The next Frontier for aI in China could Add $600 billion to Its Economy

In the past decade, China has actually constructed a solid structure to support its AI economy and made considerable contributions to AI globally. Stanford University's AI Index, which examines AI advancements around the world throughout numerous metrics in research, development, and economy, ranks China amongst the top three nations for global AI vibrancy.1"Global AI Vibrancy Tool: Who's leading the global AI race?" Expert System Index, Stanford Institute for Human-Centered Artificial Intelligence (HAI), Stanford University, 2021 ranking. On research study, for instance, China produced about one-third of both AI journal papers and AI citations worldwide in 2021. In financial financial investment, China accounted for nearly one-fifth of global personal investment funding in 2021, attracting $17 billion for AI start-ups.2 Daniel Zhang et al., Artificial Intelligence Index report 2022, Stanford Institute for Human-Centered Artificial Intelligence (HAI), Stanford University, March 2022, Figure 4.2.6, "Private financial investment in AI by geographical area, 2013-21."

Five kinds of AI companies in China

In China, we discover that AI companies typically fall under one of five main categories:

Hyperscalers establish end-to-end AI technology capability and work together within the community to serve both business-to-business and business-to-consumer business. Traditional market companies serve customers straight by establishing and adopting AI in internal improvement, new-product launch, and client services. Vertical-specific AI companies establish software and solutions for specific domain usage cases. AI core tech suppliers offer access to computer vision, natural-language processing, voice recognition, and artificial intelligence capabilities to establish AI systems. Hardware business offer the hardware facilities to support AI need in computing power and storage. Today, AI adoption is high in China in finance, retail, and high tech, which together account for more than one-third of the country's AI market (see sidebar "5 types of AI business in China").3 iResearch, iResearch serial market research on China's AI market III, December 2020. In tech, for instance, leaders Alibaba and ByteDance, both home names in China, have ended up being known for their highly tailored AI-driven consumer apps. In reality, the majority of the AI applications that have actually been extensively adopted in China to date have remained in consumer-facing industries, hb9lc.org propelled by the world's biggest internet consumer base and the ability to engage with consumers in brand-new methods to increase customer loyalty, earnings, and market appraisals.

So what's next for AI in China?

About the research study

This research is based on field interviews with more than 50 experts within McKinsey and throughout industries, in addition to extensive analysis of McKinsey market assessments in Europe, the United States, Asia, and China particularly in between October and November 2021. In performing our analysis, we looked beyond commercial sectors, such as financing and retail, where there are currently mature AI usage cases and clear adoption. In emerging sectors with the greatest value-creation capacity, we concentrated on the domains where AI applications are presently in market-entry stages and could have a disproportionate impact by 2030. Applications in these sectors that either remain in the early-exploration stage or have mature industry adoption, such as manufacturing-operations optimization, were not the focus for the function of the study.

In the coming decade, our research indicates that there is incredible opportunity for AI development in new sectors in China, including some where innovation and R&D costs have typically lagged global equivalents: automotive, transportation, and logistics; production; business software application; and health care and life sciences. (See sidebar "About the research study.") In these sectors, we see clusters of use cases where AI can develop upwards of $600 billion in economic value yearly. (To provide a sense of scale, the 2021 gdp in Shanghai, China's most populated city of almost 28 million, was roughly $680 billion.) Sometimes, this worth will come from income produced by AI-enabled offerings, while in other cases, it will be produced by cost savings through higher performance and productivity. These clusters are likely to become battlegrounds for business in each sector that will assist specify the marketplace leaders.

Unlocking the complete potential of these AI chances typically needs substantial investments-in some cases, far more than leaders might expect-on multiple fronts, consisting of the information and innovations that will underpin AI systems, the best talent and organizational frame of minds to construct these systems, and new organization designs and partnerships to create data environments, market standards, and guidelines. In our work and international research, we find much of these enablers are ending up being basic practice among business getting one of the most worth from AI.

To assist leaders and investors marshal their resources to speed up, interrupt, and lead in AI, we dive into the research, initially sharing where the most significant chances depend on each sector and after that detailing the core enablers to be tackled initially.

