How Copilot Researcher and Analyst are Transforming Work, and What Sentiment Analysis Tells Us About Team Morale

In the latest episode of the Inspiring Tech Leaders podcast, I discuss how Microsoft's new Copilot Researcher and Copilot Analyst are fundamentally transforming the way we work.  This isn't just about automation, it's about intelligent agents that understand your goals and execute complex tasks autonomously!

Here's a look at what you'll learn:

Copilot Researcher: Discover how this agentic AI acts as your ultimate research assistant, exploring vast internal and external data sources to provide deep, traceable insights.

Copilot Analyst: Learn how this powerful tool, built on Excel, Power BI, and Microsoft Fabric, turns raw data into clear, actionable insights for everyone, with no need for advanced SQL required.

Sentiment Analysis in Microsoft 365: A fascinating look at how AI is quietly monitoring tone and intent across meetings and emails. Understand how this data can be used by leaders to measure team morale, culture, and even proactively identify burnout risk, all while navigating crucial ethical considerations around privacy and transparency.

Tune in now to unlock the full potential of these groundbreaking tools and lead with insight!

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Chapters

• 0:09 Introduction
• 1:42 What is Copilot Researcher
• 3:41 How Copilot Researcher Works
• 4:59 Real World Use Cases for Copilot Researcher
• 6:25 What is Copilot Analyst
• 7:33 How Copilot Analyst Works
• 8:58 Agentic Intelligence in Copilot Analyst
• 9:32 Real World Application of Copilot Analyst
• 9:32 [Ad] Everyday AI: Your daily guide to grown with Generative AI
• 10:20 (Cont.) Agentic Intelligence in Copilot Analyst
• 11:30 What is Copilot Sentiment Analysis
• 12:22 How Does Copilot Sentiment Analysis Work
• 13:46 Ethics and Privacy
• 16:00 Wrap Up

Transcript

Welcome to the Inspiring Tech Leaders podcast, with me Dave Roberts.  Today, we are discussing a topic that is not just changing how we work but fundamentally redefining the very nature of productivity and insight generation, this being agentic artificial intelligence.

For years, AI has been a buzzword, a promise on the horizon.  But now, with the rapid advancements we are seeing, particularly from companies like Microsoft, AI is moving from the theoretical to the intensely practical, becoming an indispensable partner in our daily professional lives.

In this episode, I am going to discuss the exciting evolution of Microsoft Copilot, with the recent launch of Copilot Researcher and Copilot Analyst.  These are not just incremental updates, they are two intelligent agents that are truly redefining how we gather insights, build strategies, and make data-driven decisions.  They represent a significant leap forward in how AI can act autonomously to achieve complex goals.

But that is not all.  I will also take a look at a fascinating, and perhaps more subtle, aspect of AI inside Microsoft 365, this being sentiment analysis.  This powerful capability is quietly monitoring tone and intent across your meetings and emails, offering a unique lens into the human side of your organisation.  I will explain how that data can be used by leaders to measure morale, understand culture, and even proactively identify burnout risk within their teams.

So, whether you are a tech leader looking to harness AI more effectively, or just curious about the cutting-edge capabilities behind the latest Copilot tools and the ethical considerations that come with them, stay tuned.  There is a lot to cover in this episode, and I promise, it will change how you think about AI in the workplace.

Let us kick things off with Copilot Researcher.  This tool is built on top of Microsoft’s Copilot architecture, leveraging generative AI and orchestration through Microsoft Graph and third-party data connectors.  But instead of simply answering questions, Researcher acts more like a context-aware research agent that truly understands your work.  It now behaves like other agentic AI systems, such as Manus AI, which I have been discussing a lot recently.

So, what exactly do we mean by agentic AI?  Unlike traditional AI that might just respond to a single prompt or execute a predefined task, agentic AI systems are designed to autonomously make decisions and perform complex, multi-step tasks with limited human supervision.  They do not just generate content; they are action-oriented, capable of analysing situations, formulating strategies, and executing actions to achieve specific goals.  Consider it as an AI that can think through a problem, break it down into smaller parts, and then go out and find the information needed to solve it, much like a human researcher would.  This is a significant shift, as it means the AI can adapt to changing situations and make decisions based on context, rather than just following rigid rules.

Copilot Researcher excels in handling complex, multi-step research tasks.  It can explore vast amounts of internal documents, emails, Teams messages, SharePoint files, and external sources, such as news and academic papers, and then extract relevant insights.

It goes far beyond a basic keyword search function.  You do not need to know the exact keywords or file names. It can even integrate data from external sources like Salesforce, ServiceNow, and Confluence to provide a more comprehensive view. Imagine asking it to build a detailed go-to-market strategy based on our internal sales data and broader competitive data from the web, or to identify whitespace opportunities for a new product based on emerging trends and internal customer feedback.  This level of contextual understanding and synthesis is what sets it apart.

Microsoft Copilot Researcher is essentially powered by agentic AI, meaning it uses reasoning loops to break down complex tasks into subtasks.  When you give it a complex query, it does not just pull up documents; it formulates a high-level plan, then iterates through cycles of reasoning, retrieval, and review, collecting and synthesising findings until it reaches a comprehensive answer.

