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

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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AI may automate more execution, but that makes judgment, coordination, and management more important. In this episode Chris sits down with Erkang “Ken” Zheng, founder and CEO of Ariso, to explore why the next major opportunity for AI at work is not simply producing more content, but helping people manage work more effectively.
Ken explains why every knowledge worker increasingly needs management skills, including tracking, prioritization, communication, accountability, and reflection. He also breaks down Ariso’s distributed approach to the “company brain,” the role of an AI chief of staff, and how individual AI partners can improve coordination while preserving access controls and privacy. Listen to understand how leaders can use AI to strengthen the management layer of work as execution becomes increasingly automated.

Chapters:

(00:00) Introduction
(04:37) Why Everyone Is a Manager Now
(06:25) The Work Around the Work
(08:28) Management Skills That Matter More With AI
(11:59) Rethinking the AI Company Brain
(14:48) Building the Company Brain From the Individual Up
(19:18) When an AI Intelligence Layer Starts Making Sense
(23:43) What an AI Chief of Staff Should Actually Do
(29:19) How Ariso Differs From DIY AI Agents
(38:11) How Quickly Teams Can See Value From AI

Resources:

🔎 Find Out More About Erkang “Ken” Zheng

Ariso: 
https://ariso.ai/
Ariso Blog:
https://ariso.ai/blog
Ariso LinkedIn:
https://www.linkedin.com/company/ariso-ai 
Erkang Zheng on LinkedIn:
https://www.linkedin.com/in/erkang

🛠 AI Tools and Resources Mentioned:
Ariso / Ari:
https://ariso.ai/
ChatGPT:
https://chatgpt.com/
Claude and Claude Code: 
https://www.anthropic.com/claude
Fathom: 
https://fathom.video/
Fireflies.ai: 
https://fireflies.ai/
OpenClaw:
https://openclaw.ai 

📩 Reach Out to Chris Daigle:
doc@chiefaiofficer.com 
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What if the biggest bottleneck in finance isn't the software, but the way work gets done?
In this episode Chris sits down with John Glasgow, Founder and CEO of Campfire, an AI-native ERP platform built for finance and accounting teams. John explains why simply adding AI to legacy systems falls short, how agentic workflows are changing accounting operations, and why finance leaders should think of AI as a teammate rather than a tool. He shares real examples of AI handling reconciliations, treasury management, reporting, and close processes while keeping humans accountable for outcomes.
The conversation explores practical adoption strategies, how finance teams can build trust in AI, and where human judgment remains essential. Leaders will come away with a clear framework for introducing AI into critical business processes while maintaining accuracy, accountability, and stakeholder confidence.
Chapters:

(00:00) Introduction
(02:00) Meet John Glasgow and Campfire
(03:00) Why AI-First ERP Is Different
(06:04) From System of Record to System of Work
(08:26) AI as Operational Capacity, Not Just Productivity
(13:47) Inside Campfire's Ember Agents
(16:04) Building Trust in AI for Finance Teams
(18:49) Treat AI Like a New Hire
(22:13) Where Humans Must Stay in the Loop
(28:49) Customer Results and Real World Impact
(31:19) Does AI Replace Finance Jobs?
(34:27) The Skills Future Finance Leaders Need
Resources:

🔎 Find Out More About John Glasgow:

John Glasgow LinkedIn
https://www.linkedin.com/in/johnglasgow/ 
Campfire 
https://www.campfire.ai
Campfire LinkedIn
https://www.linkedin.com/company/meetcampfire 

🛠 AI Tools and Resources Mentioned:

Campfire 
https://www.campfire.ai
Claude 
https://claude.ai
Anthropic 
https://www.anthropic.com
NetSuite 
https://www.netsuite.com
SAP 
https://www.sap.com
Workday 
https://www.workday.com
QuickBooks
https://quickbooks.intuit.com
Xero
https://www.xero.com
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