AI in Business: Where It’s Moving Fastest

AI has quietly crossed a line: it’s no longer a side project owned by “the innovation team.” It’s becoming core infrastructure—something companies rely on daily to answer customers, ship software, manage knowledge, and make operations run smoother.

A big reason is momentum and investment. Stanford’s 2025 AI Index reports $109.1B in private AI investment in 2024, alongside a sharp rise in organizational AI usage. And the enterprise data is starting to look less like “experiments” and more like a habit: OpenAI’s enterprise report (Dec 2025) points to much deeper workflow integration, with message volume up 8× and “reasoning token” consumption per organization up 320× year over year.

So, where is AI growing the most inside companies right now?

1. Customer operations: support, contact centers, and self-service

This is one of the fastest-moving areas because it has the cleanest scorecard: handle time, deflection rate, resolution quality, and cost per ticket. Once a team sees real deflection and better response times, adoption spreads—fast.

Industry research consistently flags service operations as a major hotspot for generative AI deployment. And the trend is moving beyond chat: voice, agent-assisted workflows, automated follow-ups, and knowledge-grounded responses are turning support into an “AI-first” channel in many businesses.

2. Marketing & sales: content velocity + personalization at scale

Marketing teams adopted generative AI early because it’s easy to start: you don’t need a massive systems overhaul to get value from better drafts, more variants, or faster campaign iteration.

McKinsey’s survey results show organizations are most often using gen AI in marketing and sales, alongside other core functions. That “most often” matters: even when companies are cautious elsewhere, marketing and sales keep pushing forward because the work is constant and measurable (leads, conversion, CAC, pipeline).

3. Software engineering and IT: from copilots to agents

If customer support is the visible wave, engineering and IT is the compounding wave. Coding copilots made AI feel normal; now organizations are trying to make AI do the work, not just suggest it.

McKinsey lists software engineering and IT among the functions where gen AI is most commonly used. And the early-2026 story is “agents with guardrails”: KPMG’s Q4 AI Pulse (Jan 2026) reports 72% plan to deploy agents from trusted providers, while leaders emphasize security, compliance, and auditability as the biggest requirements.

The practical takeaway: the winners aren’t the teams with the flashiest demos—they’re the ones who can integrate agents into ticketing, CI/CD, monitoring, and access controls without creating a security nightmare.

4. Knowledge management: internal “search” becomes internal “answers.”

This category is exploding because knowledge work is everywhere: policies, pricing, product docs, onboarding, QBRs, contracts, and internal how-tos. Teams want answers that are fast and grounded in approved sources.

McKinsey tracks knowledge management as a gen AI use area and shows that usage varies by industry, with professional services leaning into it. At the same time, enterprise usage patterns show deeper workflow integration overall—another sign that “ask the model” is becoming “build it into how we work.”

5. Small business adoption: AI is now mainstream

What used to be a “big company advantage” is rapidly becoming standard tooling for smaller teams, too. The Chamber of Commerce reports 58% of small businesses say they use generative AI in 2025 (up from 40% in 2024).

That matters because it changes expectations: customers get used to faster responses and tighter follow-up, even from lean teams. It also pressures vendors to embed AI into everyday software so adoption doesn’t require hiring specialists.

6. Healthcare: adoption is real, but governance is everything

In healthcare, the growth is striking—and cautious. A JAMA Network Open study (published Dec 2025) reports 31.5% of hospitals used generative AI integrated with EHRs in 2024, and 24.7% planned to adopt within a year.

This is exactly the pattern you’d expect in high-stakes environments: strong demand (documentation, workflow friction, staffing constraints) paired with intense evaluation needs (privacy, safety, accountability).

7. Legal and professional services: AI becomes a capability, not a tool

Professional services are treating AI as a competitive advantage—so much so that some are buying capability rather than building it slowly. Reuters reported that Cleary Gottlieb acquired Springbok AI as part of its push to develop custom AI tools.

For clients, this translates to faster turnaround and more “productized” service delivery. For firms, it’s about maintaining margins, differentiating themselves, and keeping pace with what clients now expect.

The 2026 shift: less “AI usage,” more “AI operating model”

The theme heading into 2026 is simple: everyone has access to models; the advantage is integration and control.

The companies pulling ahead are doing three things:

1. Picking a few high-volume workflows (support, sales ops, IT service desk, internal knowledge) and integrating AI end-to-end.

2. Putting governance where it belongs: data access, audit trails, and human-in-the-loop for sensitive paths.

3. Measuring impact with operational metrics, not vibes.