November 27, 2025
8 min read
By Spyros Karathanasis
CPTO, Comsys
The landscape of enterprise software development is undergoing a profound transformation. Traditional models, characterized by lengthy requirements gathering, bespoke coding efforts, extended testing periods, and protracted deployment timelines, are increasingly unsustainable in an era demanding rapid adaptation to market changes, regulatory shifts, and competitive pressures. This inefficiency not only drains resources but also stifles innovation, as organizations grapple with backlogs that grow faster than they can be cleared.
By the close of 2026, a substantial portion of large enterprises is expected to move toward an internal “App Factory” operating model: a durable, scalable, and AI-augmented system that continuously converts business requirements into deployable applications. While the shape and maturity of these “App Factories” will vary, the direction of travel is clear. AI-enhanced low-code platforms are enabling a new era of specification-driven development, governed reuse, and rapid deployment that elevates digital delivery from episodic projects to a sustained, strategic capability.
This outlook explores the drivers behind this evolution, the core elements of an effective App Factory, real-world evidence of its impact, potential pitfalls, and strategic considerations for implementation. The analysis draws on recent industry forecasts and deployment patterns to underscore why this model is poised to become the standard for enterprise digital delivery.
The Imperative for Change: Analyzing the Data
The momentum toward low-code and AI-driven development is backed by robust quantitative insights from leading analysts. According to Mordor Intelligence, the global low-code development platform market is expected to grow from US$26.3 billion in 2025 to US$67.1 billion by 20301, implying a compound annual growth rate of just over 20%. Similarly, Forrester data suggests the low-code market alone could grow at around 21% annually, reaching roughly $30 billion by 20282.
Beyond market size, the composition of users is shifting dramatically. Gartner predicts that by 2026, developers outside formal IT departments will make up at least 80% of low‑code tool users, up from 60% in 2021. This democratization empowers business technologists—often referred to as citizen developers—to take an active role in application creation, potentially outnumbering professional developers in ratios as high as 4:1 by the decade’s end in mature organizations.
These figures compel a deeper examination: Why now? The convergence of AI advancements, post-pandemic digital acceleration, and escalating talent shortages in skilled coding roles creates a perfect storm. Enterprises can no longer afford the luxury of sequential, siloed development; they require a system that amplifies human input with machine efficiency, ensuring that innovation scales with demand.
Rethinking Delivery: From Episodic Projects to Continuous Product Streams
Historically, internal applications have been treated as discrete projects—each with its own isolated scope, dedicated budget, and ad-hoc team. This approach fosters redundancy, as similar functionalities are rebuilt across departments, and it amplifies risks, such as scope creep or integration failures.
The App Factory paradigm inverts this by adopting a product-centric mindset. At its heart is a persistent platform that maintains:
(a) A curated repository of reusable components, such as pre-vetted UI elements, data connectors, and workflow templates.
(b) Standardized patterns for architecture, security, and performance, automatically applied to prevent drift.
(c) AI-orchestrated pipelines that translate natural-language specifications into executable designs, code, tests, and deployments.
(d) Mechanisms for frequent, low-risk releases, often multiple times daily, supported by automated monitoring and rollback capabilities.
This model reframes success metrics from project completion to ecosystem value: How quickly can new capabilities be disseminated and integrated? How much reuse is achieved? What is the net reduction in technical debt? The intellectual shift here is profound—enterprises move from viewing IT as a cost centre to treating it as a value multiplier, where each app built enriches the collective toolkit.
Consider the strategic implications: In volatile markets, the ability to pivot—such as rapidly deploying a new compliance workflow in response to regulatory changes—becomes a competitive differentiator. Moreover, by embedding AI for specification unpacking and generation, organizations mitigate the “knowledge silos” that plague traditional teams, ensuring consistency even as personnel rotate.
Beyond Basic Low-Code: Essential Capabilities for Enterprise Viability
While early low-code tools excelled in simple, departmental applications, they often struggled to meet the demands of enterprises, such as scalability, security, and coexistence with legacy systems. By 2026, successful platforms must integrate advanced features natively:
(a) AI-Powered Specification-Driven Development: Moving beyond drag-and-drop, these systems use natural-language processing to generate comprehensive artefacts—data schemas, APIs, user interfaces, business logic, and automated tests—from high-level descriptions. This not only accelerates creation but also maintains synchronization across changes, reducing errors by design.
