The company's internal AI platform as strategic infrastructure and a business necessity

Digital sovereignty: How companies retain control over their AI and data

The era of AI experiments is over. Artificial intelligence is no longer an optional innovation project, but has rapidly become a decisive factor for competitiveness, efficiency, and future viability. Companies are doubling their AI adoption rates and recognizing that inaction is tantamount to strategic regression. However, in their haste to unlock the potential of AI, many are resorting to quick, external cloud solutions, overlooking the long-term consequences: hidden costs, dangerous vendor lock-in, and serious risks to data privacy and digital sovereignty.

At this critical turning point, the company's own managed AI platform is establishing itself not as one of many options, but as a strategic necessity. It represents the shift from merely using external AI technology to being the sovereign architect of its own data-driven value creation. This decision goes far beyond technical implementation – it is a fundamental course correction that determines who retains control over the company's most valuable digital resources: the data, the models, and the resulting innovative power.

This article illuminates the compelling reasons for this paradigm shift. It analyzes the complex economic logic that often makes an internal platform the more cost-effective solution when scaling, and demonstrates how regulatory pressure from GDPR and the EU AI Act is transforming data sovereignty from a recommendation into an obligation. Furthermore, it the strategic trap of vendor lock-in and the critical importance of organizational readiness for unlocking the full potential of AI securely, compliantly, and sustainably.

When digital sovereignty becomes a competitive factor: Why managed AI is not an option, but a survival strategy.

The management of artificial intelligence within corporate structures is at a crucial turning point. What was considered an experimental fringe topic just a few years ago is evolving into a fundamental strategic decision with far-reaching consequences for competitiveness, innovation, and digital autonomy. The managed, in-house AI platform, as a Managed AI solution, represents a paradigm shift in how organizations deal with the most transformative technology of our time.

The global market for AI platforms has already reached a considerable size of $65.25 billion in 2025 and is projected to grow to $108.96 billion by 2030, representing an average annual growth rate of 10.8 percent. However, these figures mask the fundamental transformation at play. It's not simply about market growth, but about the reorganization of business value creation through intelligent systems that can act, learn, and make decisions independently.

In Germany, 27 percent of companies now use artificial intelligence in their business processes, compared to just 13.3 percent last year. This doubling within a year signals a tipping point. Reluctance is giving way to the realization that abstaining from AI is no longer a neutral position, but rather represents an active competitive disadvantage. Companies expect productivity increases of more than ten percent through the use of AI, which cannot be ignored in a time of economic uncertainty and skills shortages.

The sectoral distribution of AI adoption is particularly revealing. IT service providers lead with 42 percent, followed by legal and tax consultancies at 36 percent, and research and development, also at 36 percent. These sectors are united by the intensive processing of structured and unstructured data, the high knowledge intensity of their work processes, and the direct link between information processing and value creation. They serve as early indicators for a development that will spread across all sectors of the economy.

The economic rationality of in-house AI platforms

The decision to implement an in-house, managed AI platform follows a complex economic logic that goes far beyond simple cost comparisons. The total cost of ownership of typical AI implementations encompasses much more than the obvious licensing and infrastructure costs. It extends across the entire lifecycle, from acquisition and implementation costs through operating expenses and hidden costs to exit costs.

The implementation costs for AI projects vary considerably depending on the use case. Simple chatbot solutions range from €1,000 to €10,000, while customer service automation costs between €10,000 and €50,000. Predictive analytics for sales processes ranges from €20,000 to €100,000, and custom deep learning systems start at €100,000 with no upper limit. However, these figures only reflect the initial investment and systematically underestimate the total costs.

A study shows that only 51 percent of organizations can reliably assess their return on investment (ROI) for AI projects. This uncertainty stems from the complexity of the value chains that AI systems permeate and the difficulty of quantifying indirect effects. Companies using third-party cost optimization tools report significantly higher confidence in their ROI calculations, highlighting the need for professional governance structures.

Average monthly AI budgets are projected to increase by 36 percent in 2025, reflecting a significant shift towards larger and more complex AI initiatives. This increase is not uniform across all companies but is concentrated in organizations that have already successfully implemented smaller AI projects and now want to scale. This scaling dynamic significantly reinforces the importance of a strategic platform decision.

