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A recent IBM survey found that CIOs and CTOs are being held accountable for increasingly autonomous AI systems without having the visibility or governance capabilities needed to supervise them effectively. As organizations deploy more AI agents to increase productivity and accelerate operations, existing controls designed for slower, human-led workflows are struggling to keep pace. The findings show that scaling AI without embedded monitoring and governance can increase data exposure, security incidents, compliance failures, and the need for human intervention.

Source: ITPro

What to know:

• IBM surveyed 2,000 C-level technology executives about AI adoption, governance, architecture, and investment readiness.
• Two-thirds of respondents said they are responsible for AI systems they cannot realistically supervise, while only 11% considered their organizations fully prepared for AI agent deployment at scale.
• Seventy-seven percent said AI adoption is already advancing faster than their organization’s governance capabilities.
• Organizations experienced an average of 54 AI-agent incidents during the previous year that involved unintended or harmful outcomes requiring human correction.
• Of the reported incidents, 37% resulted in data exposure or security breaches, 33% caused cascading system failures, and 17% triggered compliance issues.
• Seventeen percent of incidents were classified as high severity and required more than four hours to contain.
• Organizations that embedded controls directly into their AI systems experienced 25% fewer incidents than those relying mainly on manual governance processes.
• Nearly six in ten technology leaders identified security and compliance concerns as major barriers to scaling AI agents.

Why it matters:

For mid-sized businesses adopting GenAI, the pressure to improve productivity and deploy AI faster can result in systems being introduced before sufficient visibility, monitoring, and governance are in place. When AI agents operate continuously across applications and business data, manual reviews may not detect risky prompts, sensitive data exposure, policy violations, or unintended actions quickly enough.

This reinforces the need to build AI usage visibility and governance into adoption from the beginning. Continuous monitoring, prompt-level insights, sensitive-data protection, policy-aligned guardrails, and auditability can help businesses understand how AI is being used, identify risks as they emerge, and scale GenAI without allowing productivity gains to create unmanaged security, compliance, or operational exposure.

A recent survey found that organizations are facing rising and often unexpected costs as they expand workplace AI adoption. While businesses expect AI tools and agents to improve productivity, IT leaders are struggling to measure usage and business value because adoption increasingly happens outside formal governance processes. Employees are also using unapproved tools and personal AI accounts for work, while some AI systems have taken actions that caused financial, legal, compliance, or reputational harm. The findings show that AI productivity cannot be separated from visibility, governance, and risk management.

Source: ITPro

What to know:

• More than 80% of surveyed UK IT leaders reported unexpected increases in AI-related costs during the previous 12 months.
• Over 60% said they were highly or fully accountable for AI-driven business outcomes, despite lacking complete visibility into how AI was being adopted and used across departments.
• One-quarter of workers said they frequently use AI tools that have not been formally approved by their organization.
• Another 38% regularly use personal AI accounts for work, creating potential visibility, data-protection, and governance gaps.
• IT leaders estimated that 30% of new AI tools and agents introduced during the previous year had bypassed formal IT review or approval.
• More than half of IT leaders said an AI tool or agent had taken an action that resulted in financial, legal, reputational, or compliance harm.
• AI productivity was also affected by output quality and missing business context: 37% of knowledge workers spent at least 30 minutes each day correcting or reworking AI-generated outputs.

Why it matters:

For mid-sized businesses adopting GenAI, increased AI activity does not automatically translate into measurable productivity or business value. When employees use personal accounts, unauthorized tools, or AI agents outside formal review processes, organizations may be unable to determine where sensitive data is being shared, whether internal policies are being followed, or which tools are creating risk and unnecessary costs.

This reinforces the need for organization-wide AI usage visibility, shadow AI discovery, prompt-level monitoring, sensitive-data protection, and policy enforcement. By monitoring how employees interact with GenAI tools, businesses can support productive AI adoption while identifying risky behavior, reducing uncontrolled costs, and preventing AI use from creating compliance, security, or reputational damage.