Posted On:McKinsey Archives - AppFerret
AI is receiving major R&D investment from tech giants including Google, Baidu, Facebook and Microsoft.
- Artificial Intelligence (AI) investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.
- U.S.-based companies absorbed 66% of all AI investments in 2016. China was second with 17% and growing fast.
- By providing better search results, Netflix estimates that it is avoiding canceled subscriptions that would reduce its revenue by $1B annually.
These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies’ prospects for further deployment, and AI’s impact on markets, governments, and individuals. McKinsey Analytics was also utilized in the development of this study and discussion paper.
Key takeaways from the study include the following:
- Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. The current rate of AI investment is 3X the external investment growth since 2013. McKinsey found that 20% of AI-aware firms are early adopters, concentrated in the high-tech/telecom, automotive/assembly and financial services industries. The graphic below illustrates the trends the study team found during their analysis.
- AI is turning into a race for patents and intellectual property (IP) among the world’s leading tech companies. McKinsey found that only a small percentage (up to 9%) of Venture Capital (VC), Private Equity (PE), and other external funding. Of all categories that have publically available data, M&A grew the fastest between 2013 And 2016 (85%).The report cites many examples of internal development including Amazon’s investments in robotics and speech recognition, and Salesforce on virtual agents and machine learning. BMW, Tesla, and Toyota lead auto manufacturers in their investments in robotics and machine learning for use in driverless cars. Toyota is planning to invest $1B in establishing a new research institute devoted to AI for robotics and driverless vehicles.
- McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area with the Compound Annual Growth Rate (CAGR) reaching approximately 80% from 20-13 to 2016. The following graphic illustrates the distribution of external investments by category from the study.
- High tech, telecom, and financial services are the leading early adopters of machine learning and AI. These industries are known for their willingness to invest in new technologies to gain competitive and internal process efficiencies. Many startups have also had their start by concentrating on the digital challenges of this industries as well. The MGI Digitization Index is a GDP-weighted average of Europe and the United States. See Appendix B of the study for a full list of metrics and explanation of methodology. McKinsey also created an overall AI index shown in the first column below that compares key performance indicators (KPIs) across assets, usage, and labor where AI could make a contribution. The following is a heat map showing the relative level of AI adoption by industry and key area of asset, usage, and labor category.
- McKinsey predicts High Tech, Communications, and Financial Services will be the leading industries to adopt AI in the next three years. The competition for patents and intellectual property (IP) in these three industries is accelerating. Devices, products and services available now and on the roadmaps of leading tech companies will over time reveal the level of innovative activity going on in their R&D labs today. In financial services, for example, there are clear benefits from improved accuracy and speed in AI-optimized fraud-detection systems, forecast to be a $3B market in 2020. The following graphic provides an overview of sectors or industries leading in AI addition today and who intend to grow their investments the most in the next three years.
- Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins as a result of AI adoption.McKinsey found that companies who benefit from senior management support for AI initiatives have invested in infrastructure to support its scale and have clear business goals achieve 3 to 15% percentage point higher profit margin. Of the over 3,000 business leaders who were interviewed as part of the survey, the majority expect margins to increase by up to 5% points in the next year.
- Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing according to the McKinsey study. “Click to ship” cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment
- Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide. Netflix found that customers, on average, give up 90 seconds after searching for a movie. By improving search results, Netflix projects that they have avoided canceled subscriptions that would reduce its revenue by $1B annually.
Original article here.
Driven by specialised analytics systems and software, big data analytics has decreased the time required to double medical knowledge by half, thus compressing healthcare innovation cycle period, shows the much discussed Mary Meeker study titled Internet Trends 2017.
The presentation of the study is seen as an evidence of the proverbial big data-enabled revolution, that was predicted by experts like McKinsey and Company. “A big data revolution is under way in health care. Over the last decade pharmaceutical companies have been aggregating years of research and development data into medical data bases, while payors and providers have digitised their patient records,” the McKinsey report had said four years ago.
The Mary Meeker study shows that in the 1980s it took seven years to double medical knowledge which has been decreased to only 3.5 years after 2010, on account of massive use of big data analytics in healthcare. Though most of the samples used in the study were US based, the global trends revealed in it are well visible in India too.
