The same team, doing more valuable work.
For leaders who want AI to raise what their existing people can do, not to replace them. We redesign roles around AI support, train each team on its own work, and track adoption until the new habits stick. The commercial effect is more output and more consistent quality from the team you have, with fewer hours lost to low-value tasks.
Signs you need this
If two or more of these sound familiar, there's likely time and money to recover.
Uneven, hidden AI use
A few enthusiasts use AI constantly, most people don't use it at all, and some use personal accounts you can't see. You carry the risk without getting the benefit.
Licences bought, habits unchanged
You pay for Copilot or Gemini seats, but use dropped away after the launch email. An unused licence is a cost with no return.
Quality depends on who did it
The same proposal, report or customer reply varies widely from one person to the next. Inconsistency costs rework and puts client relationships at risk.
Your experts are the bottleneck
Juniors wait on seniors for reviews, answers and sign-off. Throughput is capped by a handful of calendars.
What changes for the business
More output from the same team
Drafting, summarising and searching move to AI, while people review and decide. Teams take on more work before the next hire is needed.
Consistent quality across the team
Shared prompts, templates and worked examples set a standard everyone works from. Output stops depending on who happened to pick up the task.
Expertise that reaches further
Senior knowledge is captured in assistants, checklists and examples that juniors can use on their own. Experts answer fewer repeat questions and spend more time on high-value work.
A return on licences you already pay for
Role-based training turns idle Copilot or Gemini seats into daily tools. You get value from existing spend before buying anything new.
Less shadow AI
Approved tools and clear rules give staff a safe alternative to personal accounts. Client and staff information stays inside services you control.
Faster ramp-up for new staff
New hires learn from role guides, assistants and worked examples instead of relying entirely on shadowing. They reach useful output sooner, at less cost to the people training them.
What we build
Role and task analysis
We map the tasks in each target role, how long they take, and which suit AI support, human judgement or removal altogether.
Approved tools and setup
Selection and configuration of business-grade AI tools, with the settings, permissions and data rules that suit your organisation.
Role-based training
Hands-on sessions built on each team's real work, such as drafting a client update from meeting notes or checking a contract summary, rather than generic demos.
Prompt and playbook library
Tested prompts, templates and examples for each role, stored where staff already work and kept current by a named owner.
AI champions network
A small group of internal champions trained in more depth to support colleagues, collect ideas and raise problems early.
Adoption tracking and coaching
Usage, time sampling and quality checks reported monthly, with follow-up coaching wherever adoption stalls.
Technology we work with
- Microsoft 365 Copilot
- Copilot Studio
- Google Workspace and Gemini
- ChatGPT Business and Enterprise
- Claude for Work
- Microsoft Teams
- SharePoint and OneDrive
- Slack
How it runs
Timings are indicative and depend on your systems, data and approvals.
Assess
We review current AI use, tools, policies and appetite across teams, then choose two or three roles to start with.
1 to 2 weeks (indicative)
Redesign the work
With managers and staff, we decide which tasks AI drafts, which stay with people, and where checking is mandatory.
1 to 3 weeks (indicative)
Equip and train
Tools are configured, playbooks written and role-based training delivered, with champions trained first.
2 to 4 weeks (indicative)
Embed
Coaching, drop-in sessions and usage reviews over the first months, adjusting playbooks to what people actually do.
4 to 12 weeks (indicative)
Measure and extend
We report capacity released and quality, then extend to the next roles.
Ongoing
Worked example
Four-site dental group: from scattered enquiries to booked consultations
An illustrative four-site dental group spends heavily on implant and cosmetic advertising, but enquiries arrive through five channels and nobody can say which campaigns produce booked consultations. The system puts every enquiry in one place, answers it within minutes, follows up unaccepted treatment plans and reports accepted treatment by campaign and practice.
- Google Ads, Meta and search
- Web forms, calls and social messages
- CRM record with source attached
- Instant acknowledgement and AI-drafted reply
- Staff review and booking in the practice system
- Treatment-plan follow-up
- Booked-revenue dashboard
Guardrails, fit and measurement
Guardrails we apply
- An AI use policy and approved-tools list are in place before training starts, so people know what is allowed.
- Staff are trained to keep personal and sensitive information out of publicly available AI tools, as the OAIC recommends.
- Clear rules on when a person must check AI output, scaled to the risk of the task.
- We report adoption and capacity at team level; this is not a tool for monitoring individuals.
- Changes to roles are discussed with managers and staff before rollout, not announced after it.
Not the right fit if
- The goal is to cut headcount next quarter. We design for capacity and capability, and adoption depends on staff trusting the purpose.
- Leadership won't use the tools themselves. Teams take their cue from managers, so we would start with the leadership team or not at all.
- A small team with no plans to grow. A business AI subscription and a single workshop may be all you need.
What we measure
- Weekly active users of approved AI tools, by team
- Hours released per role, from time sampling before and after
- Rework or correction rate on AI-assisted work
- Utilisation of paid AI licences
- Time for new starters to reach agreed output
- Staff confidence from short pulse surveys
Pricing
Workforce programs are scoped individually, usually after an Altitude Diagnostic or a fixed-scope discovery, so we don't publish project prices.
Questions, answered.
Is this about replacing staff?
No. We design for people using AI to remove low-value work so they can do more valuable work, and we report capacity created rather than jobs removed. What you do with that capacity, such as growing without extra admin roles or improving service, is your decision.
What if staff don't use it?
That is the risk this service exists to manage. Training uses each team's real work, champions support colleagues day to day, and we track weekly use so a stalled team gets coaching, not another reminder email.
Can you run general AI training for all staff?
We can, but we recommend role-based sessions instead. A session on drafting a quote cover letter for estimators lands better than a generic overview for 80 people, and it produces something staff can use the next day.
Which AI tools should our staff use?
Usually the AI built into platforms you already pay for, such as Microsoft 365 Copilot or Gemini in Google Workspace, plus a business-grade assistant for tasks those don't cover. Consistent with OAIC guidance, we keep personal information out of public consumer tools, and we choose business services whose published policies say your data isn't used for model training by default.
How long until people are actually using it?
Training can start within weeks, but habits take longer. We plan for an embedding period of 1 to 3 months with coaching and usage reviews, and measure adoption weekly throughout.
Related services
- AI assistants & knowledge systemsRole-specific AI assistants that answer from your own documents, cite the source and respect permissions. Built on Microsoft 365, Google or a custom stack.
- AI governance & implementationAI governance for Australian businesses: a use policy, approved tools, data rules and human oversight, informed by OAIC and National AI Centre guidance.
- AI strategy & roadmapsAn AI roadmap built on evidence: nine lenses, a do-nothing baseline, confidence-labelled estimates and a Now/Next/Later plan your board can act on.
- Business process automationAutomate intake, quoting, follow-up, onboarding and reporting across the systems you already use. Measured in hours released, with people on exceptions.
Where this matters most
Start with two roles
Tell us which teams carry the most admin, and we'll outline what AI support and training for those roles would involve.
Sources
Third-party facts on this page, checked 14 October 2026.
- Guidance on privacy and the use of commercially available AI products, Office of the Australian Information Commissioner (OAIC). www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products
- Business data privacy, security, and compliance, OpenAI. openai.com/business-data
- Is my data used for model training?, Anthropic Privacy Center. privacy.claude.com/en/articles/7996868-is-my-data-used-for-model-training