The team was not the problem. The systems around them were.

A university brought us in when its Integration, Data & Analytics function was under pressure. Work requests were coming in from multiple directions, making them difficult to track and prioritise. Priorities were constantly shifting and the backlog had grown to around 450 active items, all while demand continued to increase.

It would have been easy to assume the answer was to add more resources. But before recommending headcount, we wanted to understand whether the existing team, processes and tools were set up to manage that demand effectively.

Stepping into the Delivery Manager role, our consultant led a 14-person multidisciplinary team of Business Analysts, Developers and Data and Analytics Specialists, managing day-to-day delivery while taking the time to understand how the function actually worked.

That meant looking at where work entered, how priorities were set, how capacity was managed, where work stalled, and which manual processes were slowing the team down and making delivery harder than it needed to be.

What she found became the basis for a future-state operating model focused on clearer intake pathways, better capacity planning, defined roles and decision ownership, more consistent Agile practices and greater visibility across delivery.

Importantly, the solution was not centred on buying another platform. The university already had much of the technology it needed through its existing Microsoft 365 investment.

The proposed future state used Microsoft Forms, SharePoint Lists, Power Automate and Power BI to improve intake, automate workflows and provide better visibility and reporting.

The team did not have to wait for the future state to start seeing improvements. Through backlog grooming, approximately 450 active items were reduced to 150 by removing duplicated, completed and obsolete work. That gave the team a much clearer picture of its actual workload, priorities and capacity before any decisions about additional resourcing.

Growing demand does not automatically mean you need more people, just as transformation does not automatically mean you need new technology.

Understand the demand, understand your capacity and find the friction first. Then decide what you actually need.