Angela Q. Daniels, CTO (Americas) for Consulting and Engineering Services at DXC Technology – Interview Series

Angela Q. Daniels, Chief Technology Officer (Americas) for Consulting and Engineering Services (CES) at DXC Technology, oversees technology strategy and transformation for the company’s Consulting & Engineering Services across the Americas. She focuses on scaling AI-augmented delivery through DXC’s Xponential framework, advancing innovation with DXC’s AI Solutions, and helping clients realize greater business value through modern platforms and people. Previously, she led global software development and applications delivery initiatives, guiding agile modernization, cloud-native engineering, and talent development to strengthen DXC’s delivery excellence and technological leadership.

DXC Technology is a global IT services and consulting leader that helps large enterprises run mission-critical systems and operations across hybrid IT environments. The company partners with leading technology providers to deliver solutions that combine advanced analytics, cloud, security, and AI. Through its Enterprise Technology Stack, DXC modernizes IT infrastructures, drives digital transformation, and enables clients across industries to innovate, optimize costs, and improve customer experiences worldwide.

You’ve had a lifelong passion for programming that started at the age of eight. Can you share what first drew you to software development and how that early curiosity has shaped your approach to leading AI-driven innovation today?

When I was eight, my mother brought home a Commodore computer from a garage sale. It came with a stack of manuals and a few games. While everyone else wanted to play, I was the one reading the manuals, trying to figure out how the computer actually worked.

My first program was simple:

10 PRINT "Angela"

20 GOTO 10

RUN

Seeing my name repeat endlessly across the screen felt magical. It wasn’t just about what the computer could do, it was about what I could make it do.

From a young age, my mom noticed my curiosity and supported it, enrolling me in college summer enrichment programs during elementary school and career explorer programs throughout high school. These experiences further fueled my passion and ultimately inspired me to pursue mathematics and computer science in college.

That same curiosity has guided my entire career. I’ve always been drawn to exploring what’s possible, to understand how something works, and then figure out how it can be applied in meaningful ways. Whether it’s AI, software development, or any technology, my approach has always been rooted in exploration that leads to impact. Innovation has been a steady thread from that first Commodore program to the work I lead today.

As Global Software Development Offering Lead, you’ve played a key role in creating and deploying DXC’s new Xponential framework. What inspired this initiative, and what problem in enterprise AI adoption were you most determined to solve? 

Executives today are under tremendous pressure to show real productivity gains from AI, yet many organizations struggle to move beyond experimentation. They’re launching pilots without first aligning on a cohesive strategy that connects AI to their people, processes, and technology.

That challenge inspired the creation of DXC Xponential. We wanted to provide a structured, repeatable blueprint for orchestrating AI adoption—one that embeds governance from the start and delivers early, measurable wins to help organizations scale with confidence.

My conversations with clients, and our own internal experiences developing AI-powered applications, shaped its foundation. The most common question we heard was: “We’ve tried the pilots, but how do we move from here?” That became the voice of the customer behind Xponential.

We also heard it from our own engineering teams. There’s genuine AI tool fatigue. Teams are surrounded by tools that promise transformation and are trying to discern which ones truly deliver value. Xponential helps cut through that noise by focusing on integration, orchestration, and tangible outcomes rather than tool proliferation.

My roots at DXC include being a part of our New Orleans Customer Experience Center. Our approach to solving our customer business challenges was instrumental in shaping this approach. That center operates on design thinking principles.  We bring client challenges into an environment where we empathize, ideate, prototype, test, and scale. We used that same mindset to design Xponential, ensuring every AI solution we deliver is practical, human-centered, and scalable in the real world.

Industry data shows that 95% of AI pilots fail to meet business expectations. From your perspective, what are the most common reasons behind these failures, and how does Xponential directly address them?

Companies are investing millions of dollars in technology that impresses in demos. But once companies move to implement this technology, they’re often met with low-quality data to train AI, inaccurate models, a lack of governance, human validation bottlenecks, and complex system integration.

The problem isn’t the technology, it’s the execution. Without a cohesive strategy that integrates people, processes, and technology, scaling and delivering measurable results with AI is an uphill battle.

DXC’s Xponential framework ensures all people, processes, and technology are taken into consideration with five different pillars:

• Insight – Governance is foundational at DXC, which is why every AI agent, automated decision, and intelligent process is designed with built-in observability and compliance from the start.
• Accelerators – To meet customers where they’re at and remove barriers to success, DXC combines purpose-built innovations with partner solutions to provide ready-to-use tools, eliminating the need to build everything from the ground up.
• Automation – DXC’s agentic-AI framework goes beyond task execution. It continuously refines workflows by learning from outcomes and adapting in real time, driving a shift toward systems that evolve through experience rather than relying on temporary fixes.
• Approach – As AI evolves, organizations must stay agile and continuously refine their strategies — not to replace people, but to empower them. By offloading routine tasks to AI, teams are free to focus on high-impact work, innovation, solving complex problems, and delivering measurable value for our customers.
• Process – Real change starts with safe experimentation. At DXC, we build MVPs to quickly validate ideas, prove impact, and scale what works to avoid the pilot stage that stalls most AI initiatives.

The Xponential blueprint emphasizes five pillars — Insight, Accelerators, Automation, Approach, and Process. Which of these do you see as the most transformative for enterprises just beginning their AI journey?

