Helping enterprises leverage AI for a data-driven edge

1. AI agents move centre stage

Microsoft’s headline reveal, Agent 365, positions AI agents as the new operational layer of the digital workplace. It provides a central hub to register, monitor, secure, and coordinate agents across the organisation.

At the same time, Microsoft 365 Copilot introduced dedicated Word, Excel, and PowerPoint agents, capable of autonomously generating, restructuring, and analysing content based on business context.

Why this matters

Enterprises are shifting from “asking AI questions” to “assigning AI work”. Agent-based architectures will gradually replace many single-purpose assistants.

What organisations can do

• Identify workflows suitable for autonomous agents
• Standardise agent behaviour and permissions
• Start pilot deployments inside Microsoft 365 ecosystems

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2. Integration and orchestration become non-negotiable

Microsoft emphasised interoperability through the Model Context Protocol (MCP). Agents across Teams, Microsoft 365, and third-party apps can now share context and execute coordinated multi-step workflows.

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Why this matters

Real automation requires more than standalone copilots — it requires orchestration between tools, data sources, and departments.

What organisations can do

• Map cross-app workflows
• Connect productivity, CRM/ERP and operational platforms
• Design agent ecosystems rather than isolated assistants

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3. Governance and security move into the spotlight

As agents gain autonomy, Microsoft introduced governance capabilities such as:

• visibility into permissions
• behavioural monitoring
• integration with Defender, Entra, and Purview
• centralised policy control
• data-loss prevention

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Why this matters

AI at scale must be fully observable and compliant. Governance will become a foundational requirement for all agent deployments.

What organisations can do

• Define who is allowed to create/modify agents
• Establish audit and monitoring standards
• Build guardrails before rolling out automation

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Read the official Microsoft article with all security updates & news - Link

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4. Windows, Cloud PCs, and the rise of the AI-enabled workspace

Microsoft presented Windows 11 and Windows 365 as key components of the AI-first workplace. Features include:

• AI-enhanced Cloud PCs
• support for shared and frontline devices
• local agent inference on capable hardware
• endpoint-level automation

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Why this matters

Distributed teams gain consistent, secure work environments with native AI capabilities.

What organisations can do

• Evaluate Cloud PC scenarios
• Modernise workplace setups for agent-driven workflows
• Explore AI-enabled devices for operational teams

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5. AI infrastructure and Azure evolution

Ignite highlighted continued investment in Azure AI capabilities, including:

• improved model hosting and versioning
• hybrid CPU/GPU inference
• faster deployment pipelines
• more cost-efficient fine-tuning
• enhanced governance for AI training data

Full report here - Link

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Why this matters

Scalable data pipelines and model infrastructure remain essential foundations for any agent-driven environment.

What organisations can do

• Update data architecture for AI-readiness
• Implement vector indexing and retrieval pipelines
• Optimise model hosting costs

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6. Copilot Studio and plug-in ecosystem expand rapidly

Copilot Studio received major updates, transforming it into a central automation and integration hub. New capabilities include:

• custom agent creation with visual logic
• no-code multi-step workflows
• plug-ins for internal APIs and line-of-business systems
• improved grounding using enterprise data
• expanded connectors for CRM/ERP/event platforms

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Why this matters

Organisations can build specialised copilots and agents — connected to their internal systems and business logic.

What organisations can do

• Develop domain-specific copilots
• Use connectors to integrate existing systems
• Leverage visual logic for quick experiments

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7. Fabric + Azure AI integration

Microsoft Fabric now provides deeper AI readiness features:

• tight integration with Azure AI Studio
• automated pipelines for AI data preparation
• vector indexing and RAG capabilities inside OneLake
• enhanced lineage and governance
• performance boosts for large-scale analytics

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Why this matters

AI agents depend on clean, governed, real-time data. Microsoft states that Fabric now enables building unified data + AI environments more efficiently.

