Using AI at Work: AI in the Workplace & Generative AI for Business Leaders

How do you scale enterprise AI without exposing the sensitive data your business cannot afford to lose? In this episode Chris sits down with Steven Walchek, Founder and CEO of Liminal, to explore enterprise AI security, AI governance, data privacy, and how leaders can safely accelerate generative AI adoption.
Steven explains how enterprises can protect sensitive information before it reaches an LLM, why governance needs to enable AI adoption rather than block it, and how AI agents and tools like Claude Code are changing what teams can build and automate. He also shares practical AI use cases, including his Pocket Sales Engineer and AI-powered product prioritization, showing how non-engineers can turn ideas into working applications without writing code. Leaders should listen for a practical look at balancing AI security, governance, productivity, and innovation as generative AI spreads across the enterprise.
Chapters:
(00:00) Introduction
(03:47) The Data Privacy Risks Behind Generative AI
(07:13) How Enterprise AI Security and Governance Work
(10:29) Protecting Sensitive Data Before It Reaches an LLM
(18:22) How AI Coding Tools Are Changing Software Development
(23:16) A Crawl, Walk, Run Strategy for Enterprise AI Adoption
(28:52) Why AI Governance Should Enable Innovation, Not Block It
(31:53) AI Agents, Automation, and the Enterprise Complexity Problem
(37:30) Building a Pocket Sales Engineer With Generative AI
(42:44) How to Build AI Applications Without Writing Code
Resources:
🔎 Find Out More About Steven Walchek
Steven Walchek on LinkedIn
https://www.linkedin.com/in/swalchek/
Liminal
https://www.liminal.ai/
Liminal Resources and Blog
https://www.liminal.ai/resources/blog
🛠 AI Tools and Resources Mentioned:
ChatGPT
https://chatgpt.com/
Claude
https://claude.ai/
Claude Code
https://claude.com/product/claude-code
OpenAI Codex
https://openai.com/codex/
Perplexity
https://www.perplexity.ai/
Grok
https://grok.com/
Meta AI
https://www.meta.ai/
Gong
https://www.gong.io/
OpenClaw
https://github.com/openclaw/openclaw

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Why do companies invest in AI without changing how work gets done? In this episode Chris sits down with Justin Watt, co-founder and CEO of Switchboard, to examine why AI adoption often stalls after teams get access to tools. They explore the move from individual AI use to AI workflow automation that supports work across departments.
Justin explains why successful business process automation depends on clear handoffs, usable data, and the right permissions. He shares how simplifying a complicated proposal process created a practical opportunity for AI, then discusses how leaders can find a first use case and build the shared context needed for AI agents. Listen for a grounded approach to enterprise AI implementation that starts with the work your team actually does.
Chapters:
(00:00) Why AI Adoption Hasn’t Changed the Business
(02:23) From Individual AI Use to Team Workflows
(05:10) Where AI Workflow Automation Fits
(09:57) Finding the First Business Process to Automate
(16:12) Mapping How Teams Actually Work
(20:13) Choosing an Internal AI Implementation Lead
(24:42) Human Oversight for AI Agents
(30:01) Building Shared Context From Business Data
(36:15) AI Tools for Meetings and Delegation
(40:11) Using Claude Code for Business Workflows
(43:15) Automating Recurring Updates With an AI Skill
Resources:
🔎 Find Out More About Justin Watt
Switchboard: 
https://www.withswitchboard.com/
Justin Watt on LinkedIn: 
https://ca.linkedin.com/in/wattjustin
🛠 AI Tools and Resources Mentioned:
Granola:
 https://www.granola.ai/
Claude, Claude Code, and Claude Cowork: 
https://claude.ai/
ChatGPT and Codex:
https://chatgpt.com/ and https://openai.com/codex/
Grok Bot: 
https://grok.com/
Notion: 
https://www.notion.so
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AI transformation does not start with a massive AI project. In this episode Chris sits down with Denis Romanovskiy, Chief AI Officer at SOFTSWISS, to explore what enterprise AI adoption looks like when someone is actually responsible for turning the technology into business results. Denis explains why the Chief AI Officer role sits at the intersection of technology, operations, change management, security, and employee enablement.
They discuss how companies can identify internal AI leaders, why Denis prefers measuring "economic effect" before chasing traditional ROI, and how small employee-led experiments can build momentum across an organization. 
Denis also shares lessons from SOFTSWISS's company-wide AI hackathon, including how non-technical employees became active builders when given the right support. For leaders trying to move AI from scattered experimentation into everyday work, this conversation offers a practical look at building adoption, accountability, and human oversight at scale.

