Michael Brawn Consulting

20/08/2026

Small Businesses Go Bust Due to Poor Governance – Not Poor Sales and Marketing

The failure of small businesses is mainly due to poor governance. The fact is that few small businesses fail due to poor sales and marketing alone. Poor governance enables a cascade of negative outcomes such as inadequate strategic planning, weak risk management, and a lack of accountability, all of which are crucial for the success of a business.

Small businesses, and the non-profit and charity sectors are particularly prone to failures of governance, risk management and compliance. This is because their early focus from startup is on achieving the vision and delivering on the mission – which often translates into a laser focus on sales and marketing. However, what sustains a new small business, charity or non-profit organisation over the longer term are the solid foundations they build from day one, and the strong scaffolding of governance, risk management and compliance that protect them from inevitable headwinds down the line.

Poor Governance, Risk Management and Compliance Put Small Businesses at Risk

Strong governance, practical risk management and consistent compliance aren’t “big business” luxuries, they’re survival tools for small enterprises operating in today’s volatile environment. When these foundations are weak, small businesses become exposed to financial shocks, regulatory breaches, and rapid loss of stakeholder confidence. Recent Australian insolvency trends show just how quickly gaps in oversight can turn into business failure.

ASIC reported 5,520 liquidations and administrations in 2022/23, a 17.2% increase from the previous year, with small and medium enterprises dominating insolvency numbers.

Current Economic Risks Facing Small Businesses:

• Aggressive interest rate rises, from 0.10% to 4.35% in just 18 months, sharply increasing borrowing costs and reducing consumer spending.
• Cost of living pressures, households cutting back due to rising fuel, food, insurance, tax and mortgage costs, reducing demand for many small business offerings.
• Supply chain disruptions, including global transport issues such as those in the Middle East, delaying goods and increasing costs.
• Skilled labour shortages, lingering impacts from COVID 19 immigration restrictions, making it harder to maintain service levels or complete projects.
• Shift to online shopping, accelerated during COVID 19, leaving traditional retailers struggling to adapt fast enough.
• Slim margins, small and medium businesses are often the first to feel economic shocks because they operate with limited buffers.

Common Causes of Business Failure include:

• Failure to adapt business models to changing consumer behaviour
• High operating costs and poor cost control discipline
• Excessive debt and covenant breaches
• Weak cash flow monitoring and forecasting
• Inadequate negotiation with suppliers, lenders or the ATO
• Poor inventory management and pricing strategies
• Lack of contingency planning for downturns
• Compliance failures, including trading while insolvent, a serious breach under the Corporations Act (s588G)

Governance, Risk and Compliance Matter More Than Ever

In turbulent times, good governance is not optional. Directors and owners must actively monitor cash flow, negotiate with stakeholders, cut unnecessary costs, manage debt early, and maintain transparent communication with staff and customers.
Poor governance leads to slow decision making, missed warning signs, and failure to act early, all of which accelerate insolvency. Weak risk management leaves businesses exposed to predictable shocks. And poor compliance can trigger penalties, legal action, or forced closure.

In short: small businesses fail not only because conditions are tough, but because they are unprepared for tough conditions.

Call me if you would like to build strong governance foundations for your business

Michael Brawn — Michael Brawn Consulting (MBC)
📱 0414 987 129
📧 [email protected]
🌐

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

28/07/2026

Small Language Models are a gift for socially and environmentally responsible AI

Small Language Models (SMLs) can run without data centres. They provide flexibility, keep your data on your PC, overcome internet latency and enable good governance.

In this blog I focus on the opportunity to reduce data centre dependence when using AI.

SLMs are designed to run on the modest hardware requirements of AI laptops and offer a viable alternative to the voracious energy and water demands of data centre AI.

A wide range of SLMs are available to choose from. SLMs cover diverse topics with good enough quality that an AI PC can now be a rational investment. The SLM market is mature, fast moving, and no longer niche.

2025–2026 produced dozens of strong models across the 1B–14B parameter range. More parameters = more capable AI, covering general chat, reasoning, coding, multilingual tasks, and even multimodal input - but more parameters also increase memory, processing and storage requirements.

