Cross-note synthesis

Uncovering barriers to practical collaboration

How the published AI CoLab notes, taken together, speak to the Uncovering barriers to practical collaboration evaluation theme. Public, general synthesis — safe to draw on without checking. It generalises the lessons; the sources below link back to the particular workshops (some members-gated).

25 insight notes so far. Not every workshop becomes one — recurring sessions and smaller gatherings usually don't. For some of the larger workshops a transcript is turned into an insight note so its lessons can be shared — with the room's consent, through time, and with people who weren't there.

The barriers that bind are upstream of the technology. The core every note shares is institutional: limits on which tools teams may use at all — platform mandates in some agencies (AI for Insight Notes), security frameworks and cost ceilings constraining choices in delivery work (AI for the Systems You Inherited). Practical AI for Policy People puts the same point to public servants directly — which tool is sanctioned for which sensitivity (protected versus public), guidance on the use of cabinet material, and access itself gated by whether an agency has signed onto the shared service — with the standing reminder that what is technically possible is not an endorsement to do it. What the model can do matters less than what the organisation permits, can afford, and has data ready for.

Each domain then adds a layer the others would miss. Unlocking career potential shows the human-process layer: language fluency, accent and writing style treated as proxies for capability, and uneven scrutiny of diverse applicants. Growing with AI adds the physical and economic layer software-centric thinking overlooks — connectivity, power, ruggedness, high and unpredictable cost, vendor lock-in, supplier failure — plus structural data friction: no benefit-sharing models, no common standards, on-farm systems that do not interoperate. AI for the Systems You Inherited adds the institutional-dynamics layer: sunk-cost pressure to keep failing projects alive, and the unsolved problem of governing how agents interact. Beyond the Principles adds the evidence-and-governance layer: groups would not recommend either a human or an AI process without comparative studies that do not yet exist, smaller agencies questioned whether an independent review function is even affordable, procured black-box tools resist the explainability good decisions need, and ungoverned "shadow" AI use is already outrunning the rules meant to contain it. When the Algorithm Goes Rogue adds the political-economy layer that precedes any build: a business case built on point-estimate savings and pre-announced staff cuts, limited-tender procurement that rewards over-promising, single approved-cloud bottlenecks, in-house capability hollowed out to contractors, and budget pressure that quietly descopes the guardrails — so in high-stakes public deployments the failure is, in effect, procured before it is coded. Tourism data adds the data-estate layer: fragmented, siloed and sometimes paywalled sources with inconsistent geographies and methods, complex data-sharing frameworks where every custodian has reasons for caution, and the legal-ethical exposure of mobile-movement data — all before a single useful tool can be built. Beyond the Black Box adds the comprehension-and-cost layer: fear narratives and uneven AI literacy as the main brake on genuine adoption ahead of any technical limit, explainability methods that are mathematically sound yet fail with real decision-makers, and below-cost hyperscaler pricing that distorts model choice toward oversized, costlier tools. People and Culture Leadership adds the workforce layer: project-by-project rollouts with no overarching people strategy, training pitched at tools rather than durable behaviours, shifting usage-based pricing and vendor-dependency risk, and the hollowing-out of entry-level roles that would otherwise supply the experienced judgement needed to supervise AI-augmented work. Authoritative by Source adds the public-sector adoption layer: government uptake is slowed less by the technology than by procurement, accreditation, financial delegations and the cost of pilots, while the opacity of vendored AI — third-party components nested inside one another, so data flows to unexpected places — makes the full software bill of materials hard to see; and much public data, though public in principle, stays practically inaccessible until someone does the unglamorous work of structuring it.

A cluster of late-2025 sessions extends the same map outward and upward. At national scale the constraint becomes physical and geopolitical — energy and grid capacity, supply-chain concentration in a few firms, and sovereign exposure to foreign suppliers (AI and the geopolitics of compute). In the economics session the binding gap was evidentiary: a shortage of Australian-specific data and a mismatch between headline forecasts and operational reality that makes confident planning hard (Exploring the economics of transformative AI). Local-innovation practice named the funding-and-culture layer directly — pilots that stall because short-term project money never covers operating costs, per-seat licence costs set against unclear benefit ownership, and an equity gap between confident early adopters and colleagues unsure how to begin (AI in practice). And the values work adds a subtler operational barrier: when a model's stance drifts with language and version, teams cannot reason about its outputs consistently without shared tools to track it (How AI Speaks Our Values). The pattern holds across all of them — the model is rarely the thing in the way.

A missing evidence base is itself a collaboration barrier. Where there are no studies comparing the AI option with the existing process, groups defaulted to wanting to run the two in parallel as a deliberate pilot rather than choose blind (Beyond the Principles) — which only sharpens Growing with AI's point that, without benefit-sharing and common standards, the data such pilots would generate may never be pooled. The blocker is not that the answer is hard to compute; it is that no one has gathered the evidence, and the incentives to share it are weak.

The notes disagree instructively about data risk. Growing with AI argues data residency is largely a government preoccupation — what producers care about is who uses their data and who benefits. AI for the Systems You Inherited shows the government side of the same coin: security frameworks dictating which models may touch which data at all. These are not contradictory so much as two risk postures any cross-sector collaboration has to satisfy simultaneously — one side asking "who profits from my data?", the other "where is my data allowed to go?".

Capability gaps are a collaboration barrier in their own right. Practitioners who do not know what they do not know have no obvious place to turn (Growing with AI), and AI is an asset for people who can validate its output but a liability for those who cannot (AI for the Systems You Inherited). Beyond the Black Box names the affective side of the same gap: fear crowds out the bandwidth to learn what is publicly knowable, so the realistic bar is a "plumbing-level" literacy — enough to know the failure modes and who to call, not mastery. Across every note, AI is framed as a mitigating aid for these barriers — never the fix (Unlocking career potential).

Sources

Last compiled 2026-06-23 from 25 published note(s).

Notes contributing to this theme: