You've done the bootcamp. Your marketing team hasn't—or they did, and now they need the marketing-specific playbook.

AI for Marketing is a 6-week program built for marketers from day one. Not foundations repackaged. This covers what the general bootcamp doesn't: ICP validation in Clay with 1:1 personalizers, content systems that actually scale, AIO strategy for AI Overviews, and ad testing that learns faster with less waste.

You leave with deliverables, not just ideas: a Context Prompt Pack for your brand, a working content calendar process, an Analytics Toolbelt, and an Ad Testing Kit.

Interested? Reply with "marketing" and we'll send the details. Or visit kiingo.com to learn more.

• Anthropic published Claude's new constitution, a detailed framework explaining how the company wants its AI to behave and why—treating the document as "primarily for Claude" rather than just corporate policy theater, signaling a shift toward transparency in model training. They also partnered with Teach For All to bring Claude access and AI training to over 100,000 teachers across 63 countries serving 1.5 million students, positioning educators as co-architects rather than passive consumers—a refreshing change from the usual "here's AI, figure it out" approach to education. (Anthropic, Anthropic)

• OpenAI signed a three-year deal with ServiceNow embedding GPT-5.2 directly into enterprise workflows for IT support and customer service, with ServiceNow paying based on usage—a model that lets OpenAI chase enterprise revenue without building business software from scratch. They also rolled out age prediction for ChatGPT using behavioral signals like usage patterns and time of day to identify under-18 users and restrict sensitive content, because nothing says "we're serious about child safety" quite like an AI system that guesses your age based on when you ask about homework. OpenAI also detailed how it scaled PostgreSQL to handle 800 million ChatGPT users, managing millions of queries per second with one primary database and dozens of read replicas, demonstrating that sometimes boring infrastructure decisions matter more than fancy AI models. (CNBC, SiliconANGLE, CNBC, The Register, Microsoft, DEV Community)

• Google made two major AI moves: expanding Ask Gemini in Meet to Business Standard subscribers starting late January with mobile support in February, dropping the price to $14/user/month from $32—proof that even Google knows charging premium prices for AI meeting notes was getting out of hand. They also rolled out Personal Intelligence to AI Mode in Search, letting AI Pro and Ultra subscribers connect Gmail and Google Photos so search results can reference hotel bookings, travel photos, and purchase history for personalized recommendations. (WinBuzzer, Google)

IBM's new Enterprise 2030 study surveyed 2,000 C-suite executives and found ambitious momentum: 79% expect AI to significantly contribute to revenue by 2030—up from 40% today—and they're planning to increase AI investment by 150% between now and then. Even more telling, executives expect 62% of AI spend to shift toward innovation by 2030, compared to 47% focused on efficiency today. Companies are also planning to reinvest 70% of AI-powered productivity gains back into growth initiatives, with productivity expected to increase 42% by 2030. Meanwhile, 67% of executives believe AI will eliminate the resource and skills constraints holding their organizations back today.

Translation: Companies aren't just automating tasks—they're using AI productivity gains to fuel innovation and growth. The real opportunity isn't replacing people; it's freeing them to focus on higher-value work and capturing the compound benefits.

Sources: IBM Institute for Business Value "Enterprise 2030" Study (Jan 2026)

Role: You are an executive briefing specialist who works with C-suite leaders who need to digest dense reports and immediately understand what matters. Your job is to extract the most decision-relevant insights—not summarize everything equally, but focus on what changes the reader's understanding or action.

Task: Analyze the attached report and produce a concise executive summary organized into three sections:

1. Key Findings (what the data actually shows)
2. Implications for Decision-Making (what this means for our business/operations)
3. Recommended Actions (what we should do based on the findings)

Context: The reader has limited time and needs to make decisions based on this report. They care about accuracy—if you cite a statistic, trend, or conclusion, you must include the exact page number or section reference from the original document. If a claim can't be traced back to a specific part of the report, don't include it.

