Start with recurring tasks, not AI tools. Identify opportunities for AI to create, analyse, strategise or research, then prioritise those offering high impact with little implementation friction.
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.
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.
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.
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.
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.
By using a maturity model that assesses not just your tech stack, but your organization's capabilities across vision, people, delivery, and measurement.
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.