AI Playbook
for CRM

Einstein Trust Layer encrypts data before LLM — verify it is enabled and audited

Agentforce agents require clear human escalation paths for edge cases

Lead scoring models need 3+ months of deal data to reach reliable accuracy

Activity Capture may require privacy disclosure to contacts depending on jurisdiction

Review Einstein Copilot outputs before sending to customers

Set approval thresholds for any agent-initiated actions

Audit AI-generated content quarterly for accuracy and brand tone

Verify GDPR compliance when using Breeze Intelligence enrichment on EU contacts

AI-generated content requires human review before publishing or sending

Use lead scores as one signal, not the sole prioritization input

Define human escalation paths for Breeze Agent edge cases

Monitor unsubscribe rates; high-volume AI sequences can feel intrusive

Watch for AI hallucinations in company research summaries

Graph data access requires admin consent — review permissions periodically

Copilot outputs must be reviewed before sending to customers — especially externally

Relationship intelligence pulls from email metadata — disclose to team

Copilot Studio agents must have guardrails for sensitive customer data

Ensure Dynamics data residency requirements are met for regulated industries

Review AI forecasting accuracy vs. actuals monthly and recalibrate

Zia predictions need 3+ months of consistent deal data to reach reliable accuracy

Email sentiment analysis can misread tone — use as signal not final verdict

Zoho AI data stays within Zoho infrastructure — verify for enterprise compliance requirements

Blueprint automation should require human approval for high-value deal changes

Monitor Zia anomaly alerts — tune thresholds to avoid alert fatigue

GDPR consent required for EU contacts in automated email workflows

Freddy scores reflect historical patterns — retrain if ICP shifts significantly

AI email drafts are starting points only — always personalize before sending

Auto Profile enrichment uses third-party data — verify accuracy on key accounts

Chatbot escalation paths must be fully tested before go-live

Review Freddy Insights alert volume — tune thresholds to prevent decision fatigue

Enrichment data subject to GDPR consent requirements for EU contacts

AI deal probability needs consistent activity logging — enforce this habit before relying on scores

Email AI outputs are generic starting points — personalize every draft before sending

Smart Contact Data from third parties may be outdated — spot-check on strategic accounts quarterly

Workflow automations should have off-switches for reps encountering edge cases

Monitor AI tip acceptance rate monthly — low acceptance signals poor relevance

Monday AI automations can create data clutter if poorly designed — audit and prune quarterly

AI document generation needs review before sending to customers

Complex enterprise CRM needs may outgrow Monday's capabilities — evaluate fit before large deployment

Ensure data privacy settings align with GDPR for EU customers

Test all automations in a staging board before pushing to production

Set clear board ownership to prevent governance sprawl

Copper is optimized for Google Workspace — limited value outside that ecosystem

Auto-created contacts from email may capture incorrect job roles — review weekly

Email AI is less sophisticated than dedicated tools like Lavender — supplement for advanced personalization

Limited customization vs. Salesforce — evaluate fit before large-scale deployment

GDPR consent required for EU contacts in any automated email workflows

No-code power can lead to uncontrolled workflow sprawl — establish a governance review process

AI models require labeled historical data — plan data preparation before training begins

On-prem deployment adds IT burden and limits AI feature access — cloud strongly preferred

Platform complexity grows with use cases — assign a dedicated Creatio administrator

Validate AI scoring model performance against actual outcomes every quarter

SugarPredict external data (Bombora, Clearbit) requires separate licensing and data agreements

On-prem AI features are more limited than cloud deployments — verify capability matrix before deciding

Sugar Hint enrichment from public sources may be outdated — verify accuracy on enterprise accounts

BPM workflows require thorough testing in staging before enabling for all users

Review SugarPredict model drift quarterly — retrain if win rates shift by more than 10%

Compliance industries should validate data handling against HIPAA or FINRA requirements

Attio is a newer platform — validate enterprise-grade security certifications (SOC 2 Type II) before deploying sensitive data

Flexible schema can become inconsistent across teams without governance — assign a CRM admin

AI enrichment from external sources needs GDPR review for EU contacts

Custom object design requires upfront planning — avoid over-engineering before product-market fit is clear

Evaluate long-term scalability for large enterprise before committing to the platform

Close AI email generation needs personalization added — review every draft before sending without exception

Call recording requires two-party consent disclosure in applicable US states and international markets

AI call summaries can miss nuance in complex negotiations — listen to key calls directly

SMS automation subject to TCPA compliance — verify opt-in process is documented and enforced

Smart Views are only as good as data quality — enforce activity logging as a non-negotiable team standard

