Leveraging Copilot Technology

In 2026, Copilot isn’t a writing assistant. It’s a decision engine embedded inside your workflow. Most teams use Copilot to draft emails. High-performing marketing teams use it to reduce decision latency. If you’re leading digital marketing, here’s how to think about Copilot strategically 👇 📝 Copilot in Word Objective: Sharpen thinking, not just writing. What it actually unlocks: • Draft structured campaign briefs from bullet prompts • Reframe messaging by ICP or funnel stage • Compress 10-page strategies into exec-ready summaries 2026 Marketing Application: • Turn growth ideas into structured GTM documents • Convert performance reports into board-ready insights • Stress-test positioning across segments 👉 Strategic prompt: “Summarise this strategy into 5 executive-ready decisions and associated risks.” That’s not writing help. That’s clarity compression. 📊 Copilot in Excel Objective: Convert data into action. Core capabilities: • Build pivots, charts, formulas instantly • Identify anomalies and variance drivers • Surface hidden patterns in large datasets 2026 Marketing Application: • Diagnose ROAS volatility • Detect spend inefficiencies • Simulate budget reallocations • Flag underperforming segments before scale 👉 Strategic prompt: “What’s driving this performance shift and what action should we test first?” Now Excel becomes a performance co-pilot, not a spreadsheet. 🎤 Copilot in PowerPoint Objective: Translate analysis into narrative. What it accelerates: • Create decks from Word or Excel • Auto-generate structured speaker notes • Turn dense analysis into visual summaries 2026 Marketing Application: • Transform KPI dashboards into board-level stories • Convert campaign retros into 7-slide strategic narratives • Build executive summaries in minutes 👉 Strategic prompt: “Turn this report into a 7-slide executive narrative with recommendations and risk flags.” Insight → influence. 🤖 Copilot in Microsoft 365 Chat Objective: Context intelligence across the org. Capabilities: • Pull insights from emails, Teams, docs • Generate pre-meeting briefs • Summarise discussions and decisions 2026 Marketing Application: • Weekly performance synthesis • Cross-functional campaign alignment • Decision recap before budget shifts 👉 Strategic prompt: “Summarise key marketing decisions made this week and highlight unresolved risks.” 🎯 The 2026 Leadership Shift AI in digital marketing is no longer about: ❌ Writing emails faster ❌ Formatting slides quicker ❌ Automating surface tasks It’s about: ✅ Reducing decision latency ✅ Increasing signal clarity ✅ Compressing time-to-action ✅ Turning analysis into execution Copilot doesn’t replace thinking. It removes friction between thinking and doing. 🔁 Repost if you believe decision speed is the new growth lever ➕ Follow Sandeep Gulati🎯for AI × digital marketing × frameworks built for what’s coming next 👉 Join Proptifi.com for more AI-powered home design ideas IC: Aiswarya Venkitesh

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

Microsoft released 4 major updates for their #AI tool Copilot. I wrote this guide on how you can use it for #finance and FP&A: 1. Copilot in Excel New Features One of my favourites. Copilot in Excel with Python is generally available in US (EN-US) for Windows. With the ability to describe the desired analysis in natural language, Copilot can automatically generate, explain, and insert Python code into Excel spreadsheets. This lowers the barriers to entry, making advanced analytics accessible to a broader audience, and unlocks powerful analytics via Python for visualizations, cleaning data, machine learning, predictive analytics, and more - without needing to be Python proficient yourself. I explain how to access it in the pdf but in case you still don't have the Windows US (EN-US) version, you can also try the other method too: https://lnkd.in/ePeWEpKN You can also create tables, pull in data from the graph and search the web and use it to summarise FP&A commentary and insights 2. Copilot Actions: Automation for Finance Workflows. Copilot Actions uses a simple fill-in-the-blank interface, allowing you to create automated workflows in seconds. For example if you have the task of summarizing emails related to a specific topic (e.g., “employee benefits”) from the past week and receiving the summary via email, you can easily fill the blanks in the prompt. 3. AI-Powered Agents in Copilot Studio, SharePoint and Teams. AI agents are being embedded into SharePoint and Teams, enabling dynamic document and collaboration management. For specific projects or tasks, any SharePoint user can create a customized agent based on the relevant files, folders, or sites, with just one click. I explain how in the pdf guide. 4. Copilot Pages. Now, you will be able to prompt Copilot to create everything from interactive flow charts to blocks of code—drawing from data across Microsoft Graph—then share them in durable Pages your team can build upon. Pages will allow you to: Edit and Build on Content: Easily refine and expand on AI-generated drafts. Collaborate in Real Time: Work alongside teammates and Copilot on the same page, seeing updates and contributions instantly. Leverage Multiple Data Sources: Seamlessly integrate data, files, and web resources into your Pages. Hope you enjoy the guide and let me know if you have questions on how to apply #AI for FP&A! Also, Nicolas Boucher and I created a course on how to use Copilot for Finance: https://lnkd.in/eaFaNky8

