AI’s Real IT Ops Test: 90 Seconds, Three Days

Key Takeaways

• Unipol Assicurazioni, one of Italy’s leading insurers with the country’s largest agency network, cut its IT event response time from 20 minutes to 90 seconds after building NAMI, an AI-powered operations platform on IBM’s watsonx ecosystem, and moved from monitoring 26 percent of events in near real-time to 100 percent since June 2025. Blue Pearl, a South African cloud consultancy, used IBM’s software development assistant, Bob, to complete a Java upgrade in three days instead of the roughly 30 days such work typically took, a 90 percent faster delivery cycle with zero post-deployment defects.
• Both figures are company-reported, standard for this kind of case study, and worth reading as such rather than as independently audited results. Against the wider benchmarking picture, though, Unipol’s 90-second event response looks exceptional rather than merely good: cross-industry mean-time-to-resolve figures commonly cited in IT service management benchmarking run into hours, with even high-performing site reliability teams typically targeting under an hour for major incidents.
• Blue Pearl’s upgrade moved BlueApp from Java 11, a long-term support release for which Oracle’s premier support for paying subscribers ended in September 2023, to Java 25, the current long-term support release, published on 16 September 2025. That’s not a cosmetic version bump; Java 11 remains patchable under Oracle’s extended support track through January 2032, but running years behind the current LTS is a real, accumulating source of security and compatibility risk, the kind of work engineering teams often deprioritise.
• Neither case study presents AI as removing human judgement from the process. Blue Pearl kept its engineers in control through governance checkpoints and CI/CD validation at every stage; Unipol’s platform still escalated 538 of the roughly 800 events it analysed in its first two months rather than resolving everything autonomously. That’s consistent with wider guidance, including from OWASP’s work on generative AI security, that AI-assisted code changes need the same scrutiny as human-written ones, not less.
• Read together, the two cases separate two different jobs AI is being asked to do inside IT: Unipol’s is continuous, ongoing operational monitoring; Blue Pearl’s is a bounded, one-off technical debt project. A UK C-suite evaluating either kind of AI investment should ask which job it’s actually buying, since the evidence for what “good” looks like differs sharply between the two.

Two case studies published months apart, one from an Italian insurer with 16.8 million customers, the other from a South African cloud consultancy, land on strikingly similar numbers: work that used to take minutes now takes under two, work that used to take a month now takes days. Neither story is really about a single dramatic AI moment. Both are about applying AI to unglamorous, recurring internal work, IT event monitoring in one case, a Java version upgrade in the other, and both are worth reading precisely rather than as generic proof that “AI speeds things up.”

Unipol: from 26 percent coverage to 100

Unipol Assicurazioni, one of Italy’s leading insurance groups, serving 16.8 million customers through the country’s largest agency network, needed a more secure and scalable IT foundation to support enterprise-wide AI adoption as operational demands grew. Unipol partnered with IBM to build NAMI, next automation monitoring insurance, an AI-powered automation platform built on IBM’s watsonx ecosystem. IBM watsonx.ai provided the AI studio for generative AI use cases tailored to Unipol’s operations, with natural language interfaces delivering instant insights and streamlined workflows. IBM watsonx Orchestrate handled agentic AI workflows and natural language interactions, IBM Cloud Pak for Data supported data preparation, and IBM Cloud Pak for AIOps gave Unipol unified visibility across its applications. The platform, initially built on IBM Cloud, was migrated to a hybrid cloud using Red Hat OpenShift for scalability, with IBM Fusion Hyper-Converged Infrastructure providing the on-premises architecture needed to meet Unipol’s governance standards. NAMI takes a multi-model approach, combining Meta’s Llama, Mistral’s Pixtral AI and IBM Granite foundation models to match each task to the right model. “With NAMI, we’re building an AI ecosystem that not only modernizes IT operations but also delivers predictive insights to guide smarter business decisions,” said Riccardo Perrotta, Head of IT Service Operations & Infrastructure at Unipol Assicurazioni.

NAMI cut event response times from 20 minutes to 90 seconds and, in its first two months, analysed more than 800 system events, autonomously resolving most and escalating only 538. Unipol’s control room previously monitored just 26 percent of events in near real-time; since June 2025, NAMI has covered 100 percent, freeing technical staff to focus on higher-value work. Time for accounting and claims processes dropped from 21 to 18 hours, and incident handling time fell by 90 percent. Those figures are Unipol and IBM’s own reporting rather than an independently audited result, which is worth stating plainly rather than glossing over, but the underlying architecture, a named multi-model platform running across a hybrid cloud with a specific governance rationale, gives the numbers more grounding than a bare percentage claim usually carries.

