Peter Sorgenfrei

Live a happier and healthier life. I'll get you there in 3 months or less.

If you are a founder, the outside story probably looks good.

Growing revenue, active pipeline, publicity, and a team firing on all cylinders.

Inside, it feels like you are sprinting while everyone else is jogging. 

Like no one understands what you say or who you are.

Most days are a blur of investors, product, people issues, and late-night email. 

Sleep is short you are sharper than you want to be at home and with your team.

Quietly worrying, you have become “that” founder.

You have done the usual loop: Podcasts (DOAC anyone?), founder books (Hey Alex H), maybe a coach or therapist.

Useful ideas, but nobody has fully sat where you sit. 

Where burn rate, board pressure and a tired relationship collide in one nervous system.

I have. 

I am a six-time founder who burned out in 2019 and rebuilt from there.

Now I coach founders of 10–400 person companies who are done pretending it is fine. 

Within the first month, meeting load is often down 15–30 percent, sleep is up, and someone on the team says, “You seem different.”

What we do together is simple, not easy.

- One weekly session to see the whole system. You, your Company, and your relationships in one picture

- A few non-negotiable habits. Protected mornings, cleaner goals, listening more, being present at home

- Ongoing WhatsApp so you are not alone when the next escalation hits or when you feel alone.

Clients sometimes call me the wise monk. 

Calm, direct, caring, and not afraid to say what others will not.

If you are reading this on your phone in bed, DM me “Calm” and tell me how things really are.

Executive and Board Advisory

I work with Investors, Boards, and Leadership teams to develop people and grow companies

I have held leadership roles across technology, ecommerce, travel, and digital transformation. I was APAC Regional GM at CloudKitchens, founder of AI enabled review startup revuefinda, and ANZ Country GM at Zomato, where I led teams of 90+ and owned P&L and people performance. Today, I lead the activation and scaling of AI across complex organisations, doing strategy, enablement, and execution. 

I bring extensive experience in AI program management and transformation, with deep expertise in large language models, generative AI, and enterprise deployment. My focus is on strategic alignment, roadmap execution, and cross-functional leadership to turn AI concepts into operational systems that deliver business value. I work with senior stakeholders to identify and prioritise use cases, build enablement frameworks, manage intake pipelines, and guide teams.

𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗦𝗸𝗶𝗹𝗹𝘀:
• Strategic leadership in executive management, financial oversight, and go-to-market strategies
• Track record in launching products, creating revenue models, building high-performing teams
• Commercial acumen, leading organisations through change and market opportunity
• Expertise in marketing, pricing, and operational models for scale
• Experienced P&L leader managing large teams of 80+

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
• Strategy & Business Development: Built and executed business plans aligned with strategic goals, hitting GOP targets while tracking market trends and expansion opportunities
• Sales & Customer Success: Led teams to revenue and acquisition targets, managed key accounts, shaped pricing strategy, and grew brand presence
• Relationship Management: Built stakeholder trust, represented brands at industry events, and led senior conversations on growth and performance
• Operations & Finance: Oversaw day-to-day operations, managed P&L, improved efficiency, and ensured compliance
• People & Culture: Led recruitment, performance, and talent development, building collaborative, high-performing teams

𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝘀:
- I’m an ILEC-certified executive coach and manage shackleford. - a culture and executive coaching company. Here, I help emerging leadership teams address leadership development & organisational culture performance opportunities through John Mattone's executive coaching programme.

- I lead AI Strategy Consulting, specialising in executive education and ethical AI governance. We help leaders adopt AI responsibly through readiness programs, governance frameworks, and applied training in generative AI and automation.

Ryan Flanagan

Can AI add $19 billion to government capacity by 2030? Australia's APS AI Plan says yes. Here's how the numbers work.

Two numbers underpin the Plan: a 13% public sector gain worth $19 billion, and a 4.3% market sector uplift worth $116 billion. 

Both come from task-level analysis of what AI can automate, augment, or accelerate.

## The 13% Public Sector Gross Value Added Uplift

The 13% uplift comes from economist Shahar Merom's October 2024 report, Australia's AI Opportunity: Adopt, Develop, Lead.

Merom broke government work into individual tasks, assessed which AI could automate or enhance, and modelled the impact if agencies reached AI maturity by 2030.

