Podcast Summary: The Analytics Power Hour

Episode #294: Adapting an Analytics Team to an AI World
Date: March 31, 2026
Host(s): Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, Julie Hoyer
Guest: John Lovett (VP of Analytics, SEER Interactive)

Episode Overview

This episode dives deep into how analytics teams can adapt to—and thrive in—an increasingly AI-driven world. The hosts and guest John Lovett bring candid, hands-on insights about the realities, challenges, and opportunities that come with integrating AI across analytics workflows, upskilling teams, and evolving both culture and deliverables. With practical stories, memorable anecdotes, and detailed discussion about rollout strategies and organizational shifts, the episode is essential listening for digital analytics practitioners navigating the "AI-first" edict.

Key Discussion Points & Insights

1. The Organizational AI Mandate at SEER Interactive

• Top-Down Commitment:
  John Lovett describes a bold pivot in December 2025:
  “It was a mandate for every single person in our company... had to take an AI training course and get certified in AI. So it was like mandatory, requisite. Everybody gets trained.” (03:36)
• Cultural Shift:
  The agency empowered every employee, from associate to executive, to experiment, build AI prototypes, and share them with clients. New ideas could be rapidly piloted, getting real client feedback on emerging innovations (04:15).

2. Approaching AI: Personal First, Then Professional

• Hands-On Learning:
  Lovett recommends starting AI experimentation in everyday life to build comfort:
  “Just use it for your personal life. Like, I would literally open my refrigerator door, take a picture with ChatGPT, and say, what can I make for dinner?” (06:22)
  • Memorable anecdote: ChatGPT remembering his family vacation and referencing it later, illustrating AI’s conversational persistence (07:00).

3. Navigating Team Dynamics: Enthusiasm vs. Apprehension

• Adoption Curve:
  Teams had both eager adopters and skeptics. SEER tackled hesitancy by systematically inventorying company workflows, then asking, “Where can AI help us do these things faster, more efficiently, better?” (09:15)
• Workflow Integration:
  Example: Automated timecard population and daily briefings with custom AI agents saved meaningful time and reduced “billable hour” friction (09:45).

4. Balancing Analog and AI in Knowledge Work

• Notetaking Styles:
  Despite digital advances, Lovett (and others on the show) still value handwritten notes, especially for requirement-gathering and thinking creatively, even as AI handles transcription and reminders (13:08).
• Active Listening Adaptation:
  Moe: “For me, how I absorb the information... If I’m not taking notes, I will... my brain will probably go off on five different things.” (18:13)

5. The Next Generation Analyst—Skills in the AI Era

• Teaching Kids and Students:
  Lovett advocates integrating AI tools into education:
  “The university that says to me, oh, it’s off limits. They can’t use it. I’m going to be like, you know what? You’re not going to prepare my kid for the workforce.” (21:40)
• Developing Personal AI Styles:
  Lovett customizes his agents to match his writing/personality, and has created deployment guides for transferring that style to new models (brand voice, writing quirks, instructions)—practical for both personal and organizational continuity (23:00).

6. From Play to Production: Building Trustworthy AI Agents

• Guardrails & Validation:
  Inspired by Twyman’s Law (“if any number looks too good to be true, it probably is”), Lovett builds probabilistic outputs with deterministic checks into conversational analytics agents:
  “Every number that you generate, I want to see the SQL query, I want to see the math behind it... what you can’t show me reliably.” (28:40)
• Scaling Safely:
  SEER’s process: Individual “vibe-coded” AI agents are eventually stress-tested and productionalized for company-wide deployment (30:56).
  “Everybody’s encouraged to bring ideas... just opens up the door to possibilities and everybody trying things...” (32:00)

7. Driving Adoption: Moving from Hobby to Habit

• Combating Bystander Effect:
  Broad shoutouts for help yield “crickets.” Direct asks were much more successful:
  “I went to four of my people... I need you to be a leader here... And every single one...was like, I will do that.” (35:04)

• Process Integration:
  Michael Helbling: “Some people just need you to... give them...the layout, the how do I do these steps? Even though... others will just start with the AI and work through and learn as we go.” (37:43)

• Reusable Prompts as Knowledge Transfer:
  Lovett always asks AI agents to write instructions for themselves, so analysts who are more reluctant or unsure have a guided starting point (39:09).

8. Shifting the Analytical Workflow

• Blank Page vs. Reaction:
  Starting with an AI output is a fundamentally different analytic style—some analysts felt it slowed them down at first, but validation practices (cross-checking AI with dashboards) improved trust and workflow (42:00).

