Last updated: Monday, 21 June 2021
$1 trillion
global AI market in 2028
Grand View Research (June 2021)
87%
of AI and ML initiatives never make it to production
VentureBeat (July 2019)
55%
of organizations have not deployed a single ML model to production
Algorithmia (Dec 2019)
40% of organizations spend more than
90 days
deploying a single ML model to production
Algorithmia (Dec 2020)
95%
of production ML systems is 'glue code', not model code
Google (2014)
Cloud-native ML platforms have only been developed in the last
5 years
Databaseline (Sept 2020)
880+
cloud-native tools
CNCF (Feb 2021)
270+
AI and ML tools
Linux Foundation (Feb 2021)
50+
end-to-end ML platforms
Databaseline (Feb 2021)
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 70% of companies will employ hybrid or multi-cloud management technologies by 2022 | prediction | McKinsey | edge | 2021-06-15 |
| Global AI market is expected to reach $1t in 2028 with 40.2% CAGR | prediction | Grand View Research | — | 2021-06-07 |
| The impact comes from the last 20% of the journey: 87% of leaders (vs 23% of all others) spend more than half of their analytics budgets on landing the last mile | insight | McKinsey | — | 2021-04-23 |
| Leaders spend more on proactive change management | insight | McKinsey | — | 2021-04-23 |
| Leaders invest >15% of their IT spend on AI | insight | McKinsey | — | 2021-04-23 |
| Leaders have structured data governance vs siloed POCs | insight | McKinsey | — | 2021-04-23 |
| Leaders have enterprise-wide AI talent acquisition vs pockets of AI talent | insight | McKinsey | — | 2021-04-23 |
| Leaders have a high degree of automation in data engineering/science with a modern architectures that make extensive use of unstructured data and external sources | insight | McKinsey | — | 2021-04-23 |
| Companies fall into three groups: leaders (8%), aspirants with 1.8x (17%), and laggards (75%) | insight | McKinsey | — | 2021-04-23 |
| 26% have AI in production (+1 pp), 35% are evaluating AI (+ 2 pp), 26% are considering AI (-2 pp), and 13% are not using it (-2 pp) | insight | O'Reilly | — | 2021-04-19 |
| 79% of leader expect to use enterprise open source for emerging technologies to increase over the next two years: 72% IoT/edge (vs 55% today) and 65% AI/ML (vs 48% today) | prediction | Red Hat | — | 2021-03-03 |
| 72% expect container use to grow | prediction | Red Hat | — | 2021-03-03 |
| Barriers to enterprise open source: support (42%), compatibility (38%), security (35%), talent (35%) | insight | Red Hat | — | 2021-03-03 |
| 90% use enterprise open source | insight | Red Hat | — | 2021-03-03 |
| 83% of technology leaders are more likely to select a vendor who contributes to the open-source community | insight | Red Hat | — | 2021-03-03 |
| The ML platforms market is expected to grow to $126.1b by 2025 | prediction | Cognilytica | — | 2021-01-20 |
| Global AI software market expected to grow to $37b by 2025 | prediction | Forrester | — | 2020-12-10 |
| 42% have a hybrid cloud/on-premises infrastructure | insight | Algorithmia | — | 2020-12-09 |
| Successful AI adopters use automated tools to produce and test AI models: 48% vs 20% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters use advanced processes (e.g. data operations, microservices) to deploy AI: 57% vs 23% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters use a standardized tool set to create production-ready data pipelines: 44% vs 23% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters use a standardized end-to-end platform for AI-related data science, data engineerring, and application development: 40% vs 20% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters use a standard protocol to build and deliver AI tools: 33% vs 16% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters track a comprehensive set of KPIs to measure the impact of AI initiatives: 52% vs 27% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters rapidly integrate internal structured data for use in AI initiatives: 56% vs 28% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters have standard tool frameworks and development processes in place: 51% vs 19% | insight | McKinsey | — | 2020-11-17 |
| Successful AI adopters generate synthetic data to train AI models to complement data sets: 49% vs 16% | insight | McKinsey | — | 2020-11-17 |
| Explainability is identified as the third most relevant risk to enterprises: 41% in 2020 vs 39% in 2019 | insight | McKinsey | — | 2020-11-17 |
| Companies with more than 20% AI contributions to EBIT have: 1) better overall performance, 2) better overall leadership, and c) more resource commitment to AI | insight | McKinsey | — | 2020-11-17 |
| 22% attribute more than 5% of EBIT in 2019 to AI | insight | McKinsey | — | 2020-11-17 |
| 16% have taken deep learning beyond the prototyping stage | insight | McKinsey | — | 2020-11-17 |
| 61% of teams need between 2 and 6 weeks to build a model prototype; 28% need more than 6 weeks for a prototype | insight | Spotify | — | 2020-11-12 |
| 38% (32%) of teams need 2 – 6 (6 – 12) weeks to go from prototype to production | insight | Spotify | — | 2020-11-12 |
| Public clouds account for 36.6% as the main location of enterprise applications: 22.3% on premises, 21.6% hybrid, and 17% in private clouds | insight | Lightbend | kubernetes | 2020-10-26 |
| 75% host the majority of their applications in cloud infrastructure: public, private, or hybrid environments | insight | Lightbend | — | 2020-10-26 |
| 30% are in the early stages of Kubernetes adoption as a production platform, with 19.9% still evaluating it | insight | Lightbend | kubernetes | 2020-10-26 |
| 14.8% have no plans to adopt Kubernetes as a production platform | insight | Lightbend | kubernetes | 2020-10-26 |
| The winners of [the model performance monitoring] market will have a bottoms-up go-to-market strategy, tailored towards a developer or data scientist user persona and have an open-source component | insight | Two Sigma Ventures | — | 2020-10-21 |
