After struggling to understand proposal practitioners from the outside, I tried something much simpler: spending several hours reading what people were already saying when nobody had asked them to participate in research.
For the past several weeks, I have been trying to understand a problem that should be familiar to many proposal professionals: where does expensive human effort actually disappear inside complex RFP and tender workflows?
I approached the question seriously. I studied proposal-software companies, practitioner discussions, freelance demand, knowledge management, compliance, SME coordination and the growing capabilities of AI. I became increasingly convinced that the interesting problem was not simply whether AI could write proposals, but how good proposal teams actually operate and where their systems still break down.
Then I ran into a more basic research problem.
I could construct increasingly sophisticated theories about proposal operations, but getting enough direct access to practitioners was difficult. Cold outreach produced little response, even when I offered to pay people for interviews. I was learning a great deal about the market while remaining strangely distant from the people actually living inside it.
That made me question not only what I was researching, but how I was trying to discover worthwhile problems in the first place.
1. Desk Research Can Become Surprisingly Convincing
The danger with serious desk research is not necessarily that the information is bad.
Much of what I learned about proposal work was useful. Practitioner discussions repeatedly pushed me away from simplistic ideas such as “AI should write the proposal” and toward harder issues involving organisational knowledge, compliance, review discipline, SME attention and competitive strategy.
For example, proposal professionals discussing their own use of AI described general-purpose systems such as ChatGPT and Claude being useful for requirements analysis, first drafts and related work, while simultaneously emphasising that good organisational information and disciplined proposal processes matter enormously.
Reddit discussion: Anyone here using AI for proposals?
Another practitioner response challenged the way I was framing the problem altogether. Instead of starting with AI, the criticism pushed me toward the less glamorous but much more operational question of how proposal knowledge is actually maintained and governed.
Reddit discussion that challenged my proposal-AI framing
Those corrections were valuable.
But they also exposed a weakness in my research loop.
A possible market can be studied through a sequence such as:
-
Observe and investigate
- find an apparently expensive workflow;
- study competitors and existing software;
- read published practitioner discussions;
- estimate what technology can already accomplish.
-
Construct a theory
- identify a remaining bottleneck;
- reason about how it could be improved;
- infer who should care;
- infer why somebody might pay.
Every step can be logically defensible.
The final commercial conclusion can still be wrong.
This is the same distance between theory and reality that I described in I Was Looking For The Right Problem In The Wrong Way.
The missing variable is often how real people actually behave.
2. I Tried A Different Research Loop
I therefore started spending time in r/smallbusiness.
I did not go there asking people what software I should build. I did not announce that I was conducting customer discovery. I simply opened discussions and read.
During one session I went through roughly two dozen threads. In many cases I also read the surrounding comments, sometimes dozens of them. I skipped most opportunities to respond because I either did not understand the subject sufficiently or other people had already contributed better answers.
That distinction matters.
I was not treating Reddit as a database of “pain points” to scrape.
I was trying to understand the conversation.
Someone describes a problem. Other people interpret it differently. Some challenge the premise. Some draw from experience. Some give poor advice. Occasionally one comment exposes a principle that seems much more useful than the original discussion.
That creates a very different research loop:
Real person → voluntarily described problem → community interpretation → my interpretation → possible contribution → response or correction
The person did not agree to a research interview.
They simply cared enough about the problem to describe it publicly.
3. Sometimes The Comments Were More Valuable Than The Problem
One discussion concerned a business that had lost about $700 because an employee left and post-employment API or software usage had not been handled properly.
Discussion: post-employment API cost
I did not have some brilliant automation recommendation.
What interested me was the discussion underneath it. Several people correctly reframed the incident as a systems problem rather than merely an employee problem. One suggestion particularly stayed with me: design the operation around a maximum acceptable loss, so that even if something goes wrong, the damage remains bounded.
That sounds obvious after hearing it.
But it is a useful operating principle.
Many systems are designed around the assumption that the process should never fail. A more robust approach may be to assume that eventually somebody forgets something, a credential remains active, an API continues running or an employee leaves unexpectedly.
The question then becomes:
How much can the organisation lose when the failure happens?
I had almost nothing to contribute beyond thanking the original poster and the person whose comment I found useful.
That was still a productive research interaction.
I learned something.
4. A Good Question Sometimes Produced More Value Than An Answer
Another person wanted to import affordable European healthcare products into Morocco.
Discussion: importing from Europe to Africa
I know almost nothing about Moroccan healthcare distribution.
My instinct therefore was not to explain how they should execute the business. I asked more basic questions. What part of healthcare did they actually understand? Which customers could they realistically speak with? Where did their advantage come from?
The subsequent discussion revealed that they had essentially no healthcare experience and were attracted to the sector because it appeared commercially promising.
That interested me because the structure was familiar.
I had recently gone through something conceptually similar in my own RFP exploration. Before committing to a market, I had tried to understand my own unfair advantages and blind spots. The RFP direction eventually became much less compelling for me, but that failure exposed another blind spot: I did not have enough direct connection to the practitioners whose problems I was theorising about.
The Moroccan case therefore reinforced a broader pattern:
Sometimes the useful contribution is not specialised domain knowledge. Sometimes it is recognising which unanswered question makes everything downstream premature.
