📢 Can open source, AI, and enterprise analytics really coexist? Absolutely—and Bruno Trimouille from Posit is here to explain how.
How are open source tools reshaping enterprise data science? What role does AI play in bridging business and technical teams? In this episode, Posit CMO Bruno Trimouille breaks down how his team supports millions of users—while staying true to an open source mission.
🎯 Whether you’re a data leader, marketer, or innovator, you’ll learn practical approaches to balancing innovation with governance, productizing models into apps, and using AI for both technical and marketing acceleration.
🔍 Key Takeaways:
1- Why a code-first approach delivers trust, transparency, and reproducibility
2- How AI bridges the gap between business users and data scientists
3- What Posit’s B2B open source flywheel model looks like behind the scenes
4- Why governance doesn’t have to kill speed—in fact, it can enable scale
5- How marketing teams can harness Gen AI for content, segmentation & insights
⏳ Timestamps for Easy Navigation:
00:00 – Intro & guest welcome
00:52 – What is Posit? (formerly RStudio)
02:06 – Bruno’s journey: Engineer to CMO
04:53 – Open source, code-first, and the future of data science
07:32 – AI’s impact on productivity and risk in analytics
09:13 – Solving the governance vs. speed tension
11:29 – Using models & apps to make insights business-ready
13:13 – Building a business on open source: Posit’s flywheel
15:31 – Why organizations bet on open data tools
18:09 – Community-building as a growth engine
21:31 – How Posit marketing uses Gen AI every day
24:04 – AI for personalization, segmentation & ABM
27:51 – Multimedia & interactive learning with Gen AI
30:31 – Using AI for data insights & campaign analysis
33:14 – How AI is reshaping the marketing org chart
34:04 – Future of data-driven marketing leaders
35:49 – Final thoughts from Bruno
📩 More insights & resources:
👉 https://tinytechguides.com/blog/category/data-faces-podcast/
🔗 Connect with Bruno Trimouille:
💼 LinkedIn: https://www.linkedin.com/in/brunotrimouille
🌎 Website: https://posit.co
💬 What do you think? Drop your thoughts in the comments!
👍 Enjoyed this video? Like, share & subscribe for more AI insights!
#OpenSourceAI #DataScienceLeadership #ResponsibleAI
📢 Most AI failures don’t come from the model—they come from the data feeding it.
In this episode of the Data Faces Podcast, Tina Chace, VP of Product Management at Solidatus, explains why incomplete lineage, missing context, and silent upstream changes quietly undermine AI systems long before anyone notices.
Tina shares lessons from deploying AI and machine learning in major banks, breaking down how column-level lineage and business context prevent cascading failures across systems, teams, and decisions.
🔍 Key Takeaways:
1- Why 90% of AI production issues trace back to data quality problems.
2- How technical and business lineage work together to build trust.
3- Why column-level tracking exposes the hidden transformations behind every metric.
4- How visibility without control increases anxiety across data teams.
5- Where organizations should start to get quick wins without “boiling the ocean.”
⏳ Timestamps for Easy Navigation:
00:00 – Intro: David Sweenor introduces Tina Chace
00:54 – Tina’s early career and the origins of her data skepticism
02:28 – The 90% data problem in AI and ML deployments
03:24 – What data lineage actually captures
06:47 – The rounding-error problem that compounds at scale
07:59 – Bridging the language gap across data, reporting, and business teams
09:46 – Who really owns data quality and lineage?
12:43 – Technical vs. business lineage, with real examples
16:24 – Managing complexity across systems, teams, and tech stacks
18:16 – Why documenting “everything” never works
23:28 – Data lineage in generative AI and RAG systems
30:57 – Why AI makes complete lineage non-negotiable
33:26 – The trust paradox: more visibility, more skepticism
34:27 – How to get started without boiling the ocean
35:35 – Closing remarks
📩 More insights & resources:
👉 Blog: https://tinytechguides.com/blog/data-lineage-for-ai-why-truth-beats-hope-in-banking/
🎧 Listen to the Data Faces Podcast:
YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurR
Spotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yF
Apple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487
🔗 Connect with Tina Chace:
LinkedIn: https://www.linkedin.com/in/tina-chace-rho-5433133b/
Solidatus: https://www.solidatus.com
💬 What’s the biggest data trust challenge in your organization? Tell us in the comments.
👍 Like, share, and subscribe for more conversations with leaders shaping AI, analytics, and data strategy.
#DataLineage #DataQuality #AITrust
📢 What happens when culture and technology collide—do they compete, or do they enhance one another?
In this episode of Data Faces, Gina von Esmarch, Founder and CEO of Adesso Associates, shares how cultural heritage, storytelling, and emerging technologies work together to shape stronger communities and brands.
