Use Cases

Pick an issue. Pick a department. Pick the software. Each use case shows one AI solution to accelerate your business.

Select

Issue

Department

Software Platform

Showing 50 use cases

FOUNDATION
Poor quality leads and messy CRM data slow growth
Inbound leads land in the CRM clean, enriched, and routed fast.
Problem to Solve
Leads arrive with missing fields and duplicates
Routing rules stay inconsistent across reps
Slow response time lowers conversion
What You Get
Target list built from clear ICP rules
Enriched contacts in the CRM with duplicate checks
Outreach drafts based on approved templates
Reply routing to the right rep, with alerts
Before
Leads land across forms and inboxes
Manual entry creates missing fields and duplicates
Ownership stays unclear, follow-up starts late
After
Leads land in CRM in one format
Duplicates flagged, enrichment fills key fields
Owner assigned fast, alerts trigger follow-up
Outcomes / Benefits
Calling within 5 minutes instead of 30 minutes raises odds of contacting a lead by 100x.
Source
Calling within 5 minutes instead of 30 minutes raises odds of a lead entering the sales cycle by 21x.
Source
Book a FREE AI audit
FOUNDATION
Follow-ups slip after meetings, deals slow down
Notes turn into tasks and follow-ups the same day.
Problem to Solve
Notes stay trapped in docs
Owners and due dates stay unclear
Follow-ups go out late or never
What You Get
Task list with owners and due dates
Follow-up email draft for each meeting
CRM or project updates from action items
Weekly rollup of open commitments
Before
Notes stored in docs with no task conversion
Action items missed or assigned late
Follow-up depends on memory
After
Tasks created from notes with owners and due dates
Follow-up drafted the same day
Status updated in tracker or CRM
Outcomes / Benefits
Faster follow-up after meetings
Fewer dropped action items
Book a FREE AI audit
FOUNDATION
Proposals take too long to send, deals stall
Proposals assemble from approved blocks with fewer rewrites.
Problem to Solve
Drafting repeats the same content
Scope and pricing details get missed
Approval cycles slow sending
What You Get
Draft proposal built from your templates
Auto-filled scope based on intake answers
Internal review checklist before sending
Version history and approval notes
Before
Reps rebuild proposals from old docs
Scope and terms vary by rep
Approvals happen in email threads
After
Proposal drafts from approved templates
Standard terms and scope blocks
Review step before sending
Outcomes / Benefits
Faster proposal turnaround
More consistent scope and terms
Book a FREE AI audit
FOUNDATION
Invoices go out late or wrong, cash flow suffers
Approved work turns into invoices with fewer mistakes.
Problem to Solve
Billing details live across tools
Manual invoice creation creates errors
Delays push cash collection out
What You Get
Invoice draft from approved work logs
Line items mapped to the right customer
Human approval before sending
Invoice log with status tracking
Before
Invoices created by hand from notes
Line items missed or mispriced
Finance chases approvals for billing
After
Invoices drafted from approved work
Validation checks for totals and customer
Approval gate before send
Outcomes / Benefits
Faster invoice turnaround
Fewer invoice errors and rework
Book a FREE AI audit
FOUNDATION
Late payments hurt cash flow and planning
Collections follow-ups run on a clear schedule with escalation.
Problem to Solve
Overdue invoices slip without follow-up
Messaging varies by person
Escalation happens too late
What You Get
Reminder sequence by aging bucket
Polite follow-up drafts with invoice links
Escalation rules for high-risk accounts
Collections dashboard and notes
Before
Finance sends reminders manually
Overdue lists updated by hand
Escalations happen after weeks
After
Follow-ups scheduled by rules
Overdue list stays current
Escalations trigger on thresholds
Outcomes / Benefits
More consistent follow-up on overdue invoices
Fewer missed collections touches
Book a FREE AI audit
FOUNDATION
Support backlog grows and response slows
Tickets triage fast, route to the right owner, and get resolved sooner.
Problem to Solve
Urgent tickets hide in the queue
Wrong routing wastes time
Backlog grows while customers wait
What You Get
Auto-tagging by topic and urgency
Routing rules by product and team
Escalation alerts for high-risk tickets
Daily triage digest for leads
Before
Tickets sorted by hand
Routing depends on tribal knowledge
Escalations discovered late
After
Tags and priority applied on intake
Tickets routed to the right queue
Alerts fire before SLA risk
Outcomes / Benefits
Intercom reports Fin resolves 41% of customer conversations on average.
Source
Lower backlog as routine questions get handled automatically.
Book a FREE AI audit
FOUNDATION
Support repeats the same answers, costs rise
Resolved tickets turn into published help articles.
Problem to Solve
Tribal knowledge stays in tickets
New agents ask the same questions
Customers repeat the same issues
What You Get
Draft article from resolved ticket patterns
Screenshots and steps pulled into a template
Review step before publishing
Article update suggestions from new tickets
Before
Answers live in agent replies
Docs lag behind product changes
Customers open repeat tickets
After
Articles drafted from real resolutions
Review and publish workflow
Deflection improves over time
Outcomes / Benefits
More self-serve answers for customers
Lower repeat ticket volume over time
Book a FREE AI audit
FOUNDATION
Leaders lack clear weekly numbers, decisions slip
A weekly KPI pack ships on schedule with highlights and risks.
Problem to Solve
Reporting pulls take too long
Numbers vary by owner and format
Risks show up late
What You Get
Weekly KPI doc or slide pack
Variance notes and trend callouts
Action list for owners
