Category: Apex 1 on 1

Technology in the Circular Economy

By Dwayne Alves, IT Director, Emterra Group

How would you describe your role within your organization? What are your key responsibilities?

My primary responsibility is to ensure our IT strategy aligns with Emterra’s sustainability, operational efficiency, and business objectives. To do this I advise our executives on technology trends, risks, and opportunities.

Secondarily I oversee our infrastructure, cybersecurity posture, enterprise applications, and the rollout of transformative technologies like, AI agents, and predictive analytics.

How do you ensure effective communication when working with cross-functional teams, especially those without a technical background?

Technical people tend to explain everything, but most business teams only need the top layer. Non-technical teams care less about how the technology works and more about why it matters. We are a business partner, not a technical silo.

Most of our organization is front line operational staff so I’ve found to effectively communicate with them to always anchor technology explanations to

• time saved
• risks reduced
• operational efficiency
• sustainability or
• improved customer experience

Our people absorb visuals faster than technical language. At Emterra, this is especially useful for explaining our cybersecurity posture or showing progress on IT projects.

Can you share an example of a significant IT transformation or change initiative you’ve led and the lessons you learned?

Emterra’s frontline teams historically relied on paper-based forms which created several problems:

Weeks of delays before data made it into digital systems, lost visibility into real‑time operations, transparency gaps around production and incident tracking along with increased administrative overhead.

And most importantly it contradicted our sustainability values due to large paper usage.

My task was to define the digital strategy for data collection. Select and implement a real‑time, mobile‑first data platform. Digitize all forms used by collection teams and integrate them with existing digital data. Pilot the solution at a single site before scaling—reducing risk and building confidence. Enable drivers to enter collection data directly from any mobile device. Consolidate all collection information in one central location, accessible instantly.

Digitizing this data allowed us real‑time visibility into production and downtime, centralized, accurate data that supports dashboards, automation, and future AI use cases. It allows improved incident tracking for safety and compliance and a reduction of paper waste aligning with company sustainability goals.

The largest lesson I learned was to be flexible. The strategy was enterprise wide, but the implementations differed by country, province and line of business.

As sustainability becomes a top priority, how are you incorporating eco-friendly practices into your IT architecture design, and what innovations are emerging to reduce the environmental footprint of IT infrastructure?

My team looked at trends across the business units and one thing very noticeable was that printing was a black hole outside of a few key sites. We thought about designing an internal solution with all the bells and whistles but ended up using a vendor’s managed print services across the organization. We are seeing the Roi through increased printer uptime, extended device lifespan and consolidation of devices into more efficient units.

This has allowed us to support our sustainability goals of lower energy consumption and reduced waste from consumables & repairs. It has also allowed us to find sites with high print usage and provide digitized solutions for some procedures previously unknown.

Not every design needs to be first of a kind to get a measurable impact.

What strategies do you use to identify, develop, and upskill talent in critical areas like emerging technologies and cybersecurity to stay ahead of industry demands?

When we build our company roadmaps and project lists, I get my team involved. We take big initiatives and break that down into smaller projects and then figure out who will be doing which pieces and what skills would need to be developed. Training is a mandatory part of each team member’s performance review, and the upskilling is tied to any gap in our roadmap we want to address.

Can you share some of the key challenges your team faces in aligning IT and business objectives with regulatory requirements, and how do you ensure that risk management is integrated into the company culture?

We provide centralized services for the group of companies and while they’re all related oftentimes one solution does not fit all requirements. This leads to constantly improving our security stack without increasing complexity for our end users. As an organization we have a benchmark or gold standard we use for all vendors or applications we use regardless of business unit and we validate it via third parties. “Trust but Verify” is hardcoded into all functions of the company.

About Dwayne Alves, IT Director, Emterra Group

Dwayne Alves is a seasoned IT executive with over 20 years of progressive leadership experience spanning infrastructure, cybersecurity, business intelligence, digital transformation, and enterprise‑scale automation. Best known for scaling tech stacks to support significant growth and change. Dwayne is an active speaker and contributor within the Toronto CIO community.

LinkedIn: https://www.linkedin.com/in/dwayne-alves-11747713/

Apex Executive Insights

From Chaos to Clarity: Governing High-Stakes Portfolios at Scale 
An Apex 1:1 with Sheneq Aranda, Vice President of Enterprise Architecture and IT Governance

Apex chats with Sheneq Aranda, a Vice President of Vice President of Enterprise Architecture and IT Governance with 15+ years of experience leading complex, cross-functional initiatives across financial services, energy, and technology. Known for bringing structure and momentum to high-stakes environments where priorities compete and ownership is distributed, Sheneq has scaled enterprise BPO and automation capabilities delivering ~$30M in savings, built portfolio governance across global organizations, and partnered with CIOs and executive teams to turn ambiguous mandates into durable operating models. In today’s Apex 1:1, Sheneq shares her philosophy on turning enterprise chaos into clarity and what it takes to lead without losing speed.