🔍 Key Takeaways:1- How AI and cultural identity can evolve together, not apart.2- Why authenticity and values are the foundation of innovation.3- How diversity of thought drives long-term business success.
📩 Watch the full episode here: https://www.youtube.com/watch?v=F8-j7nqzsGk
👉 More insights: https://prompts.tinytechguides.com/s/the-data-faces-podcast
💬 How do you see culture influencing technology in your industry?
👍 Like, share & subscribe for more insights.
#DataFaces #AI #CultureAndTech #Leadership #Innovation
📢 What happens when AI becomes your sales coach? In this episode, we explore how artificial intelligence is reshaping how sales teams learn, ramp, and perform.Join David Sweenor, Founder of TinyTechGuides, as he talks with Matt Magne, Senior Enablement Manager of Revenue Enablement at LaunchDarkly, about the new frontier of AI-driven sales enablement — from AI role plays to digital coaching and the future of human + machine collaboration.🔍 Key Takeaways:-- Why AI is redefining how SEs and AEs practice and master sales conversations-- How LaunchDarkly integrates AI role plays to accelerate ramp time and confidence-- The balance between automation and authentic human coaching-- The data challenges still holding back innovation in enablement-- Why the future of enablement is about augmentation, not replacement⏳ Timestamps for Easy Navigation:00:00 – Welcome to Data Faces with David Sweenor01:00 – Meet Matt Magne and LaunchDarkly06:10 – What revenue enablement really means08:25 – How AI role plays improve ramp and coaching11:00 – Do reps prefer training with bots or humans?14:00 – Space repetition, practice, and performance17:10 – Building hybrid AI + human onboarding programs21:00 – Overcoming skepticism about AI training26:00 – The “skeptical buyer” prompt and realistic simulations27:15 – Human + machine collaboration: finding the right balance31:20 – Beyond scale: personalization and productivity with AI34:50 – The future of sales enablement: data, integration, and intelligence39:30 – Final takeaway: staying human in an AI-driven world📩 More insights & resources:👉 Read more at: https://open.substack.com/pub/davidsweenor/p/augmented-intelligence-the-future?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true🔗 Connect with Matt Magne:💼 LinkedIn: https://www.linkedin.com/in/exrocker/🌎 LaunchDarkly: https://launchdarkly.com💬 What do you think — can AI make us better sales coaches? Drop your thoughts in the comments!👍 If you enjoyed this episode, like, share, and subscribe for more insights from Data Faces.#AIinSales #SalesEnablement #DataFacesPodcast
📢 What’s the real end game of AI? Is it about smarter machines—or smarter organizations?In this episode of Data Faces, Eric Kavanagh, AI analyst and syndicated radio host of DM Radio, joins David Sweenor, founder of TinyTechGuides, to explore what success in AI truly means for business, technology, and society.Eric has spent decades helping enterprises navigate the data-driven world. He argues that AI isn’t a monolith—it’s a tool that works best when humans stay in control, governance evolves, and organizations learn to use intelligence responsibly.🔍 Key Takeaways:1- Why the “end goal” of AI is human productivity, not machine autonomy2- How corporate hierarchies will evolve as AI becomes embedded in workflows3- Why transparency, audit logs, and explainability will define governance success4- The risks of “big model” complexity and why small, focused models may win5- Why AI will change how we work more than what we do⏳ Timestamps for Easy Navigation:00:00 – Welcome and introduction to Eric Kavanagh01:00 – How Eric built a career at the intersection of data and media05:20 – Defining the “end goal” of AI: business, life, and technology08:45 – Where humans remain central—and why that won’t change12:10 – Transparency, bias, and the challenge of giant AI models19:00 – Real-world AI agents: when automation works and when it doesn’t22:50 – The rise of small language models and the age of execution29:45 – Governance, audit logs, and minimal viable oversight33:00 – The future of jobs, creativity, and collaboration39:00 – Where to find Eric Kavanagh and DM Radio📩 More insights & resources:👉 Blog recap: https://tinytechguides.com/blog/why-80-of-ai-projects-fail-and-the-three-boring-decisions-that-save-the-other-20/🔗 Connect with Eric Kavanagh:💼 LinkedIn: https://www.linkedin.com/in/erickavanagh/🌎 Website: https://dmradio.biz💬 What do you think? What should the end game of AI be? Drop your thoughts below!👍 Enjoyed this conversation? Like, share & subscribe for more real-world AI and data insights.#AI #DataFacesPodcast #EricKavanagh
📢 AI agents are moving fast from hype to enterprise reality. But how do leaders ensure they deliver real ROI—without creating risk and chaos?