The distinction between cloud-based and on-premises solutions is gaining importance in this context. While cloud solutions offer lower barriers to entry and enable rapid experimentation, on-premises implementations can be more cost-efficient with sufficient usage intensity. The capitalization of on-premises systems, amortization over several years, and tax depreciation options, combined with the initial training costs for large language models on enterprise-wide data, make on-premises solutions economically attractive when scaling.

The pricing models of external AI providers follow different logics. License-based models offer planning security with high upfront investments. Consumption-based pay-per-use models allow flexibility in the face of fluctuating demand, but can lead to exponentially increasing costs with intensive use. Subscription models simplify financial planning, but carry the risk of paying for unused capacity. Freemium approaches attract customers with free basic features, but the costs can rise rapidly with scaling.

A practical example illustrates the economic dimension. A company with ten employees, each spending eight hours per week on reporting, ties up 3,600 working hours annually in this task. An AI solution that reduces this time to one hour per report saves 2,700 working hours annually. At an average hourly rate of €50, this equates to cost savings of €135,000 per year. Even with implementation costs of €80,000, the investment pays for itself within seven months.

An overall analysis of AI investments shows that companies with the highest AI maturity report a return on investment up to six percentage points higher than organizations with limited adoption. Nearly two-thirds of AI users, specifically 65 percent, are satisfied with their generative AI solutions. This underscores that the economic value of AI is not hypothetical, but measurable and achievable.

Governance, data protection and regulatory compliance

The European General Data Protection Regulation (GDPR) and the EU AI Act create a regulatory framework that not only enables but effectively mandates in-house AI platforms. By its very nature, the GDPR requires accountability, data minimization, purpose limitation, and transparency in the processing of personal data. These requirements fundamentally clash with the business models of many external AI providers, which are based on data collection, model training with customer data, and opaque decision-making processes.

The AI ​​Act introduces a risk-based classification of AI systems, ranging from prohibited to high-risk to minimal-risk classes. This categorization requires comprehensive documentation, testing, governance processes, and human oversight for high-risk systems. Organizations must be able to demonstrate that their AI systems do not produce discriminatory effects, are transparent in their decision-making processes, and are continuously monitored for bias.

Data sovereignty is evolving into a strategic imperative. It refers to the ability of states or organizations to maintain control over their data, regardless of where it is physically stored or processed. Sovereign AI systems store and manage AI models and data while adhering to national or regional regulations and limitations. They control who has access to data and where models are trained.

Implementing GDPR-compliant AI systems requires several key measures. Privacy by Design and Privacy by Default must be integrated into the system architecture from the outset. Data Protection Impact Assessments are mandatory for virtually all modern AI tools due to the high risk to data subject rights. Comprehensive documentation of all data flows, processing purposes, and security measures is essential. Standard contractual clauses for international data transfers are indispensable when data leaves the EU.

The practical implementation of these requirements differs considerably between various deployment scenarios. Cloud-based solutions from large US providers often operate under the EU-US Data Privacy Framework, which, however, is subject to increased legal uncertainty following the Schrems II ruling. Companies must conduct transfer impact assessments and demonstrate that data transfers comply with GDPR requirements.

Storing prompt data poses a particular risk. Google Gemini stores prompts for up to 18 months, which can cause significant compliance issues if personal data is accidentally entered. While Microsoft Copilot offers comprehensive governance tools with Microsoft Purview, these must be configured correctly to be effective. ChatGPT Enterprise allows for the separation of usage and training data and offers EU server locations, but requires appropriate contractual agreements.

Having your own in-house AI platform offers crucial advantages. Data never leaves the company infrastructure, minimizing data privacy risks and simplifying compliance. Complete control over access restrictions, processing procedures, and auditability is automatically achieved through internal management. Companies can tailor governance policies specifically to their needs without relying on generic vendor policies.

Establishing a formal governance structure for AI should be at the C-level, ideally with a Chief AI Officer or an AI Governance Committee. This leadership level must ensure that AI strategies are aligned with overarching business objectives. Clear roles and responsibilities for data stewards, AI leads, and compliance officers are essential. Developing repeatable AI policies that serve as service-level standards facilitates scaling and the onboarding of new employees.

