<h2>Key Takeaways</h2>
<ul>
<li>Unipol Assicurazioni, one of Italy&#8217;s leading insurers with the country&#8217;s largest agency network, cut its IT event response time from 20 minutes to 90 seconds after building NAMI, an <a rel="nofollow" href="https://www.networksjournal.com/ai">AI</a>-powered operations platform on <a rel="nofollow" href="https://www.ibm.com/solutions/artificial-intelligence">IBM</a>&#8217;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&#8217;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.</li>
<li>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&#8217;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.</li>
<li>Blue Pearl&#8217;s upgrade moved BlueApp from Java 11, a long-term support release for which Oracle&#8217;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&#8217;s not a cosmetic version bump; Java 11 remains patchable under Oracle&#8217;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.</li>
<li>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&#8217;s platform still escalated 538 of the roughly 800 events it analysed in its first two months rather than resolving everything autonomously. That&#8217;s consistent with wider guidance, including from OWASP&#8217;s work on generative AI security, that AI-assisted code changes need the same scrutiny as human-written ones, not less.</li>
<li>Read together, the two cases separate two different jobs AI is being asked to do inside IT: Unipol&#8217;s is continuous, ongoing operational monitoring; Blue Pearl&#8217;s is a bounded, one-off technical debt project. A UK C-suite evaluating either kind of AI investment should ask which job it&#8217;s actually buying, since the evidence for what &#8220;good&#8221; looks like differs sharply between the two.</li>
</ul>
<p>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 &#8220;AI speeds things up.&#8221;</p>
<h2>Unipol: from 26 percent coverage to 100</h2>
<p>Unipol Assicurazioni, one of Italy&#8217;s leading insurance groups, serving 16.8 million customers through the country&#8217;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&#8217;s watsonx ecosystem. IBM watsonx.ai provided the AI studio for generative AI use cases tailored to Unipol&#8217;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&#8217;s governance standards. NAMI takes a multi-model approach, combining Meta&#8217;s Llama, Mistral&#8217;s Pixtral AI and IBM Granite foundation models to match each task to the right model. &#8220;With NAMI, we&#8217;re building an AI ecosystem that not only modernizes IT operations but also delivers predictive insights to guide smarter business decisions,&#8221; said Riccardo Perrotta, Head of IT Service Operations &#038; Infrastructure at Unipol Assicurazioni.</p>
<p>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&#8217;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&#8217;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.</p>
<h2>Blue Pearl: three days instead of thirty</h2>
<p>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&#8217;s engineers roughly 30 days or more of effort. Blue Pearl used IBM Bob, IBM&#8217;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&#8217;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&#8217;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.</p>
<p>&#8220;Working with IBM through Bob was a highly collaborative experience,&#8221; said Saireshan Govender, Group CEO of Blue Pearl. &#8220;The framework brought structure, speed, and confidence to the engagement, enabling us to deliver measurable value for our business and our clients.&#8221; 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.</p>
<p>The version jump itself is worth being precise about. Java 11 is a long-term support release for which Oracle&#8217;s premier support for paying subscribers ended in September 2023; it remains supportable under Oracle&#8217;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&#8217;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&#8217;s uplift moved BlueApp from a platform running multiple LTS generations behind its own premier-support cutoff to the current baseline in three days.</p>
<h2>What &#8220;fast&#8221; actually means here</h2>
<p>Unipol&#8217;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&#8217;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.</p>
<p>The technical debt Blue Pearl&#8217;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&#8217;s consistent with the specific friction Blue Pearl describes, deprecated APIs and dependency incompatibilities slowing delivery until the upgrade was actually done.</p>
<h2>The caveat that matters for both</h2>
<p>Neither case study treats AI as a replacement for human oversight, and there&#8217;s good reason a UK C-suite should expect that pattern to hold elsewhere. Guidance from bodies including OWASP&#8217;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&#8217;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&#8217;s platform, similarly, didn&#8217;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&#8217;t handle alone.</p>
<h2>Two different jobs, not one trend</h2>
<p>It&#8217;s worth resisting the temptation to fold both case studies into a single &#8220;AI speeds up IT&#8221; narrative, because they&#8217;re really answering two different questions. Unipol&#8217;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&#8217;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&#8217;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.</p>