Distribution to the right channel
Before
Analysts compile data by hand
Metrics live in many sheets
Stakeholders wait for updates
After
Data pulls run on schedule
Pack follows one template
Owners get clear actions
Outcomes / Benefits
Faster weekly reporting cycle
Clearer visibility into trends and risks
Book a FREE AI audit
FOUNDATION
Approvals stall work, projects drift
Approval requests route fast with reminders and audit trail.
Problem to Solve
Approvals sit in inboxes
Work stalls while teams wait
No visibility on who blocks progress
What You Get
Approval request built from a standard form
Routing to the right approver
Reminder and escalation schedule
Approval log for audit and reporting
Before
Approval requests scattered across email and chat
Approver unclear, no SLA for decisions
Teams chase approvals manually
After
Approvals routed to the right approver
Reminders and escalation keep work moving
Audit trail shows who approved and when
Outcomes / Benefits
Shorter approval cycle times
Fewer stalled projects from missed approvals
Book a FREE AI audit
FOUNDATION
New hires start without access, ramp slows
Onboarding steps run from a checklist with routed access requests.
Problem to Solve
Onboarding steps get missed
Access requests stay untracked
Managers chase setup across teams
What You Get
Role-based onboarding checklist
Access requests routed to owners
Reminder schedule for overdue steps
Onboarding status view for managers
Before
Manual checklist in email or docs
Access requests sent one by one
New hires wait for basics
After
Checklist starts from role and start date
Requests routed with owners and deadlines
Status visible for all stakeholders
Outcomes / Benefits
Faster ramp for new hires
Fewer missed onboarding steps
Book a FREE AI audit
FOUNDATION
Vendor onboarding drags and risk stays unclear
Vendor intake runs on a standard flow with tracked review.
Problem to Solve
Risk questionnaires take too long
Missing info causes back-and-forth
Approvals lack traceability
What You Get
Standard intake form and required fields
Auto-extraction into a review table
Flagged risk answers for human review
Approval log and renewal reminders
Before
Questionnaires handled over email
Answers copied into sheets by hand
Risk review delayed by missing details
After
Intake standardized and validated
Answers extracted and flagged
Review routed with clear owners
Outcomes / Benefits
Faster vendor onboarding
Clearer vendor risk visibility
Book a FREE AI audit
FOUNDATION
Manual data entry creates errors and delays
Docs get captured, extracted, and validated before downstream steps.
Problem to Solve
Teams retype data from PDFs and forms
Errors create rework and delays
Validation happens too late
What You Get
Document upload intake with required fields
Field extraction into a table
Validation checks and exception queue
Human review step for flagged items
Before
Docs stored in folders with no structured capture
Manual extraction into spreadsheets
Errors found downstream
After
Docs flow into a structured intake
Extraction fills the right fields
Exceptions flagged for review
Outcomes / Benefits
Lower error rates from validated extraction
Faster processing of inbound documents
Book a FREE AI audit
GROWTH
Reps chase the wrong leads, pipeline quality drops
Scores route leads fast and focus reps on best-fit buyers.
Problem to Solve
Reps waste time on low-intent leads
High-intent leads wait too long
Prioritization varies by rep
What You Get
Lead score based on form and CRM signals
Routing rules based on score and region
Alerts for high-intent leads
Score explanation for rep trust
Before
First-come lead handling
Manual scoring varies by rep
Pipeline fills with low-fit leads
After
Lead score drives routing and speed-to-lead
High-intent leads trigger alerts and tasks
Reps focus time on highest fit leads
Outcomes / Benefits
Improved lead quality and conversion rates
Faster speed-to-lead for high-intent buyers
Book a FREE AI audit
GROWTH
Reps show up unprepared, calls miss key context
A one-page brief arrives before each call with key signals.
Problem to Solve
Research time eats selling time
Key risks and opportunities get missed
Prep varies across reps
What You Get
One-page account brief per meeting
Key contacts and recent activity summary
Talk track suggestions and objections
Next steps and follow-up prompts
Before
Reps research accounts one by one
Notes scattered across tools
Calls start without a plan
After
Brief generated from CRM and web signals
Consistent prep format across reps
Follow-up steps clear after the call
Outcomes / Benefits
More consistent call preparation
Less time spent on manual research
Book a FREE AI audit
GROWTH
Deals slip and risk shows up late, forecasts break
Risk signals get flagged early with a mitigation plan.
Problem to Solve
Risk signals hide in notes and inboxes
Forecast misses reality until late
Managers chase status instead of coaching
What You Get
Risk detection rules for common patterns
Daily or weekly risk digest
Suggested next steps per deal
Escalation alerts for high-value deals
Before
Risk discovered during forecast calls
Signals spread across notes and email
Surprises show up late in the quarter
After
Signals detected and summarized
Alerts trigger mitigation tasks
Fewer late surprises and clearer forecast
Outcomes / Benefits
Earlier visibility into at-risk deals
More stable forecasting and coaching focus
Book a FREE AI audit
GROWTH
AP processing stays slow and costly, exceptions pile up
Invoices route fast with coding suggestions and flagged exceptions.
Problem to Solve
Invoices arrive in many formats
Coding and approvals take time
Exceptions hide until payment is late
What You Get
Invoice capture and extraction
Suggested coding and GL mapping
Exception flags for outliers
Approval workflow with audit trail
Before
