diff --git "a/iconiq_report_index/docstore.json" "b/iconiq_report_index/docstore.json"
new file mode 100644--- /dev/null
+++ "b/iconiq_report_index/docstore.json"
@@ -0,0 +1 @@
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"13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c72023a3-8bda-4f18-8370-a19dd7ed7653", "node_type": "1", "metadata": {}, "hash": "41869fc3674abec46ff05d5213b1a0dd6896991f7dc1d9b36a49fbd37cf0e25a", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "September 2024\n\n# The State of AI\n\nNavigating the present and promise\nof Generative AI\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All Rights Reserved\n\n| Authors | |\n|---------|--|\n| Austin Liang
Investor | Seth Pierrepont
General Partner |\n| Matt Jacobson
General Partner | Sruthi Ramaswami
Investor |\n| Mariano Payano
Investor | Tommy Dwyer
Portfolio Operations |\n| Ryan Koh
Investor | Vivian Guo
Portfolio Analytics |\n\n# Author's Note\n\nWe believe the rise of Generative AI is not just a trend; it's a revolution reshaping the business landscape. In particular, large enterprises are recognizing the transformative power of artificial intelligence, moving from mere experimentation to strategic implementation.\n\nGenerative AI adoption is a key imperative for enterprises this year, with 89% of CXOs in our June 2024 survey rating adoption of Generative AI tools in their company as a 4 or 5 in terms of importance.\n\nIn this report, we delve into the current state and future potential of Generative AI, exploring how enterprises are leveraging Generative AI to drive innovation, enhance productivity, and maintain a competitive edge, leveraging insights from a proprietary survey we conducted with CEOs, CTOs, CIOs, and functional leaders in June 2024 as well as conversations with AI leaders across the ICONIQ community.\n\n[Explore our AI perspectives]\n\nPrivate & Strictly Confidential 2\n\n# Meet the Team\n\n## ICONIQ Growth Analytics\n\nContact us at ICONIQGrowthInsights@iconiqcapital.com\n\nSeeking to empower our portfolio with proprietary insights and advisory across business operations, hiring, and strategy\n\n| Christine Edmonds | Ani Reddy | Caroline Brand | Claire Davis |\n|-------------------|-----------|----------------|--------------|\n| Head of Portfolio Analytics | Portfolio Data Analyst | Portfolio Analytics | Portfolio Analytics |\n\n| Emre Garih | Katherine Dunn | Sam O'Neill | Vivian Guo |\n|------------|----------------|-------------|------------|\n| Portfolio Analytics | Portfolio Analytics | Portfolio Data Manager | Portfolio Analytics |\n\nICONIQ Growth\n\nPrivate & Strictly Confidential\n\n3\n\n# Follow our research\n\n\ud83c\udf10 in \ud83d\udc26 \ud83d\udce7SUBSCRIBE\n\n## Go-To-Market Series\n\nGuides to sales, customer success, marketing compensation - and more\n\n## Decoding the SaaS IPO Landscape\n\nThe Path to IPO\nSaaS Enterprise Version\n\nThe metrics that matter and the market realities of 2024 and beyond\n\n## 2023 Growth & Efficiency Report\n\nICONIQ Growth\nThe New Era of\nEfficient Growth\n2023 Growth & Efficiency Report\n\nExplore our research on best-in-class SaaS growth and efficiency\n\n### The ICONIQ Growth Enterprise Five\n\n| Metric |\n|--------|\n| 1. ARR Growth |\n| 2. Net $ Retention |\n| 3. Rule of 40 |\n| 4. Net Magic Number |\n| 5.", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 2824, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "c72023a3-8bda-4f18-8370-a19dd7ed7653": {"__data__": {"id_": "c72023a3-8bda-4f18-8370-a19dd7ed7653", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "1d1d555b-9293-4aff-bda1-d3cdc0e8edd6", "node_type": "1", "metadata": {}, "hash": "21148645195fb911f61d8c8082f6f0da0b516ed914ecca0950c274882a4046c9", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f90b5018-8172-46e3-9675-6c168907095c", "node_type": "1", "metadata": {}, "hash": "fa7dbac4e21f97073f16e4b066cfbc41f69413cd8c636efb5239b81b24da06ab", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "ARR Growth |\n| 2. Net $ Retention |\n| 3. Rule of 40 |\n| 4. Net Magic Number |\n| 5. ARR per FTE |\n\nKey performance indicators of Enterprise SaaS companies\n\n### The SaaS Glossary\n\nA guide to understanding and tracking key SaaS metrics\n\n### Hiring Your Next Marketing Leader\n\nLeadership Analytics\nHiring a Marketing\nExecutive from\n$50M ARR to IPO\n\nWhat to prioritize when hiring a Marketing executive from $50M ARR to IPO\n\nICONIQ Growth\n\nPrivate & Strictly Confidential\n\n# Table of Contents\n\n| Section | Topic | Page |\n|---------|-------|------|\n| Introduction | Data Sources & Methodology | 6 |\n| | Predictions for the Future | 7 |\n| | Executive Summary | 8 |\n| Navigating Generative AI Purchasing Decisions | Leading AI Goals for Enterprise | 10 |\n| | A Decision-Making Framework | 11 |\n| | Generative AI Budgets | 12 |\n| | AI Decision Makers | 14 |\n| | Vendor Preferences | 15 |\n| | Build vs Buy | 16 |\n| | Infrastructure vs Applications | 17 |\n| Measuring ROI | Barriers to Adoption | 19 |\n| | Hiring | 20 |\n| | Use Cases | 22 |\n| | Estimated ROI | 23 |\n| Deep Dive on Infrastructure | Key Purchasing Criteria | 25 |\n| | Cloud Deployment Method | 26 |\n| | Proprietary vs Open Source | 27 |\n| | Models | 29 |\n| | Infrastructure Tooling | 30 |\n| Deep Dive on Applications | AI Usage by Function | 32 |\n| | Spotlight: Product & Engineering | 33 |\n| | Spotlight: IT | 34 |\n| | Spotlight: Marketing | 35 |\n| | Spotlight: Sales | 36 |\n| | Spotlight: Finance | 37 |\n| | Spotlight: Operations | 38 |\n\nICONIQ | Growth\n\n# Data Sources & Methodology\n\nThis study summarizes data from a June 2024 survey\u00b9 of 215 executives\u00b2 at enterprises with $500M+ annual revenue, including CEOs, CIOs, CTO, and functional leaders.\n\nThroughout this series, we also weave in perspectives, insights, and what we believe to be best practices from AI leaders from the ICONIQ Growth community.\n\nAll industry perspectives shared in this report have been anonymized to protect company-level information.\n\n## Respondent Firmographics\n\n### Industry\n\n| Industry | Percentage |\n|----------|------------|\n| Technology | 33% |\n| Financial Services | 24% |\n| Healthcare / Pharma | 12% |\n| Advanced Manufacturing | 6% |\n| Consumer Goods | 6% |\n| System Integrators | 4% |\n| Marketing / Advertising | 3% |\n| Retail | 3% |\n| Other | 11% |\n\n### Revenue Range\n\n| Revenue Range | Percentage |\n|---------------|------------|\n| $500M to less than $1B | 13% |\n| $1B to less than $2B | 17% |\n| $2B to less than $3B | 7% |\n| $3B to less than $5B | 11% |\n| $5B to less than $10B | 18% |\n| $10B to less than $20B | 9% |\n| $20B+ | 24% |\n\n### Headquarters\n\n| Location | Percentage |\n|----------|------------|\n| United States | 91% |\n| United Kingdom | 7% |\n| Canada | 1% |\n| Other | 1% |\n\nNotes: \n(1) This data was collected anonymously by an external survey. Survey responses include some but not all ICONIQ Growth portfolio companies as well as companies not part of ICONIQ Growth's portfolio.\n(2) Certain questions in the survey were optional. Accordingly, some N-Size numbers in this presentation are less than 215\n\nPrivate & Strictly Confidential\n\n# 6 Predictions for the Future\n\n| Themes | Predictions |\n|--------|-------------|\n| The Rise of Specialized Use Cases | 1. Well-entrenched infrastructure and application software vendors will force AI native startups to focus on specialized use cases or underserved business functions to demonstrate value and quick paths to ROI |\n| | 2. While coding co-pilots like GitHub Co-Pilot have seen outsized adoption\u00b9, significant opportunity still exists for specialized tools for the R&D organization to solve age-old problems where AI can uplevel engineering work (site reliability, DevOps, QA testing, code refactoring, penetration testing, etc.) |\n| The Battle of Models | 3.", "mimetype": "text/plain", "start_char_idx": 2742, "end_char_idx": 6513, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "f90b5018-8172-46e3-9675-6c168907095c": {"__data__": {"id_": "f90b5018-8172-46e3-9675-6c168907095c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "c72023a3-8bda-4f18-8370-a19dd7ed7653", "node_type": "1", "metadata": {}, "hash": "41869fc3674abec46ff05d5213b1a0dd6896991f7dc1d9b36a49fbd37cf0e25a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "63346c4d-a45b-4cb1-b000-6c82af123ee3", "node_type": "1", "metadata": {}, "hash": "b55f941091f1a4bb071869af6d64de9328c1b4d3938bf4d373fef83fd6392ca3", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "(2) Certain questions in the survey were optional. Accordingly, some N-Size numbers in this presentation are less than 215\n\nPrivate & Strictly Confidential\n\n# 6 Predictions for the Future\n\n| Themes | Predictions |\n|--------|-------------|\n| The Rise of Specialized Use Cases | 1. Well-entrenched infrastructure and application software vendors will force AI native startups to focus on specialized use cases or underserved business functions to demonstrate value and quick paths to ROI |\n| | 2. While coding co-pilots like GitHub Co-Pilot have seen outsized adoption\u00b9, significant opportunity still exists for specialized tools for the R&D organization to solve age-old problems where AI can uplevel engineering work (site reliability, DevOps, QA testing, code refactoring, penetration testing, etc.) |\n| The Battle of Models | 3. Large model vendors like OpenAI and Anthropic and big tech vendors like Meta, Google, MSFT, AWS will make it difficult for newer startups building out private general-purpose foundation models given those vendors' scale, distribution, and robust balance sheets |\n| | 4. Further, we expect to see an increase in smaller and domain or industry specific models as enterprises increasingly rely on a mixture of models that can drive specific business outcomes under performance, speed, and cost constraints |\n| Exponential Growth | 5. Current spend on gen AI within enterprises is only ~10% of the total software procurement spend\u00b2, indicating that we are still in the early innings of adoption and could expect GenAI adoption to accelerate significantly in the coming years as things move into production and enterprises start to see real business outcomes and ROI from GenAI investments. |\n| | 6. Co-creation between employees and AI tools will drive much of the adoption of generative AI within enterprises. Companies that deliver products or solutions that augment existing workflows stand to benefit the most; similarly, teams that adopt these products and rethink jobs-to-be done should reap the greatest benefits |\n\nNotes:\n(1) Quantifying Github Copilot's Impact in the Enterprise with Accenture (May 2024)\n(2) Perspectives from the ICONIQ Growth GenAI Survey (June 2024)\n\nSource: Perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Executive Summary\n\nWe believe Generative AI brings with it significant promise to augment product offerings and improve internal productivity, but also challenges for large organizations trying to implement AI.\n\nMany enterprise buyers are still developing their vision of the future and understanding of when or how generative AI might change their businesses; however, we are starting to see initial spikes in adoption across core use cases and early signals of ROI and business value.", "mimetype": "text/plain", "start_char_idx": 5683, "end_char_idx": 8631, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "63346c4d-a45b-4cb1-b000-6c82af123ee3": {"__data__": {"id_": "63346c4d-a45b-4cb1-b000-6c82af123ee3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f90b5018-8172-46e3-9675-6c168907095c", "node_type": "1", "metadata": {}, "hash": "fa7dbac4e21f97073f16e4b066cfbc41f69413cd8c636efb5239b81b24da06ab", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f2b538ba-bccb-4acb-8a76-b22245656ed5", "node_type": "1", "metadata": {}, "hash": "e3ad59a7ad4db7ce7825cae6edbeaeb6b1af3f5ae6853fd7084c358ea6b7da11", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Many enterprise buyers are still developing their vision of the future and understanding of when or how generative AI might change their businesses; however, we are starting to see initial spikes in adoption across core use cases and early signals of ROI and business value.\n\n## 1 Navigating Generative AI Purchasing Decisions\n\n- With AI adoption being a key imperative for companies, 88% of companies analyzed have an approved budget for AI investments; however, most AI dollars are coming from existing budgets vs net new dollars\n\n- On average, companies allocated 10-15% of their software procurement budget towards GenAI, implying there is additional white space for AI solutions to capture in the future\n\n- The decision makers for GenAI decisions seem to be predominantly CTOs, likely driven by the fact that most AI spend is coming from existing budgets (R&D being the most common source)\n\n- New AI-native vendors might have a difficult road ahead to break into enterprises; when procuring generative AI products, CXOs generally prefer to source from existing vendors, followed by tech incumbents\n\n- CXOs generally prioritize the performance of LLMs above all other factors, with cost being the least important purchasing criterion\n\n## 2 Quantifying Business Value and ROI\n\n- There remain several barriers to adoption in enterprises, including lack of in-house expertise, quality and accuracy, data security / privacy, infrastructure readiness, and the unproven ROI of generative AI\n\n- A key component of AI spend and readiness in enterprises is upskilling and identifying the right resources to enable AI adoption; the majority of AI roles being hired include data scientists, machine learning engineers, and data engineers\n\n- While a large portion of generative AI budgets are still going toward experimental use cases, enterprises have started to see ROI for use cases spanning customer service, IT, software code development, operations, and sales\n\n- Executives expect ROI from generative AI to be in the 5-20% range on average, with productivity gains and cost savings easier to quantify than true revenue impact\n\n## 3 Deep Dive on Implementation and Use Cases\n\n- A greater proportion of technology firms prefer to build GenAI products, whereas financial services and healthcare companies prefer to buy from existing providers\n\n- ~70% of enterprises are augmenting their generative AI models via finetuning or retrieval augmented generation (RAG)\n\n- Enterprises generally prefer to utilize proprietary models like GPT-4 over open-source models like Llama with on average ~60% of workloads being built with proprietary models\n\n- In addition to investments in foundation models, enterprises are also procuring infrastructure tooling to support areas like data observability, database augmentation, and data pre-processing\n\n- Technical teams lead in adoption of generative AI for internal productivity use cases, while HR and legal functions lag, likely hindered by data privacy and quality concerns\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from AI leaders in the ICONIQ Growth network\n\nICONIQ | Growth\n\nPrivate & Strictly Confidential\n\n8\n\n# Navigating Generative AI Purchasing Decisions\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All Rights Reserved\n\n# Leading AI Goals for Enterprises\n\nWe believe Generative AI brings with it significant promise to augment product offerings and improve internal productivity, but also challenges for large organizations trying to implement AI\n\n> Many enterprises are starting with leveraging AI to boost internal productivity before expanding to product use cases.\n\n## AI Investments\n\n1. Improved Employee Productivity\n - This could yield significant operational efficiency opportunities for enterprises, including the potential for labor cost savings, process automation, and additional capacity for humans to focus on higher-level tasks.\n\n2. Increasing Operational Efficiency\n\n3. Growing Revenue\n - 75% of companies surveyed have applied generative AI to one or more product use cases.\n - The majority of CXOs think adoption of generative AI has resulted in either defending / gaining market share or increasing pricing power.\n\n## Key Considerations\n\n- We are starting to see a slow down in the initial hype from generative AI, with CXOs having found some of the initial investments in AI a heavy lift to implement\n\n- Showing ROI from productivity gains has generally been a lot easier and quicker than quantifying revenue impact, especially as CXOs are defaulting to purchasing solutions rather than building in-house\n\n- There are also significant costs involved with ensuring enterprises are ready to adopt AI, across infrastructure, training, change management, etc.", "mimetype": "text/plain", "start_char_idx": 8357, "end_char_idx": 13171, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "f2b538ba-bccb-4acb-8a76-b22245656ed5": {"__data__": {"id_": "f2b538ba-bccb-4acb-8a76-b22245656ed5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "63346c4d-a45b-4cb1-b000-6c82af123ee3", "node_type": "1", "metadata": {}, "hash": "b55f941091f1a4bb071869af6d64de9328c1b4d3938bf4d373fef83fd6392ca3", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ca22772c-fc17-4ae4-8470-ecab2016e587", "node_type": "1", "metadata": {}, "hash": "845d4687d1a636c6cc363cb06ec506063f3128c84366a28203f2cbf714b520f0", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "2. Increasing Operational Efficiency\n\n3. Growing Revenue\n - 75% of companies surveyed have applied generative AI to one or more product use cases.