Technology Trends 2019: IT, PCs and Server Hardware

What’s important to remember about 2019 — and why it matters now

2019 is often remembered as the year when many IT solutions stopped being “experimental” and became standard practice. If you’re planning to refresh PCs, servers or infrastructure today, it’s useful to understand habits and expectations that formed then. This helps avoid overpaying for trendy features and, conversely, not missing what became baseline.

By “2019 trends” we mean practical shifts that affected purchasing and operations: changes in processors and platforms, where data centers headed, how clouds and hybrid models matured, what happened in cybersecurity, and why supply and origin of equipment became more visible.

This is an overview without “vendor wars” and without naming a single winner. In real life organizations rarely build IT around one brand. Decisions are made based on a mix of factors: lifecycle, support, compatibility, risks, budget, regulatory and procurement requirements. The focus is therefore on conclusions that work regardless of vendor.

You can read the text as a navigation by tasks:

• For office workstations — see the PC section and common selection mistakes.
• Need performance and reliability — go to server hardware and data‑center infrastructure.
• Building hybrid or already using cloud services — read the cloud and AI sections.
• Need to pass audits and reduce risks — don’t miss cybersecurity and supply topics.

At the end there’s a short checklist and an example of turning conclusions into an upgrade plan. It’s useful for conversations between IT, security and procurement, especially when supply chain transparency and on‑site support matter (for example, when choosing equipment and integration from local manufacturers and integrators like GSE.kz).

Major shifts in PC and server “hardware” in 2019

2019 changed expectations for computers and servers. Where “is there enough power?” used to mean CPU clock, by the end of the year cores, fast SSDs and networking came to the fore. This is visible in office fleets, workstations and data‑center racks.

Processors and GPUs: more parallel work

CPU core counts continued to grow. In the mass market multi‑core became common even in regular PCs. In servers the logic was similar: companies started to consider not “the single fastest chip” but “how many threads for the same money”, because virtualization, databases and containers favor parallelism.

GPUs in 2019 were increasingly seen not only for “graphics” but as compute accelerators. AI tasks, analytics and video processing in corporate systems increased demand for workstations and servers with headroom for graphics and support for modern libraries.

Memory, storage, networks: speed became noticeable

SSDs became the standard for new builds. Moving to NVMe had a tangible effect where there are many small operations: OS boot, launching heavy apps, database and VM workloads. In practice users saw the same applications but fewer “waiting for the screen” moments.

At the same time expectations for network and internal buses rose. Data centers migrated to 10/25/40/100GbE, because fast drives and many‑core CPUs started hitting the network before other limits. This matters for backups, clusters and storage.

Translated into procurement choices:

• Office PCs usually need an SSD and a reasonable core count more than the highest clock speed.
• Workstations need GPU and RAM when doing CAD, video or analytics.
• Servers require NVMe, network bandwidth and a balanced CPU/RAM/disk configuration.
• Virtualization benefits from cores and fast storage but requires careful sizing.

Simple example: when a hospital moved to electronic records and a centralized database, the bottleneck was often not CPU but slow storage and network. Many projects therefore started with NVMe for databases and network port upgrades, then increased compute. Manufacturers and integrators like GSE.kz reflected this in typical configurations: more focus on disk subsystem, network and workload support, not only “top CPU”.

Infrastructure and data centers: where companies moved

In 2019 “server for the sake of a server” purchases declined. Solutions started to be built around workloads: virtual desktops, databases, analytics, video surveillance, corporate services. This helped estimate TCO more accurately and avoid idle resources or localized bottlenecks.

By that time virtualization was already a baseline for most organizations. Even where “bare” servers remained, they were usually dedicated to specific needs. The main idea: infrastructure should be manageable and predictable, not just powerful. That drove more attention to operation and reliability.

Containers and Kubernetes were widely discussed in 2019 because businesses wanted to release updates faster and move applications between environments. For many organizations this was not a “magic switch” but a new discipline: deployment standards, observability, access control and team training.

Hyperconvergence (HCI) also became louder. It made sense where quick deployment of typical services and small‑step scaling were needed and teams didn’t want to assemble multiple layers manually. But for heavy databases or tightly defined network architecture classic separate servers and storage often remained a more transparent choice on price and control.

Another shift: remote management and monitoring ceased to be “optional”. Without them it’s hard to maintain SLAs, plan capacity and investigate incidents.

