Shail Khiyara

SWARM Engineering

Chief Executive Officer

PeakSpan Capital

Operating Advisor

Workato

Advisory Board Member

VOCAL Council

www.thevocalcouncil.com 
VOCAL is a peer powered global customer think tank of decision makers and users of Automation technologies including RPA, Intelligent Automation, Machine Learning, Natural Language Processing, Process Mining, Intelligent Document Processing, Artificial Intelligence and more.

Founder & CEO

Skan.ai

Plutoshift AI

AI for the Industrial World

Plutoshift AI is on a mission to bring data intelligence and AI to the world’s physical infrastructure. Think electronic medical record for industrial systems. Plutoshift AI helps global leaders realize the power of their operational data to increase efficiency, reduce costs, and build more resilient and sustainable businesses.

Founder, Chief Executive Officer

Robocorp

Turbotic

Automation optimization is on the rise and is the umbrella term that refers to a range of tools for monitoring, managing and improving automation performance.

Turbotic provides a single pane of glass to manage your entire automation lifecycle.  We are pioneers of a multi-vendor, multi-technology platform that reduces your operational costs and significantly improves your license infrastructure utilization.  
 
Worldwide brands today are turning to Turbotic to drive automation optimization.

President & COO

Turbotic is a pioneer and leader in Automation Optimization and delivers a fully integrated cloud platform built to support enterprises scaling, monitoring, and managing their Automation efforts. Turbotic integrates with the leading best-of-breed vendors such as UiPath, Microsoft Power Automate, Automation Anywhere & Blue Prism. The world's leading brands use Turbotic across the end-to-end Intelligent Automation lifecycle, from identifying opportunities to delivery, automated operations, and benefits tracking.

Member - Board of Directors

AV8 Ventures

AV8 Ventures is a Palo Alto and London based venture capital firm.

UiPath

Driving Customer Experience for one of the World's fastest growing robotic process automation (RPA) and AI companies that also has an amazing culture of innovation, integrity and collaboration.

Customer Experience Officer (CXO)

I bring over two decades of leadership across enterprise software, SaaS, and industrial transformation. 

My journey started in civil and structural engineering, leading global infrastructure projects across Energy, Water, and Industrial sectors. Since then, I’ve led enterprise SaaS companies—both public and private—as CEO and board advisor, focused on driving systemic transformation through AI and automation.

Today, I serve as CEO of Swarm Engineering, where we build agentic AI systems that enable enterprises to automate complex decisions and unlock new levels of efficiency and resilience. AI isn’t a bolt-on. It’s the logic layer shaping how organizations operate in real time.

This isn’t gradual progress—it’s a leap. Technologies like federated learning and constraint-based reasoning are enabling smarter, decentralized decision systems across the supply chain and beyond.

My second book, Agentic Artificial Intelligence, became an Amazon #1 Best Seller at launch, exploring how AI is shifting from passive tools to autonomous collaborators.  It builds on my first book, Intelligent Automation, which remains widely used in both industry and academia.

At my core, I think like a strategic operator—fusing systems thinking with executional clarity. Whether I’m writing, building, or advising, my focus is simple: compress complexity, elevate outcomes, and challenge conventional limits.

I founded VOCAL, a global initiative of 90+ brands collaborating on the future of intelligent automation and AI—because in a world saturated with technology, the real advantage comes from how we think about it.

🔷🔹🔹 𝗬𝗼𝘂𝗿 𝗕𝗿𝗮𝗶𝗻 𝗼𝗻 𝗖𝗵𝗮𝘁𝗚𝗣𝗧: 𝗪𝗵𝗮𝘁 𝗠𝗜𝗧 𝗝𝘂𝘀𝘁 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗪𝗶𝗹𝗹 𝗖𝗵𝗮𝗻𝗴𝗲 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗨𝘀𝗲 𝗔𝗜 

Massachusetts Institute of Technology just scanned what happens to your brain when you use #ChatGPT.

And the findings are both fascinating and a little frightening. 
<𝘢𝘤𝘤𝘦𝘴𝘴 𝘵𝘩𝘦 𝘭𝘪𝘯𝘬 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴>

When participants wrote essays with no tools, their brains lit up.

#EEG scans showed strong alpha waves (creativity) and theta waves (working memory).

They owned their words. They remembered them. Their writing had voice, texture, soul.

When they used Google, their engagement dipped, but the brain still worked - searching, filtering, deciding.

When they wrote with ChatGPT, neural activity dropped to its weakest.

🚩The essays were homogenous.
🚩The writers forgot what they wrote.
🚩Their sense of ownership faded.
🚩Over repeated sessions, they stopped wrestling with ideas - they simply copied.

And here’s the twist:

❗️When brain-only users switched to ChatGPT, their brains spiked with novelty.

❗️When ChatGPT users switched back to solo writing, their brains stayed dim.

This study is not saying don’t use AI.

It’s saying this: HOW you use it matters.

🧠 If AI is your first step, your thinking atrophies.

🧠 If AI is your last step - refining what you’ve already struggled with - it amplifies you.

This is the first time we’ve seen brain scans proving what many have felt:
#AI can either sharpen our cognition or dull it.

The choice is in the sequence.
The choice is in the design.
The choice is in you.

💬 Do you start with AI or finish with it?

That one decision might determine whether you’re outsourcing your brain or expanding it.

#AI #Leadership #Learning #Neuroscience #FutureOfWork #ChatGPT

The water is rising fast.

