How do you defend AI models that jump from on-prem racks to three different public clouds? When petabytes need to travel faster, where do you place the data without stalling innovation and how do you visualize and aggregate fragmented data across multicloud or on-prem to give context to AI models? And when every dollar is judged by outcomes, why sign a capital-heavy deal instead of a consumption-based contract?

Monica Gille wrestles with questions like these every day. As Vice President of Sales for Global and Enterprise Accounts at Hewlett Packard Enterprise (HPE), she guides a portfolio of the company’s top 500 clients, producing more than $7 billion in annual revenue and fueling 30% year-over-year growth. In this conversation, she surfaces the real pain points haunting boardrooms today: AI security inside messy hybrid estates, data gravity across multicloud footprints, runaway costs, and the demand for proof of outcome.

Gille also explains why the next wave of transformation hinges on co-creation, not transactions, sharing stories of healthcare providers, telcos, and manufacturers that are building new stacks side by side with HPE engineers.

Edited Excerpts

Q: Your journey to becoming the VP of Sales at HPE is impressive. How has your career progression influenced your approach to sales leadership and business development?

It’s been a rewarding journey. I began with an engineering degree in computer science and followed that up with an MBA in business management. This combination of technical grounding and business strategy has been instrumental in shaping my leadership style.

Early in my career, I worked extensively in the startup and innovation space, and transitioned into sales leadership across EMEA, Western Europe, and later as Managing Director and Sales Leader of HPE Switzerland. It gave me a strong understanding of global markets and client expectations.

Moving to the Bay Area and taking on the role of VP of Sales Strategy, Planning, and Operations for North America was a turning point, it refined my ability to think long-term and align technology with real-world outcomes.

My technical background allows me to speak the language of our clients, understand their architectures, and help craft solutions that don’t just meet quotas – they solve business problems.

Q: Large enterprises often hesitate when adopting new technologies and driving transformation. What are some common concerns you hear from clients, and how does your team work to address them?

Change can be challenging, especially at scale. A major concern area is hybrid cloud adoption. Clients often ask how they can manage workloads seamlessly across on-premises environments and multiple cloud providers. It’s not just about having a hybrid setup, it’s about managing it efficiently. 

We help clients through HPE GreenLake, which provides a unified control plane. This makes orchestration much simpler and enables consistent governance and automation across the board.

Another common concern is around data gravity and interoperability. Clients worry about how to move and synchronize data across environments without creating silos or compromising performance. We work closely with them to build intelligent data placement strategies. With tools like Zerto and our data fabric capabilities, we ensure migration and synchronization are as seamless as possible.

Cost is another key topic of discussion. Clients want predictability and optimization for their cloud spend. Through our acquisition of Morpheus Data, we’ve been able to provide powerful cost management, visibility, and budgeting tools that make a big difference.

Also, when it comes to integrating AI, security and privacy become paramount. Clients want to make sure their data is protected, especially when working in hybrid environments. We help them deploy private cloud AI solutions that adhere to strict governance standards. Integration with existing MLOps tools is also key. 

Clients don’t want to rip and replace what they already have. So we take a collaborative approach, understanding their environment and then deploying AI through either greenfield PCAI solutions or brownfield AI factories at scale, using containerization and microservices to make integration smooth.

Q: With AI and cloud adoption accelerating, what are the top technology demands you’re hearing from enterprise clients today? Have you noticed any key shifts in how enterprises approach purchasing decisions over the years?

The demands have evolved significantly. Enterprises are now looking for hybrid and multicloud solutions that provide a unified way to manage security, data, and costs across different environments. Security, in particular, needs to be integrated end-to-end. With workloads scattered across cloud and on-prem, consistent policy enforcement and compliance are critical.

Scalability of AI infrastructure is another big area. Clients want platforms that can support not only the training but also the deployment of AI models at scale, without compromising on performance or security. And underlying all of this is the need for solid data management. Enterprises are focused on governance, data quality, and lineage because they understand that bad data leads to bad outcomes, especially in AI.

Edge computing is gaining traction too. Clients want to process data at the edge for use cases like IoT, autonomous systems, and real-time analytics. Sustainability is becoming non-negotiable too. With the energy demands of AI workloads, enterprises are increasingly interested in energy-efficient infrastructure, things like liquid cooling are no longer nice to have, they’re essential.

