The central implication for leaders is blunt: stop relying on star performers and build a performance system that makes their behaviours teachable, repeatable, and compounding. Two top agents told Real Estate Business that joining a network accelerated results by coupling individual ambition with team scaffolding. The lesson travels well beyond property—across services, sales-led industries, and product organisations. The strategic edge comes from translating personal drive into a shared operating model, underpinned by data, AI-enabled knowledge flows, and incentives that reward collective outcomes.

Business impact: the operating model beats the hero model

In hero-led organisations, revenue scales linearly with the capacity of a few rainmakers. In networked models, revenue scales with playbooks, enablement assets, and cross-team learning. That shift improves three levers of the P&L:

- Efficiency: onboarding ramps faster when playbooks are codified. McKinsey’s 2025 report on AI in the workplace argues the core challenge is “not a technology challenge… [but] a business challenge that calls upon leaders to align teams” around new ways of working. Alignment cuts duplication and reduces cycle time across deal, delivery, and service workflows.

- Resilience: systematised knowledge reduces key-person risk and variance in performance; quality improves as practices are versioned and certified.

- Growth: networks create internal distribution for best practice. Consider Google’s 94% share of Australian search (ACCC, 2024): dominance hinges on distribution and defaults. Inside companies, the internal “default” becomes the shared playbook and platform. Make the winning behaviour the easiest behaviour.

Competitive advantage: codify, instrument, and reward the right things

Early adopters move beyond training decks to a “performance OS” that is: (1) codified (task-level playbooks with context), (2) instrumented (tracked with leading indicators), and (3) rewarded (incentives that recognise team contribution). The prize is learning velocity. A case from global gen AI practice shows what this looks like at meaningful scale: a platform enabling 30+ business domains to create and share machine learning models, accelerating data processing times and propagating improvements across functions (Real-world gen AI use cases, 2025). The competitive moat is not the model; it’s the organisation’s ability to distribute know-how and standardise excellence.

Market trends: Australia’s adoption-to-innovation gap and the team mandate

Australia’s AI ecosystem has grown but still shows a significant gap in commercialisation (June 2025 analysis). Translation: we experiment, yet struggle to convert prototypes into enterprise-grade capability. Bridging that gap is a team sport. Public sector agencies are already formalising guardrails—see the ATO’s work on governance of general-purpose AI and the Federal Government’s 2024 interim response on AI—while Australia’s AI Ethics Principles set expectations for safety, reliability, and accountability. Private sector leaders should mirror this clarity: policy first, then platform, then practices.

Team topology is shifting too. Marty Cagan (SVPG, 2025) highlights how generative AI will reconfigure roles and product team structures. Cross-functional pods will need embedded data/AI fluency to turn tacit, individual knowledge into shared, continuously improving systems.

Implementation reality: five levers to turn ambition into system performance

- Objectives and incentives: Tie a portion of variable compensation to team-level leading indicators—win-rate uplift, time-to-first-value for new hires, cross-sell success—alongside individual targets. Recognise the “assist”, not only the “goal”.

- Psychological safety as a productivity tool: Performance networks depend on candid retros and error reporting. The Clean Energy Council’s 2025 recognition of leaders elevating mental health underscores the business case: psychologically safe teams learn faster and retain talent longer. Treat this as risk management, not perks.

- Legal guardrails: Collaboration must avoid competition law pitfalls. The ACCC’s cartel provisions prohibit collusion between competitors. Within a corporate group or franchise, document boundaries: what can be shared (process, enablement) vs. what must not (pricing agreements with competitors).

- Knowledge architecture: Move from tribal tips to a governed knowledge base: versioned playbooks, annotated examples, and “pattern libraries” of successful deals. Tag by customer segment, product, and context to enable retrieval and A/B testing of methods.

- Measurement cadence: Instrument leading indicators that predict revenue—proposal cycle time, first-meeting-to-proposal conversion, content reuse rate, and adherence to playbooks—rather than lagging revenue alone.

Technical deep dive: build a performance OS with AI at the core

Leaders don’t need frontier models to win; they need an architecture that makes individual excellence scalable:

- Data pipeline: Centralise interaction data (emails, calls, proposals), outcomes (wins/losses), and context (industry, persona). Establish data quality SLAs and lineage.

- Playbook engine: Convert top-performer behaviours into modular steps with embedded artefacts (templates, talk tracks, calculators). Treat playbooks as products—versioned, owned, and sunset when obsolete.

- Gen AI retrieval and guidance: Use retrieval-augmented generation to serve context-specific prompts and content from your knowledge base at the moment of work. The model surfaces relevant case studies and next-best actions, but your corpus—curated examples and outcomes—does the heavy lifting.

- Governance and ethics: Apply Australia’s AI Ethics Principles across fairness, privacy, transparency, and accountability. Borrow from the ATO’s governance posture: classify AI use cases, assign risk owners, and require human-in-the-loop for material decisions.

- Security and IP: IP Australia’s case studies (e.g., Dermcare-Vet’s protection strategy) are a reminder: codified know-how is an asset—treat it like one. Rights, access tiers, and audit logs are essential.

Case-in-point playbooks: lessons from adjacent domains

- Search dominance as a platform lesson: The ACCC’s finding on Google’s 94% share reflects the power of defaults. Internally, standardise on one enablement platform; fragmentation kills learning velocity.

- Public-sector roadmaps: Transport for NSW’s technology roadmap approach (2021–2024) shows how to stage capability, governance, and stakeholder alignment. Private enterprises should run a similar roadmap for performance systems—prioritised use cases, guardrails, and progressive rollout.

- Cross-functional AI enablement: The global example of a shared ML platform serving 30+ domains illustrates the multiplicative effect when teams can publish, discover, and reuse models—precisely the dynamic agency networks seek with sales methods and listing playbooks.

Future outlook: a 12–24 month roadmap leaders can execute

Month 0–3: Define governance (ethics, privacy, ACCC boundaries), select an enablement platform, and identify three high-value workflows to codify. Establish metrics and incentives that include team-level outcomes.

Month 4–9: Build the knowledge base and RAG layer; pilot with two cross-functional pods. Run weekly retros; deprecate low-performing playbook steps; publish updates like product releases.

Month 10–18: Scale to adjacent teams; integrate telemetry into CRM/ATS; launch an “assist leaderboard” recognising knowledge contributors; tighten quality gates for content and data.

Month 19–24: Industrialise—automated evaluations, red-teaming for AI outputs, and continuous certification for playbooks. Begin externalising select, non-sensitive playbooks to partners to extend the network effect.

The contrarian view is worth stating: networks do not magically fix poor strategy or weak propositions. But when leaders treat individual brilliance as the R&D function for the organisation—and build the operating system to commercialise it—team wins stop being episodic. They become the default.