Q: You’ve spent your career in environments where complexity is the default-financial services, energy, global operations. What draws you to that kind of work?

A: Honestly, I’ve always been most energized when the path forward isn’t obvious, but I’m empowered to do something about it. Early in my career, working across Shell in Singapore, Deutsche Bank in London, and later Chevron and now Huntington, I noticed that the organizations that struggled most weren’t short on talent or resources. They were short on clarity: about what mattered, who owned what, and how to move. That gap between strategy and execution became the space I wanted to operate in. I find real satisfaction in being the person who helps a team see the signal through the noise and build something that actually holds.

Q: What does “chaos to clarity” actually mean in practice? Is it a methodology, a mindset, or something else?

A: Both and neither, fully. It starts as a mindset: you have to resist the urge to act before you understand. When you enter a complex initiative, the instinct is to start moving. But the most valuable thing you can do in the first 30–60 days is listen, map the terrain, and identify where the real friction is. From there, it becomes methodology: establishing governance that creates accountability without bureaucracy, building operating cadences that keep teams aligned without slowing them down, and designing decision frameworks that help leaders prioritize without second-guessing each other. The goal is always a durable operating model, not a project that ends, but a structure that continues to perform after the initial momentum and excitement fades.

Q: You scaled an enterprise BPO capability from infancy inside a $53B regulated institution and delivered ~$30M in savings. What did that actually require that isn’t obvious from the outside?

A: The easy answer is governance, process design, and vendor management, and marketing. But the harder answer is trust. In a regulated environment, no one will hand over operational processes to an external provider unless they believe the oversight model is sound and the people running it have sound judgment. I spent significant time building credibility with the CIO, the CRO, and 10 lines of business before we could move with real speed. I also had to design intake and prioritization processes that felt fair, because if business units don’t believe their work will be handled correctly by, they’ll work around the model instead of into it. The savings came from the execution, but the execution was only possible because of the trust architecture we built first.

Q: You’ve led in highly matrixed environments without formal authority over many of the stakeholders you needed to move. How do you think about influence at that level?

A: I think about it as earning the right to be in the room and then using that position to create alignment rather than advocacy. When you don’t have a direct reporting line, you can’t rely on positional authority. What you can rely on is being prepared, being transparent about trade-offs, and making it easier for leaders to say yes than to say no. I’ve found that the executives who are hardest to move are usually the ones who haven’t been given a clear picture of what’s at stake or what’s being asked of them. When you do that work upfront, anticipating their concerns, framing the ask in their language, connecting it to outcomes they care about, alignment follows much more naturally.

Q: AI and automation are reshaping enterprise operations. From your vantage point, what are organizations getting wrong about this moment?

A: They’re treating AI adoption as a technology initiative when it’s actually an operating model transformation. The technology is increasingly accessible. What’s hard is the governance: deciding what to automate, ensuring the right controls are in place, managing the human side of displacement and reskilling, and building the metrics to know whether it’s actually delivering value. I saw this pattern with BPO and RPA, and I’m seeing it again with AI. The organizations that get the most value are the ones that slow down enough to design the operating model before they scale the technology, not after.

Q: What advice would you give to a mid-level leader who wants to operate at the enterprise level but isn’t there yet?

A: Get comfortable with ambiguity and then learn how to reduce it for others. The shift from functional leader to enterprise leader isn’t just scope; it’s a different relationship with uncertainty. At the enterprise level, the problems are rarely well-defined, the ownership is rarely clean, and the answers are rarely obvious. What distinguishes the leaders who thrive is their ability to bring structure to ambiguous situations without waiting for someone else to do it first. I’d also say: build relationships before you need them. The executives I’ve seen advance most consistently are the ones who’ve invested in cross-functional relationships long before they needed to call on them.

“Clarity isn’t the absence of complexity. It’s the discipline to understand it well enough to act. That’s the work I show up to do every day.”

About the Author

Sheneq Aranda is a Vice President of Enterprise Architecture and IT Governance specializing in enterprise transformation, portfolio governance, and global business services. With 15+ years of experience across financial services, energy, and technology, she has scaled BPO and automation capabilities delivering ~$30M in savings, built governance frameworks across 10+ lines of business, and partnered with CIOs and executive teams to translate strategy into durable operating models. She has held leadership roles at Huntington National Bank, Chevron, Deutsche Bank, and AIG, and serves on the boards of Multiplier and the Upper Kirby Foundation – Levy Park Conservancy.

LinkedIn: https://www.linkedin.com/in/sheneqaranda/

Apex Executive Insights

Balancing Innovation: What AI Has Taught Me About Leadership in Global Logistics 
by Stephen Chen, CTO, NuCompass Mobility

When people ask me about AI adoption at NuCompass Mobility, they expect a technology story. What I give them is a people’s story.

We’re a logistics company, and relocating employees is what we do—it’s about real-life transitions and big personal moments. So, when we started looking into AI, we didn’t jump straight to “How much can we automate?” Instead, we asked, “Where does AI actually help our people do more, and where do we need humans to call the shots?”

