A free, practical session showing sales teams how to put AI to work in their daily routine, no technical background required. Hosted by Josh Sullivan, COO of Kiingo AI. We’ll cover how to:

• Clean up your pipeline without the busywork: flag stale deals and keep your CRM accurate automatically
• Walk into every meeting prepared, with an AI agent that scans your week ahead and researches who you’re meeting
• Follow up faster with personalized emails and instant call summaries
• Close smarter by spotting buying signals and prioritizing the deals most likely to move

Tuesday, June 16 • 10:00 AM PT / 12:00 PM CT / 1:00 PM ET
It’s just a week out, but there’s still time — save your spot →

• Apple rebuilt Siri on a custom Google Gemini model and, for the first time, let users pick their assistant — choosing Claude, ChatGPT, or Gemini to power Apple Intelligence across iPhone, iPad, and Mac. It’s Claude’s first native spot on Apple devices, and a clear sign that even Apple is buying frontier AI rather than building it. (CNBC, MacRumors)

• Anthropic launched Claude Fable 5, a new flagship for general use, plus a restricted Mythos 5 for vetted security and research partners. Stripe says Fable 5 compressed a 50-million-line migration from two months to a single day (a vendor claim), and it runs about 3x better than Opus 4.8 on long-context memory tasks. Pricing lands at the premium end of frontier models — $10 per million input tokens and $50 per million output. (Anthropic, TechCrunch)

• A bipartisan pair of House members released a 269-page draft “Great American AI Act” that would freeze state AI laws for three years and require large AI developers to publish safety frameworks. For now it’s only a discussion draft — nothing to act on, but a signal of where federal AI rules may be heading for businesses. (Roll Call, FedScoop)

• The EU AI Act becomes fully applicable on August 2, bringing high-risk-system obligations and new transparency rules (disclosing AI interactions, labeling synthetic content) with penalties up to 7% of global revenue. Colorado, by contrast, narrowed its own AI law and pushed the effective date to January 1, 2027 — so the EU deadline is the real near-term one if you use AI for hiring, lending, or other consequential decisions touching EU residents. (European Commission, Hunton)

• The race to sell AI implementation services — the layer above the models — accelerated. Anthropic launched a Services Track and Partner Hub to connect companies with vetted Claude implementation partners (more than 40,000 firms have applied), and IBM and Google Cloud announced a joint consulting practice to move enterprise AI projects from pilot into production. The shared admission: the hard part is integration, and that’s now what they’re selling. (Anthropic, IBM)

• OpenAI handed enterprise IT more control over ChatGPT and pushed Codex beyond coding. A new “Lockdown Mode” lets admins disable web access, Agent Mode, and file downloads to reduce the risk of AI being tricked into leaking company data, while Codex added role-specific bundles for sales, finance, and design teams — non-developers are now about 20% of Codex users and growing 3x faster than developers (OpenAI’s figures). (TechCrunch, VentureBeat)

• Ramp raised $750 million at a $44 billion valuation, betting that AI agents belong on the company card. The spend-management platform, now past $1 billion in annualized revenue with more than 70,000 customers, is pushing AI tools for procurement, expense review, and automated accounting close. As TechCrunch put it, investors are hungry for fintechs with an AI story. (TechCrunch, American Banker)

• Bots now generate more web traffic than humans for the first time, Cloudflare reported — 57.4% automated versus 42.6% human, driven by AI agents that may hit thousands of sites for a single task. Cloudflare’s CEO had expected the crossover no sooner than 2027. For anyone running a website, it scrambles analytics, ad economics, and bot-management assumptions. (Tom’s Hardware, NBC News)

• ChatGPT now builds and updates its memory of you automatically after conversations end, keeping preferences, projects, and deadlines without being asked and dropping stale details, rolling out to US Plus and Pro users first. If your team feeds client information into ChatGPT, review memory settings before the EU’s transparency rules take effect in August. (OpenAI, TechTimes)

• Small and midsize businesses using AI are far more likely to report gains than setbacks, Intuit’s 2026 AI Impact Report found. Drawing on more than 34,000 SMB owners plus anonymized data from 5.3 million QuickBooks businesses across four countries (developed with University of Chicago economists), it found 43% of US businesses say AI has increased their revenue against just 2% who say it decreased, and 78% say AI has improved productivity, up from 46% in July 2024. Four times as many say AI increased hiring as say it reduced it. (Intuit, May 12, 2026)

• Most employees say AI is already making them better at their jobs. In Gallup’s survey of 23,717 employed US adults (fielded February 4–19, 2026), 65% say AI has improved their productivity and efficiency, 16% call the effect extremely positive, and fewer than one in 10 report any negative impact. Only about one in 10 yet say AI has transformed how work gets done in their organization — which is the opportunity: the day-to-day gains are real and widespread, while the bigger workflow redesign is still ahead. (Gallup, April 13, 2026)

• Mid-market companies are moving AI from experiment to operating priority. In Deloitte’s survey of 100 US private-company leaders at firms with $100M to $1B+ in revenue (fielded March 18–24, 2026), 52% now rank increasing AI use across the organization as a top-three priority, up from 22% a year earlier, and 63% say they’re actively investing in digital transformation including AI, versus 33% still in limited or pilot mode. The shift from pilots to production is exactly where the value starts to show up. (Deloitte, April 28, 2026)

Our June 16 webinar covers spotting buying signals and prioritizing the deals most likely to move. Here’s the piece it doesn’t cover: protecting the one deal you can’t afford to lose. We all build the case for why a deal will close; almost nobody builds the case for why it won’t. So flip it — picture the deal already gone, and have the AI work backward to why. Give it the situation and it names the most likely reasons it slipped away, what each was really about, and the one move this week that keeps the deal on track.

The Lost-Deal Drill
Assume a deal I was counting on has just fallen through, 60 days from today. Your job is to work backward through why, then hand me a recovery plan.

Here’s the deal:
- Prospect: [role, company, industry, size]
- What I’m selling: [offer, and price if it’s on the table]
- Where we are now: [stage — after the demo, proposal out, gone quiet, etc.]
- The outcome they care about: [the result they’re measured on]
- Optional: [paste my last email, notes, or the proposal]

Step 1 — List the 5 most likely reasons this deal was lost, ranked, and for each name what was really going on underneath (no budget, no internal champion, lost to “do nothing,” a competitor, a risk I never surfaced).

Step 2 — For each reason, give me one specific action I can take this week to prevent it, plus the early signal that would tell me it’s already happening.

Return it as a table: Likely Reason Lost | What’s Really Going On | Move This Week | Early Warning Signal.

Run it the moment a deal starts to matter more than the others, while you still have time to act on what it surfaces. The first time you catch a deal-killer before it shows up, this earns a permanent place in your pre-call routine.

Here’s a test I’ve started running before I call a big project done.

Ask Claude to draft the message to your team or manager explaining what’s in the deliverable. Then read it. If you can’t explain every part of that message yourself, you’ve still got work to do before you ship. The parts you can’t explain are usually the gaps, the places where your model did or knew something you never fully accounted for. So ask it questions about those parts until you can explain them too.

I think this is where AI-assisted work is heading. As we go, we’ll all advance to a next level of AI integration. The skill that defines that level is learning to manage the thread, or the threads, with your models: knowing what they did, why, and being able to stand behind all of it.

This week, 43% of small businesses said AI lifted their revenue. The rest are still in the meeting about the meeting.

We help mid-market companies get into the first group — finding the two or three workflows where AI actually moves revenue, and building them into how the team works.

Want to see what that could look like for your team? Get in touch.