\n - The majority of CXOs think adoption of generative AI has resulted in either defending / gaining market share or increasing pricing power.\n\n## Key Considerations\n\n- We are starting to see a slow down in the initial hype from generative AI, with CXOs having found some of the initial investments in AI a heavy lift to implement\n\n- Showing ROI from productivity gains has generally been a lot easier and quicker than quantifying revenue impact, especially as CXOs are defaulting to purchasing solutions rather than building in-house\n\n- There are also significant costs involved with ensuring enterprises are ready to adopt AI, across infrastructure, training, change management, etc.\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# A Decision-Making Framework\n\nWhen making decisions around GenAI investments, we believe it will be important to assess organization readiness, put in place a framework and processes for use case evaluation, and proactively mitigate risks\n\n## Accelerate Value\n\nFind synergies between organizational readiness, use cases, and risk mitigation when making GenAI investment decisions\n\n### Use Case Identification & Evaluation\n\nWhen determining use cases for GenAI, we believe stakeholders will need to assess business value, the fluency vs. accuracy of solutions, and the level of risk associated. Given the risks involved with using GenAI to build new products, many organizations are first starting with use cases for internal productivity.\n\nIt is also important to implement feedback loops and a system for measuring ROI to evaluate use cases.\n\n### Organizational Readiness\n\nFor enterprises adopting GenAI solutions for the first time, we believe it will be important to ensure various components of the organization are ready to support the development and integration needs involved. Organizational readiness components to assess could include:\n\n- Employee readiness and training\n- IT / data team expertise\n- Security\n- Governance structure and policies\n- Data ecosystem maturity\n\n### Risk Mitigation\n\nWe believe enterprises will need to account for various risks like data security and privacy concerns, algorithm accuracy / bias, integration complexity, etc. when evaluating GenAI solutions.\n\nOrganizations can employ various strategies to mitigate some of these risks. For example, it may make sense to invest in fine-tuning or retrieval augmented generation (RAG) techniques to mitigate concerns of model accuracy.\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Generative AI Budgets\n\nWith generative AI being a key imperative for most companies, 88% of companies analyzed have an approved budget for AI investments; however, most AI dollars are coming from existing budgets vs net new dollars being created\n\n## 88% of organizations have an approved budget for generative AI investments\n% of Respondents, N = 143\n\n| Approved Budget | Percentage |\n|-----------------|------------|\n| Yes | 88% |\n| No | 12% |\n\n## Most respondents are leveraging existing R&D budgets for generative AI investments\n% of Respondents (Multi-select), N = 126\n\n| Source of Budget | Percentage |\n|------------------|------------|\n| Coming from R&D budget | 59% |\n| Net new budget being created | 57% |\n| Coming from innovation budget (non R&D) | 47% |\n| Coming from business unit (non R&D) initiatives | 44% |\n\n> \"Having AI investments be centrally funded can enable greater speed of innovation. However, having business units take on some amount of the budget also gives those leaders skin in the game. At the end of the day, the last mile across change management, operationalizing, and enabling SMEs is the most important and often the most challenging.\"", "mimetype": "text/plain", "start_char_idx": 12337, "end_char_idx": 16741, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "ca22772c-fc17-4ae4-8470-ecab2016e587": {"__data__": {"id_": "ca22772c-fc17-4ae4-8470-ecab2016e587", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f2b538ba-bccb-4acb-8a76-b22245656ed5", "node_type": "1", "metadata": {}, "hash": "e3ad59a7ad4db7ce7825cae6edbeaeb6b1af3f5ae6853fd7084c358ea6b7da11", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "bace8f87-3626-4465-8486-b68e769b9e07", "node_type": "1", "metadata": {}, "hash": "c819bb34c7c730a972f66cdfbb0fbaeefa33ba1f29a503b56bf37552addde8e4", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "However, having business units take on some amount of the budget also gives those leaders skin in the game. At the end of the day, the last mile across change management, operationalizing, and enabling SMEs is the most important and often the most challenging.\"\n> \n> - Chief Data and Analytics Officer, Healthcare Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Generative AI Budgets\n\nThe dollars spent on generative AI in 2024 vary significantly based on company size, ranging anywhere from ~$10M to ~$200M; most companies expect to increase AI spending in 2025 by 22% on average\n\n## Approximately, what is your organization's annual generative AI budget in 2024?\n% of Respondents (N = 143)\n\n| 2024 Annual Revenue | Budget |\n|---------------------|--------|\n| $500M to less than $1B | $11M |\n| $1B to less than $3B | $10M |\n| $3B to less than $5B | $31M |\n| $5B to less than $10B | $32M |\n| $10B to less than $20B | $147M |\n| $20B+ | $214M |\n\nCXOs expect to increase generative AI budgets by **22%** on average in 2025\n\n> \"We've already earmarked spend in our 2025 budget for a tool that is driving a ~30% reduction in time savings\"\n> \n> - VP Services & Partner Strategy of F1000 Tech Company\n\nIt is also important to note that on average, companies allocated only 10-15% of their total software procurement budget in 2024 towards generative AI. This implies that there is additional white space (and budget dollars) for generative AI solutions to capture in the coming years.\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# AI Decision Makers\n\nThe key decision makers for generative AI-related decisions seem to be predominantly CTOs, likely driven by the fact that most AI spend is coming from existing budgets, with the most common source being R&D budgets\n\nPlease rank up to 5 key owners for generative AI-related decisions (software procurement, building AI capabilities, etc.) in your organization.\n\n| Role | % of Respondents Listing in Top 3 (N = 143) |\n|------|---------------------------------------------|\n| Chief Technology Officer | 77% |\n| Head of AI / Chief AI Officer | 59% |\n| CEO | 38% |\n| Head of Data / Chief Data Officer | 29% |\n| Chief Information Security / Chief Security Officer | 24% |\n| Chief Financial Officer / Head of Finance | 20% |\n| COO | 16% |\n| Chief Product Officer | 15% |\n| Head of Non-Technical Function (e.g. Sales, Marketing, etc.) | 8% |\n| Managers | 6% |\n| Individual employees | 2% |\n\nWhile CTOs and Heads of AI were selected as the most common owner for generative AI-related decisions, it is also interesting to note that CEOs were ranked #3 on the list, indicating that generative AI is a strategic imperative for enterprises at the highest levels.\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential 14\n\n# Vendor Preferences\n\nNew AI-native vendors might have a difficult road ahead to break into enterprises; when procuring generative AI products, CXOs generally prefer to source from existing vendors, followed by tech incumbents\n\nWhich of the following is your company most likely to procure generative AI-enabled products from? Please rank in order of most to least likely.", "mimetype": "text/plain", "start_char_idx": 16480, "end_char_idx": 20512, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "bace8f87-3626-4465-8486-b68e769b9e07": {"__data__": {"id_": "bace8f87-3626-4465-8486-b68e769b9e07", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ca22772c-fc17-4ae4-8470-ecab2016e587", "node_type": "1", "metadata": {}, "hash": "845d4687d1a636c6cc363cb06ec506063f3128c84366a28203f2cbf714b520f0", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "495fd7fb-d454-4ad8-acf0-5d0039a6d69a", "node_type": "1", "metadata": {}, "hash": "acb6cb06aa17c9beedb7c37775d17254242d99b8e6fc1f88b41fe937597b7d51", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Source: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential 14\n\n# Vendor Preferences\n\nNew AI-native vendors might have a difficult road ahead to break into enterprises; when procuring generative AI products, CXOs generally prefer to source from existing vendors, followed by tech incumbents\n\nWhich of the following is your company most likely to procure generative AI-enabled products from? Please rank in order of most to least likely.