AI like Lego bricks instead of a monolith: Reusable AI building blocks as the new standard in software development

Make or Buy? Why 76% of companies are currently radically changing their AI strategy

A quiet but massive transformation is taking place in software development. For years, cumbersome, monolithic AI models dominated the market – expensive to develop, inflexible in their adaptation, and often a recipe for failed IT projects. But the era of custom-built AI systems programmed from scratch is drawing to a close. They are being replaced by the "Lego principle": modular, reusable AI building blocks that can be combined flexibly and with maximum cost-efficiency, depending on the use case.

Whether in the pharmaceutical industry, the financial sector, or manufacturing – so-called composable architectures drastically reduce time-to-value from months to just a few days and fundamentally change the strategic "make-or-buy" decision in companies. This article explores why moving away from monolithic architectures is unavoidable, what enormous cost advantages modular platforms offer, and how companies can successfully master the leap into the new era of industrial AI logic without compromising their data sovereignty.

The End of the Monolithic Age: Anyone Still Thinking of AI as a Standalone Solution Has Missed the Decade.
For decades, one principle was taken for granted in software development: You build a system that can do everything – or you buy one. The monolith was the dominant architectural form because, in its early stages, it offered the simplest answer to complexity: a single codebase, a single deployment pipeline, a consistent environment. For small teams and initial products, this was often the right decision. But with growing requirements, increasing data volumes, and a new class of AI functionalities, this model is beginning to fail structurally.

The transition from monolithic to modular architectures in traditional software development already took place in the 2010s through microservices. What was true then for web applications and backend systems is now even more urgent for AI systems: Monolithic AI models—large, centralized systems trained on generic data and designed to perform many tasks simultaneously—are no longer economically viable if they have to be built or trained from scratch in every context. The era of reusable AI building blocks has begun, and it is changing not only the technology but the entire economics of the enterprise software market.

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From the Lego principle to industrial AI logic

The image of Lego bricks is not mere marketing jargon – it precisely describes the architectural changes taking place. Modular AI architectures consist of independent, clearly defined components: encoders, decoders, reasoning modules, search and retrieval engines, document processing layers, agent frameworks, and orchestration logics. Each component has a defined interface, a clear function, and can be developed, maintained, and scaled independently of the others.

The decisive economic advantage lies in reusability. Once a component has been built, tested, and validated in production, its reuse in a different context costs only a fraction of the original development costs. Frameworks like LangChain make it possible to combine generative AI models modularly without requiring code adjustments each time. Companies that adopt such approaches can shorten development cycles by up to 65 percent. What previously took six to twelve months of in-house development can now be built in days on a modular platform.

This logic is also reflected in industrial practice. Platform provider Unframe for example, claims to have developed hundreds of pre-built AI building blocks – for areas such as search and reasoning, document processing, data extraction, and agent-based automation. Because these building blocks are modular, each solution can be adapted to the customer's specific environment, goals, and technology stack without having to start from scratch. The result is deployments in days instead of months.

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The structural break with the past

To understand why this shift is so fundamental, it's worth examining the structural weaknesses of the previous approach. Companies traditionally faced a binary choice: either purchase a generic, off-the-shelf solution that didn't fit their processes, or develop a custom solution in-house, requiring significant upfront investment and lengthy project durations. Realistically, in-house developments cost between €350,000 and €500,000 for personnel, GPU infrastructure, and operations alone, while standard licensing solutions cost between €30,000 and €100,000 annually.

The result of this predicament is well-known: a long list of potential AI use cases emerges, of which only the top five to ten are actually implemented in practice. The rest remain stuck in the status quo. It is estimated that only about five percent of all AI initiatives in companies achieve a measurable return on investment. This is not because the use cases lack value, but because the path to implementation is too long, too expensive, and too risky.

Modular platforms with reusable building blocks disrupt this logic. Because development effort is drastically reduced by pre-built components, even small and medium-sized use cases become economically viable. The time-to-value – the period between idea generation and measurable business benefit – shrinks from months to weeks or even days. This changes the entire investment logic surrounding AI.

