A mere 14% of organizations globally are fully prepared to deploy and leverage AI-powered technologies, according to Cisco’s inaugural AI Readiness Index. The report highlights companies’ preparedness to utilize and deploy AI, showcasing critical gaps across key business pillars and infrastructures that pose serious risks for the near future.
The new research finds that while AI adoption has been slowly progressing for decades, the advancements in generative AI, coupled with public availability in the past year, are driving greater attention to the challenges, changes, and new possibilities posed by the technology. While 84% of respondents believe AI will have a significant impact on their business operations, it also raises new issues around data privacy and security. Findings show that companies experience the most challenges when it comes to leveraging AI alongside their data. In fact, 81% of respondents admit that this is due to data existing in silos across their organizations.
There is also positive news. Findings reveal that companies are taking many proactive measures to prepare for an AI-centric future. When it came to building AI strategies, almost one-third of respondents were categorized as Pacesetters (fully prepared), which indicates a significant level of focus by C-suite executives and IT leadership. This could be driven by the fact that most (97%) respondents say the urgency to deploy AI in their organization has increased in the past six months, with IT infrastructure and cybersecurity reported as the top priority areas for AI deployments.
“The race to AI readiness is on, with organizations under intense pressure to shift from strategic planning to execution mode to capitalize on the transformative potential that AI represents,” says Liz Centoni, executive vice president, general manager of applications, and chief strategy officer at Cisco. “To realize the benefit of AI-powered products and services, companies need solutions that secure and observe their AI models and toolchains to ensure performance, secure sensitive data and systems, and deliver trustworthy and responsible AI outcomes.”
Alongside the stark finding that only 14% of companies are Pacesetters, the research finds that more than half (52%) of companies globally are considered Laggards (unprepared) at 4%, or Followers (limited preparedness) at 48%. Some of the most significant findings are below.
- One year maximum before companies start to see negative business impacts. Approximately 61% of respondents believe they have a maximum of one year to implement an AI strategy before their organization begins to incur significant negative business impact.
- Step one is strategy, and organizations are well on their way. Nearly three-quarters (73%) of organizations are benchmarked as Pacesetters or Chasers, and only 4% were found to be Laggards. Additionally, 95% of organizations already have a highly defined AI strategy in place or are in the process of developing one, which is a positive sign, but shows there is more to do.
- Networks are not equipped to meet AI workloads. Nearly all (95%) of businesses are aware that AI will increase infrastructure workloads, but only 17% of organizations have networks that are fully flexible to handle this complexity. Nearly a quarter (23%) of companies have limited or no scalability at all when it comes to meeting new AI challenges within their current IT infrastructures. To accommodate AI’s increased power and computing demands, more than three-quarters of companies will require further data center graphics processing units (GPUs) to support AI workloads. In addition, 30% say the latency and throughput of their network is not optimal, and 48% agree that they need further improvements on this front to cater to future needs.
- Organizations cannot neglect the importance of having data “AI-ready.” While data serves as the backbone needed for AI operations, it is also the area where readiness is the weakest, with the greatest number of Laggards (17%) compared to other pillars. Approximately 81% of all respondents claim some degree of siloed or fragmented data in their organization. This poses a critical challenge as the complexity of integrating data that resides in various sources and making it available for AI implications can impact the ability to leverage the full potential of these applications.
- There is a significant mismatch in leadership and employee expectations with respect to AI. Boards and leadership teams are the most likely to embrace the changes brought about by AI, with 82% of both groups showing high or moderate receptiveness. However, there is more work to be done to engage middle management where 22% have either limited or no receptiveness to AI and among employees where close to a third (31%) of organizations report employees are limited in their willingness to adopt AI.
- AI policy adoption’s slow start. More than three-quarters (76%) of organizations report not having comprehensive AI policies in place, an area that must be addressed as companies consider and govern all the factors that present a risk in eroding confidence and trust. These factors include data privacy and data sovereignty, and the understanding of and compliance with global regulations. Additionally, close attention must be paid to the concepts of bias, fairness, and transparency in both data and algorithms.
- Little preparation but high motivation to make a priority. This pillar had the lowest number of Pacesetters (9%) compared to other categories driven largely by the fact that only 21% have comprehensive change management plans for widespread AI adoption. C-suite executives are the most receptive to embracing internal AI changes and must take the lead in developing comprehensive plans and communicating them clearly to middle management and employees who have relatively lower rates of acceptance. The good news is that motivation is high. Nearly eight in 10 (79%) say their organization is embracing AI with a moderate to high level of urgency. Only 2% say they were resistant to change.
