Enterprise AI: Build or Buy?
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Internal builds that promised six month delivery stretched into multiyear projects. Off the shelf solutions delivered value in weeks. This pattern repeats across every industry, every use case, every organization that's tried both approaches.
The gap between "AI could solve this" and "AI is solving this" isn't a technology problem. It's a timeline problem. And the timeline problem isn't about model development. It's about everything else: the infrastructure, the integrations, the governance, the endless provisioning that consumes months before a single business outcome gets delivered.
AI faster than building isn't about cutting corners. It's about skipping the infrastructure and orchestration work that consumes the majority of internal project timelines. The models are ready. The platforms exist. The question is whether you'll spend months building what's already been built.
Standard enterprise AI implementation timelines run 12 to 18 months for comprehensive rollouts. That's not a worst case estimate. That's the median.
The breakdown looks like this: four to six weeks for assessment and scoping, three to four months for pilot development, then six to eight months for scaling to production. And that assumes things go well. But most don't.
The timeline isn't model training. Training a model takes days or weeks, not months. The time goes to everything around the usual model. Compute provisioning and infrastructure setup, data pipeline engineering, security and compliance approvals, integration with existing systems, testing and validation cycles, change management and training.
Each phase has dependencies. Each dependency has stakeholders. Each stakeholder has priorities that aren't your AI project. The calendar fills up fast.
Models aren't the bottleneck. They haven't been for years. The bottleneck is infrastructure. Studies show that 60% of AI development time gets consumed by connecting systems, managing APIs, and ensuring data flow. That's integration work. It's necessary, but it's not differentiated. Every enterprise building AI from scratch solves the same problems that every other enterprise has already solved.
Compute provisioning takes weeks to months for GPU allocation and environment setup. Cloud providers have queues. Internal IT has processes. Neither moves at startup speed.
Routing and orchestration means building the plumbing that connects models to data sources, handles failover, manages load, and keeps everything running. This is complex distributed systems work.
Evals and monitoring require creating systems to measure performance and catch failures before they reach users. Without this, you're flying blind in production.
Governance demands audit trails, access controls, and compliance documentation. Regulated industries can't skip this. Even unregulated companies increasingly can't skip this.
Then there's the talent problem. ML engineers command $200K or more. Senior AI roles exceed $300K. And they're scarce. Every month spent recruiting is a month not building. Every month a key engineer is pulled to another priority is a month of lost momentum.
Platform based solutions with pre-built connectors deploy in days to weeks. Not because they're simpler. Because they've already solved the infrastructure problems.
Platforms provide pre-built integrations to Salesforce, ServiceNow, Workday, SAP, and the rest of your stack. Managed infrastructure with no GPU provisioning or environment setup. Production ready orchestration with routing, failover, and scaling handled automatically. Built in governance including audit logging, access controls, and compliance frameworks. Continuous improvement through platform upgrades that happen without internal engineering effort.
The modular approach means building blocks for search, reasoning, automation, and agents that can be configured for your specific use case. Blueprints define how components work together. Your business logic without ground up development.
Here's how the timelines compare:
Direct timeline costs are obvious. Indirect costs compound in ways that don't show up on project plans.
Knowledge loss. Key engineers leave. Context evaporates. Progress resets. An 18 month project with 20% annual turnover loses half its institutional knowledge before completion.
Scope creep. Projects expand as stakeholders add requirements. What started as a document extraction tool becomes a complete knowledge management system. Timelines stretch accordingly.
Technology drift. The LLM landscape changes faster than internal builds can adapt. The architecture you designed 12 months ago may be obsolete by the time you finish building it.
Pilot purgatory. Projects that work in demo but never reach production. The impressive POC that can't scale, can't integrate, can't meet security requirements. Sunk cost with no business value.
Maintenance burden. Every custom component requires ongoing engineering attention. The team that built the system becomes the team that maintains the system, unavailable for the next priority.
The compounding problem is real. Internal builds don't just delay the first solution. They delay every subsequent solution. Each new use case becomes a new system to design, secure, and maintain. Scaling means multiplication of complexity.
Platform approaches work differently. Every use case deployed on a shared platform enriches shared context. Subsequent solutions ship faster because the foundation improves. Scaling means configuration, not construction.
Speed isn't always the priority. But it usually is.
The 80/20 rule applies to enterprise AI. 80% of use cases are variations on solved problems: document processing, search, automation, conversational agents. These don't require custom architectures. They require configuration of proven components.
Build when AI is your core product or primary competitive advantage. When you have unique compliance requirements no platform addresses. When you have unlimited budget, timeline, and talent.
Deploy faster when time to value determines competitive positioning. When the use case is well understood. When you need to validate before investing heavily. When your team should focus on business outcomes instead of infrastructure.
Most enterprises overestimate how differentiated their AI needs are. The problems are common. The implementations should be too.
AI faster than building isn't a shortcut. It's recognition that the hard problems in enterprise AI have already been solved at the platform level. Infrastructure, orchestration, governance, monitoring. These aren't competitive differentiators. They're prerequisites.
The choice isn't build vs buy. It's weeks vs months. And in most cases, the months spent building infrastructure are months your competitors are using AI to create value.
Every month of delay has a cost. The cost isn't just engineering salaries and cloud bills. It's the business outcomes you're not achieving while you provision GPUs and debug integration code.
The infrastructure already exists. The question is whether you'll use it or rebuild it.
See how the timeline changes when you stop building infrastructure. Explore the Build vs. Unframe page for the full comparison, or book a demo to validate your use case in days.
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