Is your organization currently implementing or planning any AI initiatives (e.g., evaluations, pilots, development, deployments) within the next 12 months?

• Yes
  100%
• No
  0%

Which of the following best describes your involvement and responsibilities across your organization's overall approach to AI?

• Decision Lead
  29.6%
• Technical Sponsor
  23.2%
• Strategic Owner
  20.4%
• Contributor / Implementer
  17.3%
• Observer / Researcher
  9.6%

Please indicate if you have a significant level of involvement in making or influencing decisions with specific elements of the AI Stack. (multi-select)

• Data and Integration
  70.2%
• AI Observability & Governance
  59.2%
• AI Business Applications
  57.7%
• Model Development & Training
  54.4%
• App Development & Middleware
  51.7%
• Model Hosting & Orchestration
  46.5%

Which of the following best describes the industry which your organization serves?

• Software, Cloud & AI
  10.3%
• Technology & Hardware
  10.3%
• Banking & Financial Services
  10.1%
• Retail & Consumer Goods
  9.2%
• Manufacturing & Basic Materials
  8.5%
• Professional & Consumer Services
  8.3%
• Construction & Real Estate
  7.9%
• Telecom, Data Centers & Infra
  7.0%
• Energy & Utilities
  6.4%
• Healthcare Services
  6.2%
• Life Sciences & MedTech
  5.3%
• Agriculture
  4.2%
• Mining & Minerals
  3.9%
• Other
  2.4%

In which country are you personally located?

• United States
  13.6%
• United Kingdom
  10.5%
• Canada
  8.3%
• Mexico
  7.2%
• France
  4.6%
• Germany
  4.6%
• Brazil
  3.7%
• Argentina
  3.1%
• Nordics
  2.9%
• India
  2.6%
• China
  2.6%
• Colombia
  2.6%
• Spain
  3.9%
• Italy
  4.2%
• Japan
  2.4%
• Australia
  2.0%
• Indonesia
  2.0%
• UAE
  2.2%
• Singapore
  2.2%
• Hong Kong
  1.8%
• Poland
  1.8%
• Russia
  1.8%
• Malaysia
  1.7%
• Israel
  1.7%
• Saudi Arabia
  1.5%
• South Korea
  1.3%
• Taiwan
  1.3%
• Macau
  0.7%
• Panama
  0.9%
• New Zealand
  0.4%

Please estimate the total number of employees across your entire company or organization (worldwide).

• 2 to 999
  19.5%
• 1,000 to 2,499
  19.5%
• 2,500 to 4,999
  17.6%
• 5,000 to 9,999
  15.1%
• 10,000 to 19,999
  10.7%
• 20,000 to 49,999
  9.4%
• 50,000 or more
  8.3%

Which of the following best describes your department or functional role?

• Information Technology (IT)
  29.4%
• Operations
  15.1%
• Information Security (IS)
  10.7%
• Executive Leadership
  8.1%
• Customer Service / Support
  7.0%
• Finance & Accounting
  7.0%
• R&D / Product Development
  6.8%
• Marketing
  6.6%
• Human Resources
  4.4%
• Sales
  3.1%
• Legal and Compliance
  1.8%

Which title best describes your leadership responsibilities within your organization?

• Director, Manager, or Team Lead
  25.2%
• Mid Leadership
  20.2%
• Senior Management
  17.5%
• Project Manager
  12.7%
• C-level Executive
  12.5%
• Practitioner / Specialist
  6.8%
• Consultant / Advisor
  5.1%

Which of the following best describes your primary technical responsibilities within your organization?

• Overall AI Stack Oversight
  19.9%
• Oversight of AI-enabling IT
  10.1%
• Data Scientist
  9.2%
• Data Engineer
  9.2%
• Director of Cloud/Hybrid Cloud
  8.1%
• Oversight of AI-enabling Infra
  8.1%
• ML Engineer
  7.5%
• Senior Systems/Platform Engineer
  7.4%
• Enterprise Architect
  7.0%
• Data Architect
  6.2%
• Security/SecOps Engineer
  4.8%
• Site Reliability Engineer (SRE)
  2.6%

Which of the following best describes your organization's overall approach to adopting new technologies?

