Closing the AI execution gap
The window for AI competitive advantage is open, but it won't stay that way. Drawing from the core pillars of DXC’s enterprise AI orchestration blueprint, Xponential, organizations must rethink AI in three critical ways:
1. Bridge the execution gap, and make it every leader’s next mandate
Organizations often make the mistake of treating AI too narrowly as a trendy tech investment or an efficiency play – without a thoughtful examination of desired outcomes. Instead of asking, ‘How can we do what we already do, but faster and cheaper?’ leaders should think longer-term and ask, ‘What can AI help us do that we were never able to do before?’
This last question is more important than ever, as AI increasingly drives business-critical functions. While 47% of leaders deployed AI in IT operations over the past year, AdvisoryX’s study reveals where AI is really headed: R&D, compliance and ESG reporting will see the fastest growth over the next three years.
This shift is telling. AI delivers the most value in business-critical functions facing complex data and regulatory demands. However, 73% of leaders surveyed in our study still believe technical teams should lead AI adoption. This represents a missed opportunity: business teams must play a central role in AI strategy, as they best understand the workflows, challenges and regulatory requirements AI needs to address.
R&D, compliance and ESG teams are already proving AI's value, often without the executive backing or enterprise-wide coordination that would amplify their impact. These teams could accomplish far more if organizations make closing the AI execution gap their next leadership imperative.
2. Link AI to your people and processes
AI transformation fails when organizations treat it as purely a technology implementation. As our study reveals, successful adoption requires rethinking not only who does the work but also how the work gets done.
This approach is critical, given the integral role business leaders expect their workforce to play in their organization’s transformation. When asked about the level of human-AI collaboration they envision, most leaders see the emergence of a hybrid operating model poised to fundamentally change workflows and decision-making processes: 54% expect AI to operate with partial autonomy where humans review key decisions, while 31% see AI primarily assisting humans with no independent actions. Only 15% anticipate fully autonomous AI with minimal human oversight, according to our study surveying 2,496 technology decision makers across 22 countries.
To accommodate a workplace where people increasingly collaborate with AI, organizations will need to redesign workflows, decision rights, and governance models. Further, leaders will need new skills, as 81% of executives in the study say they anticipate that AI will drive greater demand for talent and an increase in their workforce by 2028. Nearly half (47%) pointed to the expansion of IT roles, followed by data and analytics (38%), cybersecurity and SecOps (36%), and software development and AI strategy leadership (both at 34%).
Ultimately, AI creates new workflows that require new skills. If leaders implement AI without redesigning the processes it will touch and developing the workforce capable of operating in these new workflows, they’re unlikely to see the results they anticipate.
3. Build strategic partnerships
Many organizations realize they can’t build AI capabilities alone, and they shouldn't try. Seventy-five percent of business leaders in the study say they are actively exploring partnerships or collaborations with external organizations for AI projects, and nearly half have already formed partnerships with AI or automation solution providers, as well as data and analytics partners to enhance customer or employee experiences.
What’s more, leaders rank workforce AI training and change management as their top answer when asked to identify what they would value most from a third-party service provider to help them do more with AI. Partnerships with service providers have delivered results: 79% of CIOs report that these agreements have been successful in delivering improved customer experiences.
Moving from AI strategy to AI results
At DXC, we've built a complete AI transformation methodology that addresses every stage of the AI lifecycle – from foundation to production management:
AI Core provides the fundamental tools that help organizations build the foundation required to run AI at enterprise scale, including the data stack, modeling stack, and governance controls that make AI possible.
AI Reinvent demonstrates the value of AI through customer use cases spanning human-assisted, semi-autonomous, autonomous, and AI-native implementations that drive measurable business outcomes.
AI Interact reimagines how humans and AI work together, transforming traditional paper-based processes into digital artifacts that AI can understand and interrogate. Users can ask questions, get guidance, and access expert advisors through redesigned workflows.
AI Manage keeps AI systems running reliably in production. As models and infrastructure evolve, these changes ripple through applications, testing, management, and observability, requiring people who understand both the technology and the business context to detect errors and maintain performance.
Altogether, these offerings work together by DXC’s design. Organizations can't effectively manage AI without validating it continuously, and they can’t validate without a solid technical foundation. That’s the power of an integrated approach. The time to start is now.
Pete McEvoy leads the design and delivery of advisory solutions at DXC Technology, helping enterprises navigate complex technology challenges. A seasoned executive working in regulated industries, Pete and his team specialize in applying AI to solve the most difficult challenges facing organization
