Disambiguation

Michael Fauscette

"Disambiguation is the process of removing confusion around terms that express more than one meaning and can lead to different interpretations of the same string of text." Host Michael Fauscette of Arion Research; a leading technology analyst, tech startup advisor, consultant, board member, and storyteller; and his guests "remove the confusion around" artificial intelligence (AI), generative AI and business automation by looking at the business solutions available today to improve business outcomes and gain competitive advantage. 

  1. 1d ago

    The Identity Threat: Why AI Adoption Is a Human Challenge, Not a Technology Problem

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Eva Minkoff, Founder of Bold Being, about why AI adoption failures are not technology problems but identity threats, and why organizations cannot train or explain their way through them.Eva spent two decades in healthcare across clinical research, bedside care, media, marketing, and startups as both a co-founder and early employee. She gave a TEDx talk called "Five Minutes to Fix Our Broken Healthcare System" about the patient-doctor relationship and the collapse of self-trust under relentless pressure. That same pattern of pressure-driven identity loss is now playing out across every industry as AI reaches professionals whose authority and sense of legitimacy are built on a specific kind of expertise.The conversation covers why AI adoption is an identity threat rather than a change management problem, why employee sabotage of AI initiatives (30 to 70 percent in some studies) is survival behavior rather than insubordination, the Luddite parallel (skilled workers whose livelihoods were being erased, not irrational resisters), why you cannot explain someone out of an identity threat, compliance theater (employees using AI to produce outputs and then quietly redoing the work themselves), decision latency in leaders who were previously decisive, leadership brittleness (senior leaders going rigid or withdrawing under AI pressure), Eva's three-stage framework (instability, autopilot, durable agency), sustainable adaptability versus just staying current, regulated empathy in healthcare and its parallel in every industry, a client transformation story (Chief Patient Safety Officer who eliminated ED wait times and saved $50 million after doing identity work rather than operational work and was promoted to CMO), and three practical steps leaders should take right now: name the instability out loud, audit your own autopilot, and change the metric from adoption speed to adaptive capacity.Timestamps:00:00 - Introduction00:41 - Eva's background: two decades in healthcare, TEDx talk, coaching03:20 - Daughter born on ChatGPT launch day: living in both disruptions04:04 - Identity threat versus change management06:26 - Employee sabotage: the Luddite movement of our era07:16 - Survival behavior, not insubordination09:09 - Skills training assumes a knowledge barrier, the real barrier is psychological09:59 - Not ethical unpreparedness but human unpreparedness10:09 - Decision latency, performative adoption, compliance theater11:13 - Leadership brittleness12:24 - Resistance is information, not a problem to suppress15:57 - Three-stage framework: instability, autopilot, durable agency19:22 - Sustainable adaptability: not staying current, building internal capacity22:07 - Holding ambiguity without defaulting to paralysis23:47 - Healthcare as the most human-dependent industry25:08 - Regulated empathy: suppressing emotional responses as default operating mode27:33 - Client story: Chief Patient Safety Officer to CMO in one year30:06 - Three practical steps for leaders30:49 - Step one: name the instability out loud31:40 - Step two: audit your own autopilot32:48 - Step three: change the metric to adaptive capacity34:45 - Recommendation: Brene Brown, Atlas of the Heart, shame versus guiltGuest: Eva Minkoff, Founder, Bold BeingEva is currently conducting research interviews for her book on how senior leaders are navigating AI-driven change. If that is your experience right now, request a private 30-minute conversation here: calendly.com/boldbeing/conversation To connect further: LinkedInWebsiteHuman Leadership Now Substack/NewsletterHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    The Identity Threat: Why AI Adoption Is a Human Challenge, Not a Technology Problem

  2. Jul 29

    From Pen and Paper to Agentic Workflows: Building AI That Works in the Real World