That may be one of the ways I naturally think.
Whether that ability has commercial value at scale is a completely separate question.
5. The Most Appreciated Contributions Were Not Particularly Technical
Two of my strongest interactions were much simpler.
One young business owner described feeling terrible after what appeared to me to be a relatively ordinary setback.
Discussion: “Today felt like a big business owner flop”
I essentially responded from experience: events that feel enormous in the moment often become minor memories later. Learn what can be learned, correct what can be corrected and continue.
The person replied that they needed to hear it.
Another discussion asked what business owners had failed to realise would eventually become a problem.
Discussion: unexpected business problems
My response was personal: I can often do many things, but that does not mean I possess enough time and energy to do all of them.
Again, technically unimpressive.
But recognisable.
These interactions were useful because they reminded me that value is not identical to technical sophistication.
A complicated answer is not automatically more valuable than a simple observation delivered at the moment somebody needs it.
That is relevant to software research as well.
6. But Helpfulness Is Not The Same As Commercial Value
This distinction is important.
I could spend enormous amounts of time helping people individually and never create something economically scalable.
Someone feeling better after receiving perspective is genuine value. It does not automatically create a business.
Someone repeatedly losing expensive employee hours because of an operational bottleneck is different.
If the problem:
- occurs frequently;
- consumes meaningful human time;
- creates revenue loss, unnecessary cost or operational risk;
- appears across many unrelated organisations;
- and can be substantially reduced through a repeatable system;
then it begins to intersect much more naturally with automation and financial value.
That intersection is what I am interested in.
I am not looking exclusively for “automation problems.” Nor am I interested in chasing anything that appears monetisable.
I am looking for areas where two forms of value can eventually meet:
I can provide systemisation or automation that materially helps somebody.
and
The improvement creates enough financial value that the work can support me in return.
I have not found that intersection yet.
7. Why This Matters To My Proposal Research
This may look like a detour from RFPs.
It connects to the system-level argument in The AI Is Not The Proposal System: understanding how practitioners work matters more than optimising AI generation in isolation.
I increasingly think it is the opposite.
Proposal professionals repeatedly corrected my assumptions because they were operating inside a world that I was largely studying from outside. I could understand the technology, analyse the workflows and read the literature, but experienced practitioners still knew which ideas were naive.
That is exactly what good research should expose.
The difficulty was obtaining enough of those corrections.
Reddit produces a very different environment. People are already discussing what failed today, what annoyed them, where they lost money, which tools they hate, which business decision they regret and what finally solved a problem.
The original poster provides one perspective.
The comments provide twenty more.
Some of those commenters may have decades of experience. Others may be confidently wrong. The researcher's job is not to accept everything, but to compare explanations and understand where reality appears to converge.
That is extraordinarily different from inventing a questionnaire around a hypothesis I already hold.
8. I Am Not Concluding That Reddit Is “The Answer”
This experiment is still extremely early.
A few hours of reading Reddit cannot establish what market I should enter. A handful of useful conversations cannot validate a business. Upvotes do not establish willingness to pay.
There are obvious limitations.
Reddit users are not necessarily representative of a market. Strong opinions may receive more attention than boring but economically important problems. Some advice is wrong. Important business processes may never be discussed publicly because they are confidential, specialised or simply too mundane.
I therefore would not replace conventional market research with Reddit.
What I would change is the order.
Before spending months constructing a theory about what people should care about, I want much more exposure to problems people already care enough about to discuss without being asked.
That is a lower-cost correction mechanism.
9. What I Am Looking For Next
For the next stage, I want to keep doing almost exactly what I did during this first exploration.
Read deeply.
Skip discussions I do not understand.
Contribute when I genuinely think I can help.
And, in the background, pay attention to recurring problem structures.
I am particularly interested when several signals begin appearing together:
-
Human pull
- people repeatedly describe the same frustration;
- follow-up questions appear;
- people actually try suggested solutions.
-
Economic consequence
- money is being lost;
- revenue is being missed;
- expensive people are spending time on repetitive work;
- businesses already pay employees, freelancers or software to handle it.
-
Systemisation potential
- the process repeats;
- the failure pattern is predictable;
- information or coordination is unnecessarily manual;
- software or automation could materially reduce the burden.
I do not know whether those three conditions will converge around one problem.
That is what I want the experiment to tell me.
10. The Research Question Has Changed
Earlier, I was trying to determine what should be automated inside proposal workflows.
That research was useful.
But it exposed something more fundamental about how I personally search for problems.
I can spend months building a sophisticated intellectual model of a market.
What I need more of is correction from reality.
So the question I am now exploring is:
Can sustained participation in communities where people voluntarily discuss their real problems help me discover worthwhile problems more reliably than selecting a market first and researching its problems from the outside?
I do not yet know the answer.
Reddit has already given me a richer human-feedback loop than much of my previous market research. It has exposed problems I would never have thought to ask about, shown me how experienced people reason about them and occasionally allowed me to contribute something useful myself.
What it has not yet given me is commercial validation.
That distinction matters.
I am not currently trying to find a product on Reddit.
I am trying to improve the process by which I discover which problems deserve deeper attention in the first place.
If that process works, the product question can come later.