• Mass Market Adopter
  32.5%
• Late Adopter
  19.5%
• Early Adopter
  21.5%
• Innovator
  17.3%
• Laggard
  9.2%

AI Implementation Stage — Currently

• Experimenting
  29.4%
• Developing
  24.3%
• Initial Deployments
  15.4%
• Mass Deployments
  15.1%
• Planning
  8.5%
• Learning
  7.4%

AI Implementation Stage — Within 6 months

• Mass Deployments
  31.4%
• Initial Deployments
  32.7%
• Developing
  19.7%
• Experimenting
  9.7%
• Planning
  5.1%
• Learning
  0.9%

AI Implementation Stage — Within 6–12 months

• Mass Deployments
  55.3%
• Initial Deployments
  23.0%
• Developing
  12.9%
• Experimenting
  6.1%
• Planning
  2.0%
• Learning
  0.4%

AI Implementation Stage — Within 12–24 months

• Mass Deployments
  66.4%
• Initial Deployments
  14.5%
• Developing
  11.0%
• Not Sure
  5.0%
• Experimenting
  2.4%
• Planning
  0.4%
• Learning
  0.4%

Improved operational efficiency and process automation. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Primary (within 12 months)
  71.9%
• Secondary (12–24 months)
  24.6%
• Tertiary (not a focus)
  3.5%

Enhanced decision-making through advanced analytics and insights. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  53.1%
• Primary (within 12 months)
  39.3%
• Tertiary (not a focus)
  7.5%

Accelerated innovation and new product/service development. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  47.8%
• Primary (within 12 months)
  41.7%
• Tertiary (not a focus)
  10.5%

Enhanced customer engagement and personalization. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Primary (within 12 months)
  55.3%
• Secondary (12–24 months)
  41.2%
• Tertiary (not a focus)
  3.5%

Reduced costs and improve margin performance. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Primary (within 12 months)
  46.5%
• Secondary (12–24 months)
  39.0%
• Tertiary (not a focus)
  14.5%

Improved employee productivity, acquisition or retention through AI tools/copilots. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Primary (within 12 months)
  55.7%
• Secondary (12–24 months)
  30.5%
• Tertiary (not a focus)
  13.8%

Strengthened risk management, governance, and compliance. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  48.0%
• Primary (within 12 months)
  42.1%
• Tertiary (not a focus)
  9.9%

Development of new AI-enabled business models or revenue streams. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  42.5%
• Primary (within 12 months)
  29.0%
• Tertiary (not a focus)
  28.5%

Improved competitive advantage. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  46.3%
• Primary (within 12 months)
  39.5%
• Tertiary (not a focus)
  14.2%

Established market awareness as a leading AI-enabled business. Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  54.8%
• Primary (within 12 months)
  28.7%
• Tertiary (not a focus)
  16.5%

Other (please specify) — Please identify the priority or importance in achieving the following objectives or outcomes through the adoption of AI.

• Secondary (12–24 months)
  52.4%
• Primary (within 12 months)
  38.1%
• Tertiary (not a focus)
  9.5%

Most Important Primary Objective

Of the Primary objectives or outcomes you've selected, which do you consider the most important to achieve?

• Operational Efficiency & Automation
  23.9%
• Innovation & New Products
  12.5%
• Cost Reduction & Margin
  12.0%
• Advanced Analytics
  11.4%
• Employee Productivity
  11.4%
• New AI Business Models
  9.0%
• Customer Engagement
  8.1%
• Risk Management & Governance
  6.3%
• Competitive Advantage
  3.3%
• Market Awareness
  2.0%

Which of the following thresholds must be met to greenlight an AI project? (multi-select)

• Clear operational or cost efficiency
  63.2%
• Expected ROI of 15–20% or higher
  44.1%
• Strategic alignment / innovation potential
  43.2%
• Payback period < 12 months
  25.6%

Most Important Threshold

Of the items you've selected, which do you consider the most important or primary to achieve?

• Clear operational or cost efficiency
  45.8%
• Expected ROI of 15–20% or higher
  28.3%
• Strategic alignment / innovation potential
  15.4%
• Payback period < 12 months
  10.5%

Do you have a dedicated AI governance committee or cross-functional team responsible for AI strategy and oversight?

• Yes
  40.4%
• Plan to — within 6 months
  29.2%
• Plan to — within 6–12 months
  21.5%
• No plans yet
  8.8%

Primary Leader of AI Committee

Which organization is currently (or will be) the primary leader or driver of your AI committee or team?

