{"id":2931,"date":"2026-03-05T08:07:22","date_gmt":"2026-03-05T11:07:22","guid":{"rendered":"https:\/\/suricata.la\/growth-predictions-in-artificial-intelligence\/"},"modified":"2026-03-06T03:01:16","modified_gmt":"2026-03-06T06:01:16","slug":"growth-predictions-in-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/suricata.la\/en\/growth-predictions-in-artificial-intelligence\/","title":{"rendered":"## Growth predictions in artificial intelligence"},"content":{"rendered":"<h2>Table of Contents<\/h2>\n<ul>\n<li><a href=\"#huawei-impulsa-la-ia-para-mejorar-servicios-de-telcos\">1. Huawei pushes AI to improve telco services<\/a>\n<\/li>\n<li><a href=\"#prioridades-para-los-operadores-de-telecomunicaciones\">2. Priorities for telecommunications operators<\/a>\n<\/li>\n<li><a href=\"#consumer-service-transformation-through-ai\">3. Transformation of consumer services through AI<\/a>\n<ul>\n<li><a href=\"#improving-the-home-experience\">3.1 Improving the home experience<\/a><\/li>\n<li><a href=\"#reinvention-of-voice-services\">3.2 Reinvention ofVoice services<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#eficiencia-operativa-y-su-importancia\">4. Operational efficiency and its importance<\/a>\n<\/li>\n<li><a href=\"#monetizacion-de-la-inteligencia-artificial-en-telecomunicaciones\">5. Monetization of artificial intelligence in telecommunications<\/a>\n<\/li>\n<li><a href=\"#casos-de-uso-de-agentes-de-ia-en-redes\">6. Use cases for AI agents in networks<\/a>\n<\/li>\n<li><a href=\"#el-futuro-de-la-experiencia-del-cliente-en-telecomunicaciones\">7. The Future of the Customer Experience in Telecommunications<\/a>\n<ul>\n<li><a href=\"#transformacion-digital-y-su-impacto-en-el-sector\">7.1 Digital Transformation and Its Impact on the Sector<\/a><\/li>\n<li><a href=\"#la-importancia-de-la-automatizacion-en-la-atencion-al-cliente\">7.2 The Importance of Automation in Customer Service<\/a><\/li>\n<li><a href=\"#desafios-y-oportunidades-en-la-implementacion-de-ia\">7.3 Challenges and Opportunities in AI Implementation<\/a><\/li>\n<li><a href=\"#estrategias-para-mejorar-la-retencion-de-clientes\">7.4 Strategies to Improve Customer Retention<\/a><\/li>\n<li><a href=\"#el-papel-de-los-agentes-de-ia-en-la-modernizacion-de-servicios\">7.5 The Role of AI Agents in Service Modernization<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<div class=\"ai-article\">\n<h2 id=\"huawei-impulsa-la-ia-para-mejorar-servicios-de-telcos\">Huawei pushes AI to improve telco services<\/h2>\n<ul>\n<li>Huawei foresees a leap in scale: AI capacity 10 times greater, up to 900 billion agents, and a 1,000x increase in data.<\/li>\n<li>The company places telcos in a privileged position to capture value: network, customer relationship, and large-scale operations.<\/li>\n<li>The bet involves strengthening the core business (home and consumer), increasing \u201cstickiness\u201d with consistent experiences, and gaining internal efficiency.<\/li>\n<li>Monetization, according to Huawei, should progress in phases: first efficiency, then services to enterprises, and finally high-frequency consumer scenarios.<\/li>\n<\/ul>\n<blockquote>\n<p><strong>AI will transform network design<\/strong><br \/>\nMWC 2026 (Barcelona) was the stage where Eric Yang (Huawei, carrier business) put forward a clear thesis: AI will not only add new functions, but will change the way networks are designed, operated, and \u201cexperienced.\u201d<br \/>\nIn this article, the figures and examples are presented as <strong>predictions and cases cited by Huawei<\/strong> (not as market certainties): they serve to understand what pressures could arrive (more data, more automation) and where a telco can capture value first (home\/consumer, consistency across devices, and operational efficiency).<\/p>\n<\/blockquote>\n<p>These lines compile the messages presented by Eric Yang, president of Huawei\u2019s carrier business, during his appearance at Mobile World Congress 2026 (Barcelona).