It creates research threads, queries multiple data sources, filters for credibility, and produces a traceable answer. This traceable answer aspect is incredibly important.  It means you can inspect exactly where each insight came from, verifying the sources and understanding the reasoning behind the AI's conclusions.  This transparency builds trust and allows for auditing, which is crucial in a professional environment.

It is not a black box, it’s a transparent research partner.  This traceability helps identify biases, improve model performance, and maintain compliance with regulations.  Furthermore, it integrates seamlessly with your existing Microsoft 365 environment, including Loop, Word, PowerPoint, and Teams, allowing users to pull insights directly into their workspace.

This means the research is not just a standalone report, it is actionable intelligence that can be immediately incorporated into your documents, presentations, and collaborative efforts.

Imagine a product manager preparing for a major product pitch. Traditionally, this would involve days, if not weeks, of sifting through market reports, competitor analyses, internal sales figures, and customer feedback.  With Copilot Researcher, they can ask it to provide a comprehensive competitive landscape analysis, then ask for a list of patents filed in a specific sector over the last five years, and even follow up with, which of our internal whitepapers and customer testimonials align with these emerging trends and patent activities?

Copilot Researcher handles the grunt work of fact-gathering, source-checking, and cross-referencing, providing instant, synthesized results. This frees up the product manager's time for higher-level strategic thinking, refining their narrative, and focusing on the innovative aspects of their pitch, rather than getting bogged down in data collection.

Consider another scenario, a legal team preparing for a complex case. They need to review thousands of internal legal documents, past case precedents, and external regulatory updates.  A query to Copilot Researcher like, summarise all relevant legal precedents for intellectual property disputes in the software industry from the last decade, specifically those involving open-source licenses, and highlight any internal communications related to these cases, this could save hundreds of hours.  The traceable nature of the results means the legal team can confidently cite sources and understand the AI's reasoning, ensuring accuracy and compliance.

Next, let’s talk about Copilot Analyst, which is essentially a data analyst that lives inside your Microsoft 365 environment.  It builds on Excel, Power BI, and Microsoft Fabric, and helps users turn raw data into clear, actionable insights, without needing to know advanced SQL or complex data modelling techniques.  It is designed to think like a skilled data scientist, transforming raw data into actionable insights in minutes.

Copilot Analyst helps users with data summarisation, trend forecasting, anomaly detection, and what-if analysis.  You can ask things like, What’s driving the decline in sales for our Southeast region over the last two quarters, and how does that compare to previous years? or Forecast revenue for the next quarter based on current growth rates, marketing spend, and projected economic indicators.  It can even identify patterns and correlations in datasets, and assist in hypothesis testing.

It simplifies complex data tasks, automating data cleaning and preprocessing, generating pivot tables, and creating graphs.  For example, a financial analyst can ask Copilot to quickly generate a chart highlighting revenue trends and anomalies.

This agent leverages Auto Insights within Power BI, but goes further by creating explainable narratives alongside charts and data tables.  Power BI's Auto Insights feature automatically analyses data and generates visualisations, identifying patterns, trends, and anomalies.  Copilot Analyst takes this a step further by not just showing you the data, but explaining why certain trends are occurring in plain business language.  This is crucial for non-technical users who need to understand the story behind the numbers.

It uses semantic models of your datasets and joins them with your company knowledge, for example, organisational hierarchies, product taxonomies, and customer cohorts.  A semantic model is a conceptual framework that represents the meanings and relationships of terms and concepts within a particular domain.

It provides a structured approach to organising information, making data more accessible and actionable by bridging the gap between human understanding and machine processing.  By understanding the context and relationships within your data, Copilot Analyst can provide much richer and more relevant insights.  It is also capable of simulating decisions using Excel logic, such as, What happens if we increase our ad spend by 10% in the UK market, and how would that impact our profit margins and customer acquisition costs?  It can even run Python for complex data queries, and you can view the code in real-time to check its work.

Like Researcher, Copilot Analyst uses a multi-agent orchestration layer to retrieve the right data, apply logic, format visuals, and explain the outcome in business language, making it especially helpful for non-technical users.  This agentic approach means it can iteratively refine its reasoning and analysis, mirroring human analytical thinking.

It is not just executing commands, it is engaging in a dialogue with the data, asking follow-up questions, and exploring different angles to provide the most comprehensive answer. This dynamic problem-solving capability is a hallmark of agentic AI.

Think about a CFO who needs a real-time dashboard that summarises financial health across departments. Instead of waiting for analysts to pull reports, manually consolidate data, and then build visualisations, they can just ask Copilot Analyst to Create a breakdown of department costs for Q2, highlight the top 3 variances from budget, and suggest cost-saving opportunities based on historical spending patterns.

The system handles the data preparation, the modelling, the anomaly detection, and the narrative, all within seconds of the request.  This empowers the CFO to make immediate, data-backed decisions, rather than reacting days or weeks later.

Another powerful application could be in supply chain management.  An operations manager could ask, Analyse our inventory levels across all warehouses, identify any potential stock-outs for critical components in the next three months based on current demand forecasts, and suggest optimal reorder points to minimise carrying costs while avoiding disruptions.