The trap of vendor lock-in and the importance of interoperability

Vendor lock-in is becoming a critical strategic risk in the AI ​​age. Relying on the proprietary ecosystems of individual providers restricts flexibility in the long run, increases costs, and limits access to innovations outside the chosen system. This dependency develops gradually through a series of seemingly pragmatic individual decisions and often only becomes apparent when switching has already become prohibitively expensive.

The mechanisms of vendor lock-in are manifold. Proprietary APIs create technical dependencies because application code is written directly against vendor-specific interfaces. Data migration is complicated by proprietary formats and high egress fees. Contractual obligations with long-term commitments reduce negotiating power. Process lock-in occurs when teams are trained exclusively on a single vendor's tools. The costs of switching vendors—technical, contractual, procedural, and data-related—increase exponentially over time.

Nearly half of German companies are rethinking their cloud strategy due to concerns about rising costs and dependency. Already, 67 percent of organizations are actively trying to avoid excessive reliance on individual AI technology providers. These figures reflect a growing awareness of the strategic risks of proprietary platforms.

The costs of dependency manifest themselves on multiple levels. Price increases cannot be offset by switching to competitors if migration is technically or economically unfeasible. Innovation lag arises when advanced models or technologies become available outside the chosen ecosystem but cannot be utilized. Bargaining power erodes when the supplier knows the customer is effectively trapped. Strategic agility is lost when one's own roadmap is tied to that of the vendor.

A hypothetical example illustrates the problem. A retail company invests heavily in a provider's comprehensive AI marketing platform. When a niche competitor offers a significantly superior predictive churn model, the company finds that switching is impossible. The deep integration of the original provider's proprietary APIs with customer data systems and campaign execution means that a rebuild would take over a year and cost millions.

Interoperability acts as an antidote to vendor lock-in. It refers to the ability of different AI systems, tools, and platforms to work together seamlessly, regardless of their vendor or underlying technology. This interoperability operates on three levels. Model-level interoperability enables the use of multiple AI models from different vendors within the same workflow without infrastructure changes. System-level interoperability ensures that supporting infrastructure such as prompt management, guardrails, and analytics functions consistently across different models and platforms. Data-level interoperability focuses on standardized data formats such as JSON schemas and embeddings for smooth data exchange.

Standards and protocols play a central role. Agent-to-agent protocols establish a common language that allows AI systems to exchange information and delegate tasks without human input. The Mesh Communication Protocol creates an open, scalable network in which AI agents can collaborate without redundant work. These protocols represent a movement toward open AI ecosystems that avoid vendor lock-in.

The modular architecture, designed to protect against dependency, allows for the replacement of individual AI components without requiring a complete system redesign. A technology-agnostic platform, for example, permits the change of the underlying Large Language Model without reimplementing the entire application. This approach reduces dependency on a single technology stack by over 90 percent.

No-code platforms further strengthen independence from external developers and increase the autonomy of business departments. When business users can configure and customize workflows themselves, the reliance on specialized development teams, who may only be familiar with a specific vendor ecosystem, decreases.

The strategic recommendation is therefore: consciously enter into dependencies, but protect critical areas. Alternatives and exit options should be planned for mission-critical processes. Maintain a willingness to experiment with new services, but only integrate them deeply after thorough evaluation. Continuously monitor the health of providers and the availability of alternatives. Pursue an evolutionary adaptation strategy when market conditions or needs change.

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Organizational readiness and the competence crisis

The technological availability of AI solutions does not automatically translate into organizational readiness for their effective use. The AI ​​skills gap describes the discrepancy between the rapidly growing demand for AI-related roles and the available qualified talent. More than 60 percent of companies struggle to recruit AI experts. This gap affects not only coding or data science skills, but also the combination of technical expertise, business acumen, problem-solving abilities, and ethical considerations.

The global AI talent shortage will reach critical dimensions by 2025. Demand will exceed supply by a ratio of 3.2 to 1 across all key roles, with over 1.6 million open positions and only 518,000 qualified candidates. LLM development, MLOps, and AI ethics will show the most severe bottlenecks, with demand scores above 85 out of 100 but supply scores below 35 out of 100. Average time-to-fill for AI positions will be six to seven months.

Salary expectations for AI roles are 67 percent higher than for traditional software positions, with 38 percent year-over-year growth across all experience levels. This price dynamic reflects the fundamental imbalance between supply and demand and makes recruitment a financial challenge for many organizations.

