Practical guides for evaluating Salesforce and AI implementation partners, deploying AI in your organization, and recovering when a project stalls
A wrong partner choice on a Salesforce implementation costs more than the contract. It costs the months your team spends re-architecting around a decision a partner got wrong, and the budget cycle you lose waiting for a second vendor to fix the first one's work. These guides exist to catch that mistake before you sign anything.
You'll find evaluation scorecards, interview question lists, and recovery playbooks for a project that's already gone sideways. Each guide comes from work our own consultants have done on real Salesforce and AI deployments, not from theory.
Start with the partner selection guides if you're choosing a vendor. Start with the rescue guide if you're already mid-implementation and something feels wrong.
Comprehensive guide covering selection criteria, evaluation process, and red flags to avoid when choosing a Salesforce consulting partner.
Structured approach to evaluating partners including scorecards, interview questions, and reference check templates.
Detailed criteria covering certifications, experience, methodology, and cultural fit factors for partner selection.
Essential questions covering technical expertise, project approach, pricing, and support to ensure the right fit.
Detailed comparison of costs, benefits, risks, and scenarios where each approach makes the most sense.
Step-by-step recovery strategies, root cause analysis, and turnaround approaches for struggling implementations.
Evaluation criteria covering technical depth, governance maturity, integration competence, and security posture for vetting AI consulting firms.
A five-dimension scorecard for comparing AI vendors on technical depth, governance, integration, security, and delivery discipline.
Sharp questions covering architecture, security, governance, integration, pricing, and accountability, organized by category.
A balanced cost, speed, and risk comparison with a scenario-based decision table for when each approach wins.
Why most AI pilots stall, a four-mode diagnostic framework, and the rescue sequence to get one to production.
Architecture options, SSO and SCIM setup, data residency, and a practical rollout sequence for deploying Claude.
What MCP is, the governance risks of agent connectors, and concrete controls for least-privilege access and audit logging.
EU AI Act, NIST AI RMF, ISO 42001, and DIFC Regulation 10 explained in plain terms, with a starter checklist.
A governed, repeatable prompt practice for teams: shared libraries, role-specific patterns, and an evaluation process.
The same scorecards and checklists our consultants run during client engagements
Certified Salesforce architects and AI consultants write every guide, not a marketing team
Named questions to ask, named red flags to watch for, named tradeoffs to weigh