Format:
• Write in clear, direct prose—no bullet points unless quoting a list from the report
• Cite all claims using this format: (p. [X]) or (Section [X])
• If the report contains conflicting data points, call them out explicitly
• Keep the entire summary to 500–750 words
• End with a one-sentence bottom line that answers: "If I read nothing else, what do I need to know?"

Constraints:
• Do not editorialize or add opinions that aren't supported by the report
• If the report is vague or speculative on something important, say so ("The report does not provide enough detail to assess X")
• Do not oversimplify nuanced findings—if the report says "mixed results," your summary should reflect that
• Avoid generic business jargon like "streamline," "synergy," "leverage" unless the report itself uses those terms

Tip: This structure works especially well for industry reports, financial analyses, customer research, and compliance audits. If you're frequently summarizing similar types of documents, save this prompt and adjust the "Context" section to reflect your specific use case—for example, "The reader is evaluating vendor proposals" or "The reader is preparing for a board meeting."

Feedback Loop Closer
Paste these meeting notes and decisions from the past 30 days. Identify what was decided, who committed to what, whether those commitments were actually completed, what became "zombie tasks" (acknowledged but not done), and patterns in what gets dropped. Highlight accountability gaps where the same person repeatedly doesn't follow through. Return as: Completion rate per person + top 3 zombie tasks + one systemic fix to improve follow-through.

Time Leak Identifier
Review this calendar data and meeting descriptions from the last month. Find recurring meetings where attendance has dropped, one-on-ones that could be async updates, "just checking in" calls with no agenda, and time blocked for work that gets interrupted. Calculate hours lost to each category and estimate annual waste. Return as: Total recoverable hours weekly + 3 specific meetings to eliminate/shorten + suggested replacement async workflows.

Vendor Invoice Anomaly Hunter
Analyze these vendor invoices from the past 6 months. Identify unusual charges that differ from typical billing patterns, duplicate line items across invoices, prices that increased without notification, services billed but not delivered based on usage logs, and seasonal anomalies that seem off. Flag potential overcharges and calculate total recovery opportunity. Return as: Flagged invoices ranked by $ impact + specific anomaly type + draft inquiry email for each vendor.

Hi everyone — I'm Andy, and I'm really excited to officially be here at Kiingo.

A couple weeks ago, Schuyler shared a note about something I'd been experimenting with: asking AI to respond in the spirit of a question, not just the literal wording. What stood out to me wasn't just that she tried it — it was how quickly that kind of idea traveled once it was shared.

In just my first week here, I've learned more from teammates' small experiments, phrasing tweaks, and workflow habits than I had in months in some past roles. That's already reinforced something I strongly believe: AI fluency compounds fastest when people openly share what's working for them.

Because I'm especially interested in how language choices affect AI efficiency, I wanted to pass along a few phrases I've found consistently useful:

• "Zero over-promising. All claims and statistics must be backed by convincing real evidence."
This is incredibly effective for any client- or consumer-facing copy. It sharpens output fast and reduces cleanup later.

• "Use forward-thinking."
I like this more than "think creatively." It pushes the model toward practical, future-oriented problem solving instead of vague ideation.

• When iterating, explicitly tell the model: "We're getting closer" or "We're close."
If you add why it's closer ("because you did X"), the next response usually doubles down on X while incorporating new guidance.

None of these are magic prompts. They're small linguistic nudges. But stacked together — and shared across a team — they meaningfully change outcomes.

Looking forward to learning (and riffing on good ideas) from all of you.

Kiingo is an AI consultancy & advisory firm that helps companies unlock real business value with artificial intelligence. From hands-on training to strategic planning and tailored implementation, we partner with growth-minded organizations to build AI fluency, generate more value per team member, reduce inefficiencies, and create lasting competitive advantage. We believe in humans, amplified by AI. Whether you're exploring AI for the first time or ready to scale your efforts, we'll meet you where you are and guide you forward—with clarity, confidence, and results.

Reply with one task your team does too often (and your industry). We'll send a working prompt you can test this week. Want to talk more? Let's schedule a time.