The News: Just 14% of global organizations are prepared to deploy and leverage AI, according to Cisco’s AI Readiness Index. The survey asked respondents about company metrics and activities across six key organizational pillars, including strategy, infrastructure, data, talent, governance, and culture.
The AI Readiness Index is based on a double-blind survey of 8,161 private sector business and IT leaders across 30 markets, conducted by an independent third-party surveying respondents from companies with 500 or more employees. Companies were examined on 49 metrics across these six pillars to determine a readiness score for each, as well as an overall readiness score for the respondents’ organization. More information on this survey and research can be found on the Cisco website.
Cisco Study Finds Only 14% of Global Organizations Are AI-Ready
Analyst Take: Less than one-sixth of global organizations are fully prepared to deploy and leverage AI-powered technologies according to Cisco’s inaugural AI Readiness Index released in November 2023. Developed in response to the massive acceleration of AI across all facets of life, the index focuses on companies’ preparedness to utilize and deploy AI and illustrates the critical strategic, operational, and talent gaps that pose serious risks for the near future.
Key highlights from the research include:
- Just 14% of companies are fully prepared based on the criteria in the AI Readiness Index, with 52% of companies deemed unprepared. Within the unprepared category, 48% of companies have established limited preparedness, while 4% would be considered fully unprepared.
- 84% of respondents believe AI will have a significant impact on their business operations, but 81% of respondents say that leveraging AI alongside their data is hampered by data existing in silos across their organizations.
- Networks are not equipped to meet AI workloads, with 95% of businesses stating they are aware that AI will increase infrastructure workloads, but only 17% of organizations have networks that are fully flexible to handle this complexity. Additionally, the Index found that 23% of companies have limited or no scalability at all when it comes to meeting new AI challenges within their current IT infrastructures.
- While those at the top of organizations (board members and leadership teams) are likely to embrace the changes brought about by AI (82%), those in middle management or regular employees might not be on board. About 22% of middle managers have either limited or no receptiveness to AI, and 31% of organizations say employees are limited in their willingness to adopt AI or outright resistant.
- While 90% of respondents said they have invested in upleveling existing employee skillsets, 29% expressed doubt about the availability of sufficiently skilled talent.
- Governance of AI remains a challenge, with 76% of organizations report not having comprehensive AI policies in place.
The Gaps Between AI Enthusiasm at the Top and Corporate Readiness Remain Large
After reviewing the results of the AI Readiness Index, AI aspirations are still above the actual organizational readiness to achieve them. In a briefing with technology industry analysts in mid-November, Cisco’s Executive Vice President and General Manager, Applications, and Chief Strategy Officer Liz Centoni, also acknowledged this gap, noting that “empowering our partners to be able to get more of our customers to a point of execution versus just having the strategy is a key focus.”
As they continue to engage with prospects and customers, Cisco and its partners will need to address key aspects:
- A lack of understanding about the technological requirements to deploy AI effectively among C-suite and other decision makers: In many organizations, the leaders are big-picture, big-vision people and might not understand the practical and operational issues involved with deploying new technology. It is incumbent upon technology, operational, and process leaders within the organization to clearly illustrate the requirements for deploying new technology, assessing the impact of doing so on people and processes, and laying out the steps required to smoothly integrate the technology in a measured way, allowing for feedback and improvement along the way.
- A lack of data visibility: AI is fueled by data, and many organizations do not have an adequate assessment of which data will be used to fuel AI algorithms and systems, where it is located, and how it will be managed. This lack of visibility can create a significant hurdle to the adoption of AI, particularly when it is used to fuel real-time interactions (such as during customer engagements). As such, IT leaders need to assess the company’s current data infrastructure and develop a plan to ensure that all relevant information is able to be easily accessed by the algorithm while making sure that mechanisms are in place to ensure that the most updated information is used as the single source of truth.
- A lack of clarity on how worker roles and expectations might change: AI – and particularly generative AI – likely will bring about significant changes in the way workers interact with technology, partners, and customers. However, some organizations have given little thought or planning on how the integration of this technology might change not only the day-to-day aspects of workers’ jobs but also how they are evaluated and measured. For example, if a contact center worker is being measured on interaction throughput, does the incorporation of generative AI designed to speed up interactions require a recalculation of agent performance metrics? Will there be specific training for workers or are they simply expected to learn as they go? These questions need to be addressed prior to the integration of AI.
- A lack of overarching strategy and vision for how AI is used and viewed within an organization: Perhaps the biggest challenge for organizations is mapping out and communicating their vision for how AI, and especially generative AI, will be leveraged within the company. Is it seen as assistive technology, or a technology designed to replace certain aspects of human work? Is it designed as a productivity multiplier or a way to simply let humans focus on the work that is more fulfilling? In what areas is the company not comfortable using AI? These strategy and vision questions need to be addressed and communicated throughout the organization to gain buy-in from all parties and ensure that the use of AI is targeted clearly at specific objectives rather than simply acquired to keep up with competitors.
Ultimately, AI will undoubtedly bring about significant improvements in efficiency and effectiveness across a wide range of use cases. But – as many industry observers and participants have wisely noted – generative AI is still in its infancy, and organizations that take the proper steps to ensure they have aligned their AI strategy and tactical plans will be best prepared to not only roll out generative AI successfully but also quickly pivot if priorities, capabilities, or regulations shift.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.