Leveraging Copilot Technology

Microsoft 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗻𝗲𝘄 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗪𝗵𝗶𝘁𝗲𝗽𝗮𝗽𝗲𝗿: ⬇️ The 30+ page report is a blueprint for how to govern Copilot and AI agents inside the Microsoft ecosystem. 𝗧𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗮𝗰𝗿𝗼𝘀𝘀 𝟱 𝗺𝗮𝗶𝗻 𝗮𝗿𝗲𝗮𝘀: 1. Copilot & Admin Center → Controls for agent usage, access, and compliance 2. Copilot Studio & Power Platform → Guardrails for citizen developers and low-code agents 3. Microsoft Purview → Full data governance, DLP, and risk monitoring 4. Security & Compliance → Role-based access, audit logs, and Sentinel integration 5. Rollout Framework → 3-phase adoption strategy from pilot to scale 𝗜𝗻 𝗼𝗿𝗱𝗲𝗿 𝘁𝗼 𝗮𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗲𝗿 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆, 𝘁𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝟭𝟬 𝘀𝘁𝗲𝗽𝘀 𝘁𝗵𝗮𝘁 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝗳𝗼𝗹𝗹𝗼𝘄 𝗮𝗰𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝘄𝗵𝗶𝘁𝗲𝗽𝗮𝗽𝗲𝗿:️ ⬇️ 1. Build your champion team → Select early adopters, give them Copilot licenses, and let them explore Agent Builder. 2. Set guardrails early → Use Microsoft 365 Admin Center to assign secure permissions and test the first org-wide agent. 3. Train your org → Provide structured training on Copilot Chat and agent-building basics per department. 4. Pilot with proof-of-concept agents → Roll out initial agents to test real usage and gather insights. 5. Stand up a Center of Excellence → Create a CoE to define standards, approve agents, and govern development. 6. Identify department makers → Train key users in each unit to build and manage agents tied to work data. 7. Enable cost control → Set up per-department pay-go meters and restrict oversharing in Power Platform. 8. Gate sharing with governance → Let CoE evaluate agents before org-wide access — block or approve accordingly. 9. Monitor usage and scale → Use built-in analytics to track agent activity, enforce limits, and optimize deployment. 10. Manage spend actively → Set up usage alerts to stay on top of consumption and budget impact. While this playbook is tailored for the Microsoft ecosystem, the underlying principles apply far beyond it. Whether you’re using OpenAI, Google Workspace, or building your own stack — the message is clear: You don’t just need agents: → You need governance. → You need structure. → You need a plan. 𝗣𝗦: 𝗜𝗳 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀, 𝘆𝗼𝘂'𝗹𝗹 𝗹𝗼𝘃𝗲 𝗺𝘆 𝗻𝗲𝘄 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗔𝗜, 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

In an interview with The Information, the CIO of Chevron indicated that about 20,000 employees are testing Microsoft Copilot, but, he said, “the jury is still out on whether it’s helpful enough to staff to justify the cost.” As a reminder, the cost of a Copilot license is ~$30 per user per month (although they probably pay less with that many licenses). Here’s my opinion on this: If a company can’t justify $30 for Copilot (or ChatGPT, Gemini or Claude), then it is more likely due to a lack of education, training and planning, than it is to a deficiency in the AI’s capabilities. This is both a challenge for the company licensing the technology, and a weakness in how the AI tech companies are selling and supporting the platforms. How do we solve this? Here is a five-step framework I’d recommend to businesses of all sizes: 1) Pilot with small groups in select departments over a 90-day period. Prove the value and create internal user champions, then scale it. 2) Prioritize use cases specific to employee roles and responsibilities. Break their jobs into bundles of tasks, and then assess the value of AI at the task level. Pick 3 - 5 use cases initially for each person that will have an immediate and measurable impact. 3) Provide generative AI education and training to maximize the value. Tailor learning journeys for individuals that include specific coursework and experiences in your core AI platforms. 4) Monitor utilization. Invest in the employees who are actually experimenting with and applying tech. Remove the licenses from employees who don’t use them. 5) Report performance versus benchmarks (before and after LLMs). In short, have a plan. The value is absolutely there when it’s rolled out in a strategic way, and part of a larger change management plan. 