In this episode of Data Faces, David Sweenor sits down with Catalina Herrera, Field Chief Data Officer at Dataiku, to explore how organizations can adopt AI agents responsibly. Catalina shares a practical framework for balancing speed, governance, and reliability—while keeping business impact front and center.
🔍 Key Takeaways:
1- Why “agent sprawl” is the biggest hidden risk for enterprises
2- The guardrails every AI leader should put in place on day zero
3- How bad data leads to bad agents—and what to do about it
4- Practical patterns for scaling adoption with human-in-the-loop trust
5- The next 12 months: where AI agents will make the biggest impact
⏳ Timestamps for Easy Navigation:
00:00 – Introduction and guest overview
02:10 – Why AI agents matter now for business leaders
07:25 – Governance guardrails every enterprise needs
13:40 – Data foundations and making agents reliable
19:15 – Avoiding agent sprawl and scaling adoption
25:50 – Looking ahead: what’s next for AI agents
30:20 – Final thoughts and closing
📩 More insights & resources:
👉 Blog recap: https://prompts.tinytechguides.com/s/the-data-faces-podcast
🔗 Connect with Catalina Herrera:
💼 LinkedIn: https://www.linkedin.com/in/herreracatalina/
🌎 Dataiku: https://www.dataiku.com/
💬 What do you think? How are you preparing for AI agents in your organization? Drop your thoughts in the comments!
👍 If you enjoyed this conversation, remember to like, share, and subscribe for more episodes of Data Faces.
#AIagents #AIGovernance #DataScience
📢 What if your marketing campaigns could adapt themselves in real time—without you lifting a finger?
In this episode of Data Faces, David Sweenor talks with Rajeev Kozhikkattuthodi, Co-founder & CEO at Poexis, about how Agentic AI is reshaping growth marketing across events, ABM, and inbound.
Rajeev shares practical lessons from the front lines: where Agentic AI actually delivers ROI, how to avoid the trap of “AI that doesn’t scale,” and why marketing leaders need a bias to action—not just analysis.
🔍 Key Takeaways:
1- What makes Agentic AI different from traditional AI in B2B marketing
2- How to set the right level of autonomy and guardrails for AI systems
3- Why many AI pilots stall and how to prove ROI at the P&L level
4- The new role of in-person experiences in an AI-saturated digital world
5- Leadership skills marketers need to thrive in an Agentic AI future
⏳ Timestamps for Easy Navigation:
00:00 – Intro & Rajeev’s background
03:00 – What Agentic AI really means for marketers
05:40 – Shifting from analysis to agency
10:20 – Are humans ready to cede control to AI?
14:40 – Why 95% of AI pilots stall
20:20 – Agentic AI in events: boosting attendance & engagement
27:30 – Personalization at events: pre, during & post strategies
29:30 – Using AI for inbound content that still feels human
34:50 – What marketing leaders must do to stay ahead
37:00 – How to connect with Rajeev & Poexis
📩 More insights & resources:
👉 Blog recap: https://open.substack.com/pub/davidsweenor/p/the-ai-agent-mistake-90-of-marketing?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🔗 Connect with Rajeev Kozhikkattuthodi:
💼 LinkedIn: https://www.linkedin.com/in/rajeevtk/
🌎 Website: https://www.poexis.com
💬 What do you think—are marketers ready to move from analysis to action with AI? Drop your thoughts in the comments!
👍 Enjoyed this conversation? Like, share & subscribe for more AI and data leadership insights.
#AgenticAI #B2BMarketing #DataFacesPodcast
📢 AI is more than GenAI—and sustainable success depends on governance, teamwork, and evolution, not disruption.
In this episode of Data Faces, David Sweenor sits down with Thomas Been, CMO at Domino Data Lab, to unpack how enterprises can move beyond hype and build real, lasting value with AI. From governance frameworks to the human factor, Thomas shares practical insights that every technology and business leader should hear.
🔍 Key Takeaways:
1- Why 95% of GenAI projects fail—and what successful enterprises do differently
2- How to balance experimentation with governance to accelerate adoption
3- Why teamwork and culture drive AI success more than technology alone
4- The spectrum of AI: why GenAI is only part of the picture
5- How leaders can prepare for continuous evolution instead of chasing disruption
⏳ Timestamps for Easy Navigation:
00:00 – Welcome & Guest Intro (Thomas Been, CMO, Domino Data Lab)
01:05 – Thomas’s career journey & mission at Domino
03:00 – The GenAI hype cycle: why most projects fail
06:44 – AI vs. GenAI: spectrum, not a silver bullet
10:39 – Evolution vs. disruption in enterprise AI
14:17 – Rethinking governance: from compliance to value driver
18:59 – Global perspectives on AI governance
23:03 – What separates successful AI projects from failures
27:41 – How AI is transforming marketing & marketing teams
32:55 – The impact of AI on early-career professionals
35:43 – Advice for business leaders on future-proofing AI strategy
37:54 – Where to learn more about Domino Data Lab
📩 More insights & resources:
👉 Read the blog: https://prompts.tinytechguides.com/p/the-11-month-ai-validation-trap-thats🔗 Connect with Thomas Been:💼 LinkedIn: https://www.linkedin.com/in/tbeen/🌎 Website: https://domino.ai
💬 What do you think? How is your company approaching AI governance and teamwork? Share in the comments!
