So, I was delivering an AI awareness session earlier this week to 120 eight- to ten-year-olds at my sons’ school. In the Q&A bit, one of the children asked me – “What is Agentic AI?”. Needless to say, I was quite surprised to be asked that question by a nine-year-old. We actually decided to us AI to explain it to the class…. which I must say it did a better job that I would have done.
Agentic AI Explanation for a ten-year-old.
Here is what AI said: “imagine you’ve got a super-duper smart robot friend. This friend doesn’t just follow orders and obey instructions like a regular robot. It is like a super robot, the Dyson of Robots. This Robot can make decisions on its own, like deciding what to do next or how to do something when you haven’t taught it. ‘
Agentic AI’ is like that robot friend, but way more advanced. It can make its own choices, set its own goals, and figure out how to achieve them, almost like it’s got its own mind but by following a set of guidelines and rules. So, it’s not just reacting to commands, it’s thinking ahead and planning like a little strategist!”
Agentic AI for everyone else
I was then thinking, well what if you don’t work in an IT role. Does everyone know what Agentic AI is? It is certainly the buzz term of 2025 and certainly a leap forward from the “chat bots” we have played around with over the past 2 years or so with ChatGPT, Copilot etc.
I was then thinking about how I need to explain Agentic AI to some my co-workers so in my usual style I wanted to write it down to get my wording right. here’s what I came up with which I think and hope anyone can understand. As such I wanted to share it.
So…..
Think of agentic AI as more of “system” than a chat bot. Unlike a chat bot which is generally more about responding to a request or returning information, Agentic AI operates with a high degree of autonomy. Rather than just follows predefined instructions or responding based on information it has been fed/trained on, agentic AI can set its own objectives and determine. by itself, the best course of action to achieve them. This is a very different approach to what we have seen before now since it can not only executes tasks but also identifies opportunities, develops strategies, and takes initiative without constant oversight or being asked.
This has the potential to be a powerful tool in many different roles and organisations. Here’s a few examples I have pulled together based on some of the customer converations and usecases we are exploring at the moment.
Agentic AI Use Cases
Healthcare : Agentic AI could proactively identify potential health risks in patient data, following or before treatment, suggest personalised treatment plans, and even coordinate with pharmacy and supply chains to ensure medication availability. It could even be used to help patients better understand their health and nurses better explain to patients.
Gym: It could create personalised workout plans for members, monitor equipment usage to predict maintenance needs, and even suggest new classes based on emerging fitness trends. For Mangement it could suggest changes to class schedules based on enquiries, booking history, attendance etc.
Retail : It could autonomously manage inventory, predict trends by analysing customer data, external factors such as weather, news events etc, and even optimise pricing strategies based on market demand and competitor analysis such as changing the price of suncream when it gets hot and the price of umbrellas when it rains.
Public Sector : It could streamline citizen services, anticipate infrastructure needs based on usage patterns, and improve disaster response by dynamically allocating resources. It could also pre-empt and influence bin collections based on realtime data, or take proactive action and make recommendations from transcripts based on interviews or care notes in social services.
Legal: It could autonomously manage case documentation, chase up cases, predict case outcomes based on historical data, and even recommend legal strategies or layers most likely to win particualr cases. It could provide guiance to customers, based on “learned” cases for that firm and provide “virtual lawyer” services fully automonosly.
Insurance : Agentic AI could assess risk profiles, help detect fraudulent claims, and tailor policy recommendations to individual customers.
School Admissions : It could predict enrollment trends, identify potential gaps in student demographics, and optimise the selection process to ensure a diverse and well-balanced student body.
These are just a few examples of Agentic AI’s ability to act independently and adapt to complex, changing environments makes the applications and use cases almost endless as long as we can guide it, trust it and step in when needed.
Microsoft 365 Copilot now includes two advanced AI agents – Researcher and Analyst that became generally available in this month ( June 2025). These agents use powerful reasoning models (based on OpenAI’s o3-mini and deep research models) to handle complex tasks beyond what the standard Copilot could do.
Researcher is a specialised agent for multi-stepresearch – it can securely comb through your work data (emails, files, meetings, etc.) and the web to gather information, ask clarifying questions, and produce well-structured summaries and insights. It’s ideal for tasks like market research, competitor analysis, or preparing for big meetings – work that used to take hours, now done in minutes with higher accuracy.
Analyst is a virtual data analyst/data scientist built into Copilot. It excels at advanced dataanalysis, working through messy spreadsheets or databases step-by-step using chain-of-thought reasoning and even running Python code when needed. From identifying sales trends to spotting anomalies in finance data, Analyst gives you in-depth answers and visuals that mirror human analytical thinking.
Compared to the standard Microsoft 365 Copilot, these agents go much further in reasoning and capabilities for these specific tasks. While the native Copilot mod helps draft documents or summarise content, Researcher and Analyst tackle complex reasoning tasks (deep research and data analysis) with a level of thoroughness and skill akin to an expert – essentially “like having a dedicated employee at your side ready to go, 24‑7,” according to Microsoft’s Jared Spataro. They are accessed through the Copilot interface (pinned in the Copilot app and via Copilot Chat) and come with a usage limit of 25 queries permonth per user due to their intensive workloads.
Analyst vs. Copilot for Finance:
Analyst is a general-purpose data analysis agent available to any Copilot user, whereas Microsoft 365 Copilot for Finance is a separate, role-based Copilot designed specifically for finance teams. Copilot for Finance connects to financial systems (like Dynamics 365 and SAP) and Microsoft 365 apps (Excel, Outlook) to automate finance workflows (reports, reconciliations, insights). Unlike the Analyst agent which works on data you provide, Copilot for Finance directly taps into live enterprise finance data for real-time insights. Importantly, Copilot for Finance is not limited to Dynamics 365 – it can integrate with various ERPs including Dynamics 365, SAP, etc via connectors though it is deeply optimized for Dynamics 365 Finance.
