{"id":2899,"date":"2026-03-02T17:08:21","date_gmt":"2026-03-02T20:08:21","guid":{"rendered":"https:\/\/suricata.la\/untitled-3\/"},"modified":"2026-03-03T03:01:24","modified_gmt":"2026-03-03T06:01:24","slug":"untitled-3","status":"publish","type":"post","link":"https:\/\/suricata.la\/en\/untitled-3\/","title":{"rendered":"Untitled"},"content":{"rendered":"<h2>Table of Contents<\/h2>\n<ul>\n<li><a href=\"#colt-y-microsoft-optimizan-la-fijacion-de-precios-empresariales\">1. Colt and Microsoft optimize enterprise pricing<\/a>\n<\/li>\n<li><a href=\"#desarrollo-del-motor-de-ia-agentic-por-colt-y-microsoft\">2. Development of the agentic AI engine by Colt and Microsoft<\/a>\n<\/li>\n<li><a href=\"#reduccion-del-tiempo-de-fijacion-de-precios\">3. Reduction of pricing time<\/a>\n<\/li>\n<li><a href=\"#precision-en-la-generacion-de-precios\">4. Accuracy in price generation<\/a>\n<\/li>\n<li><a href=\"#entrenamiento-del-agente-de-ia\">5. Training the AI agent<\/a>\n<\/li>\n<li><a href=\"#estrategia-de-ia-people-first-de-colt\">6. Colt\u2019s \u2018people first\u2019 AI strategy<\/a>\n<\/li>\n<li><a href=\"#impacto-en-la-experiencia-del-cliente\">7. Impact on the customer experience<\/a>\n<\/li>\n<li><a href=\"#opiniones-de-lideres-de-colt-y-microsoft\">8. Views from leaders at Colt and Microsoft<\/a>\n<\/li>\n<li><a href=\"#transformacion-digital-en-telecomunicaciones-el-futuro-de-la-experiencia-del-cliente\">9. Digital Transformation in Telecommunications: The Future of the Customer Experience<\/a>\n<ul>\n<li><a href=\"#innovacion-continua-en-la-generacion-de-cotizaciones\">9.1 Continuous Innovation in Quote Generation<\/a><\/li>\n<li><a href=\"#el-papel-de-la-inteligencia-artificial-en-la-eficiencia-operativa\">9.2 The Role of Artificial Intelligence in Operational Efficiency<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#transformacion-del-servicio-al-cliente-en-telecomunicaciones-con-suricata-cx\">10. Transformation of customer service in telecommunications with Suricata Cx<\/a>\n<ul>\n<li><a href=\"#la-necesidad-de-una-experiencia-del-cliente-optimizada\">10.1 The need for an optimized customer experience<\/a><\/li>\n<li><a href=\"#soluciones-especificas-para-problemas-del-sector\">10.2 Specific solutions for industry problems<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<div class=\"ai-article\">\n<h2 id=\"colt-y-microsoft-optimizan-la-fijacion-de-precios-empresariales\">Colt and Microsoft optimize enterprise pricing<\/h2>\n<ul>\n<li>Colt Technology Services and Microsoft have completed a proof of concept of an <em>agentic<\/em> AI engine to speed up and clarify pricing in complex deals.<\/li>\n<li>The tool reduces the time to generate and share pricing from days to about <strong>10 minutes<\/strong>.<\/li>\n<li>The agent was trained in <strong>three days<\/strong> to cover most of Colt\u2019s markets, with a reported accuracy of <strong>99%<\/strong>.<\/li>\n<li>Quotes are generated with AI, but go through <strong>review by human teams<\/strong> before being sent to the customer.<\/li>\n<li>Colt plans to extend the use of <em>agentic<\/em> AI to more stages of the customer journey, and expects to make the pricing engine available <strong>later this year<\/strong>.<\/li>\n<li>The announcement refers to a <strong>proof of concept<\/strong>; general availability is positioned as a next step.<\/li>\n<\/ul>\n<blockquote>\n<p><strong>Speed and control in pricing<\/strong><br \/>\nIn global B2B pricing, the \u201cwhat changed\u201d is not just using AI, but compressing a flow that typically depends on country-by-country rules, commercial exceptions, and internal validations. In this proof of concept, Colt and Microsoft report two guiding metrics (time and accuracy) and a trust mechanism (human review) so that the leap in speed does not mean losing control.<br \/>\nAs a public reference, the figures \u201c10 minutes,\u201d \u201c3 days,\u201d and \u201c99%\u201d are communicated in Colt\u2019s announcement and in industry coverage that repeats it (e.g., Finextra\/Telecompaper). As this is a pilot, final performance may vary when it is rolled out to more products, markets, or commercial conditions.