| The best [model performance monitoring] products will provide immediate out-of-the-box value and be very easy to use for different personas, without requiring months of implementation time or custom work | insight | Two Sigma Ventures | — | 2020-10-21 |
| Smaller businesses have found that building the technology is harder than it looks | quotation | Forbes | risks | 2020-10-20 |
| IBM has deprioritized its Watson technology after drawing scorn for ventures like on $62 million oncology project that made inaccurate suggestions on cancer treatments | quotation | Forbes | risks | 2020-10-20 |
| Companies [...] overspend on technology and data scientists, without implementing changes in the business processes that could benefit from AI | quotation | Forbes | risks, skills | 2020-10-20 |
| Amazon canned an AI recruitment tool after it showed misogynistic biases | quotation | Forbes | risks | 2020-10-20 |
| 78% of developers claim that Kubernetes add-ons cause a great deal of pain and introduce complexity | insight | D2iQ | risks | 2020-10-20 |
| 38% of developers and architects claim their work makes them feel extremely burnt out, with 51% stating that building cloud native applications makes them want to find a new job | insight | D2iQ | risks | 2020-10-20 |
| Only when organizations add the ability to learn with AI do significant benefits become likely (73%); these organizations facilitate systematic and continuous learning between humans and machines | insight | BCG | skills | 2020-10-20 |
| Only one in ten companies reports significant financial benefits from implementing AI | insight | BCG | risks | 2020-10-20 |
| 57% of companies have AI pilots or deployed AI solutions in 2020 | insight | BCG | — | 2020-10-20 |
| Visa earned $3b in revenue in 2019 selling aggregated data and analytics services | insight | Andreessen Horowitz | — | 2020-10-15 |
| UPS save 100m driving miles and $350-400m per year with their ORION route optimization system | insight | Andreessen Horowitz | — | 2020-10-15 |
| Netflix generate more than 80% of content views through their ML recommendation system | insight | Andreessen Horowitz | — | 2020-10-15 |
| Airbnb increased booking conversion rate by 4% thanks to ML | insight | Andreessen Horowitz | — | 2020-10-15 |
| With 11% CAGR forecast for the value-added services market between 2020 and 2030 compared to a 0.75% CAGR for current existing services, 5G is a considered a leading source of new revenue for the industry | prediction | MIT | edge | 2020-10-07 |
| Operators aim to deliver about 80% of enterprise requests through off-the-shelf solutions, leaving just 20% in need of a customized response | prediction | MIT | edge | 2020-10-07 |
| Executives see the enterprise and public sector digitalization programs as the most compelling market opportunities for 5G | prediction | MIT | edge | 2020-10-07 |
| Addressing the seemingly infinite number of enterprise opportunities, with all of their hardware and software complexities, means that operators cannot go it alone | prediction | MIT | edge | 2020-10-07 |
| The data center market is expected to grow year-over-year through 2024 | prediction | Gartner | — | 2020-10-07 |
| Spending on global data center infrastructure is projected to reach $200 billion in 2021, an increase of 6% from 2020 | prediction | Gartner | investments | 2020-10-07 |
| Operators with serious ambitions for 5G have a cloud-first strategy for the network and IT | insight | MIT | edge | 2020-10-07 |
| There is an ML industry shift from R&D to operations | prediction | Nathan Benaich and Ian Hogarth | — | 2020-10-01 |
| PyTorch is preferred for research, whereas TensorFlow is preferred in industry | insight | Nathan Benaich and Ian Hogarth | — | 2020-10-01 |
| ML industry shift from R&D to operations | insight | Nathan Benaich and Ian Hogarth | — | 2020-10-01 |
| AI investments have not decreased due to Covid-19 | insight | Gartner | covid, risks | 2020-10-01 |
| There is a large group of non-tech companies that are just starting to dip their toe in earnest into the world of data science, predictive analytics and ML/AI. Some are just launching their initiatives, while others have been stuck in 'AI purgatory' for the last couple of years, as early pilots haven’t been given enough attention or resources to produce meaningful results yet | quotation | Matt Turck (FirstMark) | — | 2020-09-30 |
| Orchestration engines are seeing a lot of activity. Beyond early entrants like Airflow and Luigi, a second generation of engines has emerged, including Prefect and Dagster, as well as Kedro and Metaflow | quotation | Matt Turck (FirstMark) | — | 2020-09-30 |
| Just like 'Big Data' before it, ML/AI, at least in its current form, will disappear as a noteworthy and differentiating concept, because it will be everywhere | quotation | Matt Turck (FirstMark) | — | 2020-09-30 |
| An increasing need for real-time streaming | quotation | Matt Turck (FirstMark) | — | 2020-09-30 |
| A number of large corporations are starting to see the results of their efforts. They typically embarked years ago on a journey that started with Big Data infrastructure, and has evolved along the way to include data science and ML/AI | quotation | Matt Turck (FirstMark) | — | 2020-09-30 |
| Global spending on AI is to grow to $110b in 2024 | prediction | IDC | investments | 2020-08-25 |
| Global spending on AI is to be $50.1b in 2020 | prediction | IDC | investments | 2020-08-25 |
| Global spending on AI was $37.5 in 2019 | insight | IDC | investments | 2020-08-25 |
| AI developer stacks today are like DIY craft kits, with the instructions and 70% of the parts missing | quotation | Clemens Mewald (Databricks) | — | 2020-08-18 |
| 61% believe AI will substantially transform their industry in the next 3 years | prediction | Deloitte | — | 2020-07-14 |
| AI will add $13 – 16tn to the global economy by 2030 | prediction | PwC and McKinsey | investments | 2020-06-11 |
| AI models are like six-year-olds during quarantine: They need constant attention... otherwise, something will break | quotation | Forrester | — | 2020-05-28 |