• Modern 2B–4B models now deliver useful chat, summarisation, and tool use behaviour, with some (Gemma 4 e2b) even supporting image input at tiny sizes
• The strongest 3–4B models (Phi 4 mini, Qwen3 3B, SmolLM3, Llama 3.2 3B) reach MMLU scores in the mid-60s to 70, approaching GPT 3.5 level quality on consumer hardware
• Sub 4B models run comfortably on 4–8 GB RAM, delivering 30–70 tokens/sec on a CPU which is fast enough for real time chat

This is a dramatic shift from 2023–2024, when anything under 7B struggled with following basic instructions.

• A modern laptop with 8–16 GB RAM can run 3B–8B models locally with good performance. Qwen3 (1.7B–14B), Gemma 3 (1B–12B), and Phi 4 mini (3.8B) are explicitly designed for this tier
• Even low-end PCs with no GPU can run capable models like Qwen3 1.7B or Phi 4 mini at 15–40 tokens/sec
• With a mid-range GPU (e.g., 12–24 GB VRAM), you can run Phi 4 14B, which competes with much larger models on reasoning and coding

So, you no longer need cloud inference for everyday tasks as local models are fast, cheap, private, and increasingly powerful. There are strong models for:
• General chat & reasoning: Phi 4 mini, Llama 3.2 3B
• Coding: Qwen3 3B, Granite 3.x
• Multilingual tasks: Qwen3 family
• Enterprise & compliance: IBM Granite 3.x
• Mobile/embedded: Qwen3 1.7B, Gemma 4 e2b
• Multimodal (text + image): Gemma 4 e2b/e4b

This breadth means an AI PC can run multiple specialised models locally depending on the task. That is something cloud only setups cannot do without switching providers or paying for multiple APIs.

If your goal is to run AI models locally, privately, and without cloud dependency, the investment in an AI PC may now make sense.
If you want to use AI in an environmentally and socially responsible way, local models give you predictable behaviour, zero cost inference, and full control over your workflows.

Give me a call if you would value some guidance on how to select one or more SLMs for your specific needs, how to download them and the appropriate AI Runtime Framework, and how to start reducing your dependency on AI data centres.

Mick at Michael Brawn Consulting (MBC)
📱 0414 987 129
📧 [email protected]
🌐 www.michaelbrawnconsulting.com.au

* MMLU stands for the Massive Multitask Language Understanding benchmark test used to measure how well language models can apply general knowledge and reasoning across a wide range of subjects.

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

19/07/2026

Artificial Intelligence – What it Isn’t

Artificial intelligence is not alive, it has no feelings, subjective experience or agency. AI has no understanding, no common sense, no consciousness or sentience. It is not truly Intelligent and cannot think like humans.

Artificial intelligence is not alive
AI is a collection of software, mathematical models, and code running on computer hardware. It can simulate human conversation, create art, and solve complex problems but lacks the fundamental biological, chemical, and experiential properties that define life.

Biological Limitations
• No Organism Structure: AI consists of code lines and data stored on silicon chips and servers. It does not have cells, DNA, or organic matter
• No Self-Sustenance: Living things metabolize energy, grow, repair themselves, and reproduce. AI requires human intervention to maintain servers, provide electricity, and update its code
• No Homeostasis: AI cannot actively regulate its own internal state to survive in changing environments

Consciousness and Sentience
• Absence of Feelings: AI does not experience emotions, physical pain, pleasure, or desires. When an AI says it is "happy to help," it is simply predicting and generating a socially appropriate string of text
• No Subjective Experience: AI lacks qualia, the internal, subjective experience of the world. It can process the data of a sunset or the chemical composition of a flower, but it cannot "experience" them
• Lack of Agency: AI does not have free will or personal intent. It only acts when prompted by a human user or triggered by pre-written code

AI is a ‘Virtual Machine’
Modern AI can easily fool human intuition because it is trained on vast amounts of human data. It mirrors our empathy, humour, and intelligence so accurately that our brains naturally want to anthropomorphize it (attribute human traits to non-human things). However, this is an emulation of human life, not a sign that the AI itself is alive.