• Information Technology (IT)
  40.7%
• Executive Leadership
  21.8%
• Operations
  7.1%
• Human Resources
  6.0%
• Customer Service / Support
  5.0%
• R&D / Product Development
  5.4%
• Marketing
  3.4%
• Information Security (IS)
  3.2%
• Finance & Accounting
  4.6%
• Sales
  1.2%
• Legal and Compliance
  1.4%

Which is your organization's primary approach to selecting/using LLMs?

• Proprietary/commercial LLMs only
  30.9%
• Primarily open-source, some proprietary
  20.8%
• Open-source LLMs only
  17.3%
• Primarily proprietary, some open-source
  15.4%
• Even balance of both
  11.8%
• Not decided yet
  3.9%

What are your plans for using or deploying LLMs to support Customer service/support (chatbots, virtual assistants, helpdesk automation)?

• Currently in use
  50.4%
• Within 6 months
  23.2%
• Within 6–12 months
  13.6%
• Within 12–24 months
  8.3%
• No Plans
  4.6%

What are your plans for using or deploying LLMs to support Knowledge management/research (market research, internal documentation, insights generation)?

• Within 6 months
  40.3%
• Currently in use
  35.7%
• Within 6–12 months
  14.2%
• Within 12–24 months
  5.5%
• No Plans
  4.4%

What are your plans for using or deploying LLMs to support Code generation/software development support (DevOps, automation, code assistants)?

• Within 6 months
  35.5%
• Currently in use
  28.7%
• Within 6–12 months
  22.1%
• Within 12–24 months
  10.7%
• No Plans
  3.1%

What are your plans for using or deploying LLMs to support IT management and operations (IT helpdesk, operations management, data or device security)?

• Currently in use
  43.8%
• Within 6 months
  31.1%
• Within 6–12 months
  17.8%
• Within 12–24 months
  4.4%
• No Plans
  2.9%

What are your plans for using or deploying LLMs to support Financial operations (accounting systems, fraud detection)?

• Within 6 months
  39.5%
• Currently in use
  26.5%
• Within 6–12 months
  21.5%
• Within 12–24 months
  9.6%
• No Plans
  2.9%

What are your plans for using or deploying LLMs to support Content creation/marketing automation?

• Currently in use
  41.0%
• Within 6 months
  30.0%
• Within 6–12 months
  17.6%
• Within 12–24 months
  5.3%
• No Plans
  6.1%

What are your plans for using or deploying LLMs to support other business functions (specify)?

• Currently in use
  36.4%
• No Plans
  27.3%
• Within 6 months
  13.6%
• Within 6–12 months
  13.6%
• Within 12–24 months
  9.1%

Please rank the following AI application areas from most to least important to your organization today: Customer Service.

• Rank 1 (Most Important)
  22.1%
• Rank 2
  20.0%
• Rank 3
  18.0%
• Rank 4
  13.1%
• Rank 5
  13.6%
• Rank 6 (Least Important)
  13.2%

Please rank the following AI application areas from most to least important to your organization today: Internal Operations.

• Rank 1 (Most Important)
  25.9%
• Rank 2
  14.3%
• Rank 3
  20.8%
• Rank 4
  18.4%
• Rank 5
  13.1%
• Rank 6 (Least Important)
  7.5%

Please rank the following AI application areas from most to least important to your organization today: Product Development.

• Rank 1 (Most Important)
  21.7%
• Rank 2
  20.6%
• Rank 3
  17.8%
• Rank 4
  17.6%
• Rank 5
  13.8%
• Rank 6 (Least Important)
  8.5%

Please rank the following AI application areas from most to least important to your organization today: Risk/Compliance.

• Rank 1 (Most Important)
  13.8%
• Rank 2
  18.4%
• Rank 3
  17.6%
• Rank 4
  18.8%
• Rank 5
  16.4%
• Rank 6 (Least Important)
  15.1%

Please rank the following AI application areas from most to least important to your organization today: R&D/Innovation.

• Rank 1 (Most Important)
  10.7%
• Rank 2
  16.0%
• Rank 3
  13.4%
• Rank 4
  16.0%
• Rank 5
  26.5%
• Rank 6 (Least Important)
  17.5%

Please rank the following AI application areas from most to least important to your organization today: Marketing/Sales Support.

• Rank 6 (Least Important)
  38.2%
• Rank 5
  16.7%
• Rank 4
  16.2%
• Rank 3
  12.3%
• Rank 2
  10.7%
• Rank 1 (Most Important)
  5.9%

How much is your organization concerned about the following aspects of AI and LLMs? User Data Privacy.

