DXC Technology's Pete McEvoy explains the gap in AI strategy execution and why most companies fail to see results. This article was originally published on Insight Jam, the enterprise IT community that enables human conversation on AI.
AI has become a priority that most business leaders agree on, but few know how to successfully implement this rapidly evolving technology or realize its full value.
The results of a recent global survey of technology decision makers are telling. 77% of technology leaders say AI is a board-level strategic priority, but 94% face significant adoption challenges.
Our findings show that the AI execution gap extends beyond planning to five interrelated execution challenges spanning strategy, implementation focus, leadership alignment, organizational readiness, and technical capabilities.
Closing the AI execution gap
The potential for AI competitive advantage is opening up, but it won't stay that way. Organizations 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 drives increasingly business-critical functions. Over the past year, many leaders have implemented AI into their IT operations, but the next three years are likely to see the fastest adoption of AI in business-critical functions such as research and development, compliance, and ESG reporting.
This change speaks for itself. AI provides the most value in business-critical functions that face complex data and regulatory demands. However, most leaders 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. Successful implementation requires rethinking not just who does the work, but how the work is done.
This approach is critical given the critical role humans play in making AI transformation successful. To accommodate an increasingly collaborative workplace with AI, organizations must redesign their workflows, decision-making rights, and governance models. Furthermore, as AI forces everyone to seek new skills, leaders will need new capabilities.
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 in these new workflows, they are unlikely to see the results they expect.
3. Build strategic partnerships
Organizations recognize that they cannot and should not attempt to build AI capabilities in isolation.
While many business leaders are actively considering partnerships and collaborations with external organizations on AI projects, some companies already have these agreements in place with AI or automation solution providers and data and analytics partners to improve customer and employee experiences.
These partnerships provide expertise to help organizations accelerate AI adoption while managing risk.
The path forward is clear. Companies that treat AI as a business strategy, prepare their workforce, and engage in strategic partnerships will define the competitive landscape for the next decade. That work begins today.
