Capturing first-hand perspectives from senior business and IT leaders across major industries and global markets to understand how organizations were progressing their AI journeys from AI strategy and adoption to investment, business impact, and responsible scaling.
AI adoption had moved beyond isolated experimentation for many large organizations, with businesses increasingly integrating AI into everyday workflows, decision-making, and broader transformation initiatives.
Understanding this market required more than identifying organizations that were using AI.
It required understanding how deeply AI had been adopted, where AI decisions were being made, how organizations measured business impact, and which senior leaders were responsible or accountable for AI strategy.
For this study, the client sought to understand how organizations across major industries and geographies were navigating their AI journeys, with particular focus on AI strategy and maturity, agentic AI adoption, value and ROI measurement, investment dynamics, and trust and governance.
The research specifically focused on organizations demonstrating characteristics associated with Frontier Firms — businesses that had moved toward embedding AI into their operations, redesigning work around human and AI collaboration, and scaling AI adoption alongside the data, security, and governance foundations required to create measurable business value.
The study therefore required access to senior professionals with hands-on experience with AI deployments, rather than respondents whose understanding of AI was limited to theoretical or aspirational familiarity.
Vertex Expert Network supported the initiative by identifying and recruiting professionals whose organizations, seniority, functional responsibilities, and involvement in AI decision-making aligned with the study requirements.
The objective was to provide the client with access to senior business and IT leaders who were actively involved in AI strategy and investment decisions within their organizations.
The research was focused on understanding:
- How organizations were progressing their AI strategies and maturity
- How AI was being incorporated into everyday workflows and business processes
- How organizations were approaching emerging areas such as agentic AI
- How AI initiatives were selected and evaluated
- How organizations assessed the business impact of AI investments
- How AI pilots were evaluated before moving into production
- How post-implementation AI performance was measured
- How organizations approached AI investment decisions
- How trust, security, data, and governance influenced AI adoption
- How responsibilities for AI decisions were distributed across IT and business functions
The study was designed around a senior respondent group with active responsibility or accountability for AI decision-making at either the organization-wide or business-unit level.
The research also required a balanced perspective between IT / technical leaders and line-of-business leaders, ensuring that AI adoption was viewed from both the technology and business sides of the organization.
Vertex applied a highly specific recruitment framework to identify professionals working within qualifying organizations across selected global markets.
Participating organizations were required to meet the relevant company-size thresholds, with a minimum baseline of 1,000 employees globally, while additional country-specific thresholds were applied where required.
The screening assessed organization size across multiple employee bands:
- 1,000–1,999 employees
- 2,000–4,999 employees
- 5,000–9,999 employees
- 10,000–19,999 employees
- 20,000+ employees
The study sought a proportional mix across these qualifying organization sizes rather than concentrating exclusively on the largest enterprises.
The recruitment framework therefore provided access to organizations with different levels of scale and organizational complexity while maintaining the required minimum company-size thresholds.
Geographic coverage was a central component of the study.
The research prioritized organizations across North America and EMEA, while also seeking representation from Asia-Pacific where feasible.
The target markets included:
- United States
- United Kingdom
- Canada
- Australia
- Brazil
- Germany
- India
- Netherlands
- Singapore
- New Zealand
- Hong Kong,
The market structure placed particular emphasis on the United States, United Kingdom, Canada, and Australia, with the study targeting 2 respondents per market in those priority geographies.
Additional markets were targeted with individual representation, including Brazil, Germany, India, the Netherlands, Singapore, and New Zealand.
The study also sought to include 2 Asia-Pacific respondents where feasible, while Hong Kong was treated as a best-effort recruitment market.
This geographic structure was designed to provide perspectives across multiple mature and developing AI markets rather than relying on a single regional view.
The research covered organizations across a broad range of industries, with particular attention given to sectors where AI strategy, investment, and transformation were relevant.
The industry framework included:
- Automotive, Transportation and Warehousing
- Computer Hardware and Software
- Education
- Finance and Insurance
- Healthcare and Social Assistance
- Manufacturing
- Media and Entertainment
- Mining, Quarrying, and Oil and Gas
- Public Sector
- Retail and Consumer Goods
- Telecommunications
- Utilities
The study deliberately avoided over-concentration in a single industry.