Invoices received via email and PDFs
AP codes line items manually
Exceptions found after review delays
After
Invoices extracted into structured fields
Suggested codes speed review
Exceptions routed to a queue
Outcomes / Benefits
A UiPath bot program reduced invoice processing times by 92% in a Jade Global case study.
Source
The same case study reports more than 2,700 hours saved annually.
Source
Book a FREE AI audit
GROWTH
Month-end close feels chaotic, surprises hit late
Close steps run from one checklist with variance explanations.
Problem to Solve
Close tasks get missed or delayed
Variance review takes too long
Teams lack a shared close view
What You Get
Month-end checklist with owners and due dates
Variance notes drafted from data
Exception flags for unusual movements
Daily close status digest
Before
Close tracked in spreadsheets and email
Owners update status manually
Variances explained late
After
Checklist runs from a single system
Status updates auto-roll into a view
Variances summarized for review
Outcomes / Benefits
Fewer missed close steps
Faster variance review and sign-off
Book a FREE AI audit
GROWTH
Hiring screening takes too long, strong candidates wait
Resumes score against a rubric with a human review step.
Problem to Solve
Resume review eats recruiter time
Screening criteria varies by reviewer
Strong candidates wait too long
What You Get
Rubric score and summary per candidate
Shortlist recommendations by role
Flagged risks for human review
Consistent notes for interviewers
Before
Manual reading of resumes
Notes vary and decisions feel subjective
Shortlist built late
After
Rubric score and summary per candidate
Consistent criteria across reviewers
Shortlist ready faster with human review
Outcomes / Benefits
Faster screening cycle
More consistent candidate evaluation
Book a FREE AI audit
GROWTH
Policy answers vary and create risk, teams lose trust
A policy assistant answers with citations from approved docs.
Problem to Solve
Employees ask the same policy questions
Answers vary by person
Incorrect guidance creates risk
What You Get
Policy assistant grounded in your docs
Cited answers with source links
Escalation to HR for edge cases
Weekly report of top questions
Before
Questions answered in chat and email
HR repeats the same replies
No single source of truth
After
Answers come from approved documents
Citations build trust
HR focuses on exceptions
Outcomes / Benefits
Fewer repeated HR questions
More consistent policy guidance with citations
Book a FREE AI audit
GROWTH
Work stays tribal knowledge, quality varies by person
Screen recordings turn into SOPs with checklists and review.
Problem to Solve
Work stays tribal knowledge
Training takes too long
Quality varies by person
What You Get
SOP draft in a standard template
Checklist steps and screenshots
Owner review before publishing
Update prompts when steps change
Before
Shadowing and ad hoc training
Steps live in chat and memory
Errors rise during handoffs
After
SOP drafted from recordings and notes
Checklist format with clear steps
Review and publish keeps SOP current
Outcomes / Benefits
Faster onboarding for repeat workflows
More consistent execution across the team
Book a FREE AI audit
GROWTH
Work stuck, customers wait, no owner
Stalls trigger a workflow that gathers missing info and routes next steps.
Problem to Solve
Orders stall from missing info
No clear owner for next step
Escalations reach customers first
What You Get
Stall detection rules and triggers
Missing info request messages
Routing to the right owner
Escalation rules and SLA tracking
Before
Stalls discovered late
Back-and-forth to gather missing info
Manual chasing across teams
After
Stall triggers detect issues early
Missing info requested automatically
Routing and escalation assign next owner
Outcomes / Benefits
Fewer stalled orders and requests
Clear ownership and faster resolution
Book a FREE AI audit
GROWTH
Support replies take too long, customers churn
Agents send faster replies with policy citations and review.
Problem to Solve
Agents rewrite the same answers
Policy misses create rework
Response time hurts customer experience
What You Get
Draft reply from ticket context
Inline policy citations and links
Human approval before sending
Suggested follow-up questions
Before
Agents search docs for answers
Replies vary by agent
Long handle time per ticket
After
Draft replies with cited sources
Fewer back-and-forth messages
More consistent tone and policy use
Outcomes / Benefits
Microsoft reports a 12% reduction in average handle time in early Copilot for Service deployments.
Source
Faster first response and more consistent answers with policy citations.
Book a FREE AI audit
GROWTH
SLA breaches and escalations surprise leaders, trust drops
Risk signals get flagged early with alerts and routing.
Problem to Solve
SLA breaches surprise leaders
Urgent tickets hide in the queue
Escalations happen late
What You Get
SLA risk scoring and alerts
Escalation routing rules
Leader digest for at-risk queues
Post-mortem notes for repeat issues
Before
Escalations discovered late
Priority set by gut feel
Leaders learn after breach
After
Risk signals flagged early
Alerts route work to the right responder
Preventable breaches drop over time
Outcomes / Benefits
Lower SLA breach rates over time
Fewer surprise escalations
Book a FREE AI audit
TRANSFORM
Clients keep asking for status, delivery time disappears
Structured updates power a portal view with fewer meetings.
Problem to Solve
Clients ask for status every week
PMs rebuild updates in slides
Delivery time gets eaten by reporting
What You Get
Status update template and rules
Portal view powered by structured fields
Client-ready summaries on schedule
Escalation visibility for blockers
Before
Manual status updates in email and decks
Inconsistent format across projects
