You’re Invited: AI for Executives, Building a Practical 2026 AI Roadmap
If you're heading into 2026 thinking "we need to do something with AI" but don't have a clear plan (or a data science team), this free webinar is for you.
AI for Executives: Building a Practical 2026 AI Roadmap 📅 Wednesday, January 14th | 8am PT / 10am CT / 11am ET
In 60 minutes, we'll give you a simple, beginner-friendly way to sequence your first AI initiatives—no technical background required.
Leaders we've trained report saving 5–15 hours per person per week once they move from experiments to real workflows. What would your team do with that time back?
What you'll learn:
→ How to turn vague AI interest into a clear 90-day plan
→ Which initiatives to do first (and what to explicitly save for later)
→ Real examples of low-risk, high-ROI AI workflows you can implement immediately
This Week’s AI Rundown:
ChatGPT got a dedicated "Health" space. You can (optionally) connect medical records via b.well (U.S.-only at launch) and wellness apps like Apple Health, MyFitnessPal, and Peloton so responses are grounded in your own data. OpenAI says Health operates as a separate space and Health chats/files/memories aren't used to train foundation models—230 million+ users already ask health questions weekly. (OpenAI, CNBC)
Microsoft wants you to buy stuff inside the chatbot. "Copilot Checkout" is rolling out in the U.S. on Copilot.com, letting shoppers complete purchases without being redirected, with partner activation across PayPal, Shopify, Stripe, and Etsy. Translation: assistants are trying to become the place transactions happen, not just where research happens. (Microsoft, PayPal)
Elon Musk's week in one sentence: xAI raised $20B… even amid controversy. xAI closed an upsized $20B Series E to scale models and compute. Days later, Grok restricted image generation to paid users when tagging @Grok on X after backlash over people using it to create sexualized images of women and children—European officials called the content "not spicy, illegal." (TechCrunch, Bloomberg, Euronews)
Anthropic is preparing a $10B raise at a $350B valuation. The term sheet was signed January 7, with lead investors Coatue Management and GIC (Singapore's sovereign wealth fund). Translation: investor appetite for "more compute, bigger models" is still very real—that's nearly double their September 2025 valuation. (CNBC, Reuters)
Meta's Manus acquisition is now a geopolitics story. China's Ministry of Commerce is probing Meta's $2B+ purchase of AI agent startup Manus over whether transferring ~80 Beijing staff and tech to Singapore before the sale required an export license under Chinese tech-export rules. Review typically concludes within 90 days. (AP News, Bloomberg)
BNY Mellon is running one of the biggest concrete enterprise AI programs out there. OpenAI and BNY say BNY's internal AI platform "Eliza" supports 125+ live use cases, with 20,000 employees actively building agents under heavy governance. Their legal department saw 75% reduction in contract review time. Translation: this is what "AI adoption" looks like when it's operational, not experimental. (OpenAI, BNY)
Gemini is coming to Google TV (and it's more than better search). Google says Gemini on Google TV will add narrated "deep dives," Photos search + remixing, Nano Banana/Veo creative tools, and natural-language settings fixes ("screen too dim," "dialogue is lost"), starting on select TCL devices with others following. (Google Blog, TechCrunch)
Claude Code is pushing AI pair-programming into the toolchain. Anthropic's docs now describe running parallel Claude Code sessions using Git worktrees, plus team-shared repo "memory" via CLAUDE.md, and reusable custom slash commands stored in .claude/commands/. Translation: less "chat tab," more "part of how dev teams ship." (Anthropic Docs)
Actual hope file: Stanford researchers built "SleepFM," an AI model trained on 585,000+ hours of sleep-study signals that can predict risk across 130 health conditions from a single night of data, with particularly strong accuracy for Alzheimer's (0.91) and Parkinson's (0.89). It's early, but it's a real "AI helps catch problems sooner" lane. (Nature Medicine, Stanford Medicine)
Practical: Calendar Export → Executive Time Audit + Priority Realignment in 20 minutes
You say growth is your top priority. Your calendar says you spent 11 hours last month on it—and 23 hours in status meetings that could've been emails. The gap between stated priorities and actual time allocation is where strategy goes to die. AI can show you exactly where your hours went and what that reveals about what you're really prioritizing.
Try this with: Calendar export from the past 90 days (Google Calendar, Outlook—export as CSV or copy/paste). Include your top 3-5 stated priorities for context.