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

<h2>Key Takeaways</h2>
<ul>
<li>Humana&#8217;s Provider Services Conversational Voice Agent, built on <a rel="nofollow" href="https://www.ibm.com/solutions/artificial-intelligence">IBM</a> Watson technology starting in 2016, is one of the longest continuously running enterprise conversational <a rel="nofollow" href="https://www.networksjournal.com/ai">AI</a> deployments referenced in NetworksJournal&#8217;s case-study archive, automating routine benefits and eligibility calls from healthcare providers rather than routing them to human agents.</li>
<li>The case study itself contains no measured outcome figure, no call-deflection percentage, no cost saving, only a description of the problem, more than 60% of over a million monthly provider calls were routine questions callers were nonetheless opting to escalate to a human, and the solution built to address it. Some more specific figures, roughly 7,000 live calls a day and a claimed one-third reduction in call-handling costs, appear in third-party marketing materials rather than an audited Humana disclosure, and should be read with that caveat attached.</li>
<li>This narrow, unglamorous use case has quietly outlasted IBM&#8217;s much larger, much more publicised Watson Health ambitions. IBM invested more than $4 billion building Watson Health from 2015 onward, including a partnership with MD Anderson Cancer Center that was shelved after roughly $62 million in spending, and ultimately sold most of Watson Health&#8217;s assets to Francisco Partners in 2022 for roughly $1 billion. Humana&#8217;s administrative voice agent, a considerably narrower application of the underlying technology, is still running.</li>
<li>Separately, and importantly distinctly, Humana has faced active litigation over a different AI system entirely: nH Predict, a predictive length-of-stay algorithm developed by naviHealth, owned by UnitedHealth Group&#8217;s Optum. Humana itself was separately sued in Barrows et al. v. Humana, Inc., over its own use of the tool in Medicare Advantage post-acute care coverage decisions; a federal judge granted Humana&#8217;s motion to dismiss in part and denied it in part in August 2025, and breach of contract and related claims remain active. This is a wholly different AI system, built by a different company, doing a different job, informing coverage decisions rather than administrative call routing, from the voice agent this piece is otherwise about.</li>
<li>Humana&#8217;s case study describes itself as &#8220;not something to set up and walk away from,&#8221; requiring ongoing monitoring, training and supervision. Nearly a decade on, that description has held up in a way few AI case studies get the chance to prove.</li>
</ul>
<p>Most AI case studies get read once, around the time they&#8217;re published, while the technology and the claims are still new enough to be interesting. Humana&#8217;s Provider Services Conversational Voice Agent is a rarer thing: a deployment old enough, dating to 2016, to actually test whether &#8220;requires ongoing monitoring, training and supervision&#8221; was a genuine commitment or a line inserted for balance. It&#8217;s also a useful lens for a UK C-suite into a broader, more important distinction: what a specific, narrow AI application actually achieved, versus what the much larger platform ambitions built around the same underlying technology, and even the same company, actually delivered.</p>
<h2>What Humana actually built, and what its own case study doesn&#8217;t claim</h2>
<p>Humana, one of the largest health insurance providers in the US, was transferring far too many calls from its interactive voice response system to human agents at outsourced call centres, at significant cost and to the detriment of customer satisfaction. Administrative staff at healthcare providers contact Humana more than one million times a month with questions about members&#8217; health plan benefits and eligibility, and more than 60% of those calls were routine, well-defined pre-service questions that callers were nonetheless opting out of the automated system to ask a human. Humana&#8217;s Provider Services Innovation team was tasked with finding a better way to handle these costly, repetitive calls. Humana began working with IBM Watson in 2016, before conversational assistants were as advanced or widely used as they are today, starting with a three-month proof of concept before building what became the Provider Services Conversational Voice Agent with Watson. The solution combines multiple Watson applications into a single conversational assistant running on IBM Cloud, with watsonx Assistant for Voice running on-premises at Humana, using seven language models and two acoustic models tailored to different types of provider queries. Where the old IVR system might respond to a benefits query with a seven-page fax, Humana&#8217;s own case study says, the Watson-based agent &#8220;can respond with a specific, targeted answer covering eligibility, benefits, claims, authorization and referral information, without routing the caller to a human agent at all.&#8221; &#8220;I&#8217;m excited to keep exploring the infinite possibilities of artificial intelligence,&#8221; said Sara Hines, Director of Provider Experience and Connectivity at Humana. Humana notes that the technology is not something to set up and walk away from: it requires ongoing monitoring, training and supervision to keep delivering accurate answers as provider queries and Humana&#8217;s plans continue to evolve.</p>