\n\n| Rank | The AI / tech vendors we work with today | Incumbent vendors (e.g., leading tech / AI companies) we have not worked with before | New AI-native vendors |\n|------|------------------------------------------|-----------------------------------------------------------------------------------|------------------------|\n| Rank 3 | 6% | 31% | 63% |\n| Rank 2 | 31% | 42% | 28% |\n| Rank 1 | 63% | 28% | 9% |\n\n% of Respondents (N = 65)\n\n> \"We evaluate startups all the time but we have an AI governance committee that does a detailed review and approval of every single AI tool that we explore, pilot, or adopt. This means the procurement process and timeline can be very challenging for new solutions\"\n> \n> - CIO of F500 Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Build vs Buy\n\nA greater proportion of technology firms prefer to build generative AI products, whereas financial services and healthcare companies tend to prefer to buy from existing providers\n\n## Does your organization prefer to build or buy from existing providers for generative AI enabled products?\n% of Respondents by Industry (N = 143)\n\n| Option | Technology | Financial Services | Healthcare | Other Industries |\n|--------|------------|---------------------|------------|-------------------|\n| Buy | 21% | 41% | 41% | 25% |\n| Build | 19% | 3% | 6% | 6% |\n| Both | 60% | 57% | 53% | 69% |\n\nIt is interesting to observe that despite being in more heavily regulated industries, Financial Services and Healthcare companies prefer to buy solutions which could imply that there is significant technical uplift involved with building in-house.\n\n> \"You need to challenge people who want to build everything. If it's not a core competency and it's not going to yield a competitive advantage for the company, let's buy and just build on top of a solution\"\n> - CIO of F500 Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Infrastructure vs. Applications\n\nEnterprises are spending a relatively equal amount of invested dollars across models / infrastructure and applications, with technology firms spending a higher proportion of their budget on infrastructure\n\nWhat percentage of your generative AI investments is dedicated to infrastructure software to build AI-enabled solutions vs. AI-enabled applications from third-party vendors?\n% of Respondents (N = 143)\n\n| Industry | Application Software | Infrastructure |\n|----------|----------------------|----------------|\n| Technology | 42% | 58% |\n| Financial Services | 46% | 54% |\n| Healthcare | 55% | 45% |\n| Other Industries | 55% | 45% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential 17\n\n# Measuring ROI\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC.", "mimetype": "text/plain", "start_char_idx": 19731, "end_char_idx": 24296, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "495fd7fb-d454-4ad8-acf0-5d0039a6d69a": {"__data__": {"id_": "495fd7fb-d454-4ad8-acf0-5d0039a6d69a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "bace8f87-3626-4465-8486-b68e769b9e07", "node_type": "1", "metadata": {}, "hash": "c819bb34c7c730a972f66cdfbb0fbaeefa33ba1f29a503b56bf37552addde8e4", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ee0ba756-4cea-49ed-8e6e-a733bc279855", "node_type": "1", "metadata": {}, "hash": "dc6f54cb072d64803fae10ebb2adff722ae944ef6793a8bab9392e0d18f2bdfc", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "AI-enabled applications from third-party vendors?\n% of Respondents (N = 143)\n\n| Industry | Application Software | Infrastructure |\n|----------|----------------------|----------------|\n| Technology | 42% | 58% |\n| Financial Services | 46% | 54% |\n| Healthcare | 55% | 45% |\n| Other Industries | 55% | 45% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential 17\n\n# Measuring ROI\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All Rights Reserved\n\n# Barriers to Adoption\n\nThere remain several barriers to adoption in enterprises, including lack of in-house expertise, quality and accuracy, data security / privacy, infrastructure readiness, and the unproven ROI of generative AI\n\n| Data Security & Privacy | Quality & Accuracy | Data & Infrastructure Readiness | Lack of In-House Expertise | Cost Constraints |\n|-------------------------|---------------------|--------------------------------|----------------------------|-------------------|\n| If sensitive or confidential data is used as input for generative AI models, there is a risk that this data could be exposed through model outputs. There are also potential IP risks involved with leveraging AI-generated content. | Generative AI models can produce outputs that are factually incorrect or misleading, especially if the training data contains errors or if the model misinterprets the input. Generative AI models can also inadvertently perpetuate biases present in the training data. | Many enterprises may not have underlying infrastructure in the ideal state to embrace AI (e.g., data silos across on-prem and cloud, lack of clean and labeled data, tech debt, etc). In particular, we believe strong data governance is a key prequisite for AI adoption. | Organizations will generally need to hire skilled AI professionals like data scientists, machine learning engineers, and AI researchers, which can be difficult in an increasingly competitive market. | There are several costs beyond procuring generative AI solutions, such as costs associated with labor, change management, tech debt cleanup, data management, etc. can be significant and are often required for enterprises to be ready to embrace AI. |\n\nIn addition to the costs involved with procuring generative AI solutions, enterprises estimate they will need to spend on average $75M to enable their organizations to be ready to fully adopt generative AI (e.g., training, data cleanup costs, process changes, etc.)\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nICONIQ | Growth Private & Strictly Confidential 19\n\n# Hiring\n\nWe believe a key component of AI spend and readiness in enterprises is upskilling and identifying the right resources to enable AI adoption; the majority of AI roles being hired include data scientists, machine learning engineers, and data engineers\n\n## Which of the following roles is your company hiring for in 2024 specifically for AI?\n% of Respondents (N = 143)\n\n| Role | Percentage |\n|------|------------|\n| AI data scientists | 69% |\n| Machine learning engineers | 62% |\n| Data engineers | 60% |\n| AI product owners / managers | 59% |\n| Data architects | 45% |\n| Data visualization specialists | 27% |\n| Prompt engineers | 24% |\n| Design specialists | 15% |\n| Other | 3% |\n| None of the above | 2% |\n\n> So much of AI innovation and adoption in the enterprise will be tied to organizational structure and how much managers empower their teams to experiment and figure out what works.\n> \n> CIO of F500 Professional Services Firm\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nUse Cases\n\nGenerative AI has shown significant adoption with use cases spanning product enhancements, IT, customer service, and software code development while HR and legal functions lag, likely due to data / security concerns\n\n| For which use cases has your company adopted Generative AI?", "mimetype": "text/plain", "start_char_idx": 23467, "end_char_idx": 28381, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "ee0ba756-4cea-49ed-8e6e-a733bc279855": {"__data__": {"id_": "ee0ba756-4cea-49ed-8e6e-a733bc279855", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "495fd7fb-d454-4ad8-acf0-5d0039a6d69a", "node_type": "1", "metadata": {}, "hash": "acb6cb06aa17c9beedb7c37775d17254242d99b8e6fc1f88b41fe937597b7d51", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "cf5ec2c3-3be3-4661-9ef2-111312292983", "node_type": "1", "metadata": {}, "hash": "e0d8b8ca9f5fac57ca7267212f2615bd9c9136887036bc53fe5bfaa0a447cdae", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "> \n> CIO of F500 Professional Services Firm\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nUse Cases\n\nGenerative AI has shown significant adoption with use cases spanning product enhancements, IT, customer service, and software code development while HR and legal functions lag, likely due to data / security concerns\n\n| For which use cases has your company adopted Generative