Artificial intelligence is not only changing technological systems, but also organizational structures, work processes, and corporate cultures. Change management is becoming a critical success factor for AI implementations. An IBM study from 2022 identifies a lack of knowledge as the biggest problem in using AI. Even tech giants like Microsoft initially struggled to convince their employees of the benefits of AI and to impart the necessary skills.

Successful AI integration requires comprehensive training programs and change management initiatives that involve all employees. These measures lead to greater acceptance of AI technologies and improved workforce skills. JPMorgan Chase developed the COiN platform to use machine learning for analyzing legal documents, saving approximately 360,000 working hours when processing 12,000 contracts per year. However, success depends on employees learning to use AI and being willing to do so.

Organizational AI readiness encompasses more than just technological prerequisites. It requires the interplay of technical and soft skills, organizational alignment, and the ability to build trust in AI. Key readiness factors include trust, management support, data, skills, strategic alignment, resources, culture, innovativeness, managerial capabilities, adaptability, infrastructure, competitiveness, cost, organizational structure, and size.

A key characteristic that directly contributes to an AI-ready culture is a data-driven organizational culture. Organizations that make decisions based on data and evidence rather than intuition or tradition are more likely to be AI-ready. A data-driven culture ensures that employees at all levels have the tools and the mindset to integrate AI into their daily decision-making processes.

The role of AI change managers is gaining importance. These professionals support organizations in successfully managing the transformation brought about by artificial intelligence. They focus particularly on supporting employees during this change process, aiming to foster acceptance of AI solutions, alleviate anxieties, and promote a willingness to embrace change. Their tasks include planning, managing, and implementing change processes; developing change strategies; communicating the vision and benefits; facilitating workshops and feedback sessions; analyzing change needs and barriers to acceptance; and developing training and communication measures.

Paradoxically, managing an in-house AI platform can facilitate skills development. Instead of employees having to grapple with various external tools and their differing interfaces, a central platform offers a consistent environment for learning and experimentation. Standardized training programs can be developed that are tailored to the specific platform. Knowledge transfer is simplified when everyone uses the same system.

Only six percent of employees feel very comfortable using AI in their roles, while almost a third are significantly uncomfortable. This discrepancy between technological availability and human capability must be addressed. Research identifies problem-solving skills, adaptability, and a willingness to learn as critical competencies for managing an AI-driven future.

Failure to address these skill gaps can lead to disengagement, higher turnover, and reduced organizational performance. Forty-three percent of employees planning to leave their roles prioritize training and development opportunities. Employers who invest in these areas can not only retain talent but also strengthen their reputation as a forward-thinking organization.

Market dynamics and future developments

The AI ​​platform landscape is undergoing a period of rapid consolidation and differentiation. On the one hand, hyperscalers like Microsoft Azure AI, AWS Bedrock, and Google Vertex AI dominate with their integrated infrastructure, identity, and billing systems. These providers leverage their existing cloud ecosystems to protect accounts from displacement. Pure-play providers like OpenAI, Anthropic, and Databricks, on the other hand, are pushing the boundaries in terms of model size, open-weight releases, and ecosystem extensibility.

Mergers and acquisitions activity exceeded $50 billion in 2024, with Meta's $15 billion investment in Scale AI and Databricks' $15.25 billion funding round as prominent examples. Hardware co-design is emerging as a new moat, with Google's TPU v5p and Amazon's Trainium2 chips promising cost-per-token reductions and attracting customers to proprietary runtimes.

The software component commanded 71.57 percent of the AI ​​platform market share in 2024, reflecting strong demand for integrated model development environments that unify data ingestion, orchestration, and monitoring. Services, though smaller, are expanding at a CAGR of 15.2 percent as companies seek design-and-operate support to shorten ROI cycles.

Cloud configurations accounted for 64.72 percent of the AI ​​platform market size in 2024 and are projected to grow the fastest, at a CAGR of 15.2 percent. However, on-premises and edge nodes remain essential in healthcare, finance, and public sector workloads, where data sovereignty rules apply. Hybrid orchestrators that abstract location allow organizations to train centrally while inferring at the edge, balancing latency and compliance.