“Medicine and underlying biology is now becoming a data-driven science where large amounts of structured and unstructured data relating to biological systems and human health is being generated,” says Dr Rohit Gupta of MedGenome, a genomics driven research and diagnostics company based in Bengaluru.
Dr Gupta told Firstpost that big data analytics has made it possible for MedGenome, which focuses on improving global health by decoding genetic information contained in an individual genome, to dive deeper into genetics research.
“While any individual’s genome information is useful for detecting the known mutations for diseases, underlying new patterns of complicated diseases and their progression requires genomics data from many individuals across populations — sometimes several thousands to even few millions amounting to exabytes of information,” he said.
All of which would have been a cumbersome process without the latest data analytics tools that big data analytics has brought forth.
The company that started work on building India-specific baseline data to develop more accurate gene-based diagnostic testing kits in the year 2015 now conducts 400 genetic tests across all key disease areas.
What is Big Data
According to Mitali Mukerji, senior principal scientist, Council of Scientific and Industrial Research when a large number of people and institutions digitally record health data either in health apps or in digitised clinics, these information become big data about health. The data acquired from these sources can be analysed to search for patterns or trends enabling a deeper insight into the health conditions for early actionable interventions.
Big data is growing bigger
But big data analytics require big data. And proliferation of Information technology in the health sector has enhanced flow of big data exponentially from various sources like dedicated wearable health gadgets like fitness trackers and hospital data base. Big data collection in the health sector has also been made possible because of the proliferation of smartphones and health apps.
The Meeker study shows that the download of health apps have increased worldwide in 2016 to nearly 1,200 million from nearly 1,150 million in the last year and 36 percent of these apps belong to the fitness and 24 percent to the diseases and treatment ones.
Health apps help the users monitor their health. From watching calorie intake to fitness training — the apps have every assistance required to maintain one’s health. 7 minute workout, a health app with three million users helps one get that flat tummy, lose weight and strengthen the core with 12 different exercises. Fooducate, another app, helps keep track of what one eats. This app not only counts the calories one is consuming, but also shows the user a detailed breakdown of the nutrition present in a packaged food.
For Indian users, there’s Healthifyme, which comes with a comprehensive database of more than 20,000 Indian foods. It also offers an on-demand fitness trainer, yoga instructor and dietician. With this app, one can set goals to lose weight and track their food and activity. There are also companies like GOQii, which provide Indian customers with subscription-based health and fitness services on their smartphones using fitness trackers that come free.
Dr Gupta of MedGenome explains that data accumulated in wearable devices can either be sent directly to the healthcare provider for any possible intervention or even predict possible hospitalisation in the next few days.
The Meeker study shows that global shipment of wearable gadgets grew from 26 million in 2014 to 102 million in 2016.
Another area that’s shown growth is electronic health records. In the US, electronic health records in office-based physicians in United States have soared from 21 percent in 2004 to 87 percent in 2015. In fact, every hospital with 500 beds (in the US) generate 50 petabytes of health data.
Back home, the Ministry of Electronics and Information Technology, Government of India, runs Aadhar-based Online Registration System, a platform to help patients book appointments in major government hospitals. The portal has the potential to emerge into a source if big data offering insights on diseases, age groups, shortcomings in hospitals and areas to improve. The website claims to have already been used to make 8,77,054 appointments till date in 118 hospitals.
On account of permeation of digital technology in health care, data growth has recorded 48% growth year on year, the Meeker study says. The accumulated mass of data, according to it, has provided deeper insights in health conditions. The study shows drastic increase of citations from 5 million in 1977 to 27 million in 2017. Easy access to big data has ensured that scientists can now direct their investigations following patterns analysed from such information and less time is required to arrive at conclusion.
“If a researcher has huge sets of data at his disposal, he/she can also find out patterns and simulate it through machine learning tools, which decreases the time required to arrive at a conclusion. Machine learning methods become more robust when they are fed with results analysed from big data,” says Mukerji.
She further adds, “These data simulation models, rely on primary information generated from a study to build predictive models that can help assess how human body would respond to a given perturbation,” says Mukerji.
The Meeker also study shows that Archimedes data simulation models can conduct clinical trials from data related to 50,000 patients collected over a period of 30 years, in just a span of two months. In absence of this model it took seven years to conduct clinical trials on data related to 2,838 patients collected over a period of seven years.