AI? |\n|-------------------------------------------------------------|\n| Multi-select, % of Respondents (N = 143) |\n\n| Product Differentiation | Internal Productivity | R&D | S&M | G&A |\n|-------------------------|------------------------|-----|-----|-----|\n| 69% Core product performance enhancements | 61% IT | | | |\n| 57% New products or services | 58% Customer service | | | |\n| 50% Natural language interfaces for existing product | 54% Software code development | | | |\n| | 53% Operations | | | |\n| | 46% Sales and sales operations | | | |\n| | 46% Marketing | | | |\n| | 43% Overall knowledge worker effectiveness | | | |\n| | 32% Customer onboarding | | | |\n| | 31% Finance | | | |\n| | 25% Legal | | | |\n| | 20% HR | | | |\n| | 10% Non-software R&D | | | |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential 21\n\nUse Cases\n\nWhile a large portion of generative AI budgets are still going toward experimental use cases, enterprises have started to see ROI among the top 5 use cases\n\n| What % of your generative AI budget would you consider experimental vs. defined? | What uses of generative AI have exhibited the most ROI to date? |\n|-------------------------------------------------------------------------------|------------------------------------------------------------------|\n| % of Respondents (N = 143) | % Mentions in Top 5 Use Case (N = 116) |\n\n| Category | Percentage |\n|------------------|------------|\n| Experimental | 40% |\n| Defined use case | 60% |\n\n| Use Case | Percentage |\n|---------------------------|------------|\n| Customer service | 47% |\n| IT | 42% |\n| Software code development | 41% |\n| Operations | 32% |\n| Sales and sales operations| 31% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Estimated ROI\n\nExecutives are seeing or anticipate seeing ROI from generative AI in the 5-20% range on average, with productivity gains and cost savings easier to quantify than true revenue impact\n\n## What is the estimated ROI you are seeing or anticipate seeing from leveraging generative AI?\n% of Respondents (N = 143)\n\n| Category | We are seeing negative ROI | We are seeing little to no ROI (<5%) | We are seeing some ROI (5-20%) | We are seeing significant ROI (>20%) | Too early to tell |\n|----------|----------------------------|--------------------------------------|--------------------------------|--------------------------------------|-------------------|\n| Productivity Gains | | 6% | 55% | 33% | 7% |\n| Cost Savings | 1% | 18% | 53% | 22% | 6% |\n| Quality Improvements | 1% | 14% | 57% | 15% | 13% |\n| Revenue Opportunities | | 19% | 53% | 15% | 14% |\n\n> There is a big question of when and how do you weave in ROI. Some firms have oriented on learning and treating this like a R&D cost.", "mimetype": "text/plain", "start_char_idx": 27729, "end_char_idx": 31961, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "cf5ec2c3-3be3-4661-9ef2-111312292983": {"__data__": {"id_": "cf5ec2c3-3be3-4661-9ef2-111312292983", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ee0ba756-4cea-49ed-8e6e-a733bc279855", "node_type": "1", "metadata": {}, "hash": "dc6f54cb072d64803fae10ebb2adff722ae944ef6793a8bab9392e0d18f2bdfc", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "eaee8fe7-21e0-4140-afd1-3fc660420759", "node_type": "1", "metadata": {}, "hash": "21b7aec1b8692b8f3e1b61d264ce3bc7b4e2c99365a2c56436d83f9035f4feb6", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "% of Respondents (N = 143)\n\n| Category | We are seeing negative ROI | We are seeing little to no ROI (<5%) | We are seeing some ROI (5-20%) | We are seeing significant ROI (>20%) | Too early to tell |\n|----------|----------------------------|--------------------------------------|--------------------------------|--------------------------------------|-------------------|\n| Productivity Gains | | 6% | 55% | 33% | 7% |\n| Cost Savings | 1% | 18% | 53% | 22% | 6% |\n| Quality Improvements | 1% | 14% | 57% | 15% | 13% |\n| Revenue Opportunities | | 19% | 53% | 15% | 14% |\n\n> There is a big question of when and how do you weave in ROI. Some firms have oriented on learning and treating this like a R&D cost. Ultimately what moves the needle for our business will manifest itself but it may take some time to quantify\n> \n> CXO of F100 Insurance Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n23\n\n# Deep Dive on Infrastructure\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All Rights Reserved\n\n# Key Purchasing Criteria\n\nCXOs tend to prioritize the performance of LLMs above all other factors, with cost being the least important purchasing criterion\n\n## Which of the following factors are important to your organization when selecting an LLM or generative AI foundation model?\n% of Respondents Ranked as Top Factor\n\n| Factor | Percentage |\n|----------------|------------|\n| Performance | 40% |\n| Security | 21% |\n| Customizability| 15% |\n| Control | 12% |\n| Cost | 12% |\n\nDifferent models will have tradeoffs across performance, security, customizability, control, and cost, among other factors. However, it appears that CXOs are placing a premium on performance.\n\n> \"It doesn't matter what you use. Nobody really cares what LLM you pick. What we care about is how it drives business outcomes for the business units that are allocating budget towards AI\"\n> \n> CDO of Financial Institution\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nCloud Deployment Method\n\nEnterprises are primarily hosting generative AI workloads on the cloud or via a hybrid approach; AWS and Azure are the most utilized cloud service providers\n\n| Preferred Deployment Method for GenAI Models | CSP Used for GenAI Products |\n|----------------------------------------------|------------------------------|\n| % of Respondents (N = 126) | Multi-Select, % of Respondents (N = 218) |\n\n| Deployment Method | Percentage |\n|--------------------|------------|\n| On-prem | 2% |\n| Hybrid | 42% |\n| Cloud | 56% |\n\n| Cloud Service Provider | Percentage |\n|---------------------------|------------|\n| Amazon Web Services (AWS) | 68% |\n| Microsoft Azure | 61% |\n| Google Cloud (GCP) | 40% |\n| Other | 3% |\n\nWhile Azure has captured mindshare with its OpenAI, Amazon remains ahead in terms of cloud usage given the dominant market share AWS has in cloud\u00b9\n\nNotes: (1) Statista Worldwide Market Share of Leading Cloud Infrastructure Service Providers (May 2024)\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Proprietary vs Open Source\n\nEnterprises generally prefer to utilize proprietary models like GPT-4 over open-source models with on average ~60% of workloads being built with proprietary models; enterprises are primarily procuring LLMs via cloud service or model providers\n\n## What percentage of your GenAI models are proprietary vs open source?", "mimetype": "text/plain", "start_char_idx": 31254, "end_char_idx": 35644, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "eaee8fe7-21e0-4140-afd1-3fc660420759": {"__data__": {"id_": "eaee8fe7-21e0-4140-afd1-3fc660420759", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "cf5ec2c3-3be3-4661-9ef2-111312292983", "node_type": "1", "metadata": {}, "hash": "e0d8b8ca9f5fac57ca7267212f2615bd9c9136887036bc53fe5bfaa0a447cdae", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "e52263df-58a2-4dd4-88c1-9c42e292d067", "node_type": "1", "metadata": {}, "hash": "608f8bd5eb9002b33a782d809867e4cbc8a7edb4dbc9c389cc05ff634590e50f", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Average % of Models (N = 143)\n\n| Type | Percentage |\n|------------|------------|\n| Open Source| 38% |\n| Proprietary| 62% |\n\n## How does your organization discover or procure LLMs?\n\nMulti-select, % of Respondents (N = 143)\n\n| Method | Percentage |\n|-------------------------------------------------------------------------------|------------|\n| Indirectly through our cloud service provider (e.g., Azure, AWS, GCP) | 71% |\n| Directly through a model provider (e.g., OpenAI, Anthropic, Cohere) | 68% |\n| Indirectly through a model hub (e.g., Hugging Face, Replicate) | 23% |\n\n> We have significant partnerships with the hyperscalers, so the inclination is to first explore what we can use from those partners. At the same time, we are cautious of lock-in and like to explore other vendors.\n> \n> CIO of F500 Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Models Used\n\nOpenAI's GPT models are the most widely adopted generative AI models; however, ~30-50% of enterprises are also experimenting with Google, Meta, and Anthropic models\n\n## Which LLMs or generative AI foundation model providers is your organization currently using?