AI image generation reaches commercial-grade quality. The "ChatGPT for everything" habit is dying as specialists emerge. One team's chatbot disaster becomes a cautionary tale.
After years of struggling with AI-generated product imagery for e-commerce (distorted shapes, wrong colors, uncanny valley effects), Midjourney v7 is producing results that several Shopify brands are publishing directly to product pages for lifestyle shots. The practical workflow: photograph products on a clean white background, describe the lifestyle setting, and use Midjourney's new consistency mode to maintain product accuracy. Still requires human review — roughly 1 in 10 outputs is unusable — but the hit rate has improved dramatically.
A healthcare consulting firm ran a rigorous comparison: Harvey AI vs. Claude vs. ChatGPT for legal contract review of healthcare vendor agreements. Harvey (trained specifically on legal contracts) outperformed both general models on: identifying non-standard clauses, flagging regulatory compliance issues, and referencing relevant case law. Cost was higher than general AI, but review accuracy was 15–20% better. The takeaway: for domain-specific work in regulated industries, vertical AI tools are increasingly worth the premium.
A software company's AI customer support bot was asked about a refund policy edge case. The bot confidently described a specific 45-day satisfaction guarantee that didn't exist in their terms of service. A customer screenshotted the response and requested the refund in writing. The company honored it to protect their reputation — cost: $340 and a significant internal conversation about AI guardrails. Fix: ground the AI exclusively on specific policy documents and add "I'm not certain — let me connect you with our team" as a fallback for any question the AI cannot find in its grounding documents.
We're seeing Clay mentioned in virtually every B2B sales workflow this month. Its ability to combine multiple data sources (Apollo, LinkedIn, Clearbit, website enrichment) with AI-written personalization fields in a single spreadsheet-like interface is finding real product-market fit. The learning curve is real (expect 2–4 hours to get productive), but the payoff in lead quality is significant.
The new ability to ask Notion AI questions across your entire workspace — not just individual pages — is making it genuinely useful as a knowledge retrieval tool. Teams are asking "what did we decide about X in Q1?" and getting answers with page references. This is the first Notion AI feature that feels like a step change rather than a nice-to-have.
Several teams reported significant deliverability issues after deploying AI cold email tools (specifically Instantly and Smartlead setups) without completing proper inbox warmup. Sending from new domains to large lists before warmup is complete triggers spam filters — sometimes permanently damaging domain reputation. Minimum: 3–4 weeks of warmup before sending to any real leads. Many operators skip this step and pay for it with broken deliverability.
For any competitor, open Perplexity and ask: "Give me a comprehensive competitive analysis of [competitor name] including: their current pricing, key product features, recent product updates, customer complaints (from review sites), and positioning vs. [your company]. Cite all sources." Then follow up: "Based on this analysis, what are the 3 biggest opportunities for a company competing with them?" This takes 20 minutes and produces a competitor brief that used to take a junior analyst 2 days.
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