Managed AI vs. DIY: What PI Firms Get Wrong

The pitch is always the same: 'Just sign up, connect your tools, and you're automating in minutes!' Three months later, you've spent $15,000 on a platform nobody uses, your IT person quit, and the managing partner has banned the word 'automation' from all meetings.

This isn't a knock on the platforms. Many of them are genuinely capable. The problem is the deployment model, not the technology.

DIY AI deployment assumes three things that are almost never true at PI firms: (1) someone on staff has time to configure and maintain the system, (2) that person understands both the technology and the workflows well enough to bridge the gap, and (3) the firm will tolerate a 3-6 month timeline before seeing results.

In practice, the office manager gets assigned 'the AI project' on top of their existing responsibilities. They spend evenings watching tutorials. The initial setup works for simple cases. Then the edge cases start — the lead that's also an existing client, the intake from a referral partner that uses a different format, the deadline that doesn't fit any of the standard categories.

By month three, the system handles 60% of cases correctly and creates more work than it saves for the other 40%. The office manager is exhausted. The project gets shelved.

Managed deployment inverts the burden. We handle the configuration, the edge cases, the monitoring, and the maintenance. Your firm's role is to tell us how things work and to review the agent's output.

Week 1: Discovery call. We learn your tools, your workflows, your team structure. We identify the 2-3 highest-impact capabilities to deploy first.

Week 2: Configuration and testing. We build the agent skills, connect your tools, run test cases against your actual data patterns.

Week 3: Live deployment with guardrails. The agent starts working real cases with human approval on every action. Your team reviews, provides feedback, and we adjust.

Week 4+: Gradual autonomy. Actions that consistently pass review get promoted to autonomous. The agent earns trust the same way a new hire would.

DIY platforms typically cost $200-500/month per tool. But the real cost is the 10-20 hours per week someone on your staff spends configuring, debugging, and maintaining the system. At $40/hour loaded cost, that's $1,600-3,200/month in hidden labor — often more than the platform subscription itself.

Managed deployment costs more on the invoice — typically $2,500-5,000/month depending on the number of capabilities. But the hidden labor cost is near zero. Your team spends 2-3 hours per week reviewing agent output and providing feedback. The rest is on us.

For most firms, managed is cheaper than DIY within 60 days when you account for the full cost.