\nMulti-Select, % of Respondents (N = 126)\n\n| Model Provider | Testing / Experimenting | In Production |\n|----------------|-------------------------|---------------|\n| OpenAI (GPT) | 90% | 82% |\n| Google (Gemini, PaLM 2, Gemma) | 56% | 40% |\n| Meta (Llama 2/3) | 49% | 33% |\n| Anthropic | 31% | 21% |\n| Mistral AI | 22% | 13% |\n| Cohere | 13% | 7% |\n| Contextual AI | 13% | 6% |\n| Other | 3% | 2% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Modeling Techniques\n\nA significant number of enterprises are augmenting their generative AI models via finetuning or retrieval augmented generation (RAG)\n\n## Generative AI Model Techniques Used\n% of Respondents (N = 143)\n\n| Technique | Maybe / unsure | No, we do not plan to | Yes, we plan to in the future | Yes, we currently do this |\n|-----------|----------------|----------------------|------------------------------|---------------------------|\n| Fine-tuning | 4% | 12% | 37% | 47% |\n| Leveraging RAG | 11% | 11% | 33% | 44% |\n| Training from scratch | 4% | 31% | 33% | 32% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n29\n\n# Infrastructure Tooling\n\nIn addition to investments in foundation models, enterprises are also procuring infrastructure tooling to support areas like data observability, database augmentation, and data pre-processing\n\n## Which of the following areas are you procuring infrastructure tooling for in conjunction with these generative AI models?", "mimetype": "text/plain", "start_char_idx": 35646, "end_char_idx": 39168, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "e52263df-58a2-4dd4-88c1-9c42e292d067": {"__data__": {"id_": "e52263df-58a2-4dd4-88c1-9c42e292d067", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "eaee8fe7-21e0-4140-afd1-3fc660420759", "node_type": "1", "metadata": {}, "hash": "21b7aec1b8692b8f3e1b61d264ce3bc7b4e2c99365a2c56436d83f9035f4feb6", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "fdf2f749-0684-4f6a-a4ad-5491e69c4203", "node_type": "1", "metadata": {}, "hash": "e2c668c8c68147a331cf0367dc190b99b0bfbf5e1e233c1f7d7a5be7b32df5b1", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Multi-select, % of Respondents (N = 126)\n\n| Area | Percentage |\n|------|------------|\n| Observability, evaluation, security | 50% |\n| Databases | 48% |\n| Data pre-processing | 47% |\n| Training | 41% |\n| Agent Tool Frameworks | 36% |\n| Model deployment & inference | 35% |\n| Orchestration | 32% |\n| Finetuning + RLHF | 30% |\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Deep Dive on Applications\n\nICONIQ | Growth\n\nPrivate and Strictly Confidential\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All Rights Reserved\n\n# AI Usage by Function\n\nTechnical teams lead in adoption of generative AI for internal productivity, while HR and legal functions lag, likely hindered by data privacy and quality concerns\n\nFor each department / function in your company, please indicate their level of generative AI usage on a scale of 1-5.\nWeighted Average Score by % of Respondents (N = 143)\n\n| Department/Function | Score |\n|---------------------|-------|\n| AI, Machine Learning, and Data Science | 4.5 |\n| IT | 4.0 |\n| Engineering / R&D | 3.9 |\n| Product Development & Management | 3.5 |\n| Marketing | 3.4 |\n| Operations | 3.3 |\n| Strategy and Competitive Intelligence | 3.3 |\n| Sales | 3.2 |\n| Finance | 3.0 |\n| Administration | 2.9 |\n| Human Resources | 2.7 |\n| Legal | 2.7 |\n\n> We are creating a sense of artificial FOMO among our workforce to encourage participation in pilot groups that will have early access to new GenAI tools\n> \n> Chief Information Officer, Technology Company\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nSpotlight: AI in Product & Engineering\n\nR&D teams have been quick to adopt generative AI for internal productivity, finding significant improvements to product development via tools like Github Copilot\n\n## Spotlight: AI in Product & Engineering\n\nN = 51\n\n### Most Impactful Use Cases in Product & Engineering\n\n- Improving coding velocity\n- Code refactoring\n- Augmenting test cases\n- Summarization of business requirements\n- Accelerating code reviews\n- User research\n- Prototyping and experimentation\n\n### Biggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Training and onboarding\n2. Budget\n3. Compliance and legal concerns\n\n| Average priority score (1-5 with 5 highest) for adoption of generative AI tools in Product & Engineering |\n|----------------------------------------------------------------------------------------------------------|\n| 4.1 |\n\n| Average % of employees in engineering who use GenAI tools on an ongoing basis |\n|-------------------------------------------------------------------------------|\n| 63% |\n\n### Average Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|------------------------|------------|\n| Too early to tell | 13% |\n| Little to no ROI (<5%) | 8% |\n| Some ROI (5-20%) | 58% |\n| Significant ROI (20%+) | 21% |\n\n> Generative AI has improved the productivity of existing and new engineers to understand our large, complex code bases and make changes with greater confidence.\n> \n> Engineering Leader \n> Technology \n> $2-3B Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Spotlight: AI in IT\n\nIT teams have been one of the earliest adopters of Generative AI solutions, leveraging AI for use cases across customer support and ticket management\n\n## Spotlight: AI in IT\nN = 50\n\n### Most Impactful Use Cases in IT\n\n- Ticket management\n- Chatbots\n- Customer support / troubleshooting\n- Knowledge management\n- Case summarization\n\n### Biggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Training and onboarding\n2. Budget\n3.", "mimetype": "text/plain", "start_char_idx": 39169, "end_char_idx": 43760, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "fdf2f749-0684-4f6a-a4ad-5491e69c4203": {"__data__": {"id_": "fdf2f749-0684-4f6a-a4ad-5491e69c4203", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "e52263df-58a2-4dd4-88c1-9c42e292d067", "node_type": "1", "metadata": {}, "hash": "608f8bd5eb9002b33a782d809867e4cbc8a7edb4dbc9c389cc05ff634590e50f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "976704a1-a7b7-4f91-96cb-1c2574749008", "node_type": "1", "metadata": {}, "hash": "d20b585de0a3c25274312e29c3e3d404da4eb42267b7d7cae76915eab9fa3b47", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "> \n> Engineering Leader \n> Technology \n> $2-3B Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Spotlight: AI in IT\n\nIT teams have been one of the earliest adopters of Generative AI solutions, leveraging AI for use cases across customer support and ticket management\n\n## Spotlight: AI in IT\nN = 50\n\n### Most Impactful Use Cases in IT\n\n- Ticket management\n- Chatbots\n- Customer support / troubleshooting\n- Knowledge management\n- Case summarization\n\n### Biggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Training and onboarding\n2. Budget\n3. Compliance and legal concerns\n\n| Average priority score (1-5 with 5 highest) for adoption of generative AI tools in IT |\n|-----------------------------------------------------------------------------------|\n| 4.0 |\n\n| Average % of employees in IT who use GenAI tools on an ongoing basis |\n|---------------------------------------------------------------------|\n| 45% |\n\n### Average Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|------------------------|------------|\n| Too early to tell | 6% |\n| Little to no ROI (<5%) | 32% |\n| Some ROI (5-20%) | 59% |\n| Significant ROI (20%+) | 3% |\n\n> Auto-resolution of availability or error alerts has reduced our tickets by 20-30%. We've also been able to gather more telemetry and key data for systems that we were previous not able to monitor and manage\n\nEngineering Leader\nTechnology\n$2-3B Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Spotlight: AI in Marketing\n\nMarketing use cases for GenAI include marketing campaign automation, copy writing, asset generation, and customer research\n\n## Spotlight: AI in Marketing\nN = 36\n\n### Most Impactful Use Cases in Marketing\n\n- Marketing campaign automation\n- Copy writing, proofing\n- Design, image generation\n- Market and customer research\n- Voice and conversational marketing\n\n### Biggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Compliance and legal concerns\n2. Training and onboarding\n3. Unclear ROI\n\n### Average priority score for adoption of generative AI tools in Marketing\n\n4.0\n\n(1-5 with 5 highest)\n\n### Average % of employees in Marketing who use GenAI tools on an ongoing basis\n\n42%\n\n### Average Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|----------|------------|\n| Too early to tell | 21% |\n| Little to no ROI (<5%) | 11% |\n| Some ROI (5-20%) | 26% |\n| Significant ROI (20%+) | 42% |\n\n> We have been using an AI-enabled design and image generation tool that has allowed us to save over $1 million in costs of enrollment asset production annually and allowed us to increase speed to market by up to 50%.