The Age of AI Specialists in Microsoft 365 Copilot
Microsoft 365 Copilot is evolving from a single assistant into a team of AI specialists. Earlier this year, Microsoft announced two first-of-their-kind “reasoning agents” for work: Researcher and Analyst. After a period in preview (through the Frontier program) for early adopters, these agents are now generally available to all users with a Microsoft 365 Copilot license as of June 2025. This marks a significant expansion of Copilot’s capabilities beyond its initial skill set.
The new Researcher and Analyst are advanced Copilot modes (agents) specialised for particular scenarios – complex research and data analysis. They join other Wave 2 Copilot features (like the new Agent Store, Copilot Search, Memory, Notebooks, and image generation) that Microsoft has been rolling out to enhance the Copilot experience. Jared Spataro, Microsoft’s CMO for AI at Work, describes these agents as delivering “advanced reasoning” and notes “it really is like having a dedicated employee at your side ready to go, 24-7.” In other words, Microsoft 365 Copilot is no longer just a helpful assistant within Office apps – it can now also act as an on-demand subject matter expert that tackles higher-order tasks.
From a technology standpoint, both agents leverage the latest AI models tailored for their specific domains. They use OpenAI’s powerful models (codenamed o3-mini for Analyst, and a deep research model for Researcher) combined with Microsoft’s orchestration, search, Responsible AI, and tool integrations. This means they don’t just generate quick answers; they actually reason through problems in multiple steps, consult various data sources, and produce more comprehensive results. This blog explores each agent in detail:
Microsoft 365 Researcher Agent
Researcher is the new Copilot agent that acts as a highly skilled research assistant. It’s designed to help you tackle complex, multi-step research projects right from your Microsoft 365 environment. Researcher brings together OpenAI’s “deep research model” with Microsoft 365 Copilot’s advanced orchestration and search. In practice, this means it can scour both your organisational data *and* external sources on the web to find the information you need, synthesize it, and present insights in a coherent way.
What can Microsoft 365 Researcher Agent do?
Microsoft describes Researcher as “an agent that can analyse vast amounts of information with secure, compliant access to your work data – your emails, meetings, files, chats, and more – and the web” to deliver expert insights on demand. In simpler terms, Researcher is great at doing all the digging for information, reading it and then summarising the findings for you. Some of its capabilities include:
Multisource Information Gathering: It can search through your files, emails, SharePoint, and external online / Web sources to collect relevant data and. For example, if you’re exploring a new market or analysing a topic, Researcher will pull from both internal documents and credible websites to gather material.
Smart Summaries: After collecting information, Researcher summarises what it finds in plain, easy-to-read language. You get a clear, tailored report instead of a dump of raw data. It will highlight key points, trends, and insights rather than making you sift through hundreds of pages or search results.
Trend and Insight Identification: Researcher uses its AI reasoning to spot patterns, trends, and opportunities in the information. It can draw connections and highlight things that might make a difference for your project or question. For instance, it might notice an emerging customer preference across feedback data or identify a common thread in market research reports.
Interactive Refinement: If your initial query is broad, Researcher often asks clarifying questions to narrow down the scope and ensure it’s on the right track. This interactive back-and-forth helps it deliver more relevant results. You can guide it by answering those questions or giving additional instructions, much like you would with a human researcher.
Citations and Source Transparency: When delivering its findings, Researcher provides well-sourced content. It can include citations or references for where information came from, so you can trust but verify the results. (This is crucial for workplace research, and you can ask it to only use authoritative sources for extra confidence, as in one example prompt Microsoft shared).
Use Cases for Microsoft 365 Researcher Agent
Researcher is great in situations where you need to quickly learn or compile knowledge on a topic or subject area but are not sure where to look. This could be for tasks like assessing the impact of the new Trump tariffs on business lines, preparing for vendor negotiations by gathering supplier intel, and collecting client research before sales pitches.
Researcher Agent Example
In a business context, imagine your sales / marketing team are looking for a fresh perspective on top technology investments organisations are making in the UK based on industry research which needs to be in a report. You could ask Researcher “What are the top technology investments and projects by “small to medium” and enterprise organisations in the UK. Use trusted market data from repuatble sources such as Gartner, IDC, Cisco, Microsoft, Canlays, CRN etc.”
What I love is how you see the deep thinking and reasoning Researcher is using to compile the information and generate your report. This is so much easier than manually searching the web and reading dozens of articles. Instead, Researcher gives you a report in just a few minutes.
Instead of manually having to search the web and read loads and loads of articles, Researcher gives you a report in under ten minutes. You can of course tweak the response by asking more questions or requesting adjustments to ensure it meets you needs. When the report is finished you’ll see how comprehensive and well formatted it is, allowing you to export to, add it to a collaborative Copilot Notebook or leave it as is.
Sample output from Researcher Agent.
Microsoft 365 Analyst Agent – Data Analyst
If Researcher is your content and knowledge scout, Analyst is your number-crunching, data-savvy AI team member. The Analyst agent is all about diving into data (often numerical or structured data) to extract insights, find patterns, and answer complex analytical questions. Microsoft describes Analyst as “thinking like askilled data scientist”, using an advanced reasoning approach to tackle data problems step-by-step https://www.microsoft.com/en-us/microsoft-365/blog/2025/06/02/researcher-and-analyst-are-now-generally-available-in-microsoft-365-copilot
What makes Microsoft 365 Copilot Analyst Agent special?
