Most Australian small and medium enterprises (SMEs) are already using generative AI tools — but more often than not they’re doing so without a coherent plan, leaving businesses exposed to security, cost, and strategic risks while missing opportunities to drive growth and competitive advantage. According to Decidr’s inaugural National AI Readiness Index Report 2025, an urgent adoption impulse sits beside a striking absence of formal roadmaps: 83% of SMEs expect AI to have a major impact within the next year, yet 76% report they have no formal AI strategy. This gap is being filled by point-tool usage — ChatGPT, Microsoft Copilot and similar assistants — rather than integrated systems designed to transform operations, create new revenue streams, or protect critical data.
Strengths of the situation:
For IT leaders and channel partners building the next wave of SME AI services, the immediate priorities are clear: secure the data, measure the impact, and design governance into every pilot. The window to transform from cost-saver to competitive advantage is open — but only for those who plan, protect and scale deliberately. (mediaweek.com.au, industry.gov.au, bizcover.com.au, precisely.com, decidr.ai)
Source: Mediaweek Most Australian SMEs are adopting AI with no strategy - Mediaweek
Background
What Decidr says and who they are
Decidr, an Australian AI company that positions itself as a provider of agentic AI solutions and business operating systems, published (via press and trade coverage) the National AI Readiness Index Report 2025 — a survey of 1,042 decision-makers at Australian businesses with 20–500 employees conducted from 19 May to 4 June 2025. The results, as reported in media coverage, state that:- 83% of SMEs believe AI will significantly affect their business within the next 12 months.
- 76% have no formal AI strategy or roadmap.
- 92% are using consumer or productivity AI platforms such as ChatGPT and Microsoft Copilot, while only 19% have adopted advanced AI systems capable of business transformation or revenue growth.
- The survey segments SMEs into four readiness groups: Trailblazers (17%), White knucklers (24%), Tinkerers (36%), and Sleepwalkers (23%).
How this article verified the claims
The Decidr findings are reported in trade press; independent, government and industry surveys show a mixed but consistent theme: rising AI use among Australian SMEs, but with important caveats about readiness, governance, and capability. The Australian Government’s quarterly AI adoption update and other independent surveys show lower raw adoption percentages depending on definitions, reinforcing that differences in survey method, sample frame, and wording drive large swings in headline numbers. Where Decidr’s report gives one slice of the story (20–500 employee businesses), other surveys with broader business-size mixes show differing adoption levels — a critical context for interpreting claims about “92% using ChatGPT/Copilot.” (industry.gov.au, bizcover.com.au)The findings in plain terms
Headlines that matter to IT decision-makers
- High urgency, low planning: An overwhelming majority of surveyed SME leaders expect AI to matter soon, but three quarters have no formal strategic plan. That combination creates a high-risk, high-mess environment for IT teams: many tools are being used informally without proper security, logging, governance, or integration plans.
- Tool-first adoption vs platform strategy: While nearly all respondents report using lightweight generative assistants or built-in productivity copilots, a small minority (19%) have deployed advanced AI systems — the kind that are integrated with business data, wrapped in governance, and capable of delivering measurable revenue or process transformation. That distinction is the difference between convenience and competitive differentiation.
- Efficiency over growth: Most SMEs are prioritising automation and cost savings (57%) over growth and new revenue models (25%). This short-term lens means AI is more often treated as an expense-control tool than as an engine for strategic advantage.
- Barriers are pragmatic: Budget constraints and security concerns were the most-cited impediments to broader AI deployment — not philosophical objections or lack of interest. This aligns with other Australian surveys that list skills, funding and governance as leading obstacles. (mediaweek.com.au, industry.gov.au)
The four SME readiness segments — what they reveal
Decidr’s segmentation paints four pragmatic archetypes that are useful for channel partners and IT providers:- Trailblazers (17%): These organisations have a growth focus and clearer leadership on AI; they’re the ones likely to pilot integrated platforms that tie AI to CRM, inventory and sales systems.