AI labs have a “dominate the world” mindset. They’re not just selling APIs—they’re climbing up the stack, absorbing whatever works. If you’re building on their rails without a #moat, you can get washed away.

The messier the vertical, the more nuanced the workflow, the stronger the moat. Unsexy = Defensible.

Pick domains where complexity is high, workflows are tangled, and every action throws off process exhaust—data you can capture, feed back, and use to train your own models. That’s where RL thrives. That’s where your product gets smarter with every click, every edge case.

Because in this agentic era, it’s not the model that wins.  It’s the learning loop around it.

Own that loop, and every breakthrough from the labs becomes a tailwind, not a threat.

Question for builders: Are you clinging to the surface or building where the water makes you stronger?

Excellent insights in thie #mustread by Jaya Gupta & Ashu Garg at Foundation Capital
>>> When model providers eat everything: A survival guide for Service-as-Software startups
https://lnkd.in/g-FHuRXE

#AI #AgenticAI #Strategy #Startups #ReinforcementLearning

𝗔𝗜 𝗳𝗮𝗰𝗲𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗰𝗵𝗼𝗶𝗰𝗲 𝗮𝘀 𝗮𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲: 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗼𝗿 𝗿𝗲𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲.

California’s olive oil producers are already showing us the way.

They’ve shifted from yield-at-all-costs to regenerative systems: cover crops, livestock integration, composting, and independent certification. Not just for marketing, but to restore the system itself - healthier soils, stronger trees, more resilient yields.

At California Olive Ranch alone, 𝟰,𝟲𝟬𝟬 𝗮𝗰𝗿𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝘂𝗻𝗱𝗲𝗿 𝗿𝗲𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 - proof that resilience can be measured and scaled. Healthy soils can hold more water, store more carbon, and buffer against drought.

The lesson is bigger than farming.

AI today risks the same extraction model outputs without feedback, efficiency without resilience. It dazzles in the demo, then drifts in production.

Regeneration points to a different path:

🫒𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀: Regenerative soils recycle nutrients; regenerative AI recycles outcomes into continuous learning.

🫒𝗧𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: Farmers prove their practices with third-party standards; AI must prove its decisions with lineage and audit trails.

🫒𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗮𝘀 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Healthy soils buffer against drought; robust AI buffers against drift, bias, and failure.

Here’s the truth: 
 If we build AI like extraction, we’ll scale fragility. If we build it like regeneration, we’ll scale resilience.

𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘀𝗼𝗶𝗹 𝗶𝘀 𝗺𝗲𝗺𝗼𝗿𝘆. It remembers every season, every crop. And AI needs the same memory of context, rationale, and consequence, not just outputs.

California’s olive groves aren’t just producing oil. They’re producing a blueprint for how to design intelligence that endures.

👉 Leaders in AI, supply chains, and beyond: the farmers have chosen regeneration. 𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀, 𝘄𝗶𝗹𝗹 𝘄𝗲?

#RegenerativeAgriculture #AI #AgTech #Sustaniability #SystemsThinking SWARM Engineering

𝗜𝗳 𝗗𝗮𝘁𝗮 𝗪𝗮𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗢𝗶𝗹, 𝗔𝗜 𝗝𝘂𝘀𝘁 𝗧𝘂𝗿𝗻𝗲𝗱 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿𝘀 𝗜𝗻𝘁𝗼 𝘁𝗵𝗲 𝗢𝗶𝗹 𝗙𝗶𝗲𝗹𝗱𝘀. 
𝗪𝗵𝗼 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝘁𝗵𝗲 𝗪𝗲𝗹𝗹𝘀?

What was once an IT challenge is now a $7 trillion reordering of energy grids, real estate, and supply chains.

By 2030:
➤ Data center power demand triples to 220–300 GW.
➤ AI drives 70%+ of that growth, with racks hitting 120 kW densities.
➤ $7 trillion must be invested, $5.2T for AI-ready facilities alone.
➤ Traditional hubs hit limits. New campuses rise in Wisconsin, Wyoming, and nuclear-adjacent sites.
➤ Hyperscalers secure 24/7 clean PPAs, reshaping energy with SMRs, geothermal, and long-duration storage.

💧 The hidden crisis: water !

In Texas, AI data centers used hundreds of millions of gallons during drought. By 2030, consumption 𝗰𝗼𝘂𝗹𝗱 𝗿𝗲𝗮𝗰𝗵 𝟰𝟬𝟬𝗕 𝗴𝗮𝗹𝗹𝗼𝗻𝘀, 𝗮𝗽𝗽𝗿𝗼𝘅 𝟳% 𝗼𝗳 𝘁𝗵𝗲 𝘀𝘁𝗮𝘁𝗲’𝘀 𝘄𝗮𝘁𝗲𝗿. These facilities often appear in water-stressed regions without public engagement.

This is no longer just a tech race. It’s a race for 𝗹𝗮𝗻𝗱, 𝗽𝗼𝘄𝗲𝗿, 𝘄𝗮𝘁𝗲𝗿, 𝗰𝗼𝗼𝗹𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗰𝗵𝗶𝗽𝘀—𝗮𝗻𝗱 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲𝗹𝘆, 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗜 𝗲𝗿𝗮.

The stakes:
➤ 𝗢𝘃𝗲𝗿𝗶𝗻𝘃𝗲𝘀𝘁 → stranded assets.
➤ 𝗨𝗻𝗱𝗲𝗿𝗶𝗻𝘃𝗲𝘀𝘁 → irrelevance.
➤ 𝗜𝗴𝗻𝗼𝗿𝗲 𝘀𝗼𝗰𝗶𝗮𝗹 & 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗿𝗶𝘀𝗸𝘀 → backlash and operational roadblocks.
➤ 𝗠𝗼𝘃𝗲 𝘀𝗺𝗮𝗿𝘁 → own the edge of AI’s future.