Following the cash to the most promising sectors

We took a look at the AI market in China to determine where AI could provide the most value in the future. We studied market forecasts at length and dug deep into country and segment-level reports worldwide to see where AI was delivering the greatest value across the global landscape. We then spoke in depth with specialists across sectors in China to understand where the greatest chances could emerge next. Our research led us to several sectors: automobile, transport, and logistics, which are jointly anticipated to contribute the majority-around 64 percent-of the $600 billion opportunity; production, which will drive another 19 percent; enterprise software, contributing 13 percent; and health care and life sciences, at 4 percent of the chance.

Within each sector, our analysis reveals the value-creation opportunity concentrated within just 2 to 3 domains. These are generally in areas where private-equity and venture-capital-firm financial investments have actually been high in the previous 5 years and successful proof of principles have been provided.

Automotive, transport, and logistics

China's car market stands as the biggest in the world, with the number of vehicles in usage surpassing that of the United States. The large size-which we approximate to grow to more than 300 million passenger lorries on the road in China by 2030-provides a fertile landscape of AI opportunities. Certainly, our research study finds that AI might have the greatest potential effect on this sector, delivering more than $380 billion in financial value. This worth development will likely be produced mainly in three locations: self-governing cars, customization for car owners, and fleet possession management.

Autonomous, or self-driving, cars. Autonomous vehicles make up the largest portion of worth development in this sector ($335 billion). Some of this new value is expected to come from a reduction in monetary losses, such as medical, first-responder, and vehicle costs. Roadway accidents stand to decrease an estimated 3 to 5 percent annually as autonomous cars actively navigate their environments and make real-time driving choices without undergoing the many diversions, such as text messaging, that tempt human beings. Value would likewise come from savings understood by drivers as cities and enterprises replace guest vans and buses with automobiles.4 Estimate based on McKinsey analysis. Key assumptions: 3 percent of light cars and 5 percent of heavy automobiles on the road in China to be changed by shared autonomous cars; accidents to be minimized by 3 to 5 percent with adoption of autonomous cars.

Already, substantial development has been made by both traditional vehicle OEMs and AI gamers to advance autonomous-driving abilities to level 4 (where the chauffeur does not require to focus but can take control of controls) and level 5 (totally autonomous capabilities in which inclusion of a steering wheel is optional). For instance, WeRide, which attained level 4 autonomous-driving abilities,5 Based on WeRide's own assessment/claim on its website. completed a pilot of its Robotaxi in Guangzhou, with almost 150,000 trips in one year with no accidents with active liability.6 The pilot was conducted in between November 2019 and November 2020.

Personalized experiences for automobile owners. By using AI to analyze sensing unit and GPS data-including vehicle-parts conditions, fuel intake, route choice, and steering habits-car makers and AI gamers can significantly tailor recommendations for hardware and software updates and personalize automobile owners' driving experience. Automaker NIO's innovative driver-assistance system and battery-management system, for example, can track the health of electric-car batteries in genuine time, diagnose usage patterns, and enhance charging cadence to improve battery life expectancy while drivers go about their day. Our research finds this might provide $30 billion in economic value by decreasing maintenance costs and unexpected vehicle failures, as well as generating incremental income for companies that recognize ways to generate income from software application updates and new capabilities.7 Estimate based on McKinsey analysis. Key assumptions: AI will produce 5 to 10 percent cost savings in client maintenance cost (hardware updates); cars and truck makers and AI gamers will monetize software updates for 15 percent of fleet.

Fleet property management. AI could likewise prove crucial in assisting fleet supervisors better browse China's enormous network of railway, highway, inland waterway, and civil air travel paths, which are some of the longest on the planet. Our research discovers that $15 billion in value development could emerge as OEMs and AI gamers concentrating on logistics establish operations research study optimizers that can evaluate IoT data and determine more fuel-efficient paths and lower-cost maintenance picks up fleet operators.8 Estimate based upon McKinsey analysis. Key presumptions: 5 to 15 percent cost reduction in automobile fleet fuel usage and maintenance; roughly 2 percent cost decrease for aircrafts, vessels, and trains. One vehicle OEM in China now provides fleet owners and operators an AI-driven management system for monitoring fleet locations, tracking fleet conditions, and analyzing trips and routes. It is estimated to save as much as 15 percent in fuel and maintenance costs.

Manufacturing

In production, China is evolving its credibility from an inexpensive production hub for toys and clothing to a leader in accuracy production for processors, chips, engines, and other high-end parts. Our findings reveal AI can help facilitate this shift from manufacturing execution to making innovation and create $115 billion in financial value.

