Dicker Data: Creating sustainable AI growth across the business

Q&A with Troy Stairmand, Partner Sales Specialist - Azure, Dicker Data

Q1 : What is the market getting right—and wrong—about scaling AI in Azure today?

What the market is getting right is the recognition that AI is no longer a point solution—it’s becoming a core part of how organisations operate. We’re seeing strong momentum around Azure as the platform of choice because it brings together data, AI, and governance into a single ecosystem. Businesses are starting to understand that AI can’t sit off to the side anymore—it needs to be embedded across workflows, applications, and decision-making.

Q2 : What foundations do organisations still need to get right before AI can scale effectively across the business?

There are three foundations we consistently see as make-or-break:

1. Data readiness and governance
   Most AI challenges still come back to data—quality, structure, ownership, and access. Without strong data foundations, AI outputs become unreliable regardless of how advanced the models are.
2. Scalable architecture and operational model
   AI needs an environment that supports repeatability—standardised pipelines, monitoring, deployment processes, and integration patterns. A lot of environments today were built for apps, not AI, which creates friction when moving to production.
3. Business alignment and ownership
   AI can no longer sit purely in IT. The organisations scaling successfully are those aligning AI to business outcomes—embedding it into operations with clear accountability across data, security, and business functions.

Q3 : How should partners be thinking about repeatable AI offers rather than bespoke one-off projects?

Bespoke projects don’t scale—for the partner or the customer. What’s working much better is building repeatable patterns:

• Defined use cases (e.g. knowledge search, customer service co-pilots, document processing)
• Standardised architectures (landing zones, data pipelines, model orchestration)
• Pre-packaged commercial models (POV → pilot → production)

The key is consistency. Scaling AI is about solving the same problem across multiple customers in a predictable, low-risk way—not starting from scratch every time. We’re also seeing the strongest partners anchor their offers around data + AI together rather than AI in isolation. If you don’t control or influence the data layer, it’s very hard to deliver long-term value.

Q4 : What does responsible AI look like in practical terms for organisations building in Azure?

Most successful deployments still include a human-in-the-loop model, particularly for customer-facing or high-risk processes. This is about reducing risk while maintaining speed. AI systems evolve quickly with new models, new prompts, new data. Governance needs to be continuous, embedded into the platform and processes, not just documented policies.

AI isn’t about slowing innovation—it’s about enabling organisations to scale AI safely and confidently.

Q5 : What advice would you give to partners trying to move from AI interest to AI revenue?

1. Start with real business problems, not AI technology
   Customers don’t want AI—they want better outcomes. The strongest opportunities are tied to clear use cases that improve efficiency, reduce cost, or unlock new capability.
2. Invest in capability, not just opportunity
   AI requires new skills across architecture, data, security, and operations. The partners building capability now will be best positioned as demand matures.
3. Think long-term—AI is a platform play, not a one-off deal
   The real value is in ongoing consumption, optimisation, and expansion—not just the initial project.

Q6 : How can Dicker Data help partners scale AI successfully?

From what we’re seeing in market, partners don’t struggle with AI opportunity—they struggle with execution, scale, and repeatability. That’s exactly where Dicker Data plays a role.

Dicker Data helps partners scale AI by simplifying how solutions are built, sold, and delivered. We provide technical expertise across Azure AI and Foundry, fund proof-of-concept programs to de-risk customer adoption, support go-to-market execution, and enable partners to extend capability through our partner-to-partner ecosystem. Combined with direct access to Microsoft resources, we help partners move from AI experimentation to scalable, repeatable revenue.