That balance matters more than most technology leaders acknowledge. On our development team, AI serves as an orchestrator — boosting engineer productivity by 30 to 40 percent and we want to bring that same boost to our operations, but critical decisions and quality oversight remain human responsibilities. That boundary is intentional, not a limitation.

Operations in our business aren’t simple. Relocation means dealing with messy old systems, sensitive personal data, and an industry that’s honestly been slow to catch up. Then there’s Shadow AI, where people try out new tools on their own, outside the approved channels. So security isn’t something you “fix”; it’s something you constantly manage and protect, because it keeps changing.

My transition from defense to a commercial CTO role came with many lessons, but one stands above the rest: Humility and real communication between business and tech teams aren’t just nice-to-haves—They are the foundation everything else is built on.

Tech like AI is always shifting in and out. Whether your company rides the wave or gets wiped out depends on something basic: really understanding your end users, and building a culture that’s always eager to learn. That’s what makes the difference.

To learn more you can view out my podcast, available across platforms:

YouTube (Full Episode)

Apple Podcasts

Amazon Music

iHeartRadio

About Stephen Chen, CTO, NuCompass Mobility 

Stephen is CTO at NuCompass, where he has led a full transformation of the company’s technology landscape since 2019. His role sits at the intersection of innovation and discipline, blending startup-level agility with the structure required to support a complex, global mobility organization.

With seven years in mobility and more than 30 years across product development, logistics, and complex systems, Stephen brings deep experience in program management, system integration, data engineering, cybersecurity, and product strategy. Earlier in his career, he worked in high-stakes aerospace and maritime defense environments, including reconnaissance satellites and submarine-based systems, experiences that shaped his disciplined approach to execution and risk.

Outside of NuCompass, Stephen is a minority owner of a tennis and swim club, where his operational guidance has helped more than double revenue. He is also deeply passionate about helping nonprofit organizations scale through better operational systems, allowing them to focus more fully on their missions. Weekly self-reflection is a non-negotiable habit, helping him course-correct quickly and stay aligned with his values.

LinkedIn: https://www.linkedin.com/in/sschen/

Apex Executive Insights

By Maxim Hudaley, CISO at Complete Genomics

Q: You run security for a genomics company. What makes that different from any other CISO role?

The data.

Your genome is among the most sensitive forms of personal data there is — immutable, inherited, and shared in part with your family. There’s no password reset. No breach notification that undoes the damage. And no meaningful way to revoke exposure once that information is out.

Most CISOs protect data that can be rotated, revoked, or reissued. In genomics, you’re protecting data that is permanent. A Social Security number can be replaced. A genome cannot. That changes the security equation — from risk modeling to incident response to how you think about stewardship and custody at a fundamental level.

On top of that, sequencing pipelines increasingly rely on AI models with access to highly sensitive biological data and valuable intellectual property. Securing a genomics company means securing not just the infrastructure, but also the analytical layer that drives interpretation and downstream decisions.

Q: You’ve written about AI models inside sequencing pipelines being an attack surface. Can you explain that?

This is still an under-discussed threat vector in many CISO conversations.

We spend a lot of time talking about SIEM tuning, MFA fatigue, ransomware readiness, and identity controls — all of which matter. But in genomics, one of the most important attack surfaces may be the AI model embedded in the sequencing or interpretation pipeline itself.

Recent research has started to make that risk more concrete. Work from Princeton and Stanford highlighted how DNA foundation models may be vulnerable to jailbreak-style attacks, while separate research from Johns Hopkins and Oxford raised concerns that safety controls based on data exclusion may be more fragile than many assume under adversarial fine-tuning. The broader point is not that every model is immediately exploitable in production, but that the model layer deserves the same scrutiny we apply to any other high-impact system.

Operationally, that means your AI model may function like an unmonitored privileged user. It can have access to highly sensitive data, influence outputs that drive downstream decisions, and operate with far less behavioral visibility than a human or service account would. A compromised or poorly governed genomic model could do more than leak data. It could affect variant interpretation, distort research workflows, or produce outputs that existing biosecurity controls are not designed to evaluate well.

Q: AI agents are proliferating across enterprises. How do you see this playing out from a security perspective?

I was at HumanX in San Francisco recently, and the mood shift was unmistakable. The defensive posture is out. The builder mentality is in.

Every business function — HR, Finance, Sales, even the CEO — is experimenting with AI agents, often without the kind of formal IT process or security review we’ve historically expected for new systems. That creates a familiar problem in a new form: shadow IT, but faster, more autonomous, and much more capable.

The attack surface is expanding faster than many teams can staff or govern for.

Organizations need leaders who understand both the opportunity and the blast radius, and that combination is still relatively rare.

Security professionals who can’t speak the language of product, speed, and business value are going to struggle. The next two years could create one of the most intense hiring environments cybersecurity has seen, especially for leaders who are fluent in both AI and risk.