👩⚖️ What happens when a law firm pays its lawyers to use AI? Shoosmiths is finding out — with a £1M incentive. YSK: Shoosmiths is offering a £1m bonus pool if staff collectively hit one million Microsoft Copilot prompts in the next financial year. The firm says the initiative is designed to embed AI into everyday legal work and drive firm-wide change around innovation. What's happening? Shoosmiths' million-pound AI incentive reveals a profound shift happening across professional services. While the surface goal is tool adoption, the underlying strategy tackles several fundamental challenges: 1. First, this addresses the traditional reluctance of billable-hour professionals to adopt efficiency tools. By tying financial rewards directly to AI usage rather than just outcomes, Shoosmiths circumvents the inherent conflict between efficiency and revenue in professional services. 2. Second, the collective nature of the bonus (requiring firm-wide participation) transforms technological adoption from an individual choice to a shared responsibility. This cleverly uses social dynamics to accelerate change resistance that typically plagues law firms. 3. Most significantly, the specificity of "four prompts per day" suggests Shoosmiths has already quantified the minimal effective dose of AI integration needed to drive meaningful change. They're not seeking maximum usage, but rather consistent integration into daily workflows. The broader implication? We're witnessing the emergence of explicit behavioral economics in professional upskilling - moving beyond passive training offerings to actively engineered adoption through carefully calibrated incentive structures. This signals a future where firms increasingly design compensation systems around specific behavioral metrics rather than traditional performance outcomes. I won't expect any less from Tony Randle and team :)

How we helped the legal team at CluePoints build multiple AI workflows in two days.   The team had: - Experimented with ChatGPT Enterprise with no real success - Built a Custom GPT that produced unreliable output - Tried Microsoft Copilot with mixed results They didn't need another demo. They needed structured expertise to go from experimentation to reliable workflows. We designed a four-part programme: - Two live virtual sessions covering AI fundamentals and prompting for legal work - Two half-day in-person AI build workshops in London The virtual sessions laid the groundwork — how LLMs actually work under the hood, prompting patterns and practices tailored to in-house legal practice, and a diagnosis of why AI experiments fall short. Then we built. Day One. We took their actual procurement playbook - real clauses, real fallback positions - and turned it into working review workflows in both Claude CoWork and Microsoft Copilot. Two teams, two tools, both with functioning prototypes by end of day. Day Two. We built a Claude CoWork skill for document review and redlining that cut review time by 75%. We created a chatbot from their internal knowledge base using Copilot Studio. We ran an AI use case roadmap exercise with the team, prioritising what would make a difference to their day-to-day, with ambitious self-imposed efficiency targets to hit by year-end. Alice Sahba, their VP of Legal, nailed it: "It's that prompting forces you to map your legal process precisely enough that a machine can follow it consistently." The team left with skills they own, a roadmap they built, and the ability to keep iterating without us. That's how we work with in-house legal teams. Build with them, not for them. We run this as a structured programme for in-house legal and compliance teams - live virtual AI foundations followed by hands-on build workshops tailored to your contracts, playbooks, and tools. What's the one legal workflow your team would automate first? Drop it in the comments - I'll share how we'd approach it.

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

⭐ The M365 Copilot Journey ⭐ .. and which steps you should consider for a successful start 📌 Explore how M365 Copilot works In order to plan the necessary steps for an implementation, a good understanding of the structure of the M365 Copilot is important. How does M365 Copilot access your data? Here, the "Microsoft Graph" function is particularly important to note. Where in the tenant is the AI located? And what paths does the user request from the application take? 📌 Get your Tenant ready The most important preparation step from a security and compliance perspective Are there administrators with personal accounts in the tenant? Is the least privilege approach given? Do users only have access to data that they are allowed to see? Are the SharePoint pages properly authorized? Is there no oversharing in the tenant? 📌 Update your applications Check if all application are up to date. For example, the Outlook Copilot requires the "New Outlook" desktop client. An update to the new Teams Client is currently not required. 📌 Train your users An investment in the M365 Copilot is only valuable if it is also used by the users. Good user training before, during and after the introduction is very important to avoid frustration and rejection. Set up UseCases and show the users, based on their daily work, where the M365 Copilot can make their work easier 📌 Prepare the licenses In addition to the actual M365 Copilot licenses (must be ordered separately), the following minimum license requirements must also be met: In addition to the actual M365 Copilot licenses (must be ordered separately), the following minimum license requirements must also be met: Microsoft 365 E3/E5/Standard/Premium-License 😎 Can't wait to try it out for myself #microsoft365 #microsoftcopilot #ai #cloudjourney #future

