AI Marketing Tools: Lessons from 100 Days Testing

Most founders treat AI like a slot machine: pull the lever (paste prompt), hope for a jackpot (perfect output), then get mad when it spits out fluff.

Craig Hewitt didn’t do that. He ran a 100-day public experiment—one YouTube video per day—testing AI tools the way a bootstrapped startup should: hands-on, skeptical, and focused on ROI. The result wasn’t “AI will replace your team.” It was more useful than that: a practical map of where AI marketing tools for small business actually save time, where they break, and how to choose tools when you don’t have VC money to burn.

This post is part of our AI Marketing Tools for Small Business series. Think of it as a field guide for founders who need more leads, more content, and more output—without hiring a 10-person marketing team.

The real lesson: AI works great… 80% of the time

AI is already good enough to change your marketing operations. But it’s not reliable enough to fully “set and forget.” Craig’s most grounded takeaway from 100 days: you can automate a lot, but you still need a human in the loop for the last 20%.

That 80/20 reality is exactly why bootstrappers should care. When your constraint is time (not headcount), AI is a multiplier. When you’re a big company, AI becomes a headcount reducer.

Here’s how that plays out in marketing work:

• Great at first drafts: outlines, positioning options, hooks, subject lines, ad variations
• Great at synthesis: summarizing interviews, extracting themes, turning long-form into snippets
• Weak at accountability: it won’t notice your brand drift, bad claims, or “sounds fine” copy that doesn’t convert
• Weak at edge cases: anything involving messy data, unclear inputs, or business-specific nuance

A useful mental model: AI doesn’t replace marketing judgment. It replaces the blank page.

Stop treating ChatGPT as “the tool” (it’s a consumer interface)

Craig’s spicy take from the podcast episode: most people in our space shouldn’t default to ChatGPT.

Not because ChatGPT is “bad.” Because many founders confuse popularity with fitness for the job.

ChatGPT is primarily a polished consumer product: easy chat, easy prompts, easy habit. But if you’re trying to drive leads and revenue, you often need AI that can:

• browse, gather sources, and compile reports
• run multi-step tasks without constant back-and-forth
• work with your files, transcripts, analytics exports, and docs
• produce outputs you can actually ship (not just “nice text”)

ChatGPT can do some of this, but the workflow can get brittle fast.

When ChatGPT is worth it: reusable “mini-workflows”

Craig called out one strong use case: GPTs (custom, reusable setups).

For example, a small team can create GPTs for:

• product launch email drafts (consistent format)
• customer support tone + policy enforcement
• rewriting blog intros in your brand voice
• converting a feature list into landing page sections

If you already live in OpenAI, this is a practical way to standardize output across the team.

The 3 tool categories that matter for bootstrap marketing

A mistake I see founders make: they collect AI subscriptions like Pokémon. What you want is a small stack that covers the highest-leverage marketing workflows.

Craig’s experiment naturally grouped tools into categories. Here are the ones that matter most if your goal is leads without VC.

1) Agentic research + execution: “Manus-style” tools

If you want one category that can change your pace of execution, it’s agentic AI: tools that don’t just answer questions, but go do the work.

Craig’s standout recommendation was Manus (an agentic tool using Claude under the hood). The difference versus a chat-only tool is capability:

• it can open a browser and navigate
• it can gather data from multiple sources
• it can compile structured deliverables (rubrics, briefs, scripts)
• it can run multi-step tasks while you do something else

A concrete marketing example you can copy

Craig described using an agent to improve YouTube/podcast intros (critical for retention):

1. pull the latest 20 videos from a creator’s YouTube channel
2. download transcripts
3. analyze patterns
4. produce a “rubric” for strong intros
5. generate multiple intro options for a new episode

A chat tool can’t reliably execute that full chain. An agent can.

Bootstrap takeaway: Use agentic AI for tasks that normally require a junior marketer or analyst: competitive scans, content pattern analysis, message testing, and structured briefs.

2) Coding + content systems: Claude Code (or “AI with the rails removed”)

Even if you’re not a developer, coding-capable AI matters because the best marketing advantage in 2026 is custom workflows.

Craig’s pick: Claude Code. He described it as “bare metal” AI: you can feed it files, context, and tasks without the limitations of a simple chat box.

