What if the most important jobs of the next decade already exist, but we just have not named them yet?

In this episode of AI at Work, I sit down with Marinela Profi from SAS to unpack how artificial intelligence is reshaping work at a deeper level than most headlines suggest. We are not just talking about tools, automation, or faster workflows. We are talking about new roles, new decision structures, and a fundamental shift in how humans and machines collaborate inside modern organizations.

Marinela brings a grounded, enterprise-tested perspective to agentic AI, cutting through the confusion that still surrounds the term. She explains why large language models are not agents, why autonomy is often misunderstood, and why most successful AI systems will always keep humans in the loop. We explore how agentic systems differ from traditional AI, how deterministic guardrails and probabilistic models must work together, and why governance needs to be designed into systems from day one rather than bolted on later.

One of the most compelling parts of this conversation is our discussion on future roles. A few years ago, no one imagined titles like cloud governance architect. Marinela explains why roles such as AI decision designers and AI experience designers are likely to follow a similar path. These are not abstract ideas. They are practical responses to real challenges organizations face as AI systems begin to act, decide, and operate at scale.

We also dig into where teams tend to go wrong. Too many organizations rush from pilots to hype without addressing data readiness, orchestration, or accountability. Marinela shares real examples from regulated industries, including banking, insurance, telecoms, and manufacturing, where agentic AI has moved from experimentation into production by focusing on decision workflows rather than flashy prototypes.

This is a conversation for CIOs, CDOs, business leaders, and professionals who want to understand what AI means for work beyond surface-level narratives. It is also for students and early-career listeners who want to prepare for roles that are still taking shape, but will soon be unavoidable.

If AI is becoming an expected skill rather than a specialist one, how do you prepare yourself and your organization for work that is already changing in front of us?

I would love to hear your thoughts after listening. Where do you see human judgment becoming more important as AI systems grow more capable, and which future roles do you think we will be talking about next year?

Useful Links

Connect with Marinela Profi

SAS Website

Follow SAS on LinkedIn

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

What happens when eighty percent of the global workforce receives less than one percent of technology investment, and why has this imbalance gone largely unchallenged for so long?

In this episode, I sat down with Emma Seymour, Chief Financial Officer of Deputy, to unpack the realities facing the world’s so-called invisible workforce. Deskless workers power healthcare, retail, hospitality, and frontline services, yet the tools built to support them have historically lagged far behind those designed for office-based teams. Emma brings a grounded, finance-led perspective on why this gap exists and why it is finally starting to close.

We explored how AI-driven workforce management is moving beyond hype and into practical, measurable outcomes. From optimizing staffing levels to reduce overstaffing and burnout, to giving workers more control over their schedules through self-service tools, Emma shared how Deputy is translating technology investment into real operational and human impact. We also discussed how AI is reshaping the finance function itself, automating admin-heavy tasks and freeing up teams' time to focus on higher-value work.

What also stood out in this conversation was leadership. Deputy’s predominantly female executive team offers a rare example of scaling a billion-dollar technology company while balancing high performance with high care. Emma shared how trust, accountability, and empathy shape decision-making inside the business, and why that culture matters just as much as product innovation when serving a workforce that has been overlooked for decades.

As AI continues to accelerate and workforce pressures intensify, what would it look like if more technology companies truly built for the people who keep the global economy running, and how differently might work feel if the invisible workforce finally became visible?

Useful Links

• Connect with Emma Seymour
• Learn more about Deputy,
• Follow on LinkedIn

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

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.