Disambiguation

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 - Introduction
00:44 - Andrew's background: SmartThings, SMB Live, and founding Contextualize
01:27 - The common thread: disruptive tech meets underserved markets
03:08 - Why this is a leapfrog moment for the mid-market
03:48 - AI in two flavors: new form of electricity and software accelerator
04:43 - Own your AI, don't rent a feature
05:06 - 25 years of institutional knowledge locked in people's brains
05:40 - People, process, technology, and now AI as a fourth pillar
06:24 - Inside-out disruption: how PE portfolio companies transform from within
07:33 - Fleet management example: 14,000 emails, 3,000 vendors, 13 humans
09:05 - The message to team members: removing tedium, not replacing people
09:53 - 90% of solutions include a new human-AI interface
10:49 - Vacation rental story: 3,000 properties, 10-12,000 work orders per month
13:04 - The sidecar: a new human-AI interface for quality review
13:50 - Ownership of outcome and the feedback loop
14:18 - The batteries don't have serial numbers: edge cases that build trust
15:25 - From checking to automating: the progression
16:03 - Unexpected ROI: AI catches uninvoiced items
16:48 - Multi-stage AI evolution: automation, then data insight, then prediction
18:58 - Physical security company: hurricane-driven demand forecasting
21:19 - Human in the loop vs. human in the lead at scale
24:05 - You are never getting to 100%, and that is the right answer
26:02 - Engineering firm: building code analysis with certification liability
27:48 - Governance by design: hard constraints, soft constraints, and gating
28:21 - Data governance as the most foundational layer
31:07 - Don't over-index on security at the expense of value
32:24 - Separation of concerns architecture with evaluator agents
34:22 - Interceptor agents for cultural and behavioral guardrails
36:33 - Digital Greg: capturing 25 years of refrigeration expertise
39:42 - The line between AI and human touch is moving, not fixed
40:44 - PE roll-ups and the 33-CRM nightmare
41:26 - Agent layer for normalizing data across acquisitions
46:08 - Advice for mid-market executives: where to start
48:23 - Choose an internal champion
49:33 - Recommendation: Thoreau's Walden, re-read at 51

Guest: Andrew Brooks, Founder & CEO, Contextualize
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

In this episode of the Disambiguation podcast, host Michael Fauscette talks with Patrice Greene and Kathy Macchi, co-founders of Inverta, about why marketing's rush to AI efficiency missed the point, and what it really takes to rethink go-to-market workflows with AI at the core rather than bolted on top.

Patrice is an early adopter of marketing automation who started in sports marketing before spending years in the Marketo community, eventually co-founding Inverta. Kathy brings an IT and operations background and has never had the luxury of separating marketing strategy from marketing infrastructure. Together they built Inverta to bridge the gap between strategy-led firms that lacked technical depth and tech-enabled firms that had no strategy, delivering what they call "roll up your sleeves" operational consulting for B2B marketing.

The conversation covers what CMOs told Inverta's council at the end of 2025 (they thought they'd be further along with AI), why individual efficiency gains never translated into revenue impact, why you have to redesign workflows across teams rather than just hand out tools, the European supply chain analogy (why marketing needs its own ERP moment), the McKinsey threat (if marketers don't define how AI fits their function, consultants will define it for them), how CMOs need political capital and a vision that goes beyond cost cutting, Geoffrey Moore's four-box framework applied to AI decision-making in marketing, why managing AI agents has the same challenges as managing people (including a cautionary story about an agent that eroded a premium brand by over-optimizing for discounts), how AI is creating a new role in the buyer group and making 1-to-1 ABM at scale finally possible, and where marketing leaders should start their first AI workflow pilot.

