Capturing first-hand perspectives from senior IT decision-makers responsible for AI solutions, workplace technology, hardware selection, and enterprise computing strategy to understand how organizations are supporting advanced AI workflows and evaluating the future requirements of commercial PCs.
- Understanding How AI Is Changing Workplace Computing
Artificial intelligence is influencing how organizations approach workplace productivity, employee workflows, technology infrastructure, and hardware investment.
As businesses move beyond basic AI experimentation and introduce customized AI solutions into everyday operations, IT decision-makers must evaluate whether existing computing environments can support increasingly advanced workflows.
The growing use of AI has also introduced new considerations around processing capabilities, device performance, security, connectivity, and the balance between local and cloud-based computing.
Understanding this market requires more than identifying organizations that use AI tools.
It requires understanding how AI is implemented, which employees use advanced workflows, how hardware decisions are made, and what computing capabilities organizations may require as AI adoption expands.
For this research, the client sought to understand the needs of AI power users and the enterprise IT decision-makers responsible for selecting and managing the technology used to support them.
The study focused on AI implementation strategy, advanced AI workflows, on-device AI, commercial PC categories, user requirements, and hardware brand considerations.
Vertex Expert Network supported the initiative by identifying and recruiting professionals whose organizational responsibilities, AI experience, and involvement in hardware and technology decisions aligned with the research requirements.
- Client Objective
The objective was to provide the client with access to senior IT decision-makers who were responsible for deploying and managing AI solutions and hardware within their organizations.
The research focused on understanding:
- How organizations were implementing customized AI solutions
- How AI was being integrated into advanced business workflows
- The needs and working patterns of AI power users
- The role of generalist and specialist employees in AI adoption
- How organizations approached on-device, cloud, and hybrid AI computing
- The hardware requirements associated with advanced AI workflows
- How IT leaders evaluated commercial laptops, desktops, and workstations
- The role of AI implementation strategy in hardware selection
- How enterprise PC brands were considered and evaluated
- How organizations supported AI adoption across different business functions
- The challenges influencing future commercial PC and workplace technology strategies
The study required professionals who could discuss AI implementation in depth and who were directly or jointly responsible for selecting AI tools, workflows, and related hardware.
- What We Did
3.1. Recruiting Decision-Makers Across Priority Industries
Vertex applied a targeted recruitment framework to identify senior IT professionals across selected industries.
The research focused on organizations operating in areas where AI adoption, sensitive data, advanced workflows, and enterprise hardware requirements were particularly relevant.
The priority industries included:
- Financial Services
- Healthcare and Pharmaceuticals
- Government and Defence
- Legal and Consulting
- Technology-Focused Professional Services
Each industry presented different requirements for AI implementation and computing infrastructure.
For example, financial services organizations may use AI for trading, analysis, and compliance-related activities. Healthcare and pharmaceutical organizations may have requirements connected to imaging, diagnostics, and sensitive data. Government and defence environments may place emphasis on security-critical workflows and offline requirements.
Legal and consulting organizations may use AI for confidential document analysis and case preparation, while technology-focused professional services organizations may adopt AI across project management, consulting, and broader business workflows.
The recruitment framework was designed to capture these industry-specific differences.
3.2. Ensuring Appropriate Organization Size and IT Infrastructure
The research included organizations across different company-size categories.
The screening framework considered:
- Small organizations
- Medium-sized organizations
- Mid-market organizations
- Enterprise organizations
Participating organizations were required to have a dedicated IT department, with the size of the IT function also assessed during recruitment.
The IT department screening categories included:
- 1–4 employees
- 5–19 employees
- 20 employees
This qualification helped ensure that participants worked within organizations with established technology functions and could discuss AI implementation and hardware decisions from an informed organizational perspective.
The study also sought a mix of organization sizes rather than concentrating exclusively on one enterprise category.
3.3. Reaching Senior IT Decision-Makers
The research focused on senior professionals responsible for AI solutions, technology implementation, and hardware selection.
Relevant functions included:
- Chief Technology Officer
- Chief Information Officer
- IT Director
- Head of IT
- IT Manager
Participants were required to have meaningful involvement in AI implementation strategy and to be among the primary people responsible for relevant decisions.
