Six example solution patterns, each with the challenge, the proposed approach, a sample workflow, and the operational benefits to aim for.
Please note: every solution below is a hypothetical example that illustrates a possible approach. None describes a completed customer deployment, and no performance figures are claimed.
Example scenario
Customer Service Automation
Business challenge
Support teams spend time on repetitive questions and on locating account context across several tools before they can respond.
Proposed AI solution
An agent that classifies incoming tickets, retrieves answers from approved knowledge sources, drafts replies, and escalates anything uncertain or sensitive.
Example workflow
1Ticket arrives and is classified by intent and urgency
2Agent retrieves account context and relevant help content
3Draft reply is generated with cited sources
4Low-confidence or sensitive cases route to a human agent
5Approved replies are sent and logged
Expected operational benefits
Faster first response on routine requests
More consistent answers grounded in approved content
Human attention focused on complex cases
Example scenario
Document Intelligence
Business challenge
Invoices, contracts, and forms arrive in many layouts, and staff re-key the data into business systems by hand.
Proposed AI solution
An extraction agent that reads documents, returns schema-validated fields, flags inconsistencies, and posts verified data to downstream systems.
Example workflow
1Document is received and its type identified
2Text and layout are extracted and normalised
3Fields are extracted into a typed schema
4Validation rules and cross-checks run; exceptions are queued for review
5Verified records are written to the system of record
Expected operational benefits
Less manual data entry
Validation errors caught before they reach core systems
Traceable link from every field back to its source text
Example scenario
Sales Operations
Business challenge
Reps lose selling time to CRM updates, account research, and preparing follow-ups.
Proposed AI solution
An agent that summarises call notes, updates CRM fields after confirmation, and compiles account briefs from approved sources.
Example workflow
1Meeting notes or transcript are submitted
2Agent extracts action items, stakeholders, and next steps
3Proposed CRM updates are shown for approval
4Approved changes are written through the CRM API
5Follow-up draft is prepared for the rep to review
Expected operational benefits
More consistent CRM data
Less administrative work per account
Every automated change reviewed before it is applied
Example scenario
Finance Operations
Business challenge
Finance teams reconcile records and chase exceptions across spreadsheets, bank data, and ERP exports.
Proposed AI solution
A workflow where deterministic matching handles clear cases and an agent investigates the rest, proposing explanations for human approval.
Example workflow
1Transactions and ledger entries are ingested
2Rule-based matching resolves exact and near matches
3Agent examines unmatched items using supporting documents
4Proposed resolutions are queued with evidence attached
5Approver confirms; audit log records the decision
Expected operational benefits
Smaller exception queue for analysts
Evidence gathered automatically for each exception
Complete audit trail of proposed and approved actions
Example scenario
Enterprise Knowledge Assistants
Business challenge
Policies and technical knowledge live in wikis, drives, and tickets, and employees cannot find or trust the current answer.
Proposed AI solution
A retrieval-based assistant that answers with citations and respects each user’s existing document permissions.
Example workflow
1Sources are indexed with access-control metadata
2User question is rewritten and matched against the index
3Only documents the user may view are retrieved
4Answer is generated with inline citations
5Unanswered questions are logged to improve content
Expected operational benefits
Faster access to internal knowledge
Answers that can be checked against their sources
Visibility into documentation gaps
Example scenario
Research and Data Analysis
Business challenge
Analysts spend significant time collecting sources, cleaning data, and writing first-draft summaries.
Proposed AI solution
A multi-step research agent that gathers material from approved sources, runs analysis in a sandbox, and produces a draft with its evidence.
Example workflow
1Analyst states the question and approved sources
2Agent plans and gathers relevant material
3Data is cleaned and analysed in an isolated environment
4Draft findings are written with references and caveats
5Analyst reviews, edits, and publishes
Expected operational benefits
Faster first drafts
Reproducible analysis steps
Analyst remains the final reviewer
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