• Significantly Concerned
  44.1%
• Somewhat Concerned
  41.4%
• Not Concerned At All
  14.5%

How much is your organization concerned about the following aspects of AI and LLMs? Corporate Data Privacy.

• Significantly Concerned
  47.1%
• Somewhat Concerned
  38.4%
• Not Concerned At All
  14.5%

How much is your organization concerned about the following aspects of AI and LLMs? Security, Hacks.

• Significantly Concerned
  53.1%
• Somewhat Concerned
  32.4%
• Not Concerned At All
  14.5%

How much is your organization concerned about Hallucinations / Inaccurate Outputs from AI and LLMs?

• Somewhat Concerned
  47.1%
• Significantly Concerned
  33.3%
• Not Concerned At All
  19.7%

How much is your organization concerned about Bias in LLM Data?

• Somewhat Concerned
  44.1%
• Significantly Concerned
  37.9%
• Not Concerned At All
  18.0%

How much is your organization concerned about Reliability / Accuracy of Corporate Data in AI systems?

• Significantly Concerned
  48.0%
• Somewhat Concerned
  35.3%
• Not Concerned At All
  16.7%

How much is your organization concerned about the Cost to Deploy or Maintain AI systems?

• Somewhat Concerned
  42.6%
• Significantly Concerned
  40.1%
• Not Concerned At All
  17.3%

How much is your organization concerned about Misuse of AI by Employees?

• Significantly Concerned
  42.8%
• Somewhat Concerned
  36.2%
• Not Concerned At All
  21.0%

How much is your organization concerned about Lack of Employee Training / Engagement with AI?

• Somewhat Concerned
  46.3%
• Significantly Concerned
  39.5%
• Not Concerned At All
  14.2%

Which of the concerns you've selected is the most important or primary concern that needs to be addressed?

• Security, Hacks
  32.5%
• Reliability / Accuracy of Corporate Data
  15.0%
• Corporate Data Privacy
  12.8%
• User Data Privacy
  11.8%
• Cost to Deploy or Maintain
  9.2%
• Hallucinations / Inaccurate Outputs
  5.3%
• Bias in LLM Data
  5.1%
• Lack of Employee Training
  4.3%
• Misuse by Employees
  3.9%

Our organization is actively evaluating new foundation models at least once per quarter.

• Agree
  41.7%
• Strongly Agree
  31.1%
• Neutral / Unsure
  15.3%
• Disagree
  7.4%
• Strongly Disagree
  4.6%

We anticipate having multiple foundation models concurrently deployed.

• Strongly Agree
  39.2%
• Agree
  26.7%
• Neutral / Unsure
  19.5%
• Disagree
  9.0%
• Strongly Disagree
  5.7%

We anticipate adding additional or replacing existing models every year.

• Strongly Agree
  39.0%
• Agree
  28.9%
• Neutral / Unsure
  19.3%
• Strongly Disagree
  7.5%
• Disagree
  5.3%

The ability to add or replace foundation models will provide our business with agility and improve our competitive posture.

• Agree
  33.6%
• Strongly Agree
  30.7%
• Neutral / Unsure
  22.6%
• Disagree
  10.5%
• Strongly Disagree
  2.6%

AI is strategically important to our organization's overall success.

• Strongly Agree
  46.3%
• Agree
  32.4%
• Neutral / Unsure
  9.7%
• Disagree
  7.7%
• Strongly Disagree
  3.9%

Deploying AI and leveraging LLMs will be critical to maintaining our competitive advantage over the coming 1–3 years.

• Agree
  42.5%
• Strongly Agree
  30.0%
• Neutral / Unsure
  14.7%
• Disagree
  7.7%
• Strongly Disagree
  5.1%

Agentic AI (autonomous workflows/agents) will play a significant role in our AI strategy over the coming 12 months.

• Strongly Agree
  41.7%
• Agree
  37.5%
• Neutral / Unsure
  9.0%
• Disagree
  9.0%
• Strongly Disagree
  2.8%

What steps are you prioritizing to ensure the security of sensitive data when interacting with or fine-tuning LLMs? (multi-select)

• Strict access controls, encryption, pen testing
  63.2%
• Data anonymization / de-identification
  58.3%
• Network, API security
  55.3%
• On-premises or private cloud deployment
  45.8%
• Data cleansing / minimization before training
  43.9%
• Synthetic / generated datasets for fine-tuning
  33.5%

What best describes the current state of your core data architecture (warehouse, lake, or lakehouse)?

