The rise of generative AI in the workplace – from writing code to drafting emails – has opened a new revenue stream for Managed Service Providers. Clients are hearing the buzz about tools like Microsoft 365 Copilot and want to harness AI for productivity. As an MSP, you have a golden opportunity to deliver these AI-powered solutions as managed services. But success isn’t as simple as reselling a Copilot license and saying “good luck.” You need a practical plan to roll out AI safely, securely, and effectively. In this guide, we break down how MSPs can begin selling Managed AI Services to clients step by step. Follow these stages – from assessment and licensing to governance, rollout, and ongoing value – to ensure you deliver AI solutions that wow your clients (while keeping their data safe).
Step 1: Assess Readiness and Frame the Value Proposition
Before pitching AI solutions, start with an AI readiness assessment for your client. This means evaluating their current Microsoft 365 environment, data landscape, and security posture. Is their data mostly in Microsoft 365 (SharePoint, OneDrive, Teams)? Do they have basic governance (permissions, labels) in place? Identifying gaps now will save headaches later. For example, before deploying Copilot, one best practice is to conduct an AI readiness assessment and help the client define an AI usage policy. This not only gauges technical readiness but also surfaces use cases where AI can add value (e.g. automating report generation, summarizing documents, speeding up customer responses).
Equally important is framing the value proposition. Clients won’t invest in AI just because it’s trendy – you need to connect AI capabilities to their business goals. During the assessment, gather pain points and opportunities: could Copilot help salespeople draft proposals faster, or assist HR in writing policy drafts? Quantify potential benefits (time saved, faster outcomes) to build a compelling case. Then communicate those benefits in client-centric terms: e.g., “Copilot could save each of your sales reps 5 hours a week on paperwork – giving them more time to close deals.” When clients see AI as a solution to their problems (not just shiny tech), they’ll be more eager to proceed.
Finally, use this phase to set expectations and get leadership buy-in. Introduce the concept of Copilot and managed AI services in clear, non-technical language. Emphasize that you (the MSP) will guide them through it safely – the client remains in control of their data and AI won’t “run the business” on its own. This proactive communication builds trust and positions your MSP as a knowledgeable partner. By the end of Step 1, you should have: (a) a green light from the client to explore an AI rollout, (b) an understanding of their environment and needs, and (c) initial alignment on where AI can deliver quick wins.
Step 2: Ensure Licensing and Platform Readiness
With interest secured, make sure your client’s Microsoft licensing and cloud environment are ready for Copilot. Microsoft 365 Copilot is an add-on license – it requires each user to have a qualifying Microsoft 365 plan (and then the Copilot add-on is purchased for those users). In practice, this means your client likely needs to be on Microsoft 365 Business Premium or an Enterprise (E3/E5) plan as a baseline. If they’re on a lower tier (or disparate plans), now is the time to upsell a licensing upgrade. In fact, experts recommend ensuring all users are on at least Business Premium before enabling Copilot. Upgrading a client from, say, Microsoft 365 Business Standard to Business Premium not only makes them eligible for Copilot, it also gives them enhanced security features – a win-win for both client value and your monthly recurring revenue.
Next, plan out the Copilot licensing itself. Microsoft 365 Copilot is currently priced around $30/user/month, so be prepared to discuss this cost in the context of the productivity gains it delivers. Many MSPs bundle the Copilot licenses with their managed service offering (for example, including administration, training, and support). Common licensing configurations might include a pilot group of users to start (more on that later) and then phasing in additional licenses over time. Be transparent with your client about the cost and which users will get licenses initially – this manages expectations and sets the stage for future expansion.
While sorting out licenses, ready the technical environment. Confirm the client’s Microsoft 365 tenant is in good shape: are all users in Azure AD with the proper subscriptions? Is their data largely stored in Microsoft cloud services that Copilot connects to (SharePoint, OneDrive, Exchange, Teams)? Identify any legacy systems or data silos that might limit Copilot’s usefulness and plan to address those (maybe an upsell to migrate that data into M365). Also, verify any prerequisites like enabling certain features in Microsoft 365 admin center. The good news is Microsoft has started bundling some governance tools with Copilot – for example, SharePoint Advanced Management features (like content sprawl insights and sharing audits) are included when you purchase M365 Copilot. This means you and your client will have new tools to keep tabs on data as Copilot rolls out. By the end of Step 2, your client should have the right licenses in place (or a plan to get them) and a cloud environment primed for AI.
