How self-organising, self-reliant teams are turning AI into a compounding advantage.

Microsoft as Customer Zero: prove it, then scale it

Nicole McKinley’s (Microsoft) “Customer Zero” session landed because it was specific. Rather than talking about AI as an abstract capability, she walked through repeatable patterns: pick a role or workflow, remove the obvious friction, measure the lift, then expand with sponsorship and change management.

Several examples stood out:

• Sales
  Rolling out a Sales Agent, supported by strong executive sponsorship and a network of internal champions, was linked to a 9.4% increase in revenue per seller and a 20% uplift in deal closure rates.
• Finance
  Applying agent‑assisted workflows across collections unlocked 300,000+ hours, reduced call‑preparation time by 40%, and delivered 2.5× faster enquiry resolution.
• Software engineering
  Using Copilot, researcher agents, and GitHub/Azure DevOps tooling across the lifecycle reduced effort across the board:
  – ~70% less time spent on requirements alignment and review
  – ~65% reduction in prototyping effort
  – QA cycles shrinking from 80 hours to 26 in one cited example
• Support
  At massive scale (around 145 million annual interactions), early results included 12–16% lower average handle time and 9–12% more cases handled per engineer.

What tied these together was the operating model. AI was framed as an amplifier of judgement, rather than a replacement for it. Adoption was treated as a product discipline: feedback loops, iteration, and deliberate normalisation of AI in day‑to‑day work.

Even if these reported results ran at single rather than double digits, the net impact on an organisation the size of Microsoft is formidable.

On the floor | Pavel Neuzil

“The practical demos were the standout for me. PowerApps ‘spec‑to‑solution’ experiences can compress app delivery from months to days by proposing architecture and building on Dataverse. In Dynamics, prebuilt agents plus voice + CRM could remove a huge amount of manual meeting prep and pipeline admin. And for developers, GitHub Copilot can analyse legacy apps, remove dead code, suggest improvements, even act as a ‘rubber duck’ to surface ambiguity before you build.”

Pavel Neuzil, Head of Internal Applications, Brennan

The Frontier Firm lens: moving past chat, without losing control

With a recent Deloitte survey showing 69% of Australian organisations are already using agentic AI, adoption is no longer the question. Control is. One challenge is moving from fragmented experimentation to managed capability without slowing down team momentum.

What’s emerging is a split between two pronounced patterns:

• Early movers: those who’ve created plenty of pilots, tools, and autonomy, but often at the cost of platform sprawl and inconsistent governance.
• Late movers: more structured foundations and clearer controls but now facing pressure to move faster.

The organisations getting this right — what Microsoft refers to as Frontier Firms — are finding a path between the two: enabling usage early, then layering in governance in a way that doesn’t disrupt momentum.

In our work with Australian enterprises, we’re seeing this balance become a clear differentiator between organisations experimenting with AI and those translating it into repeatable, enterprise value.

Across the sessions, a few consistent patterns stood out:

• Start with enablement, not innovation
  Focus on training and real use cases first, with clear expectations around responsible use, not just policy documents that sit on a shelf.
• Build a capability ladder
  Access to more advanced tools and agent capabilities is earned through demonstrated, responsible use, not solely training completion.
• Treat agents like employees
  Agents operate with identity, logging and auditability. They’re not anonymous tools. They’re digital workers acting on behalf of users.
• Bake in guardrails
  Data loss prevention and endpoint controls need to apply consistently. Individual productivity tools can introduce just as much risk as centralised platforms.
• Track the economics
  Instrument agent usage with telemetry, including cost per execution. At scale, even low-cost workflows can quickly add up.
• Be model pragmatic
  Different tasks require different models to balance quality, speed and cost. We’re also seeing the rise of “second opinion” agents to review and validate outputs.

The realism check: agentic AI raises the security bar

Scott Woodgate’s (Microsoft) security session was a useful counterbalance. As models improve, attackers and defenders both gain leverage. AI compresses the time between vulnerability discovery and exploitation, which means baseline security hygiene matters more, not less.

Two ideas stood out:

1. Controls must apply to agents as well as people
In one demo, data labels and DLP policies prevented an agent from accessing “secret” PO details and blocked it from emailing sensitive content externally. The principle was clear: if a human isn’t allowed to do it, neither is an agent.

2. Use agents to fight the volume problem in security
Security Copilot agents were positioned to reduce toil and surface what actually matters. Cited results from controlled studies included:

• 6.5× more malicious emails identified per analyst minute
• 77% more accurate verdicts
• Up to 78% faster alert triage

The broader message: AI adoption and security maturity have to move in lockstep.

Closing thought

This is a remarkable era. The organisations winning are combining experimentation with discipline: clear outcomes, strong guardrails, and learning loops that compound over time. That’s the frontier‑firm mindset. And the name is fitting.

Thriving in frontier environments is about self‑organisation, self‑reliance, and the courage to act without guaranteed outcomes. Agentic AI is bringing a new frontier closer, raising the premium on judgement, responsibility, and leadership across organisations.