• Efficiency Gains & Beyond:

  • Efficiency:
    Standardizing repeatable prompts and agents has sped up recurring agency tasks, enabled faster onboarding, and supported scaling best practices (45:20).
  • Internal Growth & New Capabilities:
    Example: Lovett rapidly onboarded to a new client pitch by leveraging institutional AI knowledge (47:40).
  • Curiosity-Driven Experimentation:
    “In my 20 years in analytics, this is the most fun I’ve had... so much data, so much information we can just dive into and show clients things they've never seen before.” (54:30)

9. Advice for Non-Leaders Driving AI Change

• “Build something cool. Show it to somebody... If nobody picks up on it and you think it’s brilliant, share it on LinkedIn, share it on MeasureSlack, share it somewhere, get some feedback on it.... If your leadership won’t listen, take it to LinkedIn, take it to Measure Slack, take it somewhere that you can find an audience that thinks that’s cool, and you’ll grow your brand.” (56:05)

Notable Quotes & Memorable Moments

• On trust in AI and data:
  "Getting your hands dirty with AI isn’t optional anymore... but doing it without accountability is really how you lose credibility." — John Lovett (02:27)
• On AI’s persistence:
  "I only ask you because I hate to cook. I just need the ideas. And it said to me, 'How are the boys doing? What would they like for dinner?'... That blew my mind." — John Lovett (07:59)
• On upskilling with AI:
  "My kids... I want my kids, when they get out of college, to have this as part of their skill set." — John Lovett (21:40)
• On building guardrails:
  "If you’re seeing this huge spike in traffic and it’s anomalous and it doesn’t match any of the other patterns... question it and dig in." — John Lovett (28:10)
• On operationalizing AI innovation:
  "As an organization... once they've been vibed and tested and tried, we productionalize them..." — John Lovett (30:56)
• On analyst growth:
  "In my 20 years in analytics, this is the most fun I’ve had because I’m learning new stuff, I’m playing with new tools... that's growth... it’s fun, it’s exciting..." — John Lovett (54:30)
• On being an individual contributor:
  "Build something cool. Show it to somebody... If your leadership doesn’t recognize [your innovation], that might be time to look for new leadership..." — John Lovett (56:05)
• On curiosity and experimentation:
  "All we can do right now is experiment, test things, try things and see what works." — John Lovett (52:50)

Timestamps for Key Segments

• 02:27 — John Lovett on SEER’s AI training mandate and culture shift
• 06:22 — Personal experimentation with AI: learning through daily life
• 09:15 — Overcoming team resistance and workflow review for AI integration
• 13:08 — Analog habits in a digital era: Notetaking, active listening, and combining old and new
• 21:40 — Preparing the next generation: AI as an essential skill for students
• 28:10 — The importance of building AI guardrails/Twyman’s Law in analytics
• 30:56 — Productionalizing agents: From innovation lab to release day
• 35:04 — Direct nudges to drive adoption vs. mass calls to action
• 37:43 — Process-driven AI enablement for different analyst preferences
• 39:09 — Codifying repeatable AI workflows for knowledge transfer
• 45:20 — Realized efficiency gains and the roadmap ahead
• 47:40 — AI for rapid business development and internal growth
• 54:30 — Analyst growth, curiosity, and the “most fun I’ve had” in analytics
• 56:05 — Advice for non-leader analysts: build, share, and influence

Additional Resources Shared

• AI Podcast Recommendation:
  The Artificial Intelligence Show by Paul Roetzer and Mike Kaput offers weekly news aggregations and training recommendations (57:44).
• GEO/AI Search Community:
  LinkedIn group “GEO Community” (Generative Engine Optimization) — a resource for those interested in AI search trends.
• Book Recommendation:
  Code Name Helene by Ariel Lawhon (60:22)
• Data Viz for Kids Contest:
  Data visualization contest for kids ages 7–12; encourages early data literacy (61:26).

Tone and Takeaways

This episode balances candid, sometimes irreverent humor with practical, actionable wisdom. The panelists are frank about the messiness of AI adoption but also optimistic and energized. John Lovett’s “doer’s” philosophy—prototype, test, validate, repeat—shines throughout. Whether you lead a team or are an individual analyst, the message is clear: get hands-on, share what you build, keep curiosity alive, and remember that trust, guardrails, and organizational buy-in matter as much as the AI tools themselves.

"All we can do right now is experiment, test things, try things and see what works." — John Lovett (52:50)

For more details, visit the Analytics Power Hour website or connect with the hosts and guest on LinkedIn or Measure Slack.