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AI Design & Delivery

Turning AI spend into measurable value

EVERY TASK GETS A VERDICT
32 tasks
AUTOMATE12AUGMENT15PRESERVE5
Two consultants reviewing work together on a laptop

AI Design & Delivery is Transformativ's enterprise AI consulting practice, for organisations across Australia that want to turn artificial intelligence into real value. Based in Sydney, we work with enterprises in financial services and other regulated, complex sectors. Through Catalyst+, we run two transformations, AI-native software delivery and agentic operations, and four capabilities make every engagement hold: forward deployed AI engineering, AI governance, work and role redesign, and AI value measurement. Enterprise transformation and AI delivery, in one firm, from people who have led more than $3B of transformation programs.

THE PROBLEM

The whole market is learning the same lesson: usage climbed, value didn't.

Boards are approving AI budgets. Pilots are launching. Platforms are being selected. But few organisations have identified where the real value is. Most buy the tools, drive adoption and measure usage, and the work itself never changes. Spend shows up straight away. Value doesn't.

Spend shows up straight away.Value doesn't.
95%

of AI pilots fail to produce measurable financial impact

MIT Project NANDA, The GenAI Divide: State of AI in Business 2025 (preliminary findings)
70%

of the challenge in AI is people and process. Algorithms and technology are the other 30%.

BCG, How Agents Are Accelerating the Next Wave of AI Value Creation, 2025
OUR APPROACH

AI is a transformation problem with nuances only AI has.

We change the work first. Map it, involve the people who do it, build the measurement, and let the technology follow the design.

  1. 01

    Start with the work

    We begin with your enterprise and how the work gets done, before any platform decision.

  2. 02

    Every task gets a verdict

    Automate, augment or preserve. Each task is judged on where AI adds value and where people should stay in charge.

  3. 03

    The people who do the work help design it

    The teams whose roles change help shape what replaces them. That is how adoption sticks.

THE FOUR NUANCES

Four reasons AI needs a different kind of transformation

  1. EVERY ROLE · EVERY WORKFLOW · EVERY DECISION

    It touches everything at once

    No earlier transformation reached every role, workflow and decision at the same time. Scope discipline matters more here.

  2. "Will this take my job?""I designed my role."
    FEAR → OWNERSHIP

    Fear is a design constraint

    "Will this take my job?" sits in every AI conversation. When people design their own future roles, the fear turns into ownership.

    AI Workforce Readiness Assessment

  3. COST CUT
    CAPABILITY
    EVERYONE HITS THE SAME FLOOR

    Automation alone is a race to the bottom

    Cutting cost converges on the same floor as everyone else. The prize is capability: what more each role can create.

  4. SPEND
    VALUE
    INSTRUMENT FROM DAY ONE

    Value hides unless you measure first

    Spend is visible immediately. Value stays invisible until you instrument for it, so measurement starts on day one.

AI SERVICES

Our AI services

We run two transformations. Four capabilities run inside both, and inside every Catalyst+ engagement, so what we build holds up in a regulated business.

WHAT MAKES IT HOLD
03

Forward deployed AI engineering

Senior engineers embedded in your teams, building AI agents into your systems and handing over a capability you own.

Model-agnostic agent architecture
Built so a better model next year is a plug-in decision, and today's choice never becomes a three-year bet.
Explore forward deployed AI engineering →
04

AI governance

Controls that let AI agents work in a regulated business, built into the work from the start.

Governance in the flow
Your obligations met as the work moves, with only the genuinely new stepping out for a decision.
Explore AI governance →
05

Work and role redesign

Every task gets a verdict, and every affected role gets a future, designed with the people who hold it.

The Three-Layer Model
The Future Role Canvas
Explore work and role redesign →
06

AI value measurement

A baseline before anything changes, and value tracked from day one, so the board knows the money is working.

Five-layer value measurement
The board's question, answered from the first week: is the money delivering value?
Explore AI value measurement →
CATALYST+

Six weeks to AI delivering value in your business.

Catalyst+ is our AI transformation product. It carries you from the first conversation to AI running across the organisation, in three stages.

