Erick Straghalis

As Britannica highlights, many AI systems are trained on vast datasets that may include personal or sensitive information, often without explicit consent. This raises critical questions about how data is collected, used, and protected. The more data an AI system consumes, the more vulnerable it can become to breaches, misuse, or bias. That’s why ethical responsibility must be designed into every stage of development and deployment. I believe in transparent, secure, and human-centered AI practices that prioritize data integrity and user trust. AI must serve people, not the other way around.

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This is a bit of old news, but I still wonder what the impact will be. A 6 min video on how Claude can create a product launch plan: https://lnkd.in/eDpam2qH This is great for startups and small businesses with a very small marketing team. But these businesses will still need someone experienced who understands what prompts to use, questions to ask and to evaluate/edit the AI's work. What I haven't figured out is what is the talent pipeline going to look like -- how does an entry-level person get marketing experience when the hands-on work that was typically done by this person is now being done by AI?

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Jonathan Aberman is one of the leading experts on marketing and AI. It was great to have him on my podcast, the Backstory on Marketing and AI, https://lnkd.in/erYwRwy5.

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CLO Article... Across industries, the push toward AI adoption has moved from experimentation to expectation. Budgets are allocated. Tools are deployed. Roadmaps are drafted. On paper, progress looks strong. But a closer look at the data tells a more complicated story... #FutureOfWork

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Jay Thornton wrote something worth reading this week about the IBM data on AI adoption, and I want to add my angle to it because I come at this from a different place than Jay does. Jay thinks in product architecture. I think in operators. I have spent 25 years watching small business owners adopt new technology. The pattern is almost always the same. They buy the tool. They get through onboarding. They use it for two weeks. Then the day gets busy and the tool disappears into a browser tab they never open again. That is not a discipline problem. That is not a training problem. That is the tool failing to earn a place in a real person's real day. The IBM number Jay pulls out, 93.2% of executives citing cultural challenges over technology as the primary AI adoption barrier, does not surprise me at all. I have watched that number play out in practice for years before anyone had a label for it. What strikes me about where we landed with LeadMachine is that we did not set out to build a "low friction" CRM as a marketing position. We built it that way because every version of the product that asked the operator to change their habits got quietly abandoned. The market told us, repeatedly and without apology, that the tool had to fit the day, not the other way around. The Calm Operator philosophy Jay writes about is not a brand idea. It is what we learned from watching operators walk away from software that was technically excellent but culturally invisible. If you are evaluating AI tools right now, that 93.2% is the question to ask every vendor. Not what can your tool do. Ask where does your tool live in the operator's actual Tuesday morning. The answer will tell you everything. Link to Jay's piece in the first comment.

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AI is no longer just experimental—it's being executed across the business. If your business is using AI agents for real work, you need to ensure they're designed with trust built in from the start. This blog post on Microsoft's new Frontier Suite explores what it takes to run AI securely at scale, with governance, visibility, and control across users and agents. A must‑read for IT leaders planning the next phase of AI adoption. 👉

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Quantum Marketing: Embracing Uncertainty to Drive Smarter Decisions For decades, marketers relied on patterns and deterministic models to guide decisions. But the world isn’t always predictable. Quantum Marketing treats uncertainty as a strategic advantage, using probabilistic modeling and stochastic A/B testing to explore multiple possible outcomes, anticipate customer reactions, and adapt campaigns in real time. By leveraging quantum-inspired algorithms, marketers can simulate countless scenarios simultaneously: from changing customer preferences to competitor moves, and identify strategies that maximize impact even in volatile environments. This approach transforms marketing from a reactive function into a proactive, insight-driven engine. Think of it as marketing with a sense of humor: you can test, tweak, and iterate as many times as you like, and the only thing that complains is your spreadsheet.   #QuantumMarketing #ProbabilisticModeling #PredictiveAnalytics #MarketingInnovation #AIinMarketing

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In the deluge of AI noise, simple strategic business questions need to be asked. In this snippet from The Evolve to Succeed Podcast with Warren Munson and Nick Carlile I pose some of the fundamental questions businesses should be asking themselves right now.