For bootstrapped startups, this matters because:

• you can build internal tools before you pay for yet another SaaS
• you can turn repeatable marketing processes into scripts
• you can standardize content creation while staying on-brand

What this looks like in a small business marketing workflow

Here are realistic examples founders build once and reuse:

• an SEO brief generator (keyword + intent + SERP patterns → outline)
• a content repurposing pipeline (podcast transcript → blog → 5 LinkedIn posts)
• a competitor visibility tracker (capture changes in positioning + feature pages)

This is where “AI marketing tools” stop being novelty and start becoming process.

3) Voice-to-text for faster content: Whisper-style transcription

Founders underestimate how much marketing is just turning thoughts into publishable assets.

Craig highlighted speech-to-text tools (like “Super Whisper” / “Whisper Flow” category tools) as a daily driver: talk, get text, shape it into content.

This is one of the cheapest ways to increase output because:

• speaking is faster than typing
• raw ideas become drafts in minutes
• your personal voice comes through more naturally

Practical use: Record a 7-minute voice memo after a customer call. Transcribe it. Feed it into your writing tool. Publish it as a LinkedIn post or founder email.

What “AI agents” actually are (in plain English)

“Agent” gets thrown around so much it’s nearly meaningless. Craig’s definition is useful because it’s operational.

An AI agent has three components:

1. An LLM (Claude, GPT, etc.)
2. Memory (it can remember prior work or store answers)
3. Tools (email, browser, calendar, knowledge base, databases)

That combination is what makes an agent more than a chatbot.

A legit agent use case that prints time: support deflection

Craig shared a concrete example from Castos: they implemented an AI support agent trained on their docs and site content. The impact:

• cut support burden roughly in half (for a business with ~4,000 customers)
• includes an escape hatch (“talk to a human”)
• improves documentation because the team reviews logs and patches gaps

That last point is underrated: AI support systems don’t just answer questions—they reveal what your docs fail to explain.

Bootstrap takeaway: If you get meaningful support volume, an AI support agent is one of the highest-ROI AI marketing tools you can deploy. Better support becomes better retention, which makes every lead you generate more valuable.

The 100-days-of-AI method: how to evaluate tools without wasting money

Craig didn’t win because he found “secret tools.” He won because he ran a process. You can copy that process without doing 100 YouTube videos.

The bootstrapped evaluation framework (4 steps)

1. Pick one constraint. Time is usually the real one.
2. Pick one workflow. Example: “turn one webinar into a week of content.”
3. Test tools against a deliverable. Not “seems good,” but “did we ship?”
4. Track failure rate. If it breaks 20% of the time, can you absorb that?

A rule I agree with: price floors force focus

Craig’s product lens was sharp: he’s only interested in building (and often buying) tools with enough value to charge $50–$100/month.

That’s a good forcing function for founders choosing AI marketing software too. If a tool saves you 30 minutes a month, it’s not a lead engine—it’s a toy.

Why this matters for “US Startup Marketing Without VC”

Bootstrapped marketing is about two things: distribution and consistency.

Craig’s 100-day run is a case study in both:

• Consistency: publishing daily builds momentum fast
• Distribution: list-style videos got reach, even if they weren’t his “ideal” content
• Compounding: a library of content improves discoverability over time
• Process: tool choice followed outcomes, not hype

The reality? If you’re not funded, you can’t afford random experiments. AI gives you a way to test channels and scale content production without hiring ahead of revenue.

A simple starter stack for lead-focused founders (2026 edition)

If you want a “don’t overthink it” starting point for AI marketing tools for small business, here’s a lean stack that maps to Craig’s lessons:

1. Agentic tool (for research + multi-step work): one “do the work” agent
2. Writing + strategy LLM (for messaging + drafts): one primary model you standardize on
3. Transcription (for founder-led content): voice-to-text you actually enjoy using
4. One automation layer (optional): only after you have a repeatable workflow

Don’t add more tools until you can name the workflow and the output.

Next steps: run your own 14-day AI marketing sprint

You don’t need 100 days. Two weeks is enough to see results.

Pick one lead-driving workflow:

• publish 6 LinkedIn posts from one customer interview
• ship 2 SEO articles from one subject matter expert outline
• build a “support deflection” agent from your top 50 tickets

Then ask one question: Did we ship more marketing with the same team?

That’s the bar. Everything else is noise.

If AI is going to reshape how customers discover products (and it already is), what’s your plan for being the startup that publishes, tests, and compounds—while your competitors are still prompting for clever taglines?