Cross-industry reuse as a competitive advantage

One of the most powerful, yet least discussed, aspects of modular AI architectures is their potential for cross-industry application. Many business processes that appear industry-specific at first glance share the same basic structure at an abstract level. Document processing, anomaly detection, compliance monitoring, customer classification, and reporting—these tasks arise in the insurance industry just as they do in pharmaceuticals, finance, and manufacturing.

This is particularly evident in the insurance sector. Modular AI hubs for insurance companies combine specialized agents for underwriting, claims processing, fraud detection, and compliance monitoring. These agents are based on the same technological foundations as comparable systems in other industries – only the industry-specific rules, thresholds, and data schemas differ. A document extraction module that processes policy data in an insurance company would do the same for clinical trial reports or regulatory submissions in a pharmaceutical company.

In the pharmaceutical and life sciences sectors, AI has already achieved measurable breakthroughs directly attributable to modular approaches. A leading biopharmaceutical company achieved efficiency gains of 30 to 40 percent through AI-supported automation of documentation processes. Clinical trial reports, which previously took 17 weeks, are now reduced to 10 to 12 weeks by GenAI solutions – with the prospect of further reductions to five weeks. The potential cost advantage in research and development alone amounts to over US$45 million for a mid-sized company.

In manufacturing, modular AI is fundamentally changing the ERP landscape. The manufacturing ERP market will reach a volume of US$23 billion by 2025 and is growing at an annual rate of eight percent. Composable architectures are replacing monolithic deployments: IT departments can replace individual planning engines or production modules without destabilizing the entire ERP infrastructure. AI-based predictive maintenance systems report double-digit reductions in unplanned downtime, which directly impacts profitability in a capital-intensive industry.

In the financial sector, modular architectures enable the rapid integration of AI into existing core banking systems without jeopardizing the notoriously fragile legacy stacks. Composable architecture fabrics in finance offer standardized API interfaces, real-time event streaming, and integrated compliance reporting—precisely the building blocks that banks and asset managers need for their AI use cases, without each institution having to build this infrastructure separately.

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The Economics of Reuse: Figures and Relationships

The economic implications of modular AI architectures are tangible not only qualitatively but also quantitatively. Companies that combine AI with zero-base process restructuring achieve cost savings of up to 25 percent, according to analyses by Bain & Company. One asset manager that consistently pursued this approach identified annualized savings of one billion US dollars – roughly 20 percent of its total cost base. In finance and compliance, AI-powered approaches reduced the workload for reporting and analysis by more than 40 percent.

BCG data shows that companies with knowledge-intensive processes—such as software development, marketing, or document management—can use GenAI to make production processes up to 50 times more efficient and reduce costs by 20 to 30 percent. In operational areas with field service or maintenance teams, individual productivity gains can reach another 20 to 30 percent. One oil and gas company reduced error rates by 70 percent and lowered preventive maintenance costs by more than 40 percent through AI-supported maintenance operations.

Industry-wide trends underscore these figures. Organizations using hyperautomation—the combination of AI and robotic process automation—report 42 percent faster process execution and up to 25 percent productivity gains. Several studies have shown that integrating AI and big data enables a 42 percent reduction in process handling time, a 28 percent improvement in resource utilization, and a nearly 35 percent reduction in operating costs. For AI-powered customer service, the average ROI is $3.50 for every dollar invested.

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The make-or-buy decision in the AI ​​age

The shift towards modular platforms has fundamentally changed the strategic make-or-buy decision in companies. As recently as 2024, 47 percent of companies developed their AI solutions internally, while 53 percent purchased them. By 2025, this ratio had shifted dramatically: only 24 percent were building their own solutions, while 76 percent relied on external solutions. This is not a sign of a lack of technical expertise, but rather a rational response to the diminished added value of monolithic in-house development in areas that lack genuine differentiation potential.