The bulk of this value creation ($100 billion) will likely originate from developments in process style through using different AI applications, such as collaborative robotics that develop the next-generation assembly line, and digital twins that replicate real-world assets for use in simulation and optimization engines.9 Estimate based upon McKinsey analysis. Key assumptions: 40 to half cost reduction in producing item R&D based upon AI adoption rate in 2030 and enhancement for producing design by sub-industry (consisting of chemicals, steel, electronics, automotive, and advanced markets). With digital twins, producers, equipment and forum.pinoo.com.tr robotics providers, and system automation providers can simulate, test, and confirm manufacturing-process results, such as product yield or production-line efficiency, before commencing large-scale production so they can determine expensive procedure ineffectiveness early. One regional electronics manufacturer uses wearable sensors to capture and digitize hand and body movements of workers to design human performance on its production line. It then optimizes devices specifications and setups-for example, by altering the angle of each workstation based on the worker's height-to lower the probability of employee injuries while improving employee comfort and efficiency.

The remainder of value development in this sector ($15 billion) is expected to come from AI-driven improvements in product advancement.10 Estimate based on McKinsey analysis. Key assumptions: 10 percent cost reduction in producing product R&D based upon AI adoption rate in 2030 and improvement for item R&D by sub-industry (consisting of electronics, machinery, automobile, and advanced markets). Companies could use digital twins to quickly test and validate brand-new product styles to minimize R&D costs, improve item quality, and drive brand-new item innovation. On the international stage, Google has actually provided a look of what's possible: it has utilized AI to rapidly evaluate how various part designs will change a chip's power usage, performance metrics, and size. This technique can yield an optimum chip style in a portion of the time style engineers would take alone.

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Enterprise software application

As in other countries, companies based in China are undergoing digital and AI changes, causing the introduction of brand-new regional enterprise-software markets to support the essential technological foundations.

Solutions delivered by these business are estimated to deliver another $80 billion in economic value. Offerings for cloud and AI tooling are anticipated to offer over half of this worth development ($45 billion).11 Estimate based upon McKinsey analysis. Key assumptions: 12 percent CAGR for cloud database in China; 20 to 30 percent CAGR for AI tooling. In one case, a regional cloud provider serves more than 100 regional banks and insurance business in China with an integrated information platform that allows them to run throughout both cloud and on-premises environments and lowers the cost of database development and storage. In another case, an AI tool supplier in China has established a shared AI algorithm platform that can assist its information researchers automatically train, forecast, and update the model for a provided prediction problem. Using the shared platform has lowered model production time from three months to about 2 weeks.

AI-driven software-as-a-service (SaaS) applications are anticipated to contribute the remaining $35 billion in financial value in this classification.12 Estimate based on McKinsey analysis. Key assumptions: 17 percent CAGR for software application market; 100 percent SaaS penetration rate in China by 2030; 90 percent of the use cases empowered by AI in business SaaS applications. Local SaaS application designers can use multiple AI techniques (for circumstances, computer vision, natural-language processing, artificial intelligence) to assist companies make forecasts and decisions throughout enterprise functions in finance and tax, personnels, supply chain, and cybersecurity. A leading financial organization in China has actually deployed a local AI-driven SaaS service that uses AI bots to offer tailored training suggestions to staff members based on their profession course.

Healthcare and life sciences

In recent years, China has actually stepped up its financial investment in development in healthcare and life sciences with AI. China's "14th Five-Year Plan" targets 7 percent annual development by 2025 for R&D expense, of which a minimum of 8 percent is devoted to standard research.13"'14th Five-Year Plan' Digital Economy Development Plan," State Council of the People's Republic of China, January 12, 2022.

One area of focus is speeding up drug discovery and increasing the odds of success, which is a significant global issue. In 2021, international pharma R&D spend reached $212 billion, compared with $137 billion in 2012, with a roughly 5 percent compound annual development rate (CAGR). Drug discovery takes 5.5 years typically, which not just delays clients' access to ingenious therapeutics however also shortens the patent defense period that rewards innovation. Despite enhanced success rates for new-drug advancement, just the top 20 percent of pharmaceutical companies worldwide recognized a breakeven on their R&D investments after 7 years.

Another leading concern is enhancing patient care, and Chinese AI start-ups today are working to develop the country's reputation for providing more precise and trusted healthcare in terms of diagnostic outcomes and clinical choices.

Our research recommends that AI in R&D could add more than $25 billion in economic worth in three particular areas: much faster drug discovery, clinical-trial optimization, and clinical-decision support.

