Views: 503
Artificial intelligence (AI) has become a strategic lever for digital transformation in businesses. However, building an effective AI strategy is far from improvised. It’s essential to distinguish AI itself (machine learning, predictive analytics, etc.) from the AI strategy: a comprehensive action plan aligned with business objectives.
Without a clear strategic framework, AI initiatives often turn into isolated experiments with limited impact. In fact, according to IBM, over half of corporate AI projects never make it to production—typically due to unclear goals, poor data usability, or ineffective risk management. Implementing “AI for the sake of AI,” without a business-driven approach, is a dead end.
Using a focused checklist, we explore how to assess and structure your AI strategy effectively. From strategic alignment to technical architecture, including responsible governance, these checklists will guide Swiss and international decision-makers in building an AI ecosystem that is scalable, secure, and responsible—and that delivers measurable business value (ROI).
Strategic Alignment: AI in Service of Business Objectives
Key point: Ensure every AI initiative is aligned with the company’s strategy and delivers measurable ROI.
Every successful AI strategy begins with alignment to business goals. In practical terms, this means defining AI use cases that directly address identified business challenges or opportunities—improving customer experience, optimizing a supply chain, automating quality control, etc.—rather than deploying AI based on trends or hype.
In this context, a strategic checklist should include:
- Clear objectives (What do we want to achieve with AI? For whom? Why?)
- Success indicators (ROI KPIs, time savings, increased revenue, improved customer satisfaction)
- Strong executive sponsorship
For example, a French-speaking Swiss retail company initially launched a generic AI chatbot without a specific goal, which had little impact. By realigning the initiative with its customer strategy—to provide 24/7 support and streamline the shopping experience—it redefined the project around a virtual assistant embedded in customer service. The result: within a few months, first-level call volumes dropped by 30%, and customer satisfaction rose, delivering a clear ROI.
The lesson: AI only creates value when it addresses a real and prioritized business need.
At Edana, this ROI-driven approach is fundamental. Our experts begin by deeply understanding your business needs and identifying high-value use cases, ensuring that each AI project is calibrated toward tangible outcomes.
AI Governance & ESG: Building Responsible and Controlled AI
Key point: Establish strong governance to frame AI with ethical principles, compliance, and corporate social responsibility (CSR).
Responsible AI is more than a buzzword—it’s a cornerstone of any sustainable AI strategy. A solid AI governance checklist must include structures and processes to steer, monitor, and regulate AI initiatives.
This includes:
- Defining an ethical charter (algorithmic fairness, non-discrimination, appropriate transparency)
- Risk management (model bias, unfair automated decisions, social impacts)
- Regulatory compliance
In Switzerland, even without a dedicated AI law, companies face a complex and evolving legal environment—particularly with the new Federal Act on Data Protection (nLPD). To ensure responsible AI use, businesses must establish robust governance mechanisms.
In practice, this may involve setting up a multidisciplinary AI governance committee (business, IT, legal, CSR) to validate AI use cases and ensure compliance with relevant standards (e.g., GDPR/nLPD, upcoming AI regulations).
For instance, a Swiss cantonal bank implemented an internal data ethics board as soon as it began developing AI-based credit scoring models. This committee introduced safeguards: assessing the quality and governance of the training data, performing regular algorithm audits to detect bias, and defining transparency guidelines to be communicated to clients.
Thanks to this governance framework, the bank successfully deployed AI solutions that were both compliant and ethical, reinforcing stakeholder trust and avoiding reputational or legal pitfalls.
At Edana, we naturally embed these dimensions into every AI project. We ensure that each solution aligns with the client’s corporate values and CSR commitments, offering tailor-made approaches rooted in ethical best practices. Our internal charter, for example, guides our teams in developing secure, inclusive, and sustainable AI systems. This governance foundation ensures that AI remains an asset—not a liability—within your organization.
Edana: strategic digital partner in Switzerland
We support mid-sized and large enterprises in their digital transformation
Modular Architecture & Integration: A Flexible and Scalable Technical Foundation
Key point: Adopt a flexible architecture combining open-source components and custom development to integrate AI into your IT systems without excessive dependencies.
