Why AI matters now

Artificial intelligence is rapidly changing how organizations operate. By automating repetitive tasks, analyzing massive datasets, and augmenting human decision-making, AI is already driving measurable gains across industries. These gains include faster research cycles, lower development costs, and new opportunities for personalized services.

Tangible impacts across sectors

In health care and pharmaceuticals, machine learning and AI-powered tools are improving diagnostic accuracy, accelerating drug discovery timelines by up to 50%, and enabling more personalized treatment pathways. In supply chain and logistics, AI models help anticipate or mitigate disruptions, letting companies make better-informed decisions and strengthen resilience amid geopolitical and market uncertainty.

Across research and development, companies report potential reductions in time-to-market of about 50% and cost savings in fields like automotive and aerospace of as much as 30%. Those numbers help explain why AI is not just a technical novelty but a strategic lever for competitive advantage.

Urgency and leadership perspectives

Executives sense the speed of change. Nearly all companies report increased urgency to act on AI: 98% say they felt a heightened pressure over the last year, and 85% believe they have less than 18 months to put an AI strategy into place or face negative business consequences.

Industry leaders voice similar concerns. Patrick Milligan, chief information security officer at Ford, calls this an inflection point and notes that the long-term societal effects remain difficult to fully grasp. Jeetu Patel, president and chief product officer at Cisco, warns against waiting: ‘If you wait for too long, you risk becoming irrelevant,’ he says. Patel adds that the real danger is not AI itself taking jobs but competitors or colleagues who use AI more effectively.

Readiness gap and infrastructure challenges

Despite the urgency, only about 13% of companies globally say they are ready to use AI to its full potential. As AI workloads grow, IT infrastructure is becoming a critical bottleneck. Roughly 68% of organizations say their infrastructure is at best moderately ready to adopt and scale AI technologies.

Key infrastructure requirements include:

• Sufficient compute capacity to run complex models and train large datasets.
• Optimized network performance both across organizations and inside data centers.
• Strong cybersecurity to detect and prevent increasingly sophisticated attacks.
• Observability to continuously monitor and analyze infrastructure, models, and system behavior so performance remains reliable and optimized.
• High-quality, well-managed enterprise data, since AI outcomes are only as good as the data that feeds them.

These technical needs must be matched by culture and talent initiatives that prioritize AI-focused skills, cross-functional collaboration, and governance practices.

What companies should prioritize

To succeed with AI at scale, organizations should focus on three parallel tracks: build the right infrastructure, improve data and observability, and invest in people and processes. Infrastructure investments should be planned to support growth in model complexity and data volume. Observability and monitoring must be integrated early to ensure models behave as expected in production. Finally, data governance and upskilling programs will determine whether AI projects translate into sustained business value.

This article is based on research and reporting by Insights, the custom content arm of MIT Technology Review. The content was produced by human writers, editors, analysts, and illustrators and underwent human review for any use of AI tools in production processes.