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Omid Pakseresht, CEO of Good Folio, about what it takes to build AI systems that actually work in enterprise settings, why model quality is no longer the bottleneck, and why adoption is a design problem that starts long before the technology.Omid studied math at Oxford and quantitative finance, built risk management tools, and was the first product person at two AI startups focused on knowledge graphs for finance and supply chain. He founded Good Folio about five years ago as an applied AI venture platform that sits between a platform company and a venture studio, where each deployment improves the next through cross-domain learnings and reusable infrastructure.The conversation covers the Unilever engagement (computer vision for small retail stores across Indonesia, Philippines, and Pakistan where markets went from pen and paper straight to agentic WhatsApp workflows, skipping digitization entirely), the Inspector product (AI-driven financial promotion compliance that redesigned the function rather than automating it), why better models no longer directly lead to better outcomes, why the constraint is now system design rather than model performance, the discovery process for mapping workflows and identifying leverage points, why building agents is like hiring new people for your organization, architectural choices driven by where workflow risk sits (small distributed models for emerging markets vs. strict guardrails for compliance), the cross-vertical pattern of systems over models, why incentive alignment is the most underappreciated adoption bottleneck, why too much focus on efficiency crowds out growth thinking, why adoption is a design problem, pilot fatigue, and practical advice for business leaders.Timestamps:00:00 - Introduction00:44 - Omid's background: Oxford, finance, AI startups, the hospital backroom moment02:39 - Good Folio: applied AI that works in enterprise settings03:46 - The venture platform model: most AI problems are system-level problems06:01 - Unilever: computer vision in emerging-market retail06:51 - From pen and paper straight to agentic WhatsApp workflows08:42 - Vision AI and agent systems for orders and replenishment09:34 - 5-8% sales increase in fast-moving consumer goods10:20 - Inspector: financial promotion compliance11:19 - AI-generated content creates more marketing but also more risk11:51 - Continuous monitoring agents trained with compliance officers12:53 - Marketing happier, compliance can sleep at night14:24 - The shift from model performance to workflow design15:05 - Better models don't directly link to better outcomes15:55 - Impressive pilots that don't scale17:22 - Discovery: mapping workflows and identifying leverage points18:25 - Building agents is like hiring new people19:03 - Building is the fast part; understanding requirements is the hard part21:15 - Architectural choices driven by workflow risk23:18 - Same technology, deployed differently for compliance25:17 - Cross-vertical patterns: systems over models26:50 - The adoption bottleneck: incentive alignment28:41 - Adoption as a design problem31:00 - Don't start with "what can I do?" Start with "where does the system break?"33:23 - Pilot fatigue and why the first success unlocks the rest34:38 - Recommendation: Matt Lerner, Growth LeversGuest: Omid Pakseresht, CEO, Good FolioHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    From Pen and Paper to Agentic Workflows: Building AI That Works in the Real World

  3. Jul 22

    AI as a Human Problem: Why Change Management Is Broken and Storytelling Is the Fix

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Gavin McMahon, Co-Founder and CEO of fassforward, about why AI adoption is not a technology problem but a human problem, why traditional change management is broken, and how storytelling can move people through the fear and uncertainty that AI has created.Gavin is an engineer by training with 30 years of consulting experience across automotive, defense, and technology. He worked at Gartner during the early internet era, helping move the company from paper delivery to online. In 2001, he co-founded fassforward, which has grown into a leadership and storytelling consultancy serving clients like Verizon and Mastercard. He recently published Story Business.The conversation covers why change management is a power dynamic problem (change managers have responsibility but no power), the shift from change management to change leadership, why the rate limiter on AI adoption is the organization's appetite for change, Hemingway's iceberg theory and how people fill in scary narratives when leaders leave gaps, the staircase problem (going down toward productivity is a race to the bottom), the shopping mall to Amazon value shift and why it is happening in dog years with AI, the motive triangle of hope, fear, and reason, why employees are sabotaging AI initiatives and the IKEA retraining model as the right approach, why AI works like an army of ants at the word and sentence level while humans think at book and chapter level, the production / coordination / judgment framework for splitting work, the judgment pipeline gap, why leaders should ask "am I using traditional thinking to solve a nontraditional problem," the tragedy of the commons across individual, organizational, and societal competition, the social media parallel, and why there is no AI strategy (just AI accelerating your business strategy).Timestamps:00:00 - Introduction00:44 - Gavin's background: engineering, Gartner, fassforward, Story Business01:41 - Why AI is about human engineering: decision and judgment03:07 - Execution validates strategy04:44 - Why "change management" is the wrong term05:39 - The power dynamic: responsibility without power06:50 - Change leadership, not change management07:30 - The rate limiter: organizational appetite for change09:08 - You lead people, you manage work10:06 - The river and rapids metaphor: pools of stillness11:27 - Storytelling as a mechanism for AI adoption11:52 - Hemingway's iceberg theory: people fill in the scary parts13:26 - AI as productivity hack vs. real workflow change15:50 - The staircase: going down toward productivity is a race to the bottom17:22 - Shopping mall to Amazon: a 20-year value shift in dog years20:00 - The motive triangle: hope, fear, and reason23:38 - Employee sabotage and the origin of the word "sabotage"25:20 - IKEA's retraining model: the right way to activate AI26:38 - AI-sized peg in a square hole28:52 - AI is an army of ants, not a human-shaped replacement31:15 - Production, coordination, and judgment: the three types of work33:27 - The judgment pipeline gap: pig in a python35:19 - Am I using traditional thinking for a nontraditional problem?36:32 - Tragedy of the commons: competition at every level42:17 - The social media parallel: same path, faster45:25 - What leaders should do first: business strategy, not AI strategy47:53 - The Chief Blank Officer as a telltale sign49:09 - Recommendation: Bryce Hoffman and Red TeamingGuest: Gavin McMahon, Co-Founder & CEO, fassforwardHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    AI as a Human Problem: Why Change Management Is Broken and Storytelling Is the Fix

