Nathan Weill here.

I'm the CEO @ Flow Digital, where we help companies unleash their full potential by strategically automating every inch of their workflows. Each week, I share hot AI and automation tips to help you move your business into the future successfully.

Your team is spending hours every week hunting for information that already exists somewhere inside your organization. Perplexity Enterprise fixes that by combining real-time web search with your internal documents in one place, returning cited answers in seconds.

Key Features:

Teams like Databricks and Universal McCann use it to speed up technical knowledge access and market research. At $40/user/month for Enterprise Pro, the math is straightforward: two hours saved per team member per month covers the cost.

Explore Perplexity Enterprise or request a demo directly from their site.

Your AI agent just updated three CRM properties, qualified a lead, and closed a support ticket. But if someone asks what it did and why, most teams have no answer. That's the primary reason AI agent adoption stalls.

HubSpot's January 27, 2026 release added Audit Cards to Breeze. Every time the Customer Agent identifies a customer, qualifies a lead, or modifies a CRM property, an audit card displays exactly which actions the agent took — including previous field values and which knowledge base articles it referenced to make that decision.

The move most teams miss: Use audit cards as a training tool, not just a compliance record. Set up a weekly review cadence for the first 30 days. Focus on conversations where the agent qualified leads differently than your team would have. Adjust the agent's instructions in Breeze Studio based on those patterns. Most teams reach acceptable accuracy within two to three rounds of refinement.

Business Impact:

Audit Cards = trust + speed in one CRM automation.

Every time your AI agent starts a new task, it risks starting from scratch. No record of the vendor you ruled out. No trace of the tone standards your team agreed on. No log of the edge case ruling from last quarter.

The result: inconsistent outputs, repeated prompting, and workflows that stall when agents hit a question they should already know the answer to.

The Fix: A Shared Decision File

A decision file is a simple, shared document (in Notion, Confluence, or Google Docs) that logs every question your agents and team have already resolved. Context engineering research from GitHub shows that agents perform significantly better when given structured reference files they can consult before acting GitHub: pulling in settled logic instead of guessing or re-asking.

What belongs in a decision file:

Why it holds up — even as agent memory improves:

Better memory helps agents retain context within a session. But a decision file solves a different problem. Agents that reference records of past decisions can avoid repeating failed approaches and produce more consistent behavior over time Ema — and more importantly, those records stay human-readable, auditable, and transferable when you switch tools, upgrade models, or bring new agents into a workflow.

Decision files are governance, not just memory.

OpenAI began testing ads in ChatGPT on February 9, 2026, rolling out sponsored placements to U.S. users on the Free and Go tiers. The advertising world is paying close attention — and so should you.

The structure is unlike anything that came before it.

The beta is invite-only. Per AdWeek, OpenAI asked selected advertisers to commit a minimum of $200,000 upfront. Early participants include Adobe, Target, Expedia, and WPP-represented brands. The pricing is set at a $60 CPM — roughly three times Meta's average rates and on par with Netflix's ad tier at launch.

This isn't keyword bidding. It's conversational context.

Google Ads monetizes what users type. ChatGPT ads monetize what users mean. Advertisers submit context words — topics their audience is likely exploring — and the algorithm surfaces their brand as a natural conclusion to the conversation. ChatGPT queries average around 60 words, compared to Google's typical 3.4-word search. That intent depth is a meaningful difference.

What marketers need to understand right now.

The biggest challenge: OpenAI currently provides only impressions and click data. There is no conversion tracking, no attribution pixel, and no self-serve dashboard. Brands are competing for algorithmic selection without visibility into how the algorithm makes decisions. As Adthena's analysis of the beta notes, this "black box" model puts a premium on brands that understand conversational intent — not just keyword lists.

The practical framework for your marketing team.

Even if you're not buying ChatGPT ads in 2026, this shift has real implications for how you show up in AI-generated responses:

Paid Plus, Pro, Business, and Enterprise ChatGPT users see no ads — so the audience currently exposed skews toward free-tier, high-volume users. That matters for targeting strategy.

The bigger shift isn't the ad format. It's the move from user-driven search to AI-mediated recommendation. The brands that understand conversational intent — not just search terms — will have a meaningful early advantage.

- Seth Godin

As AI-mediated discovery replaces keyword search, the brands that win will be defined by how specifically and clearly they frame their story in conversational context.

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