AI is no longer the question. AI readiness is.
As organisations plan for 2026, executive teams are under increasing pressure to modernise. AI capabilities are now being embedded rapidly across ERP, HCM, finance, and operational platforms. Vendors are moving fast. Boards are asking sharper questions about value, risk, and return.
Yet across enterprise transformation programs, one pattern remains consistent.
AI readiness does not start with AI; it starts with data, systems, governance, and people.
Based on SMC’s experience supporting 750+ enterprise transformation initiatives across ERP, HCM, finance, and operational environments, these are the five executive priorities that consistently determine whether AI investments deliver value or create new risk.
Not sure where you stand?
For organisations wanting a fast executive-level view of current readiness, the SMC 2026 Data and AI Readiness Test provides a practical starting point in under three minutes.
A Summary of AI Readiness
AI readiness isn’t about adopting new tools; it’s about fixing the foundations first. Based on SMC’s experience across 750+ enterprise transformation programs, this article outlines the five executive priorities that determine whether AI investments deliver real value or introduce risk. It explains why data quality, system simplicity, governance, people capability, and value measurement must be in place before committing to AI, ERP, or major technology programs, and how leaders can position their organisations for confident, AI-ready decision-making in 2026.
Key Takeaways:
- AI readiness starts with data, not tools
- Prioritisation beats experimentation/li>
- System complexity is the silent blocker
- People readiness determines adoption
- Value must be measured, not assumed
- You don’t need to start with AI to become AI-ready
1. Fix the Foundations
Before investing in AI capability, analytics platforms, or new ERP functionality, leaders must answer one strategic question:
Do we trust the data that will drive decisions, automation, and insight?
Most organisations underestimate the level of technical debt embedded in HR, payroll, finance, procurement, and operational systems. Over time, manual workarounds, spreadsheet logic, duplicated data fields, and legacy rules quietly erode data quality.
This creates risk long before AI is introduced. This is where independent, client-side advisory adds the most value: helping leaders diagnose data risk early and prioritise what must be fixed before AI, ERP, or automation initiatives move forward.
When these foundations are not addressed, the impact shows up quickly in delivery, reporting, and decision-making.
Common consequences include:
- AI outputs that require constant manual validation
- ERP configurations that become complex and fragile
- Reporting credibility breaking down across leadership teams
- Time-to-value extends well beyond business expectations
For executives, poor data foundations don’t just delay AI adoption: they undermine confidence. When dashboards are questioned, forecasts are manually ‘adjusted’, and AI outputs cannot be trusted, transformation narratives quickly lose credibility at the board level.
Clean, connected data is not a hygiene task: It is a strategic enabler for AI readiness.
2. Map What Matters
AI readiness is not about deploying tools. It is about making disciplined decisions.
Organisations that realise value are rarely those running the most pilots. They are the ones prioritising the right problems, with a clear view of feasibility, risk, and measurable value.
High-performing executive teams consistently ask:
- Do we have the data maturity to support this use case?
- What operational or integration impact will this create?
- Where is the measurable value or risk reduction?
- What governance, ethical, or compliance considerations apply?
Without this lens, organisations drift into fragmented experimentation. With it, they build focused roadmaps that deliver value early and safely.
At SMC, we see the strongest outcomes when AI readiness is anchored in use-case-led prioritisation, not platform-led enthusiasm.
3. Strengthen the Architecture
AI, analytics, and ERP programs more often stall because of complexity than capability.
Many organisations operate with overlapping platforms, fragile integrations, unclear system ownership, and inconsistent lifecycle governance. This limits what is realistically achievable, regardless of how advanced the technology may be.
Executives need clarity on:
- Which systems are truly essential
- Where duplication or hidden risk exists
- Which integration patterns are brittle
- What a minimum viable future state actually looks like
High-performing organisations simplify before they modernise. They reduce platform sprawl, clean up integration paths, and establish stronger governance, because architecture determines what is possible in practice.
Ultimately, you cannot layer AI capability or modern ERP functionality on top of disconnected systems and expect stability.
4. Prepare Your People
AI readiness is cultural as much as it is technical.
