What steps are necessary for effective AI implementation? Your strategic roadmap to AI excellence 2025

The inevitable reality: AI as a competitive factor in 2025

Artificial intelligence (AI) has developed from a visionary technology of the future into a decisive factor for the competitiveness of companies. Companies that are already using AI specifically for automation, analysis and optimization are benefiting from measurable increases in efficiency and tapping into new opportunities for value creation. However, the digital transformation, a necessary precursor for AI, is taking place more slowly in many organizations than the rapidly advancing technological change. This discrepancy harbors the risk of being left behind.  

Typical hurdles for decision-makers on the path to AI

For many decision-makers, the path to successful AI implementation is like navigating through unknown territory. The pressure to innovate is high, but AI itself often appears to be a "black box" - the potential and specific use cases for their own business remain unclear. In addition, there are often fundamental challenges such as an outdated system infrastructure or an inadequate database that is supposed to serve as a breeding ground for AI. Figures from Switzerland illustrate this situation: 62% of small and medium-sized enterprises (SMEs) do not currently use any AI applications, and 54% express concerns regarding the use of AI. These figures underline the need for clear, comprehensible guidance. The reasons for this reluctance to adapt are complex and range from unstructured data and a lack of strategies to a lack of internal skills. It is clear that the barriers are often not purely technological in nature, but are deeply rooted in strategic and cultural aspects of the company.  

The call for a structured, holistic approach

Successful implementation of AI requires far more than just the introduction of new technologies. Successful AI integration requires a solid strategic anchoring in the company, an accompanying cultural change and, above all, a robust, high-quality database. An isolated, purely technical view of AI falls short and rarely leads to the desired success. Instead, a holistic approach is required that takes all relevant areas of the company into account and sees implementation as a transformative process. The observation that digital transformation often lags behind technological progress indicates that many companies tend to react reactively to technological developments instead of proactively shaping a strategy. A structured guide can help companies to break through this reaction trap and develop a forward-looking AI strategy.  

What you can expect in this article: A practical guide

This article provides you with a clear, practical roadmap for the effective implementation of artificial intelligence in your company. Based on the latest findings of the comprehensive CorpIn study "Towards AI Excellence (2025)". It is intended to support you as a decision-maker in understanding AI not just as a technical tool, but as a strategic necessity that must be deeply embedded in the company in order to create sustainable added value.  

II The six key steps to successful AI implementation (based on the CorpIn Hexagon model)

A structured approach is essential to master the complexity of AI implementation. The Hexagon model developed by CorpIn provides such a framework. It not only serves to assess a company's digital maturity level, but also derives targeted measures for successful AI integration. This model forms the common thread for the following six crucial steps on your path to AI excellence.  

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Step 1: The data foundation - the fuel for your AI initiatives

• Why a solid data foundation is essential: Data is the lifeblood of any AI application. Without a solid, well-structured and high-quality database, coupled with clearly regulated access rights, even the most advanced AI solution can hardly deliver the expected added value. The quality and availability of data significantly determine the performance and reliability of AI systems.   
• The reality in Swiss companies (insights from the CorpIn study): The storage strategies for data are diverse: around 40-50% of companies rely on cloud-based data storage, while 20-35% pursue classic on-premise approaches and around 20% use hybrid infrastructures. However, the consistency and integration of the data landscape is more decisive than the chosen storage model. There are clear challenges here: Only 8% of companies rate their data structures as consistently homogeneous. A relative majority of 54% state that their data structures are at least mostly uniform, but a worrying 35% struggle with heterogeneous structures in which different systems operate largely in isolation from one another. Such heterogeneous data structures often inevitably lead to the formation of data silos - a problem that, according to the study, around 5% of companies are actively struggling with. These silos hinder the company-wide exchange of knowledge and make the development of effective AI applications considerably more difficult, as AI relies on integrated and comprehensive data sets. There is also potential for optimization when it comes to data access. It is true that 79% of companies regulate access on a role-based basis and regularly review these structures, which indicates an awareness of data security. Nevertheless, 14% report largely open data access for all teams, and just under a third (30%) tend to act reactively and without fixed controls when assigning access rights. This discrepancy between established guidelines and their consistent implementation represents a risk that should not be underestimated, especially in the context of sensitive data used for AI applications.   
• Your fields of action for a strong data foundation:
  
