While government agencies have to carefully navigate AI changes while maintaining dependability, the private sector can move like a high-speed bullet train in comparison; faster, more agile, and ready to change direction. However, when it comes to AI, speed without strategy can be as dangerous as standing still.

Our latest AI survey with 864 business leaders and tech professionals shows that 48% of organisations overall are still in the experimental or pilot stage of AI adoption. In the private sector, this can be exciting with tools being trialled, data flows unlocked, and quick wins celebrated… But without clear ownership and governance, experimentation can quickly spiral into risk.

The private sector’s AI advantage

Private organisations have more flexibility than government agencies, which means they can:

• Pilot AI use cases without length approval processes
• Redirect budgets and talent more quickly
• Partner with vendors or start-ups to accelerate capability

In-house AI expert Jack Jorgensen, General Manager of Data, AI & Innovation at Avec, explains, “In the private sector, leadership can decide today that AI is a priority, and tomorrow there’s a project team in place.” This agility allows them to capitalise on emerging opportunities, from automating repetitive tasks to improving customer experience.

The strategy gap

It’s important to note that speed is an advantage, until it isn’t. Our survey data shows that:

• 41% of organisations cite “no strategy” as a major obstacle to AI adoption
• 41% say “unclear goals” are holding them back
• 34% cite “unclear ownership”

“We’ve seen this before with automation. Without a cross-business strategy, AI gets walled into a single department and it never reaches its full potential,” says Jack.

In many cases, the enthusiasm is there at the executive level, but ownership is unclear. Is AI a technology initiative? A business transformation project? A data function? Without a clear answer, adoption can stall or become fragmented.

Security and governance risks

Organisations in the private sector are split in their approach to AI security:

• 3% have restrictions or policies limiting the use of external AI tools
• 9% use tools like ChatGPT with minimal governance
• 9% are exploring secure, fit-for-purpose AI solutions
• 11% have implemented secure, in-house AI capability

Practice Manager from our office in Auckland, JP Browne observes, “You either lock it down completely or let it run free, and the private sector is doing both, often within the same organisation.”

The role of talent in AI maturity

AI success in the private sector is often tied to talent strategy, and the current roles in highest demand according to our recruitment experts include:

• Data engineers and analysts
• Systems engineers to build infrastructure
• Change managers to drive adoption across business units

But while technical capability is critical, so is critical thinking and the ability to bridge technical and commercial priorities.

What private sector leaders should do next

• Define ownership and accountability for AI strategy
• Prioritise secure data infrastructure before scaling
• Pilot AI projects with clear and measurable goals
• Invest in cross-functional teams that blend technical skill with business insight
• Develop a company-wide AI policy that balances innovation with risk management

The private sector’s ability to move quickly is a strength, but only if it’s guided by clear strategy, governance, and talent. The leaders in AI adoption will be those who can balance the hype and excitement of rapid innovation with the discipline to scale it safely and sustainably.

If you’re looking to hire AI and data talent, get in touch with our team. Or if your business is planning a high-impact data, AI or innovation project, drop a message to Jack’s team at Avec.