Navigating Enterprise AI Implementation: Risks, Rewards, and Where to Start

At Snyk, we believe that AI innovation starts with trust, which must be earned through clear governance, sound security practices, and proven value delivery. As we scale our AI initiatives across the business, we’re continually refining how to implement AI in a way that is not just fast and functional, but also secure and responsible.

Whether you’re in the early planning stages or already experimenting, here are some of the most important considerations and high-impact starting points for AI implementation in your organization.

Key concerns to address early

Data quality is non-negotiable

“Smart AI can't function effectively with poor inputs.” – Jeff McMillan, Morgan Stanley

Our first Retrieval-Augmented Generation (RAG) systems quickly proved this point: high-quality outputs require high-quality, structured data. Gaps in documentation, inconsistent metadata, or inaccessible APIs created friction and degraded results, even when using top-tier models.

Takeaway: Prioritize data hygiene before investing heavily in AI tooling. Build structured, queryable knowledge bases and expose them via APIs. In many cases, AI itself can help uncover where content gaps or inconsistencies exist.

AI introduces unique security and privacy challenges

Unlike traditional software, AI workflows often:

• Send sensitive data to third-party LLM platforms.

• Obscure how inputs are processed (“black box” inference).

• Generate content that might carry liability in customer-facing settings.

At Snyk, we’ve developed AI-specific data classification guidelines and tiered consumption models to help teams safely decide which AI tools are appropriate for each use case.

Takeaway: Embed security and governance from the start. For sensitive data, favor privately-hosted LLMs or tools that operate within your security perimeter.

Expectation management is as important as the model

It’s easy to overestimate what AI will deliver in the short term — and underestimate what it will transform long term. Generic ROI frameworks often don’t apply cleanly in the early stages of adoption.

At Snyk, we define tailored success metrics for each use case, including:

• Accuracy rates for knowledge retrieval.

• Adoption levels by internal teams.

• Satisfaction scores from feedback loops.

Takeaway: Set realistic, use-case-specific goals up front, and build in continuous evaluation.

Address the human impact head-on

AI adoption raises real concerns among employees about automation, role changes, and skills relevance. The best antidote is transparency and enablement.

Takeaway: Frame AI as augmentation, not replacement. We’ve seen success in pairing AI rollout with broad training programs and internal champion networks.

Where to start: High-value AI use cases

1. Knowledge retrieval with RAG systems

Our internal RAG apps now handle hundreds of questions per week, accelerating onboarding, reducing support cycles, and improving developer experience.

Why it works:

• Measurable value from day one.

• Built-in improvement loop (better docs = better AI).

• Broad applicability across roles.

Pro tip: Embed a feedback loop into the UX to catch hallucinations early and improve relevance.

2. Content generation and enhancement

We launched AI tooling in our Developer Relations team to assist with blog drafts, event abstracts, and doc rewrites. The results?

• Faster turnaround times.

• More consistent tone across assets.

• Less time spent on rote edits, more on strategic storytelling.

Pro tip: Start with internal content where risk is low, then scale to external-facing assets once guardrails are validated.

3. Data analysis and summarization

AI tools that can summarize threat intelligence, detect patterns in large logs, and produce digestible insights benefit security teams greatly.

Pro tip: Look for high-volume, repeatable analysis tasks with tight inputs and outputs — like alert triage or CVE clustering.

4. AI-driven workflow automation

We’re exploring AI use in back-office operations, such as contract parsing in procurement, ticket classification in support, and onboarding flows in HR.

Pro tip: Start with clearly documented workflows and low business risk. Focus on speed-to-value and measurable time savings.

Setting up for long-term success

No matter where you begin, these principles help ensure a strong foundation:

• Start small: Choose self-contained pilots with clear boundaries.

• Stay off critical paths: Prove reliability before touching production systems.

• Build feedback loops: Human-in-the-loop is key early on.

• Solve real problems: Don’t let shiny objects distract from business value.

• Share success stories: Internal storytelling builds momentum.

Security is not a feature — it’s the foundation

At Snyk, security isn’t layered on after AI systems are deployed — it’s embedded from the start. From Snyk Guard’s dynamic policy enforcement to Snyk Assist’s developer education workflows, our AI tooling is built to scale securely, in tune with how real-world developers operate.

Your first AI deployments don’t need to be massive. But they should be intentional, trustworthy, and valuable. Start small, measure obsessively, and scale with confidence.

We're committed to sharing what we learn as our AI program evolves, and we’re excited to help build a more secure, intelligent future. Download the Taming AI Code ebook to learn how to embed safety into secure development.