\n>\n> Marketing Leader \n> Financial Services \n> $20B+ Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nSpotlight: AI in Sales\n\nGenerative AI solutions have been shown to allow sales teams to streamline both lead identification and outreach, with personalized and contextual customer information\n\nSpotlight: AI in Sales\n\nN = 36\n\n| Most Impactful Use Cases in Sales |\n|-----------------------------------|\n| \u2022 Target/lead identification and outreach |\n| \u2022 Meeting summarization |\n| \u2022 Contextual writing |\n| \u2022 Insight generation |\n\n| 4.1 |\n|-----|\n| Average priority score (1-5 with 5 highest) for adoption of generative AI tools in Sales |\n\n| Biggest Challenges to Adoption |\n|--------------------------------|\n| Ranked by % of Respondents Selected in Top 3 |\n| 1. Training and onboarding |\n| 2. Unclear ROI |\n| 3.", "mimetype": "text/plain", "start_char_idx": 42903, "end_char_idx": 47414, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "976704a1-a7b7-4f91-96cb-1c2574749008": {"__data__": {"id_": "976704a1-a7b7-4f91-96cb-1c2574749008", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "fdf2f749-0684-4f6a-a4ad-5491e69c4203", "node_type": "1", "metadata": {}, "hash": "e2c668c8c68147a331cf0367dc190b99b0bfbf5e1e233c1f7d7a5be7b32df5b1", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a2fae3cd-a4da-4e02-a773-fdfcede86097", "node_type": "1", "metadata": {}, "hash": "d2e798c438ad34275d79c4d23ba443fe7a1cf8188e847dc7a2d78839a1755222", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Training and onboarding |\n| 2. Unclear ROI |\n| 3. Compliance and legal concerns |\n\n| 49% |\n|-----|\n| Average % of employees in Sales who use GenAI tools on an ongoing basis |\n\n| Average Productivity Gain |\n|---------------------------|\n| % of Respondents |\n| Little to no ROI (<5%) | 17% |\n| Some ROI (5-20%) | 42% |\n| Significant ROI (20%+) | 42% |\n\n> It is still too early to assess the impact to revenue but lead and opportunity nurturing is happening faster and better due to adoption of GenAI\n> \n> Sales Leader\n> Financial Services\n> $20B+ Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\n# Spotlight: AI in Finance\n\nWhile the finance function has been slower to adopt GenAI solutions on a regular basis, we are starting to see GenAI be leveraged for use cases like report generation, cash management, and month end book close\n\n## Spotlight: AI in Finance\n\nN = 22\n\n### Most Impactful Use Cases in Finance\n\n- Report generation\n- Cash management\n- Month end book close\n- Research\n- Memo drafting\n\n### Biggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Training and onboarding\n2. Compliance and legal concerns\n3. Lack of tools\n\n### 3.6\n\nAverage priority score (1-5 with 5 highest) for adoption of generative AI tools in Finance\n\n### 31%\n\nAverage % of employees in Finance who use GenAI tools on an ongoing basis\n\n### Average Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|----------|------------|\n| Little to no ROI (<5%) | 14% |\n| Some ROI (5-20%) | 57% |\n| Significant ROI (20%+) | 29% |\n\n> We have been able to decrease FTE hours in addition to improving quality by reducing errors from manual processes.\n>\n> Finance Leader \n> Technology \n> $20B+ Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nPrivate & Strictly Confidential\n\nSpotlight: AI in Operations\n\nUse cases in operations vary widely based on company and industry; however, common use cases include meeting summarization, ticket triage, knowledge management, and report generation\n\nSpotlight: AI in Operations\n\nN = 40\n\nMost Impactful Use Cases in Operations\n\n- Meeting summarization\n- Ticket triage / management\n- Knowledge management\n- Report generation\n\nBiggest Challenges to Adoption\nRanked by % of Respondents Selected in Top 3\n\n1. Training and onboarding\n2. Compliance and legal concerns\n3. Unclear ROI\n\n4.1\n\nAverage priority score (1-5 with 5 highest) for adoption of generative AI tools in Operations\n\n32%\n\nAverage % of employees in Operations who use GenAI tools on an ongoing basis\n\nAverage Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|------------------------|------------|\n| Too early to tell | 10% |\n| Little to no ROI (<5%) | 13% |\n| Some ROI (5-20%) | 63% |\n| Significant ROI (20%+) | 13% |\n\n> Our Operations department has developed an internal gen AI. It automates repetitive tasks in our ops workflows, such as data entry and doc processing, using natural language processing (NLP), and machine learning algorithms. It has also helped the AML dept handling routing tasks from a Fraud identification and risk standpoint.", "mimetype": "text/plain", "start_char_idx": 47365, "end_char_idx": 51021, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "a2fae3cd-a4da-4e02-a773-fdfcede86097": {"__data__": {"id_": "a2fae3cd-a4da-4e02-a773-fdfcede86097", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "976704a1-a7b7-4f91-96cb-1c2574749008", "node_type": "1", "metadata": {}, "hash": "d20b585de0a3c25274312e29c3e3d404da4eb42267b7d7cae76915eab9fa3b47", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5e6384e4-becb-42fb-83b4-4b047c136d37", "node_type": "1", "metadata": {}, "hash": "fc7071c0368a246d0af1685e19b98abbe4ed8258c64e4e7fa93d4821a7e71ec4", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Compliance and legal concerns\n3. Unclear ROI\n\n4.1\n\nAverage priority score (1-5 with 5 highest) for adoption of generative AI tools in Operations\n\n32%\n\nAverage % of employees in Operations who use GenAI tools on an ongoing basis\n\nAverage Productivity Gain\n% of Respondents\n\n| Category | Percentage |\n|------------------------|------------|\n| Too early to tell | 10% |\n| Little to no ROI (<5%) | 13% |\n| Some ROI (5-20%) | 63% |\n| Significant ROI (20%+) | 13% |\n\n> Our Operations department has developed an internal gen AI. It automates repetitive tasks in our ops workflows, such as data entry and doc processing, using natural language processing (NLP), and machine learning algorithms. It has also helped the AML dept handling routing tasks from a Fraud identification and risk standpoint.\n\nOperations Leader\nFinancial Services\n$20B+ Annual Revenue\n\nSource: Perspectives from the ICONIQ Growth GenAI Survey (June 2024) and perspectives from the ICONIQ Growth team and network of AI leaders consisting of our community of CIO/CDOs overseeing AI initiatives in enterprises, CTOs, our Technical Advisory Board, and others in our network\n\nICONIQ | Growth Private & Strictly Confidential 38\n\n# A global portfolio of category-defining businesses\n\n| | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|\n| 1Password | acuity MD | adyen | Age of Learning | airbnb | Airtable | ajaib | Alibaba.com | alteryx | Altruist |\n| APPRENTICE | APTTUS | articulate | aurora | AUTOMATTIC | AXONIUS | bambooHR | Benchling | BetterUp | bill |\n| BLACKLINE | braze | Calendly | Campaign Monitor | Canva | CaptivateIQ | causaly | chime | CLARA | coast |\n| Collibra | conexiom | coupa | CROWDSTRIKE | cyber GRX | DATADOG | dataiku | dbt Labs | DeepL | DevotedHealth |\n| dexcare | dialpad AI | docusign | DRATA | enfusion | EPIC | EvolutionIQ | ez cater | fastly | fetch REWARDS |\n| Figma | Fireblocks | Fivetran | Flipkart | #floQast | FREEWILL | FTX | Gem | GitLab | glean |\n| gofundme | GoodRx | GreenSky | Groww | Guild | HashiCorp | headspin | HEPTAGON | highradius | HIGHSPOT |\n| hightouch | hippo | HONEST | houzz | iex | INTERCOM | in VISION | komodo | loom | Lucid |\n| MARQETA | miro | MONTE CARLO | motorway | Moveworks | Nayya | netskope | ninjaOne | notable | Notion |\n| ORCA security | panther | people.ai | Pepper | Pigment | Pinecone | PLURALSIGHT | Pontera | Primer | PROCORE |\n| QGenda | ramp | recharge | RED VENTURES | ReifyHEALTH | Relativity | Re prise | Restaurant365 | Robinhood | SANITY |\n| sendbird | ServiceTitan | shopmonkey | Side | skuid | SMARTLING | snowflake | SPOTHANA | sprinklr | SQUIRE |\n| TGE | TRUCKSTOP | turbonomic | twin | Twistlock | Uber | Unit21 | UNITE US | VIC.AI | virtru |\n| WARBY PARKER | wayfair | Wealthsimple | Wolt | WRITER | zinier | ZOOM | | | |\n\nThese companies represent the full list of companies that ICONIQ Growth has invested in since inception through ICONIQ Strategic Partners funds as of the date these materials were published (except those subject to confidentiality obligations). Trademarks are the property of their respective owners. None of the companies illustrated have endorsed or recommended the services of ICONIQ.