Step 3: Establish a Governance-First Foundation
This step is critical: before turning on any AI capabilities, implement data governance and security measures. Rushing a Copilot deployment without governance is a recipe for trouble. In fact, the difference between a chaotic rollout and a smooth one boils down to this preparation. Consider a cautionary tale: one company enabled Copilot broadly without proper controls – within 72 hours, sensitive contract terms and even confidential R&D info were inadvertently exposed by the AI. Copilot had been given free rein over an environment full of overshared data: open finance and HR folders, no sensitivity labels, no audit logs. The result? A breach that left leadership demanding answers, legal scrambling, and the MSP in firefighting mode. The lesson: don’t roll out AI in an unstructured way – the risks to client data and trust are simply too high.
To avoid that fate, take a “governance-first” approach. Start by auditing data access and sharing. Identify where the client’s important data lives (e.g. the top SharePoint sites or Teams channels) and who currently has access. Lock down any wildly open permissions – for instance, eliminate any “Everyone except external users” blanket access on SharePoint and switch those sites to private. It’s reported that MSPs should run reports on the top 100 SharePoint sites and check for oversharing as part of the preparation. This helps you pinpoint which content could be at risk if an AI had access.
Next, classify and label sensitive information. Leverage Microsoft Purview (included in many M365 plans) to auto-scan and label files with sensitivity tags (e.g. Confidential, HR Only, etc.). By labeling data, you ensure Copilot’s underlying algorithms know what’s sensitive – you can configure Copilot not to show content with certain labels or to respect DLP (Data Loss Prevention) rules. If the client doesn’t have a data classification scheme, now is a great time to implement one as part of your service (position it as strengthening their compliance and readiness for AI). Also enable audit logging and insights: turn on Microsoft Purview Audit (if available) and use the new Copilot usage analytics to monitor what data Copilot is accessing. Microsoft’s Copilot control system can even detect risky prompts or unusual data access patterns – make sure those features are enabled so you get alerts if, say, someone tries to prompt the AI for things outside their scope.
In short, treat this stage as laying the groundwork and guardrails. Key tasks in this governance stage include:
- Data Access Reviews: Identify and remediate any overly broad access to data repositories.
- Sensitivity Labels & DLP: Apply sensitivity labels across M365 (client records, financial data, etc.) and enforce DLP policies so Copilot won’t divulge protected info.
- Limit Copilot’s Scope: Use Microsoft 365 settings to scope what Copilot can index/search. For example, maybe initially allow it to draw from SharePoint sites that have been reviewed and cleaned, while excluding high-risk sites until they’re governed. One successful approach limited the Copilot pilot to a few departments and even restricted search indexing to specific SharePoint libraries.
- Security Monitoring: Enable threat detection for AI. Microsoft Purview can help detect things like prompt injection attempts or if Copilot is quoting from sensitive files in its responses. This adds an extra layer of security oversight unique to AI.
By embracing a governance-first mindset, you flip the script – instead of AI running wild, it operates within a framework you control. You can confidently tell your client that their data is protected and compliant from day one of the AI rollout. And as an MSP, you’re not just selling a tool, you’re selling peace of mind. (This approach also becomes part of your value proposition: unlike a quick “flip the switch” competitor, you’re delivering AI with proper governance, which greatly reduces the client’s risk.)
Step 4: Start with a Low-Risk Pilot
With the groundwork laid, it’s time to pilot Copilot – in a controlled way. You and your client should identify a small group of initial users and use cases to kick the tires. The goal here is to prove the value of AI on a small scale, while validating that your governance controls work as intended.
