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Optional. Choose your industry and this piece opens with a situation from your world and closes with a first move for you. Your choice carries across the guides and analysis on this site.
You lead a licensee counting licences while clients wait
Investment Trends research shows 15.9 million Australians have unmet advice needs, and the industry sees hybrid advice as key to closing the capacity gap. A licensee can roll out AI tools to every adviser and still see the same number of clients served by each one.
Base camp in advice is a full licence count beside an unchanged client list. The climb begins when advisers bring their own use cases and the practice redesigns how advice is prepared.
What this means for you
- Measure clients served per adviser and time to advice, alongside tool adoption.
- Track how many advisers bring new use cases each quarter.
- Count the workflows you have redesigned, such as the path from fact-find to Statement of Advice.
You lead stores that all have the tool and work the same way
Australian organisations trail their global peers, with 12% saying AI is transforming their business against 25% globally. In retail it is easy to roll a tool out to every store and report full deployment while store managers keep working the old way.
Base camp in a territory is a dashboard that shows every store live. The climb begins when store managers bring their own ideas for what agents should do next.
What this means for you
- Ask each store manager what they have changed because of the tool.
- Track how quickly a good practice in one store spreads to the rest of the territory.
- Count the routines you have redesigned, such as weekly ordering or rostering.
You lead clinicians who have adopted AI scribes
GPs' use of AI scribes rose from 22% in August 2024 to 40% in November 2025, as the Medical Republic reported. The RACGP also cautions that scribe output can be incomplete in structured fields, which affects the quality of records.
Adoption is the easiest number to report. The climb is whether clinicians are learning to use the tools well, whether records are better and whether patients get more of the clinician's attention.
What this means for you
- Measure record quality and after-hours documentation, alongside adoption.
- Track how often clinicians share a better way of using the tool.
- Count the workflows you have redesigned, such as referrals or care-plan reviews.
You lead teams where adoption is high and stability is falling
The 2025 DORA report, based on about 5,000 technology professionals, found that 90% use AI at work and that AI adoption still has a negative relationship with delivery stability.
Base camp is the adoption figure. The climb is measured in stability, lead time and how quickly teams learn from each other.
What this means for you
- Report stability and change failure rate next to adoption.
- Track how often teams share prompts, rules and patterns that work.
- Count the workflows you have redesigned around agents.
You run an installer in a boom where six in ten inspected jobs fell short
More than 500,000 batteries have now been installed under the Cheaper Home Batteries Program. Of the 1,278 installations the regulator inspected, 60.8% were substandard. It is a national example of deployment running ahead of learning.
The measure that matters for your AI tools is the one that matters for your installations: how many jobs pass inspection the first time.
What this means for you
- Measure first-time pass rate and call-backs alongside the number of agent-prepared quotes.
- Track how quickly a lesson from one crew reaches the others.
- Count the processes you have redesigned, such as compliance paperwork.
Base camp
Climbers on big mountains spend weeks at base camp and on shorter climbs, letting their bodies adjust to the altitude before they try for the summit. Teams that rush that stage rarely fail dramatically. People get sick and turn back, the summit stays in view, and everyone decides the mountain was harder than expected.
Many AI programs are at base camp and reporting it as the summit. The board pack says the tools are deployed. Most teams still work the way they did a year ago.
Why we measure deployment
Deployment gets measured because it is easy to see and easy to report. A roadmap has milestones and licences have a count, and a board can read both in a minute. Anyone who has had to show progress to a board will understand the appeal.
The changes that move results are harder to see. Six months of teams learning to use AI on their own work does not fit on a slide. What you can measure is the rate of learning: teams bringing their own problems forward, practices spreading from one team to the next, and workflows being redesigned around what AI does well.
The evidence supports this. BCG’s AI at Work 2025 found about half of companies are moving from using AI for productivity to reshaping workflows, and employees in those companies save more time and make better decisions. Fortune’s write-up of MIT NANDA’s 2025 report described the core issue as a “learning gap” for both tools and organisations.
Australia shows the gap clearly
Deloitte’s 2026 State of AI in the Enterprise found 12% of Australian organisations report that generative AI is already transforming their business and industry, against 25% globally. Only 65% of Australian respondents plan to increase AI investment next financial year, compared with a global average of 84%.
My read is that most Australian organisations I talk to already have an AI strategy. The missing piece is the base camp stage: time spent with teams, on their actual work, until AI becomes part of how they do it.
What boards will ask
Boards are catching up. Deloitte’s boardroom survey found 66% of directors and executives say their boards still have limited or no AI knowledge or experience, down from 79% in the previous edition.
As that number falls, boards will ask harder questions. Why has the investment not compounded? Why do teams still do by hand what AI was meant to handle? Why is productivity flat when the tools are live? A program that can answer only with deployment numbers will struggle.
What to change
The sequence. Set the strategy, spend time with teams in the field, then scale. The field work is the foundation the rest of the plan stands on.
The investment. Fund people who can work alongside teams and turn the strategy into something useful on a Tuesday morning. One good guide in the field will do more for adoption than another vision document.
The measures. Track how fast teams are learning and how often they bring their own use cases. When requests start coming from inside the business, the base camp stage has done its job.
Good mountain guides spend most of their time at base camp, making sure each climber is ready. Planning the summit day is comparatively easy. Getting people ready to attempt it is what the guide is paid for.
Replace one deployment measure in your next report with a learning measure: the number of advisers who changed how they prepare advice this quarter.
On your next round of visits, ask each store manager for one thing they now do differently with AI. Share the best three with every store within a week.
Ask clinicians at two sites to audit five AI-drafted records each for completeness, and share what the best users do differently.
Add the change failure rate for agent-authored work to your next engineering report, and ask each team for one practice worth spreading.
Start tracking first-time pass rate for every job this month, and review it with the crews each fortnight.
Questions readers ask
How do you measure rate of learning?
Track a few things each month: how many use cases come from teams themselves, how many workflows have been redesigned, and how long a new practice takes to spread from one team to the next.
Is deployment data useless?
You still need it, because you need to know the tools are live. It tells you the expedition has reached base camp. It cannot tell you whether anyone is ready to climb.
Why is Australia behind?
Deloitte's 2026 data shows Australian organisations planning smaller increases in AI investment and reporting less transformation than global peers. My judgement is that the biggest gap is time spent with teams on their real work and on redesigning workflows.
Sources
- Australian organisations lag global peers in realising AI's transformational potential (State of AI in the Enterprise 2026), Deloitte Australia, 12 February 2026
- AI at Work 2025: Momentum Builds, but Gaps Remain, BCG, June 2025
- MIT report: 95% of generative AI pilots at companies are failing, Fortune, 18 August 2025
- Governance of AI: A critical imperative for today's boards, 2nd edition, Deloitte, 2025
The views here are my own, not those of my employer or its clients.