| 90% of tech executives see AI/ML as key to be incorporated in products | insight | Bain | — | 2020-05-26 |
| 87% of tech companies are not satisfied with their use of AI | insight | Bain | — | 2020-05-26 |
| 54% growth in global AI market in 2019 | insight | Statista | — | 2020-05-13 |
| $10.1b revenue of global AI software market in 2018 | insight | Statista | investments | 2020-05-13 |
| In 2022, AI technologies will be adopted in (compared to 2019): 1. Customer service (73%, +13 pp), 2. Sales and marketing (59%, +26 pp), 3. IT management / AIOps (50%, +3 pp), 4. R&D (44%, +4 pp), and 5. Finance (31%, +5 pp) | prediction | MIT | adoption | 2020-04-28 |
| More than half identify as mature adopters of AI technologies | insight | O'Reilly | — | 2020-03-18 |
| 26% plan to have formal data governance processes and tools by 2021 | insight | O'Reilly | risks | 2020-03-18 |
| 59% meet their ML needs by adopting enterprise software with built-in ML | insight | Deloitte | adoption | 2020-03-01 |
| Enterprises are between early adopters (2020) and early majority (2022/2023) on the technology adoption lifecycle for cloud native technologies | insight | Loodse | adoption | 2020-01-13 |
| 75% have explored or adopted ML technologies | insight | IBM | adoption | 2020-01-03 |
| 45% with 1000+ employees and 29% with <1000 employees have adopted AI | insight | IBM | adoption | 2020-01-03 |
| 33% plan to deploy ML models in 2020 | prediction | H2O | — | 2019-12-13 |
| 50% will not have deployed a single ML model in 2020 | prediction | Algorithmia | risks | 2019-12-13 |
| 20% have deployed ML models | insight | H2O | — | 2019-12-13 |
| The top ML priority for companies with more than 1,000 employees is cost reduction | insight | Algorithmia | — | 2019-12-13 |
| The top ML priority for companies with fewer than 1,000 employees is generating customer insights and intelligence | insight | Algorithmia | — | 2019-12-13 |
| Smaller companies deploy models faster than larger, if they deploy models within 30 days | insight | Algorithmia | risks | 2019-12-13 |
| 65% with 5,000 – 10,000 employees have no models in production | insight | Algorithmia | — | 2019-12-13 |
| 55% have not deployed a single ML model | insight | Algorithmia | — | 2019-12-13 |
| 21% are evaluating use cases | insight | Algorithmia | — | 2019-12-13 |
| 17% are starting to develop models | insight | Algorithmia | — | 2019-12-13 |
| 17% are ready to deploy models to production | insight | Algorithmia | — | 2019-12-13 |
| The space of ML operations ("MLOps") is just now emerging and will no doubt be big news in the next few years | quotation | Forbes | — | 2019-12-12 |
| AI specialists and ML engineers (1st: 74% annual growth), data scientists (3rd: 37% annual growth), data engineers (8th: 33% annual growth) are among the top-15 emerging jobs in the US | insight | skills | 2019-12-10 | |
| 30% use ML for exploration and insights generation (i.e. not production) | insight | Kaggle | — | 2019-12-02 |
| Explainability, reliability, and fairness of models are more critical in the enterprise than in consumer products, where the risk of bad predictions is minimal | insight | CMSWire | — | 2019-12-02 |
| ML adoption: 95% North America, 72% Asia, and 64% Europe | insight | Finances Online | adoption | 2019-08-27 |
| The main arc here, which has been playing out for years but seems to be accelerating, is a three-phase transition from Hadoop to the cloud services to a hybrid/Kubernetes environment | quotation | Matt Turck (FirstMark) | — | 2019-07-02 |
| In this new multi-cloud and hybrid cloud era, the rising superstar is undoubtedly Kubernetes | quotation | Matt Turck (FirstMark) | — | 2019-07-02 |
| $28b spent globally on machine learning applications in 2019 Q1 | insight | Statista | investments | 2019-05-10 |
| $14b spent globally on machine learning platforms in 2019 Q1 | insight | Statista | investments | 2019-05-10 |
| Through 2022, only 20% of analytics will deliver business value | prediction | Gartner | risks | 2019-01-03 |
| Through 2022, 85% of AI initiatives will continue to fail | prediction | Gartner | risks | 2019-01-03 |
| Through 2020, 80% of AI will remain alchemy | prediction | Gartner | risks | 2019-01-03 |
| Computational resources will increase 5x from 2018 to 2023 | prediction | Gartner | — | 2019-01-03 |
| By 2023, 70% of AI workloads will use containers using a serverless model | prediction | Gartner | — | 2019-01-03 |
| Machine learning platforms are of high important to traditional decision makers (e.g. CIOs) compared to medium importance to decision makers associated with a business unit (not associated with IT) | insight | Forrester | — | 2017-12-06 |
| 80% of views on Netflix come from recommendations | insight | Mobile Syrup | — | 2017-08-22 |
| 99.9% of spam is caught by Gmail's AI | insight | Wired | — | 2015-07-09 |
| 75% of views on Netflix come from recommendations | insight | McKinsey | — | 2013-01-01 |
| 35% of purchases on Amazon come from recommendations | insight | McKinsey | — | 2013-01-01 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Nothing will stifle an AI deployment like a lack of alignment on the purpose of AI itself | quotation | Forbes | — | 2021-05-21 |
| Top barriers to adoption: talent (19%), data quality (18%), use case identification (17%), company culture (14%), and infrastructure (12%) | insight | O'Reilly | risks | 2021-04-19 |
| Only 30% of business leaders consider AI adoption to be fast | insight | NTT Data | — | 2020-11-20 |
| Covid-19 has increased adoption of AI in healthcare from 45% to 84% | insight | Intel | covid, healthcare | 2020-11-18 |
| AI adoption in retail is 42% across the board: 33% in apparel and footwear, 29% in grocery and food, and 26% in home improvement | insight | Noodle AI | retail | 2020-11-17 |
| 70% of financial services firms use ML to predict cash flow events, fine-tune credit scores, and detect fraud | insight | Deloitte | finance | 2020-10-31 |
