Closing the AI execution gap
The potential for AI competitive advantage is opening up, but it won't stay that way. Building on the core pillars of DXC’s Enterprise AI Orchestration Blueprint, exponentialorganizations need to rethink AI in three important ways:
1. Close the execution gap and make it every leader’s next mission.
Organizations often make the mistake of treating AI narrowly as a fad technology investment or efficiency drive without carefully considering the desired outcomes. Instead of asking, “How can we do what we're already doing faster and cheaper?”, leaders need to think long-term and ask, “What can AI help us do that we couldn't do before?”
This last question is more important than ever as AI increasingly powers business-critical functions. meanwhile 47% According to the percentage of leaders who have adopted AI in their IT operations in the past year, AdvisoryX research reveals where AI is really heading. R&D, compliance and ESG reporting will see the fastest growth over the next three years.
This change speaks for itself. AI provides the most value in business-critical functions that face complex data and regulatory demands. but, 73% A percentage of leaders surveyed in our survey still believe that technical teams should lead AI adoption. This represents a lost opportunity. Business teams should play a central role in your AI strategy because they best understand the workflows, challenges, and regulatory requirements that AI needs to address.
R&D, compliance, and ESG teams are already proving the value of AI, but often lack executive support and enterprise-wide alignment to scale its impact. These teams can accomplish even more if organizations make closing the AI execution gap their next leadership challenge.
2. Link AI to your people and processes
AI transformation fails when organizations treat AI purely as a technology implementation. As our research makes clear, successful implementation requires rethinking not just who does the work, but how the work is done.
This approach is critical given the critical role business leaders expect their employees to play in transforming their organizations. When asked about the level of human-AI collaboration they envision, most leaders see the emergence of hybrid operating models as poised to fundamentally change workflows and decision-making processes. 54% We expect AI to operate with partial autonomy and humans to review important decisions. 31% See AI primarily assists humans without independent action. only 15% A survey of 2,496 technology decision makers in 22 countries shows promise for fully autonomous AI with minimal human oversight.
To accommodate an increasingly collaborative workplace with AI, organizations must redesign their workflows, decision-making rights, and governance models. Additionally, leaders will need new skills. 81% A higher percentage of executives surveyed say they expect AI to drive demand for talent and increase the number of employees by 2028. nearly half (47%) Point out the expanding role of IT, followed by data and analysis (38%)Cybersecurity and SecOps (36%)Software Development and AI Strategy Leadership (both 34%).
Ultimately, AI creates new workflows that require new skills. If leaders implement AI without redesigning the processes it involves and developing a workforce that can operate with these new workflows, they are unlikely to see the results they expect.
3. Build strategic partnerships
Many organizations recognize that they cannot and should not build AI capabilities in isolation. 75 percent A higher percentage of business leaders surveyed say they are actively seeking partnerships and collaborations with external organizations on AI projects. almost half has already formed partnerships with AI or automation solution providers and data and analytics partners to improve the customer and employee experience.
Additionally, when asked what they would most value from a third-party service provider to further leverage AI, leaders cited employee AI training and change management as top answers. Partnerships with service providers are paying off. 79% of CIOs report that these agreements have successfully improved the customer experience.
Moving from AI strategy to AI results
At DXC, we have built a complete AI transformation methodology that addresses every stage of the AI lifecycle, from fundamentals to production management.
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AI core provides essential tools to help organizations build the foundation needed to run AI at enterprise scale, including AI-enabling data stacks, modeling stacks, and governance controls.
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Reinventing AI demonstrates the value of AI through customer use cases across human-assisted, semi-autonomous, autonomous, and AI-native implementations that drive measurable business outcomes.
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AI interact Rethink how humans and AI work together, transforming traditional paper-based processes into digital artifacts that AI can understand and explore. Users can ask questions, get guidance, and access expert advisors through a redesigned workflow.
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AI verification We ensure your system stays on track through continuous testing and quality control to manage errors and risks. Humans play a central role in this critical component, and will become increasingly important as the use of agentic AI becomes more widespread.
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AI management Keep your 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 business context to detect errors and maintain performance.
Overall, these products work together by DXC design. Organizations cannot effectively manage AI without continuously validating it. Additionally, verification is not possible without a solid technical foundation. That's the power of an integrated approach. The time to start is now.
Pete McEvoy At DXC Technology, you lead the design and delivery of advisory solutions that help companies address complex technology challenges. An experienced executive working in a regulated industry, Pete and his team specialize in applying AI to solve the toughest challenges facing organizations.
