Using AI at Work

Using AI at Work The podcast that teaches YOU how to Use AI at Work.

10/17/2025

On the latest episode of Using AI at Work, Maria Elena Duron discussed one of the most important questions companies face today: Who owns AI inside the organization?

Her answer: ownership requires structure.
Even if your “AI department” is just two or three people, you still need governance, security, and visibility into what tools employees are actually using.

Maria shared how, during her first AI roadmap project, team members all said they were using ChatGPT because leadership had encouraged it. But when she returned to verify, the reality looked very different.

Her takeaway?
Without governance, enthusiasm turns into shadow AI.
Leaders must build lightweight but clear structures for how AI tools are introduced, monitored, and improved, before they scale across the company.

🎧 Link to the full episode is in the comments.

10/16/2025

On the latest episode of Using AI at Work, Maria Elena Duron shared how she begins every AI adoption project, by aligning leadership first.

Before any tools or training sessions, she meets directly with the leadership team to understand their goals, priorities, and end vision.

Because as she explains, “There are so many things you can do, it’s just like the early days of the internet, the wild, wild west.”

That initial one-hour conversation often includes CEOs, consultants, or entire department heads, from marketing to finance to operations, depending on how the organization is structured.

Her message is simple but powerful:
AI adoption starts with clarity at the top. Without it, even the best initiatives can lose direction.

🎧 Link to the full episode is in the comments.

10/15/2025

On the latest episode of Using AI at Work, Maria Elena Duron shared a powerful insight for anyone stepping into AI consulting or fractional leadership roles.

When working with clients, clarity and confidence are non-negotiable.
As she explained, being upfront about scope, whether you’re serving as a short-term fractional Chief AI Officer or as a consultant, builds trust and authority from the start.

Maria emphasized that clients can sense uncertainty immediately.
If you don’t have structure or a plan, they’ll feel it, and it can undermine the relationship before it begins.

Her approach?
Lead with confidence, set clear expectations, and guide the client through change with structure, not speculation.

🎧 Link to the full episode is in the comments.

10/14/2025

On the latest episode of Using AI at Work, Maria Elena Duron shared what she’s seeing firsthand as businesses try to understand where AI fits in, and how it compares to their long-standing focus on Google and search.

As Maria explained, interest in AI is high, it’s the buzz across every industry. Leaders know they can’t ignore it because the marketplace is shifting and search itself has changed.

But she also sees two extremes emerging:
One group wants to wait it out, hoping AI and search will somehow “just work” for them.
The other is ready to act, but often without the clarity or structure to make AI practical and aligned with their goals.

Her message to both: AI isn’t magic, and it’s not luck. It requires the same thoughtful approach as search did years ago, grounded in strategy, consistency, and real understanding.

🎧 Link to the full episode is in the comments.

10/13/2025

On the latest episode of Using AI at Work, Maria Elena Duron, fractional Chief AI Officer and marketing strategist, broke down one of the most overlooked parts of AI adoption, managing fear and building trust inside teams.

As Maria explained, when AI initiatives begin, the first reaction across companies, large or small, is often fear:
“I’m going to be let go. I’m going to lose my job.”

That’s why, before introducing tools or workflows, she focuses on showing teams that AI adoption is an investment in them, not a replacement for them. Leadership’s choice to bring in AI isn’t about automation for automation’s sake; it’s about training people to work smarter, not faster.

Maria shared how she helps teams move past uncertainty by putting structure in place, interviewing employees, identifying the routine pain points that can be measured and improved, and avoiding flashy “AI for AI’s sake” projects.

Her approach emphasizes profitability in both monetary and human resources, because moving too fast can overwhelm people just as much as moving too slow can stall progress.

It’s a masterclass in practical, human-centered AI adoption.

🎧 Link to the full episode is in the comments.

10/11/2025

On the latest episode of Using AI at Work, Jim Curry compared the current wave of AI adoption to the rise of open source software.

Just like developers once started using open tools long before corporations caught on, AI adoption inside enterprises is being driven from the bottom up, by individuals experimenting, learning, and proving value on their own.

As Jim explained, that grassroots innovation is what he’s watching most closely.
Not just the technology itself, but how people are using it, what’s working, and where it’s adding real value.

