Artificial Intelligence (AI) adoption is accelerating globally, transforming business and government. However, a new study by Cisco Systems reveals that while intentions to adopt AI are rising sharply, actual abilities to effectively leverage these technologies continue to lag.
An overwhelming 97 percent of business leaders state that the urgency to deploy AI technologies has increased significantly or somewhat at their organizations in just the past six months.
This mounting urgency is coming from the top-down, with over 50 percent saying it is being driven by their CEO and board of directors. Meanwhile, the C-suite is being tasked to prepare for action.
As a result of this mounting pressure, 95 percent of organizations now have an AI strategy in place or under development. Looking ahead, 84 percent believe AI will have a major impact on their business operations.
New AI Deployments Underway
Many organizations have already started an AI deployment, prioritizing IT infrastructure and defensive cybersecurity solutions.
The types of AI technologies seeing the most use currently are Machine Learning (ML) solutions and Predictive AI. Moreover, 40 percent have plans to deploy Generative AI (GenAI) in the next 12 months.
Areas seeing the most AI adoption include improving efficiency of systems and operations (63 percent priority), boosting innovation capabilities (51 percent), and enhancing customer experiences (47 percent).
However, only 25 percent of respondents are focused on opening new digital business revenue streams with AI capabilities.
AI Solution Readiness Gaps Persist
Despite strong ambitions, most organizations are still not ready to fully capitalize on AI's digital transformation potential.
Overall, just 14 percent of respondents meet the criteria of what Cisco researchers have characterized as "Pacesetters" who are leading global AI readiness efforts.
The remaining 86 percent are "Chasers" moderately prepared (34 percent), "Followers" in early stages (48 percent), or the "Laggards" largely unprepared (4 percent).
Note, significant gaps exist across the six pillars assessed in the study:
1) Strategy: 29 percent Pacesetters, 44 percent Chasers, 23 percent Followers, 4 percent Laggards.
While considerable progress on developing strategies, only 41 percent have defined metrics and 45 percent have long-term funding plans for measuring and supporting AI deployment.
2) Infrastructure: 17 percent Pacesetters, 30 percent Chasers, 45 percent Followers, 8 percent Laggards.
Ninety-five percent foresee AI workloads surging, demanding increased computing power, data center upgrades, network performance, cybersecurity, and energy efficiency. But most infrastructures have limited scalability and adaptability for complex AI applications.
3) Data: 13 percent Pacesetters, 30 percent Chasers, 40 percent Followers, 17 percent Laggards.
Eighty-one percent admit data remains siloed in IT systems, limiting AI effectiveness. Only 21 percent have optimal network latency for demanding AI workloads. Data management shortcomings risk model reliability and security.
4) Governance: 17 percent Pacesetters, 27 percent Chasers, 45 percent Followers, 11 percent Laggards.
Just 34 percent have comprehensive AI policies and protocols in place to address emerging ethical risks around areas like bias, transparency, and privacy. And 25 percent lack processes to correct data bias issues detected. Gaps can create legal, reputation, and financial liability.
5) Talent: 17 percent Pacesetters, 27 percent Chasers, 45 percent Followers, 11 percent Laggards.
Forty-seven percent are moderately well-resourced for AI talent. But 90 percent are investing in re-skilling to address gaps in employee proficiency. Recruiting and retaining qualified AI professionals persists as a roadblock.
6) Culture: 9 percent Pacesetters, 40 percent Chasers, 38 percent Followers, 13 percent Laggards.
While 79 percent are embracing AI urgently, receptiveness varies greatly between senior leaders and middle management or employees hindering adoption. Only 26 percent have comprehensive change management plans to smooth AI integration.
Seizing the AI Competitive Advantage
With intentions far ahead of abilities currently, companies have a pressing need to invest in building robust foundations across these pillars to achieve AI success.
Those able to effectively leverage AI have the opportunity to gain sustainable competitive advantage through improved efficiency, innovation, customer experiences and more.
But this window of opportunity is closing quickly – 61 percent predict negative business impacts if AI strategy implementation is delayed beyond 12 months. The time for action is deemed to be now.
AI Readiness Index Recommendations
- Adopt long-term strategic planning to guide AI deployments beyond immediate efficiencies towards continuous innovation and growth.
- Build highly scalable, flexible IT infrastructures with strong compute power, network performance, and cyber resilience to handle increasing AI workloads.
- Break down data silos through centralized management for greater accessibility, uniformity, and reliability in fueling AI systems.
- Implement comprehensive policies, protocols, and training to address ethical risks around areas like bias, transparency, privacy, and security.
- Foster an inclusive AI culture via change management plans that increase receptiveness across all staff levels through up-skilling, accessibility, and engagement.
According to the Cisco researchers assessment, the window of opportunity to harness AI for competitive advantage is narrowing quickly. Organizations must act now across these critical areas to effectively keep pace with accelerating technology innovation and adoption.