Particularly noteworthy is the shift towards private/edge AI for data sovereignty, driven by the EU and expanding into Asia-Pacific and regulated US sectors, with an estimated 1.7% impact on the long-term CAGR. The regulatory push towards model auditability, led by the EU with US federal adoption pending, adds another 1.2% to the long-term CAGR.

In Germany, the picture is mixed. While the absolute use of AI in companies is at 11.6 percent, exceeding the EU average of eight percent, this usage has surprisingly stagnated since 2021. This stagnation contrasts with the dynamic development of GenAI applications like ChatGPT and seems counterintuitive given the positive productivity effects.

However, a more nuanced analysis reveals a significant increase. When companies that reported using AI in previous surveys but did not in 2023 – possibly because AI processes are so integrated that respondents no longer consider them noteworthy – are included, a clear increase in AI usage emerges in 2023 compared to 2021. This suggests a normalization of AI in business processes.

91 percent of German companies now see generative AI as an important factor for their business model and future value creation, compared to only 55 percent last year. 82 percent plan to invest more in the next twelve months, and more than half plan budget increases of at least 40 percent. 69 percent have established a strategy for generative AI, which is 38 percent more than in 2024.

The benefits companies expect from AI include increased innovation, efficiency, sales, and automation, as well as product and growth opportunities. However, the backlog of governance, ethical guidelines, and training remains a challenge, and the trustworthy use of AI continues to be a key hurdle.

Agentic AI will dominate IT budget expansion over the next five years, reaching over 26 percent of global IT spending, with $1.3 trillion in 2029. This investment, driven by the growth of agentic AI-enabled applications and systems for managing agent fleets, signals a transformation within enterprise IT budgets, particularly in software, towards investment strategies led by products and services based on an agentic AI foundation.

The forecast shows a clear alignment between the growth of AI spending and the confidence of IT leaders that effective AI use can drive future business success. Application and service providers that lag behind in integrating AI into their products and fail to enhance them with agents risk losing market share to companies that have made the decision to place AI at the heart of their product development roadmap.

The AI ​​market in Germany is estimated to reach over nine billion euros in 2025 and is projected to grow to 37 billion euros by 2031, representing an annual growth rate that significantly exceeds overall economic development. Germany's AI startup landscape comprised 687 startups in 2024, corresponding to year-over-year growth of 35 percent. Berlin and Munich dominate the AI ​​startup landscape, accounting for approximately 50 percent of all AI startups in the country.

73 percent of companies in Germany believe that clear AI regulations can offer a competitive advantage for European companies if implemented correctly. This underscores the opportunity presented by the European regulatory approach: Trustworthy AI made in Europe can become a differentiating factor.

The strategic decision matrix for deployment scenarios

The choice between cloud, on-premises, and hybrid deployment models for AI platforms does not follow a universal logic but must reflect the specific requirements, constraints, and strategic priorities of each organization. Each model offers distinct advantages and disadvantages that must be carefully weighed against business objectives.

On-premise deployment models offer maximum security and control over data and intellectual property. Highly sensitive data, intellectual property, or data subject to strict regulatory compliance requirements, such as in the finance or healthcare sectors, are best handled here. High customizability allows models to be tailored to specific needs. Potentially lower latency for critical real-time applications results from local processing. Cost advantages during scaling result from capitalization opportunities and lower variable transaction costs.

The challenges of on-premises solutions include high initial infrastructure investments, longer implementation times, the need for in-house expertise for maintenance and updates, and limited scalability compared to cloud elasticity. These challenges can be mitigated by selecting a partner who can offer a standard product, configuration services, and support for on-premises deployment.

Cloud deployment offers a fast time-to-value for initial experimentation or proof-of-concept. Lower startup budgets are required because no hardware investments are necessary. Automatic scalability allows adaptation to fluctuating workloads. Rapid go-live for standard products accelerates value creation. The vendor handles maintenance, redundancy, and scalability.

The disadvantages of cloud solutions manifest themselves in potentially exponentially increasing costs with intensive use, as pay-per-use models become expensive at high volumes. Limited competitive differentiation arises because rivals can use the same off-the-shelf solutions. Data and model ownership remains with the provider, creating privacy, security, and vendor lock-in issues. Limited customizability restricts advanced experimentation.