Q: What’s your approach to AI security governance?

I start from a simple principle: if an AI model has access to your data, it has to be governed like a privileged user.

That means having a real inventory of models, clear access controls, logging around inference activity, visibility into training and fine-tuning data provenance, and monitoring for drift or anomalous behavior. AI Security Posture Management is becoming a core capability, not a future nice-to-have.

The World Economic Forum’s Global Cybersecurity Outlook found that the share of organizations assessing the security of AI tools rose significantly in a single year. That’s an encouraging shift, but it also shows how quickly expectations are changing. AI governance needs to be part of the security baseline, not a special project.

Beyond tooling, I advocate for red-teaming models, not just perimeters. Adversarial ML testing should become as routine as penetration testing, especially for systems that touch sensitive data or influence important decisions. And if a vendor’s assurance begins and ends with “we removed dangerous data from training,” that’s not enough. The real question is how they validate model behavior under adversarial conditions.

Q: How do you think about the intersection of AI and leadership in cybersecurity?

The CISO role is being redefined in real time.

It’s no longer enough to be the person who says “no.” You have to be the person who helps the business move quickly without taking on unacceptable risk. That requires a different posture — one that combines technical depth with business fluency.

AI is the accelerant. It speeds up innovation, threats, and decision-making all at once. The leaders who will thrive are the ones who can operate in both dimensions: understanding the technology well enough to assess risk, and understanding the business well enough to articulate opportunity.

I use AI tools in my own work — for threat analysis, automating parts of security operations, and keeping up with research. The CISO who isn’t fluent in AI is already behind. But fluency isn’t just about using the tools. It’s about understanding their failure modes, attack surfaces, and governance requirements.

Q: What keeps you up at night?

The convergence of three things: the sensitivity of genomic data, the pace of AI capability growth, and the lag in regulatory frameworks.

AI models can now help predict the functional impact of non-coding genetic variants, support interpretation workflows, and accelerate research in ways that would have seemed distant not long ago. The analysis layer is already here, and it is moving faster than most regulatory and governance frameworks can adapt.

At the same time, most people have very little visibility into what happens to their genomic data once they provide it to a consumer or healthcare organization. The intersection of genomics, AI, and security remains underexamined relative to its importance. That gap between technical capability and governance maturity is where a great deal of the real risk lives — and it’s where I focus my energy.

Final Thought

The most sensitive data many of us will ever generate sits inside our own cells.

As an industry, we’ve built increasingly sophisticated defenses around financial data, health records, and intellectual property. But the data that defines us at a biological level still is not protected or governed with the rigor its sensitivity demands. Securing the AI-genomics frontier is not just a technical challenge. It is a long-term responsibility.

About Maxim Hudaley, CISO at Complete Genomics

With more than 25 years of experience in cybersecurity, infrastructure, and digital transformation, Maxim leads security for one of the world’s leading genomic sequencing companies. A CISSP-certified executive and AI practitioner, he sits at the intersection of cybersecurity, artificial intelligence, and biotechnology — helping protect some of the most sensitive data on earth: the human genome. Maxim is a member of the NIST NCCoE Working Group on Cybersecurity and Privacy of Genomic Data, the Alliance of Chief Executives, The CISO Society, and (ISC)².

LinkedIn: https://www.linkedin.com/in/maxim-hudaley/

Apex Executive Insights

By Rasheen Whidbee, Vice President of Information Technology – Rendrcare

Technology evolves rapidly; how do you stay current with new tools, trends, and methodologies in the industry?

I stay current by combining structured learning with real-world application. I follow industry reports, participate in professional communities, and continuously test emerging tools in practical environments. Given how quickly technology evolves, especially with AI, I believe learning has to be intentional and ongoing, not reactive.

What is your favorite quote and why?

My favorite quote is, “Intelligence without wisdom is dangerous.” It resonates with me because we are rapidly advancing AI capabilities, but without thoughtful governance and ethical awareness, those advancements can create more risk than value.

How are you preparing your IT team to adapt to future challenges, such as automation, AI, and skills shortages?

To prepare my IT team for future challenges, I focus on building adaptability. That includes cross-training, encouraging curiosity, and exposing the team to automation and AI tools early. I also emphasize critical thinking and problem-solving over rigid technical specialization, because the tools will change, but those skills endure.

What are some of the personal experiences or compelling arguments that have influenced your thinking around gender and technology and have motivated you to get involved in being an advocate for change?

My perspective on gender and technology has been shaped by observing both subtle and overt disparities in opportunity and recognition. I have seen talented individuals overlooked, and it reinforced for me that leadership has a responsibility to actively create inclusive environments. Advocacy is not optional; it is necessary for building stronger, more diverse teams.

What skills or roles do you believe are most critical for building a strong AI-driven team, and how do you attract and retain such talent?