<\/p>\n<p>Huawei sketches a scenario of accelerated expansion of artificial intelligence that, if it materializes, will put pressure on and at the same time benefit operators. At Mobile World Congress 2026 in Barcelona, Eric Yang, president of Huawei\u2019s carrier business, stated that AI will be \u201cincreasingly reliable\u201d and that its capacity will grow by a factor of 10.<\/p>\n<p>The prediction is accompanied by two figures that point to a structural change: the emergence of up to <strong>900 billion AI agents<\/strong> and a <strong>1,000x increase in data<\/strong>. In telecommunications terms, this implies more traffic, greater demands for latency and quality, and a growing need for automation to operate networks and services efficiently.<\/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:720px;table-layout:auto !important;white-space:normal !important;word-break:normal;overflow-wrap:anywhere;hyphens:auto\">\n<thead>\n<tr>\n<th>Signal (according to Huawei \/ Eric Yang at MWC 2026)<\/th>\n<th style=\"text-align: right;\">Cited magnitude<\/th>\n<th>What it means for a telco (practical impact)<\/th>\n<th>Operational question to make it concrete<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Growth in AI capacity<\/td>\n<td style=\"text-align:right;\">10x<\/td>\n<td>More \u201cin-production\u201d use cases (not just pilots) and more automated decisions in the network\/service.<\/td>\n<td>Which processes today are manual due to lack of reliability and could move to supervised automation?<\/td>\n<\/tr>\n<tr>\n<td>Number of AI agents<\/td>\n<td style=\"text-align: right;\">Up to 900 billion<\/td>\n<td>Multiplication of machine-to-machine interactions and assistants acting on behalf of users\/companies.<\/td>\n<td>Does your identity, permissions, and audit architecture support agents acting \u201cfor\u201d the customer?<\/td>\n<\/tr>\n<tr>\n<td>Data growth<\/td>\n<td style=\"text-align: right;\">1,000x<\/td>\n<td>More pressure on transport, storage, observability, and data governance; more value if it is used to prevent incidents.<\/td>\n<td>Which part of the data is truly actionable (telemetry, tickets, QoE) and with what latency do you need to see it?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 id=\"prioridades-para-los-operadores-de-telecomunicaciones\">Priorities for telecommunications operators<\/h2>\n<p>Huawei\u2019s message to telcos can be summed up in three operational and commercial priorities:<\/p>\n<ol>\n<li><strong>Upgrade consumer and home services with AI<\/strong> to strengthen the core business (fixed and mobile connectivity, and associated services).<\/li>\n<li><strong>Offer consistent experiences across devices<\/strong> \u2014from mobile to router, from home to car\u2014 to increase loyalty and reduce churn.<\/li>\n<li><strong>Optimize internal operations<\/strong> before \u201cexporting\u201d AI capabilities to other industries, first validating the impact on costs, quality, and resolution times.<\/li>\n<\/ol>\n<blockquote>\n<p><strong>Priorities turned into action<\/strong><br \/>\nHow to turn the 3 priorities into an actionable plan (without changing the thesis):<br \/>\n&#8211; 1) AI home\/consumer (strengthen the core)<br \/>\n  &#8211; Focus: support and self-management of Wi\u2011Fi\/home, diagnosis and resolution.<br \/>\n  &#8211; Goal: fewer repeat incidents and better perceived quality.<br \/>\n  &#8211; Suggested KPI: first-contact resolution rate (FCR) and reduction in ticket \u201creopens\u201d.<br \/>\n&#8211; 2) Consistent experience across devices (increase \u201cstickiness\u201d)<br \/>\n  &#8211; Focus: experience continuity (mobile-router-TV\/car) and coherent policies (prioritization, profiles, controls).<br \/>\n  &#8211; Goal: the customer feels a \u201csingle\u201d service even if they change screens or networks.<br \/>\n  &#8211; Suggested KPI: churn and adoption of bundles\/premium (as a proxy for perceived value).<br \/>\n&#8211; 3) Internal efficiency first (fund and validate)<br \/>\n  &#8211; Focus: O&amp;M, quality inspection, automation of repetitive tasks.<br \/>\n  &#8211; Goal: lower cost per incident and shorten cycle times.