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1.
Demographics
(Required.)
Name
Organisation
Country
Email Address
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2.
What's your primary role?
(Required.)
Core product and technology
Consulting and advisory
Delivery, operations and customer success
Sales and revenue
Marketing
Business and strategy
Other (please specify)
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3.
What level best fits your role in the organisation?
(Required.)
C-Suite
Head of a Business Function
VP / Sr. VP
Director / Sr. Director
Manager / Sr. Manager
Other (please specify)
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4.
What's the primary business focus of your organisation?
(Required.)
HR technology, IT and software
HR consulting and advisory services
HR services (e.g., recruiting consultancies, training providers, implementation partners)
Venture Capital focusing on investing in HR and work tech
Other (HR academician, influencer), please specify
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5.
How is the HR technology vendor landscape changing in 2026? (select all that apply)
(Required.)
Significant platform consolidation — large players acquiring or displacing point solutions
Proliferation of AI-native point solutions creating a crowded, fragmented market
New product categories emerging (Agentic AI workflow automation, workforce data quality & governance, etc.)
Rise of AI-native challengers putting pressure on established enterprise platforms
Blurring boundaries between HR tech and broader enterprise/productivity tech
Other (please specify)
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6.
What are your top business challenges, including but not limited to rapid tech evolution? (Select up to 3)
(Required.)
Longer and more complex sales cycles
Pressure to continuously innovate
Limited access to capital for innovation
Increased demand for clear proof of value / ROI
Demand for AI-enabled solutions without well-defined use cases
Clients’ organisational and workforce readiness for AI adoption
Intensifying competition in a crowded market
Other (please specify)
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7.
How has your product or service portfolio evolved in the last 12 months in response to AI-led demand? (select all that apply)
(Required.)
Built or acquired native AI/ML capabilities into the core product
Partnered with LLM or AI infrastructure providers to add capabilities
Re-architected the core platform to support agentic workflows
Added an AI capability layer on top of an existing product
Launched new advisory or implementation services for AI adoption success
No significant change in the past 12 months
Other (please specify)
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8.
Which HR tech capabilities do you believe will shift from a market differentiator to table stakes within the next 12 months? (select top 3)
(Required.)
Natural language interfaces / conversational AI for HR)
Agentic AI for end-to-end workflow automation
Skills intelligence and real-time skills mapping
AI-powered internal talent marketplace
AI explainability, governance, and bias detection
Integrated workforce planning with live skills data
Predictive talent analytics (attrition, performance, workforce risk)
Predictive employee well-being and burnout analytics
HR data fabric (data quality, integration, unified analytics layer)
Embedded ROI and impact measurement for HR tech
GenAI-powered learning (personalised content, adaptive learning systems)
Manager self-service and decision intelligence tools
Employee experience platforms (support, service delivery, EX insights)
Other (please specify)
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9.
How would you rate the average AI readiness of your clients across the following dimensions?
Rating scale per row: Not started | Experimenting | Operational | Scaled | Optimized ROI
(Required.)
Not started
Experimenting
Operational
Scaled
Optimized ROI
Data infrastructure and quality for AI
Not started
Experimenting
Operational
Scaled
Optimized ROI
AI governance frameworks and policies
Not started
Experimenting
Operational
Scaled
Optimized ROI
HR teams’ AI capability and literacy
Not started
Experimenting
Operational
Scaled
Optimized ROI
Leadership alignment and sponsorship for AI adoption
Not started
Experimenting
Operational
Scaled
Optimized ROI
Effective change management initiatives for AI adoption
Not started
Experimenting
Operational
Scaled
Optimized ROI
Workforce readiness for AI-led productivity, HR & enterprise solutions
Not started
Experimenting
Operational
Scaled
Optimized ROI
Workflow redesign – reimagining existing processes instead of just automating them
Not started
Experimenting
Operational
Scaled
Optimized ROI
Clear AI ROI or success framework
Not started
Experimenting
Operational
Scaled
Optimized ROI
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10.
What percentage of your current client base would you describe as AI-ready — i.e., able to deploy and realise ROI from AI — vs. still at the experimenting or pilot stage?
(Required.)
Less than 10% are AI-ready
10% – 30% are AI-ready
30% – 50% are AI-ready
50% – 75% are AI-ready
More than 75% are AI-ready
11.
How would you rate the following challenges impacting the pace of AI adoption in organisations and their HR’s effectiveness in addressing them?
Challenge(High/Med/Low)
Effectiveness (High/Med/Low)
AI tool overload and fragmentation
High
Medium
Low
High
Medium
Low
Relevance of tools to HR workflows
High
Medium
Low
High
Medium
Low
Selection of the correct AI talent solutions
High
Medium
Low
High
Medium
Low
Integration between tools
High
Medium
Low
High
Medium
Low
Customisation / Configurability
High
Medium
Low
High
Medium
Low
Adoption and employee clarity on tool usage
High
Medium
Low
High
Medium
Low
AI fatigue across the workforce
High
Medium
Low
High
Medium
Low
Inability to show ROI of growing investments
High
Medium
Low
High
Medium
Low
Lack of leadership and manager trust in AI solutions
High
Medium
Low
High
Medium
Low
False sense of AI readiness
High
Medium
Low
High
Medium
Low
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12.
