Inside the Room: What AI Consultants Recommended to CEOs This Year

Last year, we spent countless hours in boardrooms, strategy sessions, and workshops with CEOs and executive teams across Australia, discussing what’s coming in 2026. These weren’t sales pitches or trend briefings. They were practical conversations about what’s genuinely working with AI, what’s quietly failing, and what leaders should do next.

Across industries; construction, professional services, healthcare, logistics, finance, and retail; a clear pattern emerged. While the technology keeps moving fast, the advice from our AI Consultants has become more grounded, more cautious, and far more focused on execution than hype.

Here’s what our AI Consultants consistently recommended to CEOs this year, based on what we saw firsthand.

1. Stop Treating AI as a Tool Purchase

One of the strongest messages from our AI Consultants was simple: AI is not a tool you buy and “roll out”. Too many organisations are still approaching AI like another SaaS subscription. The result is predictable; scattered usage, unclear ownership, and little return.

Instead, we advised CEOs to treat AI as an operating capability. That means asking different questions:

• Which parts of the business rely most on manual decisions?

• Where does knowledge live only in people’s heads?

• Which processes slow down as the business grows?

  
  

When AI is positioned as part of how work gets done, not just software, adoption improves, and outcomes become measurable.

2. Start With Internal Efficiency, Not Customer Hype

Many leaders came into the room wanting customer-facing AI straight away: chatbots, AI sales tools, AI marketing automation. While these can be valuable, our AI Consultants often recommended starting internally.

The reason is simple. Internal use cases are easier to control, cheaper to test, and faster to improve. We saw strong results when organisations focused first on:

• Knowledge base agents for staff

• Document handling and summarisation

• Internal reporting and analysis

• Admin and back-office automation

  
  

These projects reduce workload, improve accuracy, and build confidence across the business. Once teams trust AI internally, external use cases become far easier to manage.

3. Fix the Data Before Scaling AI

Another recurring theme in CEO conversations was frustration. Many had already “tried AI” and felt underwhelmed. When we looked closer, the issue was rarely the model or the tool. It was the data.

Our AI Consultants spent a lot of time advising leaders to slow down and clean up the basics:

• Where does your data live?

• Is it consistent across systems?

• Who owns it?

• Can it be trusted?

  
  

Without clear, structured data, AI systems struggle to deliver reliable outputs. CEOs who invested time in data foundations, even small steps, saw better results than those who rushed into complex AI builds.

4. Assign Clear Ownership Early

One mistake we saw repeatedly was unclear accountability. AI initiatives were often “owned by everyone”, which meant they were owned by no one.

This year, our AI Consultants strongly recommended appointing a clear AI owner or steering group. Not necessarily a new hire, but someone with authority to:

• Prioritise use cases

• Approve changes

• Manage risk and governance

• Measure results

  
  

When ownership was clear, AI projects moved faster and avoided becoming stalled experiments.

5. Focus on Measurable Outcomes, Not Demos

CEOs are shown a lot of demos. Many look impressive. Fewer deliver real value.

Inside the room, our AI Consultants consistently pushed conversations away from features and towards outcomes. Instead of asking, “What can this AI do?”, we encouraged leaders to ask:

• What time does this save per week?

• Which costs does this reduce?

• What errors does this remove?

• How does this change decision-making?

  
  

The most successful organisations defined success early and measured it often. If an AI solution did not show progress within weeks, it was adjusted or dropped.

6. Build for Governance From Day One

AI risks that came up in nearly every CEO discussion; Data privacy, compliance, accuracy, and brand risk are no longer abstract concerns.

Our AI Consultants advised leaders to embed governance early, not as an afterthought. That included:

• Clear rules on what data AI can access

• Human review points for sensitive outputs

• Logging and traceability

• Clear escalation paths when AI is unsure

  
  

This approach reduced resistance from legal, compliance, and operations teams and helped AI projects move forward with confidence.

7. Train People, Not Just Systems

A quiet but important recommendation this year was around people. AI does not fail because staff resist change. It fails because they are unsure how to use it well.

We encouraged CEOs to invest in practical, role-specific AI training. Not generic sessions, but guidance that shows teams how AI fits into their daily work. When people understand how AI supports them, rather than replaces them, adoption improves quickly.

What Stood Out Most

If there is one thing that stood out in conversations with CEOs, it is this: the role of our AI Consultants has shifted. The focus is no longer on selling potential. It is on helping organisations make AI work quietly, reliably, and sustainably.

The best outcomes came from businesses that treated AI as a long-term capability, not a quick win. They moved slower at the start, but faster overall. They asked hard questions early and avoided expensive resets later.

As we look ahead, the advice from our AI Consultants is becoming clearer and more consistent. Build strong foundations. Start with real problems. Measure everything. And bring your people with you. That’s what we’ve been recommending inside the room this year, and it’s what continues to work.

FAQ

1. What do AI Consultants actually recommend to CEOs today?

Our AI Consultants recommend focusing on practical use cases, strong data foundations, clear ownership, and measurable outcomes rather than chasing AI trends.

2. Why do AI Consultants suggest starting with internal AI use cases?

Internal AI projects are easier to control, faster to test, and help teams build trust in AI before rolling it out to customers.

3. What is the biggest mistake companies make when adopting AI?

Treating AI as a one-off tool purchase instead of a long-term business capability is the most common mistake our AI Consultants see.

4. How important is data quality when working with AI Consultants?

Data quality is critical. Without clean and consistent data, AI systems struggle to deliver reliable and useful results.

5. Do AI Consultants recommend training staff on AI?

Yes. Our AI Consultants consistently recommend practical, role-specific training so teams know how to use AI in their daily work.

GPT AI Chat, Copilots | AI Consulting Firm

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