Getting Started with Enterprise AI

Enterprise AI is not a demo. It’s production software that touches real data, real users, and real risk. The fastest way to adoption is to treat generative AI like any other enterprise capability: align on outcomes, design with security and evaluation from day one, ship a focused pilot, then scale with a repeatable platform.

This guide lays out a pragmatic path to get started — and explains how the right consultant helps you move faster with less rework.

What “enterprise AI” actually means

Enterprise AI is reliable, secure, and measurable. You can deploy it across teams without turning every release into a fire drill.

At minimum, an enterprise-ready AI system has:

• Defined scope (what it will and won’t do)
• Measured quality (evals, regression tests, and acceptance criteria)
• Security controls (least-privilege access, data handling, and auditability)
• Operational visibility (latency, cost, failures, and feedback loops)
• Clear ownership (who maintains it, who approves changes, who responds to incidents)

If any of those are missing, you can still pilot — but scaling will stall.

Step 1: Choose the right first use case (avoid “generic assistants”)

The best first use cases have high volume, clear success metrics, and bounded scope. Examples:

• Customer support deflection: reduce ticket volume, improve time-to-resolution
• Internal knowledge retrieval (RAG): cut time-to-answer for ops, legal, finance, IT
• Sales and onboarding: qualify leads, answer product questions, draft proposals with guardrails
• Workflow automation: summarize calls, extract fields, draft follow-ups, trigger downstream tasks

Define success up front:

• Business metric: revenue, cost, cycle time, CSAT, deflection rate
• Quality metric: accuracy on a “golden set” of questions, citation/grounding rate
• Risk metric: PII leakage rate, policy violations, unacceptable responses

Avoid starting with:

• “An assistant for everything”
• High-stakes decisions with no human-in-the-loop (legal/medical/financial determinations)
• Open-ended agents with broad tool access and unclear boundaries

Step 2: Get data and access right (the real bottleneck)

Most enterprise AI failures are data failures. Before you pick a model, map:

• Where the truth lives (docs, tickets, CRM, data warehouse, product specs)
• How access works (roles, permissions, tenancy, audit requirements)
• How fresh it must be (minutes vs days)
• What must never be sent to a model (sensitive fields, regulated data, secrets)

For most companies, the first “platform” investment is a secure retrieval layer:

• Document ingestion and indexing
• Per-user/per-role authorization checks
• Versioning and provenance (what source did an answer come from?)
• Latency and cost controls

Step 3: Pick the right approach: RAG, fine-tuning, or agents

Start simple and earn complexity.

• RAG (retrieval-augmented generation) is usually the best first move. It’s explainable, updateable, and works with existing knowledge.
• Fine-tuning makes sense when you need consistent format/style or domain behavior that retrieval can’t solve — but it increases operational burden.
• Agents are powerful when the workflow is bounded and tools are safe (e.g., “check inventory → draft response → create ticket”). Avoid “do anything” agents until you have strong guardrails and telemetry.

If you’re unsure, ship a pilot with RAG + tight scope, then iterate.

Step 4: Build evaluation and observability from day one

If you can’t measure it, you can’t improve it — and you can’t safely scale it.

Minimum evaluation system:

• A golden set of representative questions/tasks
• Regression tests in CI (so quality doesn’t degrade silently)
• Human review for edge cases and policy-sensitive outputs
• Runtime metrics: latency, cost per interaction, tool-call success rate, fallback rate

Treat prompts, retrieval configs, and routing rules like code:

• Version them
• Review them
• Roll them out gradually

Step 5: Secure the system (prompt injection is not theoretical)

Enterprise AI needs the same security posture as any integration platform — plus AI-specific controls:

• Prompt injection defenses: test for data exfiltration and tool misuse
• Least-privilege tool access: allow only required actions, with guardrails
• Data handling: PII redaction, retention policies, and audit logging
• Model/vendor governance: where data flows, what’s stored, and what’s used for training

If you have compliance requirements, involve security early. It’s faster than re-architecting later.

Step 6: Operationalize and scale (LLMOps)

Once the pilot works, scaling is a platform problem:

• Standardize patterns (RAG service, model gateway, prompt/versioning conventions)
• Make quality measurable (evals, dashboards, alerting)
• Define release discipline (staging, canaries, rollback)
• Enable teams (docs, templates, reference implementations)

This is how you go from “one chatbot” to a repeatable enterprise capability.

Don’t skip the operating model (ownership + approvals)

Enterprise AI adoption slows down when nobody owns the end-to-end system or when stakeholders show up late. Clarify these early:

• Executive sponsor: sets the outcome, budget, and escalation path
• Product owner: defines scope, metrics, and rollout plan
• Engineering owner: owns reliability, security implementation, and incident response
• Security/compliance: defines data handling, policy constraints, and approval gates
• Data owners: validate sources, permissions, and freshness requirements

Adoption also requires workflow integration. Aim to ship AI where work already happens (ticketing, CRM, internal portals) rather than as a separate “AI app” that teams forget.

Common failure modes (and how to avoid them)

Most teams don’t fail because the model is “not smart enough.” They fail because the system is under-scoped or under-governed.

• No evals → quality churn: start with a golden set and regression tests before broad rollout
• Weak data access model → security blocks: design least-privilege retrieval and auditing early
• Unbounded scope → unusable product: ship a narrow workflow, then expand based on telemetry
• No ownership → stalled adoption: assign an engineering owner and define support/SLAs
• Cost surprises → leadership backlash: measure cost per interaction and set budgets/limits
• Tool access without guardrails → incidents: restrict actions, validate inputs, and log decisions

A simple 30–60–90 day plan

If you want a straightforward starting point:

• Days 0–30: pick one use case, map data + permissions, define success metrics, build a baseline prototype, create a golden eval set
• Days 31–60: productionize (auth, retrieval, logging), add evals + dashboards, pilot with a small user group, iterate weekly
• Days 61–90: harden (security testing, playbooks), expand coverage, launch the second use case on the same platform patterns

Where an enterprise AI consultant speeds things up

A consultant doesn’t replace your team — they compress the learning curve and reduce rework. The highest leverage areas:

• Use case selection: avoid low-ROI pilots and scope traps
• Architecture decisions: choose the simplest approach that meets constraints (latency, cost, security)
• Security and governance: threat modeling, prompt injection testing, and safe tool access patterns
• Evaluation: setting up an eval harness and quality gates early
• Delivery acceleration: proven templates, reference implementations, and a clear execution plan
• Enablement: upskilling your engineers so you can own the system long-term

If you’re trying to move quickly, the biggest cost is not the model — it’s building the wrong system and having to redo it under pressure.

What a good engagement looks like

A practical path that many enterprise teams follow:

1) Assessment (1–2 weeks)

Deliverables typically include:

• Readiness scorecard (team + architecture)
• Use case shortlist with ROI and risk
• Risk register (security, compliance, reliability)
• Architecture recommendations and a 90-day roadmap

2) Pilot (3–6 weeks)

Ship one focused use case with:

• Secure retrieval + access controls
• Evals + dashboards
• Clear success metrics and ownership

3) Scale (60–90 days)

Platformize patterns so multiple teams can ship safely:

• Reusable RAG and model gateway
• CI quality gates and incident playbooks
• Internal documentation and team enablement

Next steps

If you want to move fast without betting the company on a fragile pilot:

• Start with an AI skills and readiness assessment: /ai-skills-assessment
• Or book a call to map your first 30–60 days: /contact

The goal is simple: ship one enterprise-quality AI capability, prove value, then scale with a repeatable foundation.