<\/p>\n<\/blockquote>\n<h2 id=\"desarrollo-del-motor-de-ia-agentic-por-colt-y-microsoft\">Development of the agentic AI engine by Colt and Microsoft<\/h2>\n<p>Colt Technology Services, a global provider of digital infrastructure, and Microsoft have jointly developed an <em>agentic<\/em> AI engine aimed at one of the most friction-filled points in the B2B telecommunications business: pricing complex deals across multiple geographies.<\/p>\n<p>According to the company, the goal of the project is to \u201caccelerate, simplify and clarify\u201d pricing for enterprise customers, especially when international growth introduces market variables, commercial conditions, and service structures that often make the process opaque. The initiative was presented as a proof of concept already validated, and is part of a broader Colt program to apply <em>agentic<\/em> AI throughout the customer lifecycle.<\/p>\n<blockquote>\n<p><strong>Colt\u2013Microsoft collaborative quoting flow<\/strong><br \/>\nHigh-level workflow describing the Colt\u2013Microsoft collaboration in this pricing use case:<br \/>\n1) Input of the\n <\/p>\n<\/blockquote>\n<p>Agreement: customer requirements (sites\/countries, capacity, timelines, SLAs) and commercial constraints.<br \/>\n2) Normalization: mapping to catalog\/services and rules by market (currency, availability, local conditions).<br \/>\n3) Generation: the agent proposes an initial quote and its components (service lines, assumptions, and dependencies).<br \/>\n4) Checks: consistency validations (across countries, discounts, compatibilities) and exception detection.<br \/>\n5) Human review: specialized teams review and adjust before issuing.<br \/>\n6) Issuance and communication: delivery to the customer faster and, ideally, with more clarity about what is included.<br \/>\nPoints where failures tend to appear in the real world: incomplete agreement data, exceptions not encoded in rules, or last-minute changes in availability\/conditions by country.<\/p>\n<\/blockquote>\n<h2 id=\"reduccion-del-tiempo-de-fijacion-de-precios\">Reduction of pricing time<\/h2>\n<p>In this context, \u201cpricing\u201d refers mainly to the <strong>generation of quotes<\/strong> and their communication to the customer in complex enterprise agreements.<\/p>\n<p>The most striking data point from the pilot is the jump in speed: the engine reduces the time spent developing and sharing prices <strong>from days to 10 minutes<\/strong>. In an environment where global infrastructure projects can take weeks to obtain an accurate commercial proposal, this compression of the quoting cycle aims to eliminate a classic bottleneck between presales, engineering, and commercial teams. (The 10-minute figure is reported in public communications of the announcement and its industry coverage, such as Finextra\/Telecompaper.)<\/p>\n<p>The operational promise is twofold: on the one hand, to speed up customer decision-making; on the other, to allow Colt to respond more agilely in competitive processes where response time can tip the balance.<\/p>\n<div class=\"cc-table-wrap\" style=\"overflow-x:auto;max-width:100%;-webkit-overflow-scrolling:touch\">\n<table class=\"cc-table\" style=\"width:100% !important;max-width:100% !important;min-width:540px;table-layout:auto !important;white-space:normal !important;word-break:normal;overflow-wrap:anywhere;hyphens:auto\">\n<thead>\n<tr>\n<th>Aspect<\/th>\n<th>Before (traditional process)<\/th>\n<th>After (PoC with <em>agentic<\/em> AI)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Time to develop and share pricing<\/td>\n<td>Days (and in global projects, it can extend to weeks for a complete proposal)<\/td>\n<td>~<strong>10 minutes<\/strong> (as reported by Colt in the pilot)<\/td>\n<\/tr>\n<tr>\n<td>Internal iterations<\/td>\n<td>Several rounds between presales\/engineering\/commercial<\/td>\n<td>Fewer rounds if the first proposal comes out consistent<\/td>\n<\/tr>\n<tr>\n<td>Consistency across markets<\/td>\n<td>Depends on teams and local rules applied manually<\/td>\n<td>Greater standardization if the rules are encoded and validated<\/td>\n<\/tr>\n<tr>\n<td>Final checkpoint<\/td>\n<td>Human review and internal approvals<\/td>\n<td>Human review before issuance (maintained)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 id=\"precision-en-la-generacion-de-precios\">Accuracy in price generation<\/h2>\n<p>Colt claims that the agent delivers complex pricing with <strong>99%<\/strong> accuracy (as reported by the company itself in the announcement). In practical terms, this aims to reduce inconsistencies between markets, minimize manual errors, and improve the comparability of proposals when a customer requests connectivity and services in multiple countries.