Rapid drug discovery. Novel drugs (trademarked prescription drugs) presently account for less than 30 percent of the overall market size in China (compared with more than 70 percent internationally), wiki.dulovic.tech indicating a considerable opportunity from introducing unique drugs empowered by AI in discovery. We approximate that utilizing AI to speed up target recognition and novel molecules style might contribute approximately $10 billion in value.14 Estimate based upon McKinsey analysis. Key assumptions: 35 percent of AI enablement on unique drug discovery; 10 percent revenue from novel drug advancement through AI empowerment. Already more than 20 AI start-ups in China funded by private-equity companies or local hyperscalers are teaming up with traditional pharmaceutical business or separately working to develop unique rehabs. Insilico Medicine, by using an end-to-end generative AI engine for target recognition, molecule design, and lead optimization, found a preclinical candidate for lung fibrosis in less than 18 months at an expense of under $3 million. This represented a significant reduction from the average timeline of six years and an average expense of more than $18 million from target discovery to preclinical candidate. This antifibrotic drug candidate has now effectively completed a Stage 0 medical study and entered a Stage I medical trial.

Clinical-trial optimization. Our research recommends that another $10 billion in financial worth might result from optimizing clinical-study designs (process, procedures, sites), optimizing trial shipment and execution (hybrid trial-delivery design), and creating real-world evidence.15 Estimate based on McKinsey analysis. Key assumptions: 30 percent AI utilization in clinical trials; 30 percent time savings from real-world-evidence sped up approval. These AI usage cases can reduce the time and expense of clinical-trial advancement, offer a better experience for clients and healthcare specialists, and enable higher quality and compliance. For example, an international top 20 pharmaceutical business leveraged AI in mix with process improvements to reduce the clinical-trial registration timeline by 13 percent and conserve 10 to 15 percent in external expenses. The worldwide pharmaceutical business focused on three areas for its tech-enabled clinical-trial advancement. To accelerate trial design and functional preparation, it made use of the power of both internal and external information for enhancing procedure design and website choice. For streamlining site and client engagement, it established an ecosystem with API requirements to leverage internal and external developments. To develop a clinical-trial development cockpit, it aggregated and pictured operational trial information to enable end-to-end clinical-trial operations with full transparency so it might predict potential risks and trial delays and proactively take action.

Clinical-decision assistance. Our findings suggest that the use of artificial intelligence algorithms on medical images and information (consisting of examination outcomes and symptom reports) to predict diagnostic outcomes and assistance clinical decisions might create around $5 billion in economic value.16 Estimate based upon McKinsey analysis. Key presumptions: 10 percent higher early-stage cancer diagnosis rate through more accurate AI diagnosis; 10 percent increase in effectiveness enabled by AI. A leading AI start-up in medical imaging now uses computer vision and artificial intelligence algorithms on optical coherence tomography arises from retinal images. It immediately browses and determines the signs of lots of chronic illnesses and conditions, such as diabetes, high blood pressure, and arteriosclerosis, speeding up the medical diagnosis procedure and increasing early detection of illness.

How to unlock these opportunities

During our research study, we discovered that recognizing the value from AI would require every sector to drive considerable investment and development across six essential allowing locations (exhibit). The very first 4 areas are data, talent, technology, and considerable work to shift mindsets as part of adoption and scaling efforts. The remaining 2, community orchestration and browsing regulations, can be considered collectively as market partnership and must be resolved as part of strategy efforts.

Some particular challenges in these locations are special to each sector. For instance, in automotive, transportation, and logistics, keeping pace with the most current advances in 5G and connected-vehicle innovations (typically referred to as V2X) is vital to unlocking the worth because sector. Those in health care will wish to remain present on advances in AI explainability; for providers and patients to trust the AI, they must have the ability to comprehend why an algorithm made the choice or recommendation it did.

Broadly speaking, four of these areas-data, talent, technology, and market collaboration-stood out as typical challenges that our company believe will have an outsized impact on the economic worth attained. Without them, dealing with the others will be much harder.

Data

For AI systems to work appropriately, they require access to top quality information, implying the data must be available, functional, trustworthy, appropriate, and higgledy-piggledy.xyz protect. This can be challenging without the right structures for keeping, processing, and managing the vast volumes of information being generated today. In the automotive sector, for instance, the capability to process and support up to two terabytes of data per vehicle and roadway data daily is required for enabling autonomous lorries to understand what's ahead and delivering tailored experiences to human chauffeurs. In health care, AI designs require to take in vast quantities of omics17"Omics" includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, and diseasomics. information to understand illness, determine brand-new targets, and design new particles.

