AI is exciting. Urgent, even.
In my role leading product management and partnering with forward deployed engineers, I’ve worked with countless companies on AI adoption. Across sizes, budgets, and ambitions, I see the same pattern: teams start with the right intentions and still end up disappointed.
The problem isn’t that AI doesn’t work. The problem is that AI done wrong wastes time, money, and trust — and most teams aren’t set up to vet tools, ask the right questions, or structure implementation for success.
To help teams evaluate and deploy with confidence, I often point leaders to The AI Agent Blueprint. It’s a practical roadmap for a moment when everyone’s trying to figure out what comes next.
In this post, I share the lessons I wish every team had before they started. Whether you’re evaluating a solution like Intercom’s Fin or just exploring what gen AI can do, these are the patterns I rely on to make smart, scalable decisions.
Core concepts to help you vet AI solutions like an expert
Before we get into the common pitfalls, let’s cover a few key concepts. You don’t need to become an engineer to thoroughly evaluate AI Agents, but you do need to understand a few foundational terms. This knowledge will help you:
– Ask sharper questions during demos.
– Spot red flags in vendor pitches.
– Choose scalable, future-proof solutions.
– Guide internal alignment and buy-in.
– Build confidence in your final decision.
A little technical fluency goes a long way. Keep in mind these are just a few of the many terms out there. But here are the ones I’d suggest getting comfortable with today:
Retrieval-Augmented Generation (RAG)
RAG enhances generative AI by pulling in real-time, relevant information from your company’s data sources before generating a response.
Why it matters: Most AI tools claiming to “know your business” only use pre-uploaded or static training data. RAG-based systems dynamically search live sources like help centers, product docs, or internal wikis, making them far more accurate and adaptable (assuming your data hygiene and permissions are in good shape).
Easy way to remember: Think of RAG as an AI assistant with an open-book exam. Instead of relying only on memory (pre-trained data), it searches for the latest, most relevant information before responding. This makes RAG especially useful for AI Agents, customer support systems, and AI-driven search engines, ensuring responses are more accurate and up to date.
Vector search
Vector search enables AI to match by meaning, not just keywords. It converts both the user’s question and your documentation into numerical vectors and retrieves the closest semantic match even when the phrasing differs.
Why it matters: Without vector search, your AI may only work if the user phrases things “just right.” With it, users can speak naturally and still get the correct response.
Easy way to remember it: Vector search is like finding a song by its vibe, not its title. It works by intent, not exact match – essential for intuitive AI experiences.
Agentic AI
Agentic AI goes beyond answering simple questions; it can initiate actions, pursue goals, and carry out multi-step tasks.
Why it matters: Most AI tools today are passive. They only respond when prompted. Agentic AI drives outcomes. For example, Intercom’s Fin is evolving to handle actions like checking order status, triggering refunds, or escalating issues, all without human involvement.
Easy way to remember it: Agentic AI is like a rockstar project manager, not just a note-taker. It doesn’t just reply with information when simple questions are asked. It plans, acts, and follows through to get the job done.
MCP (Model Context Protocol) Server / Client
MCP is an emerging approach for managing AI agents at scale. It involves three core components:
– The model (the AI system itself).
– The context (what data and information it can access).
– The protocol (the rules for how it talks to other tools and data).
Why it matters: As AI gets embedded across your organization, centralized governance becomes critical. MCP ensures agents act within rules, respect permissions, and scale responsibly – without needing to hard-code logic into every use case.
Easy way to remember it: Think of MCP as a control tower for your AI agents. It manages what they know, what data they can use, and what boundaries they stay within.
Understanding concepts matters because they help you ask better questions and spot red flags during vendor evaluations. But understanding terminology alone isn’t enough.
Common mistakes I see teams make
Here are five mistakes I see even well-informed teams make, and how I advise product and support leaders to avoid them.
Mistake #1: Treating all AI tools the same
The AI space is moving fast. It’s a constantly evolving landscape and full of buzzwords, which can create confusion. I often see teams treat “chatbots” and AI Agents as interchangeable, without realizing there’s a massive difference between things like:
– A legacy rules-based bot with generative copy slapped on top.
– A true agentic AI system that takes action, learns from context, and scales with your business.