<p>Worth being precise about what&#8217;s missing: the case study describes the problem in detail and the technical build in detail, but contains no stated outcome metric at all, no percentage of calls successfully deflected from human agents, no cost saving figure, no accuracy rate. More specific numbers do circulate, roughly 7,000 live provider calls handled a day and a claimed one-third reduction in call-handling costs compared with the prior IVR system, but they appear in third-party marketing case studies rather than an audited or dated Humana disclosure, and should be treated as directionally plausible rather than independently confirmed. Separately, the case study&#8217;s own description of Humana as serving &#8220;more than 13 million customers&#8221; is worth flagging as likely dated or referring to a specific product line rather than total enterprise membership: Humana&#8217;s own disclosed figures put total medical and specialty membership well above that number for most of the period this system has been running, closer to 19 to 20 million combined in more recent filings.</p>
<h2>The narrow deployment that outlasted the ambitious one</h2>
<p>The most instructive fact about this case study doesn&#8217;t concern Humana at all; it concerns what happened to the broader IBM Watson platform underneath it. IBM launched Watson Health as a dedicated business unit in April 2015, the year before Humana&#8217;s own project began, built on more than $4 billion in acquisitions including Merge Healthcare and Truven Health Analytics. Its flagship ambition was Watson for Oncology, an AI system meant to help recommend cancer treatments, developed in partnership with MD Anderson Cancer Center from 2013. That partnership was shelved in 2016 and 2017 after roughly $62 million in spending, amid documented concerns about unsafe or unreliable treatment recommendations and project governance. IBM ultimately sold most of Watson Health&#8217;s assets to private equity firm Francisco Partners in a deal that closed in June 2022, for roughly $1 billion, a fraction of what IBM had put into building it; the divested business became a new company, Merative.</p>
<p>Humana&#8217;s voice agent is a meaningfully narrower application of the underlying Watson conversational technology than the oncology ambitions that failed, built for a well-bounded, well-defined administrative task rather than open-ended clinical judgement, and it&#8217;s still running nearly a decade later, long after Watson Health&#8217;s flagship healthcare ambitions were sold off for a fraction of their cost. That contrast is genuinely instructive for any UK C-suite weighing its own AI investment: the version of &#8220;AI in healthcare&#8221; that succeeded here wasn&#8217;t the one attempting to replace clinical judgement, it was the one automating a well-understood, repetitive administrative task with a clear, measurable job to do.</p>
<h2>A separate, distinct, and important story: what this case study is not about</h2>
<p>It&#8217;s necessary to be precise here, because Humana has also been the subject of separate, genuinely serious litigation involving a different AI system entirely, and conflating the two would be a real disservice to readers. nH Predict is a predictive length-of-stay algorithm developed by naviHealth, a post-acute care management company owned by UnitedHealth Group&#8217;s Optum division, not an IBM product and not related to the Provider Services Conversational Voice Agent covered in this piece. Humana separately licensed and used a version of this tool, and was sued in Barrows et al. v. Humana, Inc., filed in the Western District of Kentucky, over allegations that the tool was used to prematurely deny or limit Medicare Advantage members&#8217; coverage for post-acute and rehabilitation care. In August 2025, Judge Rebecca Grady Jennings granted Humana&#8217;s motion to dismiss in part, dismissing several state unfair-practices and bad-faith insurance claims with prejudice, while allowing breach of contract, breach of the implied covenant of good faith, unjust enrichment and common-law fraud claims to proceed. The litigation remains active. UnitedHealth Group, separately named in a related Minnesota suit over the same tool, has disputed that nH Predict itself makes coverage determinations, saying the tool only informs them. This is a wholly separate AI system, developed by a different company, applied to a fundamentally different and higher-stakes decision, clinical coverage determination rather than administrative call routing, and it should never be read as part of the same story as the voice agent this piece otherwise describes.</p>
<h2>What a UK C-suite should actually take from Humana&#8217;s two, very different AI stories</h2>
<p>Read together, and kept carefully distinct, Humana&#8217;s two AI stories say something genuinely useful about where enterprise AI risk and reward actually concentrate. The narrow, well-bounded, low-stakes automation, answering a provider&#8217;s routine benefits question instead of routing it to a human, has run successfully for nearly a decade, survived the collapse of the much larger platform ambition it was built on top of, and required exactly the ongoing maintenance its own case study promised rather than a one-off launch. The high-stakes, judgement-adjacent application, an algorithm intended to inform coverage decisions for vulnerable patients, is the one now generating years of active litigation. Neither outcome was inevitable, and neither should be read as proof that &#8220;AI works&#8221; or &#8220;AI doesn&#8217;t work&#8221; as a blanket verdict. It&#8217;s a reminder that the actual variable worth interrogating, for any board considering a similar deployment, isn&#8217;t which company or which underlying model is involved. It&#8217;s how narrow and well-defined the task actually is, and how much genuine human harm follows if the system gets it wrong.</p>