  4. Jul 15

    AI-Ready Is Not AI-Enabled: The Architecture Gap Most Enterprises Are Ignoring

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Justin Bolles, CTO of Resultant, about the critical gap between being AI-enabled and being truly AI-ready, and why most enterprises are skipping the architecture, governance, and security work that determines whether AI deployments succeed or fail at scale.Justin brings over 15 years of experience across software development, cloud infrastructure, and AI consulting. He studied computer information systems at Purdue, founded three startups, and designed software across mobile, web, and embedded systems for both government and private sector clients before becoming CTO at Resultant, a data, technology, and AI consulting firm.The conversation covers the AI-ready vs AI-enabled distinction (turning on tools vs building the security and governance to use them safely), why security through obscurity died the moment AI could traverse file trees in milliseconds, the internal data leakage risk most companies overlook, metadata-rich ecosystems as prerequisites for AI at scale, the principle of least privilege for AI agents, governance by design, multi-model validation for hallucination safeguards, the Pivot workforce recommendation engine that won State IT Innovation of the Year, AI economics paralleling the early cloud era, and why suggestions over decisions is the right AI autonomy level today.Timestamps:00:00 - Introduction00:42 - Justin's background: Purdue, startups, Resultant01:48 - The low-code parallel: history repeating with AI03:19 - AI as an intelligent new college grad04:39 - AI-ready vs AI-enabled: the critical distinction05:51 - Security through obscurity is dead07:07 - Hallucination risks: fake citations and deleted databases08:12 - People, process, technology: in that order09:49 - Metadata-rich ecosystems as AI prerequisites13:05 - Institutional knowledge: processes never written down16:25 - Access controls and permission models for agents18:12 - Silicon Valley: AI deleted the UI to fix bugs20:04 - The 100-refunds story: keep customers happy gone wrong22:33 - Governance by design: build it in from the start24:25 - Generational workforce shifts and destination employers27:01 - Cost governance and AI billing surprises29:14 - Multi-model validation: never let a developer test their own code31:10 - AI subsidies will end: Wall Street demands profitability35:21 - Pivot: AI workforce engine for Indiana38:31 - 95%+ satisfaction, outcomes exceeding predictions41:07 - AI economics: pricing mirrors early cloud uncertainty43:08 - Start with the outcome, not the technology45:32 - Don't boil the ocean: narrow use cases to production48:03 - Assessment framework: security, use case, build, scale50:20 - Suggestions over decisions: the right autonomy level51:07 - Recommendation: Chase AI on YouTubeGuest: Justin Bolles, CTO, ResultantHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    AI-Ready Is Not AI-Enabled: The Architecture Gap Most Enterprises Are Ignoring

