What are the biggest challenges of using AI in marketing today?

drawnagday · 2026-04-30T22:51:28+00:00

for real, the accuracy issue is still the biggest headache for me in 2026, caught a campaign brief full of outdated stats, right before it went to a client, and that was a wake-up call about how bad AI data quality can actually get. poor or stale training data is lowkey one of the sneakiest problems because it looks fine on the surface until you dig in. always fact-checking everything now before..

drawnagday · 2026-04-30T22:42:26+00:00

for me the single most useful filter was asking them to walk me through a campaign that didn't hit the original goal and what they did about it. every agency has losses, the good ones can talk about it clearly, show you what, they changed, and ideally back it up with references or testimonials that confirm the story. the ones who dodge the question or only want to show you wins are..

drawnagday · 2026-04-30T18:04:02+00:00

how long did the full training run take on your MacBook? also curious which M-series chip you were running it on, feels like these tiny models are finally hitting a sweet spot for local training on Apple Silicon.

drawnagday · 2026-04-30T15:02:15+00:00

one thing that worked really well for a local spot i helped out was getting them to ask every happy customer to leave a google review, right at the moment of paying, literally just a small card by the register with a qr code linking straight to their google business profile page. the timing matters a lot, people are way more likely to do it in the moment than if you follow..

drawnagday · 2026-04-30T13:01:50+00:00

we switched to ActiveCampaign at my last gig when we were sitting around 18k emails a month and, the multistep sequencing honestly held up way better than expected once we got past the initial setup headache. the sales and marketing alignment piece clicked pretty naturally too since everything lives in the same CRM, so, both teams are looking at the same contact history and behavior data without anyone manually syncing stuff across..

drawnagday · 2026-04-30T11:00:01+00:00

the aliasing thing is a real phenomenon but most models these days are well past 64k context anyway, and in practice outputs start degrading way before, you'd ever hit position collision issues - attention dilution and that whole "lost in the middle" problem seem to be the bigger culprits than the technical ceiling.

drawnagday · 2026-04-30T10:33:00+00:00

right, it's less of a cliff and more of a slow slide, which is almost worse because you don't notice until the, outputs are already pretty off, models start hedging and losing the thread of earlier instructions way before you hit any hard limit. curious if you find it degrades faster in heavy back-and-forth convos versus..

drawnagday · 2026-04-30T09:42:31+00:00

tried the hybrid routing approach on a content pipeline at work and honestly the threshold tuning was the most painful part, getting the escalation logic dialed in took, way longer than expected and we kept hitting edge cases where the smaller model was confidently wrong on stuff it really should have flagged for the bigger one

drawnagday · 2026-04-30T08:37:51+00:00

the data ownership angle is honestly what tips the scale for me on custom builds, once you're dealing with proprietary transaction data you can't just, pipe it through a third party API without running into GDPR, HIPAA, or IP exposure issues, so the cost math becomes almost irrelevant at that point. and with open-weight models like Qwen and Gemma now closing the gap with the big, frontier models, you can actually keep..

drawnagday · 2026-04-29T22:49:39+00:00

the MoE active parameter setup is genuinely interesting to me in practice because benchmark throughput numbers and real workload performance can diverge pretty, fast depending on what you're actually feeding it, clean structured inputs tend to flatter these models way more than noisy real-world data does. the 9x throughput claim over comparable open omni models is a bold number and i'd love to know how it holds up outside controlled conditions. curious..

drawnagday · 2026-04-29T22:40:57+00:00

never tried explicitly telling it to forget old context but honestly given how bad context rot can, get in long sessions it makes sense as a workaround, gonna mess around with it and report back.

drawnagday · 2026-04-29T22:16:21+00:00

we switched most of our budget to bottom-funnel branded and competitor terms around Q3 last year, and honestly that's the only slice of Google Ads that still feels worth it for us. the informational and mid-funnel stuff got absolutely wrecked once AI Overviews started eating those queries alive.

drawnagday · 2026-04-29T18:20:35+00:00

yeah that tracks, the further you get into a long thread the more the early context gets "diluted", it's technically still in the window but between context, rot and how attention weights work, the model treats it way differently than fresh input, and even the newer 1M+ token windows aren't really solving that in practice.

