The SME Bottleneck: Why AI Makes Your Experts Busier, Not Freer

A proposal manager at an enterprise software company shared something frustrating recently. Her team adopted an AI-powered RFP tool that promised to cut response time in half. The AI part worked fine. But before anyone could use it, someone had to spend hours reformatting incoming documents, marking which cells were questions, separating multi-part requests into individual items, and cleaning up the messy reality of how customers actually send RFPs.

The time saved on answer generation? Eaten up by document preparation that never used to be necessary.

This pattern plays out constantly. Organizations adopt AI tools expecting efficiency gains, then discover they've traded one kind of work for another. The AI handles the flashy part. Humans get stuck with the tedious setup work the AI can't manage.

The Work Around the Work

Most AI tools assume a clean starting point. They're designed for ideal scenarios: a spreadsheet with one question per row, a document with clearly labeled sections, a form with predictable structure. Feed them tidy inputs, and they perform beautifully.

Real RFPs don't look like that.

A single spreadsheet row might contain three related questions across different columns. One sheet might have multiple tables, each with different formats. A Word document might embed a questionnaire inside narrative sections, with sub-questions nested under main questions. Excel files arrive with merged cells, conditional formatting, and hidden columns. PDFs come as scanned images or locked forms.

When AI tools can't handle this complexity, they push the problem back to users. Someone has to restructure the document to match what the AI expects. That restructuring work didn't exist before. It's pure overhead, created by the tool that was supposed to save time.

Where the Time Actually Goes

Talk to teams using RFP automation tools and ask where their time goes. You'll hear variations of the same story.

Intake formatting. Before the AI can process anything, someone manually identifies questions, marks answer locations, splits compound requests, and resolves ambiguities in document structure. For complex RFPs, this can take hours.

Exception handling. When the AI's document parser breaks on an unusual format, users have to manually extract content and reformat it. The more diverse your incoming documents, the more exceptions you'll encounter.

Output cleanup. AI-generated answers often need reformatting to match the original document's structure. If the RFP asked for answers in specific cells with character limits and formatting requirements, someone has to ensure the AI output fits those constraints.

Quality verification. Did the AI correctly identify all the questions? Did it miss a column? Did it confuse a header with a question? Someone has to check, and checking takes time.

Add it up, and teams often spend more time on these peripheral tasks than on the actual answering work the AI was supposed to accelerate.

The ROI Problem

This creates a math problem that many organizations don't see coming.

Say your AI tool generates answers 80% faster than manual drafting. That sounds compelling. But if document preparation now takes two hours that you never spent before, and output formatting takes another hour, you need a lot of answer generation to break even.

The issue isn't that AI doesn't work. The answer generation genuinely is faster. The issue is that gains in one area get offset by new burdens in another. You're not measuring the same workflow before and after. You're comparing an old workflow without prep overhead to a new workflow that requires it.

Why Vendors Leave the Hard Stuff to You

There's a reason most RFP tools punt on document complexity. Handling messy real-world inputs is genuinely difficult. It requires sophisticated document understanding, not just text extraction. The AI needs to recognize structure, infer relationships, distinguish questions from context, and handle edge cases gracefully.

Building that capability takes significant investment. It's easier to define strict input requirements and let users do the normalization. From the vendor's perspective, the product still works. It just requires clean inputs.

From the user's perspective, you've outsourced the hard problem to yourself.

This is a pattern across enterprise AI more broadly. Tools optimize for the tractable middle of the problem while leaving the messy edges to humans. The demos look great because they use ideal examples. Production reality is rougher because real data rarely matches the ideal.

What to Look For When Evaluating

If you're assessing AI tools for RFP response or similar knowledge work, here are questions worth asking.

Can it handle your actual documents? Not a cleaned-up sample. Your real incoming files, in their native formats, with all their structural quirks. Ask vendors to process a few representative examples and see what breaks.

What preparation does it require? Does someone need to mark questions? Restructure spreadsheets? Convert formats? Map columns? Every manual step is overhead that should factor into your ROI calculation.

How does it handle exceptions? When document parsing fails, what happens? Do you get an error and have to fix it yourself? Does the system degrade gracefully? Can it flag uncertainty without requiring complete manual takeover?

What's the total workflow time? Don't just measure the AI step. Measure everything: intake processing, preparation, generation, review, and output formatting. Compare that total to your current process. That's your real efficiency gain or loss.

Where does human time go? If the AI shifts human effort from high-value work (crafting differentiated answers, strategic positioning) to low-value work (reformatting documents, cleaning data), that's a bad trade even if total hours decrease.

A Different Approach

The alternative is building AI that handles the complexity instead of avoiding it. This means investing in document understanding that works with real-world messiness rather than requiring users to sanitize inputs first.

What does that look like in practice?

The system processes incoming documents as they arrive, regardless of format. It identifies questions automatically, even when they're spread across columns or nested in complex structures. It handles multiple tables, merged cells, and unconventional layouts without manual intervention. Users start working on answers immediately instead of spending hours on setup.

This isn't a small difference. If document preparation takes two hours per RFP and you handle fifty RFPs per quarter, that's a hundred hours of overhead. Eliminate that overhead and you've freed meaningful capacity for actual proposal work.

More importantly, you've removed a source of friction that kills adoption. Tools that require extensive prep work get used reluctantly or inconsistently. Tools that just work get used for everything.

The Broader Pattern

This isn't unique to RFP tools. Across enterprise AI, there's a tendency to optimize the showcase capability while neglecting the surrounding workflow. The AI can summarize documents brilliantly, but someone has to upload and tag them first. The AI can analyze data insightfully, but someone has to clean and format the data first. The AI can generate content impressively, but someone has to structure the brief first.

Each of these "firsts" represents work that may not have existed before. And that work often lands on the same people who were supposed to benefit from the AI.

When you evaluate any AI tool, look at the complete picture. Ask what the tool requires from you before it can do its job. Ask what you'll need to do after it produces output. Add up all the human touchpoints, not just the ones the vendor highlights.

The real test of AI value isn't whether the core capability works. It's whether the total workflow improves. That includes all the work around the work.

What's been your experience with AI tool overhead? Have you found yourself doing prep work that didn't exist before you adopted automation? We'd be curious to hear what patterns you're seeing.