AI Use, Preparedness, and Risk Survey

Executive summary

This summer we surveyed our clients about AI Use, Preparedness, and Risk.  Here’s what we discovered. The aggregated survey responses reveal that data privacy and security, lack of internal expertise, and ethical use are the most common concerns about AI adoption. Most organizations feel unprepared for AI implementation, with limited formal deployment and reliance on informal or individual experimentation. Key challenges include unclear ROI, training gaps, and integration with legacy systems, while support needs center on training, governance, and peer examples. These insights highlight a broad need for education, governance, and upskilling to advance AI maturity and risk management.

Respondents Profile

Current AI Usage

50% of organizations reported no AI use.

50% of organizations reported using off-the-shelf platforms
like ChatGPT, Microsoft Copilot, and Salesforce Einstein.

Planned Usage

Focus areas include sales and marketing, process automation, data analysis, and forecasting.

AI Readiness Levels

Only 1 organization feels very prepared

Only 2 organizations completed a formal AI Readiness Assessment.

AI Security and Governance

Establishing Security Baselines

35 organizations have fully implemented and monitored core security practices

39 are unsure of their status

Use of Professional Services

50 have engaged external experts to assess cybersecurity posture

45 have not

Top Concerns with Implementing AI Solutions

In order of frequency

• Lack of internal expertise
• Data privacy and security
• Ethical use of AI
• Cost of Implementation
• Regulatory compliance
• Workforce Impact / Job Displacement

Recommendations for Organizations Implementing AI

Build Internal Expertise

• Invest in training programs and certifications for staff.
• Hire or consult with AI specialists and data scientists.

Strengthen Data Governance

• Implement robust privacy policies and security protocols.
• Use encryption, access controls, and regular audits.

Ensure Ethical AI Use

• Develop ethical guidelines for AI deployment.
• Conduct bias audits and ensure transparency in AI decisions.

Plan for Financial Sustainability

• Start with low-cost pilot projects to demonstrate value.
• Create a phased investment strategy with clear ROI metrics.

Stay Compliant

• Monitor regulatory changes and consult legal experts.
• Align AI practices with industry standards and government policies.

Prepare for Workforce Transitions

• Launch reskilling initiatives and change management
• Communicate openly about AI’s role and impact on jobs.

Conduct Readiness Assessments

• Use formal tools to evaluate AI maturity and organizational preparedness.
• Identify gaps and prioritize areas for improvement.

Establish Governance Frameworks

• Define roles and responsibilities for AI oversight.
• Create policies for access control, usage monitoring, and risk management.