(b) Inherent Governance and Compliance: Features such as zero-trust security models, granular access controls, data sovereignty enforcement, and automated audit trails are non-optional. Platforms must align with standards like GDPR, ISO 27001:2022, and sector-specific regulations without requiring custom configurations, ensuring that speed does not compromise safety.
(c) Legacy Integration and Modernization Strategies: Industry analyses referencing Gartner indicate that a majority of enterprise applications are treated as legacy, and that organizations often spend 60–80% of their IT budgets just to keep existing systems running, leaving relatively little for innovation5. Enterprises need low‑code that can progressively wrap and extend heritage systems rather than rip and replace them. This allows gradual migration without disruptive overhauls, preserving institutional knowledge while introducing modern efficiencies.
Platforms lacking these elements will be relegated to niche uses, while those that deliver them holistically will power true App Factories. The "more thinking" required here involves anticipating hybrid environments: How does AI handle context from legacy data? What safeguards prevent citizen-led changes from introducing vulnerabilities? Addressing these proactively is key to sustainable adoption.
Operationalizing the App Factory: A Practical Workflow
In practice, an App Factory operates through an iterative, human-AI collaborative loop:
(a) Initiation: A stakeholder articulates a need via plain text (e.g., "Develop a procurement approval system integrating with existing ERP and enforcing budget thresholds").
(b) AI Augmentation: The platform unpacks the specification into detailed requirements, proposes architectures, and assembles reusable components.
(c) Human Oversight: Domain experts and developers refine only the nuanced elements, typically 10–20% of the total effort.
(d) Validation and Deployment: Automated pipelines conduct security scans, compliance checks, performance simulations, and integration tests before pushing to production.
(e) Feedback Integration: Post-deployment metrics feed back into the system, enhancing future generations and expanding the component library.
This workflow yields tangible efficiencies: In well-run App Factories, delivery timelines can shrink by 50–90% compared to traditional projects, and reuse of shared components often becomes the norm, with a majority of new features assembled from existing building blocks rather than built from scratch. However, deeper consideration reveals secondary benefits, such as improved employee satisfaction—non-technical staff gain agency over their tools—and reduced vendor lock-in through standardized, portable components.
Navigating Challenges: Risks and Mitigation Strategies
No transformation is without hurdles. Potential risks include over-reliance on AI, leading to opaque decision-making, resistance from traditional IT teams who fear obsolescence, or initial integration complexities with entrenched systems.
Mitigation requires thoughtful planning:
(a) Foster a culture of collaboration, positioning professionals as stewards of the factory rather than gatekeepers.
(b) Invest in phased rollouts, starting with non-critical applications to build confidence.
(c) Prioritize platforms with explainable AI and robust simulation tools to demystify outputs.
Strategically, organizations must weigh the opportunity cost of inaction: Competitors with App Factories will iterate faster, respond to disruptions more nimbly, and attract talent drawn to innovative environments.
Strategic Horizon: Positioning for 2026 and Beyond
As 2026 approaches, the App Factory will evolve from an operational tool to a strategic asset, integrating with broader ecosystems such as edge computing and generative AI to enhance predictive capabilities. Enterprises that embed ethical considerations—such as bias mitigation in AI generations—will lead responsibly.
In essence, this shift demands more than technology; it requires organizational foresight. By reimagining development as a factory floor—efficient, scalable, and innovative—enterprises can unlock latent potential, turning digital aspirations into everyday realities.
The foundational elements are available now. For organizations contemplating this path, the question is not if, but how swiftly to proceed.
To discuss tailoring an AI-powered App Factory to specific enterprise needs, visit: www.jaggle.eu
References
1 https://www.mordorintelligence.com/industry-reports/low-code-development-platform-market
2 https://www.forrester.com/blogs/the-low-code-market-could-approach-50-billion-by-2028/
5 https://www.linkedin.com/pulse/cost-legacy-systems-how-outdated-holds-companies-back-andre-occec/
Spyros Karathanasis
CPTO, Comsys
Spyros Karathanasis is a technology leader with extensive experience in software development, IT strategy, and enterprise architecture. As CPTO at Comsys, he drives the company’s product innovation and oversees the delivery of scalable, high-performance solutions across customer engagement and digital transformation initiatives.
Throughout his career, he has guided cross-functional teams, shaped technology roadmaps, and designed mission-critical systems for complex enterprise environments. His background in informatics, statistics, and financial analysis enables him to combine technical insight with strategic decision-making, helping organizations adopt modern, data-driven operating models.
