War Story: Becoming AI-Ready- Beyond the Buzzwords

An interview with Andrew, Lead Consulting Partner at Hudson & Hayes

AI readiness isn’t just a technology goal, it’s a leadership one. Andrew’s insights remind us that true transformation happens when operating models evolve to make AI a natural part of how decisions are made every day.

Chelsea:
When organisations talk about “becoming AI-ready,” what does that actually look like in practice?

Andrew:
Becoming AI-ready isn’t about installing the latest tools, it’s about redesigning how your organisation learns, decides, and delivers value.

In practice, it means creating the right conditions for AI to have a lasting impact- technically, operationally, and culturally. That starts with being clear about why AI matters, where it adds value, and how it fits into everyday decision-making.

AI-readiness is less about technology and more about building a business that can learn, adapt, and use insights confidently. You need good data foundations, clear process ownership, and collaboration between business, IT, and data teams; plus people who are data-literate, ethically aware, and comfortable working alongside intelligent systems.

In short: becoming AI-ready means building a business that can think and act intelligently at scale.

Chelsea:
That’s a great way to put it. From your experience, what’s the biggest disconnect between board-level AI strategy and operational execution?

Andrew:
The biggest gap is usually between intent and integration.

At board level, AI is often positioned as a strategic enabler, a lever for innovation or efficiency. But that’s where the conversation tends to stop. Ambitious AI strategies get approved without recognising the dependencies, things like data quality, process redesign, and behavioural change.

The result is often lots of pilots that never scale.

True alignment happens when AI is woven into the organisation’s operating rhythm, its governance, decision rights, and performance measures — rather than treated as a side project. The board’s vision only becomes reality when the operating model evolves to support it.

Chelsea:
Can you share a specific example where aligning the operating model made a measurable difference to AI adoption?

Andrew:
Sure, I worked with a national infrastructure organisation where AI adoption had completely stalled despite heavy investment. The issue wasn’t the technology, it was structure.

Each business unit owned its own data and priorities, so insights couldn’t flow across the organisation. Once the operating model was aligned- data ownership clarified, digital and field teams integrated, and decision frameworks standardised- everything changed.

Predictive models for asset health and network resilience were rolled out nationally, supported by shared data and central governance. Within a year, outages dropped, scheduling improved, and leaders gained a much clearer view of operational risk.

The real success didn’t come from the algorithms, it came from redesigning how the business worked to make AI part of everyday life.

Chelsea:
What are the most common pitfalls leadership teams face when trying to embed AI into existing processes?

Andrew:
There are a few familiar ones:

❌ Treating AI as a bolt-on instead of a redesign
❌ Dropping tools into old workflows and expecting change
❌ Forgetting the people side- trust, confidence, and capability

AI transformation fails when teams don’t understand or believe in it. Success comes from retraining people, aligning incentives, and redesigning workflows so that AI becomes a natural extension of how the organisation learns and decides.

Chelsea:
If you could give one piece of advice to organisations starting their AI transformation journey, what would it be?

Andrew:

Start with purpose, and design for scale.

The best question isn’t “Which AI tools should we buy?” It’s “Which decisions do we want to make smarter?”

Successful organisations start small, with a clear value case, and build governance, ethics, and accountability from the start. AI transformation isn’t a digital project- it’s an operating model challenge.

Align people, process, and data around a shared goal, and AI becomes a capability, not a project.

In the early days of digital transformation, many organisations were a hammer looking for a nail. Today, we’re more like a nail gun looking for a surface- faster, more powerful, but potentially dangerous without precision.

The challenge for leaders isn’t how often to fire the nail gun- it’s how to aim it, with purpose and control. That blueprint is your operating model, the bridge between ambition and action.

When it’s designed for intelligence, AI becomes not just a capability, but a competitive advantage.