Account research + meeting prep
Account + contact → relevant context → hypotheses → meeting brief.
Give sellers more time in the conversation.
The first sales return from AI is often not a better chatbot. It is removing the research, CRM administration, proposal preparation and follow-up work surrounding a good commercial conversation. The AI transformation company for Australian small and medium-sized businesses.
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AI TRANSFORMATION · AUSTRALIAN SMALL & MEDIUM BUSINESSESRevenue teams lose expensive selling time to preparation, data entry, internal coordination and repeated proposal work. AI can compress that work when it is connected to the CRM, proposition, account context and approval process.
Buyer: CRO · Head of Sales · Head of Growth
01 / WHERE TO START
Three workflows worth testing first.
These are not universal prescriptions. They are practical starting patterns with a clear owner, observable work and a measurable outcome.
Proposal workflow
Opportunity context → approved proof / offer → first draft → human commercial review.
CRM + follow-up
Meeting → structured notes → CRM update → next-step draft.
02 / 12 WORKFLOWS
Go deeper than a list of AI tools.
Every workflow is framed as work moving from input to action. That is what lets us map systems, context, human review and the business measure behind it.
Account research
Account → trigger → commercial contextMeeting preparation
CRM + account context → briefingLead qualification
Enquiry → criteria → routingCRM updates
Conversation → structured record → reviewProposal drafting
Opportunity → approved components → draftRFP response support
Requirements → evidence library → response matrixFollow-up sequencing
Meeting outcome → next action → draftPipeline risk summaries
Pipeline → gaps / stalls → manager viewExpansion signals
Customer activity → opportunity hypothesisWin / loss synthesis
Closed deals → themes → actionSales knowledge retrieval
Question → approved commercial knowledgeForecast commentary
CRM changes → signal → leadership narrativeSet up and train your agents.
We configure agents with your department's approved knowledge, instructions and examples. Agree permissions, human approvals and exception handling, then test before connecting live systems. Your industry shapes the rules.
TRIGGER→CONTEXT→AI ACTION→HUMAN OWNERSHIP→EXCEPTION→MEASURE
04 / WHAT TO MEASURE
AI usage is not the outcome.
Use operating measures that already matter to the function, then compare the new workflow against a baseline.
01Seller time returned
02Speed to proposal
03Lead response time
04CRM completeness
05Conversion
06Pipeline velocity
07Follow-up completion
08Revenue capacity
Keep accountability visible.
Automation should make the operating boundary clearer, not hide it.
Final commercial commitmentsPricing exceptions without approvalUnverified proof claimsAutonomous outreach outside approved policy
06 / FIRST 90 DAYS
Map. Build. Activate.
Audit
Review your actual processes and agree five practical improvements with clear owners, first steps and measures of success.
OUTPUT: FIVE-PRIORITY PLANTrain & Build
Train your people, set up and train AI agents, and test their work against approved examples before connecting live systems.
OUTPUT: TRAINING + REVIEWED PILOTSImplement
Activate what is ready, review real results and agree how the team will maintain and improve the work. Timescales are indicative, not a guarantee.
OUTPUT: VERIFIED PROGRESSShow me what AI could change in my department.
Select your team, size and pain point. Identify will generate five workflow opportunities, the impact dimensions to test and a recommended starting point.
RUN THE SALES & GROWTH FINDERCan Identify set up and train agents for our department?
We configure agents with approved knowledge, clear instructions, examples and permission limits, then test and refine them with your team. This does not mean training a new AI model from scratch or letting agents learn without review. Your department's workflow, industry requirements and existing systems determine what is in scope.
Where should Sales & Growth start with AI?
Start with a repeated workflow that creates visible friction, has a clear owner and can be measured before and after. The strongest first move is usually not the most ambitious use case. It is the one that proves the organisation can Map, Build and Activate a useful change.
Do we need to replace our existing systems?
Usually not. Identify designs around the systems, data, permissions and workflow already in the business. The first question is where AI should sit in the operating model, not which platform should replace everything.
What should we measure?
Use the measures already used to run the function: time, cycle time, throughput, service, conversion, quality, exceptions, cost to serve or another agreed business outcome. AI usage on its own is not the result.
What should AI not own?
Material decisions, sensitive judgement and exceptions need clear human accountability. The exact boundary depends on the workflow, risk and industry.