Blanket restrictions are unlikely to eliminate Shadow AI. Enterprises need approved tools that are easy to use, safe paths for experimentation, and governance that scales with actual risk.
Read more →Agent skills work mainly by giving AI agents reliable procedures to follow, not by teaching them new facts. Their value depends just as much on retrieval, applicability and adaptation as on the quality of the skill itself.
Read more →AI is making automated traffic a permanent part of digital infrastructure. The challenge is no longer just blocking bots, but governing which automation supports the business and which automation exploits it.
Read more →AI is changing work at three levels at once. Employees gain more capability, leaders must redesign how work gets done, and organizations need to become faster learning systems.
Read more →Treat AI resistance as a leadership signal, not an employee problem. Most resistance stems from unclear strategy, weak manager support, limited training, and uncertainty about how AI will affect real work.
Read more →Encrypting an AI model’s reasoning does not automatically make it secure. Researchers found that encrypted reasoning traces could be replayed across sessions, users and models, creating risks around data leakage, model extraction and hidden prompt injection.
Read more →AI agents are already operating inside enterprise workflows, but governance, visibility, and runtime controls are not keeping pace. The real risk is no longer just who has access, but what autonomous systems can do once they have it.
Read more →Treat AI less like a technology rollout and more like an operating model redesign. The value will not come from adding more AI tools, but from fixing the data, process, ownership, and governance gaps that stop AI from working at scale.
Read more →AI adoption is rising fast, but workforce confidence is not keeping pace. The next productivity challenge is not just deploying AI tools, but building enough trust, skills, and clarity for people to use them well.
Read more →AI automation may not arrive as sudden disruption to a few tasks. It may spread broadly across many text-based tasks, with capability improving steadily across the labor market.
Read more →Human Risk Management (HRM) is becoming essential in the era of agentic AI because cyber risk now comes from both people and the AI agents acting on their behalf. Training alone is no longer enough; organizations need visibility, context, and targeted action.
Read more →AI-native companies are not just giving employees AI tools. They are redesigning how work gets done, with AI agents acting as teammates, knowledge systems becoming core infrastructure, and adoption treated as an operating discipline.
Read more →AI tools may improve individual productivity, but enterprise productivity depends on whether people are engaged, supported, and led well. The biggest AI bottleneck may not be the technology. It may be the manager.
Read more →Agentic outsourcing is the shift from human-led managed services to AI-led operating models where agents execute work, policies define boundaries, and humans provide oversight and control.
Read more →Identity compromise is surging, browsers are now prime attack surfaces, and AI risk is showing up in two ways: attackers using AI to scale threats, and attackers targeting AI systems to reach sensitive data.
Read more →It is usually not the AI model. It is fragmented data, disconnected workflows, weak governance, and operating foundations that were never built for real-time execution.
Read more →The future of work is not humans vs AI. It is humans working with agents and robots, with the biggest winners being people and companies that redesign work around that partnership, not just automate tasks.
Read more →The biggest shift is not just more threats. It is faster threats. Attackers are moving from stealthy persistence to rapid execution, browser credential theft, cloud identity abuse, and supply chain compromise. Defenders need detection and response models built for minutes, not months.
Read more →Most organizations are moving too fast on agent deployment and too slow on operating-model redesign.
Read more →AI is already creating measurable productivity gains, but the near term story is less about mass layoffs and more about uneven adoption, stronger execution, and a recalibration of work toward higher value tasks.
Read more →It is usually not the technology. It is the gap between what the agent knows and what the business can actually do with that insight. Forrester calls this the “agentic action gap,” and says only 15% of organizations see material returns from AI agents.
Read more →AI is making cybersecurity teams faster, but it is also expanding the attack surface faster than many organizations can govern it.
Read more →Most companies do not have an AI problem. They have an orchestration and trust problem.
Read more →It comes down to 3 things: engagement, activation, and expertise. If one is missing, AI adoption stays shallow.
Read more →The biggest friction points are month-end closing (88%), supplier invoice processing (85%), and manual data consolidation from emails, PDFs, and spreadsheets (80%).
Read more →AI is not just creating new productivity upside. It is also turning data protection into a security quagmire, especially when agents, cloud apps, and sensitive data are all connected.
Read more →They do not just use AI. They redesign the entire company around it.
Read more →The biggest shift is not more chatbots or AI assistants. It is enterprises moving toward multi-agent systems that can handle real operational work, not just fancy demos.
Read more →Pay still matters, but purpose, growth, and culture seem to matter more when it comes to lasting workplace happiness. At the same time, trust in AI at work is becoming harder to earn.
Read more →While AI agents are 88% faster and 90% cheaper than human workers , they approach work through a strictly programmatic lens. For example, they would write code even for visual tasks like design whereas humans tend to rely on UI-centric, intuitive workflows.
Read more →In practice, AI often intensifies the workday. Instead of reclaiming time, people use efficiency gains to colonize their remaining "white space"—taking on more tasks and effectively narrowing the gap between working and recovering. This creates a modern Jevons Paradox: as AI makes our time more "efficient," we don't work less; we simply consume that efficiency by increasing the volume and velocity of our output until we hit a wall.
Read more →Approximately 45% of U.S. employees now report using AI at work at least a few times a year, but frequent and daily use remain far less common.