Clients ping repeatedly for the same info
After
Structured updates feed a portal view
Consistent format across projects
Fewer status pings and clearer trust
Outcomes / Benefits
Fewer status meetings and pings
More time spent on delivery work
Book a FREE AI audit
TRANSFORM
Client onboarding drags and feels unclear, churn risk rises
A guided assistant collects inputs, answers questions, and tracks progress.
Problem to Solve
Onboarding drags with back-and-forth
Missing info stalls setup
Clients feel lost before value starts
What You Get
Guided intake for required inputs
FAQ answers from your onboarding docs
Progress tracker for client and team
Handoff tasks to internal owners
Before
Back-and-forth emails for inputs
Steps unclear and repeated
Internal teams wait on missing details
After
Guided flow collects required inputs
Clear steps and owners for setup
Faster handoff and earlier time-to-value
Outcomes / Benefits
Shorter onboarding cycle time
Fewer missing inputs and handoff delays
Book a FREE AI audit
TRANSFORM
Customers disengage from generic experiences, revenue stalls
Recommendations adapt to behavior, intent, and segment.
Problem to Solve
Customers disengage from generic messaging
Cross-sell signals get missed
Manual segmentation falls behind
What You Get
Recommendation rules and signals
Personalized messages and next steps
Measurement plan for impact
Guardrails for approved offers
Before
One-size experience for all customers
Static segments updated by hand
Recommendations depend on guesswork
After
Behavior signals drive recommendations
Next steps tailored by segment and intent
Results measured with simple tests
Outcomes / Benefits
McKinsey reports personalization can lift revenues by 5% to 15%.
Source
McKinsey reports personalization can increase marketing ROI by 10% to 30%.
Source
Book a FREE AI audit
TRANSFORM
Internal requests interrupt real work, context switching wins
Requests route through one intake, missing fields get collected, owners stay clear.
Problem to Solve
Internal requests interrupt deep work
Missing details create back-and-forth
Requests fall through the cracks
What You Get
Structured intake for common requests
Auto-questions to collect missing fields
Routing to the right owner and queue
Status view without extra pings
Before
Ad hoc requests in chat with missing context
Constant pings and context switching
No tracking for status and ownership
After
Structured intake collects required fields
Routing creates tasks with owners and SLAs
Status visible without extra pings
Outcomes / Benefits
Less context switching across teams
Fewer dropped internal requests
Book a FREE AI audit
TRANSFORM
Renewal risk shows up late, expansion moments get missed
Signals trigger a playbook for outreach and retention.
Problem to Solve
Renewal risk shows up late
Expansion moments get missed
Signals scattered across tools
What You Get
Signal definitions and thresholds
Weekly digest for at-risk accounts
Recommended playbooks per signal
Routing to the account owner
Before
Reactive account work after issues escalate
Signals hidden in tickets and usage
No consistent playbooks for outreach
After
Signals trigger alerts and recommended plays
Weekly digest highlights at-risk accounts
Account team follows a repeatable plan
Outcomes / Benefits
Earlier renewal risk detection
More consistent expansion follow-up
Book a FREE AI audit
TRANSFORM
Audit prep disrupts the business, teams scramble for evidence
Evidence collection runs from a checklist with traceability.
Problem to Solve
Audit prep disrupts the team
Evidence lives across folders and tools
Missing artifacts create last-minute risk
What You Get
Evidence checklist by control
Auto-collection prompts and routing
Index with links and timestamps
Gap flags for missing evidence
Before
Manual hunting across folders and inboxes
Control evidence assembled late
Gaps discovered at the deadline
After
Evidence checklist drives collection
Artifacts indexed with traceability
Gaps flagged early for fixes
Outcomes / Benefits
Faster audit prep with fewer fire drills
More complete evidence packs with traceability
Book a FREE AI audit
FOUNDATION
Slow response to inbound leads loses revenue
Leads route to the right owner fast, with SLA reminders until first touch.
Problem to Solve
Leads sit unclaimed or get routed wrong
No SLA visibility for first response
Managers learn about missed follow-up too late
What You Get
Routing rules by region, product, and score
SLA timers with reminders and escalation
Auto-created tasks for first touch and next steps
Daily digest of unclaimed or overdue leads
Before
Leads land in inboxes and forms with no owner
Reps follow up when they remember
No reporting on response time or SLA breaches
After
Each lead gets an owner in minutes
Reminders run until first touch is logged
Overdue leads escalate automatically with a clear trail
Outcomes / Benefits
Calling within 5 minutes instead of 30 minutes raises odds of contacting a lead by 100x.
Source
Calling within 5 minutes instead of 30 minutes raises odds of a lead entering the sales cycle by 21x.
Source
Book a FREE AI audit
GROWTH
Discount approvals drag, deals stall, margins leak
A deal desk packet builds automatically, routes for approval, and logs decisions.
Problem to Solve
Reps submit incomplete discount requests
Approval chains differ by deal and rep
Policy exceptions get approved without context
What You Get
Standard deal desk checklist by deal type
Auto-built approval packet with deal facts and rationale
Routing rules by discount level and risk
Approval log with comments and timestamps
Before
Discount approvals happen in chat and email threads
Missing context causes back-and-forth
Approvals are hard to audit after the fact
After
Every exception request includes the same required fields
Approvals route by policy with reminders