  • Develop a data strategy: Define clear, company-wide guidelines for data quality, availability, storage and integration. Define responsibilities.
  • Break down data silos: Invest specifically in the harmonization of your IT landscape. The goal must be a consistent and well-integrated data landscape that enables the seamless flow of data between systems.   
  • Manage access rights: Implement a clear, role-based authorization concept. This should be subject to regular audits to ensure security and compliance.   
  • Ensure data quality: Establish robust processes to continuously monitor, cleanse and improve data quality. This includes aspects such as the correctness, completeness, up-to-dateness and relevance of the data. Companies such as Auto Lang AG, for example, have created a solid foundation for future AI solutions through a detailed analysis of their infrastructure and customer data, supported by external expertise.
    

Step 2: Strategic objectives - anchoring AI with foresight

• Why AI needs a clear strategic direction: Without a clear anchoring in the corporate strategy, AI projects run the risk of getting stuck in the experimental stage or failing to develop their full potential. AI should not be seen as an isolated technical gimmick, but as an integral part of the company's long-term direction that helps to achieve overarching business goals.   
• The reality in Swiss companies (insights from the CorpIn study): A majority of companies (65%) recognize AI as at least partially strategically relevant. However, only 26% consider AI to be a central component of their strategy, while 39% are primarily examining the use of AI in selected areas and 30% are only carrying out initial feasibility analyses without a clear strategic priority. Further action is needed when it comes to specifying goals: Only 13% of companies are already pursuing specific and measurable AI goals. Around 40% only have rough ideas, and a further 34% have not yet fully developed objectives. Support from top management is a critical success factor. Although 42% of companies actively promote AI at the highest level, almost as many (44%) restrict support to individual projects or only provide it to a limited extent. A crucial point is the measurement of success: only 8% of companies measure the success of their AI initiatives using concrete key performance indicators (KPIs), while more than half (51%) currently do without such metrics altogether. This lack of definition of measurable goals and the absence of KPIs have a direct impact: Without proof of the value proposition of AI projects, it is difficult to justify further investment and gain the full support of top management. This can lead to a cycle of underinvestment and limited strategic backing. Furthermore, the alignment of AI projects with the company's overall strategy is often insufficient. Only 21% ensure that their AI activities are closely aligned with the overall strategy. For around a quarter (27%), AI projects are even initiated almost independently of overarching objectives, and for a further 24% there is no coordination at all. This often indicates a "bottom-up" approach or thinking in departmental silos, in which AI initiatives are created for a good reason but run the risk of remaining isolated stand-alone solutions without broad strategic benefits.
  
• Your fields of action for an effective AI strategy:
  
  • Define your AI vision: Anchor AI as a central and integral part of your overarching corporate strategy. Communicate this vision clearly throughout the entire company.
  • Set measurable goals (SMART): Formulate specific, measurable, achievable, relevant and time-bound (SMART) goals for your AI initiatives. What exactly do you want to achieve with AI?
  • Win top management as a driver: Secure the active support and sponsorship of top management. Top management must exemplify the strategic importance of AI and provide resources.
  • Establish KPIs to measure success: Define clear key performance indicators (KPIs) to evaluate the progress and actual value contribution of AI projects and justify investments. This is also relevant in the context of SEO developments for 2025, which suggest a focus on conversion quality rather than pure traffic metrics.   
  • Synchronize AI projects with the overall strategy: Ensure end-to-end alignment between individual AI projects and the company's strategic goals. This ensures that AI initiatives are geared towards long-term value creation.
    