Building a strong AI-driven team requires a mix of technical and strategic roles. Data engineers, AI practitioners, and cybersecurity professionals are critical, but equally important are translators who can bridge business and technology. To attract and retain talent, I focus on purpose, growth opportunities, and creating an environment where people feel valued and challenged.

About Rasheen Whidbee, Vice President of Information Technology, Rendrcare

I’ve spent over two decades working across IT operations, cybersecurity, and leadership roles, helping organizations solve complex problems and navigate risk in highly regulated environments. Today, as a Vice President of IT Operations, I focus on building resilient systems, leading high-performing teams, and aligning technology with real business impact.

Beyond my day-to-day role, I’m deeply interested in the future of AI, particularly around bias, ethics, and governance. I’m currently pursuing my doctoral studies in cybersecurity, and I enjoy writing and speaking about the intersection of technology, risk, and society. At my core, I’m driven by a simple goal—using technology to create meaningful, responsible, and lasting impact.

LinkedIn: https://www.linkedin.com/in/rasheenwhidbee/

Apex Executive Insights

By Michael Irwin, CISO, Odyssey Logistics

What is your favorite quote and why?

“Culture eats strategy for breakfast.” Over the years, this one has always stuck with me because even the best plans fall apart without the right people behind them.

When I look back over my career, the wins I’m most proud of didn’t come from some perfect strategy deck. They came from teams that trusted each other and took real ownership of the work. When people feel like they’re protecting something that matters, they show up differently. They collaborate more. They do their best work.

No strategy document can create that.

Strategy defines direction. Culture determines whether you ever reach it.

What lessons or advice have helped you get to where you are today?

A few principles have guided me.

Stay curious. The moment you assume you’ve mastered technology is the moment you fall behind. The most effective leaders are the ones who stay curious and keep learning rather than defending what they already know.

Anchor decisions in business outcomes. Technical elegance has value, but alignment with growth, resilience, and customer trust matters more.

Invest in your people before your tools. Great teams will solve problems that great software alone never could.

I also learned to say, “Yes — and here’s how we do it responsibly,” instead of defaulting to “no.”

Lastly, I’ve had to work on letting go of perfectionism. Knowing when something is good enough and deciding to move forward often creates more value than chasing the “ideal” solution.

What is the biggest challenge for a CISO today?

Speed.

Companies are moving fast with cloud adoption, AI, automation and new digital products — and the threat landscape is evolving just as quickly.

The challenge isn’t choosing innovation over security. It’s helping the organization move quickly without creating risk it can’t manage.

At the same time, you have to keep your team sharp on technologies that are still emerging. With AI, for example, we’re securing systems that are still being defined. That demands a forward posture — anticipating secondary effects and unintended consequences before they emerge.

How has the role of the CISO changed over your career?

The core job has not changed. You are still protecting the business. But the respect for the role has grown a lot. Security used to be treated as a technical function tucked away from real business conversations. Now it is a business risk function, and CISOs are expected to speak the language of the boardroom.

Part of what drove that shift is reality. Organizations have spent more and more on security while still dealing with breaches. That pushed the role toward outcomes over controls. The industry moved away from a “protect the castle” mindset toward zero trust, and the CISO role adapted with it.

How are you preparing your IT team for automation, AI, and skills shortages? 

We’re focused on embracing this paradigm shift of how people spend their time.

Automation should handle the more repetitive work so our teams can focus on areas that require judgment, creativity and strategy. We invest heavily in ongoing learning — certifications, cloud training and AI literacy — so people stay current as the landscape evolves.

We’re also shifting talent from operator roles into engineering and architecture roles. The teams of the future will do so much more than just run systems. They’ll design and continuously improve them.

How do you balance cutting-edge technology with legacy systems?  

Most organizations operate in layered environments shaped by years of growth, acquisition and operational demands — not a clean slate. And we’re no different.

Legacy systems do not go away overnight, so we segment and isolate them, modernize deliberately and apply controls proportionate to exposure. The goal is to modernize without disrupting the business.

Additionally, AI has created the opportunity to quickly modernize underlying code basis and make rapid transformational changes to legacy systems. But every decision has to balance desired outcomes against operational constraints. Large, overnight migrations are rarely the right answer.

What challenges exist when scaling infrastructure? 

In logistics, a lot of growth comes through acquisitions and global expansion. That means consistently integrating disparate networks and platforms that were never originally designed to work together.

Standardizing globally while managing cost and maintaining security is genuinely complex. We address it through common architecture standards, cloud adoption, network modernization and automation wherever practical.

How do you prepare for ransomware threats? 

We prepare in layers.

Prevention starts with the fundamentals: multi-factor authentication, network segmentation, active vulnerability management, strong RBAC, least privilege and disciplined account auditing. The basics still move the needle more than most organizations realize.

Furthermore, if we agree that humans are the biggest risk factor, our focus needs to reflect that — robust training, clear policy communication and reasonable user controls.

Detection requires 24/7 monitoring and actionable threat intelligence. And when something does happen, we rely on tested response and communication plans. Immutable backups and solid disaster recovery ensure we can recover without paying a ransom.