The Decade-Old AI Deployment Nobody Talks About: What Humana’s 2016 Voice Agent Proves About Maintenance Over Launch

<p>Bridgewater Associates, the asset management firm, had built its security around a traditional perimeter that made remote and mobile access difficult and limited how far cloud collaboration could go. &ldquo;We developed a vision to improve productivity and collaboration and deliver comprehensive mobile access, but to achieve this goal, we needed to overhaul our technology,&rdquo; said Anthony Golia, Head of Productivity and Endpoint Engineering at Bridgewater Associates.</p>
<p>Xantrion, working alongside Microsoft, rebuilt Bridgewater&rsquo;s security model around Zero Trust principles using Microsoft 365, replacing reliance on the corporate firewall with granular, identity-based access controls.</p>
<p>&ldquo;Since implementing a Zero Trust strategy using Microsoft 365 technologies, our employees can fulfill their company duties from anywhere in the world while maintaining tight control over core security needs. This has been extremely valuable now that all of our company is working from home due to COVID-19. Everyone is working from an insecure environment today, and with our Zero Trust strategy in place, we are confident we can keep our data safe,&rdquo; said Igor Tsyganskiy, Chief Technology Officer at Bridgewater Associates.</p>
<p>The new model let staff keep working normally while travelling. &ldquo;Because we collaborated with Microsoft to improve our security controls, I can access my files on OneDrive as if I was in the office. When we went to the World Economic Forum meetings at Davos this year, I accessed my files from my iPad just like I would in the office, so my team could continue to adjust the presentations throughout the conference. In the past, we wouldn&rsquo;t have allowed this kind of access from a public location, making that collaboration more difficult,&rdquo; said Greg Jensen, co-Chief Investment Officer at Bridgewater Associates.</p>


Bridgewater Associates Enables Global Remote Access With a Zero Trust Security Model

<p>Multiloop was building a system to replace single-use shipping packaging with reusable packages, but its early-stage MVP couldn&#8217;t meet retailers&#8217; actual operational needs. The system also suffered layout and performance problems on smaller screens, a real constraint for a product meant to be used by warehouse staff and retail partners on the move.</p>
<p>Sigma Software redesigned the system architecture across Multiloop&#8217;s three core applications, user, admin and warehouse, and extended each with new modules to support real business workflows. In the admin back-office app, the team optimised data retrieval and filtering to cut load time to two seconds and added CRM features covering contracts, package ordering and retailer onboarding. In the warehouse app, a Keyence hardware scanner replaced Scandit to boost scanning speed and reliability, while the user app migrated from Google Maps to OpenStreetMap to cut mapping service costs.</p>
<p>&#8220;Without the expertise, speed, and skills of Sigma Software, we would still be stranded deep in prototyping. The engagement changed our whole product development to be quick in iterating and fast in delivery,&#8221; said Maik Siedschlag, Head of Product at Multiloop GmbH.</p>
<p>Multiloop successfully brought its reusable packaging system to market and has already secured several customer contracts, with the platform now integrating cleanly into retailers&#8217; existing workflows so they can hit sustainability goals without sacrificing operational efficiency.</p>


Multiloop Cuts App Load Time to 2 Seconds While Scaling a Reusable Packaging Platform

<p>Hundreds of thousands of Siemens CT scanners installed worldwide feed into a monitoring platform that tracks device condition and collects usage data for analytics. As that data volume grew, Siemens Healthineers needed to increase the platform&#8217;s scalability and speed up processing, a migration to Azure Cloud that required coordinating across multiple vendors and BigTech providers rather than a single team working in isolation.</p>
<p>Sigma Software&#8217;s team, ECIF-compliant Microsoft partners brought on in April 2023, took on migrating the Usage Analytics component responsible for data collection and CT scanner usage analysis, including typical user flows and error patterns. The team migrated analytics business logic and ETL pipelines feeding Qlik and Power BI, developed a unified template for ETL pipelines using Azure Databricks and Spark, and improved duplicate-removal functionality to use parallel computation, adapting Structured Streaming to work on replaced and updated data rather than only newly added records.</p>
<p>&#8220;Sigma Software has strong Data Engineering competence, and they provided us with an expert team in a very short time thanks to their global presence and wide talent network. We can tell that we work with true professionals who have the right balance of engineering and business mindset. They stick to their commitments and deliver effective solutions, contributing to the success of Siemens Healthineers&#8217; business goals and projects planned together with Microsoft. Open communication and a work culture that aligns with our values are among the reasons why we like working with Sigma Software. We feel that they truly care,&#8221; said Alexey Toryanik, Head of Business Data Analytics at Siemens Healthineers.</p>
<p>Beyond the initial migration, Sigma Software&#8217;s engineers now support a Pattern Detection Engine that reduces time-to-detect for equipment issues, and Qlik dashboards for equipment reliability monitoring that help cut defects and optimise maintenance costs across Siemens Healthineers&#8217; product lines, all while a Sigma Software Scrum Master helps embed agile practices across multiple internal teams working the broader migration.</p>