By Spyros Karathanasis
CPTO, Comsys
The landscape of enterprise software development is undergoing a profound transformation. Traditional models, characterized by lengthy requirements gathering, bespoke coding efforts, extended testing periods, and protracted deployment timelines, are increasingly unsustainable in an era demanding rapid adaptation to market changes, regulatory shifts, and competitive pressures. This inefficiency not only drains resources but also stifles innovation, as organizations grapple with backlogs that grow faster than they can be cleared.
By the close of 2026, a substantial portion of large enterprises is expected to move toward an internal “App Factory” operating model: a durable, scalable, and AI-augmented system that continuously converts business requirements into deployable applications. While the shape and maturity of these “App Factories” will vary, the direction of travel is clear. AI-enhanced low-code platforms are enabling a new era of specification-driven development, governed reuse, and rapid deployment that elevates digital delivery from episodic projects to a sustained, strategic capability.
This outlook explores the drivers behind this evolution, the core elements of an effective App Factory, real-world evidence of its impact, potential pitfalls, and strategic considerations for implementation. The analysis draws on recent industry forecasts and deployment patterns to underscore why this model is poised to become the standard for enterprise digital delivery.
The Imperative for Change: Analyzing the Data
The momentum toward low-code and AI-driven development is backed by robust quantitative insights from leading analysts. According to Mordor Intelligence, the global low-code development platform market is expected to grow from US$26.3 billion in 2025 to US$67.1 billion by 20301, implying a compound annual growth rate of just over 20%. Similarly, Forrester data suggests the low-code market alone could grow at around 21% annually, reaching roughly $30 billion by 20282.
Beyond market size, the composition of users is shifting dramatically. Gartner predicts that by 2026, developers outside formal IT departments will make up at least 80% of low‑code tool users, up from 60% in 2021. This democratization empowers business technologists—often referred to as citizen developers—to take an active role in application creation, potentially outnumbering professional developers in ratios as high as 4:1 by the decade’s end in mature organizations.
These figures compel a deeper examination: Why now? The convergence of AI advancements, post-pandemic digital acceleration, and escalating talent shortages in skilled coding roles creates a perfect storm. Enterprises can no longer afford the luxury of sequential, siloed development; they require a system that amplifies human input with machine efficiency, ensuring that innovation scales with demand.
Rethinking Delivery: From Episodic Projects to Continuous Product Streams
Historically, internal applications have been treated as discrete projects—each with its own isolated scope, dedicated budget, and ad-hoc team. This approach fosters redundancy, as similar functionalities are rebuilt across departments, and it amplifies risks, such as scope creep or integration failures.
The App Factory paradigm inverts this by adopting a product-centric mindset. At its heart is a persistent platform that maintains:
(a) A curated repository of reusable components, such as pre-vetted UI elements, data connectors, and workflow templates.
(b) Standardized patterns for architecture, security, and performance, automatically applied to prevent drift.
(c) AI-orchestrated pipelines that translate natural-language specifications into executable designs, code, tests, and deployments.
(d) Mechanisms for frequent, low-risk releases, often multiple times daily, supported by automated monitoring and rollback capabilities.
This model reframes success metrics from project completion to ecosystem value: How quickly can new capabilities be disseminated and integrated? How much reuse is achieved? What is the net reduction in technical debt? The intellectual shift here is profound—enterprises move from viewing IT as a cost centre to treating it as a value multiplier, where each app built enriches the collective toolkit.
Consider the strategic implications: In volatile markets, the ability to pivot—such as rapidly deploying a new compliance workflow in response to regulatory changes—becomes a competitive differentiator. Moreover, by embedding AI for specification unpacking and generation, organizations mitigate the “knowledge silos” that plague traditional teams, ensuring consistency even as personnel rotate.
Beyond Basic Low-Code: Essential Capabilities for Enterprise Viability
While early low-code tools excelled in simple, departmental applications, they often struggled to meet the demands of enterprises, such as scalability, security, and coexistence with legacy systems. By 2026, successful platforms must integrate advanced features natively:
(a) AI-Powered Specification-Driven Development: Moving beyond drag-and-drop, these systems use natural-language processing to generate comprehensive artefacts—data schemas, APIs, user interfaces, business logic, and automated tests—from high-level descriptions. This not only accelerates creation but also maintains synchronization across changes, reducing errors by design.