As per this report in 2016 results of 25,400 number of clinical trial was publically available against 1,900 in 2009.
The study also shows that data simulation models used by laboratories have drastically decreased time required for clinical trials. Due to emergence of big data, rise in number of publically available clinical trials have also increased, it adds.
Big data in scientific research
The developments grown around big-data in healthcare has broken the silos in scientific research. For example, the field of genomics has taken a giant stride in evolving personalised and genetic medicine with the help of big data.
A good example of how big data analytics can help modern medicine is the Human Genome Project and the innumerous researches on genetics, which paved way for personalised medicine, would have been difficult without the democratisation of data, which is another boon of big data analytics. The study shows that in the year 2008 there were only 5 personalised medicines available and it has increased to 132 in the year 2016.
In India, a Bangalore-based integrated biotech company recently launched ‘Avestagenome’, a project to build a complete genetic, genealogical and medical database of the Parsi community. Avestha Gengraine Technologies (Avesthagen), which launched the project believes that the results from the Parsi genome project could result in disease prediction and accelerate the development of new therapies and diagnostics both within the community as well as outside.
MedGenome has also been working on the same direction. “We collaborate with leading hospitals and research institutions to collect samples with research consent, generate sequencing data in our labs and analyse it along with clinical data to discover new mutations and disease causing perturbations in genes or functional pathways. The resultant disease models and their predictions will become more accurate as and when more data becomes available.”
Mukerji says that democratisation of data fuelled by proliferation of technology and big data has also democratised scientific research across geographical boundaries. “Since data has been made easily accessible, any laboratory can now proceed with research,” says Mukerji.
“We only need to ensure that our efforts and resources are put in the right direction,” she adds.
Challenges with big data
But Dr Gupta warns that big-data in itself does not guarantee reliability for collecting quality data is a difficult task.
Moreover, he said, “In medicine and clinical genomics, domain knowledge often helps and is almost essential to not only understand but also finding ways to effectively use the knowledge derived from the data and bring meaningful insights from it.”
Besides, big data gathering is heavily dependent on adaptation of digital health solutions, which further restricts the data to certain age groups. As per the Meeker report, 40 percent of millennial respondents covered in the study owned a wearable. On the other hand 26 percent and 10 percent of the Generation X and baby boomers, respectively, owned wearables.
Similarly, 48 percent millennials, 38 percent Generation X and 23 percent baby boomers go online to find a physician. The report also shows that 10 percent of the people using telemedicine and wearable proved themselves super adopters of the new healthcare technology in 2016 as compared to 2 percent in 2015.
Collection of big data.
Every technology brings its own challenges, with big data analytics secure storage and collection of data without violating the privacy of research subjects, is an added challenge. Something, even the Meeker study does not answer.
“Digital world is really scary,” says Mukerji.
“Though we try to secure our data with passwords in our devices, but someone somewhere has always access to it,” she says.
The health apps which are downloaded in mobile phones often become the source of big-data not only for the company that has produced it but also to the other agencies which are hunting for data in the internet. “We often click various options while browsing internet and thus knowingly or unknowingly give a third party access to some data stored in the device or in the health app,” she adds.
Dimiter V Dimitrov a health expert makes similar assertions in his report, ‘Medical Internet of Things and Big Data in Healthcare‘. He reports that even wearables often have a server which they interact to in a different language providing it with required information.
“Although many devices now have sensors to collect data, they often talk with the server in their own language,” he said in his report.
Even though the industry is still at a nascent stage, and privacy remains a concern, Mukerji says that agencies possessing health data can certainly share them with laboratories without disclosing patient identity.
Original article here.
McKinsey outline the range of opportunities for applying artificial intelligence in their article. They say:
‘For companies, successful adoption of these evolving technologies will significantly enhance performance. Some of the gains will come from labor substitution, but automation also has the potential to enhance productivity, raise throughput, improve predictions, outcomes, accuracy, and optimization, as well expand the discovery of new solutions in massively complex areas such as synthetic biology and material science‘.
