Why MSPs need a data platform mindset to deliver on AI strategy

‘AI is only as good as the data behind it.’

For Managed Service Providers (MSPs), this isn’t just a slogan - it’s the difference between AI hype and AI value.

Today, every MSP is under pressure to ‘do AI.’ Customers expect predictive insights, proactive service and ever-increasing automation. At the same time, vendors are releasing AI-driven features across ITSM, ITOM and collaboration platforms. The pace of change is relentless.

But here’s the hard truth: without a data platform mindset, even the best AI strategy will stall.

In this blog, I share my perspective on how MSPs operate today and what shifting to a data-first mindset really means for building a sustainable AI strategy.

The problem with today’s approach

MSPs generate massive volumes of data every day: ITSM tickets, monitoring alerts, security logs, cloud telemetry and more. Yet in most cases the systems are siloed -ServiceNow, Azure, M365, monitoring tools and finance systems all store data in isolation; our reporting is reactive - customers still get static dashboards or spreadsheets, often built manually and most importantly our usage of AI is underpowered - without clean, integrated data, AI features are limited to small use cases like chatbots or ticket categorisation.

The outcome? AI pilots that look promising in a demo, but fail to generate sustainable, business-wide impact.

The path forward is clear: if we want AI to deliver real value, data must be central to our business strategy - not treated as an afterthought or by-product of processes and workflows.

What does data platform mindset mean?

A data platform mindset means treating data not as scattered outputs of different tools, but as a strategic asset that underpins every decision, service and innovation.

It’s not about adding another tool - it’s about building an ecosystem where data flows seamlessly, is trusted, and is ready for AI.

The key elements of this mindset include:

• Centralised data foundation: Create a single source of truth that integrates ITSM, monitoring, financial and cloud data.
• Governance by design: Ensure data quality, cataloguing, metadata and security built in from the start.
• Multi-tenant design: For MSPs, separate customer datasets securely while still enabling cross-cutting insights.
• Future-proofing: A platform that scales to support today’s reporting, tomorrow’s automation and next-gen AI use cases.

Adopting this mindset shifts the role of data from being a by-product of business processes to becoming the foundation for operational efficiency, customer value and AI strategy.

How does data platform power AI strategy?

With a solid data platform mindset and foundations, AI moves from being a collection of isolated pilots to a true business strategy. All the cool AI buzz words suddenly become real. AIOps with correlating logs, metrics and CMDB data from ITSM and monitoring platforms will do the anomalies detection, predict incidents and reduce MTTR.

The Agentic AI will kick in with workflows that don’t just recommend actions but execute them, examples like resetting accounts, auto-updating CMDBs, triggering runbooks and more such scenarios that will not require any manual intervention. Customer and commercial insights with AI spotting churn risks, highlighting optimisation opportunities or benchmarking service quality. And of course, the one we are all most familiar with, the generative AI - natural language queries across your data lake: ‘Show me which customers are trending towards higher ticket volumes and why.’

Without clean, connected and governed data, these scenarios remain limited to PowerPoint slides and product demos. With a platform-first approach, they become real, repeatable and scalable outcomes that strengthen both operations and customer value.

What are the benefits for MSPs?

Adopting a data platform mindset doesn’t just enable AI. It transforms how MSPs operate and compete.

1. Operational excellence: Move from reactive firefighting to proactive, predictive operations that reduce downtime and speed up resolution.
2. Customer stickiness: Deliver insights that customers can’t get anywhere else. Data-driven services become part of your USP.
3. Commercial growth: Package data-driven insights, automated reporting and AI-enabled services as offerings that drive new revenue streams.
4. Future readiness: Build an adaptive foundation that keeps you ahead as vendors and customers demand more AI-driven capabilities.

How you can get started

Making the shift doesn’t require a massive ‘big bang’ transformation. Start small and scale:

1. Define your data strategy: Anchor it to business and customer outcomes, not just technology. Decide what problems we are solving and what value we want to prove first.
2. Build foundations: Start with core integrations - for example ITSM and financial data to establish a reliable single source of truth.
3. Apply governance: Set ownership, catalogues, lineage and access controls before the data landscape becomes unmanageable.
4. Deliver quick wins: Automate customer facing reporting through tools like Power BI to show immediate value and reduce manual effort.
5. Scale to AI: Once the data is clean, connected and trusted, layer in AIOps, agentic AI workflows and customer insight models to unlock predictive and proactive services.

This ‘crawl → walk → run’ approach ensures momentum without overwhelming teams or customers, while keeping the business outcomes front and centre.

Final thoughts

Digital transformation, AI strategy, service innovation - they all come back to one thing: data.

For MSPs, the choice is simple. Treat data as fragmented outputs of tools and you’ll always be chasing the next feature. Treat data as a strategic platform and you’ll build a foundation for AI-driven services that deliver real, lasting value.

A project ends. A platform evolves. And that evolution is where AI truly comes to life. 

Ready to build your data platform mindset? Contact us today to start your journey.