<\/p>\n<p>Even so, the workflow design maintains human control: <strong>specialized Colt teams review them before issuing<\/strong>. In practice, this positions AI as the initial generator and the teams as the final validation before sharing the quote with the customer. This \u201chuman-in-the-loop\u201d approach aims to sustain output quality and strengthen customer trust in a process that, by its nature, has a direct impact on budgeting, planning, and cost control.<\/p>\n<blockquote>\n<p><strong>The real scope of the \u201c99%\u201d<\/strong><br \/>\nWhat the \u201c99%\u201d contributes (and what it doesn\u2019t) in a pricing case like this:<br \/>\n&#8211; Quality signal: it suggests that, within the scope of the pilot, most generated quotes match the expected result under the available rules and data.<br \/>\n&#8211; Operational control: the text already indicates that the quote is not sent \u201cas is\u201d; it goes through review by human teams before being issued, which reduces the risk of an error reaching the customer.<br \/>\n&#8211; Important limitation: the percentage depends on the scope (products\/markets\/rules) and on how \u201caccuracy\u201d is measured (for example, matching line items, amounts, discounts, or assumptions). That\u2019s why it\u2019s relevant that this is a proof of concept and that human control is maintained.<\/p>\n<\/blockquote>\n<h2 id=\"entrenamiento-del-agente-de-ia\">Training the AI agent<\/h2>\n<p>The project also stands out for its rapid rollout: Colt and Microsoft trained the agent in <strong>three days<\/strong> to deliver pricing for complex deals in <strong>most of Colt\u2019s markets<\/strong>.<\/p>\n<p>The collaboration combines Colt\u2019s domain knowledge\u2014pricing, commercial terms, and telecom-specific nuances\u2014with Microsoft\u2019s cloud AI capabilities. The result, according to both companies, is a system capable of automating highly complex tasks and scaling process consistency without relying exclusively on manual cycles.<\/p>\n<blockquote>\n<p><strong>Fast track to operational pricing<\/strong><br \/>\nHow a \u201ctrained in 3 days\u201d milestone is usually achieved in a pricing agent (stage-by-stage view):<br \/>\n1) Data and rules (preparation): catalog, country-by-country rules, discount policies, technical dependencies, and permitted assumptions.<br \/>\n2) Configuration\/training: tuning the agent to interpret requirements of the<\/p>\n<\/blockquote>\n<p>agreement and apply rules consistently (often more \u201corchestration + rules + prompts\u201d than training from scratch).<br \/>\n3) Validation: testing with real or historical cases, detecting exceptions, and adjusting rules\/templates.<br \/>\n4) Controlled deployment: use in a pilot with mandatory human review and tracking of recurring errors.<br \/>\nThis sequence helps explain why the time can be so short in a PoC: it speeds up when well-defined rules and catalogs already exist, and when deployment is limited and supervised.<\/p>\n<\/blockquote>\n<h2 id=\"estrategia-de-ia-people-first-de-colt\">Colt\u2019s \u2018people first\u2019 AI strategy<\/h2>\n<p>The pricing engine is one of the first developments within Colt\u2019s AI strategy called <strong>\u201cpeople first\u201d<\/strong>. The company describes it as an approach to create <strong>safe, scalable, and responsible<\/strong> AI ecosystems for employees and customers, with the goal that both can operate with confidence in a future defined by AI.<\/p>\n<p>In practice, the pricing use case serves as a showcase: automation where it brings speed and consistency, with human oversight to ensure the final standard. Colt also notes that it is exploring applying <em>agentic<\/em> AI from pricing to <em>onboarding<\/em> and beyond.<\/p>\n<blockquote>\n<p><strong>Key trade-offs in enterprise pricing<\/strong><br \/>\nTrade-offs that this \u201cpeople first\u201d approach tries to resolve in enterprise pricing:<br \/>\n&#8211; Speed vs control: compressing timelines to minutes requires maintaining a final validation point to avoid costly errors.<br \/>\n&#8211; Consistency vs commercial flexibility: standardizing rules improves comparability, but exceptions (discounts, bundles, local conditions) must be managed without breaking the model.