Decisions are searchable and reportable
Outcomes / Benefits
A study cited in a CPQ paper reports a 30% to 40% reduction in quote processing time after a CPQ rollout.
Source
Aberdeen research cited in a CPQ overview reports organizations saw 27% less time to produce quotes and proposals.
Source
Book a FREE AI audit
GROWTH
Orders slow down because contract data is trapped in PDFs
Contracts and order forms extract into structured fields with validation before fulfillment.
Problem to Solve
Teams retype key fields from order forms
Missing fields cause rework and delays
Errors show up after the order is in motion
What You Get
Upload and intake step with required fields
Auto-extracted fields into Airtable or CRM
Validation rules with an exception queue
Human review step for flagged fields
Before
Contracts stored in folders and emailed around
Manual copy-paste into trackers
Errors found downstream during fulfillment or billing
After
Contracts enter one intake channel
Key fields extracted into the right system
Exceptions routed for review before downstream steps
Outcomes / Benefits
A UiPath case study reports mortgage document processing time dropped from 30 days to 3 days after automation.
Source
Fewer data entry errors and fewer order delays.
Book a FREE AI audit
GROWTH
CRM data is messy, forecasting turns into guesswork
An audit flags missing fields, stale stages, and risky deals, then routes fixes to owners.
Problem to Solve
Key fields stay blank or inconsistent
Deals sit in stages with no next step
Managers chase updates instead of coaching
What You Get
Weekly audit of required fields and stage hygiene
Cleanup queue by rep and manager
Suggested fixes and reminders
Dashboards for data completeness and aging
Before
Ops runs manual audits in spreadsheets
Reps update CRM only before forecast calls
Bad data compounds quarter after quarter
After
Hygiene checks run on a schedule
Fixes route to the right owner with SLAs
Forecast inputs get cleaner over time
Outcomes / Benefits
Improved data completeness and more reliable forecasting inputs.
Lower rework from fewer missing fields and stale stages.
Book a FREE AI audit
FOUNDATION
Escalations waste hours because the context is scattered
Tickets summarize into a clean handoff note with next steps and links.
Problem to Solve
Engineers get long ticket threads with no summary
Handoffs miss key details and reproduction steps
Customers wait while teams re-read history
What You Get
One-page handoff summary with timeline and key facts
Repro steps and environment fields pulled forward
Links to related tickets and docs
Suggested next steps for the receiving team
Before
Support pastes long threads into chat
Engineering re-reads the full history
Back-and-forth to gather basics
After
Handoff note includes key details in one place
Receiving team starts with repro and context
Fewer clarification loops before work starts
Outcomes / Benefits
Observe.AI reports customers using its generative AI saw a 23% reduction in average handle time.
Source
Faster escalations and fewer repeat questions during handoff.
Book a FREE AI audit
FOUNDATION
Status pings interrupt your team all day
A status request form pulls the latest update and replies with one source of truth.
Problem to Solve
Requests arrive in chat with missing context
Owners stop work to answer the same questions
Updates differ by person and channel
What You Get
Simple status request intake (chat or form)
Auto-lookup of project or ticket status
Auto-generated reply with next milestone and owner
Escalation path for urgent blockers
Before
People ask where this is at in chat
Work pauses to investigate and reply
Status answers vary by who responds
After
Requests route through one intake
Replies pull from the system of record
Owners only get pulled in for true exceptions
Outcomes / Benefits
Asana reports employees spend 60% of their time on work about work like status updates and coordination.
Source
Less interruption and more time on delivery work.
Book a FREE AI audit
FOUNDATION
Requests arrive in email, tasks get missed, deadlines drift
Emails turn into structured tasks with required fields, owners, and due dates.
Problem to Solve
Requests arrive with missing details
Manual task creation is inconsistent
No clear SLA for responses or completion
What You Get
Email-to-task parsing with required fields
Auto-assign owner based on request type
Default due dates and SLA timers
Exceptions routed back for missing info
Before
Requests sit in inboxes
People create tasks in different formats
Managers learn about misses too late
After
Every request becomes a task with required fields
Owners and due dates are consistent
Missing info gets requested immediately
Outcomes / Benefits
Asana reports employees spend 60% of their time on work about work, including coordination and tracking.
Source
Fewer dropped requests and faster turnaround on internal work.
Book a FREE AI audit
GROWTH
Work requests scatter across tools, no one owns the next step
One ops hub standardizes intake, assigns owners, and tracks completion.
Problem to Solve
Requests come through chat, email, and hallway conversations
No consistent fields for priority and due dates
Leaders cannot see backlog health
What You Get
Standard intake form with required fields
Single backlog with owners and due dates
Routing rules and SLAs by request type
Weekly digest and dashboard for visibility
Before
Requests live in threads and spreadsheets
Ownership changes without tracking
Backlog surprises leaders late
After
Requests land in one backlog
Owners, SLAs, and status are visible
Leaders manage by data, not interruptions
Outcomes / Benefits
Asana reports employees spend 60% of their time on work about work, including coordination and tracking.
Source
Clearer ownership and fewer dropped requests.
Book a FREE AI audit
GROWTH
