{"answer":"Model access is only one part of useful adoption. Organisations also need suitable workflows, reliable review, clear responsibilities and a reason to change. Some delay may be avoidable; some reflects genuine costs and constraints.","body":"AI capability can advance faster than a business’s ability to put it to work. My view is that the gap involves more than access or technical skill. Leaders have to make sense of the change, decide what is worth pursuing and alter the routines through which work gets done.\n\nEarlier research helps explain why these are separate challenges. It provides a useful starting point, while leaving important questions about AI unresolved.\n\n## Spending money and changing work are different\n\nClark Gilbert’s 2005 study of eight newspaper organisations distinguished changes in resource commitments from changes in operating routines. A strong sense of threat could unlock investment while making established ways of working more rigid. Spending more did not necessarily mean adapting well. [Gilbert, 2005](https://doi.org/10.5465/AMJ.2005.18803920).\n\nImagine a firm buying AI licences, appointing a lead and funding pilots while leaving decision rights, incentives and everyday processes largely intact.\n\nFor example, producing a draft faster may achieve little if it then waits in the same approval queue. The model has improved one task; the surrounding process still determines how much of that improvement reaches the customer.\n\n## What leaders think the change means matters\n\nTripsas and Gavetti’s study of Polaroid offers a related lesson. The company invested in digital imaging and developed technical expertise, yet senior managers’ attachment to its established business model constrained how that expertise was commercialised. Recognising a technology and understanding what it might require of the business were different achievements. [Tripsas and Gavetti, 2000](https://www.jstor.org/stable/3094431).\n\nLater research by König and colleagues, across book retailing and telecommunications, separated expectations of gain or loss from perceived control. Seeing a possible benefit does not necessarily mean believing the firm can secure it; recognising a threat does not establish that leaders see a workable response. Their findings also made strategic relevance important to understanding resource commitment. [König, Graf-Vlachy and Schöberl, 2021](https://doi.org/10.1177/0149206320908630).\n\nFor AI, this suggests looking beyond whether managers are enthusiastic or worried. What do they think will change in their own business? Which decisions seem available to them? What would make an experiment worth the effort? Those questions connect awareness to action.\n\n## Direct AI evidence adds detail, rather than one universal answer\n\nThere is evidence of substantial gains in particular settings. Brynjolfsson, Li and Raymond’s 2025 study followed the staggered introduction of an AI assistant among 5,172 customer-support agents serving one company. They estimated a 15% average increase in issues resolved per hour, with larger benefits for less experienced and lower-skilled workers. The system offered suggestions within an existing workflow, and agents retained responsibility for the conversation. This was evidence about a specific deployment, rather than a return every business should expect from current models. [Brynjolfsson, Li and Raymond, 2025](https://doi.org/10.1093/qje/qjae044).\n\nThe OECD’s 2025 report, drawing on a 2024 survey of SMEs in seven countries, found reported benefits alongside limited preparation: a third or fewer of users had undertaken measures such as staff training or internal guidelines. Australia was outside the sample. Use and preparation can therefore differ, but the survey cannot establish which support would improve Australian outcomes. [OECD, 2025](https://doi.org/10.1787/2d08b99d-en).\n\n## Why the question still needs research\n\nThe historical studies concern particular industry transitions, largely in established organisations. They cannot tell us how common those mechanisms are among Australian SMEs, or how they operate when AI can assist both everyday tasks and the thinking involved in changing a business.\n\nA smaller firm may have fewer layers of approval but less time and specialist support for experimentation. Neither greater agility nor greater inertia should be assumed. Some uses may justify immediate action; others may remain unreliable, uneconomic or irrelevant.\n\nThis is why my research examines how Australian SME leaders interpret AI and how those interpretations relate to their accounts of decisions, resource commitments and changes in practice. Existing theory helps identify worthwhile questions. The explanation must remain open to what the cases reveal.\n\n## What follows in practice\n\nMy position is that useful adoption deserves practical support. That means helping firms connect a worthwhile use to the changes needed around it, with affected staff involved and someone clearly responsible for the result.\n\nEvaluation should include checking and rework, service quality, staff learning and the distribution of workload alongside time and cost savings. As tools change, firms need to reassess their uses and consider whether their information and workflows can move to another provider.