A maximum of 2 respondents from any one industry was targeted across the overall respondent group.
This helped ensure that the research captured differences in how AI adoption, investment, and business impact were approached across sectors rather than reflecting the experience of one industry alone.
The screening went beyond broad industry classification and examined the specific business areas represented within major sectors.
Within Finance and Insurance, the study distinguished between areas including:
- Banking
- Capital Markets
- Property & Casualty Insurance
- Life Insurance
- Health Insurance
- Wealth Management
- Other Insurance
Within Healthcare and Social Assistance, the study considered:
- Health Payors
- Health Pharma
- Health Providers
- MedTech
- Pharmacies
- Social Assistance
Within Retail and Consumer Goods, the study covered areas including:
- Consumer Goods Manufacturing
- Grocery
- Clothing and Accessories
- Consumer Electronics
- Pharmacies
- Ecommerce / Online Retail
- Specialty and General Retail
- Food, Beverage and Tobacco
- Toys and Sports Equipment
- Other Consumer Goods
The framework also distinguished specific areas within Education, Manufacturing, Energy and Resources, Public Sector, Automotive and Transportation, allowing the research to capture more precise industry perspectives.
This level of classification helped ensure that participants represented meaningful areas of business activity rather than simply broad industry labels.
A central qualification requirement was determining whether participating organizations demonstrated the characteristics of a Frontier Firm.
Respondents were asked to assess their organization’s adoption of three key traits:
- Intelligence Embedded Across Work
AI had been incorporated into everyday workflows, decisions, and experiences rather than remaining limited to isolated pilots or individual point solutions.
- Work Redesigned Around Human + AI Collaboration
Organizations had reconsidered how work was performed so that employees and AI systems could operate together to increase speed, capacity, and business impact.
- Transformation Scaled Within Trust
Organizations had paired broader AI adoption with the data, security, and governance foundations required to scale AI responsibly and generate measurable business value.
Respondents were required to achieve an average score above 3 across all three traits to qualify.
This ensured that the research focused on organizations demonstrating meaningful progress in AI adoption rather than businesses with only early-stage or aspirational AI activity.
The recruitment framework was designed to reach professionals with meaningful influence over AI strategy and investment.
Qualified seniority levels included:
- Senior executives, including Chief Executive Officers (CEOs), Presidents, Chief Information Officers (CIOs), and Chief Strategy Officers (CSOs)
- Executives, including General Managers (GMs), Executive Vice Presidents (EVPs), and Executive Directors
- Upper-level management, including Senior Vice Presidents (SVPs), Vice Presidents (VPs), and Senior Directors
Mid-level management, team leads, individual contributors, owners, and other lower-seniority profiles were excluded from the core respondent pool.
The study therefore concentrated on leaders with sufficient organizational seniority to understand and influence AI strategy at a meaningful level.
The research was structured around a 50/50 balance between IT / technical and line-of-business respondents.
The IT / technical group included professionals working across areas such as:
- Artificial Intelligence
- Cybersecurity
- Data and Analytics
- Software Engineering
- Technology Infrastructure
- Technology Support
- Procurement
The line-of-business group included professionals working across functions such as:
- Finance and Accounting
- Human Resources
- Marketing and Public Relations
- Operations
- Procurement
- Product Development
- Sales and Business Development
- Supply Chain
- Logistics and Fulfilment
- Research and Development
- Manufacturing
- Sustainability
- Quality Assurance
This balance allowed the research to capture both the technical foundations of AI adoption and the business functions responsible for applying AI to organizational priorities.
Seniority alone was not sufficient to qualify.
Participants were required to have direct responsibility or accountability for AI decision-making within their organizations.
Qualified respondents were responsible or accountable for AI decisions:
- Organization-wide
- Within their business unit or function
Professionals who were merely consulted on AI decisions or kept informed of them were excluded.
This distinction ensured that the study engaged professionals who were genuinely involved in shaping AI strategy and decisions rather than individuals with only peripheral awareness of their organization’s AI activity.
The research also required respondents to demonstrate strong working knowledge of how their organizations assessed the business impact of AI initiatives.
Participants were evaluated across three stages:
- Pilot Selection
Understanding how organizations assessed and selected AI initiatives for pilot programs.
