Enterprises Quit the Pilot Phase: Info‑Tech’s Picture of AI Going to Work in 2025

Why this report matters: the shift from testbeds to live systems

Info‑Tech Research Group, a global research and advisory firm, says 2025 was the year many companies stopped treating AI as a curiosity and started treating it like infrastructure. The firm’s review of enterprise programs shows a large number of organisations moving projects out of small pilots and into operational programs that touch customers, employees, and core workflows.

That matters because pilots can be inexpensive learning exercises. Operational programs, by contrast, change how work gets done day to day, how budgets are spent, and how risks are managed. If the report is right, the question for boards, CIOs and managers is no longer whether to experiment with AI, but how to run it responsibly at scale.

What the report highlights about adoption and where AI is being used

Info‑Tech’s review pulls together survey results, case studies and advisory work to show a clear shift in scale and scope. Rather than tiny lab projects that touched a handful of users, the report finds lots of projects now serving hundreds or thousands of people. A majority of respondents described at least one AI capability as being in regular production — not just a demo.

Areas of deployment are practical and familiar. Customer service automation and chat tools are the most common uses, followed by process automation in finance and HR, sales forecasting and basic demand-sensing in supply chains. The report also notes rising use of AI in IT operations — monitoring, incident triage and automated remediation — which helps explain the move from pilot to routine.

Info‑Tech points to measurable business KPIs that leaders are tracking. Common metrics include time-to-resolution for customer issues, percentage of routine work automated, and cycle time for back-office processes. Firms are reporting improvements on those measures when projects are sustained rather than abandoned after a pilot phase.

The report also sketches a year-over-year change: where previous years’ work showed experimentation and research, 2025 shows integration. More programs connect to production data and live user interfaces, and many teams have built repeatable deployment patterns rather than one-off models.

How IT leaders made AI part of running the business

Info‑Tech describes a few common engineering and organisational choices that let teams move from prototypes to production. One is modular architecture: firms build standardized model-serving layers and data pipes so different projects reuse the same plumbing. That keeps teams from rebuilding the same pieces every time.

Another pattern is clearer governance. Rather than leaving decisions to individual project leads, organisations created review gates for privacy, security and performance before models move into production. These gates often sit with cross-functional committees that include legal, security, and the business unit sponsoring the work.

Tooling choices matter too. The report finds widespread use of managed model hosting and monitoring services alongside traditional data platforms. Teams tend to combine build-and-train tools with off-the-shelf models when possible, balancing speed with control. Procurement and skills moves are pragmatic: many firms hire or retrain engineers who can operate models in production, and they shift budget from one-off pilots to platform investments that serve multiple teams.

Behind the scenes: what operational life looks like now

Moving AI into operations changes how work happens. The report flags several practical impacts. First, project timelines lengthen in different ways: initial rollouts can be faster because of reusable platforms, but sustaining and updating models becomes an ongoing effort that requires ongoing budget and staff.

Second, security and privacy become front-and-center. Production models touch real customer and employee data, and teams must build monitoring to detect drift, unfair outputs, and data leaks. Info‑Tech says many organisations now treat model monitoring like system monitoring — a continuous task rather than a checkbox before launch.

Third, the workforce mix shifts. The report documents growing demand for people who can bridge business and machine learning work: data engineers who understand model operations, privacy analysts who can review datasets, and product managers who run AI features. At the same time, routine jobs change as automation handles repetitive tasks — creating both efficiency and reskilling needs.

Finally, budgets move from capitalizing single projects to funding platforms and governance. That makes some programs cheaper per use but raises the stakes: platform outages, bad model behaviour, or compliance failures now affect broad parts of the business.

Signals for vendors, partners and policy watchers to track next

Info‑Tech’s report suggests several market-level implications. Vendors that offer end-to-end operational tooling — model hosting, monitoring, and governance — are well placed to grow because enterprises want repeatable solutions, not bespoke proof-of-concept work. This could accelerate vendor consolidation around a smaller set of platform providers.

Partners and system integrators that can help organisations wire AI into existing workflows will also see demand. The emphasis is on operational skills and change management as much as on pure model building.

On the policy side, the report signals growing pressure for clearer rules and standards. As models enter production at scale, regulators are likely to push for stronger auditability, data controls, and accountability. Companies that have already installed governance gates may be better prepared for that scrutiny.

For readers watching this space, Info‑Tech recommends tracking adoption metrics (how many models are in production and which KPIs they target), vendor moves (consolidation or new platform launches), and emerging regulation. The big idea is simple: AI is no longer only a lab issue. It is now an operational responsibility, and that changes who owns it, how it is funded, and how risks are managed.

Photo: MART PRODUCTION / Pexels

Sources

• prnewswire.com