<br \/>\n  &#8211; Suggested KPI: cost per ticket\/order and MTTR (mean time to resolution).<\/p>\n<\/blockquote>\n<p>The underlying thesis is that operators don\u2019t just transport data: they can orchestrate experiences and automate processes at the edge of the network, where AI can act with context and speed.<\/p>\n<h2id=\"consumer-service-transformation-through-ai\">Transformation of consumer services through AI<\/h2>\n<p>Huawei positions the consumer front as the most immediate ground for differentiation, especially in the home and in traditional services such as voice, which can be \u201creinvented\u201d with agents.<\/p>\n<p>In this context, \u201cAI agents\u201d are understood as assistants capable of interacting (for example, by voice), executing operational actions (such as network tests or traffic prioritization) and, when they cannot resolve, escalating the case by opening a ticket and coordinating a visit.<\/p>\n<blockquote>\n<p><strong>Operable Home\/Voice Diagnostic Flow<\/strong><br \/>\nPractical agent flow (home\/voice) with checkpoints to avoid \u201cmagic\u201d and make it operable:<br \/>\n1) Detect<br \/>\n&#8211; Signal: user complaint (\u201cit\u2019s slow\u201d), QoE degradation, or anomaly in telemetry.<br \/>\n&#8211; Checkpoint: confirm context (device, location, time, type of use: gaming\/streaming\/call).<br \/>\n2) Prioritize<br \/>\n&#8211; Action: apply temporary policies (e.g., prioritize latency-sensitive traffic).<br \/>\n&#8211; Checkpoint: log the change and its duration; allow it to be automatically reverted.<br \/>\n3) Test<br \/>\n&#8211; Action: run tests (connectivity, Wi\u2011Fi, latency, loss, call noise).<br \/>\n&#8211; Checkpoint: distinguish \u201cnetwork problem\u201d vs \u201cdevice\/environment problem\u201d to avoid false escalation.<br \/>\n4) Resolve<br \/>\n&#8211; Action: apply safe fixes (guided reboot, channel change, profile adjustment, noise suppression, etc.).<br \/>\n&#8211; Checkpoint: validate with a simple metric (improved latency\/stability or perceived quality) and ask for user confirmation.<br \/>\n5) Escalate (if not resolved)<br \/>\n&#8211; Action: open a ticket with the diagnosis attached, propose a visit window, and coordinate scheduling.<br \/>\n&#8211; Checkpoint: ensure the ticket includes \u201cwhat was tested\u201d and \u201cwhat changed\u201d to avoid repetition and speed up field work.<\/p>\n<\/blockquote>\n<h3 id=\"improving-the-home-experience\">Improving the home experience<\/h3>\n<p>The home emerges as a central opportunity: the customer doesn\u2019t want technical menus or endless calls, but rather for it to \u201cwork.\u201d According to Yang, a <strong>home AI agent<\/strong> could manage network performance with voice commands and act when quality degrades.<\/p>\n<p>The example described: if the home network gets worse, the agent can <strong>prioritize latency-sensitive applications<\/strong> (such as gaming), <strong>automatically test the network<\/strong>, <strong>resolve the problem<\/strong> if possible or, if not, <strong>open a ticket<\/strong>, <strong>schedule a visit<\/strong>, and guide the maintenance process.<\/p>\n<p>Huawei cited <strong>China Telecom<\/strong> as an ongoing case: an upgrade of VIP services with <strong>fiber-to-room<\/strong> packages combined with <strong>AI agents for the home<\/strong>, a formula that blends infrastructure improvement with an experience layer and automated support.<\/p>\n<h3 id=\"reinvention-of-voice-services\">Reinvention ofVoice services<\/h3>\n<p>Voice, historically a mature service under competitive pressure, is presented as a natural candidate for AI. Yang highlighted that AI-powered call agents can <strong>significantly reduce background noise<\/strong> and enable value-added services: <strong>reservations<\/strong>, <strong>translation<\/strong>, <strong>food ordering<\/strong>, or <strong>handling requests<\/strong> during the call.<\/p>\n<p>In this vision, voice stops being just minutes and coverage: it becomes an interface for executing tasks, with the potential to expand the service\u2019s reach and improve the user experience.