Companies increasingly made the following a minimum:

• unified monitoring (performance, capacity, network, app errors)
• remote server management and updates without rack visits
• deployment standards (templates, roles, configurations)
• regular recovery tests and clear RTO/RPO
• logging and change control to know “what changed and when”

Practical example: when an organization upgrades servers for virtualization and critical services, choosing a platform for the workload and planning support (up to 24/7) reduces downtime and simplifies scaling. For such projects the experience of integrators and manufacturers working with local data centers is useful, especially where delivery times and service networks matter.

Clouds and hybrid: what became normal

In 2019 many companies stopped arguing “cloud or on‑prem” and assembled a pragmatic mix. They chose by scenario, not ideology.

Cloud wins where speed of launch and flexibility matter. For example, an analytics team needs an environment for a month and can turn it off afterward rather than keep hardware idle.

Where the cloud typically helped

Cloud was convenient for:

• test and temporary environments
• backup capacity for peaks
• email, collaboration and some office services
• rapid scaling of web services
• geo‑distributed access for branches

But the downside became clear: cloud can be problematic when strict data requirements, predictable latency and full control are needed. For some systems it’s cheaper and more reliable to keep compute close to users, especially when traffic is large and constant.

Hybrid became normal: critical services stayed on local servers, while the variable part moved to the cloud. Practically this often looked like a primary on‑prem contour and cloud for backup, data marts and separate apps. For organizations in Kazakhstan this was sometimes limited by bandwidth availability and data residency rules rather than preference.

Backup and disaster recovery

In 2019 backup and DR were increasingly bought as a managed service: with defined RPO/RTO, regular recovery tests and clear pricing.

Before choosing a hybrid scheme teams usually checked:

• quality and redundancy of communication channels
• where backup copies are physically stored
• personal data and industry regulations
• compatibility with current virtualization and backup tools
• cost model: one‑time purchase vs monthly payments

Procurement approach changed too: TCO began to include support, downtime, licenses, channels and the cost of mistakes. Many arrived at a model: local primary on predictable hardware plus cloud as insurance and accelerator. In such projects delivery and service matter, so manufacturers and integrators with local support, like GSE.kz, often joined the design stage.

AI and analytics: what actually changed in 2019

2019 moved AI from “demos” to understandable pilots. Companies talked less about abstract neural networks and more about concrete questions: what data exists, who ensures quality, where measurable effect is, and how to embed results into processes.

GPU acceleration played a big role. Training and data processing on CPUs often took too long, while GPUs shortened time and saved specialist hours. This was especially felt in industries with many images, video, signals or complex statistics: quality control, security, healthcare, finance, industry.

The main takeaway was simple: models depend on data. If data is scattered, poorly described or not updated, results will vary regardless of the algorithm. So storage, access and data hygiene — unified catalogs, clear rights, regular exports, quality checks — came to the front.

Typical cases that succeeded

In 2019 projects with clear metrics and short validation cycles fared best:

• demand and inventory forecasting (less waste and shortages)
• anomaly detection in transactions and events (fewer losses)
• document recognition and field extraction (faster processing)
• computer vision for process control (less defect)
• predictive maintenance (less downtime)

A real‑life example: to reduce handling time for requests a pilot starts not with the “smartest model” but with data cleanup: where requests are stored, how reasons are tagged and who closes statuses. Only then analytics and simple classification models are added.

Where AI doesn’t pay off

2019 also taught honest evaluation. AI often fails to pay off if data is scarce or unpredictable, processes change weekly, or results can’t be operationalized (no owners, procedures, or business support).

Practically, pilots should go together with infrastructure and support: reliable servers for load, clear storage architecture and a team to maintain the solution. Integrators and server manufacturers like GSE.kz often helped not by “doing magic” but by building a working contour: compute, storage and 24/7 support.

Supply, quality and “technological sovereignty” as a trend

In 2019 trust in the supply chain became louder. Companies and public bodies began asking not only “what’s faster” but “where did this come from”, “who guarantees quality” and “what will happen with supplies and updates in a year”. Against trade disputes, import‑substitution rules and rising cybersecurity risks this became a practical selection criterion.

One answer to dependence on a single supplier is a vendor‑neutral approach: architecture and procurement are not tied to one brand and solutions are chosen for the task. This reduces the risk of a closed ecosystem where expansion is expensive and conditions are dictated by one vendor.

