Raise Networks | Managed IT Services & Cybersecurity | Est. Read Time: 7-9 min
Target: CTOs, IT Directors, VPs of Engineering — Bay Area companies (50-500 employees)
Introduction
The Bay Area runs on AI ambition. From SoMa startups building LLM-powered products to enterprise giants in San Jose deploying AI agents across their workflows, Silicon Valley is moving fast on artificial intelligence. But there’s a gap most companies aren’t talking about: the IT infrastructure beneath these AI initiatives was largely built for a different era.
We’ve worked with companies across the enterprise spectrum — from Series A startups to Fortune 100 organizations. The ones whose AI projects stall have one thing in common: they skipped the infrastructure conversation. They invested in models, licenses, and talent before auditing whether their network, compute, security, data, and support layers could actually handle AI workloads.
This checklist isn’t about buying the latest GPU cluster or signing up for every AI SaaS tool on the market. It’s about the five foundational IT decisions that determine whether your AI investment delivers value — or quietly fails.
“Before you invest in AI, find out if your infrastructure can actually support it.” Work through each section and tally your score at the end. We’ll tell you exactly where you stand.
| SECTION 1 • NETWORK INFRASTRUCTURE Can your network handle AI workloads? |
Why it matters
AI workloads — especially inference at scale and real-time data pipelines — are bandwidth intensive and latency-sensitive. Most enterprise networks weren’t designed for this. Legacy switching hardware, flat network architectures, and underprovisioned uplinks are the most common failure points we see.
Checklist
* Sufficient internal bandwidth between compute and storage nodes (minimum 10 Gbps; 25-100 Gbps recommended for training pipelines)
* LAN segmented to isolate AI traffic from standard business traffic
* WAN performance audited for cloud-to-on-prem AI data transfer
| RAISE NETWORKS | Your Infrastructure, Our Priority. 24/7. raisenetworks.com |
* SD-WAN or intelligent routing in place
* Network monitoring granular enough to detect AI-driven latency spikes
Raise Networks: Our network architecture and deployment solutions are built to handle the traffic patterns AI workloads generate — before your first model hits production.
| SECTION 2 • COMPUTE & CLOUD Do you have the right engine? |
Why it matters
Running AI isn’t just about GPUs. It’s about the right mix of on-prem, cloud, and edge compute — and the flexibility to scale without overprovisioning. Cloud sprawl is an AI budget killer: companies often spin up resources for AI projects without governance, leading to massive unexpected bills.
Checklist
* AI workloads mapped to the right tier: on-prem, cloud, or edge
* A managed cloud strategy in place (not ad-hoc provisioning)
* Cloud environments optimized for AI: spot instances, reserved capacity, right-sizing □ Clear GPU/TPU access plan — owned, rented, or cloud-burst
* Virtualization layer compatible with containerized AI workloads (Kubernetes, Docker)
Raise Networks: Our managed cloud solutions ensure your AI compute is provisioned efficiently — with full visibility into costs, utilization, and performance from day one.
| SECTION 3 • SECURITY Is your security stack ready for AI’s attack surface? |
Why it matters
AI expands your attack surface in ways most security teams haven’t planned for. Model poisoning, prompt injection, data exfiltration via LLM outputs, and insecure API endpoints are now real enterprise risks. Most organizations’ security reviews were written before generative AI existed.
Checklist
* Endpoint protection extended to AI-adjacent systems: model servers, vector databases, API gateways
* Data policies define which data can be fed into AI models — especially third-party LLMs □ IAM access control configured for AI service accounts and model pipelines □ Penetration test conducted that includes AI system components
* AI APIs protected against abuse, scraping, and injection attacks
* Post-quantum cryptography being evaluated for long-term AI data protection
Raise Networks: From penetration testing to endpoint protection and our quantum-safe managed security initiative (in partnership with PQCrypto.ai), we help you secure the full AI stack — not just the perimeter.
| SECTION 4 • DATA INFRASTRUCTURE |
| RAISE NETWORKS | Your Infrastructure, Our Priority. 24/7. raisenetworks.com |
| Is your data actually usable by AI? |
Why it matters
AI is only as good as the data it learns from and operates on. Bad data infrastructure is the number one silent killer of AI projects. Teams spend months building models before discovering the underlying data can’t support them.
Checklist
* Data centralized — or silos (CRM, ERP, databases, file servers) mapped and connected □ Data governance policy in place: ownership, access, and quality standards defined □ Storage infrastructure performant enough for AI data pipelines (NVMe or high-throughput object storage recommended)
* Backup and recovery plans include AI training datasets and model weights □ Sensitive data appropriately masked or anonymized before entering AI workflows
Raise Networks: Our infrastructure consulting surfaces data bottlenecks early — so your AI investment doesn’t get derailed by problems that could have been solved in week one.
| SECTION 5 • OPERATIONAL READINESS Can your team support an AI-powered environment? |
Why it matters
AI tools change how employees work — and create new support demands that catch IT teams flat footed. From model outages to API failures and employee onboarding, operational readiness is where AI ambition meets daily reality.
Checklist
* Helpdesk team has a process for AI tool-related support requests
* SLAs updated to account for AI system incidents: model outages, API failures □ NOC monitoring strategy includes AI infrastructure uptime
* Change management plan in place for onboarding employees to AI tools □ Escalation path defined for AI-specific issues internal team can’t resolve
Raise Networks: Our onsite and remote helpdesk support, combined with 24/7 NOC services, keeps your AI-powered environment running and your employees unblocked.
Your Score — How AI-Ready Are You?
Add up your checked items across all five areas:
| Checklist Area | Max Items | Your Score |
| Network infrastructure | 5 | _____ / 5 |
| Compute & cloud | 5 | _____ / 5 |
| RAISE NETWORKS | Your Infrastructure, Our Priority. 24/7. raisenetworks.com | ||
| Security | 6 | _____ / 6 |
| Data infrastructure | 5 | _____ / 5 |
| Operational readiness | 5 | _____ / 5 |
| TOTAL | 26 | _____ / 26 |
What your score means
22-26 checked AI-ready. You’re in a strong position to move fast with confidence. 15-21 checked Partially ready. A few critical gaps need addressing before major AI investment.
Under 15 checked Not yet ready. A foundational IT infrastructure review is needed before AI deployment.
Conclusion
AI-readiness isn’t about hardware — it’s about infrastructure discipline. The companies winning with AI in Silicon Valley aren’t necessarily the ones with the biggest budgets. They’re the ones whose IT foundations can support rapid iteration, handle new security demands, and scale without breaking.
This checklist covers a lot of ground. Not every company needs to check every box on day one — but every company needs visibility into where their gaps are. The earlier you know, the less expensive it is to fix.
| Not sure where you land? Our team offers a complimentary AI Readiness Assessment for Bay Area companies. We’ll walk through your infrastructure against this checklist, identify your highest-priority gaps, and give you a prioritized roadmap — no sales pitch, just a clear picture of where you stand. Book Your Free Assessment → raisenetworks.com |
Related reading: Small businesses are adopting AI faster than anyone expected. Here’s how to do it right. and AI workforce enablement: how we get teams actually using Claude AI.
