AI research feels magical until you try to use it for something that actually matters.
That’s what happened when our team recently asked an AI system to produce a set of competitive research reports. On the surface, the output looked impressive: 18 neatly formatted documents, each packed with market insights, competitor lists, and strategic takeaways.
But when we started reading closely, the illusion fell apart.
One report listed one set of competitors. Another, for the same company, listed a completely different set. In a third, the AI confidently suggested that BOL was a competitor to our own client, an answer that was not just wrong, but dangerous if it were to make its way into a board-level deck.
In other words: the AI “sounded” smart. It was not reliably smart.
That’s the moment this post is about, when we stopped asking, “How do we write a better prompt?” and started asking, “What does a responsible AI research process actually look like?”
The 18 research docs had all been generated using what we call a “one-shot” approach:
One large, ambitious prompt.
One model.
One pass.
No deeper research motion.
The idea was simple: ask a large language model to produce comprehensive research based on its training data, with light tool access as needed. In theory, this should save time. In practice, it created a new kind of risk.
Because one-shot prompting has hard limits:
When we asked a senior strategist to review the documents, the verdict was blunt: these reports were not usable as-is. They contained hallucinations, inconsistencies, and incorrect competitive intelligence that would never pass muster with a client.
And validating 18 dense documents manually would have taken longer than just doing the research from scratch.
That was the turning point. The problem wasn’t “AI research.” The problem was how we were using AI.
This is exactly where a lot of B2B teams are right now. Someone says, “We’ll just stand up Jasper or ChatGPT and have it do our research.”
And to be clear, tools like Jasper, Claude, and ChatGPT are incredibly powerful. We use them every day.
But there’s a fundamental misconception baked into that idea, that access to AI equals outcomes with AI. In reality, quality outcomes come from workflow architecture, not just tools.
That’s where an agency like BOL still earns its keep in the AI era. Not because we have access to special models or secret APIs. Our advantage comes from knowing how go-to-market motions actually work, how research gets used in real decks, and where the line is between “interesting” and “safe to present to an executive.”
Our job isn’t to celebrate AI blindly. It’s to design systems that make AI reliable enough to trust with real decisions.
When we fed our concerns back into the AI (in this case, Claude) and asked: “How would you make this more accurate?” the answer wasn’t “try a better prompt.”
It recommended something more sophisticated: a multi-agent architecture.
Instead of one model doing everything, we break the work into specialized roles:
And then, and this is crucial, a human reviewer as the final gate.
Think of it less like asking a single intern for a finished report, and more like running a newsroom:
AI becomes the staff. Your human team remains the editor-in-chief.
The first agent is designed to answer a very specific research question in a very specific way.
Instead of one generic “Do competitive research” prompt, we define different behaviors depending on the question type:
Each research pattern has its own prompt template, its own logic, and ideally its own data sources. That’s how we move from vague “AI research” to structured prompt research: the practice of designing targeted, repeatable research prompts tied to real data.
The output from this agent still isn’t trusted automatically. Instead, it becomes the raw material for the next step.
The fact checker is the skeptic in the room.
This agent reads what the research agent produced and identifies specific claims that should not be taken at face value. Then it goes out, sometimes using different tools or sources, and tries to validate or refute each one.
Did the research agent claim a competitor has X% market share? The fact checker looks for corroborating data.
Did it say a brand operates in a particular region? The fact checker checks the company’s own site or recent press.
At the end of this stage, each key statement is tagged: confirmed, contradicted, or unverified, with links back to sources. This is where we start to move from “AI says so” to “Here’s what the evidence supports.”
Once research and fact-checking are complete, the editor agent steps in.
Its job is to take the initial research, the fact-check results, and the project’s goals to produce a clean, coherent document.
If a claim was refuted, the editor removes or corrects it. If a section is redundant, the editor simplifies it. If multiple docs are being generated, the editor works to keep definitions and competitive sets consistent across them.
This agent doesn’t decide what is true. That’s the fact checker’s job. Its focus is narrative quality and structural integrity: making sure what is true is said clearly and consistently.
Even the best AI architecture still stops one step short of what we would ever send to a client or an internal stakeholder, human validation.
We don’t base a go-to-market strategy on “95% accurate.” We base it on “accurate enough that a strategist is comfortable standing behind it in a room full of executives.”
That’s why our process always ends with a strategist or subject matter expert reviewing the output:
AI can do the heavy lifting and reduce time-to-insight by orders of magnitude. But the decision to trust and act on that insight stays human.
Back to those 18 original research documents. When we evaluated them, we realized they fell into three buckets:
Instead of trying to “clean up” all 18, we chose a smaller subset as a pilot. From there, we did the work:
Most importantly, we committed to design before build. No new agents, no new code, no new automation until we had a clear, shared understanding of:
That discipline, resisting the temptation to “just let the AI run”, is what turns AI from a toy into infrastructure.
If you’re a marketing or revenue leader, the takeaway isn’t “you need to copy our exact architecture.” Your use cases, tech stack, and risk tolerance will differ.
The takeaway is simpler. You cannot get reliable research or strategy out of AI with one-shot prompts alone.
You need:
Agencies like BOL don’t exist just to “use AI.” We exist to design the systems that make AI safe, useful, and aligned with how business decisions actually get made.
In other words: our value isn’t that we have access to AI. Its that we know where AI ends and where rigor begins.
If you’re exploring how to bring AI research agents into your own marketing or GTM motion, the question isn’t whether the tools are powerful enough. They are.
The question is whether the workflow behind them is robust enough to trust.
Ready to design an AI research and fact-checking process that’s actually safe to use in front of your executive team?
That’s the work we’re doing every day, and we’re happy to help you do it, too.