The Role of AI in Business Operations

Surveys show that more than 75% of organizations now use AI in at least one business function, and adoption is growing every year. What used to be limited to innovation labs is now embedded in routine workflows across finance, HR, operations, and marketing.

Executives expect a major shift from pilots to scale: about 46% say their organizations will focus on using AI to optimize core processes, while 44% will use it to drive new products and services. This means AI is no longer a side project—it is becoming part of the operating model itself.

A major emerging trend is **agentic AI**—systems that can pursue goals, take multi‑step actions, and coordinate workflows with limited human input. Instead of waiting for a single prompt, these agents can monitor data, trigger actions, and loop in people only when needed.

Today, businesses use AI agents for tasks like employee onboarding, password resets, ticket routing, and proactive analytics summaries. In operations and supply chain, coordinated agents can forecast demand, adjust procurement, and track logistics to reduce delays and disruptions. This shifts human work toward exceptions, strategy, and relationship‑driven tasks.

In supply chains, over half of surveyed companies already use AI to anticipate and mitigate disruptions, such as delays or inventory imbalances. AI agents are increasingly used to collaborate with ecosystem partners, helping synchronize demand, inventory, and logistics across organizations.

Customer support teams deploy generative AI and chatbots to handle common queries, search knowledge bases, and generate responses, often integrated with retrieval‑augmented generation (RAG) to stay up to date. Finance and analytics teams rely on AI for anomaly detection, forecasting, and scenario planning, improving both speed and quality of decisions.

Business leaders widely believe AI will enable both operational excellence and new revenue streams: 85% say AI will drive business model innovation, and 89% say it will power product and service innovation in the coming years. Cloud providers and software vendors are racing to offer enterprise‑grade AI platforms with better reasoning, security, and cost efficiency.

As models become more efficient and open‑weight options mature, AI is becoming cheaper and more accessible, lowering the barrier for smaller firms to compete. Organizations that move fastest to industrialize AI—standard tools, shared data platforms, and clear governance—are most likely to see outsized ROI and sustained competitive advantage.

Despite the momentum, many organizations struggle with regulation, talent gaps, and technical debt that slows AI adoption. Executives highlight governance, compliance, and workforce readiness as top barriers, especially as agentic and autonomous systems become more capable.

Leading companies respond by investing in reskilling, establishing AI risk frameworks, and building tools to monitor and evaluate AI behavior and outcomes. Those that treat AI as an ongoing transformation—not a one‑off technology deployment—are better positioned to capture value safely and at scale.