We’ve had the privilege of working with some of the most forward-thinking enterprise AI adopters. These are the numbers.
We’ve had the privilege of working with some of the most forward-thinking enterprise AI adopters. Our contacts typically belong to teams responsible for managing AI within their organizations—overseeing security, IT, and data protection.
The Rapid Rise of AI Use
Across our install base, we’re seeing a significant surge in AI usage in just the last three months, spanning:
- Generative AI applications—including homegrown AI solutions, public AI services, and AI features embedded in SaaS applications.
- Users across multiple departments—leveraging AI for diverse tasks at all levels of the organization.
The numbers tell a compelling story of AI's accelerated adoption at these companies:
- More than 90% of employees are using AI in some capacity.
- 400 to 700 unique AI services are in use within organizations—this includes public AI tools, homegrown LLM applications, and AI-enabled SaaS features.
- The number of AI services in use has grown by 25% to 250% in just the last three months.
- Organizations are adding 8 to 50 new AI services per week, averaging 31 per week.
- The average enterprise now uses 85 SaaS applications with embedded AI features.
Emerging AI Risks
While AI adoption is accelerating, so are potential risks:
- Nearly 50% of AI services in use are classified as “High Risk” based on our AI Trust Feed(TM) automated risk scoring.
- 75% of employees using AI rely on unvetted or unsanctioned AI tools.
- Among AI-enabled SaaS applications:
- 20% have default settings that automatically enable new AI features.
- 30% incorporate 4th-party AI models, introducing additional risk factors.
Employees are not waiting for AI onboarding, and are starting to bring their own AI to work.
The AI Governance Challenge
The rapid rise in AI adoption is a positive sign—employees are moving beyond experimentation and integrating AI into their workflows. However, governance is struggling to keep pace:
- AI Onboarding is slow, creating bottlenecks for business requests to vet and approve AI tools.
- Employees are resorting to Shadow AI, adopting unapproved services to meet their needs. Creating potential risk, AND increasing the workload on the onboarding team.
- Organizations are paying for enterprise license AI services that employees aren’t using, and paying for multiple personal account subscriptions, creating an AI spending sinkhole.
Optimizing AI Usage and Reducing Costs
By closely monitoring AI adoption patterns and implementing granular AI policies and enforcement actions, organizations can improve efficiency and security while managing costs:
- Eliminating unused AI subscriptions—By identifying low adoption rates, companies can discontinue underutilized paid AI services.
- Encouraging adoption of strategic AI investments—Organizations can promote the use of approved AI tools while discouraging employees from using unsanctioned alternatives.
- Minimizing data leakage risks—Some employees use both enterprise-approved AI tools (with data protection settings) and personal versions of the same tools, which could lead to data exposure. Consolidating accounts prevents this risk and reduces costs.
The Future of AI in Enterprise
AI should drive business value and innovation—not introduce unknown risks. With Singulr, enterprises can move faster, reduce costs, and mitigate risk, ensuring AI adoption is both strategic and secure.
Additional Reading
- Singulr Blog: What We’re Hearing from Enterprise AI Teams