The logic behind this is economically compelling. In-house development is worthwhile if AI is a core element of the business model, if a strategic unique selling proposition is to be secured through proprietary IP, or if regulatory requirements enforce complete data sovereignty. For everything else – and this is the vast majority of use cases – platform solutions with pre-built components offer a superior economic equation: faster deployments, lower upfront investments, continuous technical updates without in-house R&D costs, and – in the usage-based billing model – a significantly reduced risk profile.

The model of licensing only after proof of business value – no upfront commitment, no scoping project, payment only upon measurable success – represents the logical next step in this development. It shifts the risk to the provider and creates a strong incentive to deliver quickly and precisely. This is only possible because reusable components reduce delivery costs to such an extent that such a guarantee becomes economically viable.

The human-machine symbiosis: Neither replacement nor coexistence

A key misconception in the discussion about modular AI platforms is the idea that they would replace internal IT teams. The reality in companies that successfully implement these approaches is quite different. The top use cases—those with strategic importance and the highest differentiation potential—continue to be developed and managed internally. Modular platforms address the vast majority: the 40 to 45 use cases out of a list of 50 that would otherwise require either individual solutions or internal rapid projects—and fail on both counts.

This aligns with Gartner's 2026 forecast: 40 percent of all enterprise applications will integrate task-specific AI agents, compared to less than five percent in 2025. These agents will not replace the IT department—they will be controlled, monitored, and integrated into existing systems by it. The real disruption lies not in the substitution of human labor, but in the shift in the balance of value: from clicking and configuring to natural language interaction with intelligent, modular systems.

Fraunhofer researchers emphasize the role of value stream management as a crucial success factor in this context: only when the entire process, from concept to delivery, is transparent can companies identify and improve bottlenecks. AI platforms must therefore not only ensure technical quality but also orchestrate the collaboration between humans and AI. The framing of "human-machine symbiosis" precisely captures the economic essence: neither pure automation nor mere tool use, but a structural redistribution of tasks and responsibilities along the value stream.

Technical maturity and remaining risks

As compelling as the model sounds, it would be dishonest to ignore the challenges. Modular AI architectures increase complexity at the orchestration level: when many independent components have to work together, managing interfaces, error handling, data flows, and versioning becomes a critical bottleneck. The strength of the modular approach—the independence of the parts—creates new dependencies at the system level that must be carefully managed.

Another risk lies in ensuring the quality of AI-generated output. Fraunhofer experts warn that the speed at which AI systems operate necessitates a fundamental adaptation of verification and validation processes – both technically and culturally. Architectures, CI/CD pipelines, and review processes must be designed to reliably verify AI-generated output without creating new bottlenecks.

Added to this is the question of data sovereignty. In regulated industries such as pharmaceuticals, insurance, and finance, the uncontrolled outflow of sensitive data to external platforms is not only a reputational risk but also a compliance problem. Composable architectures solve this problem through selective deployment: Sensitive workloads remain in controlled on-premises environments, while low-risk tasks can run on external services. Modular building block platforms must not only promise this deployment flexibility but also implement it in a technically robust manner.

Outlook: The new standard is emerging right now

Software development in the coming years will largely no longer consist of programming functionality from scratch, but rather of intelligently combining, configuring, and orchestrating pre-built AI components. This does not mean the displacement of developers, but a shift in their work to higher levels of abstraction – from implementation to architecture, from coding to configuration and quality assurance.

For companies across all sectors, this represents a new strategic starting point. The question is no longer: "Can we afford AI?" – but rather: "How many of our 50 use cases can we implement in the next twelve months, and which model delivers the best ROI per use case?" Those who still answer this question with the binary logic of in-house development or standard software will be overtaken by competitors who use modular platforms as operational accelerators.

The figures are clear: By 2030, 45 percent of all organizations will be orchestrating AI agents at scale and embedding them across all business functions. The global automation market will reach nearly $214 billion by 2026. The question is not whether, but with which architecture and model. And in this regard, the Lego principle – modular, reusable, combinable – provides the most compelling answer that software development has to offer this decade.

Konrad Wolfenstein

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