Step 3: Cultural dimension - getting people excited about change

• Why corporate culture is a key factor in AI success: The introduction of artificial intelligence is not just a technological transformation, but also a cultural one. The attitude of employees towards AI is a key success factor. Even the best technology will fail if the people who are supposed to use it are not open to it, have fears or put up resistance.   
• The reality in Swiss companies (insights from the CorpIn study): The mood in companies is mixed: in just under 40% of the companies surveyed, a predominantly positive attitude towards AI is reported, while 47% perceive a mixed opinion. Pronounced skepticism is rare at 3%. Encouragingly, 34% of companies report no noticeable resistance or fears towards AI. In 41% of cases, isolated reservations and uncertainties are actively addressed. One critical point, however, is the involvement of employees: Only a quarter (24%) of companies have systematic feedback channels such as regular surveys or structured discussion forums. 28% only collect feedback selectively, and 21% have no defined structures for employee feedback in the context of AI. This lack of systematic feedback channels can lead to uncertainties and resistance not being recognized early on and becoming entrenched, which makes cultural change more difficult. It is also interesting to note which tasks are considered particularly time-consuming and therefore offer potential for AI support: Data processing and analysis activities were mentioned 819 times, followed by administrative tasks with 717 mentions. These activities are often repetitive. AI can not only bring efficiency gains here, but also relieve employees of monotonous tasks, which can have a positive impact on job satisfaction and thus on the acceptance of AI.   
• Your fields of action for an AI-friendly corporate culture:
  
  • Open communication and transparency: Communicate clearly and proactively about the goals, benefits and potential impact of introducing AI. Address employees' concerns and questions openly and honestly.
  • Establish change management: Accompany the AI implementation process with targeted change management measures. This includes preparing employees for new ways of working and new ways of thinking.   
  • Involve employees: Create institutionalized feedback channels and actively involve your employees in the design and introduction of AI solutions. Their perspectives and experiences are valuable.   
  • Address fears, highlight opportunities: In your communication, focus on how AI can take over repetitive and burdensome tasks, freeing up space for more creative, strategic and human-centered activities.
    

Step 4: Technical requirements - creating the infrastructure for innovation

• Why a flexible IT infrastructure is crucial: The existing technical infrastructure forms the backbone for the implementation of AI. It plays a key role in determining how quickly, flexibly and effectively new AI applications can be integrated into existing business processes. Rigid, outdated or poorly integrated systems can slow down or completely prevent even the most promising AI initiatives.   
• The reality in Swiss companies (insights from the CorpIn study): For important basic systems such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management) or HR software, the organizations surveyed show relatively high levels of flexibility and integration capability. This indicates that many companies rely on adaptable solutions, at least in the core area of their IT infrastructure. However, a different picture emerges for more specialized applications, for example for business intelligence (BI) or supply chain management (SCM). In a significant proportion of companies, these systems are difficult to integrate or are not used at all. This discrepancy between flexible core systems and special applications that are difficult to integrate is problematic. It often leads to media disruptions, isolated data islands and a fragmented IT landscape, which makes the holistic and efficient use of AI technologies more difficult. The lack of integration of specialist systems such as BI and SCM not only has technical consequences in the form of data silos, but also directly hinders strategic decision-making. If important operational data from these systems cannot be incorporated into AI analyses, or can only be incorporated incompletely, decisions based on this data are based on incomplete information, which undermines the potential benefits of AI. The fact that many companies have invested in standard software but are reluctant to integrate advanced, domain-specific solutions points to a gap where external partners with combined technical and business expertise can provide support.   
• Your fields of action for an AI-enabled IT landscape:
  
  • Carry out a system audit: Conduct a thorough inventory of your existing IT systems. Evaluate their flexibility, scalability, security and, above all, their integration capability with regard to planned AI applications.
  • Improve integration capability: Invest in modern interfaces (APIs), middleware solutions and data integration platforms to enable seamless networking of all relevant systems. The goal is end-to-end digitization, where data can flow freely.   
  • Plan a scalable infrastructure: Take into account the potentially high computing power and storage requirements of AI applications. Cloud solutions often offer the necessary flexibility and scalability here.
  • Modernize or replace legacy systems: Develop a clear roadmap for modernizing or replacing outdated systems (legacy systems) that are acting as roadblocks to your AI initiatives.

