Stop Buying AI Features. Start Building an AI Advantage.
I work with Australian SMB leaders to turn scattered AI experiments into a focused portfolio of projects that lift profit, reduce risk and actually fit the way your business runs. 20+ LinkedIn articles and 13 ebooks in 2025 on AI implementation—frameworks you can use.
Frameworks and playbooks used across insurance, manufacturing and services.
AWS Architect
Salesforce Architect
TOGAF Architect
ask CLI Agent
Terminal-based AI that plans and executes (not just suggests). One of the first CLI agentic agents with planning/acting loop and self-verification.
View on GitHub →Discovery Accelerator
Multi-agent reasoning using chess engine principles (MCTS). Shows rejected alternatives for board defense. Council of AI: 97% accuracy vs 80% single-model.
Why Most AI Projects Fail: The Three Traps
Industry studies document a 40-90% AI project failure rate. Most failures stem from three common patterns that SMBs fall into when treating strategic transformation like technology procurement.
The First Idea Trap
Seeing AI opportunities through only one lens. Taking the first obvious idea without genuine multi-perspective debate.
Example:
Operations sees "automate customer intake = save 2,200 hours/month." Revenue sees "$300K expansion revenue at risk from lost upsell calls." HR sees "attrition risk from removing meaningful work." Net result: lose $300K to save $200K.
Impact: $30-40B in wasted AI investment. 95% of pilots fail because companies solve the wrong problem by seeing it through only one dimension.
The One-Error Death Spiral
Deploying AI without baseline metrics or observability. When the first visible error happens, no data to prove AI outperforms humans.
Example:
Agent makes 15 mistakes out of 1,000 tasks (98.5% success). Executive asks "How often?" No observability = no data. Project cancelled despite possibly outperforming humans at their 3.8% error rate.
Impact: "One error = kill it" dynamic destroys projects that might be succeeding.
Maturity Mismatch
Treating AI deployment as SaaS procurement instead of software development. Jumping to R3-R4 automation when only ready for R0-R1.
Example:
Going straight to "AI handles customer refunds automatically" when organization lacks prompt version control, regression tests, or observability.
Impact: 80%+ failure rate, wasted $50K-$300K, team concludes "AI doesn't work for us."
The AI Investment Steward Model
Portfolio management + governance frameworks + ownership transfer = compounding returns
Capital Allocation Discipline
Treat AI like CFO treats investments—quarterly reviews, kill/fix/double-down decisions, strategic alignment. No random experiments, systematic evaluation.
As % of AI budget: 10-30% advisory ensures 70-90% compounds
Automated Controls, Not Committees
PII redaction layers, observability tracing, decision memos built INTO systems. 15-minute deployment vs. endless policy meetings. Safety = automatic.
Langfuse observability + Presidio PII redaction = $500-$1K/month governance
Build Capability, Not Dependency
Composable open-source stack (LangChain, Temporal), documented handoff, trained team. After 12-18 months, many clients reduce to quarterly reviews—we made ourselves redundant.
You own code, frameworks, infrastructure—no vendor lock-in
Proven Frameworks,
Research-Backed Insights
These aren't blog posts optimized for SEO. They're systematic explorations of AI deployment challenges and solutions—frameworks you can apply, research-backed insights, and counter-intuitive thinking tested against real-world implementations.
Why 42% of AI Projects Fail: The Three-Lens Framework for AI Deployment Success
Unspoken misalignment between CEO, HR, and Finance kills AI deployments despite working technology. Pre-deployment checklists and phase gates ensure all stakeholders align before building—preventing the organizational readiness gaps that doom 42% of initiatives.
The AI Paradox: Why 68% of SMBs Are Using AI But 72% Are Failing
68% of SMBs use AI, 72% struggle with integration. The problem isn't access or education—it's "Add-On Purgatory." Introducing the AI Bridge role and economic inversion: custom AI now cheaper than SaaS subscriptions over 18-24 months. Includes 90-day implementation plan.
Why AI Projects Are Failing - Explained
Running 2005 procurement playbooks against 2025 technology. Only 5% of enterprises extract consistent AI value (BCG/S&P). Four mental shifts framework: requirements→RFP→install becomes hypotheses→experiments→operating model. Addresses vendor AI-washing and procurement anti-patterns.
