In a previous blog, I explored why organisations need to rethink their end-user computing strategies in light of shifting business demands, evolving user expectations, and operational challenges.
Building on that, this post offers a strategy template: a living framework to guide sustainable, responsible tech procurement. Use it to define clear requirements that reflect your business goals, regional context, and workforce needs. Then tailor it further to suit your industry standards and organisational realities, revisiting it regularly as your environment evolves.
Click here to download “End-User Computing Strategy Checklist” as a PDF.
1. Business Alignment and Objectives
- Primary business goals such as productivity, collaboration, security, and innovation.
- Strategic initiatives, including digital transformation, hybrid working, and compliance.
- Alignment between EUC objectives, overall business strategy, and industry-specific drivers.
2. Industry-Specific Considerations
- Regulatory requirements (Data privacy, compliance frameworks, cybersecurity).
- Industry-specific applications (e.g., finance platforms, healthcare EMR, retail POS).
- Business-critical workflows and processes supported by EUC.
3. Geographic & Regional Factors
- Infrastructure considerations (network availability, connectivity quality, 4G/5G, Wi-Fi).
- Regional compliance (local privacy laws, cybersecurity regulations, data residency requirements).
- Support and logistics (local vendor availability, language support, supply chain).
4. Persona-Based Device Strategy
- Employee personas including:
- Frontline/Mobile workers
- Knowledge workers
- Power users/Technical teams
- Executives
- Hybrid/Remote workers
- Device types, operating systems, and form factors suited to each persona.
5. Technology and Platform Choices
- Operating system selection (Windows, macOS, Chrome OS, Android, iOS).
- VDI/DaaS selection (Citrix, VMware Horizon, AWS WorkSpaces, Azure Virtual Desktop).
- Cloud-based productivity suite selection (Microsoft 365, Google Workspace).
- Unified Endpoint Management (UEM) platform selection.
6. Security and Compliance Strategy
- Endpoint security model (Zero Trust, EDR, MFA, biometrics).
- Data encryption and privacy management strategy.
- Identity and Access Management (IAM) policies.
- Incident response and threat detection framework.
7. User Experience and Employee Engagement
- Employee experience objectives (ease-of-use, personalisation, productivity).
- Self-service portals and automation for IT support.
- End-user training, change management, and continuous feedback loops.
- Plans for local AI capabilities – Agents, Information and data management, etc.
8. Operational Excellence and Lifecycle Management
- Device procurement, deployment, and lifecycle policies.
- Automation and AI-driven analytics for device management.
- Sustainability and environmental impact (device recycling, energy efficiency).
- Other GRC requirements (anti-slavery etc).
9. Cost Optimisation and Budgeting
- Total Cost of Ownership (TCO) calculation framework.
- CAPEX vs OPEX considerations (purchase, lease, consumption-based).
- Vendor financing and budgeting strategies.
10. Vendor and Partner Management
- Vendor evaluation criteria (support, innovation, geographic coverage, pricing).
- Partnership strategy (managed services, system integrators, technology alliances).
- Vendor risk management and vendor performance monitoring framework.
11. Metrics and Measurement
- Outcome-focused success metrics (e.g., productivity, satisfaction, security).
- Monitoring and reporting structure.
- Continuous improvement plan based on metric analysis.
12. ESG
- Eco-labels, ISO 14067 or PAS 2050 carbon disclosures, and climate-condition testing to avoid energy waste.
- Vendor take-back in all regions, minimums for firmware support and repairability to slow refresh cycles.
- Supply-chain ethics evidence, including up-to-date RBA VAP scores or modern slavery reports.
- Tracking of tightening regulations to stay ahead of compliance risks.
At the end of last year, I had the privilege of attending a session organised by Red Hat where they shared their Asia Pacific roadmap with the tech analyst community. The company’s approach of providing a hybrid cloud application platform centred around OpenShift has worked well with clients who favour a hybrid cloud approach. Going forward, Red Hat is looking to build and expand their business around three product innovation focus areas. At the core is their platform engineering, flanked by focus areas on AI/ML and the Edge.
The Opportunities
Besides the product innovation focus, Red Hat is also looking into several emerging areas, where they’ve seen initial client success in 2023. While use cases such as operational resilience or edge lifecycle management are long-existing trends, carbon-aware workload scheduling may just have appeared over the horizon. But two others stood out for me with a potentially huge demand in 2024.
GPU-as-a-Service. GPUaaS addresses a massive demand driven by the meteoric rise of Generative AI over the past 12 months. Any innovation that would allow customers a more flexible use of scarce and expensive resources such as GPUs can create an immediate opportunity and Red Hat might have a first mover and established base advantage. Particularly GPUaaS is an opportunity in fast growing markets, where cost and availability are strong inhibitors.
Digital Sovereignty. Digital sovereignty has been a strong driver in some markets – for example in Indonesia, which has led to most cloud hyperscalers opening their data centres onshore over the past years. Yet not the least due to the geography of Indonesia, hybrid cloud remains an important consideration, where digital sovereignty needs to be managed across a diverse infrastructure. Other fast-growing markets have similar challenges and a strong drive for digital sovereignty. Crucially, Red Hat may well have an advantage where onshore hyperscalers are not yet available (for example in Malaysia).
Strategic Focus Areas for Red Hat
Red Hat’s product innovation strategy is robust at its core, particularly in platform engineering, but needs more clarity at the periphery. They have already been addressing Edge use cases as an extension of their core platform, especially in the Automotive sector, establishing a solid foundation in this area. Their focus on AI/ML may be a bit more aspirational, as they are looking to not only AI-enable their core platform but also expand it into a platform to run AI workloads. AI may drive interest in hybrid cloud, but it will be in very specific use cases.
For Red Hat to be successful in the AI space, it must steer away from competing straight out with the cloud-native AI platforms. They must identify the use cases where AI on hybrid cloud has a true advantage. Such use cases will mainly exist in industries with a strong Edge component, potentially also with a still heavy reliance on on-site data centres. Manufacturing is the prime example.
Red Hat’s success in AI/ML use cases is tightly connected to their (continuing) success in Edge use cases, all build on the solid platform engineering foundation.