Lots of organisations are trialling Microsoft Copilot, but few share the results. Vendors provide glowing case studies, but what about the mixed ones? That’s why I was excited to see a public study from the Office of Digital Government Western Australia. It was more nuanced than the usual rose-tinted vendor stories, offering valuable insights into AI adoption, raising questions about implementation strategies the rest of us can learn from 5,765 licenses deployed: solid sample size for a robust trial 33% adoption rate: Decent for a new, little-understood workplace technology, but hardly transformative The primary use? Summarising meetings & drafting—important but isolated tasks that lack the integration needed for broader impact. Copilot is doing work that might otherwise not get done, but it’s not yet the game-changer AI could be Observations: Limited integration: Meeting summaries and drafts are isolated activities. Without connecting tools to broader workflows, the potential for transformative value is lost Lack of process analysis: A comprehensive process review was recommended but appears not to have been done. Dropping tools into workflows without context limits ROI Adoption gaps: Why did only 33% adopt when meetings are universal? Barriers—technical, cultural, or support-related—likely played a role Training matters: People who undertook more than one type of training (eg workshops, peer learning, self-paced modules) showed much higher adoption rates. Varied, ongoing training is essential to building confidence and capability Technical limitations: Issues with Excel & Outlook and inaccuracies hurt productivity. Familiarity bias toward enterprise platforms like Microsoft might not always serve users best Prompt engineering struggles: Challenges with prompts suggest gaps in training or change management rather than tool design Over-reliance risks: Concerns about losing deep knowledge are valid. Organisations must balance efficiency with accountability and critical thinking Early adopter bias: Early users were perceived as more productive, which may foster resistance or fear—a common hurdle in change management If you’re planning a trial: Invest in varied training: Training shouldn’t be a one-off. Use diverse formats and reinforce adoption over time Choose fit-for-purpose tools: Don’t default to familiar vendors. Smaller, more agile tools can often deliver better results Conduct a discovery phase: A thorough process review ensures tools align with organisational needs, reducing risks and maximising ROI Set clear metrics: Measure costs, benefits, and adoption outcomes to guide experimentation and ensure accountability If your organisation is running a Copilot trial, or considering one, these steps can help you maximise success. And of course, you can always come talk to us at Lithos Partners. You knew that, right? Have you worked with AI tools like Copilot? I’d love to hear your experiences or tips for successful adoption.

As an Excel trainer and MVP, I get asked constantly, by individual analysts and managers alike, what they should be doing if they can’t get paid Copilot yet. The silver lining? Most of the pieces you need to be "AI-ready" have nothing to do with actually having Copilot license. Those foundations have to be in place long before you ever turn on advanced AI tools. Here’s what I tell every team: 1. Fix the data you already have Across dozens of organizations, I see the same problems: • Files copied 20 different ways • Refreshes breaking • Hard-coded CSV dumps • Overwritten raw data • “Muscle-memory workflows” nobody can trace Copilot won’t fix that. But you can, right now, with tools you already have: • Turn ranges into proper Excel Tables • Move recurring cleanup steps into Power Query • Pull external data the same way every time • Stop overwriting raw data • Stop pasting CSVs on top of last week’s work • Stabilize your sources before you touch anything AI 2. Practice AI thinking with what you already own If you’re on Microsoft 365, you already have Analyze Data in Excel. It’s free, secure, and perfect for practicing "AI-style" questions without sending anything sensitive to an external model. 3. Build the skills Copilot will rely on A small amount of Python in Excel literacy goes a long way: • Generate clean sample data • Reshape messy tables • Understand what Copilot is suggesting • Extend or validate Copilot’s code After all, Copilot’s Advanced Analysis runs Python under the hood. Understanding the basics gives you leverage and credibility. The same is true for Office Scripts and Power Automate, not as replacements for Copilot, but as complementary skills that make handoffs cleaner when automation enters the picture. 4. Use free AI responsibly If you’re using free Copilot or ChatGPT while waiting for paid enterprise tools, great! Just keep sensitive data out of it. Stick to formulas, structure, logic, and synthetic datasets. Save real business data for the paid, secure environment. Ultimately, getting “AI-ready” takes far more than purchasing a Copilot license. It requires getting your data into shape, building a few adjacent skills, and creating an environment where Copilot can actually help you once it arrives. Most of the heavy lifting happens long before the AI shows up. Teams that take the time to clean up their inputs now are the ones that see the fastest payoff later. If you want help getting your team ready for all of this—data foundations, Python, Copilot, Power Query, Office Scripts, or anything in between—I teach this every week for organizations of all sizes. Reach out if you want to talk through what a practical, non-disruptive path to AI-powered Excel looks like for your team.