Teams do the same work twice because duplicates go unnoticed
Duplicate requests get detected early, merged, and routed to one owner.
Problem to Solve
Multiple people submit the same request
Work starts in parallel with no coordination
Updates diverge and stakeholders get confused
What You Get
Duplicate detection rules and similarity checks
Merge suggestions and consolidated thread
Single owner assignment and status
Stakeholder notifications when duplicates merge
Before
Duplicates discovered after work begins
Multiple owners respond to the same ask
Time is wasted and status gets messy
After
Duplicates flagged on intake
Work consolidates under one ticket
Stakeholders get one consistent update
Outcomes / Benefits
Asana reports employees spend 60% of their time on work about work, including coordination and tracking.
Source
Less duplicated effort and fewer conflicting updates.
Book a FREE AI audit
GROWTH
Experts get pinged all day for the same internal questions
An internal assistant answers from approved docs, with citations and escalation.
Problem to Solve
Answers vary by person and channel
New hires do not know where to look
Incorrect answers create risk and rework
What You Get
Assistant grounded in your approved docs
Answers include citations and links
Escalation to an owner when uncertain
Weekly report of top questions and doc gaps
Before
Questions answered in chat and email
Experts repeat answers and context
No feedback loop to improve docs
After
Answers come from a single source of truth
Citations build trust
Experts focus on edge cases
Outcomes / Benefits
Intercom reports Fin resolves 41% of customer conversations on average.
Source
Fewer repeated questions and faster answers for employees.
Book a FREE AI audit
GROWTH
Follow-up slips, warm leads go cold, pipeline slows
Signals trigger sequences, drafts, and handoffs so no lead gets lost.
Problem to Solve
Reps forget follow-ups when workload spikes
Messaging varies and lacks personalization
Hand-off points between SDR and AE are inconsistent
What You Get
Follow-up triggers from CRM and email signals
Drafted messages from approved templates
Task creation and reminders until completion
Escalation when a lead hits high intent
Before
Follow-up depends on memory
Leads fall through cracks during handoffs
Managers lack visibility into missed touches
After
Follow-up runs from triggers and SLAs
Messaging is consistent and personalized
Leaders see missed touches before leads go cold
Outcomes / Benefits
Microsoft reports sellers using Copilot for Sales saved an average of 90 minutes per week and 83% said it improved productivity (survey).
Source
Faster speed-to-lead and more consistent follow-up.
Book a FREE AI audit
GROWTH
Bad CRM data wastes selling time and breaks reporting
Duplicates, missing fields, and stale records get detected and routed for fixes.
Problem to Solve
Duplicates inflate pipeline and activity counts
Missing fields break routing and reporting
Stale opportunities stay open without next steps
What You Get
Duplicate detection and merge queue
Missing-field prompts and auto-enrichment suggestions
Stale record alerts with next-step reminders
Weekly hygiene scorecard by team
Before
Data fixes happen only during fire drills
Reps spend time searching for correct records
Reports do not match reality
After
Hygiene checks run continuously
Fixes route to the right owner
Forecast and reporting become more reliable
Outcomes / Benefits
Improved forecast accuracy from cleaner CRM data.
Less rep time spent fixing records and searching for correct info.
Book a FREE AI audit
TRANSFORM
Implementations stall, customers get frustrated, churn risk rises
Blockers get detected early, routed to leaders, and updates draft automatically.
Problem to Solve
Blockers hide in tickets, notes, and chat
No clear owner for removing roadblocks
Customers do not get proactive updates
What You Get
Stall and blocker detection rules
Routing to the right owner with SLAs
Customer update drafts from the latest status
Leader digest for at-risk implementations
Before
Blockers discovered during recurring calls
Updates written from scratch
Escalations happen after frustration builds
After
Signals flag blockers early
Owners get tasks and escalation
Customers receive proactive updates
Outcomes / Benefits
Asana reports employees spend 60% of their time on work about work, including coordination and tracking.
Source
Fewer stalled implementations and more proactive customer communication.
Book a FREE AI audit
GROWTH
QBR prep eats days, leaders still lack clear insight
A QBR pack auto-builds with metrics, wins, risks, and next actions.
Problem to Solve
Data pulls come from too many systems
Narratives are rebuilt from scratch each quarter
Action items get lost after the review
What You Get
Slide or doc pack in a standard template
Auto-pulled KPIs and pipeline metrics
Wins, risks, and key deals summarized
Action list with owners and due dates
Before
Ops teams build QBR decks manually
Leaders debate numbers and definitions
Actions are tracked inconsistently
After
Pack follows one template and refreshes fast
Numbers are consistent and sourced
Actions route into task tracking automatically
Outcomes / Benefits
Faster QBR prep and clearer decisions from consistent metrics.
Less time spent rebuilding the same narrative each quarter.
Book a FREE AI audit
GROWTH
Handoffs drop context, customers repeat themselves, retention suffers
A handoff packet captures goals, stakeholders, risks, and next steps in one place.
Problem to Solve
CS starts without full context of what was sold
Key stakeholders and success criteria are unclear
Implementation risks are discovered late
What You Get
Handoff packet template with required fields
Auto-filled deal summary and promised outcomes
Customer goals, stakeholders, and risks
Kickoff agenda and first-30-days plan draft
Before