A high-performing AI strategy requires strong technological foundations. Your architecture must be designed to support AI solutions while remaining scalable and adaptable. In practical terms, this means favoring a modular, service-oriented architecture that supports future evolution and scale. It also means prioritizing seamless integrations between AI and third-party systems using standardized protocols, such as the MCP protocol, which is becoming essential for modern AI agents.
In contrast, relying on a monolithic, proprietary “one-size-fits-all” platform can lock you into rigid and costly technologies, hindering innovation and accumulating technical debt.
Our technical checklist therefore includes:
- Integrating AI into the existing IT ecosystem (ERP, CRM, databases, IoT…)
- Using open APIs to enable data flow and connect modules
- Selecting standard open-source solutions when appropriate
Open source enables independence and control. By leveraging widely adopted open standards, your organization avoids vendor lock-in, gains full control over its tech stack, and is better positioned to manage total cost of ownership Total Cost of Ownership, maintain agility, and innovate freely.
For example, an industrial company in Zurich wanted to add a predictive AI module for machine maintenance. Instead of purchasing or renting a full proprietary system, they chose a modular approach: an open-source machine learning model was trained on their sensor data and integrated into their existing CMMS via APIs. Everything was deployed on a Swiss hybrid cloud infrastructure connected to their factory.
This tailored integration avoided a costly system overhaul. The AI was seamlessly embedded into the existing ecosystem, resulting in a 20% reduction in equipment downtime without disrupting operations.
This kind of hybrid approach—blending proven open-source components with custom code—is central to Edana’s methodology. We assemble solutions using mature frameworks and APIs, combined with tailored development that fits each client’s unique context and requirements.
Security & Scalability: Safeguarding and Future-Proofing Your AI Ecosystem
Key point: Ensure data security and the scalability of AI solutions from day one to protect your business and secure long-term success.
Security and sustainability are inseparable pillars of any well-structured AI strategy. From a security perspective, a solid checklist must address:
- Data protection (encryption, anonymization of sensitive data, compliance with laws like GDPR and Switzerland’s LPD)
- Cybersecurity for AI models and applications (access controls, strong authentication for AI tools, regular audits of code and training data)
- Rights management (ensuring AI respects copyright and privacy rights when using third-party or generated data)
Swiss companies—especially those in regulated sectors such as finance and healthcare—place a premium on data location and confidentiality. It’s crucial to carefully decide where your AI solutions are hosted (sovereign cloud, on-premises, etc.), depending on regulatory and strategic needs.
For example, a private healthcare group in Switzerland deployed an AI system to assist with medical imaging diagnostics. Fully aware of the trust and compliance stakes, the organization chose to host the AI solution on its own highly secure Swiss-based servers, with end-to-end encryption of patient data. Only certified physicians could access AI-generated analyses, through a secure interface integrated into the medical records system, after strong authentication. This approach reassured both regulators and patients about AI security, while enabling the group to scale the solution across more clinics thanks to a flexible infrastructure.
On the scalability front, it’s vital to anticipate the AI platform’s ability to handle growth. What happens if data volume doubles, or if five new business units want to integrate AI into their processes? A proven best practice is to start with a focused pilot and scale the infrastructure progressively as value is demonstrated (elastic cloud architecture, additional servers, GPU capacity, etc.).
It’s equally important to plan for ongoing maintenance and updates: AI models need to be retrained regularly to remain accurate and relevant, and security patches must be applied promptly.
At Edana, as architects of digital ecosystems, we make security and scalability top priorities. From the earliest design stages, we ensure every AI solution we build adheres to strict information security standards, while being structured to grow alongside your business. Our commitment is to future-proof your AI platforms—robust, scalable, and maintainable solutions that deliver long-term value without compromising on quality or data control.
By integrating these considerations from the outset through a dedicated checklist (data security, compliance, scalability, business continuity), you shield your organization from risk and empower your AI ecosystem to evolve with confidence.
Conclusion
Structuring a high-performing AI strategy means checking all the critical boxes: strategic alignment with real business needs, strong governance and ethics for responsible AI, a modular and open architecture that integrates with your IT systems, and secure, scalable platforms that ensure long-term success.
By following these checklists, business leaders can transform AI from a trendy buzzword into a genuine driver of ROI and sustainable innovation for their organization.
AI only fulfills its promise when it is approached holistically, with control, foresight, and a clear focus on value creation. Interested? Let’s talk.