• Hybrid setup
  36.6%
• Cloud-based data lake or lakehouse
  26.1%
• Legacy on-premises data warehouse
  23.0%
• Fully modernized AI-ready architecture
  14.3%

What is the status of modernizing your organization's data architecture?

• In process now
  36.2%
• Complete
  20.6%
• Planning within 6 months
  20.0%
• Planning within 6–12 months
  12.7%
• Waiting 1 year or more
  8.5%
• No plans to modernize
  2.0%

How important is the following as a driver for modernizing your data architecture? Need for better scalability, performance, and real-time access.

• Very Important
  66.0%
• Somewhat Important
  30.8%
• Not Important
  3.2%

How important is the following as a driver for modernizing your data architecture? Integration and governance of siloed or fragmented data.

• Very Important
  65.3%
• Somewhat Important
  32.3%
• Not Important
  2.4%

How important is the following as a driver for modernizing your data architecture? Reduction of infrastructure costs and technical debt.

• Somewhat Important
  49.9%
• Very Important
  45.0%
• Not Important
  5.1%

How important is Compliance, security, and data sovereignty requirements as a driver for modernizing your data architecture?

• Very Important
  69.6%
• Somewhat Important
  26.5%
• Not Important
  3.9%

Which of the drivers you've selected is the most important or primary driver for modernizing your data architecture?

• Integration & governance of siloed data
  29.5%
• Scalability, performance & real-time access
  27.6%
• Compliance, security & data sovereignty
  23.6%
• Reduction of infrastructure costs & technical debt
  19.3%

To what extent is your data platform designed or ready to support AI/ML workloads?

• Barely (less than 50%)
  28.0%
• Mostly (75% there)
  26.2%
• Completely (100%)
  24.0%
• Somewhat (50% there)
  21.8%

Which strategy best describes how your enterprise is primarily adopting Artificial Intelligence?

• Customize or Adapt AI
  38.9%
• Buy AI (Embedded in software)
  31.9%
• Build AI (Proprietary)
  16.4%
• Explore AI (Evaluating)
  12.8%

Which of the following are (or were) primary barriers to updating your data architecture for AI/ML? (multi-select)

• Scalability / performance
  49.4%
• Skills / talent shortages
  40.6%
• Budget limitations or cost uncertainty
  39.3%
• Security / privacy / regulatory
  35.8%
• Operationalization / MLOps readiness
  31.6%
• Data quality / governance
  30.5%
• Resource allocation conflicts
  15.1%

Top Most Significant Barrier

• Scalability / performance
  22.6%
• Budget limitations or cost uncertainty
  20.8%
• Skills / talent shortages
  19.9%
• Data quality / governance
  15.6%
• Operationalization / MLOps readiness
  11.2%
• Security / privacy / regulatory
  6.6%
• Resource allocation conflicts
  3.3%

How important are automated data validation and cleansing pipelines in managing and ensuring data quality, governance, and lineage for AI models?

• Very Important
  61.0%
• Somewhat Important
  34.9%
• Not Important
  4.0%

How important is a centralized data catalog and governance tools in managing and ensuring data quality, governance, and lineage for AI models?

• Very Important
  61.9%
• Somewhat Important
  34.4%
• Not Important
  3.7%

How important are feature stores or curated datasets in managing and ensuring data quality, governance, and lineage for AI models?

• Very Important
  51.7%
• Somewhat Important
  43.6%
• Not Important
  4.8%

How important is monitoring and auditing for model input/output in managing and ensuring data quality, governance, and lineage for AI models?

• Very Important
  64.0%
• Somewhat Important
  32.0%
• Not Important
  4.0%

How important are other factors (please specify) in managing and ensuring data quality, governance, and lineage for AI models?

• Very Important
  0.2%
• Somewhat Important
  0.2%
• Not Important
  0%

How well integrated is your data platform with AI/ML operationalization (MLOps/LLMOps) workflows?