| Security, privacy concerns, and the complexity of integrating AI within existing infrastructure, are at the main barriers to AI adoption | insight | Gartner | risks | 2020-10-01 |
| The main barriers to adoption for financial services are: 1) lack of available data, 2) budget constraints, 3) lack of talent, 4) data privacy concerns, 5) lack of trust, 6) lack of available platforms and tools, 7) lack of management support, and 8) lack of algorithm transparency | insight | PwC | finance, risks | 2020-05-01 |
| In 2022, AI technologies will be adopted in (compared to 2019): 1. Customer service (73%, +13 pp), 2. Sales and marketing (59%, +26 pp), 3. IT management / AIOps (50%, +3 pp), 4. R&D (44%, +4 pp), and 5. Finance (31%, +5 pp) | prediction | MIT | — | 2020-04-28 |
| 59% meet their ML needs by adopting enterprise software with built-in ML | insight | Deloitte | — | 2020-03-01 |
| Enterprises are between early adopters (2020) and early majority (2022/2023) on the technology adoption lifecycle for cloud native technologies | insight | Loodse | — | 2020-01-13 |
| 75% have explored or adopted ML technologies | insight | IBM | — | 2020-01-03 |
| 45% with 1000+ employees and 29% with <1000 employees have adopted AI | insight | IBM | — | 2020-01-03 |
| ML adoption: 95% North America, 72% Asia, and 64% Europe | insight | Finances Online | — | 2019-08-27 |
| 77% struggle with business adoption of AI | insight | NewVantage | risks | 2019-01-01 |
| Business functions with significant value from AI: manufacturing (57%), risk (51%), supply-chain management (47%), product/service development (45%), strategy and corporate finance (39%), service operations (39%), sales and marketing (35%), and human resources (33%) | insight | McKinsey | — | 2018-11-13 |
| Lack of trust in AI among patients (36%) as well as clinicians (30%) is a barrier to adoption | insight | Intel | healthcare, risks | 2018-07-02 |
| 54% expect widespread adoption of AI until 2023 | insight | Intel | healthcare | 2018-07-02 |
| Leading sectors in AI adoption: high tech and telecommunications (32%), automotive and assembly (29%), financial services (28%), energy and resources (27%), media and entertainment (22%), transportation and logistics (21%), CPGs (20%), retail (19%), healthcare (16%), education (16%), construction (15%), professional services (14%), and travel and tourism (11%) | insight | McKinsey | — | 2018-04-04 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Retailers spend on average 7% of their IT budget on AI. A quarter of large retails spend up to 10%. | insight | Noodle AI | retail | 2020-11-17 |
| 74% of AI applications in retail are spent on operations and only 26% on customers-facing applications | insight | Noodle AI | retail | 2020-11-17 |
| Spending on global data center infrastructure is projected to reach $200 billion in 2021, an increase of 6% from 2020 | prediction | Gartner | — | 2020-10-07 |
| Global spending on AI is to grow to $110b in 2024 | prediction | IDC | — | 2020-08-25 |
| Global spending on AI is to be $50.1b in 2020 | prediction | IDC | — | 2020-08-25 |
| Global spending on AI was $37.5 in 2019 | insight | IDC | — | 2020-08-25 |
| 53% spent more than $20 million during the past year on AI and talent | insight | Deloitte | skills | 2020-07-14 |
| AI will add $13 – 16tn to the global economy by 2030 | prediction | PwC and McKinsey | — | 2020-06-11 |
| $10.1b revenue of global AI software market in 2018 | insight | Statista | — | 2020-05-13 |
| US DoD funds at to the tune of $4b in 2020 | insight | PTC | defence | 2020-03-01 |
| JAIC (Joint Artificial Intelligence Center) has increased its budget from $70m in 2018 to $209m in 2020 | insight | PTC | defence | 2020-03-01 |
| DARPA spends $2b over the next five years to foster innovation in AI for critical DoD processes | insight | PTC | defence | 2020-03-01 |
| $28b spent globally on machine learning applications in 2019 Q1 | insight | Statista | — | 2019-05-10 |
| $14b spent globally on machine learning platforms in 2019 Q1 | insight | Statista | — | 2019-05-10 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Top barriers to adoption: talent (19%), data quality (18%), use case identification (17%), company culture (14%), and infrastructure (12%) | insight | O'Reilly | adoption | 2021-04-19 |
| 46% do not version data sets | insight | O'Reilly | tools | 2021-04-19 |
| Traps of AI operationalization: 1) 'Ideating about AI' is more useful than actually 'implementing AI', 2) Taking a 'set it and forget it' approach to AI, 3) What works in AI pilots will scale in production, 4) Waiting to hire the perfect 'unicorn' to operationalize AI projects, and 5) The new tool we buy will help us scale our AI initiatives | quotation | Gartner | — | 2020-12-09 |
| Only 11% of organizations deploy a model within a week | insight | Algorithmia | — | 2020-12-09 |
| Only 11% of organizations can put a model into production within a week | insight | Algorithmia | — | 2020-12-09 |
| 64% of organizations need a month or longer to deploy a model | insight | Algorithmia | — | 2020-12-09 |
| 56% of organizations struggle with governance, security, and auditability | insight | Algorithmia | — | 2020-12-09 |
| 40% or organzations need three months or longer to deploy a model | insight | Algorithmia | — | 2020-12-09 |
| 38% of organizations spend more than half of their data scientists' time on deployment | insight | Algorithmia | — | 2020-12-09 |
| Smaller businesses have found that building the technology is harder than it looks | quotation | Forbes | — | 2020-10-20 |
| IBM has deprioritized its Watson technology after drawing scorn for ventures like on $62 million oncology project that made inaccurate suggestions on cancer treatments | quotation | Forbes | — | 2020-10-20 |
| Companies [...] overspend on technology and data scientists, without implementing changes in the business processes that could benefit from AI | quotation | Forbes | skills | 2020-10-20 |
| Amazon canned an AI recruitment tool after it showed misogynistic biases | quotation | Forbes | — | 2020-10-20 |
