Redefining AI: The Future of Artificial Intelligence

When an autonomous drone picks its own target and kills civilians, who’s responsible? The commander, the developer, or the state? Cybersecurity veteran Matthew Rosenquist (36+ years, advisor to governments and corporations) explains why AI weapons need international red lines now.In this episode of Redefining AI, Lauren Hawker Zafer and Matthew discuss his Help Net Security article on autonomous AI weapons:• How weapons went from AI-assisted to fully autonomous, with no human in the loop after launch• Why a consumer AI vision chip ended up in a Russian drone used against Ukrainian civilians• The “plausible deniability” problem, and how autonomy could make war crimes easier to hide• Why banning autonomous weapons isn’t realistic, but red lines are• How the Geneva Conventions model shows international agreements can work even when not everyone signs

If you've watched generative AI over the past few years, you already understand the shift happening in military hardware. You've just seen it with lower stakes.
Weapons are following the same arc. The first generation of AI in warfare was a very capable assistant. Algorithms crunched telemetry, handled navigation, flagged shapes that looked like tanks but a human operator stayed in the loop, looked at the picture, and made the call. The failure modes were human ones: an overloaded operator, a jammed or intercepted comms link.
You hand it a mission objective and let it go. It plots its own route, coordinates with other autonomous assets, picks targets, and strikes. After launch, there's no one in the loop at all. The failure modes change accordingly: model drift, misidentified targets, and collateral damage delivered at machine speed.
In enterprise software, an agent that goes rogue books the wrong flight or deletes a database. On the battlefield, the error bars are measured in lives.
Here's the part that should make the commercial tech industry uncomfortable: none of this requires exotic, classified hardware anymore.
In his Help Net Security article, Rosenquist points to a case in which an Nvidia vision processor, a chip built for low-power edge computing, self-driving cars, and consumer photography drones, turned up inside a Russian military drone used against Ukrainian civilians in Zaporizhzhia.
His analogy is blunt. "I can design the perfect hammer to build homes for the homeless, but somebody can take that hammer and hit their neighbor in the head and commit murder."
The chip did exactly what it was designed to do: see. The problem is what it was told to look for. And because the same silicon underpins cars, factories, and the drone your cousin flies at weddings, choking it off at the source isn't realistic without kneecapping entire civilian industries. The real vulnerability isn't in the fab. It's in how little software it now takes to turn a benign chip into a weapon that thinks for itself.
The military's "kill chain" is the sequence of decisions that ends with something going boom. For decades, international humanitarian law has rested on a simple assumption about that chain: somewhere in it, a person decided.
Play it forward and you can hear the finger-pointing already. The field commander blames an algorithmic anomaly. The developer says the model performed to spec and the deployment context was unforeseeable. The state shrugs and calls it a tragic edge case. Everyone is technically telling the truth, and nobody is responsible.
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With love and light, Redefining AI the Tech Podcast.

Six words, banned for thirty minutes: transformation, journey, reimagine, unlock, leverage, AI-native.
Dave Wright (Chief Innovation Officer, ServiceNow) and Brian Solis (Head of Global Innovation, ServiceNow, and 10x best-selling author) agreed to the rules before the conversation started. 
What followed was a plain-language case for why enterprise AI keeps failing. Their argument, from their new book Infinite, is that the problem was never the technology.
It's the company.
In this episode you will find out more about:
AI-native, AI-first, AI-forward: what the labels actually mean, and why Dave thinks no company could be AI-native before generative AI

IKEA's €13.4M service-desk automation, and the €1B+ interior design business built from the capacity it freed, with no job losses

The "iteration trap": using the most innovative technology in history just to do yesterday's work faster

Why most companies are tracking the wrong AI metrics, and what "token maxing" hides

The AI tax: people using AI to write documents so long that colleagues need AI to summarise them

Whether innovation is still possible when intelligence becomes a commodity

Most companies automate what they already do, count the hours saved, and call it a result. Very few reach the second number.
One question to sit with before you press play: the workflows running your organisation were designed around a shortage of people. If that shortage disappeared tomorrow, would anyone notice?
Follow us to keep uptodate and to stay in time for the full episode.