\n\nThe objective is sustained capability: being able to do useful work reliably, learn from experience and change course when an approach fails. A licence, a pilot or an adoption statistic tells us only part of that story.","card_summary":"","change":{"confidence":"High on the access–implementation distinction; lower on which barrier dominates","new":"Model access is only one part of useful adoption. Organisations also need suitable workflows, reliable review, clear responsibilities and a reason to change. Some delay may be avoidable; some reflects genuine costs and constraints.","old":"Model access is only one part of useful adoption. Organisations also need suitable workflows, reliable review, clear responsibilities and a reason to change. Some delay may be avoidable; some reflects genuine costs and constraints.","practical_effect":"Readers can understand the mechanisms behind an adoption gap, the limits of transferring earlier findings to AI, and why practical support and further Australian SME research matter.","why":"Rewrote the argument around resource commitments, operating routines and managerial interpretations, supported by historical primary studies and direct AI evidence. Distinguishes illustrative applications from findings about Australian SMEs and includes no Paper 3 findings."},"confidence":"High on the access–implementation distinction; lower on which barrier dominates","evidence_through":"2026-10-19","previous_release":"2026-10-18-v2","published":"2026-10-19","release":"2026-10-19-v3","scope":"Australian SMEs; evidence from dated Australian and international studies","slug":"capability-and-adoption","source_records":[{"author":"Clark G. Gilbert","checked":"2026-10-19","id":"gilbert-inertia-2005","kind":"Qualitative study of eight newspaper organisations","limitations":"Historical US newspaper cases responding to digital media. Distinguishes resource and routine rigidity. Threat relaxed the former while amplifying the latter in this setting. The opportunity pathway rests on a structurally separated exception; no universal framing effect, current AI effect or Australian SME prevalence is established. Publication day not assigned.","published":"2005-10","title":"Unbundling the Structure of Inertia: Resource Versus Routine Rigidity","url":"https://doi.org/10.5465/AMJ.2005.18803920"},{"author":"Mary Tripsas and Giovanni Gavetti","checked":"2026-10-19","id":"tripsas-gavetti-imaging-2000","kind":"Historical qualitative single-firm case study","limitations":"Polaroid digital imaging case, Strategic Management Journal 21(10/11), 1147–1161. Connects managerial beliefs, technological capability and commercialisation. One large US company, not evidence of current AI mechanisms or Australian SME prevalence. Published issue October–November 2000; day not assigned.","published":"2000","title":"Capabilities, Cognition, and Inertia: Evidence from Digital Imaging","url":"https://www.jstor.org/stable/3094431"},{"author":"Andreas König, Lorenz Graf-Vlachy and Markus Schöberl","checked":"2026-10-19","id":"konig-appraisal-inertia-2021","kind":"Longitudinal qualitative replication and extension","limitations":"Ten book-retailing and telecommunications cases in European settings. Separates gain/loss framing, perceived control and strategic relevance. Does not establish a universal effect or its prevalence in Australian SMEs using AI. Journal issue March 2021; first online 26/04/2020.","published":"2021-03","title":"Opportunity/Threat Perception and Inertia in Response to Discontinuous Change: Replicating and Extending Gilbert (2005)","url":"https://doi.org/10.1177/0149206320908630"},{"author":"Erik Brynjolfsson, Danielle Li and Lindsey Raymond","checked":"2026-10-19","id":"brynjolfsson-ai-at-work-2025","kind":"Observational staggered-deployment workplace study","limitations":"5,172 customer-support agents serving one company; estimated 15% mean increase in issues resolved per hour, with heterogeneous effects. Purpose-built historical conversational assistance, not a current-model benchmark or a universal business return. Non-randomised rollout; no aggregate employment, wage or Australian SME effect established.","published":"2025-02-04","title":"Generative AI at Work","url":"https://doi.org/10.1093/qje/qjae044"},{"author":"OECD","checked":"2026-10-18","id":"oecd","kind":"Seven-country SME survey","limitations":"Survey of 5,232 SMEs in seven economies, excluding Australia. Reports adoption, tasks and preparation; it cannot estimate Australian prevalence or establish that a particular training intervention causes successful adoption. Chapter 4 reports limited preparation among users.","published":"2025-11-05","title":"Generative AI and the SME workforce: New survey evidence","url":"https://doi.org/10.1787/2d08b99d-en"}],"sources":["gilbert-inertia-2005","tripsas-gavetti-imaging-2000","konig-appraisal-inertia-2021","brynjolfsson-ai-at-work-2025","oecd"],"standing":"Established view, with open empirical questions","title":"Why can AI do so much while business change takes so long?","topic":"Business capability","version":"v3"}