| 78% of developers claim that Kubernetes add-ons cause a great deal of pain and introduce complexity | insight | D2iQ | — | 2020-10-20 |
| 38% of developers and architects claim their work makes them feel extremely burnt out, with 51% stating that building cloud native applications makes them want to find a new job | insight | D2iQ | — | 2020-10-20 |
| Only one in ten companies reports significant financial benefits from implementing AI | insight | BCG | — | 2020-10-20 |
| Security, privacy concerns, and the complexity of integrating AI within existing infrastructure, are at the main barriers to AI adoption | insight | Gartner | adoption | 2020-10-01 |
| Only 7% say limited AI skills are a barrier of AI implementation | insight | Gartner | skills | 2020-10-01 |
| AI investments have not decreased due to Covid-19 | insight | Gartner | covid | 2020-10-01 |
| Frantic handoffs, manual monitoring, and loose governance impede organizations' ability to deploy more business-worthy AI use cases faster | insight | Forrester | — | 2020-09-10 |
| 95% have concerns around ethical risks for AI initiatives | insight | Deloitte | — | 2020-07-14 |
| The main barriers to adoption for financial services are: 1) lack of available data, 2) budget constraints, 3) lack of talent, 4) data privacy concerns, 5) lack of trust, 6) lack of available platforms and tools, 7) lack of management support, and 8) lack of algorithm transparency | insight | PwC | adoption, finance | 2020-05-01 |
| Industrial (safety-critical) systems are more affected by worst-case than average performance | insight | ML | — | 2020-04-23 |
| 28% have a negative return on ML investments due to model deployment complexities | insight | IBM | — | 2020-04-14 |
| 26% plan to have formal data governance processes and tools by 2021 | insight | O'Reilly | — | 2020-03-18 |
| 50% will not have deployed a single ML model in 2020 | prediction | Algorithmia | — | 2019-12-13 |
| Smaller companies deploy models faster than larger, if they deploy models within 30 days | insight | Algorithmia | — | 2019-12-13 |
| 58% have trouble scaling models up | insight | Algorithmia | — | 2019-12-13 |
| 5% of companies with more than five years of experience with models in production still occassionally spend more than a year to deploy a model | insight | Algorithmia | — | 2019-12-13 |
| 41% have trouble with versioning and reproducibility | insight | Algorithmia | — | 2019-12-13 |
| 34% have trouble with organizational alignment and executive buy-in | insight | Algorithmia | — | 2019-12-13 |
| 25% of data scientists' time is spent on deployments and infrastructure | insight | Algorithmia | — | 2019-12-13 |
| 10% of companies with more than five years of experience with models in production still occassionally spend more than a 90 days to deploy a model | insight | Algorithmia | — | 2019-12-13 |
| 50% spend between 8 – 90 days to deploy a single ML model | insight | Algorithmia | — | 2019-12-11 |
| 87% of ML initiatives fail | insight | VentureBeat | — | 2019-07-19 |
| Through 2022, only 20% of analytics will deliver business value | prediction | Gartner | — | 2019-01-03 |
| Through 2022, 85% of AI initiatives will continue to fail | prediction | Gartner | — | 2019-01-03 |
| Through 2020, 80% of AI will remain alchemy | prediction | Gartner | — | 2019-01-03 |
| 77% struggle with business adoption of AI | insight | NewVantage | adoption | 2019-01-01 |
| Lack of trust in AI among patients (36%) as well as clinicians (30%) is a barrier to adoption | insight | Intel | adoption, healthcare | 2018-07-02 |
| The story of enterprise Machine Learning: 'It took me 3 weeks to develop the model. It's been >11 months, and it's still not deployed.' | quotation | Gina Blaber (O'Reilly) | — | 2018-03-07 |
| 74% of IoT initiatives fail | insight | Cisco | edge | 2017-05-20 |
| When you think about data as a project, you get the Data Wheel of Death: Data Isn't Constantly Maintained → Data Becomes Irrelevant / Flawed → People Lose Trust → They Use Data Less | quotation | Brian Balfour (HubSpot) | — | 2017-04-04 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Machine learning research tends to get its fair share of hype because that’s where all the cutting-edge stuff happens, all the AlphaGo and GPT-3 and what-not. But for many companies, especially early-stage ones, the bleeding-edge state-of-the-art may not be what’s needed anymore. Getting a model that’s 90% of the way there but can scale to 1000+ users is often more valuable to them. | quotation | Mihail Eric | — | 2021-01-14 |
| Companies [...] overspend on technology and data scientists, without implementing changes in the business processes that could benefit from AI | quotation | Forbes | risks | 2020-10-20 |
| Only when organizations add the ability to learn with AI do significant benefits become likely (73%); these organizations facilitate systematic and continuous learning between humans and machines | insight | BCG | — | 2020-10-20 |
| Only 7% say limited AI skills are a barrier of AI implementation | insight | Gartner | risks | 2020-10-01 |
| 53% spent more than $20 million during the past year on AI and talent | insight | Deloitte | investments | 2020-07-14 |
| AI specialists and ML engineers (1st: 74% annual growth), data scientists (3rd: 37% annual growth), data engineers (8th: 33% annual growth) are among the top-15 emerging jobs in the US | insight | — | 2019-12-10 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Covid-19 is driving investments in AI and nanotech in healthcare to grow at a rate of nearly 50% | insight | Vector Innovation Fund | healthcare | 2021-01-03 |
| Covid-19 has increased adoption of AI in healthcare from 45% to 84% | insight | Intel | adoption, healthcare | 2020-11-18 |
| AI investments have not decreased due to Covid-19 | insight | Gartner | risks | 2020-10-01 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| By 2025, more than 50 billion IIoT devices will generate 79.4 ZB of data yearly | prediction | McKinsey | manufacturing | 2021-06-15 |