The Analyst agent runs on a finely-tuned AI reasoning model (post-trained on OpenAI’s o3-mini model specifically for analytical tasks). Unlike a standard chatbot that might try to answer a data question in one go (and often make mistakes), the Analyst agent uses a chain-of-thought process to break problems down and solve them iteratively. It can even generate and execute actual code (like Python) in the background to manipulate data, perform calculations, or generate charts. Throughout this, it adjusts to new complexities and can recover from errors autonomously – essentially debugging and refining its approach as it goes, much like a human analyst would. The end result is a thorough analysis with reasoning that is transparent to the user.
Here are some of the key capabilities of the Analyst agent:
Data Analysis Across Formats: Analyst can work with Excel spreadsheets, CSV/TSV files, databases, Power BI reports, and other structured datasources . It can even extract financial data from PDFs. It is possible to upload or point it to a dataset, even if the data is messy or hidden across multiple files. For example, if you have sales data split across a few different Excel sheets and files, you can use Analyst Agent to ingest them all. The agent can also clean up many of the typical issues found in spreadsheets such as wrong delimiters in a CSV, or values buried in an unexpected place before it starts to work. This means that your data does not need to be perfectly prepared beforehand .
Iterative Reasoning and Problem Solving: When you ask Analyst a question, it will hypothesise, test, and refine repeatedly. For instance, you might ask, “What insights can you find about our Q4 sales data, and why did some teams underperform?”. Here, Analyst might break this down into steps: first identifying overall sales by region, then noticing why one sales team is lower, then digging into possible factors (maybe inventory issues or lower marketing spend), then correlating that with other data. It takes as many steps as needed to arrive at a sound answer. This multi-step approach leads to more accurate and nuanced results than a one-shot response.
Code Generation and Execution: A standout feature – Analyst can write and run Python code behind the scenes to perform calculations or data transformations. If your data question requires a formula, statistical analysis, or creating a chart, Analyst will generate the code to do it. Even better, it shows you the code in real time as it works, so you have complete transparency into how it’s reaching its conclusion. You effectively have an AI that can program on the fly to solve your data problem. This is like having a data analyst who is also a programmer working for you instantly.
Insight Generation and Visualisation: Analyst doesn’t just provide text based results – it will also explain the “story” behind the numbers in plain language and can also create simple charts or graphs to illustrate key points. It could, for example, produce a trend line graph of sales over time or a bar chart of top-performing products if those help answer your question. It will highlight findings such as “Sales Team A had a 20% increase in Q4, outpacing their previous year results ,,,, ” By narrating and illustrating the data, it helps you quickly understand the business implications.
Actionable Recommendations: Analyst can often suggest next steps or recommendations based on the data patterns it finds. If it discovers, say, that a certain region’s sales are lagging due to low inventory, it might recommend increasing stock or marketing in that region. Or if a customer segment is showing poor engagement, it could suggest targeted outreach. These suggestions turn raw analysis into useful advice, bridging the gap from insight to action.
Microsoft 365 Analyst Agent Use Cases:
The Analyst agent is useful anywhere you have data and questions about that data. Some real-world examples Microsoft has noted include using Analyst to assess how different discount levels affected customer purchasing behavior to identify the top customers who aren’t fully utilising the products they bought, and to visualise product usage trends and customer sentiment for informing go-to-market.
Analyst Agent Example
In the example below, I took some Customer Support Tickets from an excel (see below).
Sample Customer Support Ticket Export
I then have asked the Analyst Agent to “review the support ticket and create me an exective summary of the tickets, pulling out trends and themes that my team should look at and how they might reduce future support call duration.”
The results below are the first run with data that represeted as I have asked.
How Do Researcher and Analysts Agents Compare to the Standard Microsoft 365 Copilot Experience?
With all the excitement around Researcher and Analyst, you might wonder how they differ from the core Microsoft 365 Copilot Chat experience that users have been trying out in apps like Word, Excel, Teams, and Outlook.
The key difference comes down to depth of reasoning and specialisation. The core Copilot Chat experience is like a well-rounded generalist – great at everyday productivity tasks, such as drafting an email, summarising a document or thread, writing in Word, generating a PowerPoint outline, or pulling insights from a single Excel worksheet. It uses a large language model (LLM) to understand your prompt and the context from the active document, then provides a response.
However, it typically gives a direct answer or action based on available content, without doing prolonged multi-step reasoning. For example, standard Copilot can summarise a document or create a draft from prompts, but if you ask it to perform a very complex analysis that requires digging through multiple files or doing calculations, it may hit its limits. Thats where these specialist agents differ:
Advanced Reasoning vs. Quick Responses: “Standard” Copilot Chat is designed for quick assistance within the flow of work (one-shot answers or short tasks). In contrast, Researcher and Analyst use advanced reasoning algorithms (chain-of-thought) that allow them to work through a problem in multiple steps). They will plan, execute sub-tasks (like searching sources and creating and executing code), and then refining its output. This means they can handle questions or tasks that the regular Copilot would either answer superficially or not manage at all.
Tool Use and Data Access: These specialist agents have access to a much broader set of information and models. Researcher can tap into web search and internal knowledge bases simultaneously, something standard Copilot doesn’t proactively do by itself. Analyst can use the equivalent of a built-in scripting engine (Python) to manipulate data. These abilities let the agents produce more accurate, data-backed results (for instance, Analyst can compute exact figures or generate a pivot table behind the scenes, rather than guessing).
Use Case Focus: Out of the box, Microsoft 365 Copilot has a breadth of capabilities across Word, Excel, PowerPoint, Outlook, Teams, etc., but each in a somewhat scoped way – e.g. helping write, summarise, or create within that app. It is “broad but shallow”. Researcher and Analyst are narrower but much deeper in their domains. If you don’t need multi-step research or advanced data analysis, you might not need to use them and the regular Microsoft 365 Copilot Chat or in app Copilot experience might suffice. But if you do have those needs, these agents provide a level of expertise that feels like a specialist joining your team.
For example, consider interpreting a complex financial report: Standard Copilot in Excel can summarise that report or maybe answer something about it if asked directly, but Analyst could take multiple financial files (ledgers, budgets, forecasts) and do a cross-file analysis, then produce a summary and suggest optimisations – a far more sophisticated outcome.
Interaction Model:Using Researcher/Analyst is a bit like launching a specific mode of Copilot meant for heavy tasks. They’re accessible via the Copilot app’s Agent Store or as pinned which is a different entry point than simply typing to Copilot in Word. This interface guides the user to ask bigger questions (“Help me investigate X” or “Analyse Y data for Z”) rather than the smaller in-app prompts. The agents also tend to show their working process (especially Analyst showing its code or reasoning steps), whereas standard Copilot just delivers the end answer in a friendly tone. This transparency is great for users who want to trust the results – you can literally see how Analyst arrived at an answer, step by step.
Analyst vs. Copilot for Finance – What’s the Difference?
With the introduction of the Analyst agent, you might also hear about Microsoft 365 Copilot for Finance – another AI offering that targets data and analytics, but specifically for finance professionals. It’s important to clarify how the Analyst agent and Copilot for Finance differ, because their names might seem related. In fact, they serve different needs:
Microsoft 365 Copilot for Finance (formerly introduced simply as “Copilot for Finance”, now in preview) is a role-based Copilot experience tailored for finance departments. This was announced in early 2024 as a way to “transform modern finance” by bringing generative AI into the daily workflows of finance teams. Unlike the Analyst agent – which any user with Copilot can use for various kinds of data analysis – Copilot for Finance is a separate add-on Copilot designed to integrate deeply with financial systems and processes. It essentially combines Microsoft 365 Copilot with a specialized finance agent and connectors to your financial data.
From what I have managed to assess these are the main differences between the Analyst agent and Microsoft 365 Copilot for Finance:
Aspect
Analyst Agent (Microsoft 365 Copilot )
Microsoft 365 Copilot for Finance
Purpose & Domain
General-purpose data analysis for any domain or department. Helps users analyse spreadsheets, databases, or other data to get insights.
Designed to work across certified and connected systems such as Microsoft 365 Dynamics, Salesforce and some others
Integration and Data
Works on provided or accessible data in Microsoft 365 (e.g. Excel files, CSVs, SharePoint data). No built-in direct connection to ERP systems – user typically uploads data or points to files for analysis
Connected to enterprise financial systems and data sources. Draws context from ERP systems (like D365 Finance & SAP) and the Microsoft Graph . Integrates in real-time with live finance data, assuming connectors are set up. Optimised for D365 Finance (seamless data access). Can connect other systems via custom or pre-built connectors).
Features and Skills
Uses chain-of-thought AI reasoning and Python code execution to perform analytics. Ideal for ad-hoc data analysis: e.g. combining sales data with customer data to find trends, identifying anomalies in operational data, generating charts from raw data. Acts as AI data analyst for any project.
Uses AI to streamline finance-specific processes and provide insights within finance workflows. For example, can automate variance analysis in Excel, perform reconciliations between systems, generate reports, summaries, and even draft emails for collections with relevant account info. Understands accounting principles and the company’s financial data.
User Experience
Accessed through the Copilot app as one of the agents (no special deployment beyond having Microsoft 365 Copilot license). The user asks questions or tasks in natural language and often provides the data files to analyze. The output is an interactive analysis in Copilot chat with optional visuals and code transparency.
Integrated into the tools finance teams use: primarily Excel, Outlook, and Teams in the context of finance work. For example, in Excel a finance user might invoke Copilot for Finance to run a budget vs. actual report or find anomalies in ledger data. In Outlook, it can summarise a customer’s account status from ERP data to help a collections officer. Works in flow of existing finance tasks, bringing AI where needed.
Availability & Pricing
Included as part of the Microsoft 365 Copilot (the Analyst agent is available to any user who has Copilot enabled). General Availability as of mid-2025. Usage is capped at 25 queries/month for heavy reasoning tasks.
Available as add-on to Copilot targeted at enterprises. Paid offering for organisations that use Microsoft 365 and want AI assistance in finance for supported systems like D365.
Dependencies on Microsoft Dynamics
Not dependent on Dynamics 365 – Analyst can analyse any data you give it. If your financial data is in Excel exports from SAP or Oracle, Analyst can still work with those exports, but it won’t directly pull from those systems on its own.
Deeply integrates with D365 Finance & Operations. Designed to plug into D365 modules so can act within that ecosystem (e.g., directly reading transaction data, posting results back). Through “connectors”, it can interface with other ERP or CRM systems too. Advantage is native use with D365 – without manual data exporting or integrations
To put it simply, the Analyst agent is like an AI data expert you can use for virtually any type of analysis by feeding it data, whereas Copilot for Finance is a comprehensive AI-powered solution built into Microsoft’s ecosystem to assist with a company’s financial operations in real-time. They might overlap in the sense that both can do things like variance analysis or finding trends in financial figures, but the context is different: Analyst would do it when you ask and give it the data (say, a couple of Excel files containing financial info), while Copilot for Finance would do it as part of your normal finance workflow, already knowing where the data is (in your ERP and Excel models) and proactively helping you in that domain.
Does Copilot for Finance only work with Dynamics 365?
No. Copilot for Finance is not limited to Dynamics 365, though that’s a primary integration. It brings together Microsoft 365 Copilot with a finance-focused agent that connects to your existing financial data sources including ERP systems like Dynamics 365 and SAP. So if your company runs SAP for finance, Copilot for Finance can use that data as well. Microsoft has built it to be flexible via connectors, because they know not everyone is on Dynamics. That said, organizations using Dynamics 365 Finance get a more seamless experience – Copilot for Finance can sit right inside the D365 Finance interface and offer insights without any data transfer.