How has your own organisation's AI product or delivery capability evolved in the past 12 months? (select all that apply)
(Required.)
Built or significantly expanded an internal AI/ML engineering team
Integrated large language model (LLM) capabilities into our core offering
Deployed agentic AI features (autonomous, multi-step workflows)
Achieved AI-related certification, compliance, or audit (e.g., ISO 42001, EU AI Act alignment)
Launched an internal AI adoption programme for our own workforce
No significant change in the past 12 months
Other (please specify)
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13.
How would you assess the current impact of AI across the following HR areas?
(Required.)
Level of AI Impact
Talent acquisition (automation, screening, recruiter productivity)
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Agentic AI workflows / copilots (task execution, productivity gains)
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Employee experience and personalisation
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Workforce analytics and decision dashboards
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Learning platforms and content generation
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Performance management and feedback systems
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
End-to-end HR workflow automation
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Skills-based workforce transformation
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
HR operations efficiency (cost, effort reduction)
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Manager self-service and decision-making
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Employee support and service delivery (helpdesk, resolution time)
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Talent insights for leadership decisions
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Overall workforce productivity improvement
Limited impact or mostly experimental
Delivering measurable operational value
Driving significant business and workforce transformation
Other (please specify)
14.
What is the most common mistake organisations make in HR tech investments? (Choose top 3)
Investing in AI tools without clear use cases
Prioritising features over adoption and usability
Scaling pilots without validating value
Overloading the organisation with too many tools
Ignoring integration and architecture design
Underinvesting in data readiness
Treating AI as a technology initiative, instead of business transformation
Other (please specify)
15.
Where do organisations face the greatest challenge in transformation execution? (Choose top 3)
Moving from pilot to scale
Aligning HR, IT, and business stakeholders
Redesigning workflows (not just automating them)
Driving sustained adoption
Measuring transformation impact
Managing organisational resistance
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16.
How do you expect the HR technology market to grow (in market size, revenue) over the next 12 months?
(Required.)
The market will remain largely flat or stable
Moderate growth (up to 10%)
Strong growth (10–25%)
Accelerated growth (25–50%)
Hyper-growth (more than 50%)
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17.
How do you expect investments in HR technology and services companies to grow over the next 12 months? (e.g., venture funding, private equity investment, and overall capital inflows)
(Required.)
Investment activity will remain cautious or unchanged
Slight increase in investment activity (up to 10%)
Healthy increase in funding and capital inflows (10–25%)
Significant rise in investment momentum (25–50%)
Major surge in capital and investor activity (more than 50%)
18.
How do you expect your organisation’s investments in AI-first HR solutions and service capabilities to shift across the following areas over the next 12 months?
State of Investments
Attracting the right people
Going Up
Going Down
Staying the same
Impactful onboarding programs
Going Up
Going Down
Staying the same
Talent development and upskilling
Going Up
Going Down
Staying the same
Building a high-performing culture
Going Up
Going Down
Staying the same
Career growth and succession planning
Going Up
Going Down
Staying the same
Total rewards and well-being
Going Up
Going Down
Staying the same
Personalised employee experience
Going Up
Going Down
Staying the same
Identifying and retaining top performers
Going Up
Going Down
Staying the same
Raising productivity through GenAI/automation
Going Up
Going Down
Staying the same
Continuous and fair performance management
Going Up
Going Down
Staying the same
Leadership and manager development
Going Up
Going Down
Staying the same
Increasing HR effectiveness with AI and analytics
Going Up
Going Down
Staying the same
Agentic AI workflows for HR services and operations
Going Up
Going Down
Staying the same
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19.
What will differentiate organisations that are successfully scaling AI in HR and successfully transforming in 2026?
(Required.)
Clear linkage between AI enterprise strategy and HR and business outcomes
Strong leadership sponsorship and alignment
High-quality, integrated data foundations
Focus on workflow and operating model redesign
Investment in skills and workforce readiness
Building a culture of AI readiness through effective change management and adoption
Balanced focus on technology and human capabilities
Strong governance and risk management
20.
Which one of the following is the single biggest bottleneck to scaling your AI-led growth?
Sales & Business Development
- Strengthening consultative selling by clearly articulating AI-led business value, ROI, and enterprise outcomes beyond product features
Marketing & Brand
- Building sharper differentiation and trust through insight-led narratives, thought leadership, and conversion-oriented positioning
Customer Success / Account Management -
Driving adoption, measurable ROI, and long-term business impact after deployment
Product / Solution Capability -
Moving beyond isolated AI features toward scalable, integrated, workflow-driven enterprise solutions
Implementation / Delivery
- Reducing deployment complexity and accelerating time-to-value through smoother integration and change enablement