| 70% of companies will employ hybrid or multi-cloud management technologies by 2022 | prediction | McKinsey | — | 2021-06-15 |
| 5G will reach 80% of the global population by 2030 | prediction | McKinsey | — | 2021-06-15 |
| 56% of manufacturers will kick off edge computing pilots in the next two years | insight | MIT | manufacturing | 2021-05-24 |
| 27% of manufacturers have edge computing in production, 17% will move pilots to production in the next two years | insight | MIT | manufacturing | 2021-05-24 |
| 17% of manufacturers will move edge computing pilots to production in the next two years | insight | MIT | manufacturing | 2021-05-24 |
| Top use cases of edge: 1) industrial IoT, 2) telco, and 3) image processing | insight | CNCF | — | 2021-05-01 |
| Top devices: 1) cameras, sensors, IoT (70%), 2) controllers and actuators (44%), 3) compute offload (25%), and 4) leaf nodes (25%) | insight | CNCF | — | 2021-05-01 |
| Top challenges: 1) security, 2) edge devices going offline, and 3) observability at the edge | insight | CNCF | — | 2021-05-01 |
| Top access methods: wifi (62%), 4G (54%), 5G (51%), CDN (21%) | insight | CNCF | — | 2021-05-01 |
| 10% manage 100,000 nodes/devices or more; 49% manage <100 nodes/devices | insight | CNCF | — | 2021-05-01 |
| The power footprint for infrastructure edge deployments is to grow to 40 GW (2028) from 1 GW (2019) | prediction | Linux Foundation | — | 2021-03-10 |
| By 2028, the share of edge deployments are expected to be: 37.7% in Asia Pacific, 29% in Europe, 20.5 in North America, 7.0% in Latin America, and 5.8% in Middle East and Africa | prediction | Linux Foundation | — | 2021-03-10 |
| By 2028, 37% (down from 45%) of edge energy consumption will come from mobile (21.7%) and residential consumers (14.8%), 63% from enterprises (11.9%), telecommunications (11.9%), automotive (9.7%), healthcare (8.6%), manufacturing (6.2%), energy (4.6%), smart cities (6.1%), retail (4.5%), logistics, and transportation | prediction | Linux Foundation | — | 2021-03-10 |
| $400b to be spent on edge computing facilities between 2019 and 2028 | prediction | Linux Foundation | — | 2021-03-10 |
| Europe's 5G coverage to grow from around 1% of mobile subscriptions in 2020 to 55% in western countries and 27% in central an eastern states over the next five years | prediction | Ericsson | — | 2020-12-28 |
| China accounts for 80% of global 5G mobile subscriptions; North America accounts for only 4% | insight | Ericsson | — | 2020-12-28 |
| With 11% CAGR forecast for the value-added services market between 2020 and 2030 compared to a 0.75% CAGR for current existing services, 5G is a considered a leading source of new revenue for the industry | prediction | MIT | — | 2020-10-07 |
| Operators aim to deliver about 80% of enterprise requests through off-the-shelf solutions, leaving just 20% in need of a customized response | prediction | MIT | — | 2020-10-07 |
| Executives see the enterprise and public sector digitalization programs as the most compelling market opportunities for 5G | prediction | MIT | — | 2020-10-07 |
| Addressing the seemingly infinite number of enterprise opportunities, with all of their hardware and software complexities, means that operators cannot go it alone | prediction | MIT | — | 2020-10-07 |
| Operators with serious ambitions for 5G have a cloud-first strategy for the network and IT | insight | MIT | — | 2020-10-07 |
| By 2023 more than half of new enterprise IT infrastructure deployed will be at the edge rather than in corporate data centres | prediction | IDC | — | 2020-06-01 |
| Increased demand in government for AI at the edge | insight | IDC | — | 2020-04-29 |
| AI at the edge is 90% consumer market, not enterprise | insight | IoT World Today | — | 2020-03-19 |
| On-device intelligence is expected to be around 100% in 2025 (up from 10% in 2018): mobile devices, automotive, XR, PCs/tablets, and smart speakers | prediction | Qualcomm | — | 2020-02-01 |
| 7.8b smartphone units to be shipped between 2018 – 2022 | prediction | Qualcomm | telecommunications | 2020-02-01 |
| 74% of IoT initiatives fail | insight | Cisco | risks | 2017-05-20 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 73% of tech employees agree governments ought to regulate AI | insight | Protocol | — | 2021-03-15 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 85% of technology leaders agree Kubernetes is key to cloud-native application strategies | insight | Red Hat | — | 2021-03-03 |
| 40% of end-to-end machine learning platforms on Kubernetes are based on at least one Kubeflow component | insight | Databaseline | tools | 2021-02-02 |
| Kubeflow Pipelines (with Kale) and Katib are still in trial phase for centralized ML service, after 8 months with 2 engineers | insight | CERN | — | 2020-12-01 |
| Data scientists don't care about Kubernetes: they need abstractions that make it possible to perform high-performance data science on complex, modern infrastructure without needing to be systems expert[s] | insight | Determined AI | — | 2020-11-30 |
| It took a team of 7 people 8 months to release an alpha for Kubeflow Pipelines that integrates into Spotify’s ecosystem; beta release expected after 12 months | insight | Spotify | — | 2020-11-12 |
| Public clouds account for 36.6% as the main location of enterprise applications: 22.3% on premises, 21.6% hybrid, and 17% in private clouds | insight | Lightbend | — | 2020-10-26 |
| 30% are in the early stages of Kubernetes adoption as a production platform, with 19.9% still evaluating it | insight | Lightbend | — | 2020-10-26 |
| 14.8% have no plans to adopt Kubernetes as a production platform | insight | Lightbend | — | 2020-10-26 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Top model/experiment trackers: custom (29%), MLflow (27%), Kubeflow (18%), Weights & Biases (8%) | insight | O'Reilly | — | 2021-04-19 |
| Top frameworks: scikit-learn, TensorFlow, PyTorch, and Keras | insight | O'Reilly | — | 2021-04-19 |