  5. Jul 8

    AI Exposes Lazy Management: Why Work Redesign Has to Come Before the Technology

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Jackson Lynch, Founder and President of Talent Sherpa, about why most organizations are not ready for AI agents, not because the technology is lacking but because the work itself was never properly designed. Jackson's thesis: AI does not make a system work, it just reflects how broken the system already was. Faster and at scale.Jackson started in industrial engineering at Boeing on the 777 program, then moved through HR leadership at International Paper, PepsiCo, Nestle, Clearwater Paper, and Sonoma Energy. Now he runs Talent Sherpa as a consigliere for CHROs and CEOs trying to treat human capital as infrastructure rather than overhead.The conversation covers why AI exposes lazy management (vague job descriptions and activity-based metrics worked because humans filled gaps, but agents cannot), activity-based vs. outcome-based role definitions (the accounts receivable example), work disaggregation before selecting a tool, the freed-up time problem (8 hours saved, 1 hour of productivity), the customer service agent misstep ("keep the customer happy" led to millions in refunds), why "human in the loop" signals the work has not been rethought, pivotal roles (the 5-7% with outsized influence on performance), the CHRO as Chief Workforce Optimization Officer, the Fortune 50 vs. SMB bifurcation, and practical advice for leaders who have not started.Timestamps:00:00 - Introduction00:47 - Jackson's background: Boeing, HR at PepsiCo, Nestle02:19 - Systems thinking, Deming, operating model over org chart03:03 - AI exposes lazy management03:48 - When AI hits fog, it scales the fog05:11 - Lazy management was invisible because humans filled the gaps06:02 - Automating a broken process breaks it faster and at scale07:10 - Activity-based vs. outcome-based role definitions08:53 - Outcomes enable creativity; activities drive blame10:06 - Agentic AI requires goals, and we are not good at them11:18 - The 1000 cold calls problem12:17 - Customer service agent misstep: millions in refunds13:02 - Work disaggregation: tasks, processes, high vs. low value15:49 - The freed-up time problem: 8 hours saved, 1 gained16:29 - Who owns work design?18:18 - Guardrails: deterministic vs. probabilistic boundaries20:35 - A misaligned agent damages a thousand relationships23:05 - The CEO-CHRO relationship24:11 - Fortune 50 vs. SMBs: company-size bifurcation27:14 - The hybrid workforce: employees, agents, fractional talent28:12 - Pivotal roles: the 5-7% with outsized influence30:05 - Engineering lens vs. change management lens31:31 - Undefined handoffs are where value leaks33:12 - Human in the loop as a transitionary stage36:00 - "Human in the loop" means you have not rethought the work37:17 - Three misconceptions about AI and workforce39:51 - 75% of AI strategies are for show; 90-95% no ROI40:46 - Start with pivotal roles, not tools42:15 - One team first, disaggregate, experiment fast43:07 - Onboarding as a safe starting experiment44:12 - Recommendation: Tony SarsenGuest: Jackson Lynch, Founder & President, Talent SherpaHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    AI Exposes Lazy Management: Why Work Redesign Has to Come Before the Technology

  6. Jul 1

    When AI Does the Building: Innovation, Ideation, and the New Creative Advantage

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Dr. Alex Mehr, Founder and CEO of Famous Labs, about why the most important competitive advantage in the AI era is no longer engineering skill but taste, judgment, and knowing what to build. Alex argues that AI has made execution so much easier that the bottleneck has moved upstream: the people who will win are the ones with the strongest product instincts and the clearest sense of what the market actually needs.Alex's path is distinctive. He grew up in an academic family with plans to become a physicist and university professor. He earned a PhD in mechanical engineering and worked at NASA Ames Research Center in California. Through proximity to Silicon Valley, he caught the entrepreneurship bug and co-founded Zoosk, a dating platform that grew to include a large engineering team. He describes becoming an entrepreneur as crossing the Rubicon: once you do it, there is no going back. He even kept publishing research papers during Zoosk's early years just in case he wanted to return to academia. He never did. Now he runs Famous Labs, which he describes as the ultimate playground for really smart people, constantly launching new AI-powered products.The conversation covers the taste shift (why engineering thinking still matters but judgment and product instinct now move the needle more than raw coding ability), how Famous Labs hires (they no longer ask technical questions but instead ask "what have you built and why does it look that way?"), the junior engineer pipeline gap (real but short-term and already dissolving as younger engineers pick up AI tools), why layoff narratives are overblown (companies have always right-sized and AI is just the latest excuse), Famous Labs' multi-product model and why AI enables "idea machines" who can pursue multiple products because execution costs have dropped, the death of the software moat (SaaS companies can no longer rely on their code as a competitive advantage), human-centric product philosophy (building things that add value without taking value from other humans), the innovation process (every major Famous Labs breakthrough has come from getting smart people together in a literal hotel room), Heisenberg as "Cursor for chemists" (vertical AI for small molecule drug discovery using chemistry-specific foundation models), why vertical AI is the next major evolution (every profession needs its own cursor equivalent), the SaaS-pocalypse pushback (nobody is going to vibe code their ERP because the real moats are compliance, testing, integration, and business logic), the hybrid workforce concept (every knowledge worker must be AI-enabled or competitors will eat your lunch), game theory dynamics (AI lets competitors enter your territory the way calorie-dense potatoes enabled New Zealand's territorial unification), and practical strategic and tactical advice for executives.Timestamps:03:20 - The taste shift: judgment matters more than coding ability05:15 - How hiring has changed: "What have you built?" replaces technical interviews06:14 - The junior engineer pipeline and the education system10:05 - Layoff narratives are overblown: AI is just the latest excuse to right-size11:55 - The Industrial Revolution parallel15:11 - The software moat is gone15:54 - Human-centric products16:30 - AI as coworker17:55 - Context windows and training19:33 - The ideation to output pipeline20:16 - Innovation workflow25:16 - Vertical AI: every profession needs its own cursor equivalent27:51 - Specialized models for specific professions30:36 - The SaaS-pocalypse pushback: nobody is vibe coding their ERP34:25 - SaaS companies should use AI to make their tools 10x better37:32 - The hybrid workforce38:44 - Every knowledge worker must be AI-enabled43:11 - The New Zealand potatoes analogy: AI enables territorial expansion45:04 - Systematically enable each role with AI46:12 - Recommendation: Nassim Taleb and Antifragile

