Reflections on AI Improvement and Business Impact

Ivy Insights
12/05/2025

My vision on AI improvements and business enablement

Written by Ludovic Staehli

Artificial Intelligence (AI) has undergone substantial evolution in the past two years, particularly driven by advancements in foundation models, generative AI (e.g., large language models like OpenAI’s GPT-4), and edge AI.

The release of transformer-based architectures and scalable deployment frameworks has enabled AI systems to perform tasks such as code generation, document summarisation, multimodal input processing, and real-time decision-making with minimal supervision. For example, GPT-4 Turbo is now capable of handling 128k-token contexts, enabling deep document comprehension and long-term reasoning, which significantly enhances knowledge management systems and virtual assistants in enterprise environments.

From a business perspective, AI is transforming operations by automating repetitive tasks, optimizing supply chains, personalizing customer experiences, and enhancing decision-making through predictive analytics. For instance, Siemens implemented AI-driven predictive maintenance using machine learning models trained on sensor data, reducing downtime and improving equipment reliability. Unilever applied AI to optimize marketing strategies and consumer insights by analyzing massive datasets using natural language processing (NLP). Morgan Stanley deployed OpenAI’s models to assist financial advisors with rapid, compliant responses to client inquiries, effectively merging human expertise with AI speed.

According to McKinsey & Company (2023), 55% of organizations have adopted AI in at least one business function, up from 50% in 2022, with reported benefits including cost savings, enhanced product quality, and faster innovation cycles.

To successfully implement AI features, the following prerequisites are essential:

  • Data Infrastructure

    Clean, structured, and labeled data is critical. A scalable data lake or warehouse setup (e.g., Snowflake, AWS Redshift) is commonly required.

  • Computational Resources

    Access to GPUs/TPUs or cloud-based AI platforms (e.g., Azure ML, AWS SageMaker) for model training and inference.

  • Talent

    Data scientists, ML engineers, and AI architects with expertise in model development, deployment, and monitoring.

  • Security & Compliance

    Adherence to data governance, regulatory compliance (e.g., GDPR), and AI ethics frameworks.

  • Executive Buy-in

    Alignment of AI projects with business strategy and commitment from leadership to scale AI capabilities.

  • Change Management

    Training, process redesign, and organizational adaptation to integrate AI into workflows effectively.


As AI continues to evolve at an unprecedented pace, the true differentiator for organizations won’t be access to technology. It will be the ability to implement it with purpose, discipline, and vision. By building strong foundations and aligning AI initiatives with strategic goals, businesses can move beyond experimentation and unlock meaningful, scalable impact. The future of AI is not just about innovation, it’s about execution. At Ivy Partners, we understand what it takes to support you at every stage of that journey.

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About the Author

Ludovic Staehli

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