Winners will:
Secure. Innovate. Collaborate.

The biggest bottlenecks are no longer just hardware, 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀.

𝗪𝗵𝗲𝗿𝗲 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱? 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘀𝘁𝗮𝗴𝗲 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆? 𝗛𝗼𝘄 𝘁𝗼 𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗲 𝘀𝗰𝗮𝗿𝗰𝗲 𝗽𝗼𝘄𝗲𝗿 𝗮𝗻𝗱 𝘄𝗮𝘁𝗲𝗿 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗱𝗲𝗹𝗮𝘆 𝗼𝗿 𝗯𝗮𝗰𝗸𝗹𝗮𝘀𝗵?

SWARM’s Engine platform helps leaders:

➣ Optimize site selection with real-time power, water, and grid data.
➣ Model trade-offs (energy, cooling, logistics) across scenarios before breaking ground.
➣ De-risk billion-dollar bets by stress-testing plans under uncertainty.

Help leaders 𝗰𝘂𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗮𝗻𝗱 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆.

Because the AI race will not wait for those who hesitate. In this $7 trillion race, control won’t come from owning the most servers or the cheapest land.
It will come from 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗮𝗹𝗶𝗴𝗻 𝗽𝗼𝘄𝗲𝗿, 𝘄𝗮𝘁𝗲𝗿, 𝗰𝗮𝗽𝗶𝘁𝗮𝗹, 𝗮𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.

Those who orchestrate this complexity will own the AI future.

💬 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝘆𝗼𝘂 𝘀𝗲𝗲 - 𝗽𝗼𝘄𝗲𝗿, 𝘄𝗮𝘁𝗲𝗿, 𝗰𝗮𝗽𝗶𝘁𝗮𝗹, 𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘀𝗽𝗲𝗲𝗱?

#AI #DataCenters #Infrastructure #EnergyTransition #WaterCrisis #Investing #Leadership #DecisionIntelligence SWARM Engineering S2G Investments

When I think about GPT-5, one phrase keeps coming to mind: 𝗪𝗶𝗹𝗱𝗹𝘆 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲.

This model doesn’t just wait for instructions. It initiates, reasons, picks the right tools. It fills in the blanks often producing what you didn’t ask for, but maybe should have. That’s not assistance anymore. That’s agency.

To me, this marks a clear shift from prompting to partnering. It feels like we’re no longer just “using AI.” We’re stepping into something more collaborative, more ambient, alive. GPT-5 isn’t simply smarter, it represents a new interface for cognition itself. And the implications are enormous.

The marginal cost of intelligence is collapsing faster than most realize. We're entering an era where users don’t even need to know exactly what to ask. The system figures it out, fills in the gaps. It suggests next steps and builds. 

And with that, something else shifts - our sense of control. Agency moves from the user to the system. That’s interesting. But also unsettling. Because if AI starts proposing paths forward on its own, we need to ask:  𝘞𝘩𝘰𝘴𝘦 𝘥𝘦𝘧𝘢𝘶𝘭𝘵𝘴 𝘪𝘴 𝘪𝘵 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘧𝘳𝘰𝘮? 𝘞𝘩𝘰𝘴𝘦 𝘷𝘢𝘭𝘶𝘦𝘴 𝘢𝘳𝘦 𝘦𝘮𝘣𝘦𝘥𝘥𝘦𝘥 𝘪𝘯 𝘵𝘩𝘰𝘴𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴?

OpenAI now automatically routes GPT-5 queries to the most appropriate model. Users don’t need to choose. The Thinking variant reasons deeply, handles long chains of logic, and executes multi-step tasks autonomously. Sam Altman described the leap as similar to Apple’s transition to Retina displays but less pixelated, more precise. The experience is smoother, sharper, and for many - unsettlingly effortless.

This isn’t just about performance or productivity anymore. It’s about trust, alignment, and governance in a world where cognition is shared.

I believe we’re in 𝗪𝗶𝗹𝗱𝗹𝘆 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 mode now and just beginning to see what that really means.

What shifts do you see coming?

#GPT5 #AI #AgenticAI #Innovation #Cognition #MachineReasoning

𝗧𝗵𝗲 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗣𝗼𝘄𝗲𝗿 𝗧𝗵𝗮𝘁 𝗛𝗼𝗹𝗱𝘀 𝗡𝗮𝘁𝗶𝗼𝗻𝘀, 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗙𝘂𝘁𝘂𝗿𝗲𝘀 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿
Denmark wrote it into law.

𝘞𝘦𝘭𝘤𝘰𝘮𝘦 𝘵𝘰 𝘵𝘩𝘪𝘴 𝘸𝘦𝘦𝘬’𝘴 #𝘚𝘶𝘯𝘥𝘢𝘺𝘚𝘱𝘢𝘳𝘬 - 𝘢 𝘴𝘵𝘰𝘳𝘺 𝘵𝘰 𝘪𝘨𝘯𝘪𝘵𝘦 𝘺𝘰𝘶𝘳 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘸𝘦𝘦𝘬 𝘢𝘩𝘦𝘢𝘥.

In Denmark, children don’t just learn math, science, and reading. 
𝗧𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻 𝗲𝗺𝗽𝗮𝘁𝗵𝘆.

