If your team is using AI in research, it’s already influencing your work. Every prompt, framing choice, and unchecked assumption feeds back into the system. Whether you notice it or not, your researchers are training the tool. Without a clear process to manage this, bias can start to appear in the outputs.
To make the most of AI in a research setting, you need clarity, structure, and human oversight. Tools like Aida by Beings.com are built for researchers, not marketers, not dabblers, and not generic productivity users. It’s made to support thinking, judgement and analysis, but it still depends on the person using it. Aida can help map ideas, track versions, and show you where outputs are coming from. That’s useful, but it’s not enough on its own.
If your agency wants to produce sharper, more credible work, not just get to the first draft quicker, here are eight principles to help your team use AI properly.
1. Begin with a Clear Brief
Starting with a vague question can lead the AI to fill in gaps based on common online assumptions. Instead, your team should begin with a detailed internal brief: Who is the client? What is the purpose of the work? What tone, culture, or audience considerations are there? Aida can process this input effectively, but only if provided with comprehensive information.
Encourage researchers to approach AI as they would a new team member, providing it with a thorough briefing before expecting meaningful contributions.
The key thing to remember here is that skipping the brief is the fastest way to get misleading results that sound confident but lack grounding.
2. Use it to explore, not to shortcut
Aida is particularly valuable early in a project, when you’re mapping the space and trying to understand what’s going on. It helps spot early patterns, identify gaps, and push past first assumptions.
It’s not where final slides should come from. If your team is using AI to write conclusions or summaries without testing or layering in human thinking, the work will lose depth fast.
Don’t let the pressure to “get something down” turn into dependency on half-formed outputs. Make space for AI to stretch the thinking, not wrap it up too soon. Use it to challenge a hunch. Use it to widen the frame. Use it when you want to surface multiple angles quickly. That’s where it earns its place.
3. Ask who’s speaking
Bias often shows up when certain voices dominate. If your AI output leans heavily on government reports or news media, you’re likely getting a narrow take, even if the information is technically accurate.
Aida gives visibility into source types. That makes it easier to check who’s getting the most space. Make it standard to review this and shift the balance when needed. Sometimes that means adding more first-person accounts. Sometimes it means asking for perspectives from less dominant regions, industries or communities. These don’t necessarily need to be part of the final output, and while a study conducted on participants based on a particular locality, such as say Scotland, may be what your client is after, by having the model fed with adjacent information from Wales and England might not relevant to the specific outcome, it builds the nuance and adds depth to the results from the targeted participants.
If the research feels too polished too early, that’s often a sign someone hasn’t checked the weighting.
4. Look at tone, not just content
Framing is just as important as facts. The way something is described has an impact on how it’s received. AI can get the data right, but it still uses language that shapes perception in unhelpful ways.
For example, if your research is about consumer behaviour, is the language too transactional? If it’s about new products, is it written as if everything has already been decided? These are subtle signals that can skew interpretation.
Ask your team to run different framings of the same insight. What does it sound like from a cultural, emotional or behavioural angle? If the tone shifts too much between versions, something probably needs tightening.
Again, another golden rule is that clarity is important. But overconfidence often hides bias.
5. Encourage prompt testing
The quality of the output often depends on how the question is asked. A vague prompt will give a vague answer. One with too many assumptions might steer the model without realising.
Get your team into the habit of running at least two versions of a prompt. That doesn’t mean writing five variations every time. But it does mean checking how different phrasing, tone, or structure affects what comes back. By acting on this, you prevent building whole arguments on autopilot.
6. Stay alert to passive bias
Not all bias is obvious. Sometimes it shows up in content that feels neutral on the surface but leans subtly towards dominant assumptions. You might spot it in the phrasing, the framing of questions, or in what’s missing rather than what’s included.
Encourage your team to challenge anything that seems too neat or resolved too early. Ask who benefits from this version of the story. Who might push back? Who isn’t part of the picture?
Bias does not always shout, but it leaves a mark that becomes clear if you slow down and examine it.
7. Use AI as a thinking partner, not a replacement
Aida is good at breaking the silences. It’s helpful when you’re stuck or working alone and need another way to solve the problem. But it doesn’t replace synthesis, creative leaps or judgment. That’s the researcher’s job.
AI can help you work faster by taking care of the structure or surfacing patterns. But it can’t decide what matters. Make it clear to your team that the outputs are there to be edited, challenged, and shaped, not dropped into decks untouched.
It’s to be viewed as an assistant that never runs out of energy, not a co-author.
8. Build in checks before things go out the door
Bias is easiest to spot when you’re not the one doing the work. Before anything gets signed off, someone else should do a quick review of the thinking, not just the formatting.
You don’t need to build a whole system around this. A five-minute chat can be enough. Ask:
- Is there a dominant voice shaping this?
- What’s been left out?
- Would this hold up if the client challenged it?
Catching a skew at this point is easier than fixing it later, especially once the client has seen the work.
Bias Is Ultimately A Leadership Risk
AI is already woven into how research is done. That’s not something to avoid. The real question is how clearly and deliberately your team is using it. Are they building thinking with it, or just using it to get to “something” faster?
Tools like Aida by Beings.com are built to support a solid research framework and system. They give your team access to the source trail, prompt testing, and version control. They allow you to frame the work clearly and revisit decisions as a project evolves. But they don’t make the tough calls for you. That still comes down to your people, your process, and what you expect from the work.
This is where leadership makes a difference. Are your teams encouraged to question what they’re seeing? Do they understand the risk of overconfidence in a first draft? Do they know how to brief, test, and challenge AI outputs? Are you carving out time to check not just what the AI said, but what it didn’t?
If not, this is the moment to get intentional. You don’t need a huge system overhaul. You need good prompts, clear review points, and permission to interrogate the work before it’s client-facing. This is where this article will help you, the little moments to check in with your team and yourself.
Try it for yourself. Use Aida on a live brief or a pitch that needs shaping. Feed it a proper internal context and test what happens when you shift the framing or widen the lens. The tool is designed to support researchers and to improve every time it’s used with care.
The quality of your thinking still sets the standard. Aida just helps you raise it, project by project.