Support also changed status: service stopped being an “optional extra”. It became part of the ownership cost: how fast faulty parts are replaced, spare parts availability, clear procedures, who handles incidents at night or on weekends. For equipment in critical processes weak support can cost more than the price difference at purchase.

Local production and transparency of origin became practical arguments. When a manufacturer controls development, delivery and support, it’s easier to confirm origin, quality and stable configurations. In Kazakhstan this is noticeable in projects where local preferences and predictable service matter. For example, GSE.kz as a domestic manufacturer since 2015 with ISO 9001, ISO 14001 and ISO 45001 can address local content and support via 24/7 service and a nationwide network.

Before a tender answer a few questions:

• What documents prove origin and quality (manufacturer status, certificates, production control)?
• Is there a clear SLA, reaction and repair times, and who provides on‑site support?
• Can components be replaced without voiding warranty or being locked to one brand?
• How will identical configurations be delivered in batches?
• What is included in the lifecycle: commissioning, updates, expansion, disposal?

This approach reduces surprises after purchase and makes “sovereignty” a measurable set of requirements about delivery, quality and responsibility.

Cybersecurity: practical lessons from 2019

2019 reinforced a simple idea: “antivirus” is not the same as “security”. Attacks increased and became quieter. Instead of one malicious file, attackers used phishing, credential theft, remote access and ransomware that reached shared folders and backups. Companies therefore began to see security as daily rules, not one program.

The most visible practical lesson: protection starts with access. If an attacker gets a password they act as a “legitimate” user and many controls miss this. So in 2019 measures that gave big effect without complex projects strengthened:

• MFA for email, VPN, admin consoles and critical services
• principle of least privilege (don’t grant admin rights without need)
• separate accounts for administration
• network segmentation so an infected PC doesn’t expose servers
• forbid direct server management from user subnets

Second lesson: patch management is painful. Vulnerabilities in OSs, hypervisors and popular services appeared regularly, and updates were postponed for fear of breakage. In practice the risk of downtime from an attack was often higher than from planned updates. A working approach is to classify systems by criticality, keep a test contour and set schedules: what’s updated within 48 hours, within a week, or on plan.

Third lesson: security moved down to hardware and firmware. CPU vulnerabilities and firmware issues showed that you must consider not only specs but also support: BIOS/UEFI updates, firmware management and supply‑chain transparency. For organizations buying workstations and servers in batches (e.g., public sector or healthcare) this became part of supplier and service requirements.

Finally, without logging and incident response protection is blind. It’s useful to at least collect events from servers and key workstations and agree in advance what to do on suspicion of an incident:

• who to notify and who decides
• what to isolate first (account, PC, segment)
• where clean backups are stored
• how quickly critical services can be restored

These steps don’t require rare technologies but do require discipline. That often distinguished resilient companies in 2019.

How to apply 2019 lessons: a step‑by‑step IT upgrade plan

2019 showed a simple truth: buying hardware by specs alone is not enough. What works fits your workloads, survives updates and has clear support. So start with a plan, not a brand.

First, record reality. How many users use office apps, where is heavy graphics, which services are critical (email, 1C, medical system, accounting), how much data grows each month. Bottlenecks hide in three places: slow disks, insufficient RAM and a network that dips at peak hours.

Then proceed stepwise; don’t try to fix everything at once:

1. Describe workloads and bottlenecks: who uses what and where it’s slow (PC, server, network, storage).
2. Classify tasks: office PCs, workstations, application servers, storage, network.
3. Set reliability and support requirements: SLA, spare capacity, repair times, spare parts.
4. Choose architecture: what stays local, what moves to cloud, where hybrid and redundancy are needed.
5. Plan migration without downtime: pilot, cutover window, rollback, user training.

After that add what’s often forgotten: operation. Who watches server and disk health, how often are updates applied, how are backups tested, where are cold spares stored (disks, PSUs, network modules). Even a good purchase quickly loses value without monitoring and update discipline.

