Five Executive Priorities for a Data-Led, AI-Ready 2026

As organisations plan for 2026, leaders are under pressure to act, invest, and modernise. AI features are landing in ERP, HCM, finance, and operational platforms faster than most teams can absorb. Vendors are moving quickly. Boards are asking sharper questions.

Yet across enterprise transformation programs, one pattern is consistent.

AI readiness doesn’t start with AI.
It starts with data, systems, governance, and people.

Based on SMC’s work across 750+ enterprise transformation projects, these are the five priorities leaders need to address before committing to AI, ERP, or major technology programs.

Take the 2026 SMC Data and AI Readiness Test (3 Minutes)

1. Fix the foundations, clean, connected and complete data

Before investing in new platforms or AI capability, the strategic question is simple.

Do we trust the data that will drive decisions, automations, and insights?

Most organisations underestimate the scale of technical debt sitting inside HR, payroll, finance, procurement, and operational systems. Over time, manual workarounds, spreadsheet logic, duplicated fields, and legacy rules quietly erode data quality.

This creates risk long before AI is introduced.

AI outputs can’t be trusted
ERP configuration becomes complex and brittle
Reporting credibility breaks down
Time-to-value stretches beyond expectations

Leaders who address data foundations early reduce program risk and materially accelerate outcomes. Clean, connected data isn’t a hygiene task. It is a strategic enabler.

2. Map what matters, prioritise the right use cases

AI isn’t a single capability. It’s a series of decisions.

Organisations that see real value are not those piloting the most tools. They are the ones prioritising the right problems to solve.

Strong executive teams ask:

Do we have the data to support this use case?
What operational and integration impact will it 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.

The advantage is knowing where to apply AI, not simply adopting it.

3. Strengthen the architecture, reduce complexity before you modernise

AI, analytics, and ERP programs fail more often due to complexity than capability.

Many organisations operate with overlapping platforms, fragile integration patterns, unclear system ownership, and inconsistent lifecycle governance. This limits what is realistically achievable.

Executives need clarity on:

Which systems are essential
Which systems create duplication or risk
Where integration patterns are brittle
What the minimum viable future state looks like

High performing organisations simplify before they modernise. They focus on fewer platforms, cleaner integration, and stronger governance, because architecture determines what is possible in practice.

You can’t layer AI or modern ERP capability on top of disconnected systems and expect stability.

4. Prepare your people, build confidence, capability and clear guardrails

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 tools should be used, and what is safe, expected, and supported.

Organisations succeed when they focus on:

Clear guardrails that reduce uncertainty
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 intent on their own. Organisations that treat capability, clarity, and confidence as core pillars of readiness consistently outperform those that bolt them on later.

5. Deliver and measure, make technology accountable for value

The ROI gap is widening.

CFOs are accountable for technology value, yet many organisations lack the frameworks to measure it beyond cost and implementation milestones.

Leaders need visibility into:

Expected value pathways
Adoption behaviours
Benefits realisation
How learnings feed back into governance cycles

Organisations that succeed in 2026 will track value with the same discipline they apply to spend. This requires structured governance, rigorous benefits tracking, strong commercial oversight, and the ability to negotiate and hold vendors accountable.

A practical starting point

You don’t need to commit to an AI platform, ERP replacement, or major transformation program to begin. You do need clarity on what to fix first.

If you want a fast executive view of where your organisation stands, the SMC 2026 Data for AI Readiness Test assesses the foundations that underpin successful transformation.

Take the 2026 SMC Data and AI Readiness Test (3 Minutes)

FAQ

What does “data readiness” actually mean?

Data readiness means your core data is accurate, consistent, complete, and governed. It also means systems are connected in ways that support reliable reporting, automation, and decision making, without heavy manual workarounds or fragile integrations.

Can we be AI-ready if we’re not replacing our ERP yet?

Yes. AI readiness is not dependent on an ERP replacement. Many organisations make the biggest gains by improving data quality, tightening governance, reducing process workarounds, and simplifying integration patterns before any major platform decision.

Why do manual workarounds matter so much?

Manual workarounds are usually a signal that data and process design has drifted away from reality. They create hidden risk, inconsistent outputs, and duplicated logic across spreadsheets, email chains, and offline steps. They also make AI, automation, and ERP configuration harder to stabilise.

How should executives prioritise AI use cases?

Start with problems that have clear value, manageable risk, and strong data foundations. Pressure test each use case for data availability, operational impact, integration complexity, governance requirements, and how success will be measured.

What should we measure to prove value, not just progress?

Track benefits that link to business outcomes, not only delivery milestones. This usually includes adoption behaviours, cycle time reductions, error rate reductions, compliance outcomes, improved forecasting accuracy, and measurable financial impacts, alongside clear accountability for benefit ownership.

How long does it take to get “ready”?

It depends on your starting point and scope. Many organisations can make meaningful progress in weeks by tightening governance, clarifying priorities, and identifying the highest impact data and integration issues. Larger remediation programs can be staged, so value starts early rather than waiting for a perfect end state.