The Digital Executive

In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Shannon Flanagan, founder and CEO of Retail Women in Tech, to make a case that the most important thing missing from most AI transformation efforts isn't better technology — it's the right people in the room. Shannon opens with a career that started at 18 folding jeans and evolved through merchandising, consulting at Accenture, and leadership roles at Gap, Lands' End, and Macy's, shaped throughout by a single question she discovered at 25: what does it take to create the conditions where people love what they do every day? That question led her to organizational design, change management, and eventually to founding a nonprofit she describes as the hardest, poorest, and most fulfilling chapter of her life. She built Retail Women in Tech not to create another exclusive senior women's network, but to get more women — at every level, function, and age — into the rooms where AI decisions are being made, because she believes the qualities women have historically been told to downplay are exactly the ones the AI era will reward most. On change management, Shannon introduces a framework as simple as it is powerful: potential minus interference equals performance, and argues that most AI transformations fail because leaders lead with technology and ROI instead of starting with human readiness, trust, and psychological safety. She closes with a forward-looking vision of what success will look like in retail and beyond — not functional expertise, but judgment, discernment, ethics, and connection — and a challenge to women entering the field to stop waiting until they feel ready for AI roles and start recognizing that what they bring is exactly what the future needs.

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In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Allen Dine, Chief Business Officer at Care2You, to explore what it actually takes to deliver emergency and hospital-level care inside someone's home — and how AI is making that possible at scale across one of the most complex urban environments in the world. Allen opens by tracing a career defined by a refusal to wait for someone else to solve the problem, from learning SQL to build his own data warehouse to deploying AI voice agents for patient intake — because in home care, if you want change, you have to build it yourself. He then walks through how Care2You uses voice AI for the highest-stakes first contact in healthcare: patient intake screening. The agents collect vitals, demographics, and recent history, cut a five-minute clinical call to under two minutes, and flag red flags — but they never make clinical decisions. Every conversation is fully audited so clinicians can review exactly what happened before stepping in, keeping the human always in the loop and the process never a black box. The dispatch and routing challenge gets equal attention: matching the right clinician to the right patient across New York City's five boroughs in real time, accounting for traffic, acuity, and a patient load that isn't fully known until the day unfolds. AI compresses what used to take ten minutes of dispatcher judgment into seconds, presenting ranked options that humans can override and adjust. Allen closes with a vision of hospital-at-home that goes beyond just moving the site of care — arguing that the real opportunity is educating patients about what conditions can be safely and comfortably treated at home, so that recovery happens surrounded by family, pets, and familiar routines rather than hospital walls, at lower cost to everyone involved.

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In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Furkat Kasimov, London-based entrepreneur, angel investor, and author, to explore what it takes to build and invest wisely in the AI era — before the crowd figures it out. Furkat opens with a origin story that predates the current AI boom by a decade, tracing his first investment to an InsureTech conference where he spotted a deepfake detection company called PinDrop Security and recognized that every dangerous technology creates a market for its antidote. That insight became the founding logic of Applayerai, a venture studio built around predicting what solutions large corporations will need in five to ten years — and building them before those corporations think to ask. He then unpacks the philosophy behind his book Don't Do This, explaining why he focused on cataloguing over 150 business mistakes rather than celebrating success stories: timing and luck can't be replicated, but someone else's mistakes can be avoided. He closes with sharp advice for executives navigating the AI stack: treat foundation models like cloud providers and build for model independence so you can swap them out without rebuilding everything; match model cost to task complexity rather than defaulting to the most powerful option for everything; and always measure the cost of completing a workflow so you can actually optimize it. His parting message cuts through the noise simply — don't lose sight of what makes your business great, treat AI as a utility, and build the knowledge base that lets your agents keep improving over time.

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In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Edwin Miranda, founder of Konsultora, to explore what it actually means to build an organization for the AI era — not just one with more tools, but one with less distance between a signal, a decision, and an action. Edwin opens by tracing the common thread across his career in advertising, political strategy, and customer experience: competition and decision-making. Every domain is ultimately about understanding what is changing, making better decisions faster, and adapting before your competitors do. AI, he argues, is the first technology that can sit inside that process and accelerate every step of it. He then addresses the economic pressure bearing down on traditional agency and consulting models — research that took weeks now takes minutes, media buying is increasingly algorithmic, content scales infinitely, and reporting can be automated — meaning businesses built on selling hours to produce those outputs are facing an existential reckoning. The answer is not to resist but to rebuild: moving from selling execution to building the systems, workflows, and decision-making capabilities that let organizations operate differently. Edwin then tackles the accountability question head-on, arguing that as AI moves from answering questions to taking actions, agents need identities, permissions, and audit trails just like any employee — because autonomy without accountability is not transformation, it is risk. He closes with a nuanced take on the human-versus-AI debate, rejecting the framing entirely. The goal is not to remove humans but to remove friction around them, freeing up human capacity for the judgment, creativity, and trust that machines cannot replicate. The companies that thrive will not be those that simply bought AI first — they will be the ones that learned to signal, decide, act, and adapt faster than the world around them changes.

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In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Antonio Mallia, founder and CEO of Seltz.ai, to explore one of the most overlooked infrastructure questions in the AI era: what happens to web search when the user is no longer a human? Antonio opens with a career that runs from the University of Pisa's information retrieval tradition through a PhD at NYU to research roles at Amazon, Pinecone, and Bloomberg — and traces the moment the idea for Seltz crystallized while building Alexa's question answering engine, when it became clear that the next consumer of search results was not a person but a machine. He then explains why Seltz took the harder path of building its own full retrieval stack from scratch rather than wrapping Google or Bing like most AI search products — and why that distinction matters deeply. Traditional search engines were optimized for human patience: ranked lists, short snippets, spell correction, ambiguous queries refined over multiple attempts. LLMs need something fundamentally different — full paragraphs without truncation, long and specific queries that nail the topic on the first attempt, and above all, freshness, because the one thing a model cannot learn in training is what happened after its cutoff date. Seltz also focuses its index on the content LLMs struggle most with — tables, financial records, SEC filings, and structured data that traditional crawlers skip and models cannot reliably memorize. Antonio closes with a prediction that reframes the entire stakes of what he is building: human search queries have plateaued, but machine queries are growing exponentially, and as people spawn multiple specialized agents to handle their knowledge work, the query load on web infrastructure will reach a scale never seen before. The companies that build for that future — with efficiency and freshness at their core — will define the next twenty years of the web.

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