\n\nICONIQ | Growth\n\nPrivate & Strictly Confidential 39\n\n# Disclosures\n\nUnless otherwise indicated, the views expressed in this presentation are those of ICONIQ growth (\"ICONIQ\" or the \"Firm\"), are the result of proprietary research, may be subjective, and may not be relied upon in making an investment decision. Information used in this presentation was obtained from numerous sources. Certain of these companies are portfolio companies of ICONIQ Growth. ICONIQ Growth does not make any representations or warranties as to the accuracy of the information obtained from these sources.", "mimetype": "text/plain", "start_char_idx": 50177, "end_char_idx": 54280, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "5e6384e4-becb-42fb-83b4-4b047c136d37": {"__data__": {"id_": "5e6384e4-becb-42fb-83b4-4b047c136d37", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a2fae3cd-a4da-4e02-a773-fdfcede86097", "node_type": "1", "metadata": {}, "hash": "d2e798c438ad34275d79c4d23ba443fe7a1cf8188e847dc7a2d78839a1755222", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "8ee3e943-2d09-4790-a21d-1c75552a70db", "node_type": "1", "metadata": {}, "hash": "1aae6cc70424b052be31fc52150ef694455e0b71f48bf8d03f2e58acb3a24be5", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Trademarks are the property of their respective owners. None of the companies illustrated have endorsed or recommended the services of ICONIQ.\n\nICONIQ | Growth\n\nPrivate & Strictly Confidential 39\n\n# Disclosures\n\nUnless otherwise indicated, the views expressed in this presentation are those of ICONIQ growth (\"ICONIQ\" or the \"Firm\"), are the result of proprietary research, may be subjective, and may not be relied upon in making an investment decision. Information used in this presentation was obtained from numerous sources. Certain of these companies are portfolio companies of ICONIQ Growth. ICONIQ Growth does not make any representations or warranties as to the accuracy of the information obtained from these sources.\n\nThis presentation is for educational purposes only and does not constitute investment advice or an offer to sell or a solicitation of an offer to buy any securities which will only be made pursuant to definitive offering documents and subscription agreements, including, without limitation, any investment fund or investment product referenced herein.\n\nAny reproduction or distribution of this presentation in whole or in part, or the disclosure of any of its contents, without the prior consent of ICONIQ, is prohibited.\n\nThis presentation may contain forward-looking statements based on current plans, estimates and projections. The recipient of this presentation (\"you\") are cautioned that a number of important factors could cause actual results or outcomes to differ materially from those expressed in, or implied by, the forward-looking statements. The numbers, figures and case studies included in this presentation have been included for purposes of illustration only, and no assurance can be given that the actual results of ICONIQ or any of its partners and affiliates will correspond with the results contemplated in the presentation. No information is contained herein with respect to conflicts of interest, which may be significant. The portfolio companies and other parties mentioned herein may reflect a selective list of the prior investments made by ICONIQ.\n\nCertain of the economic and market information contained herein may have been obtained from published sources and/or prepared by other parties. While such sources are believed to be reliable, none of ICONIQ or any of its affiliates and partners, employees and representatives assume any responsibility for the accuracy of such information.\n\nAll of the information in the presentation is presented as of the date made available to you (except as otherwise specified), and is subject to change without notice, and may not be current or may have changed (possibly materially) between the date made available to you and the date actually received or reviewed by you. ICONIQ assumes no obligation to update or otherwise revise any information, projections, forecasts or estimates contained in the presentation, including any revisions to reflect changes in economic or market conditions or other circumstances arising after the date the items were made available to you or to reflect the occurrence of unanticipated events. Numbers or amounts herein may increase or decrease as a result of currency fluctuations.\n\nFor avoidance of doubt, ICONIQ is not acting as an adviser or fiduciary in any respect in connection with providing this presentation and no relationship shall arise between you and ICONIQ as a result of this presentation being made available to you.\n\nICONIQ is a diversified financial services firm and has direct client relationships with persons that may become limited partners of ICONIQ funds. Notwithstanding that a person may be referred to herein as a \"client\" of the firm, no limited partner of any fund will, in its capacity as such, be a client of ICONIQ. There can be no assurance that the investments made by any ICONIQ fund will be profitable or will equal the performance of prior investments made by persons described in this presentation.\n\nThese materials are provided for general information and discussion purposes only and may not be relied upon.\n\nIn the European Economic Area, this presentation is only available to recipients that meet the description of a 'professional client' under Directive 2014/65/EU (as amended). In the United Kingdom (the \"UK\"), this presentation is only available to recipients that meet the description of a 'professional client' under Regulation (EU) 600/2014 as such Regulation forms part of UK domestic law, and who are (i) persons with professional experience in matters relating to investments falling within article 19(5) of the Financial Services and Markets Act 2000 (Financial Promotion) Order 2005 (the \"FPO Order\"), (ii) high-net-worth entities falling within Article 49(2) of the FPO Order, or (iii) any other persons to whom it may otherwise lawfully be communicated. Receipt, disclosure or publication of this presentation by or to any other person is strictly unauthorised. The information contained in this presentation does not constitute investment research or a research report and should not be relied on as such. Recipients must not distribute, publish, reproduce, or disclose this material, in whole or in part, to any other person.\n\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All rights reserved.", "mimetype": "text/plain", "start_char_idx": 53451, "end_char_idx": 58831, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "8ee3e943-2d09-4790-a21d-1c75552a70db": {"__data__": {"id_": "8ee3e943-2d09-4790-a21d-1c75552a70db", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e5a20644-0b53-4c91-938b-c6ef29cea4a7", "node_type": "4", "metadata": {}, "hash": "13a930a9d854caf7a7685807dda5c6e07be7ff66ccaee196572c8d52feee8a8a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5e6384e4-becb-42fb-83b4-4b047c136d37", "node_type": "1", "metadata": {}, "hash": "fc7071c0368a246d0af1685e19b98abbe4ed8258c64e4e7fa93d4821a7e71ec4", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Receipt, disclosure or publication of this presentation by or to any other person is strictly unauthorised. The information contained in this presentation does not constitute investment research or a research report and should not be relied on as such. Recipients must not distribute, publish, reproduce, or disclose this material, in whole or in part, to any other person.\n\nCopyright \u00a9 2024 ICONIQ Capital, LLC. All rights reserved.\n\nPrivate & Strictly Confidential 40\n\nICONIQ | Growth\n\nSan Francisco | Palo Alto | New York | London\n\nJoin our community [Globe icon] [LinkedIn icon] [Twitter icon] [2023 YIR]\n\nICONIQ | Growth\n\nPrivate & Strictly Confidential 41", "mimetype": "text/plain", "start_char_idx": 58398, "end_char_idx": 59240, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "b9283824-f930-4f09-8bf4-8cd9cd718eea": {"__data__": {"id_": "b9283824-f930-4f09-8bf4-8cd9cd718eea", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4a2d76ff-56fd-42fa-a193-7f387f485a41", "node_type": "4", "metadata": {}, "hash": "bb31fa689562b16a7ac060b26604404e7a34a29d7c02802fe32fef8242fc6a5c", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "img_p0_1.png", "mimetype": "text/plain", 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