Why 95% of AI Pilots Fail—And How AI Think Tanks Solve the Discovery Problem
Most companies don't know what they want from AI. Multi-agent reasoning (Operations/Revenue/Risk/People brains debate ideas) produces 2× better results. The "John West Principle": showing rejected alternatives builds more trust than hiding them. Discovery problem vs. tool problem.
The Team of One: Why AI Enables Individuals to Outpace Organizations
When thinking costs drop to near-zero, bottleneck shifts from headcount to coordination architecture. Explains why 95% of corporate AI fails: organizations can't learn fast enough. Delegation architecture (treating AI as team members) + tight learning loops beat 50-person teams. Addresses $1.3M annual coordination tax per 1,000 employees.
Stop Automating. Start Replacing: Why Your AI Strategy Is Backwards
Don't grease the cogs—replace the machine. 80% of companies have AI but no earnings impact (Gen AI Paradox). Three levels of integration show 10× performance gap: Assistive 5-10% vs. Reimagined 60-90%. Banking example: 40 employees + 10 handoffs → 4 employees + 0 handoffs (1 day vs. 60-100 days).
Discovery Accelerators: The Path to AGI Through Visible Reasoning Systems
80% of AI projects fail because recommendations can't be defended to boards/regulators. Multi-dimensional reasoning (chess-inspired search across HR/Risk/Revenue/Brand lenses) shows rejected alternatives with transparent reasoning. Council of AIs achieved 97% accuracy on medical exams vs. 67% for single GPT-4.
Research citations: BCG, S&P, NIST AI RMF, OWASP LLM Top 10, OpenTelemetry GenAI, Gartner
Real-World Projects
Strategic architecture and innovative thinking driving business transformation
Covermore Travel Insurance
Challenge: Amadeus travel systems dominated the distribution channel, limiting direct relationships with key partners.
Strategic Solution: Designed and delivered complete disintermediation architecture—removing dependency on Amadeus while maintaining continuity.
Breakthrough Impact: Enabled enduring partnership with Flight Centre that became foundation for successful IPO.
Key Insight: Strategic architecture isn't just technical—it's about removing barriers to high-value relationships. Systematic thinking about dependencies and alternatives creates breakthrough opportunities.
Dynaquest
Challenge: HubSpot subscription costs escalating while core workflows (outbound sales, training, onboarding) needed custom automation.
Innovative Solution: AI-first architecture replacing conventional SaaS. Purpose-built workflows with systematic governance from day one.
Breakthrough Impact: Owned infrastructure, no vendor lock-in, custom workflows tuned to exact business needs. Systematic approach vs. feature accumulation.
Key Insight: Challenging conventional SaaS wisdom with systematic AI-first thinking. When you control the architecture, you control the evolution. Governance isn't bureaucracy—it's ownership.
"Strategic breakthroughs come from systematic thinking about dependencies, alternatives, and ownership—not from following vendor roadmaps."
Systematic Governance in 90 Days
Fast proof or fast kill—no 6-month POCs that die quietly
Audit & Clarity
Weeks 1-4
What: AI Portfolio Review—audit all vendors, tools, projects
Output: Kill/Fix/Double-down decisions, waste identified ($50K-$200K)
Deliverable: 90-day roadmap, Readiness Scorecard, board presentation
Investment: $25-$30K
Govern & Pilot
Weeks 5-8
What: Company AI Gateway + 10-day pilot on high-value workflow
Output: Shadow AI stopped, 15-40% improvement proven
Deliverable: Observability dashboard, evaluation framework
Investment: Fractional Steward ($15K-$25K/mo)
Scale & Transfer
Weeks 9-12
What: Production deployment, team training, ownership transfer
Output: Internal AI capability, measurable business metrics
Deliverable: You own code, frameworks, can continue without us
Investment: Ongoing advisory or Guided Implementation
How We Work Together
Start with assessment. Then scope the right engagement based on your readiness, AI spend, and strategic priorities.
Assessment
10-minute Readiness Scorecard. Know your score, gaps, pathway.
FREE
Discovery
30-minute call. Discuss situation, challenges, current AI spend.
FREE
Audit
1-2 weeks. Portfolio review of all AI investments.
$5K-$10K
Engage
Custom scope based on readiness + priorities.
CUSTOM
Typical Engagements
AI Portfolio Review
Audit all AI spend, vendors, tools, pilots. Classify: Kill / Fix / Double-down. Deliver 90-day roadmap + waste report ($50K-$200K typically identified).
