Steps for a Successful Copilot Rollout

We rolled out AI across our team in 60 days. No chaos. No confusion. Just clear wins and real results. I've seen marketing departments jump into tools like ChatGPT and Claude without a plan, only to end up with inconsistent usage, security risks, and wasted time. So here’s a reality check: Giving your team access to AI tools is not the same as making them AI-ready. What works? A clear, structured rollout that builds confidence, protects your brand, and drives performance. Here’s the 7-step sequence I recommend getting your marketing team fully ready to use AI: 🔹 1. Leadership Alignment Before anyone writes a prompt, you need to answer this: → What are we actually trying to improve with AI? → Clarify your goals: content speed? campaign performance? lead quality? 💡Assign an internal AI Champion to lead adoption and make this someone’s job, not everyone’s maybe. 🔹 2. Create Your AI Usage Policy Yes, before the first prompt. Set ground rules: → No client data or credentials in tools → Human review before anything goes public → Approved tools only → A go-to person for AI questions 💡Keep it simple. A 1-page doc is better than a 20-page one no one reads. 🔹 3. Train the Team Don’t assume “digital native” means “AI fluent.” Run a short onboarding: → Demo real-world prompts for their roles → Share a centralized prompt library → Walk through how to use your company’s Custom GPT (if you have one) 💡Make it practical. Confidence creates momentum. 🔹 4. Start With Small Pilots Want to build trust in AI fast? Deliver small wins early. Assign 1–2 people per function to test real use cases: → AI for email writing → Content repurposing → Campaign briefs 💡Document results. Share what worked and build internal buy-in. 🔹 5. Bake AI Into Daily Workflows AI should enhance what already works. → Add AI to your content creation SOPs → Use it for meeting note summaries → Integrate it into campaign planning templates 💡The more friction you remove, the faster usage scales. 🔹 6. Build a Feedback Loop Set a bi-weekly or monthly check-in: → What’s saving time? → What’s confusing? → What should we expand next? 💡Refine as you go. This isn't a one-and-done rollout. It's a capability you're building. 🔹 7. Enable Long-Term Growth This isn’t just about productivity. It’s about transformation. → Encourage ongoing experimentation → Recognize team AI wins → Offer certifications or incentives to deepen adoption 💡You’re not just introducing a tool. You’re building a smarter, faster, more strategic team. ✅ Final Thought If you're leading a marketing team, you don’t need to rush into every AI trend. But you do need a clear path for AI readiness. Because the biggest risk today isn’t overusing AI. It’s being the last team in your category that doesn’t know how to use it well. ____________ ♻️ Repost if your network needs to see this. DM me if you need help creating an AI rollout plan for your team.

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. 

This was our advice on deploying Microsoft 365 Copilot a year ago (September 2023), two months BEFORE the M365 Copilot GA date of Nov 1. If you're a Gartner client, we hope you followed some of these recommendations to get ahead (and it's still not too late): ✅ Establish new generative AI skills and policies by evaluating Microsoft Copilot as a new technology stack rather than merely a productivity tool. ✅ Establish a M365 product team with direct oversight of governance of generative AI services that interact with the Copilot stack. ✅ Review and communicate to stakeholders key Microsoft online service terms and data protection and privacy commitments, all of which apply to M365 Copilot. ✅ Reinforce the need for information governance and access controls in M365 with stakeholders to ensure users don’t overshare information that could be exposed through the Copilot stack. ✅ Maximize adoption and reduce features overlap by coordinating with business unit leaders on use of Copilots and other generative AI tools from enterprise applications. ✅ Lead a coalition with your stakeholders to make your initial Copilot investments immediately valuable, and pave the way for an impactful and successful long-term integration of multiple generative AI technologies. ✅ Plan for a multivendor generative AI portfolio that includes Microsoft alongside other vendors, each likely with different approaches. ✅ Prioritize the rollouts to employees in a controlled way. A “big bang” rollout will cause confusion among employees, leading to a surge of support issues. ✅ To ensure ROI is achieved, plan and execute a meticulous rollout strategy that includes a series of communications, multichannel training and support, and a holistic change management strategy with buy-in from executives and business leaders. From: "Assessing the Impact of Microsoft’s Generative AI Copilots on Enterprise Application Strategy" https://lnkd.in/ewXescAp

Structured rollout boosts Copilot adoption and satisfaction by 20% AI Adoption: Old Lessons, New Opportunities - The more I explore the successes of AI, the clearer it becomes: the fundamentals of change management are as relevant as ever. The twist? With AI, the stakes are higher, the pace is faster, and the challenges more amplified. Adopting AI is change on steroids, requiring not just structured rollouts, but also robust training, clear messaging, and unwavering support from senior leadership. When these elements come together, the results are nothing short of transformative. Executive Summary A structured rollout and robust onboarding process are the secret ingredients to maximizing the impact of AI tools like GitHub Copilot. Companies that invest in pilot programs, training, and support see higher adoption rates, greater satisfaction, and measurable boosts in engineering velocity. In one case study, developers with structured onboarding reported 17% higher satisfaction rates, proving that a thoughtful approach pays dividends in utilization and ROI. Key Points 1. Structured Rollouts Deliver Results: Rolling out Copilot in phases, starting with a well-supported pilot group, resulted in an 81% satisfaction rate—17% higher than teams without structured onboarding. Satisfaction directly correlates with increased tool usage and overall productivity gains. 2. Training and Support Are Game-Changers: Interactive learning events, dedicated support channels, and regular check-ins help developers fully embrace Copilot’s capabilities. These efforts boost satisfaction and adoption, ensuring organizations get the most value from their investment. 3. Best Practices Drive Adoption: To maximize Copilot’s impact, provide hands-on training, create spaces for community sharing, and offer regular usage nudges. This approach not only drives adoption but fosters a culture of continuous learning and innovation within teams. Article Link --> https://lnkd.in/gxjQM66m Author - Abi Noda

Meeting with enterprise customers and learning about their experiences with our products and services is always enlightening. I want everyone to benefit from our products and features, from roll-out and adoption to realizing business impact. A recurring theme I've heard from customers is their concern about the change management required to achieve successful adoption. To help alleviate that concern, I've summarized some proven tactics from early Copilot adopters who have successfully increased user adoption, excitement, satisfaction, and productivity. -Select a subset of teams to test-pilot the licenses through an early-access program -Host training sessions to get users up to speed quickly -Conduct an education campaign with employee opportunities to show and tell best practices -Set up feedback channels (i.e. Teams, Viva Engage) for user-reporting and knowledge sharing -After users are finding value, begin new test-pilots by moving licenses to other teams -Create an employee Ambassador program to help onboard new users Create your own AI adoption playbook with these tactics and more in our Copilot Success Kit. #Microsoft365Copilot https://lnkd.in/g7UhxPTg