AI does exhibit a kind of intelligence, but its intelligence is computational, not human. So, whether AI is considered "intelligent" depends entirely on how you define the word.

How AI Expresses Intelligence
• Pattern Recognition: It finds hidden trends in massive data
• Problem Solving: It beats grandmasters at complex games
• Adaptability: It learns and improves from new data
• Task Performance: It automates highly complex human work

Why AI Is Not Truly Intelligent
• No Understanding: It processes symbols without knowing meaning
• No Common Sense: It lacks basic real-world logic
• No Consciousness: It has no awareness or feelings
• Strict Dependency: It cannot think outside its programming
In short, AI possesses a kind of narrow operational intelligence (doing smart things) but lacks general cognitive intelligence (being a smart being)

AI Cannot Think Like Humans
Artificial Intelligence can mimic human-like behaviour, e.g. writing, creating, and analysing data, but it doesn't truly think like humans – and without our biological features: embodiment, instincts, emotions, values, sentience and consciousness, it never will.

Call me if you would like to chat about your use of AI

Michael Brawn — Michael Brawn Consulting (MBC)
📱 0414 987 129
📧 [email protected]
🌐

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

11/07/2026

AI + Tax Time: What Small Businesses Need to Know.

AI can make tax time much easier, but only if you use it safely.

AI is good for:
• organising receipts, expenses and records
• explaining tax basics in plain English
• summarising long documents
• surfacing gaps, missing info and forgotten deductions
• helping you prep questions for your tax agent

But here’s the key message: AI is not a tax agent, and you’re still responsible for your return.

Use AI to think things through, but not to make final decisions.

Why the caution?
• AI sometimes mixes in overseas rules or outdated info
• It can sound confident while being completely wrong
• The ATO uses its own AI analytics to check your return

Stay safe with a few simple rules
• Never paste your TFN, myGov login or detailed financial records into public AI tools
• Ask AI to provide its sources and then check anything important against the ATO website
• Watch for non Australian wording, thresholds or dates
• For anything complex (investments, CGT, overseas income), talk to a registered tax agent

ATO’s quick pre lodgement checklist
• Did I keep personal details out of AI tools?
• Can I trace every claim back to official ATO info?
• Does everything reflect Australian rules?
• Have I checked the final figures myself?
• For complex items, have I spoken to a professional?

If you can say “yes” to all of these, AI has helped — and you’ve stayed in control.

Call me if you would like to chat about your use of AI

Michael Brawn — Michael Brawn Consulting (MBC)
📱 0414 987 129
📧 [email protected]
🌐

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

02/07/2026

The Business Case for Implementing AI in Small Business

Driving Productivity, Reducing Costs, and Improving Customer Experience

Executive Summary
Small businesses face rising operating costs, limited staff capacity, and increasing administrative burden. Artificial Intelligence (AI) offers a practical, affordable way to automate routine work, improve customer engagement, and strengthen decision-making. When implemented deliberately, with clear workflows, lightweight governance, and staff training, AI delivers 20–40% productivity gains and rapid, measurable ROI.

1. Strategic Opportunity
AI is no longer experimental. It is a practical business tool that enhances:
• Productivity: Automates repetitive tasks (emails, reports, proposals, minutes, marketing content).
• Customer Experience: Enables faster responses, personalised communication, and consistent follow ups.
• Sales: Supports lead generation, CRM updates, and targeted outreach.
• Decision-Making: Provides rapid analysis of financials, customer behaviour, and operational trends.
• Cost Efficiency: Reduces reliance on external contractors and streamlines internal processes.