The recruitment process excluded junior professionals and individuals with limited involvement in AI and hardware selection.
This ensured that the study captured perspectives from professionals who could discuss both the strategic and operational aspects of enterprise AI computing.
3.4. Screening for Customized AI Adoption
A key qualification requirement was that participating organizations must have experience with customized AI solutions.
Qualified organizations were using either:
- Customized AI models
- Existing AI models adapted for a particular industry or business use case
Organizations that were only exploring AI or using basic off-the-shelf AI tools without customization did not meet the core research requirements.
The study also excluded organizations with no active AI solutions or those whose AI adoption remained at an early exploratory stage.
This approach helped ensure that participants could provide practical perspectives on AI implementation rather than discussing only future plans or general awareness.
3.5. Identifying Advanced AI Workflows
The research focused on organizations using advanced AI workflows that involved more than basic or isolated AI interactions.
Advanced workflows could include:
- Multi-step task chaining
- Integration of multiple AI models
- Integration with other systems and services
- Tool and API calling
- Workflow automation
- Real-time adaptability
- Automated actions or decisions supported by guardrails
- Human-in-the-loop review
- Evaluation and feedback loops
- Continuous learning or improvement processes
- Large-scale data processing
Participants were required to demonstrate experience with multiple elements of advanced AI workflows.
The screening also assessed the degree of autonomy within these workflows. Organizations relying only on templates, macros, or basic non-autonomous applications did not meet the core qualification requirements.
The study included organizations using assisted, tool-using, agentic, or higher-autonomy workflows.
This allowed the research to explore how increasing workflow complexity could influence computing requirements and enterprise hardware decisions.
3.6. Understanding AI Power Users Across Business Functions
AI adoption is not limited to technical specialists.
Many organizations are introducing AI into the workflows of generalist employees working in areas such as:
- Leadership
- Operations
- Project Management
- Sales
- Marketing
- Customer Service
- Finance
- Legal
- Human Resources
- Frontline Operations
The research examined how generalist power users were using AI and how their requirements differed from those of specialist employees.
Specialist users may include data scientists, machine learning engineers, designers, technical professionals, and individuals working with CAD, modelling, 3D, or other demanding workloads.
The study focused on organizations where advanced AI workflows were being used across a broader range of business functions rather than being limited exclusively to specialist teams.
This distinction helped explore how commercial PC strategies could support AI adoption across the wider workforce.
3.7. Evaluating Device Requirements for Generalist Power Users
The research assessed the types of devices used by generalist AI power users.
The device categories included:
- Standard business laptops
- Standard business desktops
- Workstation laptops
The study placed particular emphasis on organizations where laptops and notebooks represented the primary devices used by generalist power users.
The research also considered how device requirements varied according to workflow complexity, employee mobility, performance expectations, and the use of AI-enabled applications.
These insights can help technology providers understand the relationship between user behavior, AI adoption, and commercial PC requirements.
3.8. Understanding Commercial PC and Workstation Categories
The research examined how IT decision-makers distinguished between different categories of computing devices.
- Standard Business Laptops
Standard business laptops support everyday business tasks such as communication, productivity, collaboration, security, and connectivity.
Their portability makes them relevant for employees working across office, remote, and hybrid environments.
- Standard Business Desktops
Standard business desktops are generally used for everyday office activities and may be configured as tower or small-form-factor systems.
Their suitability depends on the organization’s performance, management, security, and workplace requirements.
- Mobile Workstations
Mobile workstations are high-performance laptops designed for demanding specialist workloads. They may include workstation-class CPUs and GPUs, increased memory and storage, and certifications for professional software applications.
- Desktop Workstations
Desktop workstations are expandable, high-performance systems designed for specialist workloads such as CAD, 3D modelling, simulation, engineering, data science, media production, and local machine learning.
The research explored how decision-makers evaluated these categories and how their selection was influenced by the needs of AI power users.
3.9. Exploring On-Device, Cloud, and Hybrid AI Computing
The location of AI processing is an important consideration in enterprise technology strategy.
On-device AI refers to AI processing performed locally on the user’s device through components such as the CPU, GPU, or NPU.
