AI Advisory
Every enterprise is being asked to move on AI. The pressure comes from boards, from competitors, and from teams who can see the opportunity. The risk is not moving too slowly. It is moving on the wrong things, for the wrong reasons, without the foundations to make it stick.
Mulberry's AI Advisory practice helps you cut through the noise. We assess where AI genuinely fits your business, stand up the enterprise foundations so it works properly, prove value through working pilots that produce real tools (not slide decks), and build your team's own capability to keep going without us.
We do not sell AI platforms. We do not take commissions from vendors. We help you decide what is right, then help you make it work.
What we do
Five connected capabilities
From opportunity to operating AI at scale.
01
AI readiness and opportunity assessment
Before building anything, you need an honest answer to one question: where does AI genuinely fit, and where does it not?
We run a structured assessment across your nominated workflows, scoring each against dimensions that determine real-world viability: process maturity, decision complexity, data quality, integration landscape, compliance requirements, economic case, and change readiness. The output is a prioritised view of your opportunities, grounded in evidence, not a vendor pitch.
02
Enterprise AI foundations
An AI pilot that works on a laptop is not the same as AI that works inside an enterprise. The gap between the two is governance, access, data handling, and the operating standards that let teams build safely at scale.
We help you stand up your enterprise AI environment on solid foundations: workspace structure, roles and permissions, identity and access patterns, and data governance guardrails appropriate to your sector and regulatory context.
03
Prototyping and pilot delivery
The fastest way to prove value is to build something real. We run dedicated two-week pilot sprints that take the highest-value opportunities from your assessment and turn them into working tools your teams can use, not proof-of-concept decks.
Each sprint opens with a focused assessment of the nominated workflow, locks a sprint goal and acceptance criteria, builds a working prototype, and closes with a hands-on walkthrough and documented results. Sprint capacity is allocated rather than fixed-scope, so priorities can shift as evidence emerges.
04
Production pathway and scaling
A successful pilot is not a production system. The decisions between a working prototype and enterprise-scale AI are different: infrastructure, deployment approach, support model, cost profile, and organisational readiness all need to be resolved before you scale.
We provide a standardised methodology for deciding which prototypes should be productionised, industrialised, or retired, including a costed scaling view your leadership can take to a steering committee or investment forum.
05
Internal AI capability building
Our job is to make ourselves unnecessary. The most valuable thing an AI engagement can leave behind is not a set of tools. It is your team's ability to identify, assess, and prioritise AI opportunities for themselves, without needing to call anyone.
We build your internal AI assessment capability: a repeatable scoring framework, assessment templates, and the methodology your teams need to run their own evaluations across the business. This is Mulberry's enabling the home team principle applied directly. When we leave, the capability stays.
How we engage
A flexible engagement model, not a fixed-scope contract
AI programs need room to learn. What looks like the highest-value opportunity at week one often changes by week four, and a rigid scope locks you into yesterday's assumptions.
Our standard engagement model combines a fixed-price foundation stream with a bank of pre-purchased sprint capacity. The foundation stands up your environment, governance, and methodology. The sprints let you direct capacity to the workflows that matter most, reprioritising as evidence emerges. Each sprint produces a working outcome. The total investment stays predictable.
This structure works for organisations at different stages of AI maturity. If you need to start with a single readiness assessment before committing further, we can do that too. The entry point is a conversation, not a procurement exercise.
Typical starting points
A standalone AI readiness assessment (two to four weeks), a foundation and pilot program (three to four months), or ongoing advisory support alongside your internal teams.
Delivery network
Mulberry leads. Specialist partners extend the bench.
Mulberry owns the advisory relationship, sets the direction, and quality-assures every deliverable. Where an engagement calls for deep, specialist AI engineering or applied delivery capacity, we bring in trusted partners who operate under Mulberry's standards and our single point of accountability.
Our current delivery network includes V2.ai and Adapt2 AI, both Melbourne-based specialists in applied AI, agent design, and enterprise AI enablement. They bring the engineering depth to complement Mulberry's advisory leadership, working as an extension of the engagement team rather than a separate vendor.
You deal with Mulberry. We coordinate the right expertise for your context. The model scales without adding complexity.
Why Mulberry
Why Mulberry for AI advisory
The AI advisory market is crowded and conflicted. Platform vendors sell what they make. Large consultancies staff with graduates and certify them as AI experts. Niche AI firms build impressive demos that stall when they meet the real enterprise.