Role: "Act as an executive coach analyzing time allocation for a [title] at a [company size] [industry] company who suspects their calendar doesn't reflect their actual priorities."
Task: "Analyze this calendar data against my stated priorities to show where my time actually goes, identify misalignments, and recommend specific changes."
Context: "My stated priorities for this period were: [1. Growth/sales, 2. Product development, 3. Team building—or your actual priorities]. I have [X] direct reports. I feel like I'm constantly busy but not moving the needle on what matters."
Format: Deliver:
TIME ALLOCATION REALITY | Total hours analyzed: [X]; Hours in meetings: [X]; Hours with direct reports: [X]; Hours on stated Priority 1/2/3: [X/X/X]; Largest single time category: [X]
PRIORITY-CALENDAR GAP | Table: Stated Priority | Hours Allocated | % of Total Time | Gap Assessment (Aligned/Underinvested/Lip Service)
TIME TRAPS IDENTIFIED | Recurring meetings that don't serve priorities; Meetings where you're optional but attending anyway; Time blocks with no clear output; Patterns suggesting reactive vs. proactive work
CALENDAR REDESIGN | Meetings to decline or delegate: [list with hours reclaimed]; Time blocks to protect for Priority 1: [specific slots]; One scheduling rule to implement immediately
Constraints: Be brutally honest about the gap—I asked for this; Distinguish between "important to someone else" and "important to my priorities"; Flag if priorities themselves seem misaligned with role; Don't let me rationalize the status quo.
Reality Check: Career Development Champions Are 42% More Likely to Lead on AI
LinkedIn's 2025 Workplace Learning Report surveyed 937 L&D and HR professionals and found a stark divide: only 36% of organizations qualify as "career development champions"—companies with robust programs that actually yield business results. The rest either have limited adoption or are just getting started.
The payoff for getting it right? Career development champions are 42% more likely to be frontrunners in generative AI adoption. Meanwhile, 49% of executives report their employees don't have the skills to execute business strategy—and manager support is declining. Only 15% of employees say their manager helped them build a career plan in the past six months, down 5 percentage points from 2024.
Translation: The companies winning at AI aren't the ones with better tools. They're the ones who invested in career development before AI became urgent. The time to invest in your people isn't after the skills gap becomes a crisis—it's now.
Ready-to-Use Micro-Prompts
AI Confidence Calibrator
You just received AI-generated analysis for [decision]. Before acting, audit your response: – Confidence level the AI induced (1-10) – What a skeptical peer would challenge – Alternative interpretations AI didn't surface – Verification steps you're tempted to skip – Emotional pull toward the recommendation. Return as: calibrated confidence score + 3 questions to answer before deciding + one assumption to verify independently.
Workflow AI Fit Assessment
Describe [workflow without AI]. Diagnose fit for automation: – Bottleneck actually slowing output (is it time, judgment, or coordination?) – Data inputs already structured vs. requiring cleanup – Decision points needing consistency vs. creativity – Tasks your best people shouldn't be doing – Integration complexity with current tools. Return as: AI-fit score (1-10) + highest-value insertion point + one reason NOT to automate yet.
AI Tool Learning Sprint
You just got access to [new AI tool]. Design your evaluation: – 3 tasks from your actual work to test (easy, medium, hard) – Edge cases to probe where it breaks – Learning curve vs. productivity payoff – Integration friction with your current workflow – Where your existing method stays better. Return as: 5-day testing plan + go/no-go decision criteria + one task to try first.
Note from Schuyler (Chief Marketing Officer @ Kiingo AI)
Someone we just hired taught me something this week. Andy's been asking AI to think about the "spirit" of a question before answering—forcing it to interpret intent, not just parse words. It's a small shift that changes results in interesting ways.
Why does it work? Asking about "spirit" makes the AI go meta—interpret what you actually need rather than respond to what you literally said. Similar to having it translate something into its own language before working on it.
I'm still testing. But here's what struck me: I wouldn't have found this trick in documentation. I found it because a new teammate mentioned what he'd been experimenting with.
The fastest path to AI fluency is more people sharing what they're discovering.
Want to talk more? Feel free to reach out to me on LinkedIn.
Kiingo AI
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.
Quick CTA: Want to talk more? Let’s schedule a time. Book a short discovery call and we’ll map the fastest path to value.