He also raised one of the biggest questions on everyone’s mind:
👉 What happens to the human role as AI agents start handling more of the work?

From output evaluation to escalation tasks, the future of work isn’t fully clear, but understanding where humans fit alongside intelligent agents is now one of the most important conversations happening inside every company.

🎧 Link to the full episode is in the comments.

10/09/2025

On the latest episode of Using AI at Work, Jim Curry explored how the accessibility of AI is transforming what it means to build and operate a business.

In the early days, AI was complex and costly, limited to teams who could build advanced systems from scratch.
Today, it’s the opposite.

AI has become simple enough for anyone to adopt, across every part of the business, from products and operations to customer-facing experiences.

As Jim explained, this accessibility is lifting all boats in terms of innovation.
People who were never developers are now becoming builders.
And perhaps even more importantly, they’re becoming data scientists, gaining the ability to interact with data in real time and extract insights without depending on specialists.

That shift is redefining what teams can do, and who gets to do it.

👉 Link to the full episode is in the comments.

10/08/2025

AI is about to change the way companies engage prospects.

On Using AI at Work, Jim Curry described a future where intelligent agents replace static chatbots. These agents act as SDRs, collecting info, answering questions, even spinning up a demo instantly.

The key? Context persists. If a visitor leaves and returns later, the agent remembers where the conversation left off.

Meanwhile, human reps receive the data in real time, knowing exactly when to step in.
This balance of automation + human touch could redefine go-to-market, speeding up cycles and personalizing outreach without adding headcount.

Full episode, link is in the comments.

10/07/2025

AI isn’t just changing operations, it’s reshaping go-to-market strategy.

On Using AI at Work, Chris Daigle sat down with Jim Curry, co-founder of BuildGroup, to discuss how AI is lowering the marginal cost of growth and what that means for business models.

Jim explained that AI is making it possible to scale outreach and creativity at a fraction of the cost. That shift poses long-term challenges for venture capital, since high-growth companies may no longer need the same level of funding to achieve scale.

But while the tools are powerful, there’s still a gap. AI can generate prospect lists or outreach templates, but the results often feel spammy.

The real advantage comes when leaders take those AI-generated first drafts and refine them into targeted, custom outreach, messages that actually resonate instead of flooding inboxes with generic pitches.

Full episode, link is in the comments.

10/06/2025

AI isn’t the first wave of innovation, and it won’t be the last.

On Using AI at Work, Jim Curry compared today’s AI boom to earlier shifts in open source and data science. He shared the story of the OpenStack project, where global collaboration, including contributions from both U.S. and Chinese government agencies, drove massive software innovation.

AI, he explained, is the evolution of that same movement. Companies that understand their workloads generate unique, valuable data will be best positioned to capture the benefits.

We cover:
• Why open collaboration accelerates innovation
• How AI builds on the data science boom
• Why data-enabled companies have an edge

Full episode, link is in the comments.

10/03/2025

AI isn’t replacing SEO, it depends on it.

On Using AI at Work, Bruce Clay, the Father of SEO, reminded us that search optimization has never been limited to organic results. Done correctly, SEO strengthens every part of visibility, from organic listings to extensions that improve performance.

Google has spent 27 years building systems to detect spam, crawl content, and surface authority. With racks of servers and decades of refinements, there’s no reason for AI to reinvent this infrastructure.

Instead, AI relies on SEO. It depends on structured sites, expertise, and authority to filter out spam and hide garbage. Without strong SEO foundations, AI systems wouldn’t know which sources to trust.

Catch the full episode of Using AI at Work, link is in the comments.

10/01/2025

Not all AI-generated content is created equal.

On Using AI at Work, Bruce Clay, the Father of SEO, explained why content produced by chat tools often leaves a clear footprint.

Take the example of asking AI to “write a 1,500-word article on search engine optimization.” The output may look polished, even convincing. But under the surface, it’s just an assembled sequence of words drawn from across the web.

Bruce noted that detection tools like ZeroGPT can flag this kind of content as 92% AI-generated. Why? Because AI writing patterns are predictable. Sentences tend to be the same length. Sections often contain the same number of sentences. Bullet lists almost always repeat the same structure.

That uniformity is the giveaway, and it’s what makes obvious AI writing risky to publish without human refinement.

Catch the full episode of Using AI at Work, link is in the comments.

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