Chapters:
(00:00) Introduction
(01:54) What a Chief AI Officer Actually Does
(05:35) Why Businesses Need Clear AI Ownership
(07:26) Choosing the Right Internal AI Leader
(14:38) Measuring AI by Economic Effect, Not Just ROI
(19:45) How to Choose AI Use Cases That Build Momentum
(22:37) Turning an AI Hackathon Into a Business Tool
(27:34) Why Executive Support Drives AI Adoption
(29:16) What Non-Technical Employees Can Build With AI
Resources:
🔎 Find Out More About Denis Romanovskiy
LinkedIn: https://www.linkedin.com/in/dionique 
SOFTSWISS:
https://www.softswiss.com 
🛠 AI Tools and Resources Mentioned:
OpenAI:
https://openai.com
Anthropic:
https://www.anthropic.com

+ NotebookLM:
https://notebooklm.google 
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Most AI pilots do not fail because the technology is weak. They fail because the organization was never prepared to turn the experiment into a real operating workflow.
In this episode Chris sits down with Ronnie Kwesi Coleman, CEO of Puntt AI and a three-time founder focused on the future of work. They unpack the mistakes that keep enterprise AI stuck in AI pilot purgatory, why expert judgment matters more than simply giving AI more data, how AI agents should learn alongside experienced employees, and why productivity without measurable business outcomes and AI ROI is the wrong target.
They also explore the shift from creation to review as a new AI bottleneck, the limitations of the "single company brain" idea, and the risks of building an enterprise AI strategy around one provider. Listen to learn how leaders can move AI pilots from experimentation into production while preserving the judgment, integrations, and strategic flexibility required for scaling AI across the organization.
Chapters
(00:00) Introduction
(03:04) How to Choose the Right AI Pilots
(05:47) The Five Deadly Sins of AI Pilot Purgatory
(09:46) Starting Small While Planning to Scale
(14:04) Moving From AI Productivity to Business ROI
(18:02) Capturing Expert Judgment With AI Agents
(23:07) How Much Should You Trust AI?
(29:04) AI Agents and the New Review Bottleneck
(32:33) Why the Single AI Brain Model Falls Short
(38:22) Avoiding AI Vendor Lock-In and Hiring Agents
Resources:
🔎 Find Out More About Ronnie Kwesi Coleman

Puntt AI
https://www.puntt.ai/
Ronnie Kwesi Coleman on LinkedIn
https://www.linkedin.com/in/ronnie-kwesi-coleman/

🛠 AI Tools and Resources Mentioned:

Puntt AI
https://www.puntt.ai/
Anthropic Claude
https://www.anthropic.com/claude
OpenAI
https://openai.com/
Meta
https://www.meta.com/
Fall in Love with the Problem, Not the Solution by Uri Levine
https://www.simonandschuster.com/books/Fall-in-Love-with-the-Problem-Not-the-Solution/Uri-Levine/9781637746608
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AI can make your team faster while quietly making them worse at thinking. In this episode Chris sits down with Rana Gujral, cognitive AI leader, CEO of Behavioral Signals, and author of The AI Instinct, to explore what happens when AI moves from a productivity tool into the cognitive infrastructure businesses use to make decisions.
Rana explains why leaders need to distinguish between employees who are genuinely deciding and those who are simply approving AI output, how reversibility can determine when rigorous decision trails are necessary, and why organizations should deliberately preserve opportunities for people to exercise judgment without AI. He also shares his personal process for working with AI on strategic decisions and challenges leaders to measure what AI is doing to their team's thinking, not only their output. Listen to learn how to capture AI's advantages without quietly outsourcing the judgment your organization will need when the easy answers disappear.

Chapters:
(00:00) Introduction
(03:34) AI as Cognitive Infrastructure
(07:47) Building Rules for Human and AI Decisions
(10:46) Preventing Automation Bias and Protecting Human Judgment
(13:42) Redesigning Organizations Around AI Decision-Making
(19:58) How AI Sculpts Human Attention
(22:40) Are Your People Deciding or Just Approving?
(25:14) When AI-Assisted Decisions Need an Audit Trail
(28:26) Rana's Personal Process for Strategic AI Decisions
(31:06) Building Senior Talent When AI Does the Junior Work
(39:24) What Is AI Doing to the Way Your Team Thinks?

Resources:
🔎 Find Out More About Rana Gujral

Rana Gujral Website: 
https://ranagujral.com/
Rana Gujral on LinkedIn: 
https://www.linkedin.com/in/ranagujral
Behavioral Signals: 
https://behavioralsignals.com/
Book: The AI Instinct: The Future of AI and Human Decision-Making
https://ranagujral.com/book

🛠 AI Tools and Resources Mentioned:
Claude: 
https://www.anthropic.com/claude
Chat GPT: 
https://openai.com/
Gemini: 
https://gemini.google.com/
Chief AI Officer: 
https://chiefaiofficer.com/
Wiley: 
https://www.wiley.com
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