<br \/>\n&#8211; Automation vs expert knowledge: the agent speeds up what\u2019s repeatable; human teams are reserved for edge cases, negotiation, and verification.<br \/>\n&#8211; Scale vs trust: the more markets and products are covered, the more important the traceability of assumptions and review before issuing.<\/p>\n<\/blockquote>\n<h2 id=\"impacto-en-la-experiencia-del-cliente\">Impact on the customer experience<\/h2>\n<p>The customer experience is the narrative \u0645\u062d\u0648\u0631 of the announcement. Colt argues that, as its customers grow and expand globally, pricing complexity and lack of transparency can become \u201cconfusing and costly.\u201d Reducing quote time to minutes and improving process clarity aims to tackle two fronts: <strong>speed<\/strong> (fast responses) and <strong>understanding<\/strong> (less opacity in how the price is built).<\/p>\n<p>In business terms, a shorter quoting cycle can translate into less friction in IT purchases, greater planning capacity, and a perception of a provider that is more \u201ceasy to do business with,\u201d especially in multinational projects where the customer\u2019s internal coordination already is<\/p>\n<p>complex.<\/p>\n<blockquote>\n<p><strong>Visible improvements in customer experience<\/strong><br \/>\nConcrete signs of CX improvement that a customer usually notices when pricing is accelerated and standardized:<br \/>\n&#8211; Initial response in minutes (not days) to move internal decisions forward.<br \/>\n&#8211; Fewer back-and-forths due to inconsistencies between countries or service lines.<br \/>\n&#8211; More comparable proposals across alternatives (same structure, same assumptions).<br \/>\n&#8211; Greater clarity on what the price includes (components, dependencies, conditions).<br \/>\n&#8211; Fewer surprises in later reviews thanks to human validation before issuing.<\/p>\n<\/blockquote>\n<h2 id=\"opiniones-de-lideres-de-colt-y-microsoft\">Views from leaders at Colt and Microsoft<\/h2>\n<p>Frank Miller, Colt\u2019s <em>chief AI and platforms officer<\/em>, framed the problem as structural: enterprise IT purchasing \u201chas always been complex,\u201d and global projects can take weeks to obtain accurate pricing, which \u201ccan slow delivery.\u201d His thesis is that AI can break that pattern: by accelerating and clarifying pricing, customers can focus sooner on their objectives, while Colt maintains connectivity.<\/p>\n<p>Miller added that Colt will continue innovating with Microsoft to build an <em>agentic<\/em> application \u201cfactory\u201d in telecommunications, focused on the best possible experience: \u201csimple, accurate, secure and reliable.\u201d<\/p>\n<p>From Microsoft, Rick Lievano, <em>Worldwide CTO, Telco, Media &amp; Gaming<\/em>, underscored the potential of <em>agentic<\/em> AI to transform complex enterprise workflows in telecom, where \u201cspeed, accuracy and scale\u201d are critical. In his view, the combination of Colt\u2019s industry expertise with Microsoft\u2019s cloud AI capabilities helps automate complex processes, improve consistency, and give customers faster access to the information they need to decide.<\/p>\n<h2 id=\"transformacion-digital-en-telecomunicaciones-el-futuro-de-la-experiencia-del-cliente\">Digital Transformation in Telecommunications: The Future of the Customer Experience<\/h2>\n<p>Colt and Microsoft\u2019s proof of concept illustrates a broader trend in telecommunications: shifting automation from internal operational tasks to high-impact moments in the commercial relationship, such as quoting, onboarding, and service management. In a market where connectivity has become critical and global, differentiation shifts toward the <strong>simplicity<\/strong> and <strong>speed<\/strong> with which a provider enables buying, deploying, and operating.<\/p>\n<h3 id=\"innovacion-continua-en-la-generacion-de-cotizaciones\">Continuous Innovation in Quote Generation<\/h3>\n<p>Enterprise quoting has historically been a handcrafted process: multiple systems, country-by-country rules, commercial exceptions, and internal validations. The bet on an agent that condenses that work into minutes suggests a new standard of \u201ctime of\n<\/p>\n<p>response\u201d in pre-sales, especially for multinational deals.