At Smart Insights, we’ve been looking beyond the hype to look at specific practical applications for applying AI in marketing. Our recommendation is that the best marketing applications are in machine learning where predictive analytics is applied to learn from historic data to deliver more relevant personalization, both on site, using email automation and offsite in programmatic advertising. This high potential is also clear from the chart from McKinsey (see top of page).
You can see that ‘personalize advertising’ is rated highly and this relates to different forms of personalised messaging I mentioned above. Optimize merchandising strategy is a retail application which is related.
Original article here.
When we started this decade, the Internet of Things was a basically a buzzword, talked about by a few, acted upon by fewer, a challenge to save for the future, like 2015 or 2020.
But as a famous character once said in a movie that’s now 30 years old, “life moves pretty fast…” and now, here we are with 2015 in the rear view mirror and our 2020 vision becoming clearer by the minute.
Everyone’s talking about the Internet of Things, even the “things,” which can now request and deliver customer support, tell if you’re being as productive as you could be at work, let your doctor know if you’re following orders (or not), reduce inefficiencies in energy consumption, improve business processes, predict issues and proactively improve or resolve them based on data received.
The Internet of Things (IoT) is just getting started. These forecasts below show why organizations need to get started too (if they haven’t already) on leveraging and responding to the Internet of Things:
1. The worldwide Internet of Things market is predicted to grow to $1.7 trillion by 2020, marking a compound annual growth rate of 16.9%. – IDC Worldwide Internet of Things Forecast, 2015 – 2020.
2. An estimated 25 billion connected “things” will be in use by 2020. – Gartner Newsroom
3. Wearable technology vendors shipped 78.1 million wearable devices in 2015, an increase of 171.6% from 2014. Shipment predictions for this year are 111 million, increasing to 215 million in 2019. – IDC Worldwide Quarterly Wearable Device Tracker
4. By 2020, each person is likely to have an average of 5.1 connected devices. – Frost and Sullivan Power Management in IoT and Connected Devices
5. In a 2016 PwC survey of 1,000 U.S. consumers, 45% say they now own a fitness band, 27% a smartwatch, and 12% smart clothing. 57% say they are excited about the future of wearable technology as part of everyday life. 80% say wearable devices make them more efficient at home, 78% more efficient at work. – PwC The Wearable Life 2.0: Connected Living in a Wearable World
6. By 2020, more than half of major new business processes and systems will incorporate some element, large or small, of the Internet of Things. – Gartner Predicts 2016: Unexpected Implications Arising from the Internet of Things
7. 65% of approximately 1,000 global business executives surveyed say they agree organizations that leverage the internet of things will have a significant advantage; 19% however, still say they have never heard of the Internet of Things. – Internet of Things Institute 2016 I0T Trends Survey
8. 80% of retailers worldwide say they agree that the Internet of Things will drastically change the way companies do business in the next three years. – Retail Systems Research: The Internet of Things in Retail: Great Expectations
9. By 2018, six billion things will have the ability to request support. – Gartner Predicts 2016: CRM Customer Service and Support
10. By 2020, 47% of devices will have the necessary intelligence to request support. – Gartner Predicts 2016: CRM Customer Service and Support
11. By 2025, the Internet of Things could generate more than $11 trillion a year in economic value through improvements in energy efficiency, public transit, operations management, smart customer relationship management and more. – McKinsey Global Institute Report: The Internet of Things: Mapping the value behind the Hype
12. Barcelona estimates that IoT systems have helped the city save $58 million a year from connected water management and $37 million a year via smart street lighting alone. – Harvard University Report
13. General Electric estimates that the “Industrial Internet” market (connected industrial machinery) will add $10 to $15 trillion to the global GDP within the next 20 years. – GE Reports
14. General Electric believes that using connected industrial machinery to make oil and gas exploration and development just 1% more efficient would result in a savings of $90 billion. – GE Reports
16. Connected homes will be a major part of the Internet of Things. By 2019, companies will ship 1.9 billion connected home devices, marking an estimated $490 billion in revenue (Business Insider Intelligence). By 2020, even the connected kitchen will contribute at least 15 percent savings in the food and beverage industry, leveraging data analytics. – Gartner Research Predicts 2015: The Internet of Things
The Internet of Things is accelerating the transformation of the way we live and work. Life move pretty fast. Stop and look around, but don’t miss it. Is your organization leveraging the Internet of Things?
Original article here.