Fujifilm: Turning AI ambition into practical business value

One of the biggest challenges with scaling AI is that the term itself has become too vague to be useful.

When AI is discussed as a broad technology category, leaders struggle to understand what value is realistically achievable, where to focus first, and what success should look like. That vagueness often leads to two extremes: either AI is treated as a set of small productivity features with no clear return, or organisations jump prematurely into complex process automation without the foundations in place.

There is also a quieter issue at play. Many leaders intuitively sense that AI introduces new risks and requires careful planning but are reluctant to slow momentum by making those risks explicit. Clarifying data boundaries, ownership and accountability forces harder conversations, so it is often deferred. That reluctance doesn’t remove risk; it simply postpones it.

For most Australian organisations, moving to the public cloud already means living inside the Microsoft 365 ecosystem. That is where organisational data resides, where work happens, and where AI (through Microsoft Copilot) is first experienced at scale. As a result, this is also where the greatest opportunity for AI‑driven value exists, and where the consequences of poor planning are felt first.

This is why we frame AI readiness not as a question of advanced models or bespoke applications, but as the ability to extract measurable value safely and deliberately from Copilot in everyday work. When AI is grounded in real tasks, clear ownership, and incremental progression, it becomes far easier for organisations to mature their capability, manage risk, and scale with confidence, while genuinely augmenting staff capacity.

Q&A with Avron Welgemoed, Strategic AI Advisor and AI Practice Lead at Fujifilm:

1. Where are organisations struggling most when it comes to scaling AI, and how are we helping address those gaps?

Most organisations aren’t struggling because they lack AI capability; they’re struggling because AI is still defined too vaguely.

When AI can mean anything from Copilot drafting emails to fully autonomous systems, leaders find it hard to answer three basic questions: what value is achievable, where should we start, and what does success look like?

In practice, we see three common failure patterns:

• organisations stop at personal productivity and never move into work that changes outcomes
• others jump too far ahead into complex process automation long before they’re ready
• and many quietly avoid the planning conversations that would force clarity around ownership, data boundaries, and risk

We help address this by narrowing the focus. For most organisations, scaling AI really means getting measurable value from Copilot inside Microsoft 365, because that’s where work and data already live, and where staff are already capacity‑constrained. Through grounding AI in real tasks and roles, the conversation becomes concrete, actionable, and part of daily work.

2. Many organisations have moved to the cloud but still struggle to operationalise AI. What’s missing, and how do we help move from pilots to production?

What’s missing isn’t infrastructure; it’s decision clarity and intent. Cloud adoption makes AI possible, but it doesn’t answer:

• which work AI should actually support
• what value that support should create
• where AI should stop
• or who owns the outcome when something goes wrong

Without those decisions, AI initiatives remain pilots, or drift into production without anyone consciously accepting the trade‑offs.

We help organisations move from pilots to production by reframing AI as a work‑redesign exercise, not a technology rollout. That means starting with real tasks, setting clear expectations of task‑level value, and introducing just enough planning and governance early so adoption can scale without surprises; with risk and constraints understood as part of everyday AI use.

3. What changes to architecture and data strategy are essential to support scalable AI?

The most important architectural change is conceptual, not technical. Generative AI is probabilistic, while systems of record are deterministic. Treating them as the same thing creates risk (and occasionally embarrassment).

In practice, scalable AI depends far more on data discipline inside Microsoft 365 than on new platforms: permissions, content quality, ownership and boundaries. This work is often delayed because it’s unglamorous and surfaces uncomfortable truths about information sprawl, but it’s where most AI initiatives succeed or fail.

Good AI architecture isn’t about adding more tools; it’s about making existing data usable, intentional, and safe.

4. How are we helping organisations manage risk and build secure, responsible AI in cloud environments?

One of the biggest blockers to progress is reluctance to make AI risk explicit. Most leaders know AI changes risk profiles, but planning forces uncomfortable conversations about accountability and boundaries, so it’s often deferred in the name of momentum.

Our approach is to lower the emotional and organisational cost of planning by working at the level of use cases and tasks rather than abstract policy. When one task is clearly defined, both risk and reward become understandable and manageable.

This allows governance to be embedded naturally:

• guardrails are designed into how Copilot is used
• data access is intentional
• and oversight evolves as usage matures

Responsible AI works best when it grows with understanding and adoption, rather than being bolted on after the fact.

5. Can you share a customer example that demonstrates impact, and what advice would you give CIOs looking to scale AI?

A high‑value Copilot agent we see earning its keep is sales and deal‑desk response preparation. Traditionally, responding to an opportunity requires hours locating approved content, tailoring it, and pushing drafts through multiple review cycles with product, legal, commercial and management teams.

A focused Copilot agent changes this by producing a first‑pass response using only curated content, approved clauses, templates, brand standards and marketing techniques; returned with embedded review notes. Instead of multiple rework cycles, multiple stakeholders, and the handover delays, one manager performs a single, fast review.

The ROI is immediate: sales preparation time drops significantly per deal, review workload across teams is reduced, turnaround times improve, and response quality becomes consistent at scale.

The advice for CIOs is simple: don’t frame AI as a platform rollout or abstract automation programme. Start with one high‑value task the business already understands, make ownership and constraints explicit, and let maturity (and ROI) grow from there.