How to choose between building and buying AI solutions for your business

As a Salesforce admin who's rolled out AI solutions across retail, healthcare, and manufacturing, I've seen teams waste 6+ months building custom AI when buying would've saved them $300k. Here's how to cut through the noise.

First, Audit Your Data Maturity

Building AI without clean data is like building a house on sand. Run this SOQL to check readiness:

SELECT COUNT(Id) FROM Lead WHERE Email = '' OR LeadSource = 'Unknown' OR (Phone = '' AND MobilePhone = '')

If >15% of records are incomplete (as one healthcare client discovered), you can't build. Their custom AI churn prediction failed because 40% of patient records had missing contact data. They bought Salesforce Health Cloud instead, which auto-cleaned data during setup.

Cost vs. Time: The Real Math

Building isn't cheaper long-term. A retail client budgeted $220k for custom AI to predict cart abandonment. After 10 months, they'd spent $370k with no ROI. Meanwhile, Salesforce Einstein for Commerce (buy) cost $45k, integrated in 14 days, and drove a 19% reduction in abandoned carts in Month 1. Key question: Can your team maintain the model? If not, buying is cheaper.

When Buying Actually Saves Time

If your need is common (e.g., lead scoring, chatbots), buying beats building. Example: A manufacturing client tried building an AI parts inventory tool. It took 8 months to train the model on their legacy ERP data. They switched to Salesforce Field Service Lightning's AI inventory module—live in 48 hours, reduced stockouts by 33% in Q1.

The Build Case: Only When You Have a Unique Need

Build *only* if: (1) Your data is proprietary (e.g., IoT sensor data from machines), (2) You have in-house ML engineers, and (3) The problem isn't solved by any vendor. One client had proprietary failure patterns from 100,000+ machines. We built a custom AI model using Salesforce Einstein, integrated via REST API. It saved $2.1M in downtime annually. But that was the *only* case in 5 years where building made sense.

My Non-Negotiable Checklist

• Is the vendor's solution certified for Salesforce? (E.g., Einstein, not just "Salesforce-compatible").
• Can you test it in a sandbox first? (No exceptions.)
• Does it use your existing data model? (If not, integration costs will blow up.)

If you answer "no" to any, reconsider. I've seen clients buy solutions that required rewriting 50+ custom objects. Save the headache.

Don't let "we can build it" override reality. Your time is too valuable. Most businesses—especially in regulated industries like healthcare—need proven solutions, not experiments.

Stop guessing. Get a clear view of your Salesforce AI readiness. Our free health scan identifies data gaps, integration risks, and whether building or buying is right for you—no fluff, just actionable insights.

Get your free Salesforce AI health scan now—we’ll show exactly where to focus (and where to stop wasting time).