👍 Enjoyed this video? Like, share & subscribe for more insights on AI, data, and leadership.
#EnterpriseAI #AIGovernance #DataFacesPodcast
📢 What happens when AI agents take on real tasks in marketing, sales, and operations? In this episode, Chelsea Wise of Relevance AI shares practical insights on how AI agents are transforming the future of work.
Chelsea draws on her startup experience, academic background in consumer behavior, and role at Relevance AI to explain what leaders often underestimate about AI agents—and how to prepare teams for change.
🔍 Key Takeaways:
1- Why AI agents will shift work at the task level rather than replacing entire jobs.
2- The overlooked role of peer-to-peer learning in building trust in AI.
3- Practical, “unsexy” use cases where AI agents deliver real business value.
4- How hackathons and hands-on learning can unlock organizational buy-in.
5- Why critical thinking and ethics must guide AI adoption.
⏳ Timestamps for Easy Navigation:
00:00 – Introduction & Chelsea Wise background
04:15 – The underestimated impact of AI agents in the workplace
07:12 – Learning, trust, and human-to-human collaboration
12:00 – What AI agents do well vs. where humans remain essential
18:00 – Rethinking the marketing playbook with AI
20:42 – Lessons from internal hackathons
25:29 – Misconceptions about AI in enterprise marketing
28:36 – Real-world “spreadsheet” use cases for AI agents
32:21 – Preparing teams and leaders for AI agents
34:17 – Teaching AI ethics and critical thinking
38:11 – Final thoughts & resources
📩 More insights & resources:
👉 Blog recap: https://prompts.tinytechguides.com
🔗 Connect with Chelsea Wise:
💼 LinkedIn: https://www.linkedin.com/in/chelseawise/
🌎 Website: https://relevance.ai
💬 What do you think—are AI agents ready for your team? Drop your thoughts in the comments!
👍 If you enjoyed this video, like, share & subscribe for more conversations on AI, data, and leadership.
#AIAgents #FutureOfWork #DataFacesPodcast
📢 Are you losing customers because your product messaging is too complex? In this episode of Data Faces, I sit down with Emma Stratton, founder of Punchy and expert in B2B SaaS storytelling, to talk about how to cut through the jargon, keep your message simple, and connect with your audience.
Emma shares her proven framework for making marketing more “punchy,” and why simplicity is the secret weapon for winning in crowded markets. Whether you’re a founder, marketer, or product leader, this conversation is packed with insights you can put into action right away.
🔍 Key Takeaways:
1- Why complexity kills B2B SaaS messaging—and what to do instead
2- How to find the right “altitude” in your messaging for different audiences
3- Practical tips to make your writing more human without “dumbing it down”
4- The role of emotion in B2B buying decisions
5- How to adapt messaging in the age of AI while keeping your brand voice
⏳ Timestamps for Easy Navigation:
00:00 – Introduction & guest welcome
00:48 – What Punchy does and why simple messaging matters
02:22 – The curse of knowledge and overcomplication in B2B marketing
06:37 – What “punchy” means in SaaS messaging
08:38 – Overcoming the fear of sounding too simple
10:52 – Finding the right “altitude” for your audience
14:19 – Adapting messaging for different personas
16:22 – AI’s impact on brand voice and sounding human
21:28 – Why messaging by committee dilutes impact
22:17 – How to test if your message resonates before launch
24:36 – Common blind spots in product messaging
27:11 – The myth of category creation for startups
30:37 – Using emotion to connect with B2B buyers
33:47 – Final advice for companies struggling with jargon
📩 More insights & resources:
👉 Read the blog recap: https://open.substack.com/pub/davidsweenor/p/how-to-write-punchy-b2b-messaging?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🔗 Connect with Emma Stratton:
💼 LinkedIn: https://www.linkedin.com/in/emma-stratton-punchy/
🌎 Website: https://www.punchy.co
💬 What’s the biggest challenge you face with your product messaging? Drop your thoughts in the comments!
👍 Enjoyed this conversation? Like, share & subscribe for more insights on AI, analytics, and business leadership.