A woman looking ahead, with translucent data displays overlaid across the frame
  1. 01 · DEFINE

    A board-ready blueprint, a costed value case and, where focused, AI agents already live

    6 WEEKS · FIXED SCOPE · FIXED FEE
  2. 02 · ACTIVATE

    Live AI capability

    TYPICALLY 8 TO 12 WEEKS
  3. 03 · SCALE

    AI as business as usual

    IN WAVES

In software development, AI assistants alone lift productivity 10 to 15%. Paired with end-to-end process change, some companies report 25 to 30%. That gap is what Catalyst+ closes.

AI-Native Software Delivery Assessment

AI assistants alone
10 to 15%
With end-to-end process change
25 to 30%
Bain & Company, Technology Report 2025
AI RUNS THROUGH ALL SIX

AI is part of how we deliver every program

Agents in delivery, an AI-enabled program cadence, and data built for AI readiness run through all six of our consulting services. When AI is the transformation itself, Catalyst+ runs it end to end.

INSIGHT

Where does your organisation sit on the Five Levels of Enterprise AI?

Level 2 is where most AI investment stalls: the tools are in place, but the work hasn't changed, so usage is the only thing to measure. Level 3 is where the return becomes measurable. Our Five Levels model shows what separates each step.

1
2
3ROI BECOMES MEASURABLE →
4
5
LEVEL 1
Ad Hoc
LEVEL 2
Assisted
LEVEL 3
Embedded
LEVEL 4
Agentic
LEVEL 5
Orchestrated
LEVEL 2 · THE TRAP

Tools sanctioned, adoption high, the work unchanged. Usage is the only measure.

LEVEL 3 · THE GOAL

AI built into redesigned workflows, with value measured against a baseline.

QUESTIONS

Common questions about enterprise AI consulting

Six, all delivered through Catalyst+. Two change how the business works: AI-native software delivery and agentic operations. Four run inside every engagement: forward deployed AI engineering, AI governance, work and role redesign, and AI value measurement. Every engagement starts with Define, a fixed six-week sprint.

Most pilots treat AI as a technology rollout. The tools arrive, usage climbs, and the work stays the same, so there is nothing to measure except adoption. Value appears when the work itself is redesigned around AI and measured from the start.

Define takes six weeks and ends with a board-ready blueprint and a costed value case, with AI agents already live where the scope is focused. Activate typically puts AI into live operation over the following 8 to 12 weeks. Scale then extends it across the organisation in waves.

We build them. Our forward deployed engineers write the code, connect agents to your systems and data, test them on your historical cases and run them in production with your team. Where the scope is focused, the first agents are working on real cases by week 6.

Against a baseline of your own numbers, taken before anything changes: cost to serve, cycle time, volume and error rates, plus the full cost of AI. The same measures are tracked after go-live, and every Define ends with a costed value case the board can hold us to.

Agents need tighter controls than chat tools because they act. Each agent gets its own identity and limited permissions, logs every action and the data it used, has thresholds above which a person must approve, and is tested on historical cases before it goes live. Low-risk, repeatable actions are pre-approved; anything new or above threshold goes to a named person. Accuracy is monitored after go-live, which APRA found few institutions were doing continuously.

The target state, end to end: the operating model, redesigned roles, the AI agent and technology stack, skills, measurement and the activation plan, together with a costed value case for where AI pays first.

All the major ones: Claude, OpenAI, Microsoft Copilot and Azure, Google Gemini and AWS Bedrock, plus open-weight models where data must stay on your own infrastructure. We're not tied to any one vendor, so recommendations follow your environment, contracts and risk settings, and we build so you can change models later without rebuilding.

Every task gets a verdict: automate, augment or preserve. Many roles change, and the people in them help design what the new roles look like. The biggest returns come from what each role can do with AI, so we plan for capability as well as cost.

Catalyst+ Define is a fixed-scope, fixed-fee engagement, scoped to your organisation. Activate and Scale are sized to the work. We agree the scope and fee with you before anything starts.