How to Conduct an AI Readiness Assessment for Your Business
Leveraging AI to help your business grow and become more profitable is a well-known strategy among CEOs. According to recent research by Kearney, 89% of CEOs acknowledge the strategic importance of leveraging AI for business transformation. However, just one in four feel fully prepared to integrate it across their organizations.
That’s because AI readiness is far more than just buying software. It requires a strategic review across multiple dimensions of your business, including technology infrastructure, data maturity, how tools are leveraged, people and process, and governance and security.
When conducting an AI technology readiness assessment, consider leveraging these five pillars of AI readiness:
1. Connectivity & Infrastructure
Evaluate your network for bandwidth and uptime. AI workloads require reliable, high-speed connections to process large datasets and to perform data analysis in real-time. It’s important to test your network by simulating increased data loads to identify weaknesses.
Evaluate your current servers, networking equipment, and storage capability to ensure they can handle the data throughput and demands required by a variety of AI tools. Review your company’s use of cloud resources and data centers to determine if your current needs are being met. Determine how these resources can scale as AI is implemented across business functions and demands increase over time..
After you conduct a thorough review of your organization’s connectivity and infrastructure, compare your findings to industry standards so you can identify gaps and understand where additional improvements need to be made. With this information, a project roadmap should be created to address all additional requirements before AI is fully integrated into your business.
2. Data Maturity
In assessing data maturity throughout your organization for AI technology, you will need to go beyond the traditional “fit-for-use” assessment, as many organizations have built systems that can be independent of each other and may not have the same requirements as AI systems.
Consider leveraging a data maturity model that can enable analysis of data, breaking it down into maturity levels from siloed and unmanaged to fully integrated, fully managed, and of high quality. Cover dimensions such as governance, accessibility, and integration into current systems. This type of assessment will enable you to identify weaknesses and gaps so you can develop a remediation plan to be ready to fully integrate AI technology.
Data that is mature enough for AI technology should have the following characteristics:
- Accessible, available, and used as a trusted source throughout the organization.
- Accurate, timely, and “the single version of the truth.”
- Governed with clear ownership of management, sourcing, and cleansing.
AI technology requires high data maturity to learn, train, and perform—especially when generating actionable insights and advanced analytics.
3. Tool Landscape
A critical component of an AI technology readiness assessment is to ensure that any candidate tools selected can integrate with existing systems. If they don’t, you’ll need to identify gaps and plan remediation activities. Ideally, your organization’s tool landscape should support both current and future AI initiatives.
Steps to evaluate your tool landscape:
- Inventory existing tools and infrastructure, including SaaS capabilities, cloud platforms, hardware, software, data management systems, and any AI-adjacent tools.
- Understand the current state architecture, including process and data flows, to identify integration points.
- Define both business and technical requirements by engaging with stakeholders from across the business.
- Research available AI tools based on use cases. Evaluate each using consistent criteria: integration, scalability, usability, functionality, and security.
Any candidate tool should align with the business strategy and scale to meet future growth.
4. People and Process
When deploying AI technology within your organization, the skills and ability of your team, and your current processes will play a crucial role. A team equipped with the right skills and knowledge will make for a reliable and effective integration. Additionally, clearly defined processes will highlight areas that AI can enhance or transform your business.
Realistically, not everyone will be at the same skill or lnowledge level when AI technology is deployed. Determine the training needs of your organization so that employees know how to effectively use AI in their specific roles and daily workflows. Consider training beyond daily routines like prompting and agentic, or automation awareness.
Support change by developing a communications plan that informs employees about what’s coming and how it could affect them.
Processes should also be well-documented, and governance, compliance, and risk controls should account for the specific challenges and responsibilities AI will bring.
5. Governance and Security
The final pillar of AI readiness is ensuring your governance structures and security controls are built to support AI deployment.
Many organizations establish a governance committee to oversee implementation and monitor the outputs of AI tools used throughout the organization. These committees are typically tasked by leadership to carry out the AI strategy and provide regular updates.
Governance should include published policies that address fairness, explainability, and security. Some companies create their own internal standards, while others adopt frameworks such as the OECD AI Principles or Microsoft’s Responsible AI Standard. Policies should be reviewed regularly to reflect the evolving capabilities and risks of AI.
Security considerations include:
- Data: Assess integrity from ingestion through output.
- Infrastructure: Confirm that systems are updated and securely configured.
- Architecture: Evaluate vulnerabilities and consider zero-trust approaches.
- Access controls: Enforce authentication and monitor unusual access behavior.
- Incident response: Have protocols in place for detection, containment, and recovery.
Frequently Asked Questions (FAQs)
1. What is an AI readiness assessment for business?
An AI readiness assessment is detailed evaluation of your company’s infrastructure, data, tools, workforce, and governance to determine if you’re prepared to adopt, implement, and scale AI technologies effectively.
2. Why is data maturity critical for AI adoption?
AI systems rely on accurate and well-managed data to serve your business needs properly. Mature data allows AI to produce useful, reliable insights and prevent poor outcomes caused by low-quality, or fragmented information.
3. How does infrastructure impact AI readiness?
AI workloads require robust, high-speed networks, reliable uptime, and scalable cloud infrastructure. Without these, AI processes can bottleneck or fail to perform in real time.
4. What tools should be reviewed during an AI readiness assessment?
Your assessment should include a full inventory of SaaS platforms, cloud services, analytics systems, and integration tools. Evaluate how well they support interoperability, scalability, and data governance.
5. How do you prepare employees to use AI technology?
Provide AI upskill training based on employee job functions. Focus on practical the practical use of AI tools, automation awareness, prompting, and responsible AI use. Clear communication and documentation will also support adoption.
6. What governance and security practices are needed for AI?
Strong governance should include oversight committees, documented policies, and regular audits. Security protocols should address access controls, zero-trust architecture, and incident response plans for AI-specific risks.
7. How do I get started with AI readiness planning?
Begin by evaluating your organization using the five readiness pillars. Then build a roadmap to close gaps—ideally with help from a trusted partner like Cox Business who can support infrastructure, strategy, and implementation.
- How to Conduct an AI Readiness Assessment for Your Business - July 28, 2025
- How to Upskill Your Enterprise as AI Adoption Soars and Skills Gap Widens - July 9, 2025
- How to Choose the Right Bandwidth for Your Business in 2025 - May 13, 2025