- White knucklers (24%): Urgent to act, but struggling with integration complexity and internal capability gaps. They are prime customers for managed pilots and outsourced integration.
- Tinkerers (36%): Heavy experimentation with tools but lacking governance or leadership alignment — a recipe for inconsistent outcomes and shadow-IT risk.
- Sleepwalkers (23%): Little AI exposure, concentrated in certain sectors such as natural resources and public services — these will be the last to shift without targeted education and incentives.
Cross-checking the landscape: independent context
No single survey tells the whole story. Independent and government sources show consistent patterns — rising interest, uneven readiness, and sizable capability gaps.- Government quarterly data: The Australian Department of Industry’s AI adoption summary for late 2024–early 2025 shows steady increases in SME AI adoption but at a lower headline level than Decidr’s tool-usage number; adoption varies markedly by industry and business size. That report highlights skills gaps, funding constraints and governance readiness as persistent barriers. This corroborates Decidr’s theme of urgency but limited preparedness, while suggesting raw adoption numbers depend heavily on definitions (what counts as “using AI”).
- Independent industry surveys: Specialist surveys aimed at small businesses (for example, BizCover’s Australian Small Business AI Report 2025) show high intent and growing use — typically lower than Decidr’s 92% tool-usage figure but aligned on the central point: many SMEs are experimenting with AI for marketing, content and admin tasks without enterprise-grade governance. These independent studies reinforce that the quality of adoption (pilot vs. production, point tool vs integrated system) matters.
- Analysts and industry commentary: Broader AI readiness indices (from vendors and consultancies) repeatedly find a readiness gap: organisations want AI but lack data readiness, skills and governance to capitalise on it. This aligns with findings that many businesses treat AI as a productivity add-on rather than a strategic investment requiring cross-functional planning.
Critical analysis: why the gap exists and what it costs
Where SMEs are getting it wrong
- Confusing access with value. Access to ChatGPT or Copilot does not equal business transformation. A conversational assistant helps write emails; it doesn’t automatically increase customer lifetime value or replace a well-integrated recommendation engine. The Decidr data shows a majority of organisations are in the “shallow end” — tool usage without architectural change.
- Tactical pilots with no governance. Many early pilots fail to set success metrics, define acceptable error rates, or design human-in-the-loop checks. That leads to unreliable outputs, hidden liabilities, and user distrust. Independent industry research shows data quality and governance as the top blockers for scaling AI, underscoring how governance weakens pilots’ value. (precisely.com, industry.gov.au)
- Cost-first decision-making. With 57% of SMEs prioritising efficiency over growth, AI purchase decisions skew toward immediate cost reduction rather than strategic differentiation. When procurement is constrained by short-term budgets, businesses underinvest in integration, security and skills — all the things required to scale AI use from a local productivity boost to enterprise advantage.
- Misreading the competitive landscape. Only 25% of respondents cited competitive pressure as a driver for AI adoption. That suggests many leaders underestimate competitor moves: firms that invest strategically in integrated AI systems will widen capability gaps while others tinker. Decidr’s Executive Director warns that treating AI as an expense rather than an engine of growth risks being overtaken. Given Decidr’s commercial interests, this warning is reasonable — but it’s also a genuine strategic risk for SMEs. (mediaweek.com.au, decidr.ai)
Methodological caveats and potential bias
- Sample frame matters. Decidr’s survey targets 20–500 employee firms. Broader surveys including micro-businesses (1–10 employees) or enterprise firms will show different averages. When comparing reports, always check the definition of “SME” and the sampling frame. (mediaweek.com.au, bizcover.com.au)
- Commercial incentives. Decidr is an AI vendor active in the SME market, so their public-facing research will naturally align with their business narrative: more strategic AI adoption equals more addressable market. That doesn’t invalidate the findings, but it requires reading the results alongside independent government and industry data. (decidr.ai, mumbrella.com.au)
- Public availability of the full report. At the time of research the Mediaweek article was the primary public summary of the Index; a full, downloadable Decidr report was not discoverable in public repositories. Where a full methodology appendix or raw data is unavailable, treat precise percentages as directional but subject to revision pending access to the primary dataset. (Readers should always seek the original report for methodological detail.) (mediaweek.com.au, decidr.ai)
What this means for IT teams, channel partners and MSPs
The SME market is at a pivot: easy-to-access AI tools will continue proliferating, and the first-mover advantage will shift from those who trial tools to those who build governance, integration, and measurability into deployments. The following implications are immediate:- Security becomes the gating factor. Informal use of cloud copilots and public chatbots raises substantial data-leak risk. SMBs must define what data is permitted in external prompts and apply prompt-logging and DLP (data loss prevention) controls. This is consistently the top adoption blocker cited across surveys. (industry.gov.au, elevenm.com.au)
- Managed pilots will be the growth channel for MSPs. White knucklers need packaged pilot solutions: measurable 60–90 day projects with prebuilt connectors to Microsoft 365, CRM, or industry tooling, plus runbooks for governance and user training. Decidr’s segmentation highlights a sizeable market for implementation partners.