- Pilot-to-Production
Understanding how organizations determined which AI pilots were suitable for broader production deployment.
- Post-Implementation
Understanding how organizations evaluated the final business impact and performance of AI initiatives after implementation.
Respondents were required to demonstrate good overall familiarity or strong familiarity with the details across all three stages.
Anyone with no or limited familiarity at any one of these stages was excluded.
This ensured that the research reached leaders capable of discussing AI value and ROI across the full lifecycle of an AI initiative, rather than only its initial deployment.
The study was structured to recruit 12 senior respondents with active and meaningful involvement in AI investment and strategy decisions.
The respondent board was designed to provide a mix of:
- Global geographies
- Major industries
- IT / technical functions
- Line-of-business functions
- Senior executive and upper-management levels
- Organization sizes
- AI decision-making responsibilities
Geographically, the study prioritized North America and EMEA, while seeking additional Asia-Pacific representation where feasible.
The industry structure was designed to maintain diversity, with no more than 2 respondents from any single industry.
The functional mix was targeted at 50% IT / technical and 50% line-of-business leaders.
Participants were also required to bring real-world experience with AI deployments rather than theoretical familiarity alone.
Qualified participants took part in the research through in-depth qualitative interviews, providing a flexible setting in which their professional experience with AI strategy, adoption, investment, and business impact could be explored in depth.
The study examined AI adoption as a broader organizational journey rather than a single technology decision.
The framework considered the progression from:
AI Strategy → Adoption & Deployment → Business Impact → Scaling & Governance
At the strategy level, participants brought experience with organization-wide or business-unit AI decisions.
At the adoption stage, the research focused on organizations that had moved AI beyond isolated pilots and demonstrated meaningful integration into business processes.
At the business-impact stage, participants needed to understand how AI initiatives were assessed during pilot selection, transition to production, and post-implementation review.
At the scaling stage, the study considered the role of data, security, governance, and trust in supporting responsible AI transformation.
This created a broader view of how organizations were moving from AI experimentation toward measurable and scalable business value.
Vertex Expert Network’s role was to provide the client with access to the right senior professionals across the targeted AI market.
This involved:
- Identifying relevant organizations across priority global markets
- Screening organizations against detailed company-size requirements
- Verifying industry and sub-industry classifications
- Identifying professionals at the required seniority levels
- Confirming direct responsibility or accountability for AI decision-making
- Maintaining the required IT / line-of-business balance
- Screening for meaningful AI deployment experience
- Assessing Frontier Firm characteristics
- Verifying familiarity with AI business-impact measurement
- Confirming experience across pilot selection, pilot-to-production, and post-implementation evaluation
- Maintaining diversity across industries and geographies
- Recruiting professionals whose responsibilities aligned with the research requirements
- Coordinating qualified respondents for the research engagement
The value of the engagement came from ensuring that the client could speak directly with senior professionals whose organizational scale, industry exposure, AI maturity, decision-making responsibility, and practical experience matched the requirements of the study.
The study included senior professionals from major industries and global markets who were actively involved in AI strategy and investment decisions.
The respondent profile included:
- Senior executives and executive-level leaders responsible or accountable for AI decisions
- SVP, VP, and Senior Director-level professionals with organization-wide or business-unit AI responsibilities
- IT and technical leaders across AI, data and analytics, cybersecurity, software engineering, and technology infrastructure
- Line-of-business leaders across operations, finance, marketing, product, sales, supply chain, HR, manufacturing, and other business functions
- Professionals with hands-on experience with AI deployments
- Leaders with direct experience assessing AI initiatives from pilot selection through production and post-implementation review
- Professionals working within organizations that demonstrated meaningful AI adoption, human-AI collaboration, and AI governance foundations
- Respondents representing a cross-industry and cross-geography perspective on AI strategy, investment, adoption, and business impact
The study was structured to provide the client with access to senior professionals who could offer firsthand perspectives on how organizations were making AI decisions, measuring value, scaling adoption, and navigating the changing AI landscape.
Looking to understand how organizations are approaching AI adoption, technology strategy, and digital transformation? Connect with Vertex Expert Network to access perspectives from senior technology and business leaders with first-hand industry experience.