#B2BMarketing #SaaSMarketing #MessagingStrategy
📢 Can AI agents really transform the enterprise—or is it just more buzz?
In this episode of Data Faces, Rich Mendis, CMO at Bytemethod.ai, shares how agentic AI is (and isn’t) being used inside modern enterprises.
Rich brings deep insight from real deployments and tackles the common myths about AI replacing human roles, the technical hurdles companies face, and how to assess true enterprise readiness for automation.
Whether you're a tech strategist, B2B leader, or data professional trying to cut through the AI noise—this conversation delivers clarity, use cases, and practical wisdom.
🔍 Key Takeaways:
What AI agents actually are—and where they’re showing up in the enterprise
Why trust, training data, and governance are make-or-break factors
Common misconceptions about plug-and-play AI in B2B
The shift from AI replacing humans to AI complementing them
How to build a culture of experimentation with "risk-mitigated freedom"
⏳ Timestamps for Easy Navigation:
00:00 – Intro and welcome
01:14 – Rich’s background and journey into AI
02:21 – What is an AI agent, really?
03:53 – Use cases for AI in marketing and operations
05:57 – Misconceptions about AI capabilities in enterprise
08:16 – Design-time vs. runtime AI use cases
13:00 – Governance and validation of agent outputs
16:14 – The hidden challenges with enterprise data quality
19:18 – Cultural and technical readiness for AI adoption
22:23 – How AI is changing roles and skills in business
24:26 – Layoffs, automation myths, and the truth about efficiency
29:55 – Why trust and data provenance matter more than ever
31:46 – What remains uniquely human in a world of AI
33:48 – Final thoughts and how to get started with Bytemethod.ai
📩 More insights & resources:
👉 Read the blog recap on prompts.tinytechguides.com
🔗 Connect with Rich Mendis:
💼 LinkedIn: https://www.linkedin.com/in/rmendis
🌎 Website: https://bytemethod.ai
💬 What do you think? Drop your thoughts in the comments!
👍 Enjoyed this video? Like, share & subscribe for more AI and analytics insights!
#AIinEnterprise #AIagents #DataFacesPodcast
📢 Is AI just adding noise—or unlocking real competitive advantage?
In this episode of Data Faces, David Bryson, Principal Competitive Intelligence Manager at Splunk, breaks down how AI is changing the landscape of competitive intelligence and what it means for analysts, marketers, and business leaders alike.
Whether you're building a CI function or trying to turn data into action, this conversation reveals how to move from information gathering to actual intelligence—with the help of AI.
🔍 Key Takeaways:
Why “so what?” is the question every CI analyst should ask
How AI tools like deep research can supercharge strategic thinking
The danger of over-trusting generic AI outputs in CI workflows
Why prompt engineering is the new CI superpower
What the next generation of CI professionals need to succeed
⏳ Timestamps for Easy Navigation:
00:00 – Intro and welcome
01:00 – Dave’s path from sales engineering to CI
03:15 – How AI is changing intelligence gathering
05:45 – Separating signal from noise in AI-generated research
10:00 – Can AI replace the human side of CI?
12:30 – Deep research, prompt strategy, and personal AI workflows
16:00 – Sharing prompts and building internal CI knowledge
17:10 – Rethinking feature-function comparisons in an AI-first world
22:00 – Moving from reactive to proactive CI with AI
27:10 – Turning information into true intelligence
30:30 – What future CI professionals need to thrive
34:30 – Advice for breaking into competitive intelligence
📩 More insights & resources:
👉 https://prompts.tinytechguides.com
🔗 Connect with David Bryson:
💼 LinkedIn: https://www.linkedin.com/in/david-bryson
🌎 Splunk: https://www.splunk.com
💬 What was your biggest insight from this episode? Drop it in the comments!
👍 If you enjoyed the conversation, don’t forget to like, subscribe, and share for more practical AI and data insights.
#CompetitiveIntelligence #AIinBusiness #DataFacesPodcast
📢 Is AI just a tool—or a mirror forcing us to rethink how we lead, decide, and communicate?
In this episode, Judit Szabo, Global Head of Demand Generation and Operations at Endava, shares how AI is not just changing workflows—it’s reshaping the human side of business.
From marketing operations to leadership mindset, Judit dives into how AI is pushing B2B organizations to confront assumptions, strengthen critical thinking, and rediscover what makes us uniquely human.