- Strategic differentiation lies in data integration. Businesses that connect AI to CRM, inventory, and customer analytics — and treat models as production components — will capture revenue upside that simple productivity assistants can’t deliver. Independent data-readiness research shows poor data quality is the primary limiter to scaling AI; solving that advantage is sustainable.
- Endpoint and PC strategy matters. As AI features migrate into devices (Copilot-enabled PCs, on-device NPU acceleration), SMEs will need to weigh device refreshes, compatibility, and on-device inference tradeoffs. The Windows/AI PC debate shows vendors are pushing AI-capable hardware, but buyers must match hardware choices to real workloads and ROI. File-level guidance and standards are still emerging in this area.
Practical SME AI roadmap — a short, actionable playbook
- Inventory and classify data assets
- List data sources, classify sensitivity (PII, IP), and identify retention/residency requirements.
- Establish what is explicitly prohibited from external prompts or third-party models.
- Set measurable objectives
- Define 2–3 KPIs for the first pilot (time saved, customer response SLA improvement, lead conversion uplift).
- Timebox pilots to 60–90 days and require pre/post measurement.
- Choose the right entry point
- Start with high-frequency, low-risk processes (document summarisation, email draft automation, internal knowledge search).
- Reserve revenue-critical or regulated processes for later, once governance is proven.
- Apply risk controls from day one
- Implement DLP, prompt logging, role-based access, and content filters.
- Use model provider contractual guarantees (data use, retention, deletion) where possible.
- Integrate with existing systems
- Prioritise connectors to CRM, ERP, helpdesk and document management systems.
- Avoid siloed point tools that create shadow workflows.
- Build human-in-the-loop workflows
- Require human approval for any customer-facing or compliance-sensitive outputs.
- Log decisions and collect feedback to improve model prompts and templates.
- Train staff and measure adoption
- Run role-specific training sessions and maintain a living playbook of prompts.
- Reward teams for measurable time- or revenue-based improvements.
- Iterate, scale, and standardise
- Move from pilot to production only with clear SLAs, monitoring, and support.
- Centralise procurement and vendor management to avoid tool sprawl.
Technical considerations for IT leaders
- Model selection and hosting: Decide between hosted LLMs (faster to deploy, easier to manage) and private/on‑premise or VPC-hosted models (greater control, compliance). For regulated data, private deployments or enterprise-hosted models are often necessary.
- Prompt engineering and templates: Build reusable, documented prompt templates to standardise outputs and reduce hallucinations. Version-control prompts and log usage for audits.
- Observability and model ops: Treat models like services. Implement logging, performance metrics, and drift detection. Track model cost per inference and adjust usage patterns to control cloud bill shock.
- Data pipelines and labeling: Invest in data hygiene, consistent schemas, and human-reviewed labels where supervised fine-tuning or retrieval-augmented generation (RAG) is used. Poor data equals poor outputs.