Timestamps (approximate, verify against final edit):
00:00 - Introduction
00:45 - Patrice and Kathy's backgrounds: from Marketo and IT ops to co-founding Inverta
02:37 - Why Inverta exists: bridging the gap between strategy and tech in B2B marketing
03:58 - CMO council findings: teams thought they'd be further along with AI
05:07 - Individual efficiency gains did not translate into revenue
06:22 - Don't leave adoption to chance: clarity, accountability, and support
07:55 - Patrice: has efficiency really been realized? Now what?
09:08 - FOMO is driving rapid adoption of AI point solutions
09:56 - Automating broken processes just makes them broken faster
11:29 - The European supply chain analogy: rethink the whole workflow
13:17 - Who owns AI workflow redesign? Marketing, IT, or a translator?
15:01 - Traction, not transformation: why the big word is counterproductive
16:06 - Skills required: marketing expertise, org design, facilitation, change management
16:55 - Marketing therapists: managing anxiety and fear in teams
18:33 - Accountability: who is responsible when an AI workflow goes wrong?
19:25 - The cost cutting trap: Gartner says you'll rehire 50-60% in two years
20:40 - The story can't be all about efficiency: it has to be about growth
21:17 - AI-mediated buyer journey: if you're not investing now, you won't even show up
23:33 - The McKinsey threat: define AI's role or someone else will
27:30 - Geoffrey Moore's four-box framework: core vs. context for AI decisions
30:55 - Hybrid teams: workflow redesign before agents
32:16 - Managing agents is like managing people: goals, guardrails, performance reviews
33:29 - Brand risk story: agent over-optimized for discounts
35:00 - Creating content for machines and people
35:56 - AI as a new buyer group role: agents doing research on behalf of buyers
37:30 - 1-to-1 ABM at scale: what used to be a luxury is now possible
38:47 - Where to start: pick a workflow problem with a measurable outcome
40:49 - Recommendations: Kerry Cunningham and Jeff Woods

Guest: Patrice Greene and Kathy Macchi, Co-Founders, Inverta
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Victoria Mensch, CEO of Silicon Valley Executive Academy, about why AI is not just a technology shift but a cognitive revolution that challenges the very identity of leaders and demands a completely different human operating model.

Victoria holds a PhD in psychology, spent 25 years in Silicon Valley high tech across large and small companies in enterprise software, and founded the Silicon Valley Executive Academy to help companies and executives tap into the Silicon Valley innovation playbook. Her unique lens, combining neuroscience, psychology, and leadership strategy, frames AI adoption as a human transformation challenge, not a technology deployment problem.

The conversation covers why AI creates an identity crisis for leaders whose value was built on being the smartest person in the room, how the Silicon Valley innovation playbook applies to AI adoption (bias toward experimentation and treating failure as data), why human in the loop should evolve to human in the lead, the automation trap of applying AI to broken processes instead of redesigning work, why unrealistic productivity expectations are driving burnout, how AI unbundles job roles and creates both risk and opportunity, the shift from task management to systems design as the core leadership skill, and why empathy and motivation will define next-generation leadership.

Timestamps:
00:00 - Introduction
00:45 - Victoria's path: PhD in psychology to 25 years in Silicon Valley tech
02:19 - AI as a cognitive revolution: intelligence was the leader's identity
03:39 - The identity crisis: machines can do cognitive tasks better
04:11 - Finding your unique value: using AI as support, not replacement
04:43 - The Silicon Valley innovation playbook: what the best companies do differently
05:10 - Nobody has figured this out yet, even Silicon Valley is catching up
05:39 - Bias toward experimentation: treating pilots as data-driven experiments
06:34 - Embracing failure as a lesson, not a loss
07:25 - From human in the loop to human in the lead
08:05 - What leading an AI-augmented team actually looks like
09:07 - What you put in is what you get out: the value of human input
09:45 - Systems thinking versus task delegation
10:07 - Managing AI teams is not that different from managing human teams
10:50 - Subject matter expertise is not going away
11:23 - Ownership mindset: "AI replaced my tasks" versus "I replaced those tasks"
11:58 - Leadership versus position on the org chart
12:30 - Treat your career as your business
13:17 - AI unbundles job roles: what to automate and what to grow
14:05 - Management versus leadership in the AI era
15:04 - AI-accelerated burnout: the story of the marketing executive
16:00 - The impossible expectation: performing at machine pace
16:52 - Smart companies uplevel tasks instead of raising quotas
17:20 - Burnout warning signs: chronic fatigue, lost motivation, physiological changes
18:26 - Unrealistic productivity goals from executives who do not understand the tech
18:44 - Do not outsource thinking: the value of cognitive work
19:30 - Content flood: more output without more quality
20:21 - Rethink the KPIs: what are you actually optimizing for?
20:52 - Do not automate the broken process
21:17 - Automating a patch that covers a workflow breakage just creates more noise
22:19 - AI is a transformation opportunity, not just a tool
23:10 - What it takes to redesign work at the organizational level
24:59 - Three priorities: redesign work, build trust through clarity, elevate human qualities
27:07 - The future of leadership: from task management to systems design
28:54 - Empathic leadership and motivating free agents
29:45 - Developer story: moving from coding to conceptual design
30:49 - I want my engineers to solve problems, not write code
31:47 - Recommendation: Sol Rashidi, CIO and AI thought leader

Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

In this episode of the Disambiguation podcast, host Michael Fauscette talks with Drew Sechrist, Co-founder and CEO of Connect the Dots AI, about why AI-generated outreach is flooding inboxes, destroying cold email effectiveness, and making trusted human relationships the most valuable asset in sales.