    When AI Does the Building: Innovation, Ideation, and the New Creative Advantage

  7. Jun 24

    The End of One Model to Rule Them All: Why Enterprise AI Is Going Small, Specialized, and Multi-Model

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Calvin Cooper, Co-Founder and COO of Neurometric AI, about why the dominant narrative of scaling ever-larger frontier models is giving way to a more practical reality: smaller, specialized models fine-tuned for specific tasks that are faster, cheaper, and more accurate for the vast majority of enterprise AI workloads.Calvin started his career in early-stage venture capital at NCT Ventures in the Midwest, then founded Rove, a consumer fintech company he took public via a Nasdaq direct listing. Now he and Rob May have co-founded Neurometric AI, which builds task-specific small language model infrastructure. They went full time in August 2025, at a time when the dominant narrative was still "scale compute, scale larger models, AGI," because they were seeing something very different in the research and in practical enterprise deployments.The conversation covers the surgeon analogy (why you do not hire a surgeon to schedule an email), how their leaderboard proved that no single model is universally best and that inference time tactics can be as impactful as model choice, the AT&T case study (scaling from 8 billion to 27 billion tokens per day while cutting costs by 90%), how 24/7 AI agent runtimes turned subscription costs into six-figure monthly inference bills, why 75% of enterprise AI tasks do not need a frontier model, their marketplace of 115+ task-specific models under 20 billion parameters with fixed monthly pricing per endpoint, the Coding Swarm (orchestrating task-specific SLMs across the development lifecycle), why AI coding agents prove that AI expands jobs rather than replacing them, the four-stage enterprise AI maturity model, why calling a bubble is intellectually lazy (railroads had a bubble too), GPU underutilization and the case for both scaling capacity and improving efficiency, edge compute as the next frontier, and practical advice for enterprises on multi-model orchestration.Timestamps:00:00 - Introduction00:44 - Calvin's background: VC at NCT Ventures, founding Rove, Nasdaq exit01:37 - Following curiosity: why inference is the largest market opportunity of our lifetime03:47 - The surgeon analogy: why frontier models are overkill for most tasks04:58 - Smaller specialized models are faster, cheaper, and more accurate06:03 - Ship fast: the leaderboard as first proof point06:26 - No universal good model: different models perform differently at different tasks07:26 - Early adopter customers and the enterprise journey07:57 - Real example: Llama model at 4x cost and latency improvement10:20 - AT&T: 8 billion to 27 billion tokens per day, cut costs 90%11:30 - The 24/7 agent runtime problem: from subscription to $100K/month bills13:09 - Multi-model orchestration as the natural next step14:05 - SaaS pricing disruption and the need for cost predictability14:53 - 115+ task-specific models under 20 billion parameters15:06 - Fixed monthly pricing per endpoint with frontier fallback18:01 - 75% of enterprise tasks do not need a frontier model18:57 - The Coding Swarm: task-specific SLMs for the development lifecycle20:34 - AI and jobs: coding agents expanded demand for developers23:09 - Stage 4 maturity: from monolithic AI to dynamic resource matching23:31 - First KPI is learning, not ROI28:16 - Infrastructure: existing GPUs are underutilized31:14 - Efficiency is not just cost: latency, privacy, compliance32:11 - Privacy and compliance reasons for multi-model architecture33:09 - No one God model: the future is less Mission Impossible, more Tron34:17 - VC perspective shaping the Neurometric business model37:08 - Practical advice: cut your inference bill by 80-90%39:28 - Wrap-upGuest: Calvin Cooper, Co-Founder & COO, Neurometric AIHost: Michael Fauscette, CEO & Chief Analyst, Arion ResearchSubscribe and turn on notifications so you never miss an episode.