CS reads scattered notes and email threads
Customers repeat requirements
Success criteria changes mid-stream
After
CS starts with a complete packet
Kickoff aligns on goals and plan
Fewer surprises during implementation
Outcomes / Benefits
Cleaner handoffs and less customer re-explaining during onboarding.
Faster time-to-value from clearer goals and ownership.
Book a FREE AI audit
GROWTH
Pricing exceptions are inconsistent, margin leakage goes unnoticed
Exception requests gather evidence, route for approval, and log outcomes.
Problem to Solve
Reps request discounts without evidence
Approvals depend on who is asked
No feedback loop from exceptions to pricing policy
What You Get
Exception request form with required fields
Auto-collected deal data and competitive notes
Routing by discount level and risk
Exception log with outcomes and learnings
Before
Discount requests happen ad hoc
Context is missing and approvals are slow
Exceptions are hard to analyze after the quarter
After
Requests include required evidence
Approvals route by policy with SLAs
Exceptions become analyzable inputs to pricing strategy
Outcomes / Benefits
A CPQ study reports a 30% to 40% reduction in quote processing time after CPQ implementation, helping reduce approval back-and-forth.
Source
Faster exception processing and better discount governance.
Book a FREE AI audit
GROWTH
Pipeline surprises show up late, forecasts swing, leaders lose confidence
Weekly anomaly alerts flag unusual changes and route follow-ups to owners.
Problem to Solve
Stage changes happen with no notes
Big deal changes hide until forecast calls
Data anomalies distort KPIs and pipeline views
What You Get
Rules for anomaly definitions by metric
Weekly digest of unusual changes
Auto-created follow-up tasks
Trend notes and suggested root causes
Before
Anomalies found during QBRs and fire drills
Teams debate which numbers are real
Managers chase updates in meetings
After
Anomalies flagged early
Owners get tasks to confirm and explain
Forecast conversations focus on actions, not data cleanup
Outcomes / Benefits
Earlier visibility into pipeline risks and cleaner forecasting inputs.
Fewer surprise swings during forecast and review cycles.
Book a FREE AI audit
FOUNDATION
Meeting scheduling friction kills momentum after a lead replies
Replies trigger an instant booking flow with guardrails and handoff.
Problem to Solve
Back-and-forth scheduling slows response
Wrong calendar links get sent
Leads go cold between reply and meeting
What You Get
Auto-detected meeting intent
Time-window rules and meeting types
Calendar booking link insertion and reminders
Handoff to the right owner after booking
Before
Reps manually send calendar links
Scheduling threads drag on
Meetings booked late or not at all
After
Intent triggers a booking flow immediately
Rules ensure the right meeting type
Owners get notified as soon as a meeting is booked
Outcomes / Benefits
Calendly reports the average professional spends about 10 hours per week scheduling meetings.
Source
Faster meeting booking and fewer scheduling back-and-forth messages.
Book a FREE AI audit
GROWTH
Reps waste selling time searching for the right asset for a deal
An enablement assistant answers with links to approved decks, one-pagers, and proof points.
Problem to Solve
Content is scattered across folders and tools
Reps use outdated decks
Enablement teams get repeated requests
What You Get
Assistant grounded in your enablement library
Answers include links and best next asset
Guardrails to approved content only
Gap report of missing assets by theme
Before
Reps search folders and ask in chat
Outdated materials get used
Enablement is reactive to requests
After
Reps get the right asset in seconds
Approved content usage increases
Enablement sees gaps and demand signals
Outcomes / Benefits
HubSpot cites research that salespeople spend 31% of their time searching for or creating sales content.
Source
More consistent messaging and faster deal cycles from faster access to assets.
Book a FREE AI audit
GROWTH
Support volume stays high because customers cannot self-serve
A deflection assistant answers from your KB, then escalates when needed.
Problem to Solve
Customers ask the same questions repeatedly
Agents spend time on low-complexity issues
Incorrect answers create risk and churn
What You Get
Assistant grounded in KB articles and macros
Answer citations with links
Escalation path to an agent when uncertain
Deflection reporting and top-gap insights
Before
Customers open tickets for basic questions
Agents rewrite common replies
KB drifts behind real issues
After
Customers get instant cited answers
Tickets drop for routine issues
KB gaps become visible and fixable
Outcomes / Benefits
Intercom reports Fin resolves 41% of customer conversations on average.
Source
Lower support volume and faster responses for complex tickets.
Book a FREE AI audit
TRANSFORM
Customer feedback is scattered, themes get missed, roadmap drifts
An insights agent clusters feedback, flags themes, and proposes backlog items with evidence.
Problem to Solve
Feedback lives across tickets, calls, and notes
Manual tagging is slow and inconsistent
Product teams see issues too late
What You Get
Theme clustering across tickets and notes
Top drivers and example quotes linked to sources
Backlog item suggestions with impact estimates
Weekly digest for product and CS leaders
Before
Teams skim tickets manually
Themes debated without evidence
Backlog decisions lag real customer pain
After
Themes surface automatically with examples
Leaders see what is trending early
Backlog items link to real customer evidence
Outcomes / Benefits
Faster identification of repeat issues and clearer prioritization with linked customer evidence.
Less manual tagging and more consistent Voice of Customer reporting.
Book a FREE AI audit