• Partially integrated (<50%)
  32.4%
• Mostly integrated (50%+)
  31.6%
• Fully integrated (end-to-end)
  21.1%
• Not integrated (silos)
  14.9%

Infrastructure Providers — Today (multi-select)

• Amazon Web Services – AWS
  53.7%
• Microsoft Azure
  44.1%
• Google Cloud – GCP
  32.7%
• IBM Cloud
  24.1%
• Dell
  19.9%
• Oracle Cloud Infrastructure
  15.4%
• Alibaba
  12.1%
• Lenovo
  12.1%
• Cisco
  10.8%
• HPE
  9.7%
• Tencent
  8.1%
• Neo Cloud (CoreWeave, Vultr, OVHCloud)
  2.4%
• SuperMicro
  1.8%

Infrastructure Providers — 12–24 Months from Now (multi-select)

• Amazon Web Services – AWS
  57.0%
• Microsoft Azure
  43.0%
• Google Cloud – GCP
  34.7%
• IBM Cloud
  23.9%
• Lenovo
  22.6%
• Oracle Cloud Infrastructure
  19.7%
• Alibaba
  18.0%
• Dell
  16.0%
• Cisco
  16.0%
• Tencent
  8.1%
• HPE
  5.3%
• Neo Cloud (CoreWeave, Vultr, OVHCloud)
  4.2%
• SuperMicro
  1.7%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Performance.

• Rank 1 (Most Important)
  35.3%
• Rank 2
  24.4%
• Rank 3
  18.4%
• Rank 4
  11.8%
• Rank 5
  6.2%
• Rank 6 (Least Important)
  3.9%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Cost.

• Rank 1 (Most Important)
  24.8%
• Rank 2
  15.1%
• Rank 3
  17.6%
• Rank 4
  16.4%
• Rank 5
  11.6%
• Rank 6 (Least Important)
  14.5%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Ecosystem Compatibility.

• Rank 1 (Most Important)
  13.1%
• Rank 2
  11.9%
• Rank 3
  14.9%
• Rank 4
  21.5%
• Rank 5
  20.8%
• Rank 6 (Least Important)
  17.8%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Security.

• Rank 1 (Most Important)
  21.3%
• Rank 2
  31.4%
• Rank 3
  23.2%
• Rank 4
  11.8%
• Rank 5
  7.7%
• Rank 6 (Least Important)
  4.6%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Governance.

• Rank 1 (Most Important)
  3.3%
• Rank 2
  9.7%
• Rank 3
  14.7%
• Rank 4
  20.4%
• Rank 5
  30.7%
• Rank 6 (Least Important)
  21.1%

Please rank the following criteria from most to least important when selecting infrastructure/model vendors: Support/Training.

• Rank 6 (Least Important)
  38.1%
• Rank 5
  23.0%
• Rank 4
  18.2%
• Rank 3
  11.2%
• Rank 2
  7.4%
• Rank 1 (Most Important)
  2.2%

What percent of all distinct AI/ML projects (pilots, proofs-of-concept, and deployments) launched at your organization in the past 12 months are at the following stages?

• Stalled / In Process
  25.7%
• Pending / Planning
  16.9%
• Successful (deployed, meeting ROI)
  22.8%
• Partially Successful (deployed, not meeting ROI)
  20.7%
• Failed / Abandoned
  13.8%

How significant is Data Integration and Curation as a challenge in adopting/scaling your AI Stack?

• Very Significant
  57.4%
• Somewhat Significant
  33.5%
• Not Significant
  9.2%

How significant is Infrastructure as a challenge in adopting/scaling your AI Stack?

• Very Significant
  60.7%
• Somewhat Significant
  33.8%
• Not Significant
  5.5%

How significant is Security as a technical challenge in adopting/scaling your AI Stack?

• Very Significant
  62.5%
• Somewhat Significant
  29.6%
• Not Significant
  7.9%

How significant is Tool Complexity as a technical challenge in adopting/scaling your AI Stack?

• Somewhat Significant
  53.5%
• Very Significant
  36.0%
• Not Significant
  10.5%

How significant is Executive Buy-In as an organizational challenge in adopting/scaling your AI Stack?

• Very Significant
  48.5%
• Somewhat Significant
  33.3%
• Not Significant
  18.2%

How significant is the Talent Gap as an organizational challenge in adopting/scaling your AI Stack?

• Very Significant
  46.0%
• Somewhat Significant
  37.9%
• Not Significant
  16.2%

How significant is Organizational Resistance to Change as a challenge in adopting/scaling your AI Stack?

• Somewhat Significant
  42.8%
• Very Significant
  40.3%
• Not Significant
  16.9%

How significant is Regulatory & Compliance as an organizational challenge in adopting/scaling your AI Stack?

