December 02, 2025

Solving business challenges with AI: why advanced specialisation is just the beginning

Mark Pattie
Modern Work Practice Lead

In today’s rush to adopt AI, many organisations feel pressure to “get on the AI bandwagon”, whether that means rolling out a chatbot because everyone else has one, or using the latest large language model just because it’s trending. This technology-first mindset is backwards. At Data#3, we start with the problem, not the tool. Achieving Microsoft’s Copilot Advanced Specialisation proves our capability across Copilot, Copilot Studio and more, but the real value lies in how we apply these technologies to solve unique business challenges.

Case in point: AI automation without an agent

To see this in action, let’s look at a recent internal project here at Data#3. One of our teams was grappling with a time-consuming internal process that involved registering certain project details on a portal under specific conditions. The workflow required:

  1. Checking reports for new projects
  2. Hunting down the latest documents that describe the scope of those projects
  3. Reading through those documents to identify which solutions were deployed
  4. Submitting the eligible ones.

This was exactly the kind of repetitive, multi-step task that cried out for automation.

At first, one might think: “Let’s build a Copilot agent to handle this!” After all, an AI agent could, in theory, ask for a project name and then do all the behind-the-scenes work via conversation. However, our team paused and asked a fundamental question: “What’s the simplest way to solve this?” We broke the problem down into parts and assessed where technology could add the most value. The answer was not a single monolithic chatbot, but a set of targeted automations:

  • Intelligent workflows: We developed multiple Power Automate flows, each tackling a piece of the process. For example, one flow scans new project reports and logs key details (including where the documentation is stored) into a SharePoint list. Another flow kicks in that uses AI to search the library and fetch the latest version of the documented scope automatically. Yet another flow opens the document and analyses its content with AI, determining which workloads or technologies the document says were deployed, even if the wording is subtle. This is where AI is at its best, determining the intent of the document, not just looking for keywords.
  • AI in the loop where it counts: The AI components in this solution aren’t user-facing chatbots; they work behind the scenes to interpret unstructured data. This is akin to giving the workflow some human-like reading comprehension.
  • Automation of actions: Once the AI-powered logic determines a project is eligible, the workflow updates the SharePoint list with that decision and relevant details, even explaining why it believes it is eligible. At this point, the only remaining step is for a team member to quickly review the consolidated info in that list and submit the project registration.

The result was a dramatically reduced manual workload. What formerly took many hours of an employee’s time can now be done with a brief check of a SharePoint list, because the heavy lifting of data gathering and interpretation is handled by the automated flows and AI logic. Equally important, it improved accuracy by minimising human error in the search and reading process.

Interestingly, some might not even label this kind of system as an “AI agent,” since there’s no single chatbot interface or agent. And that’s exactly the point; we didn’t need to force an agent implementation where it wasn’t necessary. The collection of AI-enhanced workflows we built delivers the same outcome that a conversational agent might for this scenario. It’s fast, reliable and tailored to the team’s way of working. In the end we did what mattered, which was solve the business challenge.

From hype to impact: starting with the problem, not the tool

The above example highlights a broader lesson: AI initiatives succeed when they start with, and focus on, the business problem, not the technical solution. Activity alone doesn’t guarantee value. True success comes from pinpointing a business problem or opportunity first, then determining how, or if, AI can solve it.

At Data#3, this principle is core. We start by understanding the business challenge, be it speeding up a process, improving customer experience, reducing manual workload, or enabling better decisions. Only then do we choose the technology, or combination of technologies, that will best address that need. This human-centric, outcome-driven approach ensures AI isn’t adopted for novelty’s sake, but to achieve a meaningful result.

We’ve seen firsthand why this matters. Over the past few years, many early AI deployments have focused on individual productivity gains by helping employees draft documents faster, triage email, or summarise meetings. While valuable, they are relatively straightforward wins. Now, organisations are looking to tackle bigger, more complex problems that span teams or entire departments. This is where agentic AI comes in, AI solutions that can act more autonomously, integrate with business systems, and handle multi-step workflows. The marketing hype makes it sound easy to drop in an AI agent to automate these tasks, but the reality is more nuanced. Without careful design, an AI agent might give incorrect information or struggle with the company’s tone and policies. In some cases, the best solution might not even be a single chatbot or “Copilot” interface at all.

Data#3’s philosophy is to consider the full toolbox Microsoft provides to solve the problem in the simplest, most effective way. Sometimes that will involve a conversational AI agent or custom Copilot; other times a behind-the-scenes automation or predictive model might be more appropriate.

Crucially, we also make sure the surrounding pieces are in place like data readiness and security, user training, change management, and clear success metrics. This ensures the AI solution gets adopted and delivers Return on investment (ROI).

Microsoft’s Copilot Advanced Specialisation: a new benchmark in AI capability

In the Microsoft partner ecosystem, the Copilot Advanced Specialisation is a credential that signals a partner has proven capabilities with Microsoft’s AI platform. It demonstrates expertise not only with Microsoft 365 Copilot itself, but also with Copilot Chat, Copilot Studio (for building custom AI agents), and extensions that connect to business systems. In other words, a specialised partner has shown they can guide customers through secure Copilot adoption and training, build and orchestrate AI agents, and deliver tangible outcomes with AI. This achievement follows on from Data#3’s recognition as Microsoft Australia’s Partner of the Year, reinforcing our industry-leading expertise with Microsoft solutions.

The specialisation is a foundation of credibility and expertise that we bring to every customer engagement. The real value lies in how we apply these certified capabilities to solve the unique challenges our clients face.

Conclusion: converting AI hype into tangible outcomes

Success with AI isn’t measured by how many fancy tools you deploy. Data#3’s approach, honed through real-world projects, is all about delivering the right outcomes. We’re excited to continue this journey with our customers, turning the promise of AI into practical results, one business problem at a time.

For businesses embarking on their AI journey, the message is clear: start with your business challenges. Don’t worry if you’re not sure whether you need a chatbot, an app, or an automation to solve it. With a partner like Data#3, backed by Microsoft’s highest endorsements, you have a guide who can translate your objectives into an impactful solution leveraging the Microsoft ecosystem. We’ll help you avoid the pitfalls of chasing technology for its own sake and instead apply it with precision and purpose.

 

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