Guides for enterprise AI implementation.
Practical guides from practitioners who implement governed agentic systems in production — not advisors who leave at go-live.
Explore resource hubs.
- Deployment
Pilot to production, FDEs, governance, and derisking go-live
- Glossary
Enterprise AI terms — deployment, context, evaluation, governance
- Compare
Derisk360 vs consulting, vendors, and build-vs-buy options
- Frameworks
Deployment risk index and readiness assessments
- Research
Original data on enterprise AI failure rates and ROI
- Blog
Practitioner insights on production AI delivery
- Case studies
Production implementations in banking and insurance
- Locations
Enterprise AI services UK, US, and globally
Implementation knowledge from the field.
Enterprise AI Deployment Guide
Enterprise AI deployment moves from use case discovery through context engineering, agent configuration, evaluation, private cloud go-live, and continuous operations — with embedded delivery teams accountable for production outcomes.
GuidePilot to Production Playbook
The pilot-to-production playbook: stop funding generic POCs, embed FDEs, unify context, evaluate before go-live, deploy in your VPC, and operate with 24/7 accountability — typical governed go-live under 12 weeks.
GuideAI Governance Checklist
AI governance for production requires risk tiering, policy engines, audit trails, eval harnesses, red teams, and an operating model with 24/7 escalation — engineered into agents before go-live, not bolted on after incidents.
GuideContext Engineering Guide
Context engineering unifies enterprise data via MCP, knowledge graphs, and qualified retrieval — Layer 01 and the prerequisite for governed production agents.
GuideMulti-Agent Design Patterns
Multi-agent design patterns for regulated enterprises: supervisor orchestration, specialised workers, shared audit state, and FDEE eval at each layer.
GuideAI Evaluation Framework
An AI evaluation framework defines success criteria, golden datasets, automated harnesses, red teams, production monitoring, and model risk reporting.
GuideFDE Engagement Model
The FDE engagement model embeds Forward Deployed Engineers in your organisation — accountable for context, agents, eval, go-live, and capability transfer.
GuideAI Accelerator Selection Guide
Choose the right AI accelerator by matching your highest-value use case to Derisk360 industry and capability accelerators — scoped in discovery, not RFP theatre.
GuideBanking AI Implementation Guide
Banking AI implementation requires governed context from core systems, model-risk-ready eval, and agents for KYC, ops, and reconciliation — delivered by embedded FDEs.
GuideInsurance AI Implementation Guide
Insurance AI implementation grounds agents in policy knowledge, enforces HITL on claims and underwriting, and scales with catastrophe-ready ops — typical go-live under 12 weeks.
GuideAI Security Baseline
AI security baseline for enterprise: VPC deployment, zero-trust tool access, encryption, secrets management, audit logging, and red-team validation before go-live.
GuideAI ROI Measurement
Measure AI ROI on workflow outcomes — handling time, error rates, cost per case — not pilot demo metrics or model benchmark scores.
GuideVendor vs Services for Enterprise AI
Enterprise AI vendor vs services: licences optimise for product scale; outcome-based services with embedded FDEs optimise for governed production in regulated workflows.
GuideAI Operating Model Guide
An AI operating model assigns FDEE monitoring, ops runbooks, business ownership, and escalation paths for production agents after accelerator go-live.
GuideRegulatory AI Compliance Guide
Regulatory AI compliance aligns production agents with FCA, PRA, and internal model risk — eval evidence, audit trails, and explainability before go-live.
GuideAI Data Strategy Guide
Enterprise AI data strategy connects data estate to governed agent context — lineage, qualification, MCP access, and freshness for production retrieval.
GuideAgent Guardrails Setup
Agent guardrails setup configures policy engines, tool restrictions, HITL approval paths, and eval gates before agents execute high-stakes actions.
GuideMCP Integration Guide
MCP integration connects agents to enterprise systems through governed connectors deployed in your VPC — reusable across accelerators and agent fleets.
GuideKnowledge Graph for Agents
Knowledge graphs for agents encode products, policies, and relationships for explainable retrieval — combined with MCP for live data in production.
GuideAI Incident Response
AI incident response runbooks cover detection, containment, rollback, stakeholder communication, and post-mortem for production agent failures.
GuideAI Change Management
AI change management prepares ops and business teams for agent-driven workflows — training, escalation clarity, and sponsor communication.
GuideAI Procurement RFP Guide
AI procurement RFPs should specify production outcomes, VPC deployment, eval evidence, ops model, and outcome-based pricing — not licence seats or hourly rates alone.
GuideAI Talent Model
Enterprise AI talent model: embed FDEs for production delivery, hire permanent staff for sustained ops, and use accelerators to transfer capability — not endless contractor rotation.
GuideAI Portfolio Management
AI portfolio management prioritises use cases by value, readiness, and deployment risk — funding production accelerators instead of pilot sprawl.
GuideLegacy Integration for AI
Legacy integration for AI connects agents to core banking, policy admin, and mainframe workflows via MCP — without rip-and-replace modernisation programmes.
GuideAI Observability Setup
AI observability setup instruments agents with quality metrics, latency, cost, policy violations, and business outcome traces for FDEE and ops teams.
GuideRed Team AI Systems
Red team AI systems with structured adversarial tests — injection, exfiltration, policy bypass — documented for model risk before regulated production.
GuideAI Cost Optimisation
Enterprise AI cost optimisation balances inference spend, integration, eval, and ops — right-sizing models and routing without sacrificing compliance.
GuideAI Ethics Board Setup
AI ethics board setup establishes oversight for high-impact use cases — charter, membership, escalation, and integration with risk tiering and model risk.
GuideAI Deployment Timeline
Typical AI deployment timeline: 12-week accelerator from discovery to governed go-live — context weeks 1–3, agents 4–8, eval and deploy 9–12.
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Frequently asked questions
- What guides does Derisk360 publish?
- Derisk360 publishes practical guides on enterprise AI deployment, pilot-to-production acceleration, MCP integration, and evaluation frameworks — designed for AI programme owners and technology leaders.
- Are guides written by practitioners?
- Yes. Guides reflect hands-on implementation experience from Forward Deployed Engineers and delivery teams working in regulated enterprise environments.