Why do companies spend millions on AI and digital transformation while overlooking the people expected to make those investments successful?

In this episode of Business Technology Perspectives, I speak with Elena Varo, founder of Elevatio Tech & Culture, about why the biggest barriers to technology adoption are often human rather than technical, what leaders misunderstand about organizational change, and why companies need to invest in people as seriously as they invest in technology.

Elena brings more than a decade of experience across the technology industry, including work with Microsoft, Red Hat, CyberArk, and WSO2. Having worked across Spain, Ireland, and the UK, she has seen how leadership, workplace culture, communication, and organizational structures influence whether technology investments produce meaningful business outcomes.

A major theme throughout our conversation is the gap between buying technology and preparing a company to use it effectively. Elena argues that many businesses begin with the wrong question. They focus on which AI platform, cybersecurity product, or cloud provider to purchase before identifying the problem they need to solve and understanding whether employees have the skills, training, information, and support required to adopt new technology.

We discuss why AI is creating a leadership challenge that may be harder to solve than the technology itself. Employees want answers to practical questions about training, skills, changing roles, and expectations. When companies fail to communicate clearly or listen to their workforce, uncertainty grows, experienced employees leave, and technology adoption becomes harder.

Talent retention is another major part of the conversation. The technology industry frequently talks about skills shortages and recruiting new employees while paying far less attention to why talented people leave. Elena explains why salary alone rarely solves problems caused by unclear responsibilities, poor communication, limited career development, internal competition, and workplaces where employees do not feel safe speaking openly.

We also examine the relationship between employee well-being and long-term business performance. Constant technology change, economic uncertainty, demanding targets, and pressure to do more with fewer resources affect employees and managers alike. Companies that want people to collaborate, share knowledge, question assumptions, and develop new ideas need to create working environments where people feel supported and heard.

The conversation also addresses diversity in technology and why attracting more women and people from different backgrounds is only part of the challenge. Companies need to improve retention, promotion processes, sponsorship, access to leadership positions, and opportunities for people with different experiences to influence decisions.

Elena also shares why she founded Elevatio Tech & Culture and her plans for a technology congress in Córdoba that brings business leaders, technologists, universities, public institutions, and other participants together to discuss technology adoption, organizational culture, talent, leadership, and employee well-being.

For CEOs, CIOs, HR leaders, technology executives, and anyone responsible for AI adoption or digital transformation, this conversation provides practical lessons about preparing employees for change, retaining experienced talent, improving communication, supporting psychological safety, and creating workplaces where technology investments can produce better outcomes.

Elena’s advice to business leaders is clear: invest in people as seriously as you invest in technology. Technology can be purchased, but building a workplace where people trust their leaders, develop new skills, share knowledge, and want to contribute requires sustained attention.

What happens when AI can write software in seconds but lacks the context to understand whether the code it creates is built on secure foundations?

In this episode of Business Tech Perspectives, I speak with Brian Fox, Co-Founder and CTO of Sonatype, about the growing pressure facing software teams as AI accelerates development while cyber threats continue to evolve. Brian brings a unique perspective from his work overseeing Maven Central and helping organizations understand the risks hidden inside modern software supply chains.

Our conversation begins with a challenge that many organizations may not fully appreciate. While AI coding assistants are becoming increasingly capable, the information they rely on can already be months old. Brian explains why that matters when selecting open source dependencies and how outdated recommendations can leave security risks buried inside applications long after they are deployed.

We also discuss the role open source software now plays in almost every application. Developers can build products faster than ever by using existing components, but that speed comes with responsibility. Brian shares why understanding what is inside your software has become a business issue as much as a technical one.

Another major topic is the expected surge in vulnerability discoveries driven by new AI capabilities. Brian warns that both attackers and security researchers now have access to tools that can identify weaknesses at unprecedented speed. The result could be a flood of vulnerability reports that challenges maintainers, vendors, and security teams alike.

The discussion also covers the changing state of vulnerability intelligence. With NIST narrowing its focus and public resources under strain, organizations may need to rethink how they gather information, assess risk, and prioritize remediation efforts.

Throughout our conversation, Brian offers practical advice for leaders who want to prepare their organizations for what comes next. From understanding software bills of materials to improving patch management processes, he explains why preparation today could make all the difference tomorrow.

Despite the challenges, Brian remains optimistic about the future. He believes AI will help developers create software faster and help the industry address years of accumulated security issues. The question is whether organizations can adapt quickly enough to keep pace with the changes already underway.

How confident are you that your organization could identify, assess, and respond to a major software supply chain issue today, and are your teams prepared for the increase in vulnerabilities that AI may soon uncover?

What happens when the biggest barrier to AI success isn't the technology itself, but the way organizations are structured to adopt it? In this episode, I sit down with Milan Cooper, Head of Product at TWG AI, a company working alongside Palantir to help enterprises rebuild core business processes around AI. With previous leadership roles at JPMorgan Chase and Accenture, Milan brings a rare perspective from the intersection of AI, risk, governance, and large-scale transformation.

Our conversation moves beyond chatbots, pilots, and proofs of concept to examine what it actually takes to make AI part of mission-critical operations. Milan explains why so many organizations remain stuck in what he calls "AI theater," measuring success through use cases rather than business value. He shares how TWG AI approaches enterprise adoption by focusing on entire value streams, helping organizations move from isolated experiments to AI-native operations that directly influence revenue, efficiency, and decision-making.

We also discuss the growing challenge of AI concentration risk, why switching between AI models could become the equivalent of performing brain surgery on an enterprise, and how organizations can avoid locking themselves into a single provider. Milan offers insights from projects with companies including Guggenheim Investments, where AI is being embedded into investment workflows to increase deal throughput and remove operational bottlenecks.

Along the way, we tackle governance, compliance, AI accountability, the future of SaaS, and why leadership conviction may be the single biggest factor determining whether an AI transformation succeeds or stalls. Milan also shares why trust remains the missing ingredient in enterprise AI adoption and what organizations need to do before employees are comfortable using AI with their most sensitive information.

If you've ever wondered why some companies are turning AI into measurable business outcomes while others remain trapped in endless experimentation, this conversation offers a candid look at what separates the two. What do you think is holding back AI adoption in your organization, technology, culture, or leadership? Share your thoughts with me.