Where’s enterprise AI headed next? And are we ready for it?
Sadid Hasan, a leader at Microsoft’s MAIDAP, explores how enterprise AI is shifting from standalone models to deeply integrated, context-aware systems. He reflects on the nuances of translating cutting-edge research into intuitive tools, the challenges of responsible deployment, and why we need hybrid approaches to balance scale with precision.
I’m currently focused on advancing Generative and Agentic AI innovation across Microsoft’s productivity and cloud ecosystems. My work centers on enhancing user experiences and operational intelligence through developing and deploying advanced Copilot features in Office M365 and Azure Edge & Platforms. This involves exploring how multi-modal foundational AI models and agents can drive deeper contextual understanding, streamline workflows, and unlock new capabilities for users. It’s an exciting intersection of AI, platform engineering, and product strategy, where cutting-edge research meets real-world impact towards empowering people and organizations while shaping the future of technology.
The latest foundational models and agentic systems can be incredibly powerful, but without thoughtful design, their capabilities may be underutilized or misunderstood. Another key challenge is deeply understanding user needs and mapping model capabilities to address those needs in meaningful ways. To bridge this gap, I focus on establishing a strong contextual link between technical innovation and practical application via wrapping models with responsible AI and evaluation pipelines, and designing solutions that are intuitive, context-aware, and workflow-enhancing to empower users.
Embedding responsible AI principles and best practices into a meaningful user experience is key to building trustworthy AI systems. Given the vast amount of domain-specific data in the AIOps ecosystem, one challenge is ensuring the system can dynamically consider the most relevant operational context in real time and surface insights in ways that are truly actionable for users. The goal is to earn user trust by designing AI systems that feel less like a black box and more like a reliable partner in decision-making.
When evaluating whether an AI idea should be patented or shared through open science, I generally look at its potential impact and the ecosystem it’s meant to serve. For example, if the idea introduces a novel mechanism that could fundamentally improve enterprise-grade systems, it may be worth protecting to ensure responsible and scalable deployment. On the other hand, if the idea contributes to foundational understanding or benefits from broader collaboration, open science could be the path to consider. So, it’s mainly about finding the right balance between advancing innovation and accelerating progress. Also, we often publish research papers based on patented ideas, as patents primarily protect commercial use but still allow the scientific community to continue studying, understanding, and building upon them.
I believe they’ll coexist. General-purpose LLMs offer great flexibility, but task-specific models still excel when high accuracy, speed, or domain expertise is needed, especially in enterprise settings. The ideal path forward is to build hybrid systems, where general-purpose LLMs can be enhanced by domain-specific fine-tuning, retrieval-augmented pipelines, or by building agentic systems composed of collaborative specialized agents as necessary to better meet user needs. So, it's not a one-size-fits-all approach; rather, thoughtful orchestration of different techniques is key to building effective and scalable AI systems that solve real-world problems.
Sadid Hasan has over two decades of AI R&D experience and currently leads generative AI research and product development for Office M365 Copilots and Azure AIOps initiatives as part of Microsoft's advanced AI Development Acceleration Program. Previously, he was the Executive Director of AI at CVS Health and served as the Senior Scientist & Technical Lead of the AI Group at Philips Research. Sadid has a PhD. in NLP and Machine Learning, with hundreds of peer-reviewed publications and patents. He was recently recognized as one of the 100 most influential AI leaders in the USA.
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