Mia Horrigan Co-Founder, Zen Ex Machina & Agile IQ

About Mia Horrigan

Mia Horrigan is Co-Founder Zen Ex Machina & Agile IQ, Chief of Product, Business Transformation and Program Management Services. She has over 20 years experience as a Product manager launching some of the biggest products in Pharmaceutical domain for Roche, Pharmacia/Pfizer and CSL as well as senior executive experience leading and implementing software solutions. Mia has been successful in delivering business outcomes through implementation of Agile/Scrum at team and enterprise level and led transformations at some of the biggest agencies in Australia such as ATO, Defence, Dept of Employment, Finance and Dept of Industry.

The push to deliver more features to users often means we take short cuts, take on tech debt and reduce time spent maintaining the system. Some organisations just create a BAU team and toss the work over the fence once development ends, others allocate a % of time , whilst others continually deprioritise the work for a short term gain or deliverable. Some teams use their BAU workload as a reason that they can’t go Agile? In a product based model where “you built it, you fix it” is the mantra, what do we do with BAU (business as usual) activities and can AI help?

BAU activities mean many things to many people, KTLO, Maintenance, Enablers, sustainment and DevOps. Recently we have seen a perception that BAU is less important. Some teams balance this by capturing BAU work as Features and Stories in their backlog, however, new build work is seen as more interesting dev work and more likely to be prioritised higher by business as they show direct value to users. Whilst Product owners may recognise that BAU activity is important, they may not necessarily understand the consequences of prioritising new capability over BAU and shouldn’t be placed in a position to choose.this is where we have looked to AI to help improve our speed of builds, data ingestion and taming the proliferation of tools and tech.

Using metrics such as Innovation index are a good way to see the balance in your organsiation as these show that if BAU is neglected , over time, the cost to maintain and support the systems greatly increases and ability to deliver new features slows. So BAU is critical to keep our systems working. This presentation will explore how to handling BAU work within an AI Product Management approach and will provide practical real world examples of how to manage a Product backlog with BAU, enablers and product features. We will explore how to capacity plan, what governance to apply and the principles of good backlog management.