| Top deployment technologies: MLflow (22%), TFX (20%), and Kubeflow (18%) | insight | O'Reilly | — | 2021-04-19 |
| 46% do not version data sets | insight | O'Reilly | risks | 2021-04-19 |
| Python developers typically work on teams with 2 – 7 (75%) or 8 – 12 people (16) | insight | JetBrains | — | 2021-03-01 |
| Python code is run within containers (VMs) for 47% (43%) of cloud-based environments | insight | JetBrains | — | 2021-03-01 |
| Python 3 is used by 94% of developers; only 6% use Python 2 | insight | JetBrains | — | 2021-03-01 |
| Only 32% of Python developers consider themselves data scientists | insight | JetBrains | — | 2021-03-01 |
| Machine learning is the primary (secondary) activity for 49% (27%) of Python developers | insight | JetBrains | — | 2021-03-01 |
| Data analysis is the primary (secondary) activity for 48% (34%) of Python developers | insight | JetBrains | — | 2021-03-01 |
| AWS (53%), GCP (33%), Heroku (23%), and Azure (21%) are the leading cloud platforms for Python developers | insight | JetBrains | — | 2021-03-01 |
| 85% of Python developers use it as their main language with JavaScript the leading secondary language | insight | JetBrains | — | 2021-03-01 |
| 55% of Python developers use it for data analysis | insight | JetBrains | — | 2021-03-01 |
| 54% (32%) of Python developers use virtualenv (Docker) to isolate environments | insight | JetBrains | — | 2021-03-01 |
| 11% (39%) of Python developers do not use version control systems (CI tools) | insight | JetBrains | — | 2021-03-01 |
| 59% of non-managed end-to-end machine learning platforms run on Kubernetes | insight | Databaseline | — | 2021-02-02 |
| 40% of end-to-end machine learning platforms on Kubernetes are based on at least one Kubeflow component | insight | Databaseline | kubernetes | 2021-02-02 |
| The most popular geospatial analysis libraries are Folium, GeoPandas, and Shapely | insight | Deepnote | — | 2021-01-26 |
| The most popular Python libraries are Matplotlib, NumPy, and Pandas | insight | Deepnote | — | 2021-01-26 |
| The most popular Python libaries for machine learning are TensorFlow (40%), Keras (34.1%), PyTorch (23.5%), and fast.ai (2.4%) | insight | Deepnote | — | 2021-01-26 |
| The most popular NLP libraries are NLTK (63%), GenSim (19.5%), and SpaCy (11.8%) | insight | Deepnote | — | 2021-01-26 |
| Python 3.6 (3.7) are used by 55% (36.5%) of users | insight | Deepnote | — | 2021-01-26 |
| Most use open-source tools and libraries for Python | insight | O'Reilly | — | 2020-03-18 |
| 55% use TensorFlow, 48% use scikit-learn, and 36% use PyTorch | insight | O'Reilly | — | 2020-03-18 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 69% of leaders believe their employees are more digitally mature than their organizations | insight | Accenture | defence | 2020-09-02 |
| The market for AI in aerospace robotics is to reach $7.78b by 2027 with CAGR 20.5% | prediction | Fortune | — | 2020-08-25 |
| Chatbots are used in 14% of aircraft and 9% of air terminals | insight | SITA | — | 2019-09-07 |
| 68% of airlines want AI-powered chatbots | insight | SITA | — | 2019-09-07 |
| 97% of aerospace and defence executives are willing to digitally reinvent their business and industry | insight | Accenture | defence | 2018-09-07 |
| 96% plan to increase investments to digitize core operations | insight | BCG | — | 2018-05-31 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 69% of leaders believe their employees are more digitally mature than their organizations | insight | Accenture | aerospace | 2020-09-02 |
| US DoD has more than 600 active AI projects | insight | PTC | — | 2020-03-01 |
| US DoD funds at to the tune of $4b in 2020 | insight | PTC | investments | 2020-03-01 |
| Over 30% of active DARPA projects involve AI | insight | PTC | — | 2020-03-01 |
| JAIC (Joint Artificial Intelligence Center) has increased its budget from $70m in 2018 to $209m in 2020 | insight | PTC | investments | 2020-03-01 |
| DARPA spends $2b over the next five years to foster innovation in AI for critical DoD processes | insight | PTC | investments | 2020-03-01 |
| 97% of aerospace and defence executives are willing to digitally reinvent their business and industry | insight | Accenture | aerospace | 2018-09-07 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Top use cases in fintech and investment firms: 1) algorithmic trading, 2) fraud detection, and 3) portfolio optimization | insight | Nvidia | — | 2021-03-10 |
| Top use cases in banking: 1) fraud detection, 2) recommender systems, 3) sales and marketing optimization | insight | Nvidia | — | 2021-03-10 |
| 83% believe AI is important to their company's future success with 34% citing it will increase their company's revenue by 20% or more | insight | Nvidia | — | 2021-03-10 |
| Global AI market for fintech is expected to reach $22.6b in 2025 | prediction | Mordor Intelligence | — | 2020-10-31 |
| 70% of financial services firms use ML to predict cash flow events, fine-tune credit scores, and detect fraud | insight | Deloitte | adoption | 2020-10-31 |
| The main barriers to adoption for financial services are: 1) lack of available data, 2) budget constraints, 3) lack of talent, 4) data privacy concerns, 5) lack of trust, 6) lack of available platforms and tools, 7) lack of management support, and 8) lack of algorithm transparency | insight | PwC | adoption, risks | 2020-05-01 |
| Only 47% of finance organizations have AI in production | insight | KPMG | — | 2020-02-13 |
| Most promising AI finance applications: 1) process automation, 2) risk management assessments, and 3) fraud prevention | insight | KPMG | — | 2020-02-13 |
| Top-3 business functions that have adopted AI: 1) service operations (49%), 2) risk (40%), and 3) sales and marketing (33%) | insight | McKinsey | — | 2018-11-13 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| NSTC's Subcommittee on Machine Learning & Artificial Intelligence must establish a task force to find best practices in identity management, such as token-based access, and single-sign-on strategies | insight | Fedscoop | — | 2020-11-18 |