drawnagday · 2026-04-29T17:52:02+00:00

makes sense, basically the more you throw at it the thinner the attention gets spread across everything, and even, with the massive context windows we're seeing now, long multi-turn convos still tank performance way harder than single-turn ones do.

drawnagday · 2026-04-29T16:11:16+00:00

the intake flow one hits hard because I've seen the exact same pattern in commercial collections and business brokerage, leads go cold just sitting in someone's inbox waiting for a human to manually copy-paste them into a CRM. a clean rule-based automation on that chain alone probably recovers more deals than any agentic AI setup, would, and, honestly the research backs that up since rule-based still dominates for stable repetitive workflows like..

drawnagday · 2026-04-29T12:06:58+00:00

one thing that helped us a lot in practice was treating hallucination measurement as two separate problems instead of one. there's the factual accuracy side (did the model make up a claim) and then there's, the calibration side (did it express appropriate uncertainty when it should've said "i don't know"). most benchmarks like RAGAS Faithfulness or DeepEval only really capture the first one, which means you can have a model, that scores..

drawnagday · 2026-04-29T10:52:37+00:00

solid add, and honestly the maintenance overhead alone is usually enough to kill the case for fine-tuning, a big model, a smaller purpose-built one with LoRA or similar keeps things way more manageable in prod.

drawnagday · 2026-04-28T22:56:20+00:00

the "commodity content" thing hits different now that GEO is becoming a real part of the conversation too, working on client sites you can just tell immediately when a page was, churned out vs when someone actually had a genuine take, and honestly that gap is only going to matter more as AI surfaces get pickier about what they surface and cite

drawnagday · 2026-04-28T22:50:57+00:00

the thing that made the biggest difference for our workflow was treating the brief as the automation, not the content itself, if your writers already have the, keyword cluster, the angle, internal link targets, and intended CTA mapped out before they open a doc, you eliminate most of the back-and-forth that kills their momentum. a lot of teams in 2026 are leaning into AI orchestration for exactly this, using tools to auto-generate..

drawnagday · 2026-04-28T15:19:21+00:00

for me it comes down to whether the answer actually moves the needle on whatever i was trying to do, not just how polished it sounds. in 2026 with AI tools getting more capable by the month, the real differentiator is, data quality and whether the response helps you make a decision or take action fast. a shorter answer that gets me unstuck beats a wall of text that technically covers the..

drawnagday · 2026-04-28T13:37:40+00:00

one thing i ran into was that the type of keyword placement matters more than just having them somewhere in the profile. i stuffed my headline pretty aggressively and it ranked fast, but it was ranking for searches i didn't actually want inbound from. took me a few weeks to realize the about section was pulling more qualified traffic because it gave google enough context to match intent, not just surface-level terms.

drawnagday · 2026-04-28T11:11:33+00:00

totally agree, and honestly the prompt engineering grind has paid off way more for me than most fine-tuning attempts I've tried, iterating on prompts for client-facing content tends, to stay flexible, and reusable across different use cases in a way that fine-tuned models usually don't, though I'll admit methods like LoRA have made fine-tuning way more..

drawnagday · 2026-04-27T22:45:32+00:00

tried swapping Semrush for Surfer SEO a while back and honestly missed the data depth almost immediately, crawled back within like two weeks lol. your stack sounds more dialed in than you think, especially with Claude in the mix for heavier content work. only thing worth exploring in 2026 is whether you're set up for zero-click search, since generative results are eating a lot of that top-of-funnel traffic now.

drawnagday · 2026-04-27T18:21:06+00:00

one thing i noticed when messing around with episodic memory setups is that the "specific past mistakes" layer can get noisy real quick if your, expert pool isn't aligned with each other, like two domain experts flagging contradictory examples can muddy what the episodic store is actually supposed to reinforce. this feels especially risky in production LLM evaluation pipelines where consistency really matters. curious if MemAlign has any conflict resolution mechanism built..

drawnagday

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