Why Clarity of Purpose Matters More Than Ever
Something shifted in late 2025.
AI adoption didn’t just increase — it accelerated. Social feeds filled with demos. Tooling exploded. “Vibe coding” became a weekend hobby. Entire SaaS categories started looking vulnerable.
But beneath the noise sits a more important strategic question:
Is AI your differentiator — or is it just a commodity you should be consuming?
For CTOs, architects and engineering leaders, that distinction is now existential.
The Step Change: AI as a Platform Shift
We’ve seen this pattern before.
When AWS launched new services at re:Invent, entire industries would groan in the keynote theatre as a managed feature eliminated a whole category of tooling.
The same dynamic is happening again — only this time, it’s model providers.
Capabilities that startups were charging for 18 months ago are now native features in Claude, Gemini, or ChatGPT. If your product strategy is “LLM + some prompts”, you’re competing directly with a platform provider that can outpace you overnight.
That’s not strategy. That’s exposure.
Commodity vs Differentiator
Most organisations should consume AI, not build it.
Four years ago, building foundation models might have been strategic. Today? You’re unlikely to out-innovate hyperscale model providers.
The LLM itself is typically bottom-right on your value chain. It’s infrastructure.
The differentiator is:
- Your domain understanding
- Your customer insight
- Your workflow integration
- Your data context
- Your operating model
If you misidentify what is commodity versus differentiator, you’ll:
- Invest millions in capabilities that become table stakes
- Build internal systems that are instantly obsolete
- Compete in the wrong layer of the stack
Clarity of purpose matters more than ever.
Internal Acceleration vs Product Feature
There are two very different uses of AI:
- Using AI internally to accelerate your SDLC and operational flow.
- Embedding AI into your product as a feature customers consume.
The same strategic discipline applies to both.
Should you build your own internal LLM coding assistant?
Or should you use an enterprise model offering?
Should you create a bespoke agentic framework?
Or consume a hardened, sandboxed capability?
The containerisation analogy is helpful:
You wouldn’t create your own container runtime because Docker looked interesting.
So why would you build your own model infrastructure because AI is exciting?
Start with the problem. Not the technology.
The Risk Nobody Talks About: Blast Radius
Agentic AI changes risk profiles.
If you deploy agents into environments without:
- Clear boundaries
- Proper tenancy isolation
- Defined blast radius
- Strong governance
You introduce systemic risk.
An uncontained chatbot in a poorly defined SDLC can:
- Access unintended data
- Trigger workflows unexpectedly
- Surface sensitive information
- Move faster than your controls allow
This is where SaaS isolation models offer lessons. Mobile operating systems sandbox apps by design. Most enterprise environments are far more porous.
Before you deploy agents, ask:
What is the blast radius if this goes wrong?
Training Data, Sovereignty & Trust
Enterprise nervousness isn’t irrational.
If you’re feeding:
- JIRA tickets
- Requirements documents
- Architecture designs
- Customer data
into models — where does that data live? Who owns it? Can it resurface?
Enterprise AI providers promise tenant isolation. But perception and governance matter as much as architecture.
Trust becomes a strategic asset.
“Vibe Coding” Critical Systems
There’s a growing narrative that you can replace mature SaaS systems with a weekend of AI-generated code.
Technically? You might replicate a narrow workflow.
Strategically? You’re ignoring:
- Regulatory compliance
- Auditability
- Security controls
- Edge cases
- Decades of domain modelling
Rebuilding payroll or HR systems via prompt engineering is not disruption. It’s risk.
The deeper issue is misunderstanding your value chain.
Critical business systems exist inside a complex web of governance and compliance. That web does not disappear because AI can scaffold CRUD screens quickly.
Speed Changes the Organisation
AI compresses delivery cycles dramatically.
Engineering teams can now:
- Prototype in hours
- Ship in days
- Iterate continuously
If your decision-making loop still takes weeks, you’ll create friction.
Old models of:
- Six-month discovery
- Hand-offs between silos
- Static roadmaps
are increasingly incompatible with AI-accelerated delivery.
The feedback loop must tighten:
- Clear leading and lagging indicators
- North Star alignment
- Traceability between work and outcomes
- Engineering embedded in product decisions
Velocity without direction creates chaos.
You Cannot Outsource Critical Thinking
AI can:
- Help draft your North Star
- Refine impact maps
- Improve Wardley Maps
- Suggest KPIs
- Stress-test assumptions
But it cannot replace:
- Context
- Organisational alignment
- Strategic trade-offs
- Ethical judgement
With humans, shared context is implicit.
With models, context must be explicitly encoded.
And that’s hard.
If your internal processes, standards and practices aren’t codified, AI cannot reliably operate within them.
Context becomes a prerequisite for automation.
Organisational Design Still Applies
There’s a temptation to assume agentic AI eliminates coordination problems.
It doesn’t.
The same challenges that exist in human organisations — misalignment, unclear boundaries, communication breakdowns — will appear in agent systems.
Organisational design theory does not become obsolete because agents are involved.
If anything, it becomes more important.
The Value Flywheel Effect
There’s a reinforcing dynamic here.
If you:
- Understand your ecosystem
- Map your value chain
- Clarify your differentiator
- Define your feedback loops
AI will sharpen your thinking and accelerate progress.
If you don’t?
AI will amplify confusion.
Turn your flywheel first.
Then apply acceleration.
There Is No Sideline
One thing is clear: sitting this out isn’t an option.
AI adoption is happening. Capabilities are evolving weekly. Platform providers are collapsing layers of the stack.
The question isn’t whether to engage.
It’s whether you engage with discipline.
Focus on technique over tooling.
Prioritise clarity over capability.
Strengthen structure before scaling speed.
Because in this new platform shift:
The winners won’t be those who build models.
They’ll be those who understand exactly where value is created — and apply AI precisely there.