The goal can’t be solely prevention — it has to be resilience.

How do you balance innovation with risk management in system design? 

We start with the business objective and work backward.

Every technology decision should support growth, improve resilience, manage risk and stand the test of time. Architecture governance keeps innovation aligned with strategy. This doesn’t mean slowing down, instead it’s about making sure we build solutions we can actually work for the business long term

What are the most pressing cybersecurity challenges in 2026? 

Three areas stand out:

First, AI-driven attacks and deep-fake enabled social engineering. The barrier to creating convincing phishing or impersonation attempts has dropped significantly.

Second, supply chain and third-party risk. As organizations rely on more vendors and partners, exposure inevitably expands.

Third, the talent gap. The shortage isn’t improving, even as threats grow more complex. Emerging technologies can also amplify syndrome inside teams. We need to set practical expectations and create environments where people can learn and grow confidently. We need to check our expectations against what is practical and encourage our teams to learn and grow within our organizations.

What important leadership or business lessons have you learned? 

Early on, I thought being a leader meant having all the answers. I know now it means creating clarity when things are uncertain, clearing the path so your team can get work done, and giving honest feedback even when it is uncomfortable.

The other big lesson is about making decisions. Sitting on a decision too long is often worse than making the wrong one. You make the call, watch what happens, and adjust. Leadership is really about serving the people around you, not the other way around.

How do you balance innovation with robust security? 

Security has to be part of the design from the start, not something added at the end. We embed security into architecture reviews and development pipelines, apply zero trust principles, and make risk-based decisions rather than blanket restrictions. When security is done right, it actually speeds things up because teams do not have to go back and fix things later.

How do you keep IT teams agile during transformation? 

We keep delivery cycles short, encourage teams to work across functions, and push decision-making down to the people closest to the work. We measure outcomes, not just activity. Transformation stalls when teams are stuck waiting for approvals or trying to get everything perfect before shipping. Moving and adjusting is better than standing still.

How do you evaluate emerging technologies like AI and IoT? 

We look at business value first, then security and privacy impact, operational complexity, cost, and regulatory considerations. We move quickly on pilots, get something in front of real users, measure what happens, and scale what works. Risk of AI is largely related to the data you are feeding it – as an organization we should encourage experimentation in a low-risk, non-sensitive data way wherever possible. In the innovation space, you should lean yes unless the reasons are strong enough not to. The worst thing you can do with a new technology is spend a year evaluating it on paper while your competitors are already using it.

About Michael Irwin, CISO, Odyssey Logistics

Michael Irwin is a senior technology and cybersecurity leader with over 16 years of experience guiding organizations through complex transformation across logistics, media, and private-equity-backed environments. He specializes in aligning security, infrastructure, and operations to deliver measurable business outcomes while reducing risk.

As Chief Information Security Officer and Vice President of Technology Operations at Odyssey Logistics, Michael led the most comprehensive technology and cybersecurity transformation in the company’s history. He unified fragmented environments, modernized infrastructure, and established enterprise-wide governance and security programs that strengthen resilience and operational stability.

Michael is widely respected for his people-first leadership style, building strong cultures rooted in trust, accountability, and development. His teams consistently deliver against aggressive roadmaps while maintaining exceptional engagement and retention.

Beyond his executive roles, Michael is an active speaker, writer, and mentor in the cybersecurity community, participating in executive forums and professional organizations. Based in Charlotte, he is committed to local talent development, cybersecurity advocacy, and community involvement. He is also a dedicated family man and a youth soccer coach.

LinkedIn: https://www.linkedin.com/in/michaeljamesirwin/

Guardrails, Not Gatekeepers: How CIOs Are Scaling AI Responsibly

By Blaine Bryant, SVP – Global CIO, Lightera Information Technology

As CIOs move from AI experimentation to enterprise adoption, one lesson from recent peer discussions is clear: governance must enable innovation rather than slow it down. The organizations making the most progress established AI governance early and made it cross-functional from the start—bringing together Legal, Security, Architecture, and Risk leaders. Instead of relying on approval bottlenecks, these teams define standards, guardrails, and reusable architectures that allow development teams to move quickly while staying compliant. At the same time, improving AI and data literacy across the organization has become essential. Successful programs start with a handful of high-value use cases, proving measurable outcomes before scaling, and increasingly incorporate AI/FinOps models to better understand cost and scaling economics.

Security, however, is proving broader than many anticipated. Protecting AI systems means going beyond securing data pipelines to also address model reliability, generated outputs, and the intelligence produced by these systems. Strong data governance and human-in-the-loop oversight remain essential safeguards. In fact, the most common cause of AI failure is not sophisticated attacks—it is still poor data quality and immature processes.

Perhaps the most complex frontier is the rise of AI agents. Many organizations are discovering gaps in how agents are identified, documented, and governed. Traditional SDLC controls are no longer sufficient; real-time monitoring, policy enforcement, and “kill switch” capabilities are becoming necessary. As autonomous agents take on more decision-making authority, enterprises must also invest in tooling that supports auditability and decision traceability.