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Babak, your comments in Joao-Pierre Ruth InformationWeek piece from Davos, and your BBC News interview this morning, really struck a chord. That article made a strong point that AI was front and centre for business leaders at Davos, while still feeling more peripheral for many politicians. The commercial world clearly sees where this is heading, but the reality on the ground is moving far more slowly. The Wall Street Journal recently noted that AI adoption across companies is still “slow and uneven,” with most organisations stuck in pilot mode rather than real, scaled deployment. Lots of noise. Plenty of decks. Not nearly as much real impact in the day-to-day customer journey. That idea that the future of AI isn’t one giant assistant, but lots of specialised agents working together behind the scenes, feels exactly where things are heading. The retail leaders and agency teams I’m speaking to aren’t asking if they should use AI anymore. They’re asking where it improves service, conversion, and customer confidence right now, especially across the direct-to-consumer journey, and who ultimately owns the first-party data coming out of it. Working from London with the Silicon Valley-based team Mind Over Media , I’m seeing real traction from practical, brand-owned agents that sit inside the customer journey, remove friction at the exact moment it matters, and keep the relationship and the data with the brand. As founders Andy Anderson and Jamie Parker put it: “As commerce moves toward agents and agent-to-agent interactions, every brand will need its own intelligence representing it. We build for the brand, not the channel, so that brain sits with the company, and they keep ownership of the customer relationship, the data, and the commercial outcome.” And this isn’t about replacing people. It’s about giving teams better tools that actually work in the real world. As someone who’s dyslexic thinker, AI has genuinely transformed my day-to-day working life. As Sir Richard Branson often says, it can be a powerful co-pilot, but human creativity still matters most. That balance between human judgement and agent-driven systems feels like the real direction of travel. Much of the work around Mind Over Media AI sits right in the middle of this shift. Let me know what you think? #ArtificialIntelligence #AI #GenerativeAI #AgenticAI #Davos #CustomerExperience #DirectToConsumer #FirstPartyData #DigitalTransformation #Retail #Ecommerce #MarketingTechnology #FutureOfWork

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Still a role for Human Marketers - "While these tools invite more people to participate, they also risk creating a feedback loop where the platform’s “best practice” becomes everyone’s default. Brands begin to sound the same and strategies blur as the work reflects the system rather than the people behind it."

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When clients, prospects, or talent Google you, what do they see before they even click? Knowledge panels are quickly becoming the new front door for credibility, especially as AI-generated answers rise to the top of search results. In this week’s episode of the On Record PR Podcast, I sit down again with Furia Rubel Communications' Chief Innovation Officer, Leslie Richards, to break down: ✅What knowledge panels are and why they matter for law firms ✅How they connect to GEO (generative engine optimization) ✅What you can and can’t control when it comes to panels ✅Practical steps attorneys can take to build visibility ✅How firm leaders can support distributed, third-party publishing strategies 🎧 Tune in by following the link below. #LawFirmLeadership #LegalMarketing #PublicRelations #GEO #ReputationManagement

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Excited to see more of the vision behind Liz Agent, Seedtag’s new agentic AI platform. Our CEO Brian Gleason shares how she puts Seedtag’s AI directly into the hands of brands and agencies, making it easier to turn Neuro-Contextual intelligence into smarter strategies. Discover more about Liz Agent: https://lnkd.in/em4KGvs6 #LizAgent #NeuroContextual #AdTech

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The next leap in B2B marketing involves trading static automation rules for autonomous agents that actively pursue revenue goals. https://lttr.ai/Al9lE #B2B #Marketing #Technology

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AI, GenAI, Agentic… all require systemic approach, strategy, rigor, change management… Just a quick quote… « The problem isn’t the technology, but rather the absence of strategy, structure, and leadership to move from isolated successes to embedded capability » #AIstrategy https://bit.ly/3HDnOrX

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Hey all. Excited to share a recent episode where I had the opportunity to reconnect with Ardis Kadiu. We spent time catching up on his current work and discussing where AI innovation is truly driving impact. In our conversation, we explored several developments shaping the next phase of AI deployment: • Apple’s progress in AI hardware acceleration and on device intelligence • SpaceX’s continued investment in global satellite connectivity and the implications for edge computing • Anthropic’s advances in automated code review and more secure development workflows • Agent loops as open source experimentation approach and what it signals for research velocity I appreciate the dialogue, Ardis. These are the conversations that help operators separate signal from noise and stay focused on what will matter at scale.

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