Companies seeing the highest returns from AI-more than 20 percent of profits before interest and taxes (EBIT) contributed by AI-offer some insights into what it requires to attain this. McKinsey's 2021 Global AI Survey shows that these high entertainers are much more most likely to purchase core data practices, such as quickly integrating internal structured data for use in AI systems (51 percent of high entertainers versus 32 percent of other business), developing a data dictionary that is available across their business (53 percent versus 29 percent), and establishing distinct processes for information governance (45 percent versus 37 percent).

Participation in information sharing and information communities is likewise essential, as these collaborations can cause insights that would not be possible otherwise. For instance, medical huge information and AI business are now partnering with a wide variety of healthcare facilities and research study institutes, incorporating their electronic medical records (EMR) with openly available medical-research information and clinical-trial data from pharmaceutical companies or agreement research organizations. The goal is to facilitate drug discovery, scientific trials, and choice making at the point of care so service providers can much better determine the ideal treatment procedures and prepare for each patient, thus increasing treatment efficiency and minimizing possibilities of adverse side impacts. One such company, Yidu Cloud, has actually supplied big information platforms and services to more than 500 healthcare facilities in China and has, upon permission, evaluated more than 1.3 billion healthcare records given that 2017 for usage in real-world disease models to support a variety of use cases including scientific research, healthcare facility management, and policy making.

The state of AI in 2021

Talent

In our experience, we discover it almost difficult for businesses to deliver impact with AI without business domain understanding. Knowing what concerns to ask in each domain can figure out the success or failure of an offered AI effort. As an outcome, companies in all 4 sectors (automobile, transport, and logistics; manufacturing; enterprise software application; and health care and life sciences) can gain from systematically upskilling existing AI experts and knowledge employees to become AI translators-individuals who understand what service concerns to ask and can equate service issues into AI services. We like to consider their abilities as resembling the Greek letter pi (π). This group has not just a broad mastery of basic management abilities (the horizontal bar) however also spikes of deep practical understanding in AI and domain know-how (the vertical bars).

To construct this talent profile, some business upskill technical skill with the requisite abilities. One AI start-up in drug discovery, for circumstances, has produced a program to train newly worked with information researchers and AI engineers in pharmaceutical domain knowledge such as molecule structure and attributes. Company executives credit this deep domain knowledge amongst its AI specialists with making it possible for the discovery of nearly 30 particles for clinical trials. Other business look for to arm existing domain skill with the AI abilities they require. An electronic devices manufacturer has actually built a digital and AI academy to provide on-the-job training to more than 400 employees across different functional locations so that they can lead different digital and AI jobs across the enterprise.

Technology maturity

McKinsey has discovered through past research study that having the ideal technology foundation is a critical chauffeur for AI success. For magnate in China, our findings highlight 4 priorities in this area:

Increasing digital adoption. There is room across industries to increase digital adoption. In medical facilities and other care providers, numerous workflows related to patients, personnel, and devices have yet to be digitized. Further digital adoption is required to offer healthcare companies with the needed data for predicting a client's eligibility for a medical trial or offering a physician with intelligent clinical-decision-support tools.

The exact same applies in production, where digitization of factories is low. Implementing IoT sensors across producing equipment and production lines can make it possible for business to collect the data needed for powering digital twins.

Implementing data science tooling and platforms. The cost of algorithmic development can be high, and companies can benefit greatly from using technology platforms and tooling that enhance model release and maintenance, just as they gain from investments in technologies to enhance the effectiveness of a factory production line. Some important capabilities we recommend business think about include multiple-use information structures, scalable computation power, and automated MLOps abilities. All of these add to making sure AI groups can work efficiently and proficiently.

Advancing cloud infrastructures. Our research finds that while the percent of IT work on cloud in China is nearly on par with worldwide survey numbers, the share on private cloud is much bigger due to security and information compliance concerns. As SaaS vendors and other enterprise-software companies enter this market, we encourage that they continue to advance their facilities to deal with these issues and offer enterprises with a clear value proposition. This will require additional advances in virtualization, data-storage capability, performance, flexibility and strength, and technological agility to tailor business abilities, which enterprises have pertained to anticipate from their suppliers.

