Why do some businesses turn AI ambition into real results, while others do not?
That question sits at the heart of new global research from Vertage. The study surveyed 800 enterprise decision-makers and 1,200 external workers across six markets, including Australia. It also included interviews with senior client decision-makers and analyst commentary.
The findings show a clear gap. Seventy-four per cent of businesses say they define significant work around outcomes, yet only 15 per cent are set up to deliver them.
What outcome-ready means
Being outcome-oriented means knowing the result you want. Being outcome-ready means having the right organisation and resources to deliver it. That includes a clear measure of success, a named owner, and funding, people, partners, data and AI aligned around the same goal.
A clear outcome answers four questions. What should improve? Who should benefit? How much should it improve? By when?
Take this public-housing example:
- Activity: Introduce a new asset-management system and hire 12 additional maintenance officers.
- Outcome: Reduce overdue priority repairs for public-housing tenants in regional New South Wales from 4,200 to fewer than 1,000 by 30 June 2027.
This shifts the focus from what is being added to what should improve for tenants. With the result clear, leaders can then decide who or what is best placed to deliver it, whether that is internal staff, contingent talent, a specialist partner, AI or a mix of all four.
The senior client interviews brought the shift to life. Leaders described using AI to cut administration, then redirecting the time towards stronger relationships, new business and better quality. One leader said the aim was to give recruiters more time to focus on building relationships with their hiring managers and speaking to candidates. Others described moving people towards revenue-generating work once routine tasks were automated. The lesson was clear: saving time is only useful if the business decides where that capacity should go.
Australia’s opportunity
Australia is much further ahead on defining outcomes than integrating AI. Businesses scored 71 out of 100 for outcome orientation, compared with 37 for AI integration, and just 10 per cent were strong on both. The findings also reveal a gap between what leaders see and what workers feel. While 78 per cent of decision-makers said AI had created more capacity, only 35 per cent of workers agreed. AI may be making organisations more efficient without giving people more time in their day.
Australia’s next challenge is to turn its strong ambition into a repeatable way of working.
How do you become outcome ready?
1. Set the outcome. Define the change, who should benefit, how it will be measured and when it should happen. A major retail bank now funds work against an agreed result rather than the number of people assigned to a project.
Start today by taking one current project and completing this sentence: “By [date], we will improve [measure] from [starting point] to [target] for [beneficiary].” Name one person who will own the result.
2. Find the readiness gap. Look for what could stop delivery. The gap may sit in funding, ownership, processes, data, skills, culture or decision-making. One Australian insurer assesses each use case through the eyes of the customer or candidate, then weighs the risk, cost and benefit. People still review applications, check accuracy and make the final decision.
To test this, put that outcome at the centre of a one-page review. List the funding, owner, process, data, skills and decisions needed to deliver it. Circle the two missing or weakest elements and assign an action and owner to each.
3. Make the delivery decision. Break the work into tasks, skills and decisions. Then choose what to build internally, buy from a partner, borrow through flexible talent or automate with AI. One global technology talent-acquisition leader reports that automated assessments and scheduling save around 60 to 70 per cent of the team’s time. The team reinvests that time in a stronger shortlist, while people make the final decision.
For a practical first step, choose one workflow and list its main tasks. Mark each task “build” for internal capability, “buy” for a specialist partner, “borrow” for contingent talent or “bot” for AI and automation. Select one task to test, then agree the quality, speed, cost and risk measures before work begins.
Mark’s bio
Mark Coyle is APAC CEO of Vertage, based in Sydney. With more than 25 years in workforce solutions, he has built expertise across managed services, specialist staffing, BPO, and technology-enabled talent platforms spanning industries including IT, engineering, government, healthcare, and financial services. Since relocating to Australia in 2010, Mark has helped grow the APAC business from around $10 million to approaching $1 billion in turnover through organic expansion, leadership development, and building high-performance teams across the region.
About Vertage
Vertage’s vision is to become the amplification engine that rewires how work gets done. By bringing together the right mix of expert talent and AI, Vertage sets out to help people and businesses move faster, think bigger and stay ahead in the intelligence era. Built on decades of workforce expertise, enterprise-grade governance and a global community of 1.5 million specialist talent members across 80 countries, Vertage is focused on helping organisations solve talent and delivery challenges with greater speed, intelligence and control. Vertage is committed to bringing the expertise, intelligence and trusted delivery needed to move complex work forward. With €10 billion in Gross Invoice Value, Vertage’s ambition is to turn insight into action and every engagement into momentum for what comes next.