<\/p>\n<p>If the pilot\u2019s performance holds up in production, the impact will not be only internal efficiency: it could also redefine customer expectations about how long a provider should take to deliver a complete, comparable proposal.<\/p>\n<h3 id=\"el-papel-de-la-inteligencia-artificial-en-la-eficiencia-operativa\">The Role of Artificial Intelligence in Operational Efficiency<\/h3>\n<p>Beyond the pricing use case, Colt is already aiming to extend <em>agentic<\/em> AI to other stages of the customer journey. In telecom, where processes often cut across silos (sales, engineering, provisioning, support), goal-oriented automation\u2014capable of executing chained tasks\u2014promises to reduce idle time and improve consistency.<\/p>\n<p>The key, as this deployment shows, will be balancing automation with expert control: using AI to speed up and standardize, without giving up human review in decisions that affect cost, feasibility, and service commitment.<\/p>\n<div class=\"cc-table-wrap\" style=\"overflow-x:auto;max-width:100%;-webkit-overflow-scrolling:touch\">\n<table class=\"cc-table\" style=\"width:100% !important;max-width:100% !important;min-width:540px;table-layout:auto !important;white-space:normal !important;word-break:normal;overflow-wrap:anywhere;hyphens:auto\">\n<thead>\n<tr>\n<th>Customer journey area<\/th>\n<th>What typically hurts today (B2B telecom)<\/th>\n<th>What can change with automation\/<em>agentic<\/em> AI<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Quotation \/ pricing<\/td>\n<td>Rules by country, multiple approvals, long lead times<\/td>\n<td>Faster response, consistency across markets, less rework<\/td>\n<\/tr>\n<tr>\n<td>Onboarding \/ activation<\/td>\n<td>Handoffs between teams, lack of status visibility<\/td>\n<td>Task orchestration, milestone tracking, fewer waits<\/td>\n<\/tr>\n<tr>\n<td>Provisioning \/ delivery<\/td>\n<td>Technical dependencies and multinational coordination<\/td>\n<td>Automatic task sequencing and blockage alerts<\/td>\n<\/tr>\n<tr>\n<td>Support \/ incidents<\/td>\n<td>Prioritization, repeated information, resolution times<\/td>\n<td>Intelligent routing, unified context, more consistent communication<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 id=\"transformacion-del-servicio-al-cliente-en-telecomunicaciones-con-suricata-cx\">Transformation of customer service in telecommunications with Suricata Cx<\/h2>\n<h3 id=\"la-necesidad-de-una-experiencia-del-cliente-optimizada\">The need for an optimized customer experience<\/h3>\n<p>In telecommunications, the customer experience is defined by how quickly they get answers, the clarity of the information, and consistency across channels. The pressure to reduce timelines\u2014from quotation to incident resolution\u2014has made service optimization a competitive factor.<\/p>\n<h3 id=\"soluciones-especificas-para-problemas-del-sector\">Specific solutions for industry problems<\/h3>\n<p>specific to industry challenges<\/h3>\n<p>Operators and infrastructure providers often face fragmented processes, multiple tools, and dependencies between teams. Specialized customer experience solutions aim to unify workflows, reduce rework, and improve traceability of requests, orders, and cases.<\/p>\n<h3 id=\"casos-de-uso-que-marcan-la-diferencia\">Use cases that make the difference<\/h3>\n<p>Among the most common use cases are: quote acceleration, <em>onboarding<\/em> automation, proactive order tracking, incident management with intelligent prioritization, and consistent customer communication at every service milestone.<\/p>\n<h3 id=\"capacidades-funcionales-que-potencian-la-eficiencia\">Functional capabilities that boost efficiency<\/h3>\n<p>The capabilities that typically add the most value include: process orchestration, automation of repetitive tasks, real-time status dashboards, integration with existing systems, and analytics to identify bottlenecks and opportunities for improvement.<\/p>\n<h3 id=\"por-que-elegir-suricata-cx-para-su-negocio\">Why choose Suricata Cx for your business<\/h3>\n<p>Suricata Cx is positioned as an option for organizations looking to improve operational efficiency and customer experience through simpler workflows, end-to-end visibility, and tools that reduce response times in service operations.