A short document package helps avoid verbal disputes:

• service map and criticality (what must always work)
• data growth forecast for 12–18 months
• recovery requirements (acceptable downtime)
• implementation plan with pilot and success criteria

Example: a district clinic has slow registry workstations and an unstable file resource. Investigation shows the bottleneck is old storage and lack of proper backup, not CPU. The solution is not “the most powerful server” but role separation (app server and storage separate), fast disks where queues form and clear 24/7 support. In Kazakhstan this often includes requirements for local origin and a service network: consider suppliers that can guarantee delivery, support and a clear lifecycle, as GSE.kz does with its PC and server lines and system integration.

This plan saves budget and nerves: you buy what solves the problem and know how it will live for the next years.

Common mistakes when choosing PCs and servers based on 2019

The most frequent mistake: buying “the most powerful” without understanding where the load actually sits. The market moved toward many‑core CPUs, fast SSDs (including NVMe) and faster networks. But if the real load is office apps and accounting, overpaying for a top CPU yields little. Standardizing the fleet and fast replacements are more valuable.

On servers a typical problem is skimping on invisible things: memory, disk subsystem and network. A powerful CPU won’t help if there’s not enough RAM for VMs, slow disks for DB, or a 1GbE network blocking backups and migrations. The result is an expensive server that performs only average.

Another pain is “backup for the checkbox”. Many made backups but didn’t verify restore. After an incident copies turned out incomplete, keys were lost, or recovery time was unacceptable. 2019 showed that copies alone aren’t enough — recovery tests matter.

Also avoid a single point of failure. Buying fast storage or one big server without an availability plan — no secondary node, no quick response scenario, no spare power or channels — is risky. It doesn’t have to be an expensive cluster, but you need a plan for the first 30 minutes of a failure.

Chaos in the PC and server park gets costly. Dozens of different models, power supplies, RAM types, OS images and drivers increase downtime and support load. For large buyers this is often solved by choosing 1–2 standard lines for typical roles. For example, with a local manufacturer like GSE.kz you can set standard workstation and server configurations for departments and make them corporate standards.

Before buying do a quick check:

• Which 3–5 tasks generate 80% of the load (office, 1C/ERP, VDI, DB, file server).
• Where is the bottleneck: CPU, RAM, disks or network.
• How do you back up and how often do you test recovery.
• How do you handle the failure of one node or disk (downtime, spare node, timelines).
• Who is responsible for updates, firmware and basic security hygiene.

The last mistake is when “everything runs on enthusiasm”. Without a designated owner for patches, asset inventory and basic security hygiene even the best purchase quickly loses value.

Quick checklist, an example and next steps

If you look at 2019 trends without nostalgia, the main conclusion is simple: hardware purchases and infrastructure upgrades work only if you define requirements and support in advance. Otherwise initial savings turn into downtime, manual work and urgent add‑ons.

Before procurement check the basics. It takes a few meetings but removes most risks:

• Load: number of users and services in 12–18 months, where growth occurs (email, files, 1C/ERP, VDI, DB).
• Storage: data type and speed needs (many small files, video, backups), IOPS and latency requirements.
• Resiliency: what must survive without downtime (power, disks, network, nodes) and acceptable recovery times.
• Security and compliance: encryption, access control, logging, segmentation, regulator demands.
• Service and spares: reaction times, service network, who diagnoses and replaces parts.

Mini scenario. A regional organization grows, document volumes rise and new data sources appear. Office PCs are slow: old storage, mixed configs and updates disrupt work. Server backups don’t fit the window, DB hits disk limits, and monitoring is ad hoc.

Budget allocation: don’t skimp on reliable storage, backups, power and network, and on having a single standard fleet. Save on unnecessary peak power: better to plan realistic growth with a clear margin than buy the maximum now and pay later for downtime due to lacking support or wrong architecture.

To make procurement manageable prepare artifacts for approvals and tenders:

• specification (BOM): device roles, minimum parameters, compatibility, warranty requirements
• migration plan: order, windows, rollback, responsibilities, data handling
• support plan: criticality levels, SLA, contacts, replacement procedures, spares
• acceptance tests and metrics: performance checks, backup verification, failover tests

Then proceed in small steps: pilot on a limited group, fix the standard for PCs and servers, then scale. A homogeneous fleet is easier to maintain, update and secure.

If you need a partner covering supply, implementation and support in Kazakhstan, the “manufacturer + integrator” model is convenient. For example, GSE.kz can offer L200 office PCs, M200 all‑in‑ones, S200 servers, infrastructure integration and 24/7 support. This helps not only to buy equipment but to understand how it will live for the next 3–5 years.