Copilot Analyst could then process vast amounts of inventory data, sales forecasts, and supplier lead times, presenting a clear, actionable plan. This kind of predictive insight can save companies millions in lost sales or excess inventory.

Now let’s move into the human side of productivity, where data meets emotion. Microsoft has started embedding sentiment analysis across platforms like Outlook, Teams, and meeting notes in Microsoft 365.

This is not about surveillance, it is about understanding the collective pulse of your organisation, identifying trends, and proactively addressing potential issues related to employee well-being and engagement.

Copilot can now analyse the tone, urgency, politeness, and stress signals within emails.  It can analyse speaker emotion, engagement, and positivity vs. negativity within meeting notes and Teams transcriptions.  Document drafts or summaries generated by Copilot can be evaluated for tone alignment, ensuring they are formal, optimistic, critical, or whatever tone is intended.  This capability extends to identifying patterns in communication styles and team dynamics.

Microsoft uses a mix of natural language processing, transformer models, and tone classification algorithms trained on enterprise communication datasets.  Natural language processing is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.

Sentiment analysis, also known as opinion mining, uses those natural language processing techniques to interpret and classify emotions expressed in contextual data, identifying whether the sentiment is positive, negative, or neutral.

These models evaluate things like word choice, punctuation, sentence complexity, and also things like polarity, being either positive or negative, and subjectivity, being either fact-based vs. emotional responses.  For example, a manager receives a monthly sentiment report showing that team emails have become more abrupt and less collaborative, possibly signalling burnout or pressure.  This allows leaders to address issues before they escalate.

Another example might be a team’s post-meeting feedback summaries that are consistently marked as low engagement, prompting a change in how stand-ups are run.

Sentiment trendlines can help HR flag issues early, long before traditional surveys or exit interviews would. This provides real-time analytics for quick responses, allowing HR teams to address concerns as they arise.  It can even serve as an early warning system for potential resignations by spotting dissatisfaction patterns.

It is important to note that this kind of analysis is aggregate-level, not designed to single out individual employees.  Microsoft has implemented guardrails to protect privacy and ensure the data is used responsibly, typically by leadership, HR, or wellness teams to look at patterns, not individuals.

However, the ethical implications of workplace sentiment analysis are significant and require careful consideration.

The collection of data from internal communications can raise concerns about workplace surveillance.  Organisations must ensure that sentiment analysis tools respect privacy laws, anonymise data, and obtain necessary consent.

Transparency with employees about what data is collected, why, and how it will be used is paramount to building and maintaining trust.  The data should be anonymous, and employees should be assured of data confidentiality.

Employee communications contain sensitive information. Unauthorised access or improper storage of sentiment analysis data could lead to data breaches, legal consequences, and a loss of trust.  Robust cybersecurity measures are essential to prevent misuse or leakage of this confidential data.

AI models, including those used for sentiment analysis, can introduce biases based on language use, cultural context, or sentiment interpretation. Regular audits and adjustments are necessary to ensure fairness and accuracy.  Bias in AI-driven sentiment analysis could lead to misinterpretations of employee sentiment, potentially harming workplace culture or leading to unfair actions.

If employees feel that their conversations are being monitored without their knowledge or clear purpose, trust in leadership may erode. Clear communication about how sentiment analysis is conducted and used is essential.  Organisations must actively engage employees in discussions about workplace monitoring to maintain transparency and credibility.  The goal should be to improve employee well-being and organisational health, not to micromanage or monitor.  Microsoft's approach emphasises fairness, reliability, privacy, inclusiveness, transparency, and accountability in its AI systems.

The key is to use these tools to enhance, not replace, human judgment, and to prioritise the employee experience by fostering a culture of trust and transparency.

So, what does all this mean for you as a tech leader?

We are entering an era where work is not just automated, it is understood by AI.

Agents like Copilot Researcher and Analyst do not just help you do more, they help you do better. They empower you to think strategically, act decisively, and lead with insight, by handling the complex, time-consuming tasks of research and data analysis.  They are transforming business productivity by providing intelligent data analysis, automated content generation, and enhanced communication across your Microsoft 365 applications.

And with sentiment analysis now part of the Microsoft 365 toolset, we can measure the invisible, such as how people feel, how they engage, and how they are coping in a hybrid, fast-moving world.  This offers an unprecedented opportunity to proactively address issues like burnout, low morale, and disengagement, fostering a more positive and productive work environment.

But this also means tech leaders need to lead with transparency and a strong ethical compass.  These tools are powerful, but they must be used ethically, responsibly, and with a clear focus on improving the employee experience, not micromanaging it.  The conversation around AI in the workplace must always prioritise human well-being and trust.

Well, that is all for today. Thanks for tuning in to Inspiring Tech Leaders. If you enjoyed this episode, do not forget to subscribe, leave a review, and share it with your network.  You can find more insights, show notes, and resources at www.inspiringtechleaders.com

Thanks again for listening, and until next time, stay curious, stay connected, and keep pushing the boundaries of what is possible in tech.