Since 1993, every school has set aside an hour each week for 𝘒𝘭𝘢𝘴𝘴𝘦𝘯𝘴 𝘵𝘪𝘥 - literally “class time.” But it isn’t about textbooks or exams. 

It’s about listening to one another. Resolving conflict. Supporting classmates. Even 𝘴𝘩𝘢𝘳𝘪𝘯𝘨 𝘩𝘺𝘨𝘨𝘦, those small rituals of comfort and connection that define Danish culture.

The purpose is simple, but profound: 𝗲𝗺𝗽𝗮𝘁𝗵𝘆 𝗶𝘀 𝘁𝗮𝘂𝗴𝗵𝘁 𝗻𝗼𝘁 𝗮𝘀 𝗮𝗻 𝗼𝗰𝗰𝗮𝘀𝗶𝗼𝗻𝗮𝗹 𝗹𝗲𝘀𝘀𝗼𝗻, 𝗯𝘂𝘁 𝗮𝘀 𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗹𝗶𝗳𝗲. And perhaps that’s one reason Denmark consistently ranks among the happiest nations in the world.

This reminds me of two voices that shaped my thinking:

𝗦𝘂𝗴𝗮𝘁𝗮 𝗠𝗶𝘁𝗿𝗮, through his “Hole in the Wall” experiment, showed that when children are given curiosity and collaboration, they can teach themselves almost anything. Education isn’t delivered top-down; it’s unlocked through trust and connection.

𝗦𝗶𝗿 𝗞𝗲𝗻 𝗥𝗼𝗯𝗶𝗻𝘀𝗼𝗻, whom I had the privilege of working with, urged us to humanize schools - not standardize them. He argued creativity, imagination, and empathy should be treated with the same seriousness as literacy.

Three contexts. One thread.

𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗶𝘀𝗻’𝘁 𝗮 “𝘀𝗼𝗳𝘁 𝘀𝗸𝗶𝗹𝗹.”
It is the architecture of resilience that fuels creativity.

And it leaves me with a question: what if we designed empathy not as an afterthought, but as a system?

Into schools, as Denmark has.
Into organizations, so culture isn’t just words on a wall but lived behavior.
Even into AI, so the systems we’re building are not just intelligent, but understanding: capable of sensing nuance, context, and human intent.

𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗶𝘀 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 - the invisible system that makes resilience, trust, and collaboration possible. Just as nations invest in roads and power grids, leaders and organizations 𝗺𝘂𝘀𝘁 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗲𝗺𝗽𝗮𝘁𝗵𝘆 if they want to scale.

And in the century ahead, empathy may be more than infrastructure.
𝗜𝘁 𝗺𝗮𝘆 𝗯𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆.

Knowledge was the currency of the 20th century.
Understanding will be the wealth of the 21st.

Because the future won’t belong to those who simply know the most.
𝗜𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲𝗹𝗼𝗻𝗴 𝘁𝗼 𝘁𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘁𝗿𝘂𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱.

Orgs that treat empathy as optional will scale bureaucracy, not trust. In volatile markets, empathy is the system that prevents fracture. So here’s the challenge for leaders: how do we budget for empathy the way we budget for servers, salaries, and strategy?

MIT says 95% of generative AI pilots fail.

Larry Fink says we may literally run out of electricians as AI data centers multiply.

Here’s the truth:
We’re glamorizing coders (no reason not to) while ignoring the people who actually wire the future.

AI may be built on silicon chips, but it still runs on copper wires, steel beams, and skilled hands.

The U.S. will need tens of thousands of electricians every year just to keep up.  Google is already funding training programs to close the gap. Yet our narrative still points young people toward tech degrees while undervaluing the trades that keep the lights on…literally.

The future won’t be won by algorithms alone. It will be won by algorithms + skilled trades.

Until we respect and invest in both, AI’s trillion-dollar promise will keep tripping over a tripped circuit.

#Skilledtrades #TheOperatorsEdge #RealityCheck

Most AI today is still reactive. You prompt, it answers.
That’s about to change.

We’re entering an era where systems act before you ask inferring needs, preempting tasks, and shaping decisions without waiting for you.

That shift isn’t just technical. It’s political.

When agency moves from you to the machine, defaults matter: Whose priorities? Whose constraints? Whose ethics?

In this next phase, governance isn’t a compliance box.  It’s the only way to steer a system that moves faster than you can type.

The leaders who prepare now will find opportunity in that acceleration. Those who don’t will be reacting to their own technology.

When your AI starts acting unprompted, what will you insist it can’t decide?

#AI #AgenticAI #Governance #Leadership #FutureOfWork #DecisionIntelligence

A farmer once told me: “We don’t harvest when it’s ready. We harvest when it’s right.”

In agriculture, speed without timing is waste. Move too soon, you sacrifice yield. Move too late, you lose the crop.

The same applies to leadership in the AI era. We’re told to “move fast”  but when decisions are autonomous and data flows constantly, the real discipline is knowing when to act.

High decision velocity isn’t frantic pace. It’s rhythm.

Leaders who win will:
• Sense inflection points early
• Hold back until constraints align
• Move decisively once timing peaks

💬 In your business, how do you know when the moment is right not just ready?

#Leadership #DecisionIntelligence #AI #Timing #Execution #Strategy #AgriFood

Everyone is rushing to deploy AI agents.

Few are asking: Can we explain the decisions they make?
➤➤➤➤➤➤

In a multi-agent environment, work doesn’t happen in one place. An output may pass through three, five, ten agents before it lands on your desk.