In summary, Copilot for Finance is cross-platform in terms of data sources, but tightly integrated with Microsoft’s own finance solutions for maximum benefit. It’s an example of Microsoft creating role-specific Copilots (others being Copilot for Sales, Copilot for Service) that extend the core Copilot capabilities into specialised business functions.
Further Reading and Sources
As well my own experimentation, the following sources were also inferred and read when writing this blog. I did also use Copilot to help tweak the tone and flow.
Yesterday I read the “Omdia Universe: Smart Collaboration Devices 2025 report“. This was promoted by Cisco who, as you may know are currently the fastest growing Microsoft Teams Room MTR provider whilst also of course having their own Webex Meeting platform and experiences.
In this blog, I summarise the key insights that jumped out to me and cover how the report reveal how Cisco are re innovating in this crowded space and differenting themselves through their inter-connected portfolio.
I also look at what it all means for organises who use Webex and of course Microsoft Teams, and why – in this rapidly shifting collaborative landscape – why not all vendors in this space are equal.
The Omdia Universe Report
The report looks at 11 of the top-tier vendors across 18 categories and 20 subcategories, evaluating everything from AI-driven features to deployment simplicity. It looks at the current state and evolution of our hybrid work era where every meeting room is expected to deliver a seamless blend of hardware and functionality to drive productivity and foster a culture of meaningful collaboration.
Early on in the report they bring out the market trends and growth which is important.
State of the Meeting Room Market
The report reveals that one of the primary priority for most organisations is to expand video capabilities across every type of meeting room. This ambition to improve the user experience, together with regular room refresh initiatives, is fueling continuous market growth. Key sectors such as local government, finance, legal, education, and technology are actively deploying these solutions to support both hybrid and in-office workforces.
Although the video conferencing market is mature, recent innovations, especially AI enhancements like active speaker tracking, auto-framing, presenter tracking, background noise cancellation, and audio/video zone fencing, are reinvigorating this space. These features are designed to ensure meeting equity by making every participant visible and audible.
Additionally, collaborative services that offer meeting transcription and summarisaon have become transformative, as enterprises increasingly desire devices that operate seamlessly without manual intervention.
The report concludes that looking ahead, industry leaders like Microsoft and, Cisco are expected to spearhead the integration of meeting room budgets and projects through unified platform-driven experiences.
With hybrid work becoming the norm, the smart collaboration devices market is poised for further expansion, building on a 10% year-over-year revenue increase in 2024 and anticipated even stronger growth in 2025.
Key needs from business include’
Broad solutions matter: What organisations demand goes beyond just high-quality video; it’s about having the flexibility to address complex meeting room setups, catering to diverse environments – from intimate huddle spaces to large, multifunctional boardrooms.
Integration is king: Unified and connected ecosystems are essential to success. Devices must not only work well independently but also integrate with the software platforms we use every day such as like Cisco Webex and Microsoft Teams.
AI and automation: From real-time noise suppression, participant identification, smart meeting notes and inclusion, to intelligent framing and dynamic meeting analytics, AI is transforming devices into interactive partners rather than mere tools.
Refresh is at an all time high. The Covid Era of quick purchases and decisions is past us and the next 2 years show huge demand from customers and growth from partners who operate and excel in this space.
Deep Impact: Cisco’s Differentiation
In short not all collaboration vendors are equal. When we ask, “Who really understands the future of collaboration?” the answer resonates with Cisco’s long time history and performance in this space.
Image by Omdia
Despite the decline of Webex platform usage over the years and the huge adoption of Teams, the report shares how Cisco is truly the “full stack” visionary in this segment, and here’s why according to the report:
End-to-End Ecosystem Integration: the report calls out that Cisco’s Webex devices aren’t just about a good video endpoint. They are part of a growing broader ecosystem that unifies Cisco’s hardware, network infrastructure, observability platform and other software. For customers and partners, this means easier deployment, streamlined management and an elevated user experience across different meeting room types which leads to higher productivity and a sense of continuity. What really impacts this is that the integration even extends to Microsoft Teams, offering a fluid experience for organisations that want to maintain their existing Teams environment while leveraging Cisco’s robust hardware solutions.
AI-Driven Excellence: while platforms like Cisco Webex and Microsoft Teams have AI embedded in the software, the newest Cisco hardware leverages NVIDIA GPUs and NPUs to go beyond “good enough.” The report calls our how they deliver advanced features like intelligent framing, active speaker tracking, and background noise elimination at the hardware layer. Just like Copilot+PCs that do the same on the desktop, Cisco by putting in their meeting room endpoints don’t simply improve call quality, they change the dynamics of how teams interact in both in-person and remote settings. This AI-first approach creates a “meeting equity” where every participant is seen and heard clearly an essential ingredient for effective hybrid work and something that is key for inclusion and accessibility too.
Webex Control Hub & Management Simplicity: Another big call out is Cisco’s centralised management suite. Whilst Microsoft Teams has a Teams Room Pro portal which is very good, the report details how Cisco takethe headache out of device provisioning and monitoring especially where organisations have a mix of platforms but standardise on Cisco hardware. This ease of use, combined with proactive analytics, provides a level of operational insight that few competitors can match. With new AI features around management and the integration of the network this kind of thoughtful design allows IT teams to focus on strategic priorities rather than firefighting everyday issues across rooms, network etc.
Interoperability with Teams: In today’s environment, larger organisations are often split between different collaboration platforms or may be shifting from one to the other. Cisco’s revised strategy is smart, as it ensures that while Webex remains the backbone for in-room experiences, its devices are “platform agnostic” enough to also be a Microsoft partner and fully support Microsoft Teams. This means businesses don’t have to compromise on one technology over another—they can have the best of both worlds. This is good for sustainability, consistency and for Cisco and Microsoft partners a kind.
