A great deal has been written about readiness for artificial intelligence (AI). In Factors influencing readiness for artificial intelligence: a systematic literature review, the authors examined 52 papers to study AI readiness factors.

One of the more thought provoking papers was Technology readiness and the organizational journey towards AI adoption: An empirical study. This research revisited the well-known construct that success with technology relies on understanding the interdependencies between people, processes and technology as illustrated in Figure 1 below.

Yet many organizations lack an understanding of how their large cross functional processes develops, makes and delivers their products and services to customers.

Here are some areas to examine in order to assess organizational readiness.

1. Strategic Alignment
Question:

To what extent does your organization have a clearly defined AI strategy that aligns with its broader business goals?

Rationale:
AI adoption succeeds when integrated with strategic objectives rather than as isolated technology initiatives.

2. Leadership and Governance
Question:

How well does the leadership team understand AI’s potential, risks, and ethical implications?

Rationale:
Leadership commitment and governance structures are crucial to fostering trust, funding, and policy alignment.

3. Data Readiness and Infrastructure
Question:

How mature are your data collection, management, and integration processes in supporting AI applications?

Rationale:
AI depends heavily on volume, quality, and accessibility of data; inadequate data governance hinders progress.

4. Talent and Skills
Question:

Does your organization have sufficient AI talent (data scientists, ML engineers) or access to external expertise?

Rationale:
The availability of skilled staff or partnerships determines how effectively AI initiatives are executed.

5. Technology and Tools
Question:

How equipped is your organization with modern AI tools, platforms, and computing infrastructure (e.g., cloud AI services, MLOps frameworks)?

Rationale:
Having the right technology foundation ensures scalability and sustainable AI deployment.

6. Change Management and Culture
Question:

How open is your organizational culture to innovation, experimentation, and data-driven decision-making?

Rationale:
Resistance to change and lack of trust can derail AI adoption even with technical readiness.

7. Ethical, Legal, and Risk Frameworks
Question:

Has your organization established policies for AI ethics, transparency, privacy, and risk management?

Rationale:
AI deployment requires responsible guidelines to ensure compliance and prevent reputational damage.

8. Measurement and Continuous Improvement
Question:

How do you evaluate the performance, ROI, and impact of AI initiatives on operational and strategic outcomes?

Rationale:
Continuous monitoring and feedback loops turn pilot experiments into organization-wide transformation.

Cisco's 2024 AI Readiness Index is aligned with the the above factors. In 2024, Cisco found:

Leaders feel the pressure; 98% report increased urgency to deliver on AI and 85% believe they have less than 18 months to act.

Networks are not equipped to meet AI workloads; only 21% of companies report having the necessary GPUs to meet current and future AI demands.
Only 13% say they are fully ready to capture AI's potential – down from 14% last year.

More specifically, some of the most significant findings included: 

URGENCY: Companies feel they only have 18 months to showcase the impact of AI. Nearly all (85%) companies say they only have 18 months to start demonstrating the impact of AI. More than half (59%) give it only 12 months.
STRATEGY: Companies agree that AI cannot be deployed effectively in an organization without a clear strategy. Cybersecurity is the top priority for AI deployment with 42% of respondents having achieved advanced security deployment. Infrastructure follows at 40%, and data analysis and data management tied for third at 39%.
INVESTMENT: Companies are doubling down on AI despite lukewarm results from current AI projects. In the next five years, respondents anticipate that roughly 30% of IT budgets will be dedicated to AI, nearly double what it is today. Close to half of companies say AI implementations across top priorities have fallen short of expectations this year, yet 59% believe the impact from AI investments will surpass expectations after five years.
INFRASTRUCTURE: Networks are not equipped to meet AI workloads. The largest decline was in infrastructure readiness, with gaps in compute, data center network performance, and cybersecurity, amongst other areas. Only 21% of organizations have the necessary GPUs to meet current and future AI demands and 30% have the capabilities to protect data in AI models with end–to–end encryption, security audits, continuous monitoring and instant threat response.
DATA: Companies report feeling less ready to manage data effectively for AI initiatives, compared to a year ago. Nearly a third (32%) of respondents report high readiness from a data perspective to adapt, deploy and fully leverage AI technologies. Most companies (80%) report inconsistencies or shortcomings in the pre-processing and cleaning of data for AI projects. This remains almost as high as a year ago (81%). Additionally, 64% report that they feel there is room for improvement in tracking the origins of data.
TALENT: A lack of skilled talent is a top challenge across infrastructure, data, and governance, underscoring the critical need for skilled professionals to drive AI initiatives. Only 31% of organizations claim their talent is at a high state of readiness to fully leverage AI. Twenty-four percent say their organizations are under resourced in terms of in-house talent necessary for successful AI deployment. Twenty-four percent of all respondents also say that there is not enough talent available in their sector with the right skillsets to address the growing demand for AI.
GOVERNANCE: Effective AI governance is more crucial than ever, yet respondents feel that it has become more difficult. When asked about the comprehensiveness of their organizations' AI policies and protocols, 31% of the organizations said they are highly comprehensive. Fifty-one percent of respondents identified "the lack of talent with expertise in AI governance, law and ethics in the market" as a challenge in improving their readiness from the governance perspective.
CULTURE: There has been a noticeable reduction in cultural readiness to embrace AI. A lack of receptiveness to AI's changes has contributed to the decline in cultural readiness: boards have become less receptive to embracing the transformative power of AI, with 66% of them being highly or moderately receptive, down from 82% last year while 30% of organizations report employees are limited in their willingness to adopt AI or are outright resistant.

A close examination of these two reports illustrates the importance of people, processes and technology in AI deployment readiness.

How is your organization doing?

Andrew Spanyi – Editor-in-Chief

Andrew Spanyi, MBA, is an author, and formerly a consultant and an adjunct professor at Babson College. He is the author of three books on process management and operational leadership. He has written over a hundred of articles and is an advisor to the Association of Business Process Management Professionals. His current research interest is on redesigning the oncology clinical trial process. Andrew is a member of the Cognitive World Think Tank.
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Is Your Organization Ready for Successful AI Deployment