Ongoing Advisory
Portfolio stewardship, quarterly reviews, governance oversight, vendor evaluation, board presentations. Scope varies: monthly retainer or project-based.
Implementation Support
Hands-on: build Company AI Gateway, run 10-day pilot, deploy production system with governance. Hand off ownership (code, docs, trained team).
Investment Depends On
Your Current AI Spend
Are you investing $150K, $500K, or $1M+ annually? Scope scales accordingly.
Readiness Score
Score 0-10 needs foundation-building. Score 17+ ready for systematic deployment. Different pathways, different investments.
Strategic Priorities
Governance urgency? Shadow AI risk? Failed pilots to salvage? Board pressure? Each shapes the engagement.
Context for Investment
Industry data shows 72% of AI projects fail. If you're spending $500K on AI, that's ~$360K wasted annually at the documented failure rate.
Systematic governance prevents waste + improves working spend. Portfolio review typically identifies $50K-$200K in eliminable spending—often paying for itself in eliminated waste alone.
Or schedule a discovery call to discuss your situation
Is This For You?
This IS for you if:
- 25-500 employees, $10M-$100M revenue
- Currently investing $150K-$1M+ annually in AI
- Have budget authority ($15K-$25K/month commitment)
- Tried AI with underwhelming/failed results
- Board/competitors pressuring for AI ROI
- Staff using shadow AI (ChatGPT with company data)
- Value prudent investment over hype-chasing
- Willing to kill failed projects (no sunk-cost fallacy)
- Want ownership and flexibility (not vendor lock-in)
- Australian company or significant AU operations
This is NOT for you if:
This is NOT for you if:
- <25 employees or <$150K annual AI spend
- >500 employees with dedicated AI teams
- Pure startup <2 years old (too early for governance)
- Want "AI strategy deck" without implementation
- Seeking cheapest vendor (we're premium: $180K-$300K/year)
- Not yet investing in AI (come back when ready)
- Expect AI to "solve everything" (we're skeptics)
- Can't commit to killing failed projects
Common Questions From Skeptical Executives
You're already spending $150K-$1M on AI. Our $15K-$25K/month = 10-30% of that to ensure the other 70-90% compounds vs. evaporates. At 72% failure rate, you're wasting $108K-$720K—our fee prevents that. Compare to $300K+ full-time AI exec—we're 1/5 the cost. Portfolio Review pays for itself in eliminated waste within 60 days.
That's exactly the point. 72% fail because of organizational readiness (no governance, wrong metrics, no observability), not AI limitations. We address root causes with systematic frameworks. Your failures are learning opportunities. 10-day pilots prove value fast or kill fast—no 6-month experiments.
Governance as architecture ≠ bureaucracy. PII redaction: 15 minutes to deploy. Observability: install Langfuse, done. Kill switch: literal button. We handle complexity so you don't have to. Start with simplest high-value intervention (Company AI Gateway), prove value week 1.
Fractional gets senior expertise at 1/5 cost. You get breadth of experience (dozens of clients) vs. single perspective. AI landscape changing fast—fractional stays current. Scale as needed. After 12-18 months with capability transfer, many clients reduce to quarterly reviews—we built your capability, not dependency.
Portfolio Review systematically evaluates all investments against strategic direction. Kill/Fix/Double-down framework prevents betting farm on one idea. 10-day pilots with kill criteria mean wrong bets fail fast/cheap ($5K-$15K, 2 weeks) vs. slow/expensive (6 months, $150K). We help you kill bad projects—that's the value.
Portfolio Review pays for itself in 60 days (eliminate $50K-$200K waste). First pilot proves 15-40% improvement in 2-3 weeks. Fractional advisory shows gains (governance, shadow AI stopped, portfolio clarity) within 90 days. Full ROI (3-5×): 12-18 months as advantages compound.
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Take the 5-minute Readiness Scorecard (32 points across 8 dimensions). Instant results: where you are, what's missing, what to fix first.
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DIY AI Governance
Step-by-step implementation plan: Audit → Gateway → Pilot → Scale. Includes checklists, tool recommendations, success criteria.
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See How It Works
5 documented case studies: Professional services, manufacturing, healthcare, SaaS, financial services. ROI breakdowns, timelines, lessons learned.
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