Amazon Q Enterprise training for sales & marketing teams in Singapore
Amazon Q Enterprise training for sales teams in Singapore: one hands-on day, onsite, on your own files. Account research. Proposals and campaigns.
Amazon Q Enterprise for sales & marketing teams
Sales questions usually span the CRM, the data warehouse and a pile of documents. Amazon Q is built to answer across connected sources, and this programme works on asking those questions well.
Where the work happens
- Connected sources
- Business question answering
- Data discovery
- Dashboard drafting
The work here is asking business questions against sources that are already connected, which makes source coverage and access scope part of the training rather than an afterthought.
Who this programme is for
Sales, account management and marketing teams. Teams standardised on AWS.
- Account managers and sales teams preparing for discovery meetings and renewals
- Bid and proposal teams responding to RFPs and tenders
- Marketers adapting approved content across channels and audiences
- Sales and marketing leaders deciding which claims AI may draft and who approves them
Not the right fit
- Teams looking for a vendor demo rather than hands-on practice
- Anyone expecting AI to send customer communications or publish content without review
- CRM implementation or marketing-automation build projects — this is applied assistant training
What the day covers
Module 1: Account research
Account research is run against connected CRM and document sources, separating evidence from assumption.
Exercise. Build an account brief from approved sources, distinguish evidence from assumptions and prepare discovery questions.
What you leave with. A referenced account brief and meeting plan.
Module 2: Proposals and campaigns
Proposal and campaign inputs are drawn from connected data with the source named.
Exercise. Turn a customer brief into a proposal outline, campaign concept and audience-specific messages.
What you leave with. A proposal template and campaign brief.
Module 3: Content and follow-up
Pipeline and follow-up questions are standardised into a set the team reuses.
Exercise. Adapt approved content across channels and draft follow-ups from meeting notes with brand and factual review.
What you leave with. A content pack and follow-up workflow.
A worked example: Account research across CRM and documents
One exercise from the day, as it runs on your own material in Amazon Q Enterprise.
- What goes in
- Connected CRM and document sources.
- What is asked
- Summarise the account's history, open opportunities and last contact, separating evidence from assumption.
- What comes out
- An account brief with each fact sourced.
- What the reviewer checks
- The account owner confirms the brief against the CRM before the call.
Singapore rules the exercises respect
Sales and marketing output in Singapore carries obligations on both data and claims.
- Prospect data used in research and outreach is subject to the PDPA, including the Do Not Call provisions for telephone and SMS marketing.
- Marketing claims are checked against the Singapore Code of Advertising Practice and your own approved claims list.
- Customer names and deal terms stay out of prompts unless your workspace terms and internal policy allow them.
What this day does not cover
Scope is agreed before the session so the day stays hands-on.
- Connector configuration, index tuning or IAM policy design — that is work for your platform team, and it needs to be done before the session.
- QuickSight dashboard engineering or data modelling.
- Amazon Q Developer and coding assistance, which is a different product and a different audience.
Before the session
- A licensed Amazon Q Enterprise seat for each participant
- Working knowledge of your own sales process, offer and brand guidelines
- Anonymised or sample working files — an account brief, a past proposal, approved product copy
- No coding background required
Format and schedule
- Delivery
- On site in Singapore or remote, delivered live. Cohorts are kept small enough that every participant works on their own files rather than watching a screen share.
- Duration
- Typically one full day, or two half-days scheduled around quarter-end. Module order is adjusted to your calendar and tool stack.
- How each module runs
- Each module follows the same pattern: a short walkthrough of the technique, a hands-on exercise on your own data, then a review of what the assistant got wrong and how to catch it.
- Afterwards
- Participants leave with the prompt patterns, templates and review checklists produced during the session, so the work continues after the training day.
How your data is handled
- Exercises run inside your own tenant and your own licences, so your data does not leave your environment for the training.
- Files shared with the trainer in advance are anonymised extracts; customer records, pipeline data and pricing stay inside your own tenant and licences.
- Every module includes verification: checking output against the source, confirming what the assistant left out, and marking what still needs a human read.
- Customer-facing output is reviewed before it is sent or published. The programme teaches AI-assisted drafting and research, never unattended outreach.
Who runs the session
Abhijit Ghosh
Founder, Eagentix · Data & Generative AI Practitioner · Singapore
The programme is delivered by Abhijit Ghosh, who has spent 25 years in data and AI, including roles at Accenture, IBM, Deloitte, Cloudera, Tibco and Wipro. Sessions are run by the practitioner, not handed to a junior facilitator.
Questions about Amazon Q Enterprise for sales & marketing
Can it answer across the CRM and our documents at once?
Answering across connected sources is what the product is for, and the module is about asking those questions precisely — then separating what the data evidenced from what the answer assumed.
What has to be in place before the session?
At least one connected source your team actually uses, and someone who can tell us what it covers. Amazon Q answers from what it is connected to, so a session booked before anything is connected becomes a demonstration rather than training.
How do we know an answer is complete?
You check what was in scope. An answer drawn from connected sources is only as complete as the connection and the asker's permissions, so every module ends by asking what the answer could not have seen.
Can we put prospect lists into the assistant?
Only within your workspace terms and internal policy, and outreach still has to respect the PDPA's Do Not Call provisions. The session uses public sources and anonymised CRM extracts for the research exercises.
How do we keep generated claims accurate?
Every exercise works from an approved claims list. Anything the assistant produces outside that list is flagged for marketing sign-off before it reaches a customer.