Step 5: Security & privacy - ensuring trust in the age of AI

• Why data protection and IT security are central to AI: Artificial intelligence often operates with large amounts of data, often including sensitive or personal information. Data protection breaches, security gaps or the unethical use of AI can have serious legal consequences, financial damage and a lasting loss of reputation. Trust - both from customers and employees - is the fundamental currency in the age of AI.   
• The reality in Swiss companies (insights from the CorpIn study): Many companies comply with the minimum legal requirements for data protection (GDPR/DSG). However, only a few go beyond this: only 20% of the companies surveyed are certified in accordance with the internationally recognized ISO/IEC 27001 standard for information security. Although 41% meet the legal requirements, they do not have any additional certifications, and 15% only work with basic security measures. In terms of specific protective measures, around half (50%) rely on end-to-end encryption and role-based access controls, while 44% largely rely on basic solutions such as password protection and firewalls. The situation is particularly critical when it comes to AI-specific policies: Only 21% of companies have comprehensive, regularly updated guidelines for the use of AI. A fifth (20%) have not yet established any such guidelines at all. There are also shortcomings when it comes to reviewing measures: only 37% systematically carry out regular audits to ensure compliance with data protection guidelines, while 15% do not do so at all. This discrepancy between the sensitivity of data processed by AI systems and the level of security and governance measures in place is alarming. Companies risk not only compliance violations, but also a loss of trust that can jeopardize AI projects in the long term. The lack of AI-specific guidelines and regular audits could also be linked to a lack of specific awareness and expertise in the area of AI safety and ethics.   
• Your fields of action for the safe and responsible use of AI:
  
  • Ensure data protection compliance (GDPR/DSG): Implement and maintain robust processes and technical measures to ensure compliance with applicable data protection laws at all times.
  • Raise security standards: Align yourself with recognized international standards such as ISO/IEC 27001. Rely on advanced protection measures that are appropriate to the risk level of your AI applications.
  • Develop AI ethics guidelines: Define clear, internal company rules and principles for the responsible and ethical use of AI. In particular, address topics such as fairness, transparency, traceability and dealing with potential bias risks in AI systems.   
  • Conduct regular security audits: Systematically review the effectiveness of your data protection guidelines and security measures at regular intervals, ideally by internal and external bodies.
  • Create transparency: Ensure that the functioning of your AI systems and their use of data is as comprehensible and transparent as possible for the relevant stakeholders - be they customers, employees or supervisory authorities.

Step 6: Awareness & competence - strengthening AI expertise in the company

• Why AI knowledge and expertise are indispensable: Without a sound awareness of the opportunities and challenges of AI and the corresponding skills within the company, new technologies cannot be effectively integrated into value creation processes and used sustainably. Both broad user knowledge and specialized expertise are required in order to exploit the full potential of AI and manage risks.   
• The reality in Swiss companies (insights from the CorpIn study): There is a clear need to catch up here: half of companies (51%) do not offer any regular AI training for their employees at all. A further 21% provide training only irregularly and without a fixed concept. Only 9% rely on continuous, institutionalized and mandatory training measures in the field of AI. Moreover, the content of the training courses offered is often limited to AI basics and everyday tools, while strategic fields of application or technical implementation aspects are often neglected. Access to specialized AI expertise is also a hurdle: 39% of the companies surveyed state that they have neither internal AI specialists nor access to external experts. If external consulting is brought in, this is often only done on a situational basis and not as part of a strategic partnership. The integration of AI tools into existing processes is also proving difficult: over 45% of companies rate their integration performance in this regard as "poor" or "non-existent". This lack of training and access to experts often leads to poor integration of AI tools, which in turn can lead to frustration and a negative perception of AI - a vicious cycle that reduces the willingness for further AI initiatives. Although 68.4% of respondents state that they have a general awareness of risks such as hallucinations (incorrect AI-generated information) and bias (distortions in AI models). However, 23.5% are indifferent to this point, and 8.2% give it little or no consideration. However, this "general awareness" is often superficial. Without in-depth training and expertise, it is unlikely to be translated into concrete measures to minimize risk.   
• Your fields of action for increasing AI awareness and competence:
  
  • Develop comprehensive training programs: Offer regular and target group-specific training courses. These should not only cover AI basics and the operation of tools, but also department-specific applications, the strategic use of AI, ethical aspects and risk management.   
  • Ensure access to expertise: Build up internal AI skills in a targeted manner, for example by training key personnel or creating new roles. In addition, strategic collaboration with external experts, such as that offered by CorpIn through AI workshops and individual support, can provide valuable impetus and close knowledge gaps.   
  • Promote knowledge sharing: Create internal platforms and formats (e.g. communities of practice, internal conferences) for sharing AI experiences, best practices and lessons learned.
  • Train critical thinking: Sensitize your employees to the specific risks of AI, such as bias in data sets and algorithms or the danger of hallucinations. Encourage a critical and questioning approach to AI-generated results and decisions.
    