For organizations at the start of their AI journey, Insight is the most transformative pillar. Many companies rush into implementation, but success begins with understanding where AI can truly create value. Insight gives leaders clarity, not just about their data, but about their processes, talent, and readiness to change. It’s the foundation that informs every other pillar.

Closely following that is the Human+ dimension, which we see as the multiplier across all five pillars. AI is most powerful when it enhances human capability rather than replaces it. Human+ is about redesigning work so people spend more time on creativity, judgment, and innovation — the things that make organizations distinctly human.

The Approach pillar highlights the concept of “Human+ collaboration.” How do you see the balance between human expertise and AI automation evolving within enterprise environments over the next few years?

Keeping humans in the loop is crucial. Our Human+ approach uses AI to amplify human expertise, not to replace it. Our experts remain in control, making strategic decisions and ensuring quality control while AI handles repetitive tasks and does the heavy lifting. Now, engineers who once spent hours on repetitive coding tasks can spend more time designing system architectures and solving complex business problems. Combining skilled professionals with AI leads to amplified, measurable outcomes that reduce costs and improve efficiency.

One of Xponential’s promises is to move organizations from small-scale wins to enterprise-wide AI integration. What are the biggest challenges companies face when scaling from proof of concept to production, and how can they overcome them?

Many organizations are stuck in pilot phases with highly fragmented initiatives that fail to unite people, processes, and technology. The biggest hurdle we see companies navigating when implementing AI is decentralized, disorganized programs that lack a cohesive strategy.

Xponential lays out a clear path to take companies beyond the pilot phase. Its orchestrated, responsible, and repeatable approach transforms AI from a technology experiment into a business imperative. Designed to address the lack of cohesive strategies in the market, the framework is structured, but built for flexibility and scalability, enabling organizations to start small, achieve early wins, and rapidly scale across the enterprise.

Xponential is already helping customers transition from pilot stages to scaling. Its modular design allows us to meet customers where they’re at by supporting legacy upgrades and integrating seamlessly with customers’ existing data, cloud, and application environments.

For example, we partnered with Singapore General Hospital to develop the Augmented Intelligence in Infectious Diseases solution, which uses AI-driven insights and collaborative human+AI decision-making to guide antibiotic choices for lower respiratory tract infections with 90% accuracy. The tool has resulted in improved patient care while combating antimicrobial resistance.

DXC has already implemented Xponential with global clients like Textron and the European Space Agency. Could you share a specific example of how this framework delivered measurable impact in one of these deployments? 

Xponential is a proven framework already generating results with our customers Textron and the European Space Agency. We partnered with Textron to transform its IT support model using automation and AI. The optimization cut service desk tickets by 20% and proactively resolved network issues for 32,000 employees using AI-powered chatbots trained on shared knowledge bases, freeing IT staff to focus on complex issues requiring human expertise. With the European Space Agency, we utilized Xponential to implement ASK ESA, an AI-powered platform that unifies data, accelerates research, and enhances collaboration across the agency. The platform not only provides secure, efficient access to large volumes of data, it saves engineers 1-2 hours per week.

Your leadership background spans academia, enterprise software, and large-scale cloud development. How has this diverse experience influenced your view of what “responsible AI” means in practice? 

Each part of my journey has shaped how I define responsible AI. From academia, I learned the importance of rigor, questioning assumptions, validating outcomes, and understanding the “why” behind every result. From enterprise software, I gained a deep appreciation for governance, ethics, and the ripple effects technology can have on people, processes, and industries. And through large-scale application development, I saw firsthand how scale can amplify both impact and risk.

To me, responsible AI isn’t just about compliance or bias mitigation, it’s about intentional design. It means building systems that are transparent, auditable, and aligned to human values from the start. It’s about making sure innovation and accountability evolve together.

My role is to make sure that as we innovate, we do so responsibly, not slowing innovation down, but guiding it in a way that earns trust and delivers sustainable value.

Governance and observability are central to the Insight pillar of Xponential. How do you ensure AI remains both transparent and compliant in heavily regulated industries such as healthcare and aerospace? 

The Xponential framework ensures that governance, compliance, and observability are embedded from day one, not added later. We align our AI ethics with global standards and emerging regulatory frameworks coming out of NIST and the EU AI Act, meaning full traceability, auditability, and compliance are built into every workflow.

This governance model provides clear visibility into how AI operates, who’s accountable, and whether decisions follow ethical and operational standards — essential requirements for scaling AI effectively across regulated enterprises. It is our belief that AI should be trusted, transparent, and human-centered.

As AI continues to redefine software development lifecycles, what new skills or mindsets do you believe software teams will need to stay competitive in the next decade? 

The most important change isn’t just about learning new tools. It’s about adopting a new mindset. AI is transforming how we build software, and the teams that thrive will be the ones who treat AI as a collaborator, not just a utility.

The skill that matters most is curiosity. The best engineers won’t just accept what AI generates. They’ll question it, refine it, and explore how it can be applied in new ways. Curiosity fuels the ability to learn continuously, experiment responsibly, and see connections that others might miss.

Beyond that, developers will evolve from being coders to composers, orchestrating AI agents, automation, and human insight into integrated systems. That takes not only technical fluency but also systems thinking and a willingness to reimagine how work gets done.

At DXC, we’re embedding this mindset through Xponential, helping teams develop our Human+ approach. It combines technical mastery with curiosity, creativity, and ethical awareness, because in the next decade, success in software development won’t come from knowing everything. It will come from staying endlessly curious about what’s possible.

Thank you for the great interview, readers who wish to learn more should visit DXC Technology.