AI is not a tools problem
Most enterprise AI initiatives don't fail because the technology doesn't work. They fail because cost, governance, security, and operating models are treated as afterthoughts.
This section focuses on how enterprises should think about AI, before — and while — choosing platforms.
What this pillar covers
🧠 AI strategy & operating models
- Why AI pilots rarely scale
- Where AI ownership should actually sit
- Build vs buy vs augment decision frameworks
🧱 Enterprise AI platforms
- LLM platforms and orchestration layers
- Secure enterprise copilots
- Data, model, and integration considerations
- Vendor lock-in and portability risks
💸 Cost, risk & governance
- Why AI cost behaves differently to cloud cost
- Token economics and consumption models
- AI security, data leakage, and compliance risks
- Governance patterns that work outside PowerPoint
Who this is for
This content is written for:
- CIOs, CTOs, and Heads of Digital
- Data and AI leaders
- Security and risk stakeholders involved in AI decisions
- Executives accountable for AI outcomes
It assumes enterprise-scale realities, not greenfield labs.
How AI platforms are evaluated
Platforms discussed here are assessed based on:
- Architectural fit in enterprise environments
- Cost transparency and control
- Security, data handling, and access models
- Operational complexity
- Long-term lock-in risk
Affiliate relationships are disclosed where relevant and never override analysis.
Where to start
If you're new to this section:
- Enterprise AI isn't a tools problem — it's an operating model problem
- Build vs buy: how enterprises should approach AI platforms
- Why AI cost management needs FinOps discipline
From there, platform comparisons and deep-dive reviews explore real-world trade-offs.
The enterprises that succeed with AI treat it like infrastructure — not innovation theatre.