Pick the pilot participants carefully. Aim for a mix of enthusiastic, tech-savvy users from departments that can benefit, but avoid the most sensitive areas at first. For example, you might include a couple of people from the sales or operations teams who frequently produce documents or reports, but hold off on giving Copilot to the HR director or CEO right away. One expert recommendation is to avoid high-risk departments like Legal, HR, or the Executive team in the initial pilot. Those areas often handle ultra-sensitive data – it’s wise to “prove your governance first” in a lower-risk context before extending AI into those realms.
Once the pilot group (perhaps 5-20 users, depending on company size) is chosen, assign Copilot licenses to those users and double-check their permissions. Make sure they can access the data sources you want Copilot to leverage (and nothing more). It’s also a good practice to inform pilot users about the program before they suddenly see a new AI icon in their Word or Teams. Explain that they’re part of an early adopter group, what Copilot can do, and reinforce any usage guidelines. For instance, if there are certain data types or questions that are off-limits, let them know upfront. This sets expectations and makes them partners in the experiment.
During the pilot, keep a close eye on how Copilot is used and the results it produces. Monitor user feedback and system logs. Are users getting helpful answers? Are they attempting to use Copilot to find information they shouldn’t (what one might call “fishing for content”)? Microsoft’s Purview reports and Activity Explorer for Copilot will be valuable here – they can show you what types of prompts users are entering and if Copilot is pulling from any sensitive content. For example, you might see that a user tried to get Copilot to summarize a confidential finance file – which should be caught by your controls. Use these insights to adjust: maybe you need to tweak a DLP rule or provide a reminder on appropriate use.
Keep the pilot scope limited in time and breadth – say, a 4-6 week pilot phase with bi-weekly check-ins. This creates a sense of urgency and focus. At the end of the pilot, plan to gather the team and evaluate outcomes: both the qualitative feedback (did they find it useful? any issues?) and quantitative metrics (time saved, output produced). Often, early pilots will surface great success stories that you can highlight. For instance, perhaps the operations manager used Copilot to generate SOP documents in half the usual time, or the sales rep got a solid first draft of a proposal in minutes. These wins become powerful testimonials to convince the rest of the organization (and justify further investment). In short, step 4 is all about learning and iterating on a small scale – you’re validating that “Copilot can work here and here’s the proof,” which paves the way for broader deployment.
Step 5: Educate Users and Communicate Throughout the Rollout
While the pilot is running (and as you prepare to expand), user education and communication are your best friends. Introducing AI in the workplace can be as much a change management challenge as a technical one. MSPs that excel in selling AI services differentiate themselves by how well they train and inform their clients’ users and stakeholders. Don’t leave end-users in the dark – or worse, let them develop misconceptions (like thinking Copilot will replace jobs or always gives correct answers).
Start by providing role-specific training to the pilot users and eventually all users. People in different departments will use Copilot differently: a salesperson might use it to draft emails, whereas a finance analyst might use it to summarize monthly figures. Tailor your training sessions or materials to those contexts. Show practical examples relevant to each role. Emphasize that Copilot is a copilot – a helper that suggests content – but the user remains the “pilot” in control. This helps set a collaborative mindset (AI + human) rather than fear. Also cover responsible use: for example, caution users not to paste sensitive personal data into prompts, and teach them how to review Copilot’s outputs critically (AI can make mistakes, after all).
It’s also wise to establish an AI usage policy for the client (if you haven’t already in Step 1) and communicate it clearly to all users. This policy might outline what types of information can be used with Copilot, ethical guidelines (e.g. don’t ask Copilot to do something that violates company policy), and support procedures if they encounter issues. Reinforce that policy during training. One slide deck suggests having an internal usage policy that spells out what’s in scope and what’s not, and even setting up DLP rules to technically enforce parts of that policy. For instance, if something is labeled “Confidential – Client X”, Copilot should be prevented from including it in a response, and users should know that’s off-limits for prompting.