• Very Significant
  50.6%
• Somewhat Significant
  38.6%
• Not Significant
  10.8%

Which of the above challenges is the top most significant or challenging to overcome?

• Data Integration & Curation
  23.6%
• Security
  21.9%
• Executive Buy-In
  12.9%
• Tool Complexity
  11.6%
• Infrastructure
  15.5%
• Talent Gap
  6.1%
• Organizational Resistance to Change
  4.4%
• Regulatory & Compliance
  3.5%

Please rank the following barriers or inhibitors to broad-scale AI adoption today, from most to least significant: Cost.

• Rank 1 (Most Significant)
  23.7%
• Rank 2
  13.8%
• Rank 3
  14.3%
• Rank 4
  12.7%
• Rank 5
  9.0%
• Rank 6
  10.1%
• Rank 7 (Least Significant)
  16.4%

Please rank the following barriers or inhibitors to broad-scale AI adoption today, from most to least significant: Data Quality.

• Rank 1 (Most Significant)
  27.0%
• Rank 2
  19.1%
• Rank 3
  18.8%
• Rank 4
  13.2%
• Rank 5
  8.6%
• Rank 6
  7.5%
• Rank 7 (Least Significant)
  5.7%

Please rank the following barriers or inhibitors to broad-scale AI adoption today, from most to least significant: Talent Shortage.

• Rank 1 (Most Significant)
  13.4%
• Rank 2
  11.9%
• Rank 3
  11.8%
• Rank 4
  15.1%
• Rank 5
  20.0%
• Rank 6
  16.2%
• Rank 7 (Least Significant)
  11.6%

Please rank the following barriers to broad-scale AI adoption from most to least significant: Executive Buy-In.

• Rank 1 (Most Significant)
  9.4%
• Rank 2
  12.7%
• Rank 3
  10.7%
• Rank 4
  15.3%
• Rank 5
  16.0%
• Rank 6
  20.0%
• Rank 7 (Least Significant)
  16.0%

Please rank the following barriers to broad-scale AI adoption from most to least significant: Infrastructure.

• Rank 1 (Most Significant)
  9.9%
• Rank 2
  21.0%
• Rank 3
  14.2%
• Rank 4
  12.7%
• Rank 5
  20.0%
• Rank 6
  12.1%
• Rank 7 (Least Significant)
  10.1%

Please rank the following barriers to broad-scale AI adoption from most to least significant: Trust/Safety.

• Rank 1 (Most Significant)
  11.9%
• Rank 2
  15.6%
• Rank 3
  20.2%
• Rank 4
  15.1%
• Rank 5
  11.6%
• Rank 6
  17.6%
• Rank 7 (Least Significant)
  7.9%

Please rank the following barriers to broad-scale AI adoption from most to least significant: Governance/Compliance.

• Rank 1 (Most Significant)
  4.6%
• Rank 2
  5.9%
• Rank 3
  10.1%
• Rank 4
  16.0%
• Rank 5
  14.7%
• Rank 6
  16.4%
• Rank 7 (Least Significant)
  32.4%

Using a scale of 1 (lowest) to 10 (highest), please rate the maturity of your MLOps, AIOps or LLMOps practices today.

• 1 (Lowest)
  1.8%
• 10 (Highest)
  6.8%

Full distribution available in raw data export.

Using a scale of 1 (lowest) to 10 (highest), please estimate where your MLOps, AIOps or LLMOps maturity will be in 12–24 months.

• 1 (Lowest)
  0%
• 10 (Highest)
  10.3%

Full distribution available in raw data export.

Does your organization have a skills gap (or lack of expertise) in AI that you consider a barrier to successful AI implementations?

• Yes
  64.7%
• No
  35.3%

How important is upskilling existing staff as an approach to addressing the AI skills gap within your organization?

• Very Important
  58.5%
• Somewhat Important
  32.1%
• Not Important
  9.4%

How important is hiring new personnel as an approach to addressing the AI skills gap within your organization?

• Very Important
  49.1%
• Somewhat Important
  37.5%
• Not Important
  13.4%

How important is augmenting staff with contractors or consultants as an approach to addressing the AI skills gap within your organization?

• Very Important
  54.3%
• Somewhat Important
  26.4%
• Not Important
  19.3%

How important is acquiring other companies with existing expertise as an approach to addressing the AI skills gap?