I’ve delivered 15+ Microsoft Copilot trainings in the past several months—and not a single one looked the same. Every session was customized to how the team actually works: marketing, sales, ops, execs. Different roles, different data, different pain points. One pattern I keep seeing: -> Many companies only allow employees to use Copilot as the default AI chatbot. If that’s your reality, here’s how to get real value (without breaking any rules). 1️ Know which Copilot license you actually have This matters more than people think. Copilot Chat - Primarily grounded in the web - Secure, enterprise-protected—but it does not automatically know your emails, files, or Teams chats - To use internal data, you must copy/paste or upload files into the prompt - Lives in a web interface or Edge sidebar Microsoft 365 Copilot - Grounded in your organizational data via Microsoft Graph - Can securely reference emails, meetings, documents, calendars, and chats you already have access to - Deeply integrated into Word, Excel, PowerPoint, Outlook, and Teams Under your name in the bottom-left corner, you can see which Copilot license you have. When in doubt, confirm with your IT team. Knowing which version you’re using is critical—it directly impacts what Copilot can (and can’t) do for you. 2️ Learn the features – not just the chat box To use Copilot well, you need to go beyond typing prompts. Some underused power moves I teach in training: - Personalization & memory → so Copilot understands your preferences over time - Prompt Library → save prompts, reuse them, and refine instead of starting from scratch - Notebooks → pull multiple files into one place and analyze them together (great for research projects) - Create → experiment with generative visuals and images, not just text - Agents → delegate repeatable tasks once you understand the workflow 3️⃣ Just play You’re not going to break anything. The people who get the most value are the ones who explore, not the ones waiting for “perfect” prompts. BTW, there is no “perfect” prompt. If you want your team to move beyond “we have Copilot” to “we actually use Copilot”, I’d love to help. I run hands-on Copilot trainings tailored to how your team works, not generic demos. 👉 Schedule a call if you want to level up Copilot adoption and usage for your team. https://lnkd.in/efjaqMNW What’s your favorite Copilot feature so far? #Copilot #CopilotTraining #marketing #B2Bmarketing

That sound you make before starting tedious tasks isn't just frustration. It's your Copilot opportunity map. After many Copilot implementations across different organizations and conversations with many organizations and senior leadership teams, I've noticed some consistency in the team's getting actual value. We start in the same place. Begin by asking these three fundamental questions: "What tasks make me sigh before starting them?" "Where am I spending time on repetitive work?" "Which parts of my job require my uniquely human judgment?" If you're feeling stuck with Copilot adoption, these questions will help give you immediate clarity: Some "sigh tasks" might be.... • The weekly status report that takes 90 minutes to compile • That customer email template you keep rewriting • The meeting notes you perpetually format into action items • The data you manually pull from multiple sources for monthly reviews These "Copilot-ready tasks" share common traits: - They make you sigh with dread - They follow predictable patterns - They consume time better spent on creative work - They don't require your uniquely human judgment The most successful implementations focus first on the immediate pain points - the tasks draining your team's energy today. This requires a fundamental shift in thinking—moving from a "search mindset" to an "AI mindset." Instead of just looking for information or solutions, start thinking about collaboration with AI. Your initial AI wins should come from eliminating friction in your current workflows before expanding to more strategic initiatives. Sidenote: I recently heard someone say every "export" button is also an AI opportunity - look for those too! Ask yourself the three questions above, identify what makes you sigh, and you'll have your personal Copilot or AI starting point - a practical foundation for a more comprehensive implementation strategy. #GenerativeAI #Copilot #ProductivityTips #AIImplementation #WorkSmarter