- Identity and access: Use single sign-on, least-privilege roles for model access, and enterprise DLP that can inspect prompts and model outputs where allowed.
- Contracts and vendor lock-in: Negotiate clauses on data ownership, portability, and right-to-audit. Avoid architectures that hard-tie business logic to proprietary stacks without escape paths.
Risks and how to mitigate them
- Data leakage and privacy: Mitigate with DLP, redaction, private endpoints, and contractual protections against model providers using submitted data for training.
- Regulatory exposure: Monitor sector-specific guidance (financial services, health, education) and ensure human oversight for decisions that materially affect customers.
- Operational cost overruns: Track inference costs, token usage, and API calls. Use caching, batching, or on-device inference to reduce cloud bills.
- Model hallucination and liability: Implement verification layers and human approvals for any critical output.
- Staff displacement and morale: Combine role redesign with upskilling budgets and clear communication — frame AI as augmentation, not replacement.
- Vendor concentration: Diversify providers where practical and standardise around interoperable data formats and API abstractions.
The channel opportunity and public policy implications
Channel partners, resellers and managed service providers are uniquely positioned to bridge the readiness gap. Decidr’s segmentation indicates a large market for packaged pilots, governance toolkits and managed integrations that reduce complexity for White knucklers and Tinkerers. Governments and industry groups can accelerate safe adoption through subsidies, education programs and standardised procurement templates that lower risk for small buyers. Independent reports and government data repeatedly call for targeted training, improved access to secure cloud environments, and clearer governance frameworks. Combining commercial channel programs with public support would materially raise the quality of SME AI adoption across the country. (industry.gov.au, elevenm.com.au)Where the data is unclear — flagged issues
- The headline “92% using ChatGPT/Copilot” demands careful interpretation. Different reports use divergent definitions of “use” (heavy daily use vs occasional trial) and sample distinct business-size cohorts; government data and independent surveys report lower adoption rates in some samples. Without direct access to Decidr’s full methodology appendix and questionnaire, this specific number should be treated as a strong indicator of trial activity, not definitive proof of routine, governed, enterprise usage. Readers should consult the primary Decidr report or the full questionnaire when available. (mediaweek.com.au, industry.gov.au)
- The full Decidr report and raw dataset were not readily available in public repositories at the time this article was drafted; the Mediaweek report provides the summary used herein. Independent corroboration (government and industry surveys) supports the broader themes but not every granular percentage. Practitioners should seek the original Decidr document to validate measurement details before using any single stat as the basis for major investment decisions. (mediaweek.com.au, decidr.ai)
Final assessment — strength, weakness, and where to go next
Decidr’s National AI Readiness Index is a useful field lens: it highlights a real and urgent tension within Australian SMEs — strong belief in AI’s near-term importance, counterbalanced by weak strategic planning and a resource-constrained, security-conscious mindset. That combination means many firms will enjoy tactical productivity gains from tools like ChatGPT and Microsoft Copilot but will fail to harvest the deeper business value available from integrated, governed AI systems.Strengths of the situation:
- Rapid experimentation means organisations will discover use cases quickly.
- Accessible tools and cloud services lower the technical barrier to entry.
- A sizeable portion of SMEs already have the appetite to pilot integrated systems, creating a near-term market for managed solutions. (mediaweek.com.au, bizcover.com.au)
- Unmanaged tool sprawl, data leakage, and unrealistic ROI timelines.
- Underinvestment in governance and data readiness that prevents scaling.
- Vendor-driven messaging that emphasises capability over implementation reality — buyers must demand clear SLAs and data protections. (precisely.com, mumbrella.com.au)
For IT leaders and channel partners building the next wave of SME AI services, the immediate priorities are clear: secure the data, measure the impact, and design governance into every pilot. The window to transform from cost-saver to competitive advantage is open — but only for those who plan, protect and scale deliberately. (mediaweek.com.au, industry.gov.au, bizcover.com.au, precisely.com, decidr.ai)
Source: Mediaweek Most Australian SMEs are adopting AI with no strategy - Mediaweek