AI capabilities are primarily integration-dependent — validate third-party tool outputs before acting on them

Automation workflows require clean, structured data to function reliably — audit data quality before go-live

Rule-based lead scoring lacks ML adaptability — review and recalibrate criteria as market conditions change

GDPR compliance required when enriching or exporting customer data through connected tools

Automated communications should be reviewed periodically to avoid stale or inaccurate outreach

SLA escalation rules need regular auditing to prevent false triggers or missed cases

AI scoring models can reflect historical bias — audit for demographic or segment skew quarterly

Models need retraining when ICP shifts or product lines change significantly

Score threshold for MQL requires explicit marketing and sales alignment before launch

Intent data providers have different methodologies — validate overlap before paying for multiple

Manual overrides must be logged to provide feedback signal to improve model over time

Avoid full automation on strategic or enterprise accounts — human judgment required

Review model performance every quarter and retrain if conversion correlation degrades

AI forecasting is only as good as CRM data quality — enforce update discipline as a team norm

Use AI as input to the forecast call, not a replacement for human judgment

Beware sandbagging: AI models trained on artificially low commits will reflect that bias

Deal exclusions must be documented to avoid distorting historical accuracy metrics

Forecasting tools need 2+ quarters of consistent data before baselines are reliable

Present AI signals in context — not as final verdicts that override rep knowledge

Audit for systematic bias in opportunity scoring across segments and reps

Two-party call recording consent is legally required in many US states and international markets — implement disclosure statements in all meeting invites

Recording sensitive enterprise negotiations requires legal review before sharing recordings

AI transcription accuracy varies by accent and background noise — spot-check critical calls

Never use call scoring as the sole performance metric — context and deal specifics matter

Configure competitor keyword tracking carefully — false positives create review fatigue

Ensure recording storage meets data retention and deletion policies

Never auto-send AI-generated emails without human review — personalization errors cause significant damage

CAN-SPAM and GDPR require physical address and clear opt-out in all commercial emails

Over-sequencing damages domain deliverability and brand reputation — enforce contact frequency limits

AI personalization using LinkedIn data may violate platform terms of service — use only official licensed data partnerships

Monitor reply sentiment — negative replies should immediately exit sequences and trigger rep review

Test new sequences on small cohorts before rolling out broadly

AI deal health only reflects data that is logged — enforce activity logging or signals are missing and scores are wrong

Deal health scores should supplement manager judgment, not replace it

Pipeline inflation (ghost deals) will distort AI models — enforce regular deal hygiene and close cycles

Don't use at-risk flags to penalize reps — use them to coach and support

Customize AI risk thresholds for different deal types and average sales cycles

Single-threaded flags need context — some deals have a single buyer by design

Third-party enrichment data cannot be resold or shared — license restrictions apply

Email addresses from enrichment tools require explicit consent before cold outreach to EU contacts

Data freshness varies significantly by provider — tier accounts for enrichment frequency and cost

Tech stack detection is probabilistic not definitive — verify for critical enterprise accounts

Intent data is company-level not individual-level — use appropriately in outreach personalization

Never assume enriched data is fully accurate — spot-check on all strategic accounts

Comply with LinkedIn's restrictions on data scraping — use only official licensed data partnerships

Marketing automation plus AI equals scale — which magnifies both success and mistakes, so start with small cohorts

AI content generation requires brand voice guidelines and mandatory human review before publishing

Email marketing opt-in consent is required in most markets — enforce strictly and test regularly

Frequency caps prevent list fatigue — set and enforce maximum email contacts per month

Unsubscribe requests must be processed within regulatory timeframes

Attribution models have inherent bias — use multiple models and triangulate results

AI chatbot frustrates customers if it cannot escalate smoothly — design human handoff with extreme care

Never use AI to deny claims or reject high-stakes customer requests without human review

Customer sentiment data is sensitive — implement role-based access restrictions

AI case prioritization may deprioritize legitimate issues with low volume — review edge cases monthly

GDPR subject access requests cannot be handled by AI alone — require human review

Ensure accessibility compliance for all AI-powered self-service portal experiences

Revenue analytics are only as accurate as the underlying data — invest in data quality before investing in analytics tools

Rep-level performance data is sensitive — implement and enforce role-based access controls

AI predictions are probabilistic — present confidence ranges not single-point estimates

Attribution models are never perfect — use multiple and triangulate for strategic decisions

Historical analysis can embed past biases — review segmentation assumptions regularly

Don't optimize solely for AI-favored metrics — qualitative signals and rep knowledge matter too

Align all analytics definitions with finance before reporting to board or investors