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Are today’s AI tools actually doing the work, or are they still sitting on the sidelines offering advice that humans have to act on?

In this episode of the AI at Work podcast, I sat down with Oren Michels, Founder and CEO of Barndoor AI, to explore why so much enterprise AI still feels stuck in what he calls “advisor mode.” We talked about the gap between AI that summarizes and AI that acts, and why that distinction matters far more to knowledge workers than most leaders realize. Oren drew on his experience building Mashery during the early days of APIs, drawing a clear parallel between then and now, when powerful technology exists but remains inaccessible to the people who actually need to use it.

We spent a lot of time unpacking what true agentic AI really means inside the enterprise. For Oren, it is not about smarter chatbots or recycled RPA workflows, but about agents that can safely take action inside systems like Salesforce, CRMs, and other tools of record. We discussed why so many AI initiatives fail to deliver ROI, and why the missing skill is often not prompt engineering, but the ability to break real business problems into clear, executable instructions that an AI agent can actually follow.

Governance became a central theme in our conversation, especially as we dug into the Model Context Protocol, or MCP. While MCP is emerging as a powerful standard for connecting AI to enterprise tools, Oren explained why it also introduces new security, cost, and control challenges if left unchecked. We explored why governance should act as a launchpad rather than a brake, how least-privilege access changes the conversation, and why the most important question is not how a model was trained, but what it can do with access right now.

If you are thinking seriously about agentic AI, enterprise adoption, or how to prevent “bring your own AI” from becoming the next wave of shadow IT, this episode will give you a grounded, experience-led perspective on what actually needs to change inside organizations. As AI agents begin to operate at speed and scale across core systems, are your guardrails designed to stop progress, or to make it possible to move forward with confidence?

I would love to hear your thoughts after listening. How close do you think we really are to AI that acts, not just advises?

Useful Links

• Connect with Oren Michels
• Learn more about Barndoor AI

Thanks to our sponsors, Alcor, for supporting the show.

What does AI at work really look like once the conference buzz fades and teams have to turn ambition into execution?

In this episode of the AI at Work Podcast, I sit down with Diego Lomanto, Chief Marketing Officer at Writer, to unpack how marketing teams are actually using AI and agents inside real enterprise workflows. Diego brings a grounded perspective shaped by more than two decades in enterprise software, spanning analytics, automation, and now AI, including his time leading product marketing at UiPath during its rapid growth years.

We talk candidly about why AI adoption often stalls inside organizations, not because of the technology, but because leadership behavior, operating models, and incentives fail to evolve. Diego explains why C-level executives need to get hands-on first, why AI should be treated as a transformation of how work gets done rather than another IT rollout, and how marketing leaders need to rethink team structure, workflows, and success metrics in an agent-driven world.

The conversation digs into what Diego calls an agentic marketing playbook, where AI handles speed and scale while humans remain firmly in charge of narrative, judgment, and creative direction. From automating repetitive content workflows to freeing up time for deeper customer relationships and high-touch engagement, Diego shares how Writer and its customers, including large consumer brands and regulated enterprises, are using agents to support people rather than sideline them.

We also explore how Writer uses its own technology internally, what surprised Diego once AI agents were fully embedded into day-to-day marketing operations, and why change management and AI literacy matter just as much as model quality. As organizations look ahead to 2026, this episode offers a clear-eyed view of where AI-driven work is heading next, from departmental orchestration to deeper collaboration across marketing, sales, and product teams.

If AI is quickly becoming table stakes, how will your organization use it to automate the repeatable while keeping humans as the real source of differentiation?

Useful Links

Connect with Diego Lomanto

Learn More About Writer

Denodo sponsors Tech Talks Network

As AI moves beyond hype and into everyday operations, many organizations are asking harder questions about impact, trust, and return on investment. Three years on from ChatGPT’s breakout moment, leaders are no longer experimenting for novelty’s sake. They want to know where AI genuinely improves outcomes for employees and customers, and where it risks getting in the way.

In this episode of the AI at Work Podcast, I sit down with John Finch, Head of Product Marketing at RingCentral, to unpack how AI is changing customer interactions before, during, and after the call. We explore how tools like AI receptionists and real time agent assistance are helping businesses avoid missed calls, reduce friction, and support frontline teams without turning conversations into scripted or robotic exchanges.

John shares RingCentral’s perspective on why voice remains one of the richest and most strategic data sources inside modern organizations. We discuss how insights drawn from real conversations are shaping smarter routing, coaching, and workforce planning, and why sectors like healthcare and financial services are leaning into AI faster than others. At the same time, we address the common mistakes companies make when they bolt AI onto fragmented systems rather than embedding it into a unified platform.

Looking ahead to 2026, this conversation also reflects on what AI done well really looks like in the workplace. Not as a replacement for people, but as a way to remove pressure, improve performance, and create better experiences for everyone involved. As AI becomes more natural, conversational, and embedded into daily workflows, the line between digital and human support continues to blur.

So as AI becomes part of the fabric of customer operations, how are you balancing automation with empathy, and what lessons from your own organization would you share with others navigating this shift?