AI That Sales and Marketing Teams Actually Use
The most common marketing AI failure is not a bad model. It is a scoring system sales ignores, a forecasting tool that never connected to CRM, or a personalization engine producing segment-of-one output nobody reviewed. We build marketing and sales AI programs your commercial team trusts because the data quality, model logic, and CRM integration were designed with them, not for them.
Why Sales Stops Using the AI Scores
The model goes live. The scores populate in CRM. Six weeks later, sales reps are ignoring the scores and doing what they always did: calling based on gut and recency. Not because they're resistant to technology, but because the scoring logic wasn't built against their actual ICP, the CRM data feeding the model was dirty, and nobody trained the model on what a qualified deal actually looks like in your pipeline. The technology worked. The adoption problem was a data and design problem.
What Marketing AI Requires to Actually Work
Five dimensions that determine whether marketing and sales AI produces insights your team uses or dashboards they ignore.
Data Quality Foundation
AI models are as good as the data they run on. Before building any model, audit CRM hygiene, contact completeness, and historical pipeline data. Bad data produces confident wrong predictions.
ICP & Scoring Alignment
Who is your ideal customer and what signals indicate fit? If scoring logic wasn't built against your actual ICP, validated with sales, it won't be used.
Model Validation
A model that predicts accurately on training data but fails on live pipeline is worse than no model, because it generates false confidence. Validation against real outcomes is non-negotiable.
CRM & Attribution Integration
AI insights that live outside your CRM or don't connect to attribution create parallel workflows that sales ignores. The model needs to live where the selling happens.
Sales & Marketing Alignment
Who defined the scoring criteria? Did sales validate the model thresholds? Alignment is not a soft requirement. It determines whether anyone uses the output.
Specific Engagements
Each offering goes deep on one area of this service. Start where the need is clearest.
AI Sales Forecasting
Pipeline forecasting models connected to your CRM, validated against historical data, and presentable to finance without a manual adjustment layer.
Explore this serviceAI-Driven Personalized Marketing
Personalization programs with documented segmentation logic, brand guardrails, and compliance documentation, not segment-of-one output nobody reviewed.
Explore this servicePredictive Lead Scoring
Scoring models validated with sales leadership, calibrated against your actual pipeline, and integrated into your CRM, not scores sales ignores.
Explore this serviceHow a Marketing AI Engagement Works
Four phases from data audit to production model. No scoring model is built until data quality and ICP criteria are validated.
Data Audit & ICP Definition
We audit your CRM data quality, historical pipeline data, and ICP criteria. The output is a data quality gap map and a validated ICP definition that sales has reviewed.
Model Design & Alignment
We design the scoring logic, enrichment sources, and attribution model. Then validate criteria with sales leadership before building anything.
Build & CRM Integration
We build the model, integrate it with your CRM and marketing automation platform, and validate scoring output against your historical pipeline data.
Adoption & Monitoring
We configure performance monitoring, train the sales and marketing teams on how to use and interpret the scores, and establish a quarterly review cadence.
Who This Is For
We would rather say this clearly than waste your time.
This engagement is right for you if...
- Sales and marketing teams with AI scoring or forecasting tools that aren't being used in daily workflow
- RevOps teams whose CRM data quality is preventing reliable pipeline forecasting
- Marketing teams running personalization programs that generate inconsistent or off-brand output
- Sales leadership that wants AI forecasting they can present to the board without qualifying every number
- Organizations where marketing AI is producing output that sales and finance don't trust
This is probably not the right fit if...
- Companies with CRM data too incomplete to build a reliable model. Data quality must come first.
- Teams looking for a marketing AI tool without sales and RevOps alignment on the scoring criteria
- Organizations expecting AI to compensate for an undefined ICP or broken pipeline tracking
AI Your Commercial Team Will Actually Use
Start with a data quality audit to understand what your CRM can actually support before building any models.