Drew was employee number 36 at Salesforce, where he cold emailed Marc Benioff in 1999 and spent a decade helping take the company from zero to $1 billion in revenue. The biggest lesson from that experience: the cheat code in sales is knowing who knows who. Connect the Dots maps professional relationships using email history, LinkedIn career overlaps, and communication patterns, then scores relationship strength so sales teams can find warm paths into target accounts they never knew existed.

The conversation covers Gresham's Law applied to outbound sales (bad outreach drives out good), why the only things that cut through inbox noise are trusted introductions and perfectly nailed problem statements, how the ghost email system works (the same approach Drew used with Benioff for a decade, now automated), why relationship strength should be a core primitive in every CRM system, the data quality challenge of building a 99%+ accurate relationship graph, the pendulum swing from data privacy fear to competitive FOMO, why AI native CRMs will challenge Salesforce and HubSpot, the barbell theory of future work, and why human relationships may be the last thing AI cannot automate.

Timestamps:
00:00 - Introduction
00:42 - Employee 36 at Salesforce: cold emailing Marc Benioff in 1999
01:53 - The cheat code: it really is who you know
03:38 - How Connect the Dots works: mapping invisible relationship paths
05:12 - Finding warm paths you never knew existed: board members, college roommates, career overlaps
05:53 - Proprietary scoring algorithm: relationship strength across your entire graph
06:16 - The flight to relationships: Gresham's Law applied to outbound sales
08:04 - The only two things that cut through inbox noise
09:01 - Trust as the filter: if the messenger is trusted, you will read it
10:18 - Ghost emails: how Drew turned Marc Benioff into his SDR for a decade
12:04 - Automating the ghost email: reducing friction to one tap
13:10 - The people with the most relationship leverage have the least time
13:53 - How buyer behavior has shifted: 80% of buyers have already chosen their vendor
15:30 - Relationship intelligence: planting seeds before buy mode begins
16:54 - The economics of attention: trust earns the right to someone's finite time
19:55 - Where agents should automate and where the human relationship stays
20:48 - Tasks are going asymptotically toward zero, but relationships are the last holdout
22:06 - The agent as presidential aide: facilitating, not replacing, the relationship
24:17 - Data quality and privacy: three years to build a 99%+ accurate data engine
25:13 - The pendulum swing: from data privacy fear to competitive FOMO
27:33 - Not a data broker: intentional security and trust architecture
29:42 - Where Connect the Dots fits in the evolving sales tech stack
30:49 - AI native CRMs and the future of the CRM market
32:21 - The trust layer across the internet: two new primitives for every CRM
34:57 - 2026 is the year of actual AI automation of go-to-market workflows
35:24 - Your relationship graph is the one proprietary signal your competitors cannot replicate
38:57 - The hybrid workforce: the barbell theory of future work
42:22 - The 10x engineer versus the 1.2x engineer
44:47 - Recommendation: Bob Moore, CEO of Crossbeam

Guest: Drew Sechrist, Co-founder and CEO, Connect the Dots AI
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Joshua Gould, CEO of The BigWord, about the hidden economics of enterprise AI deployment and why AI agents often cost more than the humans they are meant to augment.

Joshua has spent over 20 years in language services, co-founded TBB Global, and now runs one of the world's largest language service providers operating in 80 countries across 250+ languages. The BigWord has been navigating AI disruption since the late 1990s, from machine learning-driven translation memory to neural machine translation to today's large language models.

The conversation covers the real math behind AI agent deployment in call centers, why integration and infrastructure costs dwarf license fees, why the AI industry is negatively scaling at the macro level, the parallel between today's AI hype and the dot-com boom and bust, how regulated industries like courts and healthcare are deploying AI methodically versus recklessly, why governance by design matters when errors scale at machine speed, and why the companies built like cockroaches will outlast the hype cycle.

