To deal with hyperscaler dominance, MSPs must innovate with AI strategies to stay relevant

As hyperscalers like AWS, Microsoft Azure, and Google Cloud embed AI into their platforms, managed service providers face a pivotal choice: adapt through strategic integration or risk obsolescence in a rapidly evolving cloud and AI landscape.

The rapid evolution of cloud services and artificial intelligence (AI) is reshaping the landscape for managed service providers (MSPs), forcing them to reconsider their strategies amid the dominance of major hyperscalers such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). What were once lucrative revenue streams from hosting email, data analytics, and reporting have now become baseline services, as hyperscalers embed these capabilities into their platforms and offer them at scale.

The entry of hyperscalers into the market has prompted MSPs to adapt by transitioning from standalone offerings to a model where they add value on top of hyperscaler services. For example, MSPs frequently provide Microsoft 365 email agreements where Microsoft handles licensing and infrastructure, while MSPs focus on support and additional service layers. This approach exemplifies a broader trend of MSPs becoming integrators, assembling third-party services into tailored solutions that align with specific customer outcomes.

The challenge for MSPs is particularly acute for those operating their own data centres and platforms. They face a choice: continue investing heavily to build and maintain proprietary infrastructure or leverage hyperscaler services, accepting potential compromises related to response times and service availability. The growing prominence of AI adds further complexity, as hyperscalers invest billions into AI research and embed sophisticated AI tools across their offerings. This makes in-house AI development risky and resource-intensive for many MSPs.

MSPs currently have three principal paths to navigate the AI-driven future. Building their own AI capabilities from scratch or using open-source engines like OpenAI or TensorFlow demands deep expertise and substantial ongoing investment. Alternatively, MSPs can partner with AI-focused providers who offer domain-specific AI solutions, leveraging vertical specialisation in sectors such as healthcare, law, aerospace, or education to differentiate their services. A third, and often preferred option, is relying directly on hyperscalers, benefiting from the extensive AI R&D and platform maturity that the large cloud providers offer, although this carries the risk of vendor lock-in, potentially limiting flexibility and increasing dependency on one provider’s pricing and roadmap.

Industry data reinforces the critical role of AI in this evolving ecosystem. Gartner’s research highlights that the top hyperscalers posted a remarkable 27% year-over-year growth in Q2 2025, with Azure and GCP overtaking AWS primarily due to better support for generative AI workloads. This growth underlines how AI capabilities are now central to competitiveness within the cloud market. From the MSP perspective, a 2024 report found that 95% of MSPs are either already integrating or planning to incorporate AI services within the next year, driven by customer demand for predictive analytics, intelligent customer support, and enhanced security.

Furthermore, the ongoing development of platform-driven cloud operations, as demonstrated by firms like HCL Technologies, exemplifies how integrating AI tools and automation across observability, Site Reliability Engineering (SRE), DevSecOps, and financial operations (FinOps) can improve service delivery and operational efficiency for MSPs. These advancements highlight how AI is becoming embedded across management frameworks, elevating expectations for MSPs to offer more than traditional services.

Despite the ubiquity of AI and cloud platforms, the commoditisation of these core offerings means that MSPs must pivot towards adding discrete value. The opportunity for sustainable revenue lies in creating distinct, outcome-focused capabilities that sit atop the foundational AI engines, tailored to specific industries or customer needs rather than competing on generic services alone.

The marketplace also features a broad range of MSP business models adapting to these shifts. According to Gartner, these include hyperscale-only MSPs, hybrid hosters, cloud-native MSPs, cloud-focused MSPs, and traditional MSPs, each employing different strategies for integrating and managing cloud infrastructure. Many MSPs find themselves embedded within partner ecosystems dominated by hyperscalers; over half of surveyed technology service providers identify one of the big three cloud giants as their primary partner, a pattern driven by the hyperscalers’ expansive market reach and focus on AI technology.

The trajectory for MSPs is clear: the era of all-encompassing full-function software-as-a-service models is fading. Instead, the MSP of the future will master the art of integrating third-party AI and cloud services while delivering specialised, value-added solutions that meet particular vertical demands or use cases. Providers that embrace this shift can help clients realise meaningful outcomes from AI-powered platforms and maintain resilience amid intense competition and ongoing innovation in the hyperscaler space.

However, MSPs that fail to evolve risk erosion of revenue and business viability, as hyperscalers continue to aggressively expand their influence, often at the expense of smaller players. Success will depend on a balanced approach, combining integration expertise with targeted innovation, ensuring MSPs remain indispensable intermediaries in a rapidly shifting technological landscape.

📌 Reference Map:

• ^[1] (SmarterMSP) – Paragraphs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
• ^[2] (Gartner) – Paragraph 3
• ^[3] (OpsRamp) – Paragraph 4
• ^[4] (HCL Technologies) – Paragraph 5
• ^[6] (Gartner) – Paragraph 7
• ^[7] (TechTarget) – Paragraph 7

Source: Fuse Wire