🔍 Key Takeaways:
1– Why AI is a wake-up call for leadership and decision-making
2– The human skills becoming more valuable in an AI-driven world
3– How demand gen and marketing ops must evolve with intelligent automation
4– Where AI falls short—and why human context still matters
5– How to lead AI adoption without fear or over-reliance
⏳ Timestamps for Easy Navigation:
00:00 – Intro & welcome
01:25 – Why AI is more than just a productivity tool
06:40 – Redefining demand generation in the AI era
12:10 – The new value of human judgment and soft skills
19:30 – How AI changes the role of operations leaders
27:45 – Leading teams through transformation
34:00 – Final thoughts and where to go from here
📩 More insights & resources:
👉 [Blog post or resource link here]
🔗 Connect with Judit Szabo:
💼 LinkedIn: https://www.linkedin.com/in/juditszabocloud/
🌎 Website: https://www.endava.com
💬 What do you think? Drop your thoughts in the comments!
👍 Enjoyed this conversation? Like, share & subscribe for more expert takes on AI, analytics, and business leadership.
#AILeadership #B2BSaaS #DataDrivenDecisions
📢 Are AI “hallucinations” just bad framing? Discover why agentic quality—not accuracy alone—will decide which AI products win.Generative and agent-based AI are changing how every team researches, plans, and executes. In this fast-paced talk, Hyoun Park, CEO & Principal Analyst at Amalgam Insights, joins David Sweenor to break down what agentic quality means, why “hallucinations” is the wrong metaphor, and how to keep thousands of enterprise agents from running wild. Perfect for data leaders and curious execs who want pragmatic, hype-free answers.🔍 Key Takeaways:1- Agentic quality measures goal-oriented reasoning and timeliness—metrics most benchmarks ignore.2- “Hallucination” anthropomorphizes AI; call it model mismatch and fix the data or prompt instead.3- Synthetic data and vector stores are table stakes; trust and governance will decide adoption.4- Expect agent sprawl: thousands of mini-workflows that demand new ownership and monitoring.5- Within five years, every white-collar role will spend ~25 % of its time orchestrating AI.⏳ Timestamps for Easy Navigation:00:00 – Intro & show setup01:00 – Hyoun’s analyst journey and Amalgam Insights mission03:30 – What AI agents actually do beyond meeting scheduling07:00 – Will agents make us mentally lazy? Human learning vs. automation11:30 – Defining agentic quality and why accuracy isn’t enough18:00 – Retiring the term “hallucination” and reframing model errors24:30 – Managing agent sprawl and ownership at scale29:00 – Synthetic data, vector DBs & the real impact on quality33:00 – Creative AI: music, art, and the future of human craft36:00 – The missing capability—establishing cross-agent trust37:30 – Final thoughts & episode wrap-up📩 More insights & resources:👉 Read Hyoun’s latest posts: https://amalgaminsights.com/🔗 Connect with Hyoun Park:💼 LinkedIn: https://www.linkedin.com/in/hyounpark/🌎 Website: https://amalgaminsights.com/💬 What resonated most? Tell us in the comments!👍 If you learned something useful, give us a like, share with a colleague, and subscribe for more weekly AI strategy talks.#AgenticAI #DataAnalytics #ArtificialIntelligenceSee more at https://prompts.tinytechguides.com/s/the-tinytechguides-chronicle
📢 Can we really trust AI without trustworthy data?
Field CTO Shane Murray of Monte Carlo Data shares what “AI-ready” actually means, and why most data teams are underprepared for the shift to generative AI.
In this episode, we explore the practical and philosophical challenges behind building data products that can power AI applications — from defining quality in unstructured data to the ripple effects of small changes in AI systems. Shane draws on his experience leading data at The New York Times and now helping organizations scale observability and governance at Monte Carlo Data.
🔍 Key Takeaways:
Why the term “AI-ready” is often misunderstood — and what it really takes
How unstructured data quality and observability differ from traditional structured approaches
The hidden risks of hallucinations, model drift, and multi-agent errors
Why governance can’t be “pumped in” after the fact — it must be designed in from the start
A pragmatic path for data teams: start small, keep humans in the loop, and build what matters
⏳ Timestamps for Easy Navigation:
00:00 – Intro & Shane Murray’s background
03:23 – What does “AI-ready” actually mean?
07:54 – Measuring quality in unstructured data
12:43 – The hidden causes of AI hallucinations
18:23 – Multi-agent systems and compounding errors
20:31 – Rethinking AI governance in enterprise environments
25:35 – Can we ever truly trust AI?
30:45 – The future of trustworthy AI systems
34:38 – Shane’s advice to data teams and where to start
📩 More insights & resources:
👉 [Link to blog post or Substack recap here]
🔗 Connect with Shane Murray:
💼 LinkedIn: https://www.linkedin.com/in/shanemurray5/
🌎 Website: https://www.montecarlodata.com
💬 What stood out to you most? Let us know in the comments.
👍 Like this episode? Subscribe and share for more conversations on data, AI, and analytics leadership.