Investments in AI research study and advanced AI techniques. Many of the use cases explained here will require fundamental advances in the underlying technologies and strategies. For example, in manufacturing, extra research is needed to improve the performance of cam sensing units and computer vision algorithms to find and recognize things in poorly lit environments, which can be typical on factory floors. In life sciences, further development in wearable gadgets and AI algorithms is needed to enable the collection, processing, and integration of real-world information in drug discovery, medical trials, and clinical-decision-support procedures. In automotive, advances for enhancing self-driving design accuracy and lowering modeling complexity are needed to enhance how self-governing cars perceive things and carry out in complicated situations.

For conducting such research study, scholastic collaborations in between business and universities can advance what's possible.

Market partnership

AI can present obstacles that go beyond the capabilities of any one business, which typically gives rise to guidelines and partnerships that can further AI innovation. In numerous markets globally, we have actually seen new guidelines, such as Global Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act in the United States, begin to attend to emerging issues such as data personal privacy, which is thought about a leading AI pertinent risk in our 2021 Global AI Survey. And proposed European Union guidelines created to deal with the advancement and use of AI more broadly will have implications worldwide.

Our research study indicate 3 locations where additional efforts might help China open the full financial value of AI:

Data privacy and sharing. For people to share their information, whether it's health care or driving information, they need to have an easy method to permit to use their data and have trust that it will be utilized properly by authorized entities and safely shared and kept. Guidelines associated with privacy and sharing can create more confidence and thus enable greater AI adoption. A 2019 law enacted in China to improve person health, for example, promotes the use of huge data and AI by establishing technical requirements on the collection, storage, analysis, and application of medical and health data.18 Law of individuals's Republic of China on Basic Medical and Health Care and the Promotion of Health, Article 49, 2019.

Meanwhile, there has actually been substantial momentum in market and academia to develop techniques and structures to assist alleviate privacy concerns. For example, the number of documents pointing out "privacy" accepted by the Neural Details Processing Systems, a leading artificial intelligence conference, has actually increased sixfold in the previous 5 years.19 Artificial Intelligence Index report 2022, March 2022, Figure 3.3.6.

Market positioning. Sometimes, new service designs allowed by AI will raise basic questions around the usage and shipment of AI among the different stakeholders. In healthcare, for example, as business establish brand-new AI systems for clinical-decision assistance, argument will likely emerge among government and health care companies and payers regarding when AI works in improving diagnosis and treatment recommendations and how suppliers will be repaid when using such systems. In transportation and logistics, issues around how government and insurance providers figure out responsibility have already developed in China following accidents involving both self-governing cars and cars run by people. Settlements in these mishaps have created precedents to guide future choices, however further codification can help ensure consistency and clearness.

Standard procedures and protocols. Standards allow the sharing of data within and across ecosystems. In the health care and life sciences sectors, academic medical research, clinical-trial data, and patient medical data require to be well structured and documented in an uniform way to speed up drug discovery and clinical trials. A push by the National Health Commission in China to build a data foundation for EMRs and disease databases in 2018 has resulted in some motion here with the development of a standardized disease database and EMRs for use in AI. However, requirements and procedures around how the data are structured, processed, and linked can be advantageous for further usage of the raw-data records.

Likewise, requirements can also eliminate process delays that can derail development and scare off investors and talent. An example involves the velocity of drug discovery using real-world proof in Hainan's medical tourism zone; translating that success into transparent approval protocols can help make sure constant licensing across the country and eventually would develop trust in brand-new discoveries. On the manufacturing side, standards for how organizations identify the various features of an object (such as the size and shape of a part or completion product) on the production line can make it easier for business to take advantage of algorithms from one factory to another, without needing to go through costly retraining efforts.

Patent protections. Traditionally, in China, brand-new developments are rapidly folded into the general public domain, making it hard for enterprise-software and AI players to recognize a return on their large investment. In our experience, patent laws that safeguard copyright can increase investors' confidence and bring in more financial investment in this area.

AI has the possible to reshape key sectors in China. However, among business domains in these sectors with the most valuable use cases, there is no low-hanging fruit where AI can be executed with little additional financial investment. Rather, our research discovers that unlocking maximum potential of this opportunity will be possible only with tactical investments and innovations throughout a number of dimensions-with data, talent, innovation, and market collaboration being foremost. Working together, enterprises, AI players, and federal government can address these conditions and make it possible for China to record the amount at stake.