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.
FN/
AUSTRALIA / APPLIED AIRevenue 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 narrativeThe workflow is the product.
A useful implementation defines five things before it ships: the trigger, the business context, what AI is allowed to do, where a person owns the decision, and what happens when the normal path fails.
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.
Map
Choose the business outcome. Baseline the current workflow. Map systems, data, handoffs, exceptions and ownership. Rank the first opportunities by value and feasibility.
OUTPUT: first implementation briefBuild
Design against real context and systems. Define permissions, human review, governance and exception paths. Test with the people who run the work.
OUTPUT: production-ready workflowActivate
Launch, train, measure, fix the operating design and capture the reusable pattern. Use the evidence to choose the next workflow.
OUTPUT: live workflow + outcome evidenceShow 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 FINDERFORTNIGHTLY AI BRIEFINGWhat matters in applied AI.
What matters in applied AI.
Without the noise.
Practical AI workflows, implementation lessons, Australian market signals, governance notes and upcoming Identify events for leadership teams.
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.