Communication with client leadership is equally important. Keep the stakeholders updated on pilot progress, early wins, and any adjustments you’re making. If there were any minor hiccups or near-misses (e.g. Copilot tried to show a sensitive file but it was blocked), be transparent about how those are being addressed – this will actually increase their confidence that the governance measures work. Highlight quick wins in business terms: “In the first month, Copilot helped your team produce 10 customer proposals and saved ~40 hours of work.” In one governed pilot, an organization saw zero data exposure incidents while their sales team achieved a 38% time savings on certain tasks and specialists saved 6 hours per week thanks to Copilot. Sharing such results with your client in real time builds enthusiasm and executive support to roll out wider.
Another aspect of communication is building excitement without overpromising. AI is cool, and users might have sky-high expectations from what they’ve seen in marketing. Ensure your training and comms strike a balance: Copilot is powerful for certain tasks, but it’s not omniscient. Encourage users to experiment and find where it helps them most, and to share success stories with the group. Maybe set up an internal forum or channel (which you can offer to moderate) where pilot users and later all users can post “Here’s how I used Copilot today” – this peer learning can drive adoption organically.
In summary, Step 5 is about driving user adoption through education and keeping all stakeholders in the loop. The more comfortable and informed people are, the more they’ll actually use (and love) the AI solution – which is key to delivering value.
Step 6: Roll Out Broadly in Phases and Continue to Optimize
After a successful pilot, it’s time to expand the AI service deployment to more users and ultimately the whole organization. Emphasize a phased approach: you might roll out department by department, or in waves of a certain number of users at a time. This controlled scaling ensures you can manage the load (both technically and in terms of support/training) and apply any lessons from one phase to the next. Train each group of new users before you enable Copilot for them, so they start off on the right foot. As you roll out, continue to tighten governance where needed – each phase is a chance to double-check that group’s data for any new oversharing issues or to fine-tune label policies if new types of sensitive info come into play.
During the broader rollout, maintain a high level of monitoring and iteration. Your job as the MSP doesn’t stop at flipping the switch for more users; in fact, this is where the managed service aspect truly kicks in. Set up routine reports (perhaps monthly) to review Copilot usage and any potential exposure risks across all users. Microsoft provides tools like the Service Access Monitor (SAM) and DPSM (Data Protection Score Metrics) for AI – use these to track how Copilot is interacting with the client’s data over time. If you see, for example, that usage is low in a certain department, that might signal the need for a refresher training or a meeting to find relevant use cases for them. If you spot any risky behavior (like someone consistently trying to get around restrictions), you can address it quickly – either with a policy adjustment or a direct conversation.
This phase is also about operationalizing AI as part of business-as-usual. Help the client integrate Copilot into their standard workflows. Perhaps you schedule periodic check-ins (quarterly “AI value review” meetings) to discuss how things are going and introduce new Copilot features (Microsoft will be updating it regularly with new capabilities). Encourage the client to designate internal “AI champions” or power users in each department who can continue driving adoption from within. As an MSP, you can host Q&A sessions or office hours for Copilot users as the rollout continues, ensuring questions get answered and new ideas are heard.
Crucially, demonstrate ongoing value. Remember those metrics and success stories you gathered? Keep updating them as usage grows. Maybe after 6 months, you can report to the client’s leadership that “Copilot has generated X deliverables, saving an estimated Y hours – effectively Z dollars – and no security incidents have occurred.” This helps frame the value proposition in hard numbers, reinforcing why the client invested in AI services. It also sets the stage for you to upsell additional AI capabilities in the future. For example, if Microsoft releases a new AI feature (like a domain-specific Copilot or an upgrade to capabilities), you can propose it knowing you have a track record of success.
Lastly, incorporate feedback loops. Solicit feedback from end-users and stakeholders regularly. Users might suggest new ways they wish to use AI, which could turn into new service offerings you provide. Or they might identify limitations – which could lead you to adjust governance or seek out third-party solutions to augment Copilot. Continuous improvement is the name of the game. In one successful rollout, the MSP and client moved toward automating permission reviews and other maintenance tasks to keep the AI ecosystem clean and efficient. This kind of optimization keeps the service scalable and sustainable over the long term.
With the proper groundwork and a client-first mindset, selling AI services can be a win for your clients, a win for their end-users, and a big win for your MSP business.