What happens when 50+ Australian government agencies test generative AI? The answer: a mix of optimism, efficiency gains, and important lessons.   Last year the Australian Government wrapped up a six-month trial of Microsoft 365 Copilot across 50+ agencies - the first major public sector experiment with generative AI. This report explores its impact on productivity and key risks.   Here’s what they found:   --> Efficiency gains Many participants reported saving up to an hour per day on tasks like creating first drafts, summarising documents, and finding information quickly. IT staff and mid-level public servants experienced the biggest time savings.   --> Positive productivity impact 69% agreed that Copilot helped them complete tasks faster, and 40% said it allowed them to focus on higher-value activities like strategic planning and stakeholder engagement.   --> Moderate adoption While enthusiasm was high (77% optimistic about Copilot’s future), only 1 in 3 participants used it daily. 86% wished to continue using Copilot.   --> Training matters 75% of participants who received 3 or more forms of training were confident in their ability to use Copilot, 28 percentage points higher than those who received one form of training.   --> Barriers to adoption: Integration issues, security concerns, and a lack of AI prompt skills slowed adoption. Clear policies and communication are key to addressing security, accountability, and usage expectations.   --> Broader concerns: There are fears about the impact of generative AI on entry-level jobs, potential bias in AI outputs, and environmental consequences.   My Thoughts: ✅ AI drives impact, not just efficiency. The real gains from AI come when you shift from 'saving time' to 'driving impact'. Used well, tools like Copilot can free up employees to focus on high-value impactful work, like strategy, specialist expertise, and customer engagement. ✅ A clear strategy prevents AI chaos. Without a plan, AI adoption easily becomes fragmented. Focus on quick wins and high-impact use cases - identify pain points, blind spots, and bottlenecks, then leverage AI to solve them. ✅ Ongoing training = real ROI. One-time onboarding isn’t enough. If people don’t understand what the tool does, how it helps them, or how to use it, they won’t adopt it. Continuous learning builds confidence, sharpens skills, and ensures AI is used effectively and safely   Have you started using Copilot at work? What are you finding it most helpful for?   ⚛ I'm Sarah Mitchell, PhD, AIGP, founder of AI consultancy Anadyne IQ. Need help making Copilot work for you? We run practical workshops, design tailored use cases & e-learning modules, and offer ongoing support.

AI in software development is about enabling our engineers to focus on high-value work, not replacing them. At Tesco Technology, we use AI as a copilot to boost productivity and streamline routine tasks. Here’s how we make it practical across teams: For leaders: Invest in structured AI training and upskilling. Set clear expectations about what AI can and cannot do, so teams use it with confidence and trust. For platform owners: Integrate AI seamlessly into workflows. Automate repetitive steps, display confidence scores, and create clear support/tutorials to help every engineer use AI as a transparent assistant, not a black box. For enterprise scale: Begin with small pilots and collect feedback. Plan for scaling, measure productivity improvements, share successes, and iterate so the adoption endures and provides value at every stage. AI copilots aren’t just for coding. We’ve enhanced documentation and user guides, reduced tedious work, and improved quality across materials. Success comes from integrating AI into our usual practices, supported by clear guidance and feedback mechanisms (Tools like DX are invaluable for this!). Every developer can benefit, and every team is empowered to progress more quickly. If you’re rolling out AI tooling and copilots, focus on practical wins and clear measurement. Let’s drive developer productivity together! #tescotechnology #devex #ai #teamwork #ArtificialIntelligence #Technology #Innovation #DeveloperExperience #Leadership #Productivity #DigitalTransformation #FutureOfWork #TechLeadership #EnterpriseAI

Last month, a blinking cursor quietly stole six hours of my life. A deadline breathed down my neck while tabs multiplied like rabbits and promised shortcuts they couldn’t deliver. Every “best” tool missed the simple job I needed done right now. Then it clicked. Stop hunting perfect. Start fitting tools to the work in front of you. Here’s the part that hurt. Each switch costs about 23 minutes to regain focus, so ten switches can nuke a full afternoon of deep work. And the average company already runs 93 apps, while large enterprises juggle 231, so tool sprawl is quietly eating your week. The FOCUS Method used with clients and teams today: 1️⃣ Function first: define one job to be done in plain language before testing. 2️⃣ Output quality: test on your data, score clarity and accuracy on a 1–5 scale. 3️⃣ Cost vs value: tie price to a metric like minutes saved or error rate reduced. 4️⃣ Usability: pick what people can learn in an afternoon and actually adopt. 5️⃣ Speed: time to first useful result under 5 minutes, or it won’t stick. A stack that talks to each other beats a drawer full of shiny tools. Writing, email, meetings: Microsoft 365 Copilot. Users were 29% faster on core tasks and nearly 4x faster catching up on missed meetings in controlled studies. Code: GitHub Copilot in VS Code to draft functions, tests, and docstrings where you already work. Data storytelling: Power BI (or Gamma) with Copilot to draft visuals and executive summaries directly from your model. Communication: Teams with Copilot for meeting notes, decisions, and action items without leaving your hub. What changed results for me wasn’t a miracle app. It was starting from workflow, choosing native integrations, and running 30-day pilots with hard metrics before scaling. Three moves you can run this week: 1️⃣Map one painful workflow end to end and mark the two slowest steps. 2️⃣Pilot one tool in your primary suite with five users and measure minutes saved and quality deltas. 3️⃣ Kill one redundant app once the pilot works and reallocate that budget to adoption training. What’s your biggest time‑waster when picking AI tools, and where do you feel the most context switching tax right now ?