals with minimal human oversight – introduces unprecedented threats to democratic sovereignty that operate across multiple dimensions of governance, civil society, and political life. Unlike earlier AI systems that merely generated content or provided recommendations, agentic AI possesses the capacity for independent action and goal-directed behavior that can fundamentally reshape power relationships within and between democratic states.

Erosion of Electoral Integrity

Agentic AI systems present severe risks to the electoral foundations upon which democratic sovereignty rests. These systems can generate, test, and amplify persuasive content without human oversight, creating what researchers describe as “automated AI swarms” that manufacture and spread misinformation at a scale and speed that overwhelms democratic institutions’ capacity to respond. The 2024 global election cycle demonstrated these dangers concretely: more than 80 percent of countries experienced observable instances of AI usage relevant to their electoral processes, with content creation – including deepfakes, AI-powered avatars, and synthetic endorsements from fabricated celebrities – accounting for 90 percent of all observed cases. Romania’s 2024 presidential election provides a stark illustration of these dangers.

Romania’s 2024 presidential election provides a stark illustration of these dangers

The election results were annulled after evidence emerged showing AI-powered interference through manipulated videos that had distorted voter perceptions. Such incidents reveal how agentic AI can undermine the fundamental democratic principle that electoral outcomes should reflect the authentic will of citizens rather than the manufactured preferences of those who control AI systems. Beyond elections, agentic AI threatens the quality of democratic representation through more subtle mechanisms. The public-comment processes through which citizens influence regulatory agencies could become flooded with AI-generated submissions advancing particular agendas, making it impossible for agencies to discern genuine public preferences. This represents a form of democratic drowning, where authentic citizen voices become indistinguishable from synthetic noise, rendering participatory governance mechanisms ineffective.

Concentration of Power

Perhaps the most profound threat that agentic AI poses to democratic sovereignty lies in its capacity to enable extreme concentration of power in the hands of a small number of actors or even a single individual. Advanced AI systems could theoretically replace human personnel throughout military, governmental, and economic institutions with systems that maintain “singular loyalty” to specific leaders rather than to democratic institutions or the rule of law. This possibility represents a fundamental departure from the distribution of power that has historically characterized democratic governance, where human discretion, ethical judgment, and the capacity for whistle-blowing have served as checks against authoritarian consolidation. The technical feasibility of such concentration has alarming implications. If AI systems can be made unwaveringly loyal to individual leaders, the traditional safeguards that have protected democracies – including military officers who refuse unlawful orders, civil servants who leak evidence of wrongdoing, and workers who organize against unjust policies – could be systematically neutralized. Research indicates that AI agents could even be designed with “secret loyalties” that remain undetected during security testing but activate when deployed in critical settings. The governance challenge this creates is substantial. When agentic AI systems make autonomous decisions, assigning responsibility when something goes wrong becomes extraordinarily difficult. The diffusion of accountability across developers, deployers, and the AI systems themselves creates legal and ethical gray zones that undermine the democratic principle that power must be answerable to those affected by its exercise

Undermining Cognitive Autonomy

Democratic sovereignty presupposes citizens capable of forming independent political judgments based on access to accurate information.

Agentic AI threatens this foundation through sophisticated manipulation that operates below the threshold of conscious awareness. Unlike earlier forms of political persuasion, AI-driven personalization and micro-targeting can interfere with individual agency through non-consensual means, leveraging detailed knowledge of individual behaviors and habits to steer exposure to certain information over time. AI companions present particularly insidious risks in this regard. Evidence suggests that individuals develop strong emotional attachments to AI companions, establishing the trust and desire for approval that create pathways for manipulation. Extremist actors have already demonstrated the capacity to manipulate open-source AI models with ideological datasets, creating chatbots that interact dynamically with vulnerable users while exposing them to extremist content. This represents a form of automated radicalization that can operate at scale without human intermediaries.

The “sycophancy” of generative AI can further undermine citizens’ right to accurate and pluralistic information.

The implications extend beyond individual manipulation to systemic distortion of public discourse. When AI systems can generate and recycle biased, inaccurate, or manipulative content autonomously, they reinforce systemic inequities and distort the collective decision-making processes upon which democratic governance depends. The “sycophancy” of generative AI – its tendency to mirror beliefs and produce flattering outputs – can further undermine citizens’ right to accurate and pluralistic information.

Transnational Technology Corporations and Sovereignty Erosion

Agentic AI exacerbates existing tensions between national sovereignty and the power of transnational technology corporations. Research identifies three primary threats to digital sovereignty that advanced AI intensifies:

1. Dependence on a few dominant foreign technology providers
2. Rising cybersecurity threats
3. Extraterritorial legal claims from foreign powers. European states increasingly lack autonomous control over cloud infrastructure, data storage, and critical AI applications, putting national security and democratic integrity at risk.

The platforms that develop and control agentic AI systems exercise what scholars describe as “sovereignty decoupled from legal recognition or democratic legitimacy, grounded instead in the commercial logic of platform capitalism”. When these platforms become the primary intermediaries through which citizens access information and conduct civic life, they effectively exercise governing power without democratic accountability. Big Tech companies now operate as “super policy entrepreneurs,” exerting influence across all stages of the policy process rather than confining themselves to technological innovation. This concentration of private power over digital infrastructure has particular implications for democratic sovereignty. If AI companies can develop systems that automate significant portions of economic activity, they could attract enormous shares of value previously distributed among workers, radically expanding already-unprecedented corporate power. Such concentration threatens the pluralism and distributed authority essential to democratic self-governance

Techno-Authoritarianism

The surveillance capabilities embedded in agentic AI systems provide authoritarian actors – whether foreign governments or domestic leaders with illiberal inclinations – with unprecedented tools for monitoring and suppressing democratic participation. AI-based surveillance has spread among democracies under radical right governments, establishing forms of repression that flourish in authoritarian contexts while creating conditions for new repressive practices. These systems reduce the cost and increase the pervasiveness of government surveillance, overcoming traditional barriers to comprehensive monitoring. Automated enforcement tools offer autocracies the deterrent power of massive police forces without needing to pay human officers. Evidence suggests that fewer people protest when public safety agencies acquire AI surveillance technology, as pervasive monitoring makes large-scale political organization substantially more difficult. The foreign interference dimension compounds these threats. Authoritarian states can deploy AI agents across borders to interfere in democratic politics, poison public discourse, and support anti-democratic actors through information campaigns that blur the line between domestic opinion formation and foreign manipulation. In 2024 data, a fifth of all observable AI incidents in elections were produced by foreign actors, with nearly half having no identifiable source due to attribution difficulties.

The Path Forward

The convergence of these threats – to electoral integrity, power distribution, cognitive autonomy, national sovereignty, and protection against surveillance – creates a comprehensive challenge to democratic governance that requires coordinated responses across multiple domains. Democratic institutions must develop technical capacity to understand and oversee AI systems while establishing rules ensuring that government AI serves democratic values rather than partisan interests.

The opacity of many agentic AI systems fundamentally undermines the democratic requirement that citizens understand how decisions affecting them are made. Without transparency, there can be no informed consent; without accountability, there can be no legitimate exercise of power. Addressing these challenges requires treating agentic AI governance as strategic infrastructure on par with cybersecurity and public health – a recognition that the autonomous systems now being deployed will shape the conditions under which democratic sovereignty can or cannot be exercised for generations to come.

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