2024-11-29
Microsoft Fabric has emerged as a powerful solution to solve complex data and analytics challenges, equipping companies with the capabilities needed for AI-driven innovation. In September, we had the honor of sponsoring the European Fabric Community Conference in Stockholm, where the buzz around new features and AI capabilities was undeniable. But one key question remains: are companies adequately prepared for AI adoption?
We’ve seen firsthand how Microsoft Fabric can enable enterprise agility and transform data strategy. A standout example is our work with the UK’s largest real estate business, where we combined best-in-class technology with hands-on data expertise to deliver impactful results across 200+ brands and 10+ distinct services.
Key considerations for AI implementation:
AI foundations and medallion architecture: Companies building AI factories need a solid data foundation. Using the medallion architecture — Bronze for raw data, Silver for cleaned data and Gold for aggregated business analytics — provides a robust framework for scalable AI solutions. Real-time intelligence: Fabric enables real-time insights from IoT and various data sources, helping organizations design event-driven business use cases and improve operational efficiency.
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AI governance and strategy alignment: Successful AI adoption requires a comprehensive governance framework that aligns with the company’s overall strategy. AI is a tool, not a strategy in itself, so understanding how AI can advance business goals and KPIs is crucial. Consider whether your organization has a dedicated innovation lab for proofs of concept and how your technology roadmap supports business objectives.
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Infrastructure readiness: Transitioning from legacy systems to cloud infrastructure is vital for future-proofing operations. Managing multi-cloud environments may be tempting for short-term gains, but a unified cloud strategy ensures resilience and efficiency over time.
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Data governance and FinOps: A robust data governance model is essential. Addressing issues such as data sovereignty, cost management (FinOps) and data privacy ensures your data practices are both compliant and efficient.
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Human-in-the-loop safeguards: Automating tasks can yield significant efficiencies, but human oversight is necessary to validate outcomes and keep data relevant. Incorporate user feedback into new processes to ensure that automation enhances, rather than diminishes, employee productivity.
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AI governance board and compliance: A strong AI governance board should include leaders across business, technology, finance, HR and key vendors. This board should ensure all AI initiatives are aligned and compliant with evolving regulations, such as the EU AI Act, which categorizes AI risk levels and mandates specific safeguards.
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Security and upskilling: New AI technologies come with heightened security risks. Building a team with the necessary AI expertise and learning from past projects’ challenges can significantly enhance security measures.
Valtech has numerous success stories in AI and data science. Whether it’s equipping banks with AI-driven fraud prevention or accelerating clinical trials with machine learning, partnering with an experienced AI technology partner is critical. As the saying goes, “If you want to go fast, go alone. If you want to go far, go together.” Collaborate wisely to ensure AI works for your business, responsibly and effectively.
Ready to discuss your AI strategy? Connect with us to see how we can help.
