Insights
Practical writing on custom training, AI implementation, and what's actually working for enterprise teams.
Start with a guide
Everything here is written for the people who plan and run AI training in organisations: L and D leads, IT and data leaders, and the executives who sign off. Each guide below gives the full picture on one topic and links to the shorter articles that go deeper, so it is the quickest way to find what applies to your team.
- Enterprise AI trainingChoosing a provider, what it costs, and how to run a rollout that sticks.
- AI workshops and fluencyWhat fluency means, prompting as a team skill, and getting value from Copilot and Gemini.
- Implementation and adoptionReadiness, use-case backlogs, governance and taking pilots into production.
- AI risk, governance and scamsDeepfake fraud, voice cloning, data leakage and verification habits.
- AI by industryWhat changes for government, financial services, health and professional firms.
- Databricks for enterprise teamsThe platform, certifications and training built on your own data.
- Data literacy and data trainingData literacy, analytics and engineering skills across the organisation.
- AWS and cloud trainingThe cloud skills enterprise teams need, and how to build them.
- AI training in SingaporeGovernance, tools and training for Singapore enterprises and agencies.
- AI training in MalaysiaPlanning AI training for Malaysian enterprise teams.
Claude Agentic Workflows: A Dev Guide
Learn how to build agentic workflows with Claude, from tool use and multi-step chains to safety controls and real-world patterns for Australian dev teams.
Ijan Kruizinga
Claude for Enterprise: Projects & Safe Use
How Claude's Projects feature works, what makes it safe for enterprise use, and how Australian teams can deploy it responsibly. Practical and honest.
Ijan Kruizinga
ChatGPT Enterprise Data Privacy
How ChatGPT Enterprise protects your data, what OpenAI's terms actually say, and what Australian CISOs need to verify before deploying at scale.
Ijan Kruizinga
ChatGPT for Business: What Teams Get Wrong
Most teams adopt ChatGPT for business and see underwhelming results. Here are the common mistakes and how to fix them before they cost you momentum.
Ijan Kruizinga
Data Sovereignty and AI for Government
Australian government agencies face strict data sovereignty rules when adopting AI. Here is what CISOs and IT leaders need to know before deploying.
Ijan Kruizinga
Privacy Act and AI: What Must Change
Australia's Privacy Act reforms directly affect how organisations use AI. Here's what changed, what the risks are, and what training must now cover.
Ijan Kruizinga
AI Board Oversight: Four Risks to Ask About
Four AI risks every Australian board should be asking about right now, from deepfake fraud to regulatory exposure. Practical questions for executives.
Ijan Kruizinga
AI in HR: Bias and Compliance Traps
AI in recruitment risks are real: bias, privacy breaches, and legal exposure. Here's what every HR lead needs to know before deploying AI hiring tools.
Ijan Kruizinga
Claude vs ChatGPT vs Gemini for Enterprise
Choosing between Claude, ChatGPT, and Gemini for enterprise use? This plain-English comparison covers strengths, trade-offs, and which tool fits which team.
Ijan Kruizinga
Databricks vs Microsoft Fabric
Databricks vs Microsoft Fabric: a plain-English comparison for Australian data and IT leads choosing between the two platforms in 2026.
Ijan Kruizinga
AI Training for Singapore Government
Practical AI training for Singapore government agencies and statutory boards. Build AI fluency across your workforce while meeting governance requirements.
Ijan Kruizinga
Databricks Genie Explained
Databricks Genie lets business teams ask plain-English questions of their data. Here's what it is, how Genie Spaces work, and what it costs.
Ijan Kruizinga
Databricks Data Engineer Associate 2026
The 2026 Databricks Data Engineer Associate exam adds CI/CD, troubleshooting and Lakeflow Jobs. Here is what changed and how to prepare.
Ijan Kruizinga
AI Readiness for Singapore Enterprises
Singapore GLCs and large enterprises face distinct AI readiness challenges. Here's what it takes to build genuine capability, not just tool access.
Ijan Kruizinga
Azure Databricks Explained
Azure Databricks is Microsoft's managed Databricks platform. Here's what Australian enterprise teams need to know about how it works, what it costs, and who it's for.
Ijan Kruizinga
Databricks Training for Regulated Industries
Custom Databricks training built for regulated industries in Australia. Data governance, compliance workflows, and lakehouse skills for finance, health and government teams.
Ijan Kruizinga
Why Generic Data Training Doesn't Stick
Generic data training fails because it ignores context. Learn why transfer of learning depends on relevance, and what L&D teams can do about it.
Ijan Kruizinga
Databricks Certifications in 2026
Which Databricks certification should your team target in 2026? A plain-English guide to every exam, cost in AUD, and how to choose the right path.
Ijan Kruizinga
What Is Databricks?
Databricks is a cloud-based data intelligence platform that unifies data engineering, analytics, and AI. Here's what that means for your team.
Ijan Kruizinga
Databricks Academy vs Custom Training
Databricks Academy and custom training serve different goals. Here's how to choose the right approach for your team's situation and learning objectives.
Ijan Kruizinga
Agent Bricks: Build and Govern AI Agents
Agent Bricks is Databricks' framework for building, deploying and governing AI agents on the lakehouse. Here's what data and engineering leads need to know.
Ijan Kruizinga
Databricks Unity Catalog Explained
Unity Catalog is Databricks' unified governance layer for data and AI. Learn how it works, what permissions look like, and who needs it.
Ijan Kruizinga
Databricks vs Power BI: Partners or Rivals?
Databricks and Power BI serve different layers of the data stack. Here's how they fit together, where they overlap, and how to decide what your team needs.
Ijan Kruizinga
The Databricks Lakehouse Explained
The Databricks Lakehouse combines a data lake and data warehouse into one platform. Here's what that means, why it matters, and when it makes sense.
Ijan Kruizinga