Blue Pearl: three days instead of thirty

Blue Pearl, a cloud solutions developer and consulting partner headquartered in South Africa, runs BlueApp, a high-volume consultant-matching platform powered by search, workflow automation and machine learning ranking. As the platform grew in scale and complexity, staying on Java 11 meant missing out on the security fixes and JVM improvements that come with a modern long-term support baseline, while developers increasingly ran into friction from deprecated APIs and dependency incompatibilities that slowed delivery. Similar Java uplifts had historically taken Blue Pearl’s engineers roughly 30 days or more of effort. Blue Pearl used IBM Bob, IBM’s software development lifecycle AI assistant, to accelerate analysis, refactoring and verification while keeping its engineering team in control through governance checkpoints and CI/CD validation. Bob provided architecture-aware guidance aligned with Blue Pearl’s existing design patterns, automated refactoring of deprecated APIs and code hotspots, and flagged dependency-alignment issues and viable upgrade paths, while extending Blue Pearl’s unit and integration test coverage automatically. The uplift itself ran as a structured three-day process: assessment and sequencing on day one, refactoring and dependency upgrades on day two, and testing, performance comparison, security checks and deployment on day three.

“Working with IBM through Bob was a highly collaborative experience,” said Saireshan Govender, Group CEO of Blue Pearl. “The framework brought structure, speed, and confidence to the engagement, enabling us to deliver measurable value for our business and our clients.” The Java 25 uplift was completed in three days rather than the roughly 30 days such work typically took, a 90 percent faster delivery cycle that preserved more than 160 engineering hours and shipped to production with zero post-deployment defects, while response times across key workflows improved by roughly 15 percent.

The version jump itself is worth being precise about. Java 11 is a long-term support release for which Oracle’s premier support for paying subscribers ended in September 2023; it remains supportable under Oracle’s extended support track, with the extended-support uplift fee waived through January 2032, or via free OpenJDK builds from other vendors, so it wasn’t an unpatched or abandoned platform. But running multiple LTS generations behind is a genuine, accumulating source of security and compatibility debt, exactly the kind of deprecated-API and dependency friction the case study describes. Java 25 is the current LTS release, published on 16 September 2025, so Blue Pearl’s uplift moved BlueApp from a platform running multiple LTS generations behind its own premier-support cutoff to the current baseline in three days.

What “fast” actually means here

Unipol’s 90-second event response is worth placing against a wider benchmark rather than judged only against its own 20-minute starting point. Mean-time-to-resolve figures in enterprise IT service management vary enormously by severity and industry, with no single authoritative cross-industry figure; one widely used benchmarking database puts average incident resolution around nine hours, while a commonly cited cross-industry figure for all incidents runs closer to three days, and even high-performing site reliability teams typically aim for under an hour on major incidents rather than under two minutes. Read against that spread, Unipol’s figure looks exceptional rather than simply an improvement on a slow internal baseline, though it remains a company-reported number rather than an externally audited one.

The technical debt Blue Pearl’s upgrade addressed is also a genuinely common problem, not a one-off. A widely cited 2018 survey by Stripe, still frequently referenced in this space despite its age, found developers reporting they spent roughly a third to just over 40 percent of their working week on maintenance and technical debt rather than new feature work. That figure is now several years old and self-reported, so it should be read as broadly illustrative of a persistent industry pattern rather than a precise current statistic, but it’s consistent with the specific friction Blue Pearl describes, deprecated APIs and dependency incompatibilities slowing delivery until the upgrade was actually done.

The caveat that matters for both

Neither case study treats AI as a replacement for human oversight, and there’s good reason a UK C-suite should expect that pattern to hold elsewhere. Guidance from bodies including OWASP’s work on generative AI and large language model security has flagged that AI coding assistants can generate insecure patterns or reference nonexistent dependencies, and that AI-suggested code changes need the same scrutiny as human-written code rather than less, not more. Blue Pearl’s process reflects that directly: governance checkpoints and CI/CD validation ran through all three days, and testing, performance comparison and security checks were a dedicated stage of the process rather than an afterthought. Unipol’s platform, similarly, didn’t attempt to resolve every event on its own, escalating 538 of the roughly 800 it analysed in its first two months to human teams. In both cases, the AI expanded what could be handled quickly, without removing the checkpoint that catches what it shouldn’t handle alone.

Two different jobs, not one trend

It’s worth resisting the temptation to fold both case studies into a single “AI speeds up IT” narrative, because they’re really answering two different questions. Unipol’s NAMI is built for continuous, ongoing operational monitoring, a platform that has to keep performing at 100 percent coverage indefinitely, and its value depends on sustained reliability over time, not a single result. Blue Pearl’s use of Bob was a bounded, one-off technical debt project with a clear start and end point, three days, then done. A UK board weighing a similar investment should ask which of those two problems it’s actually trying to solve, since the evidence for what counts as success, and the ongoing operational commitment required, is different for each. Judged on their own terms, both are credible, specific results rather than vague AI success stories, an Italian insurer that closed a monitoring gap it had lived with for years, and a South African software firm that cleared multiple LTS generations of accumulated technical debt in three days instead of the month it used to take.