The AI initiatives that succeed share one trait: they’re tied to real business outcomes.
As interest in GenAI and intelligent automation grows, many organisations are still struggling to convert ambition into execution. AI roadmaps are in place. Budgets have been allocated. But the delivery bottlenecks persist – often because use cases aren’t clearly framed, or they don’t align with architectural realities.
Enterprise Architects increasingly find themselves at the centre of this challenge.
Not just as system designers, but as translators between strategic intent and delivery capability – ensuring what’s being asked is not only desirable, but feasible and repeatable.
The Current Reality
Across banking, insurance, and the public sector, three themes keep surfacing:
- AI is being explored everywhere, but deployed almost nowhere
- Use cases exist, but few are tightly scoped or tied to enterprise KPIs
- Architecture and platform teams are brought in after the fact - when timelines are already set
This points to a recurring pattern: architecture is being treated as an enabler of last resort, rather than a strategic input to AI delivery planning.
Industry benchmarks suggest that 60 – 70% of AI projects stall before they scale, not because the models don’t work – but because the operational readiness and architectural scaffolding are missing.
What We’re Seeing Across the Industry
In our work with enterprise architecture and platform teams, a consistent pattern has emerged:
What’s Working
- Business-aligned AI use cases Fast, scoped PoCs with measurable value
- Early-stage architecture involvement
- Cross-functional delivery from day one
- Integration with strategic platforms
What’s Getting in the Way
- Exploratory initiatives with no clear owner
- Open-ended pilots with shifting objectives
- Innovation disconnected from infra/governance
- Risk and compliance brought in post-design
- One-off integrations, point solutions, duplication
These issues lead to misalignment between innovation and architecture governance – and ultimately create friction at the productionisation stage.
A Framework That Helps Use Cases Land
Here’s a structure we’ve seen work consistently in enterprise environments, especially when used to prioritise AI initiatives at architectural review boards and steering committees:
1. Define the Business Problem
Start with the friction – not the function.
- What process or decision is slow, inconsistent, or high-risk?
- What is the impact on throughput, cost, risk, or customer experience?
Business value decomposition can be useful here – aligning the use case to capability models or value streams.
2. Clarify the AI Leverage Point
- Is the AI enabling prediction, classification, generation, or optimisation?
- Is it augmenting human judgment or replacing rules-based logic?
Architects should assess model design patterns here – e.g. decision-support vs. straight-through processing – and the impact that has on integration and controls.
3. Assess Architectural Fit
- Can the use case be supported within the current architecture runway?
- What dependencies exist - data sourcing, model deployment, observability, integration patterns?
Run this against your existing capability map and architectural principles. Consider tech debt, reuse, and alignment with target state patterns (e.g. microservices, event-driven workflows, containerised model serving).
- Is this aligned to your AI governance reference architecture?
- Do you have sandbox or model test environments to simulate production-like conditions?
4. Quantify the Value and Impact
- What KPIs are being influenced - cost, cycle time, accuracy, customer NPS, operational risk?
- What does good look like - and how will it be measured?
Many architects now include AI use case evaluation frameworks in their governance boards – scoring for strategic alignment, tech feasibility, architectural impact, and ROI potential.
5. Validate Execution Readiness
- Can this use case be tested within 4 – 6 weeks in a way that generates data to support scale decisions?
- Are delivery owners, business SMEs, legal and compliance involved from day one?
Establish gating criteria for PoC handover – including architecture sign-off, security review, model explainability, and integration feasibility.
From Innovation Theatre to Execution Discipline
Too often, AI initiatives are scoped based on what’s technically interesting – not what’s architecturally sound or operationally viable. The enterprise architects best positioned for impact are those who can:
- Shape the upstream scoping process - not just review it after the fact
- Push for reuse over reinvention
- Align AI innovation to enterprise capability models and architectural guardrails
- Bring clarity around where AI fits (and where it doesn’t)
This is the shift from architect-as-reviewer to architect-as-orchestrator.
And it’s a critical shift as AI moves from lab environments to core enterprise workflows.
Final Thought: AI That Delivers Starts With Framing That Works
If your organisation is running ten AI experiments but struggling to move one into production, it’s likely not a tooling issue. It’s a framing issue.
Strong AI use cases are not about chasing new models – they’re about validating real business value in ways the architecture can support.
As enterprise architects, the goal isn’t to slow innovation. It’s to de-risk it – and make it scalable.
Looking to bring more structure to how AI use cases are framed, tested, and validated across your organisation?
At NayaOne, we work with enterprise architecture, innovation, and risk teams to help accelerate AI adoption – safely and repeatedly.
Our platform enables:
- Secure, production-like PoCs that reflect real data and real controls
- Cross-functional testing environments for architecture, compliance, and business teams
- Standardised frameworks for evaluating vendor or in-house models against enterprise requirements
If you’re navigating similar challenges – or want to avoid rework down the line – we’d be happy to share what we’re seeing across the industry. Get in touch to explore how your team could use NayaOne to move from idea to impact, with execution discipline built in.