| Inconsistencies in the methods of accessing and using cloud computing create barriers to using the technology for AI research | insight | Fedscoop | — | 2020-11-18 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Healthcare must move beyond explainability to recourse (giving those impacted concrete actions they could take to change the outcome) and to move beyond transparency to contestability (allowing people to challenge an algorithm) to avoid power and participation imbalances | insight | Boston Review | — | 2021-01-04 |
| Bias is endemic in medicine, especially with regard to sex (non-male), race (non-white), class, weight, and sexuality that exacerbates automated diagnoses and decisions | insight | Boston Review | — | 2021-01-04 |
| AI and ML in healthcare are forecast to grow with 22.5% CAGR through to 2027, from a $284.38 billion market in 2019 | prediction | Vector Innovation Fund | — | 2021-01-03 |
| Covid-19 is driving investments in AI and nanotech in healthcare to grow at a rate of nearly 50% | insight | Vector Innovation Fund | covid | 2021-01-03 |
| AI healthcare market to grow to $51.3b in 2027 with 41.4% CAGR | prediction | Meticulous Research | — | 2020-12-01 |
| Covid-19 has increased adoption of AI in healthcare from 45% to 84% | insight | Intel | adoption, covid | 2020-11-18 |
| 44% of healthcare and pharmaceuticals have increased their AI investments because of Covid-19 | insight | McKinsey | — | 2020-11-17 |
| 75% are concerned AI threatens security and privacy of patient data | insight | KPMG | — | 2020-02-14 |
| More than 80% of consumers are willing to wear fitness technology | insight | Business Insider | — | 2020-01-31 |
| Most promising AI healthcare cost savings potential: 1) service operations, 2) marketing and sales, 3) risk, 4) supply chain management and manufacturing, 5) operations, 6) finance and IT, and 7) HR | prediction | McKinsey | — | 2019-02-11 |
| Most promising AI healthcare applications: 1) robot-assisted surgery, 2) virtual nursing assistants, 3) administrative workflow assistants, 4) fraud detection, 5) dosage error reduction, 6) connected machines, 7) clinical trial participant identifier, 8) preliminary diagnosis, 9) automated image diagnosis, and 10) cybersecurity | prediction | Accenture | — | 2019-02-11 |
| Annual savings of AI in healthcare are expected to reach $150b by 2026 | prediction | Accenture | — | 2019-02-11 |
| Key areas of investment for AI in healthcare: digitization, engagement, and diagnostics | insight | PwC | — | 2019-02-11 |
| Top-3 business functions that have adopted AI: 1) service operations (46%), 2) product/service development (28%), and 3) supply-chain management (21%) | insight | McKinsey | — | 2018-11-13 |
| Lack of trust in AI among patients (36%) as well as clinicians (30%) is a barrier to adoption | insight | Intel | adoption, risks | 2018-07-02 |
| 54% expect widespread adoption of AI until 2023 | insight | Intel | adoption | 2018-07-02 |
| 20% of healthcare spend is wasted | insight | OECD | — | 2017-01-10 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| By 2023, 95% of supply vendors in consumer goods will leverage AI | prediction | Noodle AI | — | 2020-11-17 |
| Retailers spend on average 7% of their IT budget on AI. A quarter of large retails spend up to 10%. | insight | Noodle AI | investments | 2020-11-17 |
| AI adoption in retail is 42% across the board: 33% in apparel and footwear, 29% in grocery and food, and 26% in home improvement | insight | Noodle AI | adoption | 2020-11-17 |
| 74% of AI applications in retail are spent on operations and only 26% on customers-facing applications | insight | Noodle AI | investments | 2020-11-17 |
| Top-3 business functions that have adopted AI: 1) sales and marketing (52%), 2) supply-chain management (38%), and 3) service operations (23%) | insight | McKinsey | — | 2018-11-13 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| By 2025, more than 50 billion IIoT devices will generate 79.4 ZB of data yearly | prediction | McKinsey | edge | 2021-06-15 |
| 50% of today's work activities could be automated by 2025 | prediction | McKinsey | — | 2021-06-15 |
| 56% of manufacturers will kick off edge computing pilots in the next two years | insight | MIT | edge | 2021-05-24 |
| 27% of manufacturers have edge computing in production, 17% will move pilots to production in the next two years | insight | MIT | edge | 2021-05-24 |
| 17% of manufacturers will move edge computing pilots to production in the next two years | insight | MIT | edge | 2021-05-24 |
| 77% of semiconductor companies have adopted or are piloting AI technologies | insight | Accenture | — | 2019-08-21 |
| Top-3 business functions that have adopted AI: 1) manufacturing (49%), 2) product/service development (39%), and 3) service operations (27%) | insight | McKinsey | — | 2018-11-13 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| 7.8b smartphone units to be shipped between 2018 – 2022 | prediction | Qualcomm | edge | 2020-02-01 |
| Key areas of AI: network planning, network performance management, SLA management, product lifecycle management, network management, and revenue management | prediction | Ericsson | — | 2019-05-16 |
| 53% of service providers expect to have integrated AI into their networks by the end of 2020 | prediction | Ericsson | — | 2019-05-16 |
| Top-3 business functions that have adopted AI: 1) service operations (75%), 2) product/service development (45%), and 3) sales and marketing (38%) | insight | McKinsey | — | 2018-11-13 |
| Note | Type | Source | Categories | Date |
|---|---|---|---|---|
| Top-3 business functions that have adopted AI: 1) service operations (46%), 2) product/service development (41%), and 3) manufacturing (19%) | insight | McKinsey | — | 2018-11-13 |
| Only 23 percent of utilities executives see AI as a high strategic priority | insight | Roland Berger | — | 2018-03-13 |
| 40% lack a strategy or targets for AI | insight | Roland Berger | — | 2018-03-13 |