• Somewhat Important
  42.9%
• Very Important
  32.4%
• Not Important
  24.7%

How important is delaying AI initiatives until you have the necessary skills as an approach to addressing the AI skills gap?

• Somewhat Important
  41.8%
• Very Important
  26.4%
• Not Important
  31.8%

What is the primary or most important approach you've selected in dealing with the AI skills gap?

• Upskilling existing staff
  38.6%
• Hiring new personnel
  25.3%
• Contractors or consultants
  16.8%
• Acquiring companies with expertise
  13.9%
• Delaying AI initiatives
  5.1%

Does your organization have a clear process for evaluating, testing, and deploying new AI technologies, or is it case by case?

• Partially structured
  40.1%
• Structured process in place
  37.1%
• Case-by-case
  22.8%

On average, how long does it take to move an AI project from development to production?

• 3–6 months
  43.6%
• 1–3 months
  24.3%
• 6–12 months
  18.0%
• <1 month
  6.4%
• 12+ months
  5.5%
• Unsure / haven't made it there
  2.2%

How much AI-driven transformation do you expect in Customer Experience & Engagement over the coming 3–5 years?

• Significant Transformation
  53.1%
• Moderate Transformation
  36.6%
• Limited or No Transformation
  10.3%

How much AI-driven transformation do you expect in Operations & Process Automation over the coming 3–5 years?

• Significant Transformation
  59.4%
• Moderate Transformation
  31.8%
• Limited or No Transformation
  8.8%

How much AI-driven transformation do you expect in Product Development & Innovation over the coming 3–5 years?

• Significant Transformation
  46.9%
• Moderate Transformation
  39.3%
• Limited or No Transformation
  13.8%

How much AI-driven transformation do you expect in Manufacturing & Engineering over the coming 3–5 years?

• Significant Transformation
  56.8%
• Moderate Transformation
  26.3%
• Limited or No Transformation
  16.9%

How much AI-driven transformation do you expect in Back Office functions (HR, Finance & Legal) over the coming 3–5 years?

• Significant Transformation
  54.0%
• Moderate Transformation
  34.4%
• Limited or No Transformation
  11.6%

How much AI-driven transformation do you expect in Coding & Development over the coming 3–5 years?

• Significant Transformation
  57.2%
• Moderate Transformation
  34.2%
• Limited or No Transformation
  8.6%

How much AI-driven transformation do you expect in Sales & Marketing over the coming 3–5 years?

• Significant Transformation
  51.8%
• Moderate Transformation
  39.2%
• Limited or No Transformation
  9.0%

When do you plan to implement retrieval-augmented generation (RAG)?

• Within 6–12 months
  31.1%
• Within 6 months
  25.7%
• Already implemented
  21.9%
• Unsure / Evaluating
  21.3%

How important are vendor partnerships and ecosystem strength (e.g., cloud providers, specialized AI vendors, integrators) to the success of your AI strategy?

• Very Important — Critical to AI strategy
  54.2%
• Somewhat Important — Helpful but not essential
  34.9%
• Neutral / Moderate — Minor role
  9.2%
• Not Important
  1.7%

Which of the following best describes your organization's current position or status on Shadow AI?

• Strictly prohibited with enforcement issues
  37.3%
• Strictly prohibited and enforced
  22.8%
• Guided allowance / support
  22.8%
• Flexible / Experimental
  14.7%
• Unsure
  2.4%

How important is implementing strict monitoring and access controls in managing risks associated with Shadow AI?

• Very Important
  64.7%
• Somewhat Important
  30.3%
• Not Important
  5.0%

How important is providing training and guidelines for safe AI use in managing risks associated with Shadow AI?

• Very Important
  66.5%
• Somewhat Important
  26.1%
• Not Important
  7.4%

How important is conducting audits or reviews of AI tool usage in managing risks associated with Shadow AI?

• Very Important
  53.9%
• Somewhat Important
  37.5%
• Not Important
  8.6%

How important is accepting some risk as part of an innovation strategy in managing risks associated with Shadow AI?

• Very Important
  47.6%
• Somewhat Important
  40.1%
• Not Important
  12.3%

Primary Approach to Managing Shadow AI Risks

• Training & guidelines for safe AI use
  52.4%
• Strict monitoring & access controls
  22.8%
• Audits or reviews of AI tool usage
  18.8%
• Accepting some risk for innovation
  6.1%