Aaron Rallo is Founder & CEO of Trovia, a knowledge layer for content that makes enterprise AI accurate and trustworthy.gettyEvery business is under pressure to put AI to work. I've advised hundreds of customers, including several in the Fortune 100, on AI strategy and implementation.Here are three traps I’ve seen even the most seasoned leaders fall into:Trap 1: Shiny Object SyndromeThrowing something together with AI is easy. Building the right thing? Not so easy. AI systems let you iterate exceptionally fast, and experimenting with different use cases is a great way to learn, but without an outcome-oriented plan, flitting from one shiny object to the next creates activity without actionable results. Here's a better approach:​Before building a proof of concept, first assess whether the business unit or task is truly a good fit. If it’s not, you risk burning budget producing something that nobody trusts.​Evaluate these five criteria for each proof of concept:1. Repeatability: How repeatable are the tasks? It’s easier to replace tasks that have repeatable, predictable outcomes because you can quickly assess whether the AI tool is returning correct results. With tasks that have large amounts of variation, it's difficult to assess if AI is performing accurately.2. Data Accessibility: How easy is it to access the data needed to conduct those tasks? Is it in a data repository where it could be easily accessed by an AI system, or does critical information live in people’s heads? Ensure data is readily available.3. Knowledge Readiness: Assuming the data is accessible, what shape is it in? Are there mistakes or content gaps that select employees know the answers to but that an AI system might not? Do you have employees who can resolve these gaps? The quality of your data determines when you need to loop a human in.​4. Business Risk: What’s the level of risk to this business unit if the AI system makes a mistake? Trying to automate a critical system isn't an ideal place to start. Implementing a proof of concept in a critical unit poses a higher risk, will be subject to greater scrutiny and, therefore, will take much longer to implement.5. Ownership: Do you have a clear owner to make decisions? Dozens of projects stall because decisions are made by a committee versus an appointed owner. AI projects are particularly vulnerable because they require frequent judgment calls (What accuracy is sufficient? Whose answer is right when two subject matter experts disagree? When is the system ready to ship?). Select an owner close enough to the work to assess quality and senior enough to determine scope and risk.​After the initial assessment, make a short list of projects to pursue, cost out each and articulate quantifiable deliverables. Then decide what to execute.​Trap 2: The Sorcerer’s Apprentice Problem​Vibe coding a proof of concept is one thing. Making it operational is something else entirely.The Sorcerer’s Apprentice problem is when you build something that you don’t entirely understand how to control (Remember what happened when Mickey Mouse tried to automate carrying buckets of water?). With AI coding assistants, this is easy to do. Use these methods to maintain oversight of your AI tools:1. Create front-end and back-end guardrails. Front-end guardrails are standard operating procedures governing what goes into an LLM (and what shouldn't, such as no personal and confidential information and no production passwords). Back-end guardrails govern what comes out. Back-end guardrails assess whether the agent is operating within its mandate, flags noncompliance and escalates to a human when needed. Guardrails require an assigned owner who inspects and refines them over time.2. Put comprehension before production. Nothing should move from the proof-of-concept stage to operational until the designated owner can explain how the system works: what every component does, why it's there and how it fails. Someone other than the original builder should review the code or process diagrams. Ask yourself: If the person who built this left tomorrow, could the rest of the team operate it safely? If not, it's not operation-ready.​Trap 3: The 'Set It And Forget It' Fallacy​What seems like a good idea when you have a single test user can change drastically when you have hundreds. Although AI-coded solutions can be a cost-effective way to replace existing software, most leaders forget to factor in these aspects of ongoing operational overhead:1. Implement ops dashboards and "off" switches. There are three questions to answer about any production-grade system: What is the system doing right now? What did it do in the last 48 hours? How do I stop it immediately if necessary? This means logging every decision/action the system takes (not just errors), creating dashboards a non-author can read and having a way to revert to the original system if the new one fails. Consider this even at the proof-of-concept phase.2. Designate someone to own maintenance. Before replacing any software with an AI-coded solution, factor in operational costs and ongoing maintenance. Consider how to keep your system running 24/7, securely and accurately. Like any software, your AI solution requires upkeep, troubleshooting and improvements. Designate someone for this role.​Moving Forward With ClarityWe all want to move fast, and we all applaud employees who are curious enough to start building. If you have people like this on your team, reward them and find a way to retain them. Encourage experimentation, then be deliberate about which projects you take on. Select the right problems to solve, identify clear owners, put guardrails in place and plan with ongoing operations in mind.Lastly, you don’t have to solve all these problems on your own. A recent PwC study reported that the companies benefitting from AI have invested significantly more money than those that haven’t. Invest in your people through training from major cloud providers or bring in external consultants from your AI software vendor. These traps are easier to avoid when someone in the room has seen them before, and vendors can guide your team through these common and costly missteps.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?