AI is reshaping public services, from Medicaid eligibility to behavioral health treatment planning, but the biggest shift isn’t automation. It’s the rising importance of human judgment, governance, and critical thinking in a world flooded with machine-generated information.

In this episode of Human Work After AI, host Chris Fanchi sits down with Connor Norwood, Founder & CEO of Delineate LLC, to explore how government, healthcare systems, and consulting firms are navigating AI adoption while protecting public trust and human responsibility.

Connor brings a rare lens: academic researcher → state CDO → COVID-19 multi-agency data leader → founder. His perspective on AI governance, health workforce shortages, legal liability, and expert-human augmentation is one every tech leader needs to hear.

🔑 Key Themes

- Why critical thinking becomes the most important workforce skill in an AI-first world

- Why AI will replace some jobs, but will create entirely new ones that require reskilling

- How expert-human augmentation can ease the mental health provider shortage

- The hidden risk for companies that “ban AI” but don’t create policies

- Why public sector AI adoption is slowed by outdated statutes and risk-averse incentives

- How AI changes discovery, research, and consulting workflows

⏱️ Chapters

00:00 — Opening highlight

01:02 — Introduction to Connor Norwood & Delineate

01:58 — From pre-med → academia → state government → data leadership

04:28 — The origins of Delineate & the art of data storytelling

06:39 — Why executives need narrative clarity, not more dashboards

09:44 — What “tuning your message to the audience” really means

10:52 — What Delineate does today (government + sports + healthcare + AI)

12:55 — AI governance: why orgs can’t put their head in the sand

13:03 — Expert human augmentation in healthcare

15:26 — How clinicians perceive AI (and why definitions matter)

18:48 — The fragmented regulatory landscape

21:12 — Government constraints: legacy rules, risk tolerance, budget cycles

23:34 — Why efficiency in public services matters

26:23 — How Delineate uses AI to accelerate research & discovery

28:21 — Are jobs being replaced? Connor’s view on job creation & reskilling

30:38 — Why critical thinking still matters more than ever

31:24 — Legal liability: AI outputs, copyright, and the black box

34:03 — Why proactive guardrails matter

36:17 — Creating realistic AI policies in organizations

36:45 — Connor’s vision for improving public service with AI

38:17 — Where to find Connor

38:33 — Closing

🔗 Resources

Delineate LLC: https://delineateconsulting.com

Connect with Connor: http://www.linkedin.com/in/connorwnorwood

Chris Fanchi / Big North: https://bignorthnetwork.com

📘 Order Chris’s Book — Managing AI: Humans, Agents, and the Future of Work

Available Now!

👉 https://bignorthnetwork.com/managing-ai

“Most titles will remain. What will change is what people do within those titles.”

In this episode of Human Work After AI, Chris Fanchi talks with Chandan Maruti (CEO of Twig.so) about using agentic AI to scale customer support and success without sacrificing empathy. Twig acts like a coworker for support agents, handling instant answers and triage so humans can focus on pattern-finding, prevention, and relationships.

We cover:

- Why CS/S roles won’t disappear, but will become far more strategic as AI takes the repetitive load.

- The “thousand CSMs” idea: sensing problems earlier across every user interaction.

- Agentic support: AI handles, escalates, and routes while humans oversee and tackle the hard cases.

- Measuring value with a 7-dimension evaluation to avoid AI oversell/undersell traps.

- What a “perfect AI–human hybrid” support team looks like in the next 2–4 years.

Chapters

00:00 — Opening highlight (roles change, not titles)

00:48 — Intro: Twig as an AI coworker for support agents

03:04 — Chandan’s path: engineering → Lambda School → CS leadership

05:44 — The CS pain: high cost, limited bandwidth, slow signal flow

10:17 — “A thousand CSMs”: using AI to listen, sense, escalate early

12:21 — Retention by rigor: the 7-metric outcome framework

16:12 — Humans stay: AI does the repetitive, people do prevention & relationships

19:23 — Next step: from data deluge to actionable signals for CSMs

23:54 — Agentic tools and distributable expertise (runnable knowledge)

36:34 — The ideal AI–human hybrid support org (instant response, smart triage)

38:01 — Where to find Twig + outcomes-first mindset

Resources

Twig.so — outcomes-first AI for support & success: https://www.twig.so

Steward your people through the AI transition with Chris Fanchi's Big North Network — https://bignorthnetwork.com

Managing AI: Humans, Agents, and the Future of Work (out now!)

If this episode helped you reframe human + agent workflows, dive deeper in Chris Fanchi's new book, Managing AI - a pragmatic playbook for leaders on trust, metrics, and org design in the agentic era.

Order your copy today: https://bignorthnetwork.com/managing-ai

If every real estate agent gets AI superpowers, what still sets the best apart? For Biju Ashokan (Founder & CEO, Radius), the answer is clear: proactive AI to remove the grunt work, and human judgment to win the moment that matters. In this episode of Human Work After AI, Biju breaks down MEL, Radius’s AI assistant that builds live client personas, schedules showings, drafts offers, and acts proactively, not just reactively like most chat tools. He argues the enduring edge is interpersonal skill, negotiation, and local context, even as AI levels the playing field.What we cover (highlights):- Why Radius calls itself a software company with a brokerage license and how the model works (subscriptions + per-transaction)- How MEL gets proactive and creates a living client profile from calls, texts, email, and search behavior- Why human skills become more important when AI gives everyone similar tools (negotiation, network, local knowledge)- The future: fewer agents, more professional teams, higher throughput per agent, and likely commission compression over time- Data privacy, team-scoped AI instances, and keeping human-in-the-loop for liability and quality controlBiju’s ultimate takeaway about AI: the same thing that excites him about AI also scares him - the unknowns of the next 10 years of work.Chapters00:00 – Highlight: “Interpersonal skills will stand out when AI levels the field.”02:49 – Software company with a brokerage license03:15 – Vision, then AI’s 360° rethink of real estate software04:29 – Research first: six months inside agent offices; hiring lessons08:17 – Agents want independence; Radius builds the infra to enable it10:27 – Where AI fits: repetitive workflows → MEL assistant12:03 – From reactive to proactive: MEL books showings, sends CMAs13:55 – What stays human: negotiation, network, local context15:44 – GTM update: 100+ brokerages, 1,000+ agents, new states opening16:30 – Teams growing despite a down market; time-back → growth19:20 – One platform vs. tool soup; MEL keeps data in sync21:45 – Fewer agents, more productivity; likely commission compression26:51 – Human-in-the-loop, feedback loops, liability guardrails29:49 – Final remarks: optimism and uncertainty about jobs aheadResourcesLearn more about Radius — https://radiusagent.comGrow your revenue with host Chris Fanchi's Big North Marketing — https://bignorthmarketing.comConversations like this feed the new book "Managing AI: Humans, Agents, and the Future of Work" by Chris Fanchi — releasing November 18. Visit bignorthmarketing.com/managing-ai to learn more and pre-order today!