If you can’t trace the path, you’ve lost more than data provenance — you’ve lost decision lineage.

Without lineage:
➤ No audit for compliance
➤ No pinpointing failure
➤ No building trust

Fixing this isn’t sexy, but it’s decisive:
1️⃣ Log context, source, rationale at every agent-to-agent handoff.
2️⃣ Build audit trails into orchestration layers, not just outputs.
3️⃣ Treat lineage as part of your moat , buyers trust what they can verify.

Because in enterprise AI, control isn’t about owning the model. It’s about owning the why.

Do your current AI workflows have decision lineage or just outputs?

#AI #AgenticAI #Governance #Trust #DecisionMaking #EnterpriseAI

𝗚𝗲𝗻𝗔𝗜’𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝘀𝗻’𝘁 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸.

𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀? 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. 𝗧𝗵𝗲𝘆 𝗺𝗮𝗸𝗲 𝘄𝗼𝗿𝗸 𝗲𝗮𝘀𝗶𝗲𝗿, 𝗯𝘂𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼𝗻’𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗣&𝗟.

At the other extreme is reinvention tearing out processes and rebuilding them around agents. McKinsey & Company calls vertical reinvention “the most powerful lever for value creation,” estimating up to 90% of enterprise GenAI value comes from end-to-end process redesign. (recent report in the comments)

I agree, that’s the destination.

But here’s my POV: most industries can’t start there. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝗼𝗿𝘁𝗵𝗼𝗴𝗼𝗻𝗮𝗹 𝗽𝗮𝘁𝗵.

You don’t rip out a grain plant, a farm, or a supply chain and rebuild from scratch. You start by embedding vertical agents into the workflows you already have.

Call them 𝘢𝘱𝘱𝘭𝘪𝘦𝘥 𝘷𝘦𝘳𝘵𝘪𝘤𝘢𝘭𝘴.

And when they’re applied, the results look like this:

➤ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝘁𝗿𝘂𝗰𝗸 𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀: A grain truck shows up half-full. An agent reroutes before the next one leaves.
➤ 𝗖𝘂𝘁𝘁𝗶𝗻𝗴 𝗽𝗹𝗮𝗻𝘁 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲𝘀: A manager stares at a week-long grid. By morning, it’s solved.
➤ 𝗖𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗺𝗮𝗿𝗴𝗶𝗻 𝗹𝗲𝗮𝗸𝗮𝗴𝗲: A contract heads to signature with hidden losses. The agent flags it in time.

These aren’t side benefits. They’re millions in savings today.

And they build the trust and muscle memory that make reinvention possible tomorrow. They’re the bridge to reinvention.

So yes, process reinvention is the endgame.

But the path runs through applied vertical adoption that delivers impact now and builds the trust and muscle memory for transformation later.

#CEO #IndustrialSystems #Optimization #AI #COO #CFO SWARM Engineering

𝗔𝗜’𝘀 𝗡𝗲𝘅𝘁 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿: 𝗙𝗿𝗼𝗺 𝗦𝗰𝗮𝗹𝗲 𝘁𝗼 𝗔𝗴𝗲𝗻𝗰𝘆

For years, the story was simple: more data, bigger models, 
better results.

But industries are realizing something: sheer size doesn’t solve their hardest problems.

The shift now is toward 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 - systems that reason, adapt, and operate in messy, real-world contexts.

Scaling got us this far.
𝗔𝗴𝗲𝗻𝗰𝘆 𝘄𝗶𝗹𝗹 𝘁𝗮𝗸𝗲 𝘂𝘀 𝗳𝘂𝗿𝘁𝗵𝗲𝗿.

Scaling is a chapter.
𝗔𝗴𝗲𝗻𝗰𝘆 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗲𝗾𝘂𝗲𝗹.

👉 Where should we be investing: in bigger models, or in smarter, more adaptive ones?

#AgenticAI #ArtificialIntelligence #AIInnovation #FutureOfAI #EnterpriseAI #DecisionIntelligence #AITrends SWARM Engineering

AI's Future : Purpose not Power

🔧 Why the Next AI War Will Be Won in the Middle of the Stack

The model isn’t where this battle will be won. Inference is cheapening. APIs are everywhere.

The real fight is shifting to the orchestration layer where context, domain logic, and decision flows live.

This “middle of the stack” is where vertical specialists outmaneuver horizontal giants:
⟴ Own the workflows
⟴ Own the learning loops
⟴ Own the trust layer

That’s where defensibility lives and where margin migrates when the bottom commoditizes.

💬 Are you clinging to the foundation or owning the floors where value lives?

#AI #AgenticAI #Strategy #Startups #Moat #EnterpriseTech

𝗬𝗼𝘂𝗿 𝗼𝗿𝗴 𝗳𝗮𝗶𝗹𝗲𝗱 𝘁𝗵𝗲 𝗯𝗲𝗻𝗱 𝘁𝗲𝘀𝘁.
𝘊𝘪𝘷𝘪𝘭 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘵𝘢𝘶𝘨𝘩𝘵 𝘮𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘱𝘩𝘺𝘴𝘪𝘤𝘴: 𝘳𝘪𝘨𝘪𝘥𝘪𝘵𝘺 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘥𝘶𝘤𝘵𝘪𝘭𝘪𝘵𝘺 𝘪𝘴 𝘧𝘳𝘢𝘨𝘪𝘭𝘪𝘵𝘺.

MIT just put a number on it: ~𝟵𝟱% 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝘀𝘁𝗮𝗹𝗹. Not because the models are weak - but because the company can’t hold a new shape under stress. That’s plasticity.