Where other vendors fall short
The report also pulls out that while many organisations have vendors preferences (or at the flip end don’t – and use a mix devices, not all are equal.
This is based on the pain points reported by enterprises which are summarised in the report as follows
The report gives scores across the main key vendors and shows Cisco as a clear leader mainly because.
“Cisco emerges as a leader in this Omdia Universe report on smart collaboration devices. Cisco’s” Leader” status is attributed to its exceptional performance across all evaluated categories. The company achieved an impressive overall unweighted score of 90% for its capabilities, 85% for strategy and execution,and a solution breadth score of 97%”
Webex & Teams: Bridging the Divide
Back on Cisco, the report calls out that conversation around collaboration tools is incomplete without recognising the symbiotic (and still new) relationship between Cisco Webex and Microsoft Teams. The once enemies in the Collaboration spaces, Cisco, to avoid loosing any more market share have now truly partnered. The report calls out tha while Webex hardware is the go-to for feature-rich, AI-driven collaboration experiences, Microsoft Teams remains indispensable due to its deep integration into enterprise productivity suites like Microsoft 365 and of course Copilot. This unification of the two brings the best of both!
The report also calls out the reasons some firms still stand behind Webex and why Teams as a platform is the choice by most.
Webex: Cisco’s Webex is celebrated for its polished, intuitive interface and extensive feature set. By offering advanced meeting controls, real-time transcription, immersive audio, and intelligent device management, Webex sets the standard for what “smart collaboration” should feel like.
Teams: Teams is deeply entrenched in the daily workflow of almost every enterprise thanks to its seamless integration with other Microsoft 365,its extensibility and of course Their Copilot AI offerings. Cisco’s ability to support Teams via its hardware bridges the gap, allowing organisations to invest in robust, vendor-supported devices without needing to choose exclusively between platforms. Those that choose Teams (as long as they don’t need Windows powered systems) get a truly awesome experience.
Cisco, by ensuring that devices work seamlessly across these two major platforms, they not only reassures current customers but also attracts enterprises looking to future-proof their collaboration investments.
This value is multiplied for organisations that also invest in Cisco networking solutions with the integration, aligned management and insights across their estate that no other vendor can provide.
NOTE: Whilst this blog pulls out the huge advantages of Cisco, the full report actually show that as well as Cisco, HP Poly, Logitech, Neat, and Yealink also shine.
Regardless of your vendor of choice the message to IT leaders grappling with hybrid work challenges is to ” invest in solutions that blend robust performance with seamless platform integration. Whether you lean towards the established sophistication of Webex or the cohesive productivity experience provided by Teams, the future of collaboration demands a thoughtful, integrated approach.
My view
As a proud Cisco and Microsoft partner, I believe that Cisco’s revamped video collaboration solutions integrate seamlessly with both Webex and Microsoft Teams while driving innovation for both platforms.
Cisco is also (through help of partners and putting their money where their mouth is) effectively overcoming its legacy reputation of “complex and expensive” , where customers once perceived their devices as outdated, expensive, and burdened with complex licensing and procurement processes.
As highlighted in the report and reflected in our customer experiences, those concerns are now outdated. Cisco devices are readily available at competitive pricing through collaboration partners like Cisilion, and the benefits are further amplified when customers invest in the broader Cisco infrastructure portfolio, including networking, ThousandEyes for enhanced visibility and performance, and secure access solutions.
Cisco Live 2025 is happening this week in San Diego (after five years in Vegas) with around 22,000 attendees. As you’d image from any tech event at the moment, the focus was very much AI with the theme being summed up as “All AI, all the time”. Throghout the Day 1 keynotes, Cisco’s message was clear: the “agentic AI era” is upon us, and Cisco is positioning itself as the infrastructure backbone to support service providers, cloud providers and enterprises of this new age.
Cisco’s President and Chief Product Officer Jeetu Patel set the tone with a bold analogy: “The way that you should think about us is like the picks and shovels company during the gold rush. We are the infrastructure company that powers AI during the agentic movement,”
…….In other words, while everyone’s chasing AI gold, Cisco’s approach is to providing the bedrock tools to dig for it – unveiling new innovations spanning networking hardware, unified management software, security, and collaboration tools, all infused with AI.
I wasn’t able to attend the event myself, but here’s my break down the top announcements and innovations from the live streams I watched. Let me know what I have missd 🙂
The “Agentic AI” Era
Cisco Live’s buzzword was undoubtedly “Agentic AI.” Cisco sees a shift from basic chatbots to autonomous agents that don’t just answer questions, but perform tasks and jobs on our behalf. As Jeetu Patel said in the keynote “The world is moving from chatbots intelligently answering our questions to agents conducting tasks and jobs fully autonomously. This is the agentic era of AI”.
Like many of the other tech giants, their view is that in this fast moving era, billions of AI agents could be working for us behind the scenes, which “will soar” the demand for high-bandwidth, low-latency and power-efficient networking in Cloud Providers and Private Hosted data centers.
Cisco’s key mesage here is that they are here to help organisations and providers meet this demand. “Cisco is delivering the critical infrastructure for the AI era — secure networks and experiences, optimized for AI that connect the world and power the global economy“.
Cisco CEO Chuck Robbins said that “no organisation can hire limitless people to tackle increasing IT complexity and cyber threats – instead – machines must scale to share the burden”. He went on to say how Automation and AI-driven operations are not just nice-to-haves; they’re becoming essential and every business is looking to invest and build here and it will only accelerate in pace and scale.
Cisco also set out to explain that “generative AI” and “agentic AI” have different effects on the infrastructure needed to support them. Generaive AI creates sporadic spikes in demand, but Agentic AI creates sustained perpetual demand for inferencing capacity. This means that for agentic AI, networks and Cloud data centers need a continuous heavy-duty upgrade to what they run on today. Cisco expect that many will large enterprises, those setting out to build their own “AIs” and of course Service and Cloud Providers will likley need to “re-rack the entire datacenter and rebuild the network” to handle these new AI workloads.