Timestamps:
00:00 - Introduction
00:42 - Joshua's background: from selling beer to Wall Street language services
03:40 - The first AI disruption: machine learning and translation memory in the 1990s
05:23 - Integrations and automated workflows for banks
06:54 - Pivoting to government contracting after the Great Recession
08:25 - Building a defense contracting company from scratch to $20M
10:49 - The 2019 vision: multilingual communications platform
11:45 - Covid's devastating impact: losing 47% of revenue overnight
12:46 - Selling to Susquehanna private equity in 2021
13:06 - LLMs arrive: the realization that AI is not free
15:43 - The real math: why AI agents cost more than human agents
18:07 - Hidden costs: integrations, infrastructure, tuning, and orchestration
19:05 - AI can only do 70% of what a human does, 70% of the time
19:54 - Why AI costs will not come down as fast as people expect
21:10 - Data center rebuild cycles and the $1.4 trillion CapEx problem
22:21 - Why deploy AI if it is not cheaper? Stability, speed, and service quality
25:08 - The FOMO driving boards and the CEO firing wave
30:03 - The coming correction: dot-com parallels and who will survive
31:29 - Why enterprise SaaS is not going away
32:19 - Staging AI deployment to protect against confidence collapse
35:16 - Be a cockroach: companies built for survival versus hype
37:01 - Governance by design: when errors happen 30,000 times in three milliseconds
39:57 - Testing at scale and the danger of AI policing AI
44:11 - Lessons from three waves of AI: watch regulated industries
47:59 - Unintended consequences: data saturation and content noise
48:40 - Recommendation: Gold with Gold podcast (Larry Gould, Cornell University)

Guest: Joshua Gould, CEO, The BigWord
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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In this episode of the Disambiguation podcast, host Michael Fauscette sits down with Chris Morancie, Fractional CTO and Founder of Digital Operations Factory, for a deeply technical and practical conversation about why AI governance has to be engineered into your architecture, not bolted on after the fact.

Chris brings a unique combination of computer information systems, an MBA in business strategy, and a master's in data science to the problem of getting AI into production safely. His core argument: if your governance cannot stop your model from doing something wrong in real time, then it is not governance, it is just documentation.

The conversation covers his three-part scalability test (design for scale, make sure it doesn't break at scale, don't go broke at scale), the Goldilocks zone for model selection, why agents should be treated through a microservices security lens with least-privilege access and short-term tokens, the firewall pattern for policy enforcement, real-time semantic interceptors for customer-facing AI, operational sovereignty and vendor SLA inheritance, IP leakage through model training, and a practical trust-vs-reasoning quadrant for managing hybrid human-agent teams.

Timestamps:
00:00 - Introduction
00:44 - Chris's background: Caribbean upbringing, CIS + MBA + Data Science
03:48 - The AI production framework: design for scale, don't break at scale, don't go broke at scale
07:17 - The Goldilocks zone: model selection and cost benchmarking
09:28 - Assertion testing vs. evaluation testing for model quality
10:25 - "If your governance can't stop your model in real time, it's just documentation"
13:26 - The firewall pattern: policy agents with least-privilege, short-term tokens
16:09 - AI governance as good old-fashioned software hygiene
17:49 - Real-time semantic interceptors for customer-facing agents
21:15 - Competing goals: why prompts alone cannot prevent policy violations
24:02 - Agent security: every ingress and egress point is a vector
27:55 - RAG poisoning and downstream injection attacks
29:00 - Operational sovereignty: SLA inheritance and vendor risk
34:56 - IP leakage: when your feedback trains a competitor's model
36:16 - Trust vs. reasoning: a quadrant for managing hybrid teams
41:37 - Advice by company size: economics for SMEs, security for enterprise
45:25 - Recommendation: DALI Research Labs (YouTube)

Guest: Chris Morancie, Fractional CTO and Founder, Digital Operations Factory
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Clayton Bryan, Head of Enterprise at Quill, about why data sovereignty is becoming the defining issue for enterprise AI adoption and why most companies are on the wrong side of the trend.

Clayton spent a decade as an early-stage investor at 500 Global before joining Quill, where he leads enterprise strategy. Quill's architecture is built local-first: audio transcription never leaves your device, and enterprise clients can bring their own LLM stack so that all data stays under their control. Clayton explains why this matters for regulated industries from defense to healthcare to financial services, how CISOs are becoming advocates once they understand the architecture, and why bolt-on governance will always leave gaps.

The conversation covers why ChatGPT has a "professional trust deficit," why the line between enterprise and personal AI is dissolving, how Quill's agent (Quilliam) automates post-meeting workflows like CRM updates and project management tickets, and why data sovereignty is on the same trajectory as HTTPS -- optional today for some, table stakes tomorrow for all.