Stop debating models. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗽𝗹𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆. 
Run this 90-second 𝗣𝗹𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆 𝗜𝗻𝗱𝗲𝘅 before your next pilot:

𝟭) 𝗟𝗼𝗼𝗽 𝗛𝗮𝗹𝗳-𝗟𝗶𝗳𝗲: Time from new signal to workflow change in production. (Target: days, not quarters.)

𝟮) 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻: Approvals to ship a tiny, reversible change. 
(Target: ≤3.)

𝟯) 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗘𝗹𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆: Are operators rewarded this quarter for changing how they work? 
(Target: yes, explicitly.)

If your PI is low, your pilot will snap back to old behavior - no matter how good the model is.

𝗥𝘂𝗹𝗲 𝗼𝗳 𝘁𝗵𝘂𝗺𝗯: 𝘐𝘧 𝘺𝘰𝘶 𝘤𝘢𝘯’𝘵 𝘤𝘩𝘢𝘯𝘨𝘦 𝘢 𝘧𝘰𝘳𝘮, 𝘺𝘰𝘶 𝘤𝘢𝘯’𝘵 𝘤𝘩𝘢𝘯𝘨𝘦 𝘢 𝘧𝘪𝘳𝘮.
Rewrite one policy. Ship one workflow. Pay for one new behavior. Then launch the pilot.

 What’s your Loop Half-Life right now - days, weeks, or quarters?

#AIExecution #GenAI #DigitalTransformation #EnterpriseAI #OperatingModel

𝗔𝗜’𝘀 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀𝗻’𝘁 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁. 𝗜𝘁’𝘀 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗗𝗲𝗯𝗮𝘁𝗲.

It’s not just my view. Even Mustafa Suleyman, co-founder of DeepMind and now leading Microsoft AI, is calling out this shift.

Here’s why it matters:

𝗢𝗻𝗲 𝗚𝗣𝗧 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 𝗯𝘂𝘁 𝘀𝘁𝘂𝗯𝗯𝗼𝗿𝗻 𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲.

They think out loud. They sound convincing. But if they’re wrong, no one calls them out.

That’s 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁.

A single model reasoning step by step. Useful, but brittle.

Now imagine a 𝘤𝘰𝘶𝘯𝘤𝘪𝘭 of GPTs:
💬 One focused on accuracy.
💬 One weighing cost.
💬 One scanning for risk.
💬 One optimizing efficiency.

They 𝗱𝗲𝗯𝗮𝘁𝗲. They 𝗮𝗿𝗴𝘂𝗲. They 𝗰𝗿𝗼𝘀𝘀-𝗲𝘅𝗮𝗺𝗶𝗻𝗲.

And an 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 synthesizes the debate into the best possible answer.

That’s 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗗𝗲𝗯𝗮𝘁𝗲.

Not a solo violinist. An orchestra with a conductor.

The Strategic Shift

As GPT models get commoditized, 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗼𝗮𝘁 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻.

How you debate, weight, and synthesize 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦𝘴 will determine who wins.

This is the shift from:

🗣️ Faster answers → to better answers.
🗣️ Single reasoning → to collective intelligence.
🗣️ Models as tools → to AI as a decision-making partner.

💡 If you’re leading an enterprise today, stop asking what one GPT can do for you. Start asking:

𝗪𝗵𝗮𝘁 𝗱𝗲𝗯𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗯𝗲𝗳𝗼𝗿𝗲 𝘃𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆 𝗱𝗿𝗮𝗶𝗻𝘀 𝘆𝗼𝘂𝗿 𝗺𝗮𝗿𝗴𝗶𝗻𝘀?

👉 That’s the leap from hype to leadership. From Chain of Thought to Chain of Debate.

#AI #AgenticAI #Leadership #DecisionIntelligence #Optimization SWARM Engineering

𝗪𝗵𝗲𝗻 𝗘𝘃𝗲𝗻 𝗔𝗽𝗽𝗹𝗲 𝗢𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗬𝗼𝘂 𝗞𝗻𝗼𝘄 𝘁𝗵𝗲 𝗚𝗮𝗺𝗲 𝗛𝗮𝘀 𝗖𝗵𝗮𝗻𝗴𝗲𝗱.
𝘈𝘱𝘱𝘭𝘦 + 𝘎𝘰𝘰𝘨𝘭𝘦: 𝘛𝘩𝘦 𝘈𝘐 𝘈𝘭𝘭𝘪𝘢𝘯𝘤𝘦 𝘕𝘰 𝘖𝘯𝘦 𝘚𝘢𝘸 𝘊𝘰𝘮𝘪𝘯𝘨.

Apple, the company that built its empire on vertical integration and controlling every layer of the stack, is reportedly in early talks with Google 𝘁𝗼 𝗽𝗼𝘄𝗲𝗿 𝗦𝗶𝗿𝗶 𝘄𝗶𝘁𝗵 𝗚𝗲𝗺𝗶𝗻𝗶 𝗔𝗜.

For years, Siri has been the punchline in every AI conversation. But if this deal goes through, 𝗚𝗼𝗼𝗴𝗹e 𝘄𝗼𝘂𝗹𝗱 𝘁𝗿𝗮𝗶𝗻 𝗮 𝗰𝘂𝘀𝘁𝗼𝗺 𝗚𝗲𝗺𝗶𝗻𝗶 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗿𝘂𝗻 𝗼𝗻 𝗔𝗽𝗽𝗹𝗲’𝘀 𝗼𝘄𝗻 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲, preserving Apple’s hard line on privacy while finally giving Siri the brain it never had.