One Unified Plartform to Manage it all
As (a long time ago) IT Sys Admin, I remember how managing networks used to sometimes feel like herding cats – multiple dashboards for switches, routers, security, cloud, etc., all siloed.
Cisco has now announced Cisco Cloud Control, a new unified management console intended to “drive all its networking, security, and observability tools” from one place. In a nutshell, Cloud Control is Cisco’s approach to bring all those separate management tools into a single pane of glass – making it easier for network admins and giving a Cisco Customers a cohesive platform to showcase it’s new AI innovations in one place.
Of course Cloud Control is AI infused too. There is an AI Assistant that lets IT teams query their infrastructure in plain English. Here they could ask (as per their demo) “Hey Cisco, why is the Wi-Fi slow on the 4th floor?” and get a useful answer.
To achieve this, Cisco are using a new custom large language model trained on decades of Cisco networking knowledge (like an AI powered CCIE) to provide expert guidance. Cisco showed off a new AI Canvas (an “agentic” interface) that auto-generates relevant dashboards that work together to help identify issues, suggest fixes, and even implement changes – with human approval gating the final step. In short – you describe a problem, and the system brings forward the relevant controls and data needed to solve it, all guided by Cisco AI.
Cisco’s message is not just about adding AI for AI sake – it is designed to address real IT headache by combining formerly separate mnagement planes and interfaces into one.
Cisco also announced they are unifying management for their Catalyst and Meraki product lines (switching and wireless) into this single console, with common licensing too.
Overall, the message is that whether it’s campus networks, branch, data center, or cloud, Cisco goal is is to centralise control and inject AI assistance across them all, leading to smarter and simpler unified operations.
Splunk also got a mention – with Cisco talking about how ThousandEyes and Splunk analytics will also be able to integrate into this platform to give end-to-end visibility – from user device to application. This is part of a broader “One Cisco” vision of an integrated portfolio for networking, security, collaboration, and observability.
Net Hardware: Faster, Smarter, and Built for AI
It wouldn’t be Cisco Live without new hardware – and this year, Cisco delivered a loads of it. Recognising that AI workloads are putting unprecedented demands on Service provider and Cloud networks, Cisco unveiled a lineup of new switches, routers, and wireless devices which all give higher throughput, low latency, and security by design. This inlcuded:
Campus Switches (C9350 & C9610): Designed for campus networks and powered by its custom Silicon One chips – they boast a huge 51.2 Tbps of throughput and sub-5 microsecond latency, with quantum-resistant security built in. These are designed to handle “high-stakes AI applications” at the network edge.
Secure Branch Routers (8100, 8200, 8300, 8400, 8500 Series): To connect sites and users to AI resources, Cisco have unveiled these new Secure Catalyst Routers for branches. These are all-in-one boxes that combine SD-WAN, SASE (Secure Access Service Edge) connectivity and next-gen firewall. Cisco say they will deliver up to 3× the throughput of the previous generation too. Why? Cisco is converging networking and security at the WAN edge so that adopting AI doesn’t open new holes in your defenses.
Wi-Fi 7 (Cisco Wireless 9179F): – see new APs, tailored for stadiums and large venues. These APs support the latest Wi-Fi 7 standard bringing multi-gig speeds and better reliability and integrate Ultra-Reliable Wireless Backhaul (URWB) technology alongside Wi-Fi in one device. That means an access point can also serve as a highly reliable wireless bridge/mesh link, useful in places where running fiber/cable is hard.
Ruggedised Switches for Industry 4.0: To support AI at the edge – in places like factories, oil rigs, smart cities – Cisco unveiled 19 new rugged switches built to withstand harsh environments. These come in various form factors (tiny DIN-rail mounts, hardened casings, etc.) to fit into industrial sites where conditions are extreme. Interestingly, Cisco integrated that URWB wireless tech here too, meaning you can have a unified wireless fabric that covers both IT and OT (operational tech) environments via a combination of Wi-Fi and wireless backhaul. In plain terms, these rugged switches + wireless combos let factories and outdoor facilities achieve high-density, reliable wireless coverage as part of one unified infrastructure.
Powered by Cisco Silicon One: All Cisco’s hardware announcements reinforced a key point: networking and security are fusing together in Cisco’s strategy. All new switches and routers all come with baked-in security features (from Hypershield to post-quantum crypto) rather than treating security as an add-on. Jeetu Patel emphasised, that the future is about networks that are programmable and adaptable – Cisco’s own Silicon One custom chips are a big part of that story because it means that Cisco can update these devices for new AI workloads via software without needing to build a new chip and device. This is a major compete play and USP for Cisco.
Security in the AI Era: Zero Trust, Everywhere, All at Once
All the AI in the world won’t help if your business if your network isn’t secure. Cisco used this approach to double down on its message that security must be woven into every layer of the network, especially as AI opens new frontiers (and potentially new threats). In the agentic AI era, Cisco said that attackers will leverage AI, meaning threats could become faster and more sophisticated. The answer? “Secure by design” infrastructure and a unified security architecture that can handle the scale of AI-fueled operations.
As a result Cisco introduced a new network security blueprint anchored by what they call the Hybrid Mesh Firewall and Universal ZTNA (Zero Trust Network Access). They represent a concerted effort to integrate security across all users, devices, and applications more seamlessly including:
Hybrid Mesh Firewall: Annouced earlier this year, Cisco’s next-gen firewall for the AI era, acts as a distributed security fabric spanning your whole environment. It brings together Cisco’s own firewalls and even third-party firewall integrations into one cohesive system to to enable zero-trust segmentation everywhere – from your data center core, across clouds, out to branch offices and all the way to IoT devices at the edge. The goal is that every part of the network becomes a security enforcement point, tightly coordinated.