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 ?

Imagine you're a legal or compliance team, looking to use AI to help keep you abreast of recent legal developments and to horizon scan upcoming developments in data protection and AI — how can you do this? 🤖⚖️ Subject to all the usual warnings (beware of hallucinations, check outputs for accuracy etc.), those of you who have access to any of the mainstream LLMs (ChatGPT, Claude, Gemini, Copilot etc.) can do this easily as follows: 1. First, create a horizon scanning prompt that you can ask the AI to run. The challenge is how to create a good prompt if you're not an expert in prompt engineering. ✍️ 2. The answer is to have AI create it for you! For example, I asked ChatGPT: "Can you draft a prompt for an AI agent that will run on a weekly basis and endeavour to find: (i) major developments in EU/UK data protection, data regulation and AI laws and regulatory guidance (at both an UK and EU-wide and EU member state level), and (ii) do horizon scanning for any new or emerging issues relevant to these areas? The aim is to gather information that will be relevant to and keep up to date a legal team specialising in these areas in a leading law firm." 💡 3. ChatGPT understood the brief, and created an extremely long, fleshed-out prompt that you can see below. If you're looking for a short cut, you can cut and paste this into the LLM you use (and even ask your own LLM if it would suggest any improvements). 📋 4. If you then run this in your chosen LLM, it will provide you with an initial horizon scanning report. 🔍 5. LLMs like ChatGPT, Gemini and (I think) Copilot offer task scheduling functionality to business (i.e. paid) users — meaning you can give them this prompt and require them to execute it at predetermined intervals (e.g. once a week). For example, you can ask them to schedule these prompts to run at weekly intervals by saying: "Can you create a schedule to run the previous prompt once a week?" This then provides you with regular horizon scanning reports. 📅 6. If your chosen LLM doesn't have task scheduling capability, then you'll need to run this prompt manually at your chosen intervals. But that's what calendars and reminders are for! 🔔 The prompt created below has in-built source prioritisation, search strategies, and quality controls — but, like any AI, they won't be foolproof. Simply telling an AI not to hallucinate doesn't mean that it won't! So keep that in mind. However, if you're looking for a tool that can help make you aware of developing issues so you can check them out and validate them for yourself (you human-in-the-loop, you), then this can be a great place to start. 🚀

