Every enterprise AI conversation eventually turns to skills. “We don’t have the people.” “We can’t find data scientists.” “Our team doesn’t know how to work with AI.”

The skills gap is real, but it’s often misunderstood. The gaps that matter most aren’t the ones that get the most attention.

The Actual Skills Landscape

Let me describe what I’m seeing across Australian enterprises:

Abundant: Basic AI awareness. Most knowledge workers understand what AI is, have tried ChatGPT, and grasp general concepts.

Adequate: Data science fundamentals. There are enough people who can build models, particularly in major cities and large enterprises.

Scarce: Enterprise AI implementation. The ability to take AI from prototype to production in complex enterprise environments.

Very scarce: AI strategy and governance. The ability to define where AI fits, how it should be governed, and how to measure success.

Critical shortage: AI-business translation. People who understand both AI capabilities and business operations deeply enough to identify practical applications.

The common narrative focuses on data scientists. But most enterprises can hire or contract data scientists. The harder gaps are in implementation and translation.

Why the Translation Gap Matters Most

Consider a typical enterprise scenario:

The business has a problem: customer service costs are rising while satisfaction is falling. The AI team knows that language models can automate some customer interactions.

But connecting these dots requires understanding:

• Which customer interactions can be automated without quality loss
• How to integrate AI with existing service platforms
• What change management is needed for service teams
• How to measure success in business terms, not AI terms
• What governance is required for customer-facing AI

This translation work sits between business and technology. Neither side can do it alone. And there simply aren’t enough people with feet in both worlds.

The Australian Context

Several factors make the skills gap more acute in Australia:

Geographic concentration. AI talent clusters in Sydney and Melbourne. Organisations elsewhere struggle to hire locally and must compete for remote workers.

Immigration policy impacts. Australia’s skilled migration pathways affect AI talent supply. Jobs and Skills Australia monitors these trends closely. Policy changes have real workforce implications.

Salary competition. Global companies offer remote work at international salaries. Australian enterprises compete with global compensation, often unsuccessfully.

University pipeline. Australian universities produce capable graduates, but volume doesn’t meet demand, and curricula don’t always match enterprise needs. Research from CSIRO’s Data61 has highlighted this gap.

Industry structure. Many large Australian employers (mining, agriculture, retail) aren’t seen as AI-attractive compared to tech companies.

What’s Working

Organisations successfully addressing the skills gap are using several approaches:

Build From Within

Upskilling existing staff is often more effective than external hiring. People who understand your business can learn AI faster than AI experts can learn your business.

What works:

• Identifying employees with analytical aptitude and AI curiosity
• Providing structured learning paths (certifications, courses, projects)
• Creating apprenticeship opportunities with experienced practitioners
• Allowing time for learning, not just expecting it to happen

What doesn’t work:

• One-off training workshops with no follow-up
• Expecting everyone to upskill (focus on the willing)
• All theory, no practice

Strategic Hiring

Targeted external hiring for capabilities you can’t build.

What works:

• Hiring for specific, defined gaps (not general “AI capability”)
• Competitive compensation, including remote work options
• Clear career paths for AI roles
• Interesting problems that attract talent

What doesn’t work:

• Job descriptions requiring unrealistic combinations of skills
• Expecting AI talent to accept below-market compensation
• Treating AI roles as IT support

Partner Strategically

Using external partners to complement internal capability.

What works:

• AI consultants Melbourne who can transfer knowledge, not just deliver projects
• Partnerships that build internal capability over time
• Clear scope for what partners do vs. what you learn to do internally

What doesn’t work:

• Full outsourcing of AI capability (creates dependency)
• Partners who guard knowledge to ensure ongoing engagement
• Misaligned incentives between delivery and capability building

Building the Translation Capability

The most critical gap – AI-business translation – requires specific attention:

Create hybrid roles. Business analyst plus AI capability. Product manager plus AI understanding. Don’t expect pure business or pure technical roles to bridge the gap.

Rotate people. Move business people into AI projects and AI people into business rotations. Cross-pollination builds translation capability.

Hire for learning ability. Translation skills are learned through experience. Hire people who learn quickly and put them in bridging positions.

Value the capability. Translators often fall through career ladders designed for either technical or business paths. Create explicit career progression for hybrid roles.

A Realistic Timeline

Building enterprise AI capability takes time. A realistic progression:

Year 1: Build foundation. Train enthusiasts, hire key roles, establish partnerships, complete initial projects with support.

Year 2: Expand capability. Grow internal team, reduce partner dependency, complete projects with decreasing support, develop internal experts.

Year 3: Achieve self-sufficiency. Internal capability handles most needs, partners for specialised or peak requirements, continuous learning embedded in operations.

Trying to compress this timeline usually fails. Capability building doesn’t accelerate just because executives want it to.

The Investment Required

Skills development requires budget:

Training: $5-10k per person per year for structured learning programs

Time: 10-15% of working hours for people in development programs

Hiring premium: 20-30% above market for scarce AI roles

Partnership investment: Varies, but budget for knowledge transfer, not just delivery

Many organisations underinvest in skills while overinvesting in technology. The technology is useless without people who can apply it effectively.

Final Thought

The AI skills gap is real but manageable. It requires sustained investment in people – building from within, hiring strategically, and partnering effectively.

The organisations that will lead in AI aren’t those with the best technology. They’re those with the best people to apply technology to business problems.

Invest in your people. It’s the constraint that matters most.