    The End of One Model to Rule Them All: Why Enterprise AI Is Going Small, Specialized, and Multi-Model

  8. Jun 17

    AI Meets the Mid-Market: How PE-Backed Companies Are Leapfrogging with AI

    In this episode of the Disambiguation podcast, host Michael Fauscette talks with Andrew Brooks, Founder and CEO of Contextualize, about why mid-market and PE-backed companies are in a unique position to leapfrog with AI, and how purpose-built solutions, inside-out disruption, and a multi-stage evolution from automation to intelligence are creating value these businesses could never have accessed before.Andrew is a serial entrepreneur whose career follows a consistent pattern: identifying new disruptive technology and connecting it to underserved markets. He founded SmartThings, the smart home platform that Samsung acquired, built and sold SMB Live to ReachLocal, and now runs Contextualize, which builds AI solutions specifically for mid-market B2B services organizations, many of them backed by private equity.The conversation covers why AI operates in two flavors (a new form of electricity and a tool for accelerating software creation), why mid-market companies now have the right to own purpose-built AI rather than renting features from enterprise vendors, how inside-out disruption differs from the Silicon Valley outside-in model, a fleet management case study where 14,000 emails per month from 3,000 vendors were processed by 13 humans, the vacation rental story, the multi-stage AI evolution from automation to data insight to prediction, "Digital Greg" and the challenge of capturing 25 years of institutional knowledge, governance by design with hard constraints, soft constraints, and separation of concerns architecture, how an agent layer can normalize data across 33 CRM systems after PE roll-ups, and practical advice for mid-market executives on where to start.Timestamps:00:00 - Introduction00:44 - Andrew's background: SmartThings, SMB Live, and founding Contextualize01:27 - The common thread: disruptive tech meets underserved markets03:08 - Why this is a leapfrog moment for the mid-market03:48 - AI in two flavors: new form of electricity and software accelerator04:43 - Own your AI, don't rent a feature05:06 - 25 years of institutional knowledge locked in people's brains05:40 - People, process, technology, and now AI as a fourth pillar06:24 - Inside-out disruption: how PE portfolio companies transform from within07:33 - Fleet management example: 14,000 emails, 3,000 vendors, 13 humans09:05 - The message to team members: removing tedium, not replacing people09:53 - 90% of solutions include a new human-AI interface10:49 - Vacation rental story: 3,000 properties, 10-12,000 work orders per month13:04 - The sidecar: a new human-AI interface for quality review13:50 - Ownership of outcome and the feedback loop14:18 - The batteries don't have serial numbers: edge cases that build trust15:25 - From checking to automating: the progression16:03 - Unexpected ROI: AI catches uninvoiced items16:48 - Multi-stage AI evolution: automation, then data insight, then prediction18:58 - Physical security company: hurricane-driven demand forecasting21:19 - Human in the loop vs. human in the lead at scale24:05 - You are never getting to 100%, and that is the right answer26:02 - Engineering firm: building code analysis with certification liability27:48 - Governance by design: hard constraints, soft constraints, and gating28:21 - Data governance as the most foundational layer31:07 - Don't over-index on security at the expense of value32:24 - Separation of concerns architecture with evaluator agents34:22 - Interceptor agents for cultural and behavioral guardrails36:33 - Digital Greg: capturing 25 years of refrigeration expertise39:42 - The line between AI and human touch is moving, not fixed40:44 - PE roll-ups and the 33-CRM nightmare41:26 - Agent layer for normalizing data across acquisitions46:08 - Advice for mid-market executives: where to start48:23 - Choose an internal champion49:33 - Recommendation: Thoreau's Walden, re-read at 51Guest: Andrew Brooks, Founder & CEO, ContextualizeHost: Michael Fauscette, CEO & Chief Analyst, Arion Research

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"Disambiguation is the process of removing confusion around terms that express more than one meaning and can lead to different interpretations of the same string of text." Host Michael Fauscette of Arion Research; a leading technology analyst, tech startup advisor, consultant, board member, and storyteller; and his guests "remove the confusion around" artificial intelligence (AI), generative AI and business automation by looking at the business solutions available today to improve business outcomes and gain competitive advantage. 

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  • Creator
    Michael Fauscette
  • Years Active
    2023 - 2026
  • Episodes
    148
  • Rating
    Clean
  • Copyright
    ©️All Rights Reserved
  • Show Website
    Disambiguation