𝗟𝗲𝗴𝗮𝗹 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗧𝗮𝗹𝗸: 𝗔𝗜 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗴𝗮𝗹 𝗗𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁   I spoke to Karim Tejani, Head of Legal Switzerland at Microsoft. Karim is passionate about leveraging AI to navigate the complexities of legal frameworks.   ❓ 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗔𝗜 𝗶𝗺𝗽𝗮𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗹𝗲𝗴𝗮𝗹 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁? 🗣 AI has the power to transform legal departments by enhancing efficiency, quality, and scale. It streamlines regulatory work, improves advisory services, and strengthens compliance. An experiment In Microsoft Legal showed faster task completion and greater accuracy. Most participants found AI tools like Copilot boosted productivity and work quality, allowing focus on complex, strategic tasks. ❓ 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝗔𝗜 𝗳𝗼𝗰𝘂𝘀 𝗮𝗿𝗲𝗮𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗹𝗲𝗴𝗮𝗹 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁? 🗣 The main AI investment areas for Microsoft’s legal department include: 1️⃣ Advice: Knowledge management and self-help tools. 2️⃣ Transactions: Contract management, drafting, review and negotiation support. 3️⃣ Compliance: Insights for internal compliance. Keeping up with ever evolving regulation. ❓ 𝗛𝗼𝘄 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝘁𝗵𝗮𝘁 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸? 🗣 AI such as Microsoft Copilot, are highly reliable. They enhance work product quality, increase agility, and facilitate decision-making. I see AI as a tool that supports users as a copilot while the human remains in the driver seat. The Microsoft legal team has recently shared practical use cases on how we use Microsoft Copilot in our everyday work (accessible via my LinkedIn profile). ❓ 𝗪𝗵𝗮𝘁 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝘁𝗮𝗸𝗲𝗻 𝘁𝗼 𝗲𝗻𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝗮𝗳𝗲 𝗮𝗻𝗱 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝘂𝘀𝗲 𝗼𝗳 𝗔𝗜? 🗣 Microsoft is committed to responsible AI development and deployment. This includes implementing policies and practices to map, measure, and manage AI risks. Key principles such as accountability, inclusiveness, reliability, safety, fairness, transparency, and privacy guide these efforts. Initiatives like the Pilot Gen AI Redteaming Network by ETH and EDA, which Microsoft Switzerland recently joined, also play a crucial role in addressing safety and ethical challenges. ❓ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗸𝗲𝘆 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝗻 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗲𝘃𝗲𝗿𝘆𝗱𝗮𝘆 𝘄𝗼𝗿𝗸? 🗣 On an individual level, curiosity and an open mindset. On a department level, a strategic approach that includes experimentation, cultural change initiatives, and continuous learning. Many thanks, Karim, for the interesting conversation. #leadership #inspiration #success      

ZoomInfo Copilot just crossed $250M in ACV only 18 months after launch, while most AI pilots are failing. Here’s the exact playbook we used to build, launch and grow it: 1. Make your own company Customer Zero Before a single customer saw Copilot, every seller and account manager used it in real workflows for 4 months. We doubled down on 2 magic moments 1) rep-territory–specific signals tied to recommended actions and 2) full AI-driven account 360 - a chat where reps could ask Copilot anything about an account. 2. Build an unfiltered feedback loop and fast-track engineering We created a dedicated Slack channel for unfiltered feedback straight from reps → PMs → engineers. Every piece of feedback had to be acted on fast - we were shipping updates multiple times per day. 3. Test willingness to pay before public launch 2 small GTM teams sold Copilot in live customer conversations months before GA, with real pricing, to learn what customers would actually pay for and which gaps were deal-breakers vs. planned follow-ons. 4. Launch with a narrow ICP Copilot delivers the most value when a customer’s CRM is fully integrated with ZoomInfo, so instead of blasting everyone, we: → Filtered only CRM-integrated accounts → Prioritized those with the highest data quality and activity → Built the launch around this smaller, higher-probability segment 5. Expand post-sales capacity early We took Copilot sales forecasts and fed them into a time-allocation model to understand: → How many onboarding hours we’d need → When those hours would hit → What headcount would be required by month Then we layered that on top of existing post-sale work to plan capacity. No one was dedicated to Copilot - onboarding, L&D, and implementation teams flexed based on need. 6. Anchor selling to value We rolled out customer-level guidance which varied by firmographics, technographics, usage, and readiness. Clear value → expand. Emerging value → keep scope tight. 7. Incentivize sales We set explicit Copilot upsell and renewal attach targets for every manager, senior manager, and director. Targets were based on: → their team’s expected monthly renewal ACV → a Copilot attach-rate goal tied to that ACV Leaders were paid cash for exceeding their quarterly Copilot targets. We also layered Copilot into existing upsell spiffs - often doubling payouts when a deal included Copilot. 8. Make all metrics visible We tracked Copilot like its own business: ACV, pipeline, ASP, renewals vs. legacy, loss reasons, and internal adoption. All filterable by month, segment, team, and rep. This gave sellers clear signal that Copilot was a core product. We paired that with AI-powered enablement trained on real calls, decks, docs, Slack threads, and battlecards. If your feedback loops are slow, your ICP is fuzzy, or your post-sale motion isn’t prepared, AI exposes it immediately. There’s no special secret here. We just did the unglamorous work early and took it seriously.