Use Cases

Poor quality leads and messy CRM data slow growth

Follow-ups slip after meetings, deals slow down

Proposals take too long to send, deals stall

Invoices go out late or wrong, cash flow suffers

Late payments hurt cash flow and planning

Support backlog grows and response slows

Support repeats the same answers, costs rise

Leaders lack clear weekly numbers, decisions slip

Approvals stall work, projects drift

New hires start without access, ramp slows

Vendor onboarding drags and risk stays unclear

Manual data entry creates errors and delays

Reps chase the wrong leads, pipeline quality drops

Reps show up unprepared, calls miss key context

Deals slip and risk shows up late, forecasts break

AP processing stays slow and costly, exceptions pile up

Month-end close feels chaotic, surprises hit late

Hiring screening takes too long, strong candidates wait

Policy answers vary and create risk, teams lose trust

Work stays tribal knowledge, quality varies by person

Work stuck, customers wait, no owner

Support replies take too long, customers churn

SLA breaches and escalations surprise leaders, trust drops

Clients keep asking for status, delivery time disappears

Client onboarding drags and feels unclear, churn risk rises

Customers disengage from generic experiences, revenue stalls

Internal requests interrupt real work, context switching wins

Renewal risk shows up late, expansion moments get missed

Audit prep disrupts the business, teams scramble for evidence

Slow response to inbound leads loses revenue

Discount approvals drag, deals stall, margins leak

Orders slow down because contract data is trapped in PDFs

CRM data is messy, forecasting turns into guesswork

Escalations waste hours because the context is scattered

Status pings interrupt your team all day

Requests arrive in email, tasks get missed, deadlines drift

Work requests scatter across tools, no one owns the next step

Teams do the same work twice because duplicates go unnoticed

Experts get pinged all day for the same internal questions

Follow-up slips, warm leads go cold, pipeline slows

Bad CRM data wastes selling time and breaks reporting

Implementations stall, customers get frustrated, churn risk rises

QBR prep eats days, leaders still lack clear insight

Handoffs drop context, customers repeat themselves, retention suffers

Pricing exceptions are inconsistent, margin leakage goes unnoticed

Pipeline surprises show up late, forecasts swing, leaders lose confidence

Meeting scheduling friction kills momentum after a lead replies

Reps waste selling time searching for the right asset for a deal

Support volume stays high because customers cannot self-serve

Customer feedback is scattered, themes get missed, roadmap drifts