The result: government could produce 13% more value with the same workforce. That equates to $𝟭𝟵 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 in annual capacity by 2030. 

Where the $𝟭𝟵 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 comes from:

• 30-40% time savings in drafting, research, and data entry

• Faster FOI, grants, and procurement processing

• Stronger policy analysis with broader evidence

• Fewer errors through consistent decision pathways

• Small agencies accessing analytical capability previously limited to large departments

## The 4.3% Labour Productivity Gain

The 4.3% labour productivity projection comes from the Productivity Commission's 2024 interim report, Harnessing Data and Digital Technology.

It models AI's impact on the market sector from 2024 to 2034 under conservative assumptions, holding population, working hours, and employment patterns constant.

The result: workers produce $𝟰.𝟯𝟬 more value per hour by 2034, adding $𝟭𝟭𝟲 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 to GDP, or about $𝟰,𝟯𝟬𝟬 per person annually.

Where the $𝟭𝟭𝟲 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 comes from:

• Process automation: Streamlining repetitive admin tasks, reducing errors, accelerating workflows

• Augmented decision-making and content generation: AI-assisted analysis and drafting

• Operational optimisation: Better resource allocation, demand forecasting, and efficiency

## Why These Numbers Matter

Government accounts for 𝟮𝟳% of Australia's GDP. When public sector productivity improves, the entire economy benefits through faster approvals, clearer regulations, and reduced friction.

When government processes speed up: faster grants, clearer regulations, quicker approvals: businesses face less friction. The public sector's productivity gain removes delays that currently slow the broader economy.

But neither the 13% nor the 4.3% is automatic.

3 things will determine whether the economy captures the gains:

Training at scale: Time to experiment, permission to fail, support to redesign workflows.

Enabling governance: Clear rules, not endless approval loops.

Reliable infrastructure: Fast, stable tools integrated into daily work.

The opportunity is real. The numbers are real. The challenge is turning potential into practice.

Leading AI Activation, Enablement & Governance for Government. CAIO & ISO 42001 AI Management Systems Auditor.

How do you train 200,000 public servants in AI when most organisations can't prompt Copilot beyond an email draft?

Australia's APS AI Plan 2025 recognises this: technology alone is insufficient.

The "People Pillar" isn't soft skills window dressing. It's the difference between adoption and abandonment.

The APS is mandating foundational AI literacy for every employee through the AI Plan. Every single one.

That's a lot of morning teas.

The APS Copilot trial showed that 75% of participants who received 3+ forms of training were confident using AI tools, 28% higher than those who received only one.

One-off training doesn't work.
Multiple touchpoints & formats do.
The pattern holds across jurisdictions.
Across multiple departments and numbers.

Here's how to get it right:

• 𝗠𝗮𝗸𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 𝗮𝗻𝗱 𝘁𝗶𝗲 𝗶𝘁 𝘁𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: If completion isn't a KPI for executives and staff alike, it won't happen.

• 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿𝘀 𝘄𝗵𝗼 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: If your CAIO sits three levels below the CFO, they can't shape budget or strategy.

• 𝗣𝗿𝗼𝗽𝗲𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝘂𝗱𝗶𝘁𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: You can't close capability gaps you haven't measured.

• 𝗧𝗶𝗲𝗿𝗲𝗱 𝗽𝗮𝘁𝗵𝘄𝗮𝘆𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗶𝗰𝗸-𝗯𝗼𝘅 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀: Without clear routes to intermediate and advanced skills, your best people plateau or leave.

• 𝗥𝗲𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗼𝗳 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲, 𝗻𝗼𝘁 𝗧𝗲𝗮𝗺𝘀 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗻𝗼 𝗼𝗻𝗲 𝗿𝗲𝗮𝗱𝘀: Peer learning accelerates adoption faster than top-down mandates.

• 𝗔𝗜 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗶𝗻𝘁𝗼 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴, 𝗻𝗼𝘁 𝗯𝗼𝗹𝘁𝗲𝗱 𝗼𝗻 𝗮𝘀 𝗮 𝘀𝗶𝗱𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: If training lives separate from onboarding and performance goals, it stays optional.

• 𝗧𝗿𝗮𝗰𝗸 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗮𝗻𝗱 𝗮𝗱𝗷𝘂𝘀𝘁 𝗾𝘂𝗮𝗿𝘁𝗲𝗿𝗹𝘆, 𝗻𝗼𝘁 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆: Measure completion rates, confidence levels, use case adoption, and time saved, then adjust course.