What organisations can do

• Consolidate disparate data pipelines into Fabric
• Implement vector search for internal knowledge retrieval
• Build governed AI datasets with lineage tracking

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What this means for companies

Across all announcements, one trend is consistent: AI is becoming an operational layer—not an add-on.

For organisations in finance, energy, logistics, retail, or event management, this brings clear implications:

• It’s time to move from experimentation to real deployment.
• Automated agents will replace many single-purpose copilots.
• Governance frameworks must be in place before scaling.
• Integration across apps, data sources, and workflows is essential.
• AI will increasingly live inside productivity tools employees already use.
• The competitive advantage will come from how well agents connect to business processes—not from which model is used.

BigHub is well-positioned to guide you with for this transition—through personalized strategy, architecture, implementation, and optimisation.

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How enterprises should prepare for 2025–2026

Here are the next steps organisations should consider:

1. Map high-value workflows for agent automation

Identify repetitive, cross-team workflows where autonomous task execution delivers value.

2. Design your agent governance framework

Define roles, access boundaries, audit controls, and operational monitoring.

3. Prepare your data infrastructure

Ensure clean, accessible, governed data that agents can safely use.

4. Integrate your productivity tools

Leverage Teams, Microsoft 365, and MCP-compatible apps to reduce friction.

5. Start with a controlled pilot

Choose one business unit or workflow to test agent deployment under monitoring.

6. Plan for organisation-wide rollout

Once guardrails are validated, scale agents into more complex processes.

Bridging academia and real-world practice is key

At the Faculty of Nuclear Sciences and Physical Engineering of CVUT (FJFI), we are changing that. Since the 2021/2022 academic year, BigHub has been teaching full-semester courses that connect academia with the real world of data. And it’s not just lectures—students get hands-on experience with real technologies in a business-like environment, guided by professionals who deal with such projects every day.

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What brought us to FJFI

BigHub has a personal connection to CVUT FJFI. Many of us—including CEO Karel Šimánek, COO Ing. Tomáš Hubínek, and more than ten other colleagues—studied there ourselves. We know the faculty produces top-tier mathematicians, physicists, and engineers. But we also know that these students often lack insight into how data and AI function in business contexts.

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That’s why we decided to change it. Not as a recruitment campaign, but as a long-term contribution to Czech education. We want students to see real examples, try modern tools, and be better prepared for their careers.

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Two courses, two semesters

18AAD – Applied Data Analysis (summer semester)

The first course launched in the 2021/2022 academic year, led by Ing. Tomáš Hubínek. Its goal is to give students an overview of how large-scale data work looks in practice. Topics include:

• data organization and storage,
• frameworks for big data computation,
• graph analysis,
• cloud services,
• basics of AI and ML.

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Strong emphasis is placed on practical exercises. Students work in Microsoft Azure, explore different technologies, and have room for discussion. Selected lectures also feature BigHub experts who share insights from real projects.

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18BIG – Data in Business (winter semester)

In 2024, we added a second course that builds on 18AAD. It is taught by doc. Ing. Jan Kučera, CSc. and doc. Ing. Petr Pokorný, Ph.D. The course goes deeper and focuses on:

• data governance and data management in organizations,
• integration architectures,
• data platforms and AI readiness,
• best practices from real-world projects.

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While 18AAD shows what can be done with data, 18BIG demonstrates how it actually works inside companies.

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Above-average student interest

Elective courses at FJFI usually attract only a few students. Our courses, however, enroll 20–35 students every year—an above-average number for the faculty.

Feedback is consistent: students appreciate the practical focus, open discussions, and the chance to ask professionals about real-world situations. For many, it’s their first encounter with technologies actually used in business.

Beyond the classroom

Our involvement doesn’t end with teaching. Together with the Department of Software Engineering, we’ve helped revise curricula and graduate profiles, enabling the faculty to respond more flexibly to what companies in the data and AI fields really need. This improves the quality of education across the entire faculty, not just for students who take our electives.