Timestamps:
00:00 - Introduction
00:44 - Clayton's journey from 500 Global investor to joining Quill
03:48 - "AI without compromise" -- what that actually means
04:22 - Local-first architecture: audio never leaves your device
06:45 - Air-gapped deployments and who needs them
08:17 - GDPR demand and the EU enterprise tour
09:28 - Convenience vs. compliance: the real tradeoff
10:38 - Bottom-up adoption: when CISOs investigate and become advocates
12:34 - Why bolt-on governance is broken
14:28 - Governance by design at machine speed
14:57 - Investor-founders who understand what enterprise buyers actually need
17:11 - ChatGPT's "professional trust deficit"
19:31 - The dissolving line between enterprise and personal AI
20:48 - Beyond transcription: automating post-meeting workflows with Quilliam
24:27 - AI and the future of work: new skills, new opportunities
27:18 - Vibe coding: prototypes vs. production
30:13 - The future: local-first as minimum standard, not premium option
31:57 - Data sovereignty is the next HTTPS
33:12 - Recommendation: Matthew Berman

Guest: Clayton Bryan, Head of Enterprise, Quill
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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In this episode of the Disambiguation podcast, host Michael Fauscette sits down with Dr. Deepak Bhootra, Founder of RISEUP Career Studio, for a candid conversation about why AI is creating an illusion of progress in sales while the real, hard work remains deeply human.

With 30 years in sales, a doctoral degree studying job satisfaction and organizational commitment, and an ICF coaching certification, Deepak brings a unique lens to the collision between AI hype and sales reality. He explains why salespeople use AI for the easy stuff like drafting emails but avoid using it for the hard stuff like self-reflection and honest deal analysis. He unpacks the "seductive" nature of AI output that makes activity feel like progress, the rise of individual shadow IT stacks where top reps spend $300 a month on personal tools they refuse to share, and why automating a broken process just scales the brokenness faster.

Deepak also introduces RISEUP Career Studio, his platform designed to help early-career professionals (ages 21-35) build the judgment, self-reflection, and career navigation skills that no college degree teaches and no AI can replace.

Timestamps:
00:00 - Introduction
00:45 - Deepak's 30-year sales journey and why he founded RISEUP
04:08 - "AI is the biggest distraction" in sales
06:37 - Why salespeople will never admit AI made them better
07:58 - How sales leaders should reframe AI adoption
10:16 - The cold email nightmare: 12,000 to 62,000 emails, same revenue
12:05 - AI-written LinkedIn profiles are sending false signals
14:35 - Shadow IT goes personal: reps building their own AI stacks
18:35 - Why top reps refuse to share their prompts
20:23 - The ethics of presenting AI output as your own work
23:42 - 70% transformation failure and the vision-to-execution gap
26:31 - "Scaling stupidity": when AI automates broken processes
29:13 - The black box trust problem in sales AI
31:31 - Using generative AI to demystify predictive AI
36:01 - RISEUP Career Studio: structured guidance for early careers
44:56 - The future of sales will be more human, not less
48:20 - Generic sellers are the next geriatrics
50:07 - Recommendations: Suresh Vasu, Mark Finnick, Ethan Mollick

Guest: Dr. Deepak Bhootra, Founder, RISEUP Career Studio
Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

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Most enterprises think they are ready for autonomous IT. Most are not. And frankly, they should not pretend they are.

In this episode, Michael Fauscette sits down with Brian Amaro, Vice President of Customer Value and Partner Strategy at ScienceLogic, to map the real journey from reactive, siloed IT operations to autonomic IT. Brian shares the maturity progression his team built from hundreds of Fortune 500 engagements, the 15% adoption rate story that exposes the biggest misconception about AI in IT ops, and why the first win is never self-healing. It is better judgment under pressure.