2. Value Areas & Expected Benefits
A. Productivity & Time Savings
• 5–10 hours saved per employee per week.
• Faster turnaround times and reduced backlog.
• Less administrative pressure on owners and managers.
B. Sales & Customer Growth
• Improved lead conversion through automated follow ups.
• Enhanced customer insights from email, review, and social media analysis.
• More consistent engagement without additional staff.
C. Operational Efficiency
• Automated workflows across marketing, admin, HR, and service delivery.
• Standardised templates and documentation.
• Reduced duplication and fewer bottlenecks.
D. Better Decisions
• Clearer visibility of trends and risks.
• Faster scenario modelling and forecasting.
• Access to internal knowledge through retrieval augmented generation (RAG).

3. Requirements for Profitable Implementation
A. Identify High-Value Use Cases
Start with tasks that are repetitive, time-consuming, rules-based, and easy to measure. Examples: customer enquiries, marketing content, CRM updates, reporting, onboarding.
B. Map Workflows Before Adding AI
Create Standard Operating Procedures for your most demanding, challenging or time-consuming business processes. Document steps, decision points, approval requirements, and quality standards. AI succeeds when workflows are predictable and auditable.
C. Integrate AI with Existing Systems
Choose tools that plug into:
• Microsoft 365
• Google Workspace Apps: communication, collaboration, storage, and content creation
• CRM
• Accounting software
• Website CMS
• Booking/ticketing systems
Integration = automation + reduced friction.
D. Establish Lightweight Governance
Set simple rules:
• What AI drafts
• What humans approve
• Where data is stored
• How outputs are checked
• How errors are corrected
Protects brand, compliance, and customer trust.
E. Train Staff in Practical Use
Focus on task-based training:
• Effective prompting
• Reviewing AI outputs
• Automating workflows
• Using AI inside existing tools
Confidence drives adoption.
F. Measure ROI Monthly
Track:
• Hours saved
• Cost avoided
• Sales generated
• Customer satisfaction
• Error reduction
Adjust use cases based on results.

4. Investment & Resourcing
• Staff training (initial + ongoing).
• Workflow mapping and documentation.
• Integration with existing systems.
• Light governance setup.
• Optional: external support for change management or automation design.

5. Risks & Controls
Risks: inconsistent outputs, data misuse, over reliance, poor workflow fit. Controls: human-in-the-loop approvals, clear data rules, quality checks, documented workflows.

6. Recommendation
Implement AI in targeted workflows (admin, marketing, sales, customer service) using integrated tools, lightweight governance, and staff training. Focus on measurable outcomes and monthly ROI tracking. AI becomes profitable when deployed deliberately, not broadly or experimentally.

7. Next Steps
Implement AI as a practical, profitable tool for your small businesses with clear workflows, simple governance, and a focus on high value tasks.

Michael Brawn Consulting has extensive experience of implementing generative AI across Asia.
For a chat, contact [email protected]
0414 987 129

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

24/06/2026

Five AI challenges that will impact small businesses over the coming months and years

Before we dive into the challenges, let’s reset on the variety of AI systems out there – spoiler alert, it’s not just LLMs. Here are a few of the best-known alternatives:

1. Graph Neural Networks (GNNs) are used to process complex data structures such as graphs

2. Convolutional Neural Networks (CNNs) are for image processing and to enable computer vision

3. Transformers are an architecture model used for AI requirements beyond language processing

4. Reinforcement Learning (RL) is a training method for AI models that maximises rewards through trial and error enabling the model itself to evolve based on experience

5. Generative Adversarial Networks (GANs) generate new data to resemble test data so as to create additional training data

6. New AI chipsets enable on-device (think laptop or smartphone) AI processing to avoid expensive network, energy and data centre demand.

7. Traditional single use A models are evolving fast for specific scientific uses such as medical diagnosis and the development of new materials and medicines.

Most small businesses are not aware of these varieties of AI and cannot therefore prepare for their arrival. However, LLMs plus a host of emerging technologies have created an urgent need for governance and risk management. Here are five areas that you will need to work on now.

1. Establish a strong and foundation for your use of AI
Define and apply your core principles for AI use, understanding that these principles will inevitably trigger changes in:
• Governance,
• Operational Change Management,
• Risk Management,
• Compliance and Control, and
• The evolution and refinement of your Business Culture.

Think through the governance and ethical policies you will need. All aspects of your business will be influenced by your take up of AI. Change will be rapid and must be managed.