III. Understanding your AI maturity level: The key to targeted further development

The six dimensions in interaction: No step stands alone.

The six steps presented for successful AI implementation, which are based on the dimensions of the CorpIn Hexagon model, are not isolated from one another. Rather, they are closely interlinked and influence each other. An excellent data foundation, for example, is of little use if there are no clear strategic goals for the use of AI or if the corporate culture is opposed to change. Conversely, a high level of AI awareness in the workforce can only bear fruit if the technical prerequisites for the use of AI are also in place. A weakness in one dimension can therefore severely disrupt or even block progress in all other areas.

Why an honest inventory is the starting point.

Before companies take targeted measures to implement AI and make investments, they need to know where they currently stand. As Nicolas Quell, Co-Founder of CorpIn, aptly points out: "Many companies rate AI as important, but often don't know how prepared they really are". An honest, data-based inventory of your own AI maturity level is therefore the essential starting point for any successful AI strategy. It helps to uncover blind spots, avoid bad investments and focus limited resources on the fields of action that offer the greatest leverage for further development.  

The CorpIn Hexagon platform: your tool for data-based location determination.

CorpIn has developed the Hexagon platform to provide companies with precisely this clarity and a sound basis for implementing these six crucial steps. This innovative tool, which is based on the comprehensive findings of the Swiss study "Towards AI Excellence (2025)" and data from over 1,300 decision-makers, enables you to perform a detailed and holistic analysis of your current AI maturity level along the six dimensions discussed. The platform is therefore more than just an assessment tool; it functions as a strategic planning instrument that transforms a pure as-is analysis into a target concept and a concrete roadmap. It thus directly addresses one of the biggest challenges for decision-makers: making AI tangible and manageable as a supposed "black box".  

The core benefit of the CorpIn Hexagon platform lies in its ability to deliver data-driven and therefore objective insights:

• Clear assessment of where you stand: precisely identify your individual strengths and weaknesses in the field of AI using 56 scientifically based success factors.   
• Objective benchmarking: Compare your own AI maturity level anonymously with the average of over 1,300 other Swiss companies as well as with relevant peers from your industry and company size.   
• Concrete recommendations for action: Based on your individual results, receive tailored and prioritized suggestions for optimizing your AI strategy and closing identified gaps. As the press release on the platform states: "The platform provides companies with a clear assessment of their current position, industry-specific benchmarks and concrete next steps. The aim is to systematically and strategically anchor AI in companies.".
  

Such a data-driven analysis is the first step in managing your AI transformation in a targeted manner and unlocking the full potential of this technology for your company. Find out more about how you can determine your individual AI maturity level:[https://www.corpin.ch/hexagon].

IV. Conclusion: Shaping the future with strategic AI implementation

The successful implementation of artificial intelligence is not a one-off task, but a continuous journey that requires a long-term perspective, constant learning and a willingness to adapt. The six steps outlined here represent an iterative process that must be constantly reassessed and adjusted in order to keep pace with the dynamic developments in the field of AI.  

The first step on this journey is often the most crucial: make a structured and well-founded start. Whether this means initiating an internal discussion about the strategic importance of AI, objectively assessing your own level of AI maturity or seeking external expertise, the key is to take action. If you are ready to set the course for a successful AI future in your organization and develop a clear, data-driven strategy, now is the time to do it. CorpIn sees itself as a partner that supports companies not only with tools such as the Hexagon platform, but also with individual AI and digitalization strategies along their entire digital transformation journey.  

The transformative potential of artificial intelligence is immense. Companies that take a strategic approach to the challenges of AI implementation and develop the six dimensions of the CorpIn Hexagon model in a balanced way have the best chance of securing sustainable competitive advantages and actively shaping the future of their industry.  

V. About the authors (CorpIn)

This technical article was written by the AI and digitalization experts at CorpIn GmbH. As pioneers in the practical application of AI and authors of the comprehensive Swiss study "On the road to AI excellence (2025)" on which the six steps presented here are based, we combine a scientific foundation with entrepreneurial understanding. Our mission is to support decision-makers like you in unlocking the full potential of future technologies and establishing AI successfully and sustainably in their companies. The CorpIn Hexagon platform is a result of this expertise and offers companies a concrete tool for analyzing and managing their AI transformation.