TIMESTAMPS:
00:00 - Introduction
01:03 - Brian's Background and What ScienceLogic Does
02:51 - Why AI Can't Be Bolted On: ScienceLogic's Ground-Up Rebuild
04:55 - Autonomic IT: Where Are We Really on the Journey?
07:20 - Assisted Autonomy Is Real; Broad Autonomy Is Not a Day One Strategy
09:23 - Human in the Loop to Human in the Lead: The Trust Progression
10:05 - Trust Formation, Not Speed: Where Most AI Adoption Goes Wrong
10:53 - The 15% Adoption Rate Story: Why Engineers Resisted AI
12:19 - Human on the Loop: Supervising Outcomes, Not Approving Actions
12:41 - Human in the Lead: Defining Intent, Not Controlling Tasks
13:59 - Moving Past the Approval Trap: Policy-Based Control
16:19 - Skylar Advisor: From "What Fired" to "What Matters"
19:31 - Why Observability, AI, Automation and Compliance Must Be One Platform
22:05 - What Analyst Recognition Tells Us About Where the Market Is Heading
24:09 - AI Governance by Design: Guardrails, Auditability, and Transparency
27:13 - Real-World Customer Journey: From Reactive Ops to Governed Automation
30:17 - The First Win Is Not Self-Healing: It Is Better Judgment Under Pressure
31:00 - The Hybrid Workforce in IT Ops: Better Leverage, Not Fewer Engineers
33:56 - The 2-3 Year Outlook: AI as Operating Model, Not Feature Rollout
36:13 - Don't Wait for Perfect Data: Start Where the Pain Is High
37:15 - Brian's Recommendation: Ethan Mollick's Co-Intelligence
38:30 - Wrap-Up

ABOUT THE GUEST:
Brian Amaro is the Vice President of Customer Value and Partner Strategy at ScienceLogic, where he has spent years working across customer strategy, advisory, and operational transformation. A certified Project Management Professional (PMP) with 30 years of experience, Brian helped build ScienceLogic's maturity model drawn from over 300 Fortune 500 customer engagements.

ABOUT DISAMBIGUATION:
AI clarity for business leaders. New episodes every Wednesday.
Host: Michael Fauscette, CEO & Chief Analyst at Arion Research
Author of "Building the Digital Workforce"

SUBSCRIBE & follow for weekly episodes on AI strategy, agentic AI, and enterprise technology.

Website: https://www.disambiguationpod.com/
Arion Research: https://www.arionresearch.com/
LinkedIn: https://www.linkedin.com/in/mfauscette/

Most companies jump straight to AI use cases. They pick the hottest tools, launch pilots, and wonder why nothing scales. The problem isn't the technology. It's that they don't have a map of how work and decisions actually move through their organization.

In this episode, Michael Fauscette sits down with Minyang Jiang (MJ), Chief Strategy & Revenue Officer at Credibly, to unpack what she calls the Intelligence Model: a framework for understanding where AI excels, where humans still lead, and why the scarcity models that shaped how businesses operate for decades are now being fundamentally challenged by AI's abundance. MJ also shares hard-won lessons from leading AI transformation at a fintech lending company, including why friction is something leaders should protect, not eliminate.

TIMESTAMPS:
00:00 - Introduction
01:16 - MJ's Path: From Ford Motor Company to Fintech AI Transformation
03:17 - Why System-Level AI Change Is So Hard for Real Businesses
06:52 - The Intelligence Model: Mapping Work Before You Automate It
09:05 - The Scarcity Problem: Decisions Built Around Human Limits
10:34 - Where Humans Still Beat AI: Prioritization, Decisioning, and Intuition
13:17 - Why Human-AI Collaboration Still Produces the Best Results
15:52 - The Awkward Teenage Phase of AI Agents
18:32 - AI Has No Mental Model: The Adjacency of Expertise Problem
22:23 - Credibly AI: Patented AI Underwriting and Industry Classification
27:01 - The Explainability Tension: Complexity vs. Customer Trust
29:39 - Change Management: The Crowd, the Lab, and the Leader
33:35 - The Balloon Effect: Why AI Productivity Creates More Work, Not Less
37:19 - Human in the Loop to Human in the Lead: The Trust Progression
42:10 - Agent-to-Agent Commerce: Redesigning for Machine Buyers
45:37 - What Leaders Should NOT Do: The Case for Intentional Friction
48:48 - MJ's Recommendations: Stefano Bertoni and Lenny's Newsletter
49:51 - Wrap-Up

ABOUT THE GUEST:
Minyang Jiang (MJ) is the Chief Strategy & Revenue Officer at Credibly, a fintech lending company, where she leads the AI transformation and innovation team. Her career spans Ford Motor Company's marketing program, founding the Go Ride Health startup within Ford's mobility division, and building cross-functional AI adoption strategies in financial services.

ABOUT DISAMBIGUATION:
AI clarity for business leaders. New episodes every Wednesday.
Host: Michael Fauscette, CEO & Chief Analyst at Arion Research
Author of "Building the Digital Workforce"

SUBSCRIBE & follow for weekly episodes on AI strategy, agentic AI, and enterprise technology.

Website: https://www.disambiguationpod.com/
Arion Research: https://www.arionresearch.com/
LinkedIn: https://www.linkedin.com/in/mfauscette/