<\/p>\n<h2 id=\"eficiencia-operativa-y-su-importancia\">Operational efficiency and its importance<\/h2>\n<p>Before promising new revenue streams, Huawei insists on AI\u2019s \u201cfirst dividend\u201d: efficiency. Yang gave a concrete example in installation and maintenance: where manual inspections reached around <strong>2% of users<\/strong>, AI-based systems could review <strong>100%<\/strong>.<\/p>\n<blockquote>\n<p><strong>Real impact of efficiency<\/strong><br \/>\nQuick checklist to measure whether the \u201c2% \u2192 100%\u201d efficiency translates into real impact (and not just coverage):<br \/>\n&#8211; Clear baseline: what were the inspection rate, cost per inspection, and % of faults detected before?<br \/>\n&#8211; Coverage vs. accuracy: if coverage increases, what happens to false positives\/negatives and rework?<br \/>\n&#8211; Cycle time: does the time from installation\/intervention to quality validation go down?<br \/>\n&#8211; Total cost: does the cost per order decrease (including revisits) or is it just shifted to another area?<br \/>\n&#8211; Perceived quality: is it reflected in fewer post-installation incidents and better NPS\/CSAT?<br \/>\n&#8211; Traceability: is there an auditable record of what was inspected, what was detected, and what action was taken?<\/p>\n<\/blockquote>\n<p>The implication is twofold: improved quality (fewer undetected faults) and reduced costs (fewer manual reviews, less rework). In a sector with pressured margins, O&amp;M (operations and maintenance) automation is positioned as a prerequisite for scaling any AI strategy.<\/p>\n<h2 id=\"monetizacion-de-la-inteligencia-artificial-en-telecomunicaciones\">Monetization of artificial intelligence in telecommunications<\/h2>\n<p>Huawei proposes phased monetization, aligned with technological maturity and commercial risk:<\/p>\n<ul>\n<li><strong>Phase 1: highly mature use cases<\/strong> such as energy savings and O&amp;M efficiency, where returns are usually more measurable and faster.<\/li>\n<li><strong>Phase 2: \u201cproductize\u201d internal tools<\/strong> (office, sales, customer service) and turn them into offerings for enterprises, leveraging what has been learned within the operator.<\/li>\n<li><strong>Phase 3: high-frequency, high-fidelity consumer scenarios<\/strong>, where agents are used daily and scale quickly, strengthening the link with theclient.<\/li>\n<\/ul>\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:1080px;table-layout:auto !important;white-space:normal !important;word-break:normal;overflow-wrap:anywhere;hyphens:auto\">\n<thead>\n<tr>\n<th>Phase (according to Huawei)<\/th>\n<th>Typical investment<\/th>\n<th>Main risk<\/th>\n<th>Value horizon<\/th>\n<th>Type of value that tends to dominate<\/th>\n<th>Signal that you\u2019re ready to move on to the next<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1) Efficiency (energy, O&amp;M)<\/td>\n<td>Medium (integration + data + automation)<\/td>\n<td>Incomplete measurement: \u201ctheoretical\u201d savings that don\u2019t make it to P&amp;L<\/td>\n<td>Short<\/td>\n<td>Cost reduction, less rework, better quality<\/td>\n<td>Repeatable savings and stable metrics (cost per order\/ticket, MTTR)<\/td>\n<\/tr>\n<tr>\n<td>2) B2B (turn internal tools into an offering)<\/td>\n<td>Medium\u2013high (product, support, go-to-market)<\/td>\n<td>Product fit: what\u2019s internal isn\u2019t always sellable as-is<\/td>\n<td>Medium<\/td>\n<td>New revenue from services\/solutions and differentiation<\/td>\n<td>Standardized internal use cases + support capability and SLAs<\/td>\n<\/tr>\n<tr>\n<td>3) High-frequency consumer use ( \u201cdaily\u201d agents)<\/td>\n<td>High (experience, ecosystem, scaling)<\/td>\n<td>User trust and omnichannel operations complexity<\/td>\n<td>Medium\u2013long<\/td>\n<td>Retention, ARPU via bundles\/premium, churn reduction<\/td>\n<td>Consistent, low-friction experience; automation with clear human scaling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The order is not accidental: first the transformation is funded with efficiencies, then the B2B catalog is expanded, and finally massive scale in consumer is pursued.