#RedefiningAI #Leadership #ArtificialIntelligence #AIStrategy #FutureOfWork

Six words, banned for thirty minutes: transformation, journey, reimagine, unlock, leverage, AI-native.
Dave Wright (Chief Innovation Officer, ServiceNow) and Brian Solis (Head of Global Innovation, ServiceNow, and 10x best-selling author) agreed to the rules before the conversation started.
What followed was a plain-language case for why enterprise AI keeps failing. Their argument, from their new book Infinite, is that the problem was never the technology. It's the company.
In the upcoming episode you will find out more about:
AI-native, AI-first, AI-forward: what the labels actually mean, and why Dave thinks no company could be AI-native before generative AI
IKEA's €13.4M service-desk automation, and the €1B+ interior design business built from the capacity it freed, with no job losses
The "iteration trap": using the most innovative technology in history just to do yesterday's work faster
Why most companies are tracking the wrong AI metrics, and what "token maxing" hides
The AI tax: people using AI to write documents so long that colleagues need AI to summarise them
Whether innovation is still possible when intelligence becomes a commodity

Most companies automate what they already do, count the hours saved, and call it a result. Very few reach the second number.
One question to sit with before you press play: the workflows running your organisation were designed around a shortage of people. If that shortage disappeared tomorrow, would anyone notice?
Follow us to keep uptodate and to stay in time for the full episode.

#RedefiningAI #Leadership #ArtificialIntelligence #AIStrategy #FutureOfWork

Most of the voices we hear on AI sit in one chair.
Louisa Loran sits in several at once: operator, author, board director, and trusted advisor to Fortune 500 boards navigating once-in-a-generation change.
In episode three of Redefining AI, season five host Lauren Hawker Zafer talks with Louisa about the difference between using AI to hollow out a company and using it to amplify its strengths.

Lauren and Louisa dig into how to protect your core proposition, how to spot a real moat, the generational and gender dynamics shaping adoption, and the four leadership behaviors, envision, expand, steer, embody at the heart of her Amazon-bestselling book, Leadership Anatomy in Motion.

In the episode:
• Why the real AI divide isn’t fast vs. slow
• The three-ring framework for what to protect, match, and rent
• What tacit knowledge and a “data-access culture” mean for your moat
• The one leadership behavior you can’t outsource
• Why the AI gender gap could be an opportunity for women to lead Louisa Loran is the author of Leadership Anatomy in Motion (Fast Company Press).

Louisa has advised Fortune 500 boards, sits on the board of one of Europe’s largest business schools, and helped pivot one of the world’s biggest shipping companies into integrated logistics. Hear how she’d put AI to work, without hollowing your company out.

Follow Redefining AI for more conversations at the intersection of leadership and technology.

#RedefiningAI #Leadership #ArtificialIntelligence #AIStrategy #FutureOfWork

Most of the voices we hear on AI sit in one chair. Louisa Loran sits in several at once: operator, author, board director, and trusted advisor to Fortune 500 boards navigating once-in-a-generation change.
In episode three of Redefining AI, host Lauren talks with Louisa about the difference between using AI to hollow out a company and using it to amplify its strengths. They dig into how to protect your core proposition, how to spot a real moat, the generational and gender dynamics shaping adoption, and the four leadership behaviors, envision, expand, steer, embody at the heart of her Amazon-bestselling book, Leadership Anatomy in Motion.

In the upcoming episode:
• Why the real AI divide isn’t fast vs. slow
• The three-ring framework for what to protect, match, and rent

• What tacit knowledge and a “data-access culture” mean for your moat

• The one leadership behavior you can’t outsource

• Why the AI gender gap could be an opportunity for women to leadLouisa Loran is the author of Leadership Anatomy in Motion (Fast Company Press).

Follow Redefining AI for more conversations at the intersection of leadership and technology. She has advised Fortune 500 boards, sits on the board of one of Europe’s largest business schools, and helped pivot one of the world’s biggest shipping companies into integrated logistics.
Hear how she’d put AI to work, without hollowing your company out.

#RedefiningAI #Leadership #ArtificialIntelligence #AIStrategy #FutureOfWork