#AIReadyData #DataGovernance #TrustworthyAI
📢 What if the biggest reason your AI projects struggle isn’t the tech—but the people?In this episode of Data Faces, Danny Stout, Product Lead at EY and seasoned AI strategist, shares why alignment, communication, and soft skills matter more than ever in data and AI teams.From building the right team structure to navigating internal politics, Danny brings practical insights from his decades-long career leading data and analytics transformation efforts across major enterprises.🔍 Key Takeaways:1- Bigger AI models aren’t always better—context and alignment win.2- Organizational misalignment kills great tech before it starts.3- Communication and soft skills are now essential for AI success.4- AI teams need more than tech talent—they need translators and connectors.5- Simple solutions often outperform flashy tools in real-world AI use.⏳ Timestamps for Easy Navigation:00:00 – Intro & guest welcome01:14 – Danny’s career path: from education to AI leader03:47 – Why tech obsession blinds us to the human element06:00 – The critical role of executive alignment10:22 – Who should you hire first on an AI team?14:52 – How GenAI is changing the skills landscape17:56 – The growing importance of soft skills20:59 – Why diversity supercharges team effectiveness21:54 – Real-world story: Predicting human behavior with data25:05 – Common blind spots in AI projects27:22 – Understanding and applying guardrails in GenAI28:33 – What GenAI tools Danny is building now30:19 – Advice for aspiring data & AI leaders33:57 – Final tips + where to follow Danny📩 More insights & resources:👉 [Insert blog/resource link here]🔗 Connect with Danny Stout:💼 LinkedIn: https://www.linkedin.com/in/dannystout/💬 What resonated most with you? Drop a comment below!👍 Like, share, and subscribe for more conversations on the people shaping AI and data!#AILeadership #AnalyticsCulture #DataStrategy #GenerativeAI #SoftSkillsInTech #AIteams #TechLeadership
📢 Tired of B2B messaging that sounds like everyone else’s? You’re not alone.
Product marketing consultant Gabriela Contreras joins Data Faces to unpack why most B2B messaging falls flat—and how to actually make yours resonate.
If you're a product marketer, GTM leader, or startup founder trying to sharpen your story and cut through the noise, this episode is packed with real-world strategies and frameworks you can use immediately.
🔍 Key Takeaways:
The #1 messaging mistake B2B SaaS companies make (hint: trying to talk to everyone).
How to align messaging across product, marketing, and sales without chaos.
A simple framework (VBF: Value–Benefit–Feature) that transforms how you write copy.
What product marketers can do to capture the voice of the customer—without massive VOC programs.
How to evolve your messaging as your product matures and the market shifts.
⏳ Timestamps for Easy Navigation:
00:00 – Intro & welcome
01:00 – Gabriela's background and approach
02:15 – The biggest messaging mistake in B2B SaaS
04:40 – Navigating multiple personas and tailoring messaging
07:15 – Standing out in a saturated SaaS landscape
10:45 – Getting PMMs closer to the customer
13:40 – Cross-team alignment on messaging
19:20 – Case study: Translating tech into clarity
24:00 – Value > Benefit > Feature explained
28:30 – Emotional resonance vs. business speak
31:00 – Evolving messaging with the market
35:30 – Final advice: Your product is NOT the hero
37:00 – How to connect with Gabriela
📩 More insights & resources:
👉 https://www.skyline.marketing
🔗 Connect with Gabriela Contreras:
💼 LinkedIn: https://www.linkedin.com/in/mgcontreras/
💬 What resonated with you most? Drop your thoughts in the comments!
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#B2BMarketing #ProductMarketing #SaaSStrategy #MessagingFrameworks #VoiceOfCustomer #StartupMarketing
📢 Can you really trust AI—or are we putting too much faith in a system we barely understand?
In this episode of Data Faces, we sit down with executive advisor Robert Lake to explore what it truly means to trust AI in business—and why it starts with people, not just the tech.
Robert draws on 30+ years of experience in data science and strategic advising to break down the hidden challenges of building AI that organizations (and humans) can confidently rely on. From governance to explainability to emotional dependency on machines—this is the human side of AI you don’t want to miss.
🔍 Key Takeaways:
1- Why trust in AI is often misplaced—and how to fix that
2- The myth of AI "reasoning" and the dangers of anthropomorphizing tech
3- How business leaders can evaluate the intentions behind AI systems
4- Why core values—not compliance checklists—drive ethical AI adoption
5- How to build long-term “AI habits” that align with real business goals
⏳ Timestamps for Easy Navigation:
00:00 – Introduction & what trust in AI really means
02:23 – Robert’s background in data science & business exits
05:14 – Probabilistic AI vs. executive desire for certainty
07:55 – Humanizing machines: the real risk behind emotional AI
11:53 – Evaluating intentions: what the “trust module” really checks
17:33 – Explainability, accountability, and who owns the system
21:12 – Building AI habits: Lessons from Lean Six Sigma
23:31 – Do we need new trust frameworks in AI?