(b) Inherent Governance and Compliance: Features such as zero-trust security models, granular access controls, data sovereignty enforcement, and automated audit trails are non-optional. Platforms must align with standards like GDPR, ISO 27001:2022, and sector-specific regulations without requiring custom configurations, ensuring that speed does not compromise safety.
(c) Legacy Integration and Modernization Strategies: Industry analyses referencing Gartner indicate that a majority of enterprise applications are treated as legacy, and that organizations often spend 60–80% of their IT budgets just to keep existing systems running, leaving relatively little for innovation5. Enterprises need low‑code that can progressively wrap and extend heritage systems rather than rip and replace them. This allows gradual migration without disruptive overhauls, preserving institutional knowledge while introducing modern efficiencies.
Platforms lacking these elements will be relegated to niche uses, while those that deliver them holistically will power true App Factories. The “more thinking” required here involves anticipating hybrid environments: How does AI handle context from legacy data? What safeguards prevent citizen-led changes from introducing vulnerabilities? Addressing these proactively is key to sustainable adoption.
Operationalizing the App Factory: A Practical Workflow
In practice, an App Factory operates through an iterative, human-AI collaborative loop:
(a) Initiation: A stakeholder articulates a need via plain text (e.g., “Develop a procurement approval system integrating with existing ERP and enforcing budget thresholds”).
(b) AI Augmentation: The platform unpacks the specification into detailed requirements, proposes architectures, and assembles reusable components.
(c) Human Oversight: Domain experts and developers refine only the nuanced elements, typically 10–20% of the total effort.
(d) Validation and Deployment: Automated pipelines conduct security scans, compliance checks, performance simulations, and integration tests before pushing to production.
(e) Feedback Integration: Post-deployment metrics feed back into the system, enhancing future generations and expanding the component library.
This workflow yields tangible efficiencies: In well-run App Factories, delivery timelines can shrink by 50–90% compared to traditional projects, and reuse of shared components often becomes the norm, with a majority of new features assembled from existing building blocks rather than built from scratch. However, deeper consideration reveals secondary benefits, such as improved employee satisfaction—non-technical staff gain agency over their tools—and reduced vendor lock-in through standardized, portable components.
Navigating Challenges: Risks and Mitigation Strategies
No transformation is without hurdles. Potential risks include over-reliance on AI, leading to opaque decision-making, resistance from traditional IT teams who fear obsolescence, or initial integration complexities with entrenched systems.
Mitigation requires thoughtful planning:
(a) Foster a culture of collaboration, positioning professionals as stewards of the factory rather than gatekeepers.
(b) Invest in phased rollouts, starting with non-critical applications to build confidence.
(c) Prioritize platforms with explainable AI and robust simulation tools to demystify outputs.
Strategically, organizations must weigh the opportunity cost of inaction: Competitors with App Factories will iterate faster, respond to disruptions more nimbly, and attract talent drawn to innovative environments.
Strategic Horizon: Positioning for 2026 and Beyond
As 2026 approaches, the App Factory will evolve from an operational tool to a strategic asset, integrating with broader ecosystems such as edge computing and generative AI to enhance predictive capabilities. Enterprises that embed ethical considerations—such as bias mitigation in AI generations—will lead responsibly.
In essence, this shift demands more than technology; it requires organizational foresight. By reimagining development as a factory floor—efficient, scalable, and innovative—enterprises can unlock latent potential, turning digital aspirations into everyday realities.
The foundational elements are available now. For organizations contemplating this path, the question is not if, but how swiftly to proceed.
To discuss tailoring an AI-powered App Factory to specific enterprise needs, visit: www.jaggle.eu
References
1 https://www.mordorintelligence.com/industry-reports/low-code-development-platform-market
2 https://www.forrester.com/blogs/the-low-code-market-could-approach-50-billion-by-2028/
5 https://www.linkedin.com/pulse/cost-legacy-systems-how-outdated-holds-companies-back-andre-occec/
Spyros Karathanasis
CPTO, Comsys
Spyros Karathanasis is a technology leader with extensive experience in software development, IT strategy, and enterprise architecture. As CPTO at Comsys, he drives the company’s product innovation and oversees the delivery of scalable, high-performance solutions across customer engagement and digital transformation initiatives.
Throughout his career, he has guided cross-functional teams, shaped technology roadmaps, and designed mission-critical systems for complex enterprise environments. His background in informatics, statistics, and financial analysis enables him to combine technical insight with strategic decision-making, helping organizations adopt modern, data-driven operating models.