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It’s not about recruitment

Sometimes, a student later joins BigHub — but that’s not the goal. The goal is to ensure graduates aren’t surprised by how data work really looks. We want them to have broader, more practical knowledge and hands-on experience with modern tools. It’s our way of giving back to the institution that shaped us and contributing to the Czech tech ecosystem as a whole.

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Collaboration with FJFI goes beyond teaching. Since BigHub’s founding, we’ve supported the student union and regularly participated in the faculty’s Dean’s Cup sports event, playing futsal, beach volleyball, and more. This year, we also submitted several grant applications together and hope to soon collaborate on joint technical projects. We believe a strong community and informal connections between students and professionals are just as important as textbook knowledge.

What’s next?

Our cooperation with CVUT FJFI is long-term. Courses 18AAD and 18BIG will continue, and we are exploring ways to expand their scope. We see that students crave practical experience and that bridging academia with real-world practice truly works. If this helps improve the quality of data and AI projects in Czech companies, it will be the best proof that our effort is worthwhile.

What the AI Act is and why it was introduced

The AI Act is the first EU-wide law that sets rules for the development and use of artificial intelligence. The rationale behind this legislation is clear: only with clear rules can AI be safe, transparent, and ethical for both companies and their customers.

Artificial intelligence is increasingly penetrating all areas of life and business, so the EU aims to ensure that its use and development are responsible and free from misuse, discrimination, or other negative impacts. The AI Act is designed to protect consumers, promote fair competition, and establish uniform rules across all EU member states.

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Who the AI act applies to

The devil is often in the details, and the AI Act is no exception. This legislation affects not only companies that develop AI but also those that use it in their products, services, or internal processes. Typically, companies that must comply with the AI Act include those that:

• Develope AI
  
  
• Use AI for decision-making about people, such as recruitment or employee performance evaluation
  
  
• Automate customer services, for example, chatbots or voice assistants
  
  
• Process sensitive data using AI
  
  
• Integrate AI into products and services
  
  
• Operate third-party AI systems, such as implementing pre-built AI solutions from external providers
  
  

The AI Act distinguishes between standard software and AI systems, so it is always important to determine whether a solution operates autonomously and adaptively, meaning it learns from data and optimizes its results, or merely executes predefined instructions, which does not meet the definition of an AI solution.

Importantly, the legislation applies not only to new AI applications but also to existing ones, including machine learning systems.

To save you from spending dozens of hours worrying whether your company fully complies, BigHub is ready to handle AI Act implementation for you.

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What the AI Act regulates

The AI Act defines many detailed requirements, but for businesses using AI, the key areas to understand include:

1. Risk classification

The legislation categorizes AI systems by risk level, from minimal risk to high risk, and even banned applications.

2. Obligations for developers and operators

This includes compliance with safety standards, regular documentation, and ensuring strict oversight.

3. Transparency and explainability

Users of AI tools must be aware they are interacting with artificial intelligence.

4. Prohibited AI applications

For example, systems that manipulate human behavior or intentionally discriminate against specific groups.

5. Monitoring and incident reporting

Companies must report adverse events or malfunctions of AI systems.

6. Processing sensitive data

The AI Act regulates the use of personal, biometric, or health data of anyone interacting with AI tools.

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Avoid massive fines

Penalties for non-compliance with the AI Act are high, potentially reaching up to 7% of a company’s global revenue, which can amount to millions of euros for some businesses. 

This makes it crucial to implement the new AI regulations promptly in all areas where AI is used.

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Let us handle AI Act compliance for you

Don’t have dozens of hours to study complex laws and don’t want to risk huge fines? Why not let BigHub manage AI Act compliance for your company? We help clients worldwide implement best practices and frameworks, accelerate innovation, and optimize processes, and we are ready to do the same for you.

We offer turnkey AI solutions, including integrating AI Act compliance. Our process includes:

• Creating internal AI usage policies for your company
  
  
• Auditing the AI applications you currently use
  
  
• Ensuring existing and newly implemented AI applications comply with the AI Act
  
  
• Assessing risks so you know which AI systems you can safely use
  
  
• Mapping your current situation and helping with necessary documentation and process obligations

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