29:52 – AI maturity in executive teams & why less tech is more
34:38 – Organizational change: Why “start with why” still matters
37:17 – Final advice: “What would your grandma say?”
38:36 – How to connect with Robert
📩 More insights & resources:
👉 https://treboradvisors.com
🔗 Connect with Robert Lake:
💼 LinkedIn: https://www.linkedin.com/in/rjlake
🌎 Website: https://treboradvisors.com
💬 What’s your take on AI trust? Drop a comment below!
👍 If this sparked your thinking, like, share & subscribe for more expert insights on AI, data, and business strategy.
#ArtificialIntelligence #TrustInAI #DataEthics #AILeadership #BusinessStrategy
📢 Is Gen AI revolutionizing product marketing—or just flooding the market with generic content?In this episode of Data Faces, David Sweenor sits down with Melissa Burroughs, Director of Product Marketing at Alteryx, to unpack how generative AI is transforming the PMM role. From boosting productivity to raising ethical questions, Melissa shares real-world insights, personal lessons, and future-forward advice for marketers navigating the Gen AI shift.Whether you're a seasoned PMM or just getting started with AI, this episode is packed with valuable takeaways on strategy, trust, and thriving in an AI-driven landscape.🔍 Key Takeaways:1-Gen AI is supercharging PMM productivity—but it raises the bar for quality and strategy.2-The human touch—collaboration, expertise, and judgment—is more valuable than ever.3-Ethical use of AI requires transparency, accountability, and a BS filter.4-PMMs must balance AI efficiency with brand voice and customer trust.The PMM role is evolving—fewer junior roles, greater strategic expectations.⏳ Timestamps for Easy Navigation:00:00 – Intro & Melissa’s journey from physics to product marketing03:45 – How Gen AI is transforming the PMM role07:15 – The rise of collaboration and human-centric skills10:10 – When AI makes PMMs less effective12:00 – Why subject-matter expertise matters more than ever18:30 – Balancing AI productivity with brand authenticity23:00 – Ethical concerns: transparency, privacy & bias28:50 – The future of PMM roles in a Gen AI world33:40 – Should AI help with strategy instead of just tasks?34:50 – Advice for PMMs starting their Gen AI journey36:05 – Why protecting data matters more than ever38:00 – Final thoughts: AI as a partner, not a replacement📩 More insights & resources:👉 Explore helpful AI prompts & tools: https://prompts.tinytechguides.com🔗 Connect with Melissa Burroughs:💼 LinkedIn: / melissaburroughs 🌎 Learn more about Alteryx: https://www.alteryx.com#genai #ProductMarketing #AIForBusiness#tinytechguides #DataFacesPodcast
How are autonomous AI agents reshaping enterprise operations? In this episode of The Data Faces Podcast, John Thompson, Global AI Leader at EY, cuts through the hyperbole to provide a strategic assessment of opportunities, risks, and governance requirements based on his decades of implementation experience.
🔍 Key Topics Covered:1- The fundamental shift from analytical AI to operational agents2- Why the capabilities that make agents powerful also make them risky3- The inevitable consolidation of today's fragmented agent framework landscape4- The critical importance of appropriate agent governance mechanisms5- Realistic value expectations versus "utopian automation scenarios"
⏳ Timestamps for Easy Navigation:00:33 - John's 38-year journey through data, analytics and AI02:40 - The dual-edged nature of AI agent capabilities04:58 - The current state of AI agent maturity05:35 - Why 150 agent frameworks will consolidate like the early auto industry06:22 - Competing visions: Microsoft's closed platform vs. Google's open ecosystem10:13 - From RPA to intelligent process automation12:07 - The human element as the primary risk factor14:28 - The need for an agent management platform16:54 - Operational risks of inadequate agent governance18:46 - Wrapping agents with organizational context21:20 - Why AGI remains decades or centuries away23:44 - The importance of transparency in customer interactions25:48 - The fallacy of "utopian automation scenarios"30:53 - Using AI to monitor AI-generated content34:13 - Why there is no "post-AI world"34:33 - Resources for building organizational AI competency
💡 More AI & Data Insights:🌐 Explore our analysis: https://prompts.tinytechguides.com/p/there-is-no-post-ai-world-preparing📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com
🔗 Connect with John Thompson:💼 LinkedIn: https://www.linkedin.com/in/johnkthompson/🌎 Learn about EY's AI initiatives: https://www.ey.com/en_us/artificial-intelligence
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#AIAgents #EnterpriseAI #AIGovernance