<\/p>\n<h3 id=\"el-perfil-ideal-del-cliente-para-suricata-cx\">The ideal customer profile for Suricata Cx<\/h3>\n<p>The typical fit is in telecommunications and B2B services companies with a high volume of requests, multiple teams involved, and a need to standardize processes without losing control, especially in multinational environments or with complex catalogs.<\/p>\n<h3 id=\"valor-estrategico-y-futuro-de-la-experiencia-del-cliente\">Strategic value and the future of customer experience<\/h3>\n<p>The industry\u2019s direction points to \u201cfrictionless\u201d experiences: less waiting, more self-service, greater transparency, and faster decisions. In that context, platforms and automation engines \u2014including AI applied to critical processes\u2014 are consolidating as strategic levers to compete in a market where connectivity, by itself, is no longer enough.<\/p>\n<p>Colt and Microsoft: AI Engine for Pricing in Enterprises shows how agentic AI can compress critical cycles from days to minutes without giving up human control. At Suricata Cx we share that same \u201chuman-in-the-loop\u201d logic applied to telecom operations: automate what is repeatable to gain speed and consistency, and maintain expert oversight where the impact on customer and business demands maximum precision.<\/p>\n<blockquote>\n<p><strong>From Request to Resolution CX<\/strong><br \/>\nTypical \u201crequest-to-resolution\u201d journey in telecom CX operations (where a platform like Suricata Cx often adds value):<br \/>\n1) Intake\n<\/p>\n<\/blockquote>\n<p>omnichannel: the customer opens a request (quote, activation, order, incident) and the minimum context is captured.<br \/>\n2) Classification and routing: it is assigned to a queue\/team according to type, priority, SLA, and dependencies.<br \/>\n3) Orchestration: chained tasks across teams\/systems (sales \u2194 provisioning \u2194 support) with visible statuses.<br \/>\n4) Checkpoints: data validation, milestone confirmation (order accepted, provisioning started, testing, closure).<br \/>\n5) Communication: consistent updates to the customer at each milestone and in case of blockers.<br \/>\n6) Closure and learning: root cause, time per stage, and feedback to reduce rework.<\/p>\n<\/blockquote>\n<p>This analysis is approached from the perspective of an omnichannel CX platform for ISPs and telecom, where goal-oriented automation and human oversight are often the most reliable operational balance to scale without degrading the experience.<\/p>\n<p><em>This article is based on publicly available information at the time of its publication about a proof of concept announced by Colt and Microsoft. The cited metrics (time, accuracy, and training timeline) reflect only the scope of the pilot communicated at that time. With the move to general availability, performance and implementation details may vary by market, product, and operating conditions.<\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Table of contents 1. Colt and Microsoft optimize enterprise pricing 2. Development of the agentic AI engine by Colt and Microsoft 3. Reduction of pricing time 4. Accuracy in price generation 5. Training the AI agent 6. Colt&rsquo;s &lsquo;people first&rsquo; AI strategy 7. Impact on the customer experience &#8230; <\/p>\n<div><a href=\"https:\/\/suricata.la\/untitled-3\/\" class=\"more-link\">Read More<\/a><\/div>\n","protected":false},"author":2,"featured_media":2896,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_metadesc":"","content_central_editor_notes":"","yoast_description":"","footnotes":""},"categories":[48],"tags":[1470,801,858,1471,1472],"class_list":["post-2899","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias","tag-ai-in-pricing","tag-customer-experience","tag-digital-transformation","tag-enterprise-solutions","tag-telecommunications-innovation"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Untitled - Suricata Cx<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/suricata.la\/en\/untitled-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Untitled - Suricata Cx\" \/>\n<meta property=\"og:description\" content=\"Table of contents 1. Colt and Microsoft optimize enterprise pricing 2. Development of the agentic AI engine by Colt and Microsoft 3. Reduction of pricing time 4. Accuracy in price generation 5. Training the AI agent 6. Colt&rsquo;s &lsquo;people first&rsquo; AI strategy 7. 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