⫸ 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?

Because even the world’s most valuable company is showing us three truths about this era of AI:

➙ Speed beats pride. In generative AI, three years of “perfecting in-house” is three years of irrelevance.

➙ Alliances are tactical, not sentimental. Apple isn’t suddenly “Team Google.” It’s recognizing that partnerships can be accelerants.

➙ Privacy is the new battleground. Outsourcing the brain while keeping the data inside Apple’s walls is the compromise of the future.

If Apple is willing to outsource what it once considered sacred, what does that say about the rest of us?

It says the 𝗔𝗜 𝘄𝗮𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗼 𝗯𝘂𝗶𝗹𝗱𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗼 𝗰𝗮𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗺𝗶𝘅 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 - 𝗮𝗻𝗱 𝗱𝗼 𝗶𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 than anyone else.

Siri may finally stop being a joke. But the bigger story is this:

 𝗘𝘃𝗲𝗻 𝗔𝗽𝗽𝗹𝗲 𝗵𝗮𝘀 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝗱 𝘁𝗵𝗮𝘁 𝗶𝗻 𝗔𝗜, 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗶𝘀 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.

𝗧𝗵𝗲 𝘄𝗮𝗹𝗹𝗲𝘁, 𝘁𝗵𝗲 𝘄𝗮𝘁𝗰𝗵, 𝗼𝗿 𝘁𝗵𝗲 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿. 𝗪𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝘀𝘁 𝗶𝗻 𝗮 𝘀𝘁𝗮𝗿𝘁𝘂𝗽? 

Most leaders obsess over the wallet. Cash feels like the oxygen. And don’t get me wrong, cash is vital !

But the real burn rate isn’t money.
𝗜𝘁’𝘀 𝘁𝗶𝗺𝗲.

Cash is visible on a balance sheet.
𝗧𝗶𝗺𝗲 𝗶𝘀𝗻’𝘁.

And the calendar? That’s where you see the waste: slow hires, ghosted meetings, delayed launches, shiny object chase, endless “let’s circle back.”

𝗧𝗶𝗺𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗯𝘂𝗿𝗻 𝗿𝗮𝘁𝗲.

And once it’s gone, 𝗻𝗼 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿 𝗰𝗮𝗻 𝘄𝗶𝗿𝗲 𝗶𝘁 𝗯𝗮𝗰𝗸.

#Leadership #Startups #Execution #AI #CEO #Founder

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𝗧𝗵𝗲 𝗦𝗺𝗮𝗹𝗹 𝗔𝗰𝘁 𝗶𝗻 𝗗𝗲𝗳𝗲𝗮𝘁 𝘁𝗵𝗮𝘁 𝗥𝗲𝘃𝗲𝗮𝗹𝗲𝗱 𝗪𝗮𝘀𝗵𝗶𝗻𝗴𝘁𝗼𝗻’𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝘀𝘁 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵

𝘞𝘦𝘭𝘤𝘰𝘮𝘦 𝘵𝘰 𝘵𝘩𝘪𝘴 𝘸𝘦𝘦𝘬’𝘴  #𝘚𝘶𝘯𝘥𝘢𝘺𝘚𝘱𝘢𝘳𝘬 - 𝘢 𝘴𝘵𝘰𝘳𝘺 𝘵𝘰 𝘪𝘨𝘯𝘪𝘵𝘦 𝘺𝘰𝘶𝘳 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘸𝘦𝘦𝘬 𝘢𝘩𝘦𝘢𝘥.

Washington’s army had just suffered defeat at 𝗚𝗲𝗿𝗺𝗮𝗻𝘁𝗼𝘄𝗻.

His army was 𝗱𝗲𝗺𝗼𝗿𝗮𝗹𝗶𝘇𝗲𝗱, 𝗖𝗼𝗻𝗴𝗿𝗲𝘀𝘀 𝘄𝗮𝘀 𝗹𝗼𝘀𝗶𝗻𝗴 𝗳𝗮𝗶𝘁𝗵, 𝗰𝗿𝗶𝘁𝗶𝗰𝘀 𝘄𝗵𝗶𝘀𝗽𝗲𝗿𝗲𝗱 𝗵𝗲 𝘄𝗮𝘀𝗻’𝘁 𝗳𝗶𝘁 𝘁𝗼 𝗹𝗲𝗮𝗱.

He had every reason to b𝗲 𝗯𝗶𝘁𝘁𝗲𝗿, 𝘀𝗺𝗮𝗹𝗹, 𝘃𝗲𝗻𝗴𝗲𝗳𝘂𝗹.

Then, a stray dog wandered into camp.It belonged to 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗛𝗼𝘄𝗲.

The dog wasn’t just a pet. It was 𝘁𝗵𝗲 𝗲𝗻𝗲𝗺𝘆 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝗲𝗿’𝘀 𝗱𝗼𝗴 - 𝗯𝗲𝗹𝗼𝗻𝗴𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗮𝗻 𝘄𝗵𝗼 𝗵𝗮𝗱 𝗷𝘂𝘀𝘁 𝗯𝗲𝗮𝘁𝗲𝗻 𝗵𝗶𝗺.

Washington could have laughed, ignored it, kept it as a trophy.