AI’s promise is enormous—but realizing it responsibly requires governance, security, and operational discipline to evolve just as quickly as the technology itself.

About Blaine Bryant, SVP – Global CIO, Lightera Information Technology

Blaine Bryant is a global CIO and Board Advisor known for leading large-scale digital transformations across multinational enterprises. With more than two decades of experience spanning manufacturing, software, financial services, and high-tech industries, he has built a reputation for turning technology organizations into strategic value creators.

Blaine currently serves as the Chief Information Officer and Senior Vice President at Lightera, a global manufacturer of fiber-optic and cable products, where he is driving global IT consolidation, MES standardization, data modernization, and cybersecurity maturity across 27 locations.

Previously, he held CIO and security leadership roles at Accelya and directed enterprise transformation programs at BMC Software, where he improved delivery performance, strengthened cybersecurity, and generated tens of millions in operating efficiencies.

LinkedIn: https://www.linkedin.com/in/blaine-bryant/

Skill up or fall behind: How AI is forcing another skilling revolution

By Glen Vickers, Head of IT & CISO, ABS Wavesight

Since AI started to really rise up in all IT industries there’s been significant discussion, and even some effort, to replace jobs and even entire industries. We’ve seen this job scare before and watched it flop just as famously. However, AI does offer faster response times, quicker automation, and can even do the job of junior or mid-level engineers if properly trained.

The question being asked by college graduates and senior architects alike is the same question that was asked the last time technology took a really big leap forward, what do we do? The question has a simple answer. However, that simple answer is often one full of fear, uncertainty, and frustration. You need to skill up. You need to learn how to leverage AI and work smarter. You need to adopt new ways of working and improve your situation. You need to adapt.

If you accept the fact that you need to adapt, where do you start? The first place to start is to understand what types of AI are out there in the wild. If you’re just learning what AI is, take a course or just experiment. As your maturity model increases with your knowledge of AI, leverage AI in your daily routine. Learn how you can apply it in your work. As leaders, determine which use cases have yet to be solved and determine if those use cases can be solved with AI.

About Glen Vickers, Head of IT & CISO, ABS Wavesight

Glen Vickers has 30 years of experience in the IT industry. He has worked at startups as the head of IT and at Fortune 50 companies as a director and architect of cybersecurity. His career has serviced both IT infrastructure and Cyber Security in multiple capacities. He also obtained a Doctorates of Management in Organizational Leadership and focuses on the psychology of virtual leadership and organizational psychology as it relates to the Leader/Follower dynamic.

LinkedIn: https://www.linkedin.com/in/drglenvickers/

AI Ambition Requires Platform Discipline

By Javier Zon, Head of Platform, Click Funnels

Everyone is adopting AI. But many organizations treat it like a checkbox. Plug in a model, run a pilot, call it transformation.

It rarely works that way.

What I keep seeing across growth-stage and enterprise teams is a familiar pattern: the use case is exciting, the demo looks promising, but the foundation underneath is weak. Data pipelines are fragile. Ownership is unclear. Governance is reactive. When the pilot stalls, leadership blames the model.

The issue is not the model. It is platform maturity.

The teams getting real results start with the unglamorous work. They align engineering and business leaders early. They define clear data ownership. They invest in infrastructure that works reliably, not just during a presentation, but at 3 a.m. on a Sunday.

That discipline compounds. Every improvement to your platform strengthens every AI initiative that follows. Skip it, and you are stacking experiments on a shaky base.

One reality that is often underestimated: AI amplifies what already exists. Strong data practices become a force multiplier. Disconnected systems and unclear accountability become more visible, faster and more expensive.

The organizations that win will not be the ones who moved fastest into AI. They will be the ones who built the strongest foundation first.

About Javier Zon, Head of Platform, Click Funnels

Javier Zon is a technology leader with over 16 years of experience building and scaling high-performance platforms. He currently serves as Head of Platform at ClickFunnels, where he leads infrastructure strategy, reliability, and data architecture initiatives that support rapid growth and operational resilience. Javier also founded ScaleDB, an advisory firm that helps organizations modernize their data platforms and align infrastructure decisions with long-term business outcomes. His work centers on scalable architecture, disciplined platform design, and bridging the gap between technology ambition and operational reality.

LinkedIn: https://www.linkedin.com/in/jtomaszon/

Agentic Voice AI Is Becoming Enterprise-Critical Infrastructure

By Hari Kishan, Director of Cloud Engineering, Manulife Global Wealth and Asset Management

You are recognized for advancing enterprise Voice AI architectures. What distinguishes your work from conventional implementations?

Traditional IVR and chatbot systems rely on deterministic logic and static intent routing. My work has focused on engineering production grade agentic voice systems capable of contextual reasoning, real-time decision optimization and closed loop performance learning.