2. AI technology will not stand still – neither will the necessary governance and controls

The evolution of AI has been characterised by rapid invention and reinvention. Large Language Models (LLMs) have been augmented with prompt engineering which has been further enhanced with the use of AI agents. And I agents will very soon be augmented by agentic management and monitoring systems. Traditional Business Process Re-Engineering is already being augmented with automated with AI models and redefined with systems of agents. Plan how you will reengineer your business processes with AI.

3. Agentic AI will create chaos even as it creates order
The adoption of AI agents is expanding exponentially – without proper governance, management systems and monitoring in place. There is a significant risk for businesses that adopt AI without having sufficient controls in place that they will develop uncontrolled shadow IT. We can expect exponential growth triggering massive change, new opportunities and unmanaged risks. Implement an agent catalogue or other governance mechanism for agents now.

4. Risk management is not optional

The risks implicit in uncontrolled new AI applications will demand complex risk management from the outset. Do not wait for government regulation. Do not hesitate while jurisdictional issues are resolved. Define and enact your AI risk management approach now.

5. AI competition is ‘red in tooth and claw’

AI companies are forging ahead regardless of regulation. For national regulators, data and processing sovereignty are already top of mind – but the essential legislative and regulatory infrastructure is still in development. Expect a patchwork of disjointed policies to evolve internationally, and put your own ethical, governance, risk management and control mechanisms in place now.

For a chat about AI governance, contact [email protected]
Mobile: 0414 987 129

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

23/06/2026

Louise Brawn

17/06/2026

AI models continuously absorb and commoditize human expertise

We have been here before. The phrase "everything that can be digitised will be digitised" apocryphally attributed to Nobel laureate Paul Krugman, highlighted the ubiquitous capability from at least the early 1990s, to commoditise through duplication. Digital content (books, music, photographs and films) was available ‘for free’. The technology enabled it. It took several years for the law and digital rights management tech to build a moat around digital IP.

Economically, the ability to duplicate ad infinitum devalued the books, music, photographs and films that were copied – the value of each title tending to zero the more duplicates were made.
Now Microsoft CEO Satya Nadella says, “AI dominance could hollow out entire industries.” Allowing a few AI firms to control your data and information could cause widespread issues for companies of all sizes.

He says, "The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see," “There is no societal permission for an AI future that hollows out entire industries."

‘What is at stake is not some digital tool or system and its use, but how organizations continue to learn, build IP, differentiate, and thrive in when AI models continuously absorb the expertise of humans and organizations and commoditize it.’

Meanwhile, somewhere over at Invidia… CEO Jensen Huang admitted, “Society must change in era of artificial intelligence”, “…society has no choice but to change in the advent of AI.”
Mr Huang stressed the need for government regulation and safety standards for AI. He emphasised national security as a priority as AI technology has been powering stock market gains and much of the US economy in recent years.

He hopes “…society will adapt to AI just as it did to cars.” “When I was growing up, I used to play in the streets, … you obviously can’t play in the streets now.”

There’s the rub - apply the principle that whatever can be digitised will be digitised to human beings - not literally, but as AI agents. Further, apply the well understood and inevitable tendency for the value of duplicated and commoditised ‘things’ to drop towards zero…

An infinity of AI Agents, replicating human activities, commoditising human work and effort, would devalue human beings economically in the process.

How do we value human beings in a world where the economic value of their work tends inevitably to zero?

For a chat, contact [email protected]
0414 987 129

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

01/06/2026

Three high‑leverage, commercially realistic approaches Big Consulting could take to maintain relevance and protect billings

1. Pivot from “billable hours + juniors” to an AI‑augmented, senior‑led expertise model

Core idea: Doubles down on what AI cannot replace, i.e. human judgement, sector experience, crisis navigation, and trusted relationships, while using AI to eliminate low‑value work.