Read more →Companies can drive meaningful business impact by scaling incremental, manageable AI use cases instead of adopting a “big bang” approach. This means focusing on building capabilities, managing risk, and climbing a practical AI maturity curve.
Read more →Surprisingly, yes. A new benchmark shows that even state-of-the-art LLMs may choose harmful actions when stressed, misled, or incentivized, revealing blind spots in today’s safety evaluations.
Read more →Not really. AI agents will augment and extend the automation stack, but RPA and BPA will remain essential for deterministic and compliance-heavy workflows.
Read more →Prompt injection is a cybersecurity threat unique to AI systems where attackers embed hidden instructions that cause generative models to behave in unintended ways, for example, potentially exposing data, executing unwanted actions, or bypassing safety guardrails. Enterprises deploying AI agents must treat this as a long-term security challenge with layered defenses.
Read more →According to Deloitte, organizations that successfully deploy autonomous AI agents by 2028 could dramatically reduce operating costs, accelerate decision-making, and unlock entirely new sources of growth—all while creating new roles in orchestration, governance, and AI oversight rather than eliminating them.
Read more →ChatGPT has become a global phenomenon—used weekly by 700 million people and adopted by 10% of the world’s adults. While it’s increasingly used at work, over 70% of all messages are now non-work-related, highlighting its growing role as a personal assistant, educator, and creative partner.
Read more →Only a small group — just 13% of organizations, dubbed the “Pacesetters” — are truly ready. Most are racing ahead with ambition but lagging in infrastructure, governance, and measurement
Read more →For the majority of specialized, repetitive agentic tasks, SLMs are set to take the lead — offering sufficient capability, lower latency, and dramatically reduced costs.
Read more →Through sophisticated indirect prompt injection attacks, as demonstrated by the "ForcedLeak" vulnerability in Salesforce Agentforce, which allowed attackers to exfiltrate sensitive CRM data using malicious instructions embedded in trusted data sources.
Read more →Agentic AI initiatives often underwhelm because teams fixate on the agent technology itself, rather than the end-to-end business workflow it needs to improve. Success depends on redesigning the entire process, rigorously evaluating agent output to prevent "AI slop," and recognizing that complex AI agents are not always the right tool for the job.
Read more →Agentic AI is moving from pilots to production, with 14% of large organizations already implementing AI agents to some degree. However, this rapid adoption is running into significant headwinds from a lack of trust and a major gap in workforce preparedness.
Read more →Not necessarily — but understanding why can define the success of your AI automation strategy.
Read more →Finance has entered its “Golden Age of AI,” but the transformation is uneven — coordination, data extraction, and communication bottlenecks still slow progress, even as AI agents rise to prominence.
Read more →Cybercriminals are now using agentic AI as an active partner to automate and scale sophisticated attacks, from data extortion to Ransomware-as-a-Service. This allows a single, low-skill operator to achieve the impact of an entire criminal team.
Read more →By using a maturity model that assesses not just your tech stack, but your organization's capabilities across vision, people, delivery, and measurement.
Read more →Most enterprises are dangerously unprepared. There exists a critical readiness gap, with AI adoption far outpacing the enterprise safeguards needed to govern it.
Read more →According to a new report from ISG, while the number of AI projects in production has doubled since 2024, the business outcomes are not keeping pace. Key metrics like revenue growth and cost savings are significantly underperforming expectations, while the most consistent gains are found in compliance and risk management.
Read more →ShadowLeak is a critical zero-click vulnerability that allowed for the exfiltration of sensitive information from ChatGPT when connected to enterprise Gmail, discovered by Radware and now patched by OpenAI.
Read more →Despite their promise, today’s autonomous agents succeed on only about 50% of benchmarked tasks. Most failures stem from poor planning, flawed execution, or response errors.
Read more →Because most systems don’t retain feedback, adapt to context, or improve over time.
Read more →Yes — through zero-click exploits like EchoLeak, which weaponize voice, context, and memory against your agents.
Read more →Not very. Even the most capable AI agents complete less than one-third of professional tasks autonomously.
Read more →Generative AI is tripling task productivity for some—but not everyone is benefiting equally.
Read more →No — AI and digitalization are now strategic imperatives, not optional initiatives.
Read more →Quick Answer: Most LLMs wouldn’t — but 92% of humans would.
Read more →Quick Answer: Agentic AI is the next frontier—moving from passive assistants to autonomous collaborators.
Read more →Quick Answer: Yes—AI agents can boost output, but the match between human and AI personality plays a crucial role.
Read more →Quick Answer: Most failures stem from poor use case selection—not the technology itself.
Read more →Quick Answer: The RPA market is rapidly shifting from task automation to agentic automation platforms, and your vendor strategy needs to evolve accordingly.
Read more →Not so fast — run this checklist first
Read more →What a Harvard economist’s research tells us about automation, job growth, and the real risk behind AI
Read more →AI agents are reshaping the way modern enterprises run their finance & accounting operations
Read more →Computer-Using Agents (CUAs) vs. Robotic Process Automation (RPA)
Read more →AI performance has been doubling roughly every 7 months over the past 6 years
Read more →What’s the Difference? 🧠
Read more →IDP solutions are evolving into autonomous AI agents that can understand, decide, and act.
Read more →