If you don’t understand core terms like RAG, MCP, or the differences between LLMs and agentic AI, it’s nearly impossible to ask the right questions during your evaluation process. I’ve heard of too many teams buying solutions that are outdated or require heavy upkeep after deployment. Educating your team on the fundamentals gives you the confidence to separate real capability from flashy demos.
Mistake #2: Assuming you can build it in-house
There’s a real cost and complexity of building AI Agents internally – orchestration, retrieval systems, prompt chaining, governance, and more. It’s not just a weekend project. It’s a long-term infrastructure investment. And for most companies, it quickly becomes a distraction rather than a differentiator.
Many teams assume building their own AI Agent will be faster, cheaper, or more flexible than buying. On paper, it sounds reasonable – especially if you’ve got a strong engineering team, access to top-tier models, and a healthy budget. But in practice, that path is much harder than it looks.
I smile writing this because I’ve been there. I’ve built multiple AI apps on nights and weekends. Early wins feel amazing — then reality sets in. Shipping something truly polished, even at tiny scale, demands far more infrastructure, reliability work, and governance than most teams anticipate.
At a company level, those challenges only grow. Building an AI Agent from scratch means committing to:
– Data chunking, embedding, and relevance tuning.
– Prompt chaining, context management, and hallucination reduction.
– Real-time retrieval architecture and RAG pipelines.
– Fine-tuning, model upgrades, and fallback orchestration.
– Security, permissions, audit logs, AI governance… and so much more!
Even well-resourced teams often circle back to buying after burning time, money, and momentum. The true cost of building isn’t just engineering — it’s maintenance and velocity. High-performing teams focus on their differentiators and partner for the rest.
Mistake #3: Betting on the wrong vendor
I often see teams focus too narrowly on slick demos or assume a vendor will “figure it out later.” In a market moving this fast, that’s a risky bet. The result is a tool that can’t keep up, needs constant hand-holding, or becomes too rigid to scale.
The best vendors learn quickly, ship frequently, and keep driving value. When I evaluate, I ask:
– Is the vendor investing meaningfully in AI R&D?
– Does their team have a clear roadmap for improvement?
– Can this system adapt to your workflows without needing engineering support at every step?
– How much ongoing maintenance will be needed?
These questions separate vendors building for tomorrow from those selling yesterday’s technology. You want a partner who’s staying ahead, not catching up.
Mistake #4: Ignoring your internal foundation
Even the best AI Agents need fuel. Your content and systems are the inputs that determine quality. If your help center is outdated, documentation is thin, or APIs are missing, you’ll get “garbage in, garbage out.”
I’ve watched teams buy best-in-class AI and still stall because they hadn’t invested in the inputs that make it powerful:
– A well-structured help center.
– Clear, detailed documentation.
– Internal process visibility (for things like internal AI/copilot).
– Robust APIs.
You don’t need to overhaul everything on day one. But clean, accessible content dramatically improves accuracy, confidence, and resolution rate.
Mistake #5: Expecting instant, perfect resolution rates
Another misconception is expecting AI to resolve 100% of support conversations immediately. In reality, no AI tool starts at perfection — and your team needs a shared understanding of how resolution rate works to set expectations.
For context, Fin typically resolves over 65% of support questions out of the box, with minimal training needed, and continues to improve month-over-month. What separates great implementations isn’t just where you start; it’s how you optimize. Tightening content, closing automation gaps, and iterating on prompts and retrieval all compound over time.
If you’re not tracking your current resolution rate or don’t know how your vendor defines it, it’s hard to see progress. Establish a baseline, set realistic targets, and measure consistently. Treat resolution rate as a growth metric, not a fixed score.
Final thoughts
The teams that win with AI don’t just adopt tools — they implement future-proof systems that connect knowledge, workflows, and decision-making to drive real business outcomes.
– They don’t build everything from scratch.
– They don’t fall for flashy demos of stale technology.
– They partner with vendors already building what’s next.
If your team is exploring AI — whether you’re starting fresh or rethinking your stack — start with the concepts and lessons here. Use them to evaluate options, align stakeholders, and choose partners who are building what’s next, not just what’s trendy.
And if you want a broader strategic roadmap, The AI Agent Blueprint is a great place to dive deeper. It lays out how to go from launching an AI Agent to building successful systems that scale and drive real business value.
AI isn’t just a trend. It’s a capability your business will depend on. Done right, it becomes your most powerful teammate.
Inspired by this post on The Intercom Blog.
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