Why this fits the current AI environment

Clients now expect AI to automate research and analysis, eroding the traditional junior‑heavy model Current page.
Clients increasingly pay a premium for experienced consultants who have “been in the room when deals go wrong” (Ref)
Junior roles are being cut across the sector as AI replaces their tasks

Potential Options
- Rebuild delivery teams around small, senior pods supported by proprietary AI copilots.
- Introduce AI‑accelerated delivery guarantees (e.g., “48‑hour insights turnaround”).
- Sell judgement‑based advisory retainers rather than time-based billing.
- Position partners as “embedded strategic allies”, not deck‑producing vendors.

Commercial impact
- Protects premium pricing.
- Reduces delivery cost base.
- Differentiates Consulting from boutiques and in‑house teams.

2. Become the global leader in AI governance, AI assurance, and responsible AI operating models

Core idea: Leverage regulatory, risk, and audit DNA to own the emerging market for AI oversight, compliance, and trust frameworks.

Why this fits
Clients are already turning to consultants for help with how to use AI before rolling it out internally.
AI adoption is accelerating faster in-house than in agencies, creating a need for external assurance.

What could be done
- Build a global AI Assurance Practice (similar to cyber or ESG).
- Offer certification-style services:
- AI risk audits
- AI model transparency reviews
- AI governance frameworks
- AI regulatory readiness (EU AI Act, APRA CPS 230, etc.)
- Develop industry-specific AI control libraries (health, finance, energy, government).
- Partner with major AI vendors to become the “trusted verifier”.

Commercial impact
- Creates a new, defensible revenue stream.
- Aligns with consulting brand strengths (trust, compliance, risk).
- Positions as indispensable even when clients build AI in-house.

3. Build a hybrid “AI + human” managed services model that replaces traditional consulting

Core idea: Shift from episodic advisory to always-on, AI-powered managed services that clients subscribe to, thus blending automation with human escalation.

Why this fits

Clients are moving work in-house because AI makes it cheaper and faster. Budgets are tightening and clients want predictable, value-based spend.

What could be done
- Launch AI-enabled managed services for:
- continuous compliance monitoring
- real-time financial analytics
- automated reporting
- workforce optimisation
- supply chain risk alerts

Provide tiered subscription models (AI-only, AI + analyst, AI + senior advisor).

Use global scale to build proprietary AI platforms that boutiques cannot match.

Commercial impact
- Converts volatile project revenue into recurring revenue.
- Locks in long-term client relationships.
- Makes consulting part of the client’s operating system, not an optional consultant.

The strategic pattern across all three approaches. The path to relevance is not “more AI tools”. It’s rebuilding the business model around what clients now value:

- Speed (AI does the grunt work)
- Judgement (humans do the hard thinking)
- Trust (Consulting becomes the verifier of AI, not just a user of it)
- Predictability (subscriptions instead of time sheets)

This is the same shift we're all navigating in our own advisory practices, but at global scale.

Always happy to chat.

Mick Brawn
0414 987 129
[email protected]

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...

26/05/2026

Here's a happy thought

AI multiplies ‘notional worker’ numbers to infinity

Whereas digital automation reduced the unit cost of production of digital ‘things’ like music, books, movies – in fact anything that can be digitised and copied - Artificial Intelligence has radically increased the notional number of available workers and simultaneously reduced the cost of labour.

Just as digital automation vastly reduced the cost of production of digital ‘things’, AI has vastly reduced the cost and increased the availability of digital ‘people’.

The ‘Virtual Machine’ at the heart of early Information Technology evolution, has returned triumphant. No longer merely replicating digital ‘things’ but replicating digital people too – potentially without limit.

What will happen to the unit costs of raw materials, administration, management, production and distribution (i.e. the unit cost of products and services) as AI Agent Infrastructures take on those roles?

As our human populations inevitably decline worldwide will we be left merely to consume abundant, essentially free, products and services?

We had best hope not.

Mick Brawn
[email protected]
0414987129

In our view, ALL small businesses should build solid foundations by implementing these six essential areas: 1) Market Positioning 2) Business Strategy and Planning 3) CRM Selection and Setup 4) Risk Analysis and Management 5) Governance and Compliance 6) Policy Development Michael Brawn Consulting s...