Topic
Enterprise AI
Enterprise AI has a different failure mode from a startup MVP. The model usually works. What stalls a rollout is the operating model, the risk and compliance review, permission mapping across a dozen systems, and change management. Most enterprise pilots that fail, fail there rather than on accuracy.
This cluster covers the enterprise-specific work: designing an AI operating model, building for risk and compliance teams, integrating AI on top of Dynamics, SAP, Salesforce, and Power BI, and the discipline that gets a pilot to production instead of leaving it in staging.
What we would tell you on a call
The integration is the project
The model is a few weeks. Getting clean access to eight source systems, agreeing what the fields mean and satisfying the people who own them is the rest of it, and it is where enterprise programmes actually slip.
Security review before the first prototype
Working inside the client's own cloud and perimeter from day one avoids the rebuild that follows a prototype built somewhere convenient. PII is detected and redacted before anything reaches a model.
A sponsor who can decide
Enterprise AI stalls on decisions, not engineering: which process is in scope, whose data is authoritative, who signs off the exceptions. Name the person who can settle those before the kickoff.
Adoption is the deliverable
A system nobody uses is indistinguishable from a system that does not work. Rollout, training and the escalation path belong in the plan alongside the build.
Everything we have written on this
AI Analytics on Salesforce and Power BI
If you searched Salesforce AI Power BI, you are asking how to add AI and analytics on top of the CRM and dashboards you already run. Here are the patterns that ship in production, and the traps that quietly stall them.
AI in Customer Operations: Rollout Lessons
Customer operations teams were among the first to see AI in production at scale. The patterns that stuck were surprisingly conservative - and very effective.
AI on Dynamics 365 and SAP: Patterns
How to add AI and analytics on top of Dynamics 365, SAP, or your ERP without a rip-and-replace. The integration patterns for AI Dynamics 365 integration that actually reach production, the permission and freshness traps, and a checklist before you start.
AI Systems for Risk and Compliance Teams
Risk and compliance teams in 2022–25 didn’t want magic. They wanted tools that surfaced signals, preserved audit trails, and respected existing controls.
Designing an Enterprise AI Operating Model
From 2023 to 2025, the most successful enterprises stopped treating AI as a side project and gave it a real operating model: clear ownership, budget, guardrails, and roadmaps.
Enterprise AI: A Retrospective Playbook
Looking back at 2022–25, a clear playbook emerged for enterprise AI: start small but serious, invest in evaluation and governance early, and treat AI as a capability, not a gadget.
Evaluating AI Pilots Before You Scale
The hardest decision in 2022–25 AI programmes wasn’t "can we build a pilot?" - it was "should we scale it?" A simple evaluation frame turned hand‑wavy excitement into clear calls.
Add AI to Your SaaS Without a Rebuild
You do not need to rewrite your product to ship AI. Here is the integration-first approach we use to add AI features to an existing SaaS in weeks, and the numbers from two real builds.
Proof in production
Key terms
- SOC 2
- An audit standard covering how a service organisation handles security, availability, processing integrity, confidentiality, and privacy. Type I assesses the design of controls at a point in time, Type II assesses their operation over a period.
- Data residency
- The requirement that data be stored and processed in a specific country or region, which constrains where models can run and which providers can be used.
- Human in the loop
- A design where a person approves, edits, or overrides an AI decision before it takes effect, usually on the actions that are expensive or irreversible.
- PII redaction
- Detecting and removing personally identifiable information from text before it is sent to a model or stored in logs.
The service behind this
Enterprise AI Solutions
AI that fits your enterprise: governance, security, and compliance built in.