<\/p>\n<h2 id=\"casos-de-uso-de-agentes-de-ia-en-redes\">Use cases for AI agents in networks<\/h2>\n<p>Beyond the rhetoric, the use cases described point to a more autonomous, intent-oriented network:<\/p>\n<ul>\n<li><strong>Automatic diagnosis and resolution in the home<\/strong>: testing, traffic prioritization, opening incidents, and coordinating visits.<\/li>\n<li><strong>AI-assisted quality inspection<\/strong> in deployments and interventions, increasing control coverage from 2% to 100% according to the cited example.<\/li>\n<li><strong>Enhanced voice agents<\/strong>: noise reduction and task execution (translation, bookings, orders), integrating services on top of the connectivity layer.<\/li>\n<\/ul>\n<blockquote>\n<p><strong>Cases and expected results<\/strong><br \/>\nMini catalog (what it is, what it needs, and what result to expect) based on the cited examples:<br \/>\n&#8211; Home: self-diagnosis + prioritization<br \/>\n  &#8211; Example: prioritize gaming when quality drops, run tests, and decide whether it\u2019s resolved or a visit is scheduled.<br \/>\n  &#8211; Prerequisites: CPE\/Wi\u2011Fi telemetry, QoS policies, integration with CRM\/ticketing and field scheduling.<br \/>\n  &#8211; Expected result: fewer repeat calls<\/p>\n<\/blockquote>\n<p>and fewer technician \u201cwalkabouts\u201d without a diagnosis.<br \/>\n&#8211; O&amp;M: automated quality inspection<br \/>\n  &#8211; Example: moving from partial manual inspection (~2%) to broad review (up to 100% in Yang\u2019s example).<br \/>\n  &#8211; Prerequisites: codified quality criteria, installation\/maintenance data, sampling and auditing capability.<br \/>\n  &#8211; Expected result: early fault detection and reduced post-intervention rework.<br \/>\n&#8211; Voice: call agent with noise suppression + tasks<br \/>\n  &#8211; Example: reduce background noise and enable actions (bookings, translation, orders) during the call.<br \/>\n  &#8211; Prerequisites: real-time audio pipeline, integration with services\/partners, permission controls and action logging.<br \/>\n  &#8211; Expected result: better perceived quality and expansion of the voice service\u2019s \u201creach.\u201d<\/p>\n<\/blockquote>\n<p>The cross-cutting idea is that agents don\u2019t just \u201crespond\u201d: they act, coordinate, and close operational loops, from the user experience to the back end.<\/p>\n<h2 id=\"el-futuro-de-la-experiencia-del-cliente-en-telecomunicaciones\">The Future of the Customer Experience in Telecommunications<\/h2>\n<h3 id=\"transformacion-digital-y-su-impacto-en-el-sector\">Digital Transformation and Its Impact on the Sector<\/h3>\n<p>Huawei\u2019s roadmap suggests that digital transformation in telecommunications is entering a phase where AI stops being an add-on and becomes a structural layer: in the network, in operations, and in the relationship with the customer. With more data and more agents, the differentiator will shift toward who automates better and who turns that automation into experience.<\/p>\n<h3 id=\"la-importancia-de-la-automatizacion-en-la-atencion-al-cliente\">The Importance of Automation in Customer Service<\/h3>\n<p>AI agents promise to reduce friction: fewer steps, less waiting, and more first-contact resolution. In the approach presented, customer service is integrated with operations: if there\u2019s a problem, the agent doesn\u2019t just inform, but tests, prioritizes, fixes, or escalates with a ticket and schedule.<\/p>\n<h3 id=\"desafios-y-oportunidades-en-la-implementacion-de-ia\">Challenges and Opportunities in AI Implementation<\/h3>\n<p>it implies opportunities, but also complexity: integrating agents with network systems, field processes, and service channels. The opportunity for telcos is to turn that complexity into a competitive advantage based on their infrastructure and operational capillarity.