Even the best platforms fail when people don’t understand why change is happening, how to use tools, or what is considered safe, expected, or supported.
Organisations that succeed focus deliberately on:
- Clear guardrails that reduce uncertainty and risk
- Role-based capability uplift, not generic training
- Leadership alignment on what ‘good’ looks like
- Communication that creates clarity, not fear
Transformation breaks down when people are left to interpret policy, purpose, and risk on their own. Treating people and change readiness as core pillars, not bolt-ons, consistently separates successful AI programs from those that stall.
5. Deliver and Measure
The ROI gap is widening.
While CFOs and executives are increasingly accountable for technology value, many organisations still lack the frameworks to measure outcomes beyond cost, timelines, and implementation milestones.
Leaders need visibility into:
- value hypotheses tied to specific use cases
- adoption behaviours and usage patterns
- changes in cycle time, error rates, or forecasting quality
- compliance or control outcomes
- named ownership of benefit realisation
Organisations that succeed in 2026 will measure value with the same discipline they apply to spending. This requires structured governance, strong commercial oversight, and the ability to negotiate with (and hold) vendors accountable.
A Practical Starting Point for AI Readiness
You do not need to commit to an AI platform, ERP replacement, or major transformation program to begin. However, you do need clarity on what to fix first.
If you want a fast, executive-level view of readiness, the SMC 2026 Data and AI Readiness Test (3 minutes) assesses the foundations that underpin successful AI and enterprise transformation: data, architecture, governance, and people.
Frequently Asked Questions About AI Readiness
AI readiness means your organisation can trust the outputs it gets from technology. In practical terms, that requires clean and governed data, well-understood systems, clear ownership, and minimal reliance on manual workarounds.
AI readiness is less about whether you already have tools and more about whether your data, systems, controls, and people are ready to use them safely and effectively.
Yes, and in many cases, that’s exactly where organisations should start.
We regularly work with clients who significantly improve AI readiness without replacing their ERP. Tightening data governance, simplifying integrations, reducing spreadsheet logic, and clarifying system roles often deliver more immediate value than a major platform change. An ERP replacement should come after you understand what needs fixing, not before.
Because manual workarounds are one of the clearest signals of hidden risk. In our experience, spreadsheets, offline adjustments, and shadow processes are where data quality breaks down, and accountability becomes unclear. AI simply amplifies those issues. When we see heavy manual intervention, it tells us exactly where readiness gaps exist and where early remediation will deliver the biggest return.
We help leaders slow the conversation down just enough to ask better questions. Rather than starting with ‘what can AI do?’, we start with ‘what should AI do here?’.
We pressure-test use cases against data readiness, operational impact, governance requirements, and measurable value. This ensures AI investment is focused, defensible, and aligned to business outcomes, not experimentation for experimentation’s sake.
- Adoption patterns
- Reductions in cycle time or error rates
- Improved forecasting accuracy
- Compliance outcomes
- Financial impact
It depends on where you’re starting, but meaningful progress often happens faster than expected.
Many organisations can make visible early improvements within weeks by clarifying governance, prioritising the right fixes, and addressing high-impact data and integration issues. Larger remediation programs can be staged, so you’re building readiness and confidence incrementally rather than waiting for a ‘perfect’ end state.
About the Author
Rob Stummer
Rob Stummer is Director of Growth at SMC, leading strategic growth, market expansion, and commercial partnerships across Australia and New Zealand. With more than 25 years of experience in enterprise software, ERP, and fintech across APAC, he has a strong track record of scaling high-growth businesses and driving sustainable performance. Rob has held senior leadership roles at organisations including Pagero, SYSPRO, and IFS, where he led go-to-market execution, operational transformation, and regional expansion. He also supports SMC clients as a strategic advisor on complex ERP and transformation programs.
Get AI Ready With Clarity
AI readiness is not about choosing the next tool first. It is about understanding where the organisation is exposed, where it can move faster safely, and what must change to unlock measurable value.
If you want a practical, executive-level view of readiness before committing to AI, ERP, or major technology programs, start with the SMC 2026 Data and AI Readiness Test.
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