As for purchasing behavior, we’re seeing a clear shift from CapEx to OpEx. Clients prefer consumption-based models that offer flexibility and scalability. HPE GreenLake is designed precisely for that. There’s also a move from buying point solutions to adopting integrated platforms. Clients want to reduce complexity, and platforms like HPE Private Cloud for AI help them do that.

More importantly, decision-making is now outcome-driven. It’s not enough for a solution to look good on paper. Clients want to see tangible ROI and business impact. Proof of concept is no longer the end goal, proof of outcome is.

Q: Can you shed some light on how GenAI particularly has changed the landscape in the last 2–3 years?

It’s been transformative. Since around early 2022, we’ve seen GenAI go from a niche innovation to a central pillar of enterprise strategy. It was made possible by the democratization of access. Thanks to cloud providers and NeoClouds, it’s easier than ever for enterprises to tap into GenAI capabilities and plug them into their existing systems.

The rise of low-code and no-code tools has put power into the hands of business users, not just developers. And we’re seeing GenAI revolutionize content creation. Whether it’s text, video, or images, enterprises can now generate high-quality content faster and at scale.

It’s also become a huge productivity enhancer. From auto-generating code to personalizing customer experiences, AI is making teams more efficient. Entirely new business models have emerged and AI-as-a-Service is becoming a viable revenue stream, and we’re seeing NeoClouds leading the charge.

The underlying technology has made leaps too – larger context windows, better reasoning, multimodal inputs, and even agentic AI systems that can plan and execute tasks autonomously.

At HPE, this shift prompted us to launch an incubation team to work with AI startups and GPU service providers. We’ve been able to connect them with enterprise clients and accelerate innovation cycles, all while creating new revenue streams.

Q: What’s your take on collaborating with clients to co-develop innovative solutions that drive mutual success? Can you share any interesting examples in this regard?

Co-creation is one of the most rewarding parts of what we do. We’ve worked with telcos to co-engineer edge platforms, partnered with retailers to design next-gen store and distribution solutions, and teamed up with media and communications companies to build AI-powered 3D rendering pipelines.

In finance, we’ve collaborated on ultra-low latency platforms for high-frequency trading. In oil and gas, we’ve deployed private 5G and AI at the edge to enhance field operations. Similarly, in health and life sciences, we’ve worked on reference architectures for Cryo-EM microscopy, which accelerates drug discovery.

These aren’t just projects, they’re partnerships that reshape how entire industries operate. When both sides bring their expertise to the table, the results are exponentially more impactful.

Q: The roles of CDO/CDAO continue to evolve based on organizational maturity, industry, and business priorities. What does this look like from the lens of a partner and enabler? What changes on your side with their changing mandates and needs?

The CDO role has evolved from being focused on data quality and reporting to driving business outcomes and enterprise-wide strategy. Today’s CDOs are expected to enable advanced analytics, operationalize AI, and influence revenue growth – not just manage data pipelines.

From our perspective, that shift means we also have to evolve. We’re no longer just providing infrastructure, we’re aligning technology with strategic business goals. Our solutions are now outcome-driven and designed to support key business KPIs. Our consumption models are more flexible, and our architectures are agile enough to support fast iteration and innovation.

We see ourselves as co-pilots to CDOs, helping them not only solve technical challenges but also succeed in the boardroom by demonstrating measurable value.

Q: Without taking names, can you share a particularly unforgettable client meeting — whether it was inspiring, funny, or completely unexpected?

One meeting that really stands out was with a healthcare client. It started off as a standard discussion about upgrading infrastructure. But as the conversation progressed, it quickly escalated into something much more ambitious.

We ended up talking about full data center transformation, cloud rebalancing, new AI workloads, and even co-developing a go-to-market strategy to help other healthcare organizations.

What made it memorable was the CIO’s opening line. He said, “I don’t want anything transactional. I need something that will make us say, ‘Why didn’t we think of that?’—not, ‘That’s slightly better than what we already have.’ I want something that will make our competitors panic.”

That level of boldness was contagious. It pushed us to go beyond conventional thinking and helped create a truly transformational roadmap.

Q: If you weren’t in enterprise sales, what career path do you think you might have taken?

I’d be in product engineering or development – something that allows me to build and innovate. I’ve always enjoyed turning ideas into real-world impact, and I could easily see myself leading product and engineering teams or even leading a company in the startup world. The thrill of creating something from scratch and watching it make a difference is hard to beat.