Hybrid cloud models combine the advantages of both approaches while addressing their limitations. Sensitive AI workloads run on bare metal or private clusters for compliance, while less critical training is offloaded to the public cloud. Steady-state workloads operate on private infrastructure, while public cloud elasticity is used only when needed. Data sovereignty is ensured by keeping sensitive data on-premises while leveraging public cloud scale where permitted.

AI acceleration through generative AI, large language models, and high-performance computing workloads is reshaping infrastructure requirements. Businesses need access to GPU clusters, high-bandwidth networking, and low-latency interconnects that are not evenly distributed across providers. In multicloud environments, enterprises choose a provider based on AI specialization, such as Google's TPU services or Azure's OpenAI integration. In hybrid cloud environments, sensitive AI workloads run on-premises, while training is outsourced to the public cloud.

Regulatory pressures are intensifying globally. The EU Digital Operational Resilience Act, California's CPRA, and new data sovereignty mandates in APAC require enterprises to have visibility and control over data location. Multicloud offers geographic flexibility, allowing data to be stored in jurisdictions where regulations require it. Hybrid cloud provides sovereignty assurance by keeping sensitive data on-premises while leveraging public cloud scale where permitted.

The practical implementation of a managed AI solution as an internal platform typically follows a structured approach. First, goals and requirements are defined, along with a detailed analysis of whether, how, and where the use of AI makes sense. Technology selection and architectural design consider modular components that can be flexibly exchanged. Data integration and preparation form the basis for high-performance models. Model development and MLOps setup establish continuous deployment and monitoring processes.

The resulting benefits of an in-house AI platform include reduced development times through standardization and reuse, automated processes for training, deployment and monitoring, secure integration into existing systems while taking into account all compliance requirements, and complete control over data, models and infrastructure.

The AI ​​platform as strategic infrastructure

A managed, in-house AI platform, as a managed AI solution, represents far more than a technological decision. It constitutes a strategic shift with fundamental implications for competitiveness, digital sovereignty, organizational agility, and long-term innovation capability. The evidence from market data, company experience, and regulatory developments converges to a clear picture: Companies that are serious about AI adoption need a coherent platform strategy that balances governance, flexibility, and value creation.

Economic rationale argues for a differentiated approach. While external cloud services offer low barriers to entry and rapid experimentation, cost structures shift dramatically in favor of internal solutions as systems scale. The total cost of ownership must be considered across the entire lifecycle, including hidden costs due to vendor dependency, data exfiltration, and lack of control. Organizations with intensive AI usage and stringent compliance requirements often find the economically and strategically optimal solution in on-premises or hybrid models.

The regulatory landscape in Europe, with the GDPR and the AI ​​Act, makes internal corporate control over AI systems not only desirable but increasingly necessary. Data sovereignty is evolving from a nice-to-have to a must-have. The ability to demonstrate at any time where data is processed, who has access, how models were trained, and on what basis decisions are made is becoming a compliance imperative. External AI services often cannot meet these requirements, or only with considerable additional effort.

The risk of vendor lock-in is real and increases with every proprietary integration. Modular architectures, open standards, and interoperability must be built into platform strategies from the outset. The ability to exchange components, switch between models, and migrate to new technologies ensures that the organization does not become a prisoner of a vendor ecosystem.

The organizational dimension should not be underestimated. The availability of technology does not automatically guarantee the ability to use it effectively. Building skills, managing change, and establishing a data-driven culture require systematic investment. An internal platform can facilitate these processes through consistent environments, standardized training, and clear responsibilities.

Market dynamics show that AI investments are growing exponentially, and Agentic AI represents the next stage of evolution. Companies that lay the foundations now for scalable, flexible, and secure AI infrastructure are positioning themselves for the coming wave of autonomous systems. Choosing a managed AI platform is not a decision against innovation, but rather a decision for sustainable innovation capability.

Ultimately, it comes down to the question of control. Who controls the data, the models, the infrastructure, and thus the ability to generate value from AI? External dependencies may seem convenient in the short term, but in the long run, they delegate core strategic competencies to third parties. An in-house AI platform as a managed AI solution is the way for organizations to maintain control – over their data, their innovative capacity, and ultimately their future in an increasingly AI-driven environment and economy.

Konrad Wolfenstein

I would be happy to serve as your personal advisor.

contact me under Wolfenstein ∂ Xpert.digital

call me under +49 89 674 804 (Munich)

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