Rishi Kapoor, WW Partner Sales Engineering Leader at Alteryx, has a front-row seat to the company’s work at the intersection of data, AI, and partner success. From lessons learned through their McLaren Racing partnership to AI-ready workflows and repeatable data kits, the discussion offered practical insights for MSPs and partners aiming to grow and differentiate themselves in a competitive market.
‘AI Ambition to Impact Report’ findings
Artificial intelligence investment is accelerating at pace. Yet for many organisations, progress is stalling at pilot stage. According to new research from Alteryx, fewer than one in four AI initiatives successfully transition into full production. Despite rising budgets and executive enthusiasm, trust gaps, inconsistent data quality and legacy architectures are preventing scalable impact.
The findings, based on a global survey of 1,400 business and IT leaders, reveal a growing disconnect between AI ambition and operational reality. While nearly half of respondents trust AI for repetitive or administrative tasks, confidence drops sharply when it comes to strategic decision-making, forecasting or planning. Only 28% trust AI to support core decisions, and just 27% trust it for forward-looking business insight. For MSPs navigating this environment, AI services must be grounded in governed data, repeatable workflows and measurable outcomes.
At the same time, AI platform consolidation is accelerating. According to the research, AI platforms are projected to grow from 33% of data stacks in 2024 to 51% within three years. Forty-eight percent of leaders plan to boost AI infrastructure spending, and 89% expect to maintain or increase budgets in 2026.
The research underscores a deeper structural issue. Organisations are layering generative AI tools directly onto raw, ungoverned datasets. The result is hallucinations, inconsistent outputs and eroding executive confidence. Nearly half of surveyed leaders (49%) identify high-quality, accessible and well-governed data as the primary factor for agentic AI success. Meanwhile, 28% plan to prioritise data governance improvements in the year ahead.
For MSPs, this shift toward line-of-business ownership creates both opportunity and risk. Services must extend beyond technical deployment into workflow governance, metric definition and explainability frameworks. AI cannot remain an IT experiment. It must be embedded into daily operational decision-making.
From infrastructure to intelligence
Kapoor explains that many MSPs are actively repositioning themselves beyond traditional infrastructure management toward higher-value AI and data services. The shift is not simply technical - it is also commercial. Recurring revenue increasingly depends on delivering intelligence, automation and business insight rather than uptime alone.
Kapoor notes that, “these MSPs are now moving away from infrastructure management to more AI and technology service management,” reflecting a wider reality that “Everything is now moving in either a hybrid technical landscape or in the cloud.” From a technical standpoint, he believes the Alteryx One platform is well aligned with this evolution, giving MSPs the flexibility to monetise and operationalise their own data assets for client benefit while expanding into more advanced AI-led service models. “Alteryx as a company and as a platform is very agnostic,” Kapoor notes, adding that, “Because our platform is so agnostic, I see MSPs bringing in their own private LLM models. We're working with partners to build AI data kits.”
That agnostic positioning is significant. In a market dominated by hyperscalers and specialised AI platforms, neutrality allows partners to integrate with Google, Snowflake, Databricks and other ecosystems without being locked into a single vendor stack. For MSPs building differentiated AI offerings, that flexibility allows them to package proprietary AI data kits, embed private models and lead with their own intellectual property. The opportunity is not simply to deploy AI, but to operationalise it safely and repeatedly.
The critical role of partner sales engineering
Delivering that level of AI and data maturity demands deep technical alignment from day one. For Kapoor, it begins with the partner sales engineering function, a role he sees as a genuine force multiplier within the partner ecosystem. Far from acting as a traditional product support layer, he challenges long-held assumptions about what the role entails: “Our role isn't just around product enablement, and I think that this has been an assumption and misconception for a long time. Individuals understand what a direct sales engineer does, but the partner SE role, in my opinion, is all about solution co-creation”.
In practice, that co-creation model operates across parallel technical and commercial tracks. He explains that partner sales engineers support partners through technical validation, product demonstrations and roadmap development. But the role extends beyond architecture design and proof of concept work. “Importantly, Alteryx partner sales engineers are focusing on solution selling and use case creation with the partners”. This aims to create a clear, structured way of working where teams co-develop deployment architectures, check that integrations work properly across complex cloud environments, and build roadmaps that connect analytics projects directly to business goals.
Ultimately, differentiation is not about tooling alone; it is about measurable impact. As Rishi noted, “differentiation comes from helping the clients around reducing manual processes, accelerating cloud modernisation projects.” Automation of repetitive workflows, faster cloud migration programmes and the safe operationalisation of AI models translate directly into cost efficiency and revenue optimisation. In a subscription driven market, long term value matters from the very beginning. Building that value in from the outset is what makes recurring revenue sustainable.
Lessons from the fastest environment in sport
One of Alteryx’s most visible partnerships is its role as an Official Partner of McLaren Racing, providing a clear lens into how analytics maturity translates into real competitive advantage. The collaboration demonstrates what it means to operationalise data in a performance driven environment where decisions must be made in real time. As Kapoor explains, “In high-pressure environments, like Formula 1 racing, analytics only delivers value when it's fast, trusted, and embedded directly into that decision-making process.”
Beyond speed, the partnership reinforces the need for flexible automation, strong governance and cultural alignment. Kapoor explains that, “Supporting that ecosystem through Alteryx reinforces the importance of flexible analytics and automation that can operate in real time,” and that “the partnership has highlighted that culture, trust and accountability and speed is ultimately what turns data into that competitive advantage.”
Through working alongside a high-performance team, internal standards are said to have been raised, lifting the bar in terms of best practices around speed, scale and decision making. Kapoor says that Alteryx actively applies and transfers best practices from one partnership to the next, ensuring lessons learned in one engagement are scaled and embedded across others. He also noted that they are “learning all the time in terms of how to work at speed, but do it carefully, keeping security, governance, policy, all of those factors in mind.”
For MSPs and enterprise organisations, the opportunity is not about the brand association with motorsport. It is about applying those principles directly to core business processes. Embedding analytics into forecasting, supply chain management and regulatory reporting ensures data is not just generated but acted upon. In a fast-moving environment, speed is what turns insight into real competitive advantage, and organisations need to constantly refine how they scale and operationalise those capabilities to stay ahead.
What this means for MSPs
Across AI ambition, partner enablement and performance driven partnerships, the same principles consistently apply. Technology alone does not create advantage. Execution, governance and alignment with business outcomes do. Moving from infrastructure management to AI and data services requires more than new tools. It requires building repeatable offerings, embedding strong data governance from the start and leaning into collaborative models like partner sales engineering that accelerate solution design and deployment. Platform neutrality and flexible architecture matter because they allow partners to integrate across ecosystems and bring their own intellectual property to the table.
The lessons from McLaren show that speed, trust and accountability are not just sporting principles, they are operational ones. Organisations that treat analytics as something embedded into daily workflows, not layered on top as a reporting exercise, are the ones that turn insight into measurable impact. As AI adoption continues to accelerate, MSPs that focus on practical use cases, scalable frameworks and clear business value will stand out. The opportunity is not simply to implement AI, but to operationalise it in a way that drives sustainable, recurring value for customers and long-term growth for partners.
In an exclusive interview with MSP Channel Insights, Rishi shared how Alteryx One and the Partner SE model are helping MSPs move beyond traditional infrastructure management toward higher-value data and AI services.