Universal ZTNA: Cisco’s Zero Trust Network Access solution, now branded “Universal” because it aims to cover any user or device, anywhere. Universal ZTNA provides secure, identity-based access to applications, whether users are on the corporate LAN, at home, or on a mobile device. It extends the zero-trust mode to hard-to-manage endpoints and ensures a unified policy follows the user. For example, whether JimBob from accounting logs in from the office or from a coffee shop Wi-Fi, the system continuously verifies his identity and device posture before granting access to the finance app. The synergy here is that integrating ZTNA and the distributed firewall, Cisco can tightly control user-to-app connections and even monitor the traffic between services, all under a zero-trust philosophy.
Beyond hardware, the cloud-based Cisco Security Cloud got enhancements to help secure those emerging AI workflows. Their platform can now better secure interactions involving AI agents, using tools like Cisco AI Defense (which monitors AI model operations for tampering or misuse) as part of a “Secure AI Factory” concept co-developed with NVIDIA.
Their integration of Splunk also got a mention, where they demonstrated deeper Cisco + Splunk integrations for security analytics – such as sending security events and network telemetry into Splunk’s SIEM and using Splunk’s AI-driven insights to automate responses via Cisco’s tools.
Webex: Smarter Meetings, AI Helpers, and Cameras with a Brain
Cisco did also announce a series of Webex updates with more AI coming into Webex in ways that aim to make meetings less of a chore and customer service more efficient.
Jira Workflow Automation in Webex: For native Webex meetings, this can listen for action items discussed in a meeting and automatically create Jira tickets for them. For example, if during a team call someone says “I’ll update the budget doc next week,” the AI will note that and generate a task in Jira , Monday.com or Asana – fill in your project tool) assigned to that person. It will even capture the context by attaching relevant portions of the meeting transcript or recording. Cisco touted, the integration can also update Jira tickets in real-time if status changes are mentioned in meetings – so, if the team says “the server migration is completed,” the AI could move the Jira task to “done” and note the discussion. It’s like having a diligent virtual project manager in every meeting, so humans can focus on discussion rather than note-taking.
Webex AI Agent for Customer Self-Service: They announced enhancements to the Webex AI Agent – to make it easier to deploy and more powerful. Tgherenis a new set of prebuilt, industry-specific templates – out-of-the-box chatbot templates tailored for industries like healthcare, finance, retail, etc. Instead of a generic bot that has to be trained from scratch, Cisco provides a starting knowledge base (e.g., a healthcare template might know common questions about insurance, appointments, privacy rules, etc.). This can significantly speed up creating a virtual agent and leads to more relevant answers since it’s contextually aware of the industry. Cisco are also enabling these AI agents and features for on-premises deployments as well.
Conclusion
Cisco is all-in on AI, not by making its own AI apps, but by supercharging the underlying tech that makes AI possible.
Cisco seem fully aware of the challenges businesses face with emerging technologies. – whether it’s handling the flood of data and compute that AI workloads generate, securing a more complex threat landscape, and having a true end to end view on the user experence – Cisco is positioning itself as the enabler (and problem-solver) and has signaled it’s not sitting on the sidelines of the AI revolution.
The narrative of “One Cisco” came through strongly: networking, security, collaboration, cloud, and services all interlinking to form a complete platform for the AI era. Cisco is offering a very compelling toolkit for enterprises: blazing-fast hardware to move AI bits, smart software to manage it with minimal hassle, and built-in security every step of the way.
Cisco wants to be “the infrastructure company that powers AI” – the dependable partner under the hood while everyone chases AI magic. By unifying its platforms and injecting AI into network operations, Cisco is making a play to stay indispensable in this new era.
One of the most frustrating thing about Teams intelligent Recap and Copilot in meetings is in its ability to not understand company acroymns and internal “language” or terms.
Scheduled to rollout in July 2025, tenant administrators will be able to upload a Custom Dictionary through the Microsoft 365 Admin Portal’s Copilot Settings page.
This feature will finally enables organisations to improve transcription accuracy in Copilot and Teams meetings and calls by enabling Microsoft 365 to understand company-specific terminology. This will means that will be able to understand things such as
Industry jargon,
Internal product names and terms
Multilingual terms
This should help ensure conversations are transcribed and interpreted with greater precision.
Why this matters?
Organisations rely on Microsoft Copilot and Teams transcripts for insights, documentation, and knowledge retrieval. However, standard AI transcription can misinterpret niche terms or acronyms, leading to confusion and even sometimes humorous transcriptions.
This new Custom Dictionary feature addresses this by allowing businesses to define key terms their workforce frequently uses.
Real Benefits.
Legal & Compliance Accuracy: Law firms using specialised legal terminology (e.g., “prima facie,” “voir dire”) can ensure precise transcripts without ambiguity.
Enterprise Acronyms & Branding: Technology companies like Cisilion will be able to maintain more accurate documentation of internal project names (e.g., “Project Nebula”) and proprietary solutions.
Global Team Collaboration: Multinational organisations can optimise transcription quality across multiple languages and regional dialects.
Better AI Insights & Search:Copilot will be able to retrieve knowledge more effectively, ensuring summaries, recommendations, and contextual responses align with an organisation’s unique vocabulary.
This update is part of a broader set of Microsoft 365 enhancements including improved accessibility for sign language users in Teams meetings and expanded Copilot capabilities for 1:1 and group calls.
By refining AI-driven language models, Microsoft aims to make workplace collaboration smarter, clearer, and more inclusive.
You can read more and track this features release on the official Microsoft 365 Roadmap.
There’s instructions for enabling and configuring it here.