<\/p>\n<h3 id=\"estrategias-para-mejorar-la-retencion-de-clientes\">Strategies to Improve Customer Retention<\/h3>\n<p>Huawei links retention to two levers: <strong>improving the core service<\/strong> (home and connectivity) and <strong>ensuring consistency across devices<\/strong>. If the customer perceives that the network \u201cself-manages\u201d and that the experience is uniform, the likelihood of staying and subscribing to premium services increases.<\/p>\n<h3id=\"el-papel-de-los-agentes-de-ia-en-la-modernizacion-de-servicios\">The Role of AI Agents in Service Modernization<\/h3>\n<p>In Yang\u2019s view, AI agents are the engine of modernization: they turn connectivity into a platform for actions\u2014from optimizing an online gaming session to managing a voice reservation\u2014and enable operators to move from selling access to selling outcomes. In a mature market, that transition can make the difference between competing on price or competing on experience.<\/p>\n<blockquote>\n<p><strong>Key indicators at 12 months<\/strong><br \/>\nWhat to watch over the next 6\u201312 months to know whether this vision is becoming \u201creal\u201d at a telco:<br \/>\n&#8211; Operational signals: an increase in closed-loop automations (detect\u2192act\u2192verify) without human intervention in repeatable cases.<br \/>\n&#8211; Customer signals: sustained improvement in FCR and fewer reopenings; less friction at home (Wi\u2011Fi) and in voice.<br \/>\n&#8211; Network\/data signals: more usable telemetry in near real time and better traceability of the agent\u2019s decisions.<br \/>\n&#8211; Business signals: pilots that move to deployments with KPIs published internally (cost per ticket\/order, MTTR, churn, bundle adoption).<\/p>\n<\/blockquote>\n<p>AI growth at Huawei: Opportunities for Telcos focuses on how agents, automation, and consistency across devices can turn operational complexity into a better experience and higher retention. At Suricata Cx, we work precisely on bringing that vision down to earth in real omnichannel operations for telcos and ISPs, with AI applied to resolution and efficiency without losing human control where it matters.<\/p>\n<p>In practice, that \u201cbringing down to earth\u201d usually depends on two pieces: integrating the agents with systems and processes (tickets, field scheduling, customer\/service data) and designing flows where automation resolves what\u2019s predictable and escalates transparently when human intervention is required.<\/p>\n<p><em>The figures and examples attributed to Huawei are based on public statements made at MWC 2026 and reflect the manufacturer\u2019s own vision and predictions. In practice, the pace of adoption and results may vary depending on systems integration, data quality, and each operator\u2019s operations. This information corresponds to what was publicly available at the time of writing and may be updated if new data emerges.<\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Table of contents 1. Huawei drives AI to improve telco services 2. Priorities for telecommunications operators 3. Transformation of consumer services through AI 3.1 Improving the home experience 3.2 Reinventing voice services 4. Operational efficiency and its importance 5. Monetizing artificial intelligence in telecommunications 6. Use cases of &#8230; <\/p>\n<div><a href=\"https:\/\/suricata.la\/predicciones-de-crecimiento-en-inteligencia-artificial\/\" class=\"more-link\">Read More<\/a><\/div>\n","protected":false},"author":2,"featured_media":2928,"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":[1494,871,801,908,802],"class_list":["post-2931","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias","tag-ai-agents","tag-artificial-intelligence","tag-customer-experience","tag-operational-efficiency","tag-telecommunications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>## Growth predictions in artificial intelligence - 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\/growth-predictions-in-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"## Growth predictions in artificial intelligence - Suricata Cx\" \/>\n<meta property=\"og:description\" content=\"Table of contents 1. 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