Instead, he 𝗳𝗲𝗱 𝘁𝗵𝗲 𝗱𝗼𝗴, 𝗰𝗹𝗲𝗮𝗻𝗲𝗱 𝗶𝘁, 𝗮𝗻𝗱 𝘀𝗲𝗻𝘁 𝗶𝘁 𝗯𝗮𝗰𝗸 𝘄𝗶𝘁𝗵 𝗮 𝗻𝗼𝘁𝗲 𝗼𝗳 𝗿𝗲𝘀𝗽𝗲𝗰𝘁. He did it under a flag of truce, elevating it from a small gesture to an 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗮𝗰𝘁 𝗼𝗳 𝗱𝗶𝗴𝗻𝗶𝘁𝘆.

The note read simply:
 “𝘎𝘦𝘯𝘦𝘳𝘢𝘭 𝘞𝘢𝘴𝘩𝘪𝘯𝘨𝘵𝘰𝘯’𝘴 𝘤𝘰𝘮𝘱𝘭𝘪𝘮𝘦𝘯𝘵𝘴 𝘵𝘰 𝘎𝘦𝘯𝘦𝘳𝘢𝘭 𝘏𝘰𝘸𝘦. 𝘏𝘦 𝘥𝘰𝘦𝘴 𝘩𝘪𝘮𝘴𝘦𝘭𝘧 𝘵𝘩𝘦 𝘱𝘭𝘦𝘢𝘴𝘶𝘳𝘦 𝘵𝘰 𝘳𝘦𝘵𝘶𝘳𝘯 𝘩𝘪𝘮 𝘢 𝘥𝘰𝘨...”

𝗜𝗻𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁? 𝗛𝗮𝗿𝗱𝗹𝘆.

It was a signal. That even in loss, 𝗱𝗶𝗴𝗻𝗶𝘁𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀.

𝗧𝗵𝗮𝘁 𝘁𝗿𝘂𝗲 𝗽𝗼𝘄𝗲𝗿 𝗶𝘀𝗻’𝘁 𝗶𝗻 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗲𝗻𝗲𝗺𝘆, 𝗯𝘂𝘁 𝗶𝗻 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗼 𝗴𝗶𝘃𝗲 𝗯𝗮𝗰𝗸.

Because armies win battles. But character wins trust.

𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁.
𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗱𝗲𝗳𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻.

👉 Are you building trust through your wins or through how you lead in the quiet moments?

#Trust #Leadership #Inspiration

If your plan assumes stability, volatility will eat it alive.

𝗛𝗲𝘁𝗲𝗿𝗼𝘀𝗸𝗲𝗱𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆: 𝗧𝗵𝗲 𝗠𝗮𝘁𝗵 𝗕𝗲𝗵𝗶𝗻𝗱 𝗪𝗵𝘆 𝗠𝗼𝘀𝘁 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹𝘀 𝗕𝗿𝗲𝗮𝗸

In data, stability is rare.

𝗛𝗼𝗺𝗼𝘀𝗸𝗲𝗱𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆 is the tidy case where variance stays constant.
𝗛𝗲𝘁𝗲𝗿𝗼𝘀𝗸𝗲𝗱𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆 is the messy reality where variance grows or shrinks as conditions change.

You see it in income vs. restaurant spend. You see it in social media engagement.

And if you run a supply chain, commodity operation, or workforce at scale - you live it.

Here’s the problem:

➤ Most planning and forecasting tools are built for a homoskedastic world.
➤ They assume variance is constant.
➤ They assume the world behaves.

Optimization in the real world requires the opposite mindset. 
One that expects variance to scale, models it, and adapts decisions in real time.

In my work at SWARM, we see it every day.

➤ In food production, variance explodes when weather or demand shifts.
➤ In logistics, delays multiply past certain volume thresholds.
➤ In trading, risk scales non-linearly with market moves.

That’s not noise. That’s the real signal.

Leaders who plan for constant variance get blindsided.

Leaders who expect variance to scale and use optimization can turn chaos into advantage.

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 “𝗗𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲 𝗵𝗲𝘁𝗲𝗿𝗼𝘀𝗸𝗲𝗱𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆?”
𝗜𝘁’𝘀 “𝗪𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝘄𝗲 𝗱𝗼 𝘄𝗵𝗲𝗻 𝗶𝘁 𝘀𝗵𝗼𝘄𝘀 𝘂𝗽?”

In leadership and in data, the spread will come.
And in fast-moving markets, the deadline always arrives before you expect it. 

The leaders who model for it, and act, will take the market.

#AI #Optimization #DataScience #Leadership #SupplyChain #Operations #MachineLearning #DecisionIntelligence SWARM Engineering S2G Investments

Most AI failures aren’t about bad models. They’re about bad architecture.

🔷🔷🔷🔷

I’ve seen it in boardrooms and postmortems:

The AI worked beautifully in the demo. Then silent failures, cost overruns, and user complaints in production.

The root cause?
Teams built it like an app, not an architecture.

In probabilistic systems, you’re not just designing what the AI does, you’re designing how it behaves when reality pushes back.

If you skip that, you’re gambling with drift, compliance breaches, and runaway cloud bills.

Winners bake in resilience from day one:

🔹Evaluation – Continuous monitoring, drift detection, feedback loops.
	
🔹Governance – Guardrails, risk gates, compliance alignment.

🔹Cost Control – Usage visibility, rate limits, optimization levers.

🚩In the companies that get this right, architecture is the moat.

It’s what makes an AI system adapt instead of react.

And it’s why the AI Architect is not a coder, instead they’re the custodian of trust, reliability, and scale.

Where does your AI sit today:
🔴 Reactive
🟠 Basic
🟢 Proactive
🔵 Adaptive

If you had to close one architecture gap this quarter, which would it be?

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