Depending on the workflow, local processing may be considered for reasons involving responsiveness, privacy, security, offline access, and reduced reliance on cloud connectivity.
Cloud-based AI environments can provide access to centralized services and scalable computing resources, while hybrid models may combine local device processing with cloud infrastructure.
The research explored how organizations were using local, cloud, and hybrid environments and how IT decision-makers evaluated these approaches in relation to their AI implementation strategies.
3.10. Examining Commercial PC Brand Considerations
The research considered the role of brand selection within enterprise PC procurement and workplace technology strategy.
The brand framework included:
- HP
- Dell
- Lenovo
- Apple
- Samsung
- Toshiba
- Microsoft
- Acer
- Asus
The study explored how IT decision-makers evaluated commercial PC brands and how brand preferences related to existing technology environments, device requirements, employee needs, and enterprise purchasing decisions.
Participants were required to have experience with relevant commercial PC brands, while the research also sought to understand the differences between brand usage across organizational environments.
This provided a foundation for exploring how AI adoption and advanced workflows could influence future hardware selection.
- Understanding the Relationship Between AI Strategy and Hardware Selection
The research examined hardware decisions as part of a broader enterprise AI implementation strategy.
IT leaders must consider the relationship between:
AI Adoption → Workflow Complexity → User Requirements → Device Capabilities → Enterprise Hardware Strategy
The type of AI workflow being implemented can influence the computing resources required by employees.
For example, organizations using complex workflows involving multiple models, real-time processing, sensitive data, or automated actions may have different hardware requirements from organizations using AI primarily for basic productivity tasks.
The research therefore explored how decision-makers connected AI implementation plans with device performance, security, processing location, and commercial PC selection.
- Research Engagement
The research was designed around 60-minute in-depth video interviews with senior IT decision-makers.
Participants were required to take part using a laptop or desktop computer, with access to a camera, microphone, and reliable internet connection.
The research engagement focused on professionals who could discuss:
- AI implementation strategy
- Advanced AI workflows
- AI power-user requirements
- Generalist and specialist employee needs
- On-device AI and cloud computing
- Commercial PC selection
- Hardware brand considerations
- Enterprise technology management
- AI-related workplace innovation
The participant screening process was designed to ensure that the research team could engage professionals with practical experience and meaningful responsibility in AI and hardware decisions.
- Vertex Expert Network’s Role
Vertex Expert Network supported the client by identifying, screening, and recruiting senior IT decision-makers with relevant experience in enterprise AI and commercial computing.
The recruitment process involved:
- Identifying organizations across the priority industries
- Screening organizations based on company size
- Verifying the presence of a dedicated IT department
- Identifying senior IT professionals
- Confirming responsibility for AI solutions and hardware decisions
- Screening for customized AI model adoption
- Assessing experience with advanced AI workflows
- Evaluating workflow autonomy and integration requirements
- Identifying organizations using AI across generalist business functions
- Confirming experience with commercial laptops and desktops
- Assessing on-device, cloud, and hybrid computing environments
- Screening for relevant PC brand experience
- Coordinating qualified professionals for 60-minute video interviews
The value of the engagement came from connecting the client with professionals who could provide firsthand perspectives on AI-enabled workplace computing, enterprise hardware requirements, AI power users, and future commercial PC innovation.,
- Experts Engaged
Vertex recruited senior IT decision-makers responsible for AI implementation, enterprise computing, hardware selection, and workplace technology strategy.
The expert profile included:
- IT leaders responsible for AI solutions and technology deployment
- Professionals involved in selecting and managing enterprise hardware
- Decision-makers with experience using customized AI models
- Leaders familiar with advanced AI workflows and automation
- Professionals working with generalist and specialist AI power users
- IT decision-makers with experience across local, cloud, and hybrid AI environments
- Professionals able to discuss commercial laptops, desktops, and workstations
- Leaders with experience evaluating enterprise PC brands
- Senior stakeholders involved in AI implementation strategy and hardware decisions
- Professionals working across financial services, healthcare and pharmaceuticals, government and defence, legal and consulting, and technology-focused professional services
The research was designed to provide the client with firsthand insights into how organizations are adapting workplace computing environments to support AI adoption and the evolving requirements of enterprise users.
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