Get this right: staff use the tools, productivity rises, service delivery improves, and retention holds.

Get it wrong: the plan becomes another failed transformation that proves "AI doesn't work here."

The workforce that learns together builds capability together.

No alternative text description for this image

I tested Google Notebook LLM’s slide feature to see if white-collar jobs are feeling the same squeeze that hit 80’s blue-collar jobs.

And it’s probably true- ish.

But the actual shape of the AI transition is predictable thinking work being automated before anyone notices.

I’ve been mapping this shift on task-level automation, entry-level bottlenecks, and the erosion of routine analytical work. So I tested the new Google Notebook LLM slide feature against this exact material.

Now. I’m not an AI tool-stacker.
I don't do the “one CustomGPT will replace your ELT” thing. 

But.

What I wanted to know was simple: 

Can an AI tool handle dense content and produce a coherent structure?

I pushed the full set of sources into it: ILO & WEF displacement forecasts, skills exposure, labour-market churn, 1848 revolutions, previous technology transitions and the thesis that this isn’t a job crisis, it’s a skills crisis. 

Basically, the theory that we are all going to be jobless and become weird influencers on UBI.

It produced 2 decks in minutes: one 14-slide summary and one 24-slide version using the new image integration without breaking thinking logic (or a sweat).

I used the same tool that is compressing routine output work to produce the output attached here. It didn’t replace the thinking. 

It replaced the doing.

That’s the point.
Anything that looks like polished output is now automated. What stood out wasn’t the slide design. It was the accuracy.

It isn’t polished (it is pretty impressive), but it’s coherent enough that anyone working on AI strategy, workforce planning, or capability development will recognise what this means.

If a model can already turn detailed, multi-source material into a full presentation with this level of structure in minutes, the baseline for knowledge work has moved (again).

Maybe technologies don’t decide outcomes.
The people using them do.

The first deck is attached, the second title page in the first comment. Take a look. Not bad, not great, but good. Judge for yourself.

Notebook LLM & the shift hitting white-collar work.

What is a CAIO? The Australian Public Service is about to appoint its first wave of Chief AI Officers. By July 2026, every agency will have one.

This is recognition that AI adoption is a transformation problem, not a technology problem.

The role has a history:

The term "Chief AI Officer" was first predicted in 2013. Andrew Ng wrote in Harvard Business Review that companies should hire one in 2016.

Early examples: WPP - 2021, GE HealthCare - 2022, NASA - 2024.

The 2024 U.S. Executive Order 14110 mandated that every US federal agency appoint a CAIO. 

Now Australia is following.

So what makes a good CAIO?

Technical depth:

• RAG &  vector database fluency
• Systems integration with legacy platforms
• Risk frameworks: Voluntary AI Safety Standard, ISO 42011 AIMS
• Infrastructure economics: compute costs & cost-per-query metrics

Soft skill breadth:

• Storytelling: turning complexity into clear value.
• Org Psych: spotting power dynamics and resistance.
• Ethics: holding when pressure clashes with responsible AI.
• Diplomacy: building coalitions & keeping sceptics engaged.
• Change management: guiding resistance without defensiveness.

An effective CAIO is a simple translator. They build bridges between algorithmic possibility and business value.

The challenges every CAIO will face:

1. Shadow AI: unsanctioned tools. 
2. POC Purgatory: pilots that fail in production. 
3. Data Readiness: missing fields, inconsistent formats. 
4. Measuring ROI: proving value when benefits are diffuse.
5. Talent War: competing for skills whilst retaining domain experts.
6. Ethical Landmines: bias, privacy breaches, unexplainable decisions.
7. Organisational Inertia: legacy procurement (6 months) to approve pilots.

Why this matters:

AI moves faster than usual ICT adoption cycles. 
It impacts all aspects of public service work.
Without dedicated leadership, the APS risks lagging the private sector.

Not a technologist. 
Not a compliance officer.
A transformation leader who can contest default assumptions and lead change. 

Or as I like to think of it .....

Someone who can tell simple truths that compound instead of complex narratives that confuse.

taplio

The Battle

Peter Sorgenfrei

Ryan Flanagan

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