In regulated industries such as insurance and healthcare, these systems must operate under strict compliance frameworks(HIPAA, PCI-DSS, GDPR etc) while maintaining sub second latency and measurable business KPIs.

The contribution is not incremental automation. It is the architectural transition from scripted telephony trees to intelligent orchestration frameworks that continuously adapt based on behavioral data, performance telemetry, and customer context.

What original contributions have you made to the field of conversational AI?

My primary contribution lies in designing and operationalizing:

• Memory-aware conversational orchestration layers
• KPI-optimized routing architectures
• Guardrail-driven LLM deployment models
• Closed-loop feedback systems integrating analytics and AI retraining
• Customizing Frontier Models for Insurance use cases

These frameworks move Voice AI from experimental deployment to enterprise-critical infrastructure.

The measurable impact includes multi-million-dollar operational efficiencies, improved first-call resolution, fraud-risk mitigation, and increased customer satisfaction metrics across high-volume call ecosystems.

The broader significance is the establishment of repeatable architectural patterns now being adopted across large-scale customer experience platforms.

Why is production-grade Voice AI considered a complex systems engineering challenge?

Unlike text-based AI interfaces, enterprise voice systems operate in real-time environments where:

• Identity verification and fraud detection must occur mid-conversation
• Regulatory compliance must be enforced dynamically
• Routing decisions impact operational cost structures
• Sentiment and escalation logic influence customer retention.

This requires the integration of telephony platforms, NLU engines, large language models, CRM systems, analytics pipelines, and observability frameworks.

Engineering such systems at enterprise scale requires cross-domain expertise in distributed systems, AI governance, cloud architecture, and performance optimization.

How does your work demonstrate major significance within the industry?

In high-volume enterprise call centers, even marginal efficiency gains (3–5%) can represent millions in annual cost impact.

The systems I have led and architected have:

• Reduced unnecessary human escalations
• Improved self-service containment
• Enhanced fraud detection orchestration
• Increased measurable CX KPIs

Beyond financial metrics, the significance lies in creating scalable AI governance models that allow regulated enterprises to safely deploy advanced generative capabilities.

These architectural approaches are influencing how large organizations conceptualize next-generation conversational platforms.

In addition to your engineering leadership, you serve as a peer reviewer, public speaker, and author. How do these roles reinforce your contributions to the field?

Beyond building enterprise systems, I believe it’s important to contribute back to the broader AI community.

As a peer reviewer for international journals, I evaluate research submissions and provide feedback on technical depth, originality, and real-world applicability. It’s a responsibility I take seriously, because it means helping shape the quality and direction of work entering the field.

As a public speaker, I share lessons learned from deploying conversational AI in highly regulated, large-scale environments. These talks aren’t theoretical; they come from real production challenges, including governance, compliance, and measurable business outcomes. Engaging with practitioners across industries keeps the conversation grounded in practical impact.

I’m also authoring a book focused on Agentic Conversational AI and enterprise voice systems. The goal is to document architectural patterns, common pitfalls, and scalable frameworks that others can apply in their own organizations. Writing long form allows me to consolidate years of hands-on experience into something structured and useful for the next generation of builders.

Together, these roles extend my work beyond a single organization. They reflect ongoing engagement with the field not just implementing systems, but contributing to how enterprise AI is understood, discussed, and responsibly deployed.

How do you define leadership in enterprise AI transformation?

Leadership in this domain is not limited to model selection. It involves:

• Establishing governance frameworks
• Designing scalable AI infrastructure
• Aligning technology decisions with business KPIs
• Mentoring cross-functional engineering teams
• Driving modernization of legacy telephony ecosystems

The transition from legacy IVR to agentic voice systems represents a structural shift in enterprise architecture strategy.

Leading such transformation requires both technical depth and strategic execution capability.

What is the future trajectory of Agentic Voice AI?

The field is moving toward:

• Persistent conversational memory architectures
• KPI-aware autonomous routing systems
• Context-lake driven personalization
• Real-time compliance enforcement layers
• Self-optimizing orchestration frameworks

Voice systems will increasingly function as adaptive decision engines rather than static automation tools.

The next generation of enterprise AI will not merely respond, it will reason, adapt, and continuously optimize performance outcomes.

About Hari Kishan, Director of Cloud Engineering, Manulife Global Wealth and Asset Management 

Venkata Harikishan Koppuravuri (often referred as Hari Kishan) is a Director of Cloud Engineering and enterprise conversational AI leader driving large scale AI modernization initiatives in regulated industries. He leads the transformation of legacy IVR systems into intelligent, agentic voice platforms that combine governance, real-time analytics, and measurable operational impact.

He also serves as a peer reviewer for international AI journals, a public speaker on enterprise AI strategy, goveranc, and is authoring a book on Agentic Conversational AI. He has multiple publications on Agentic AI, Frontier and Voice AI models, his work reflects sustained leadership at the intersection of advanced AI systems and enterprise transformation.

LinkedIn: https://www.linkedin.com/in/hari-kishan/