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Which technologies have the most momentum in an accelerating world? We identified the trends that matter most.

The top trends in tech

McKinsey tech trends index

As all things digital continue to accelerate, which technology trends matter most for companies and executives? To answer that question, we developed a unique methodology to identify the ten trends most relevant to competitive advantage and technology investments. These trends may not represent the coolest, most bleeding-edge technologies. But they’re the ones drawing the most venture money, producing the most patent filings, and generating the biggest implications for how and where to compete and the capabilities you need to accelerate performance. Unifying and underlying them all is the combinatorial effect of massively faster computation propelling new convergences between technologies; startling breakthroughs in health and materials sciences; an array of new product and service functionalities; and a strong foundation for the reinvention of companies, markets, industries, and sectors. In the next decade, according to entrepreneur and futurist Peter Diamandis, we’ll experience more progress than in the past 100 years combined, as technology reshapes health and materials sciences, energy, transportation, and a wide range of other industries and domains. The implications for corporations are broad. Consider the compressive effects on value chains as manufacturers combine 3-D or 4-D printing with next-generation materials to produce for themselves what suppliers had previously provided and eliminate the need for spare parts. Watch retailers combine sensors, computer vision, AI, augmented reality, and immersive and spatial computing to wow customers with video-game-like experience designs. Imagine virtualized R&D functions in science-based industries like pharma and chemicals or a fully automated finance function in your company. Will your organization make the most of these trends to pursue new heights of rapid innovation, improved performance, and significant achievement? This interactive covers how fast these trends are moving toward you, their level of maturity, and their applicability across industries. It also presents the key technologies underlying each trend, along with their current applications and the disruptions they portend for companies going forward. These disruptions may be significant. A recent McKinsey survey describes how, during the pandemic, technology further lowered barriers to digital disruption, paving the way for more rapid, technology-driven change. Survey respondents in every sector say their companies face significant vulnerabilities to profit structures, the ability to bundle products, and operations. We’ll have more to say soon regarding the impact on specific industries—as well as the strategic questions these trends suggest for your industry, your business model, and your unique organizational capabilities. Until then, our new research helps make sense of a noisy and complex technology landscape.

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The top trends in tech

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Executive summary

About the research

Industry-agnostic trends

Process automation & virtualization

Future of connectivity

Distributed infrastructure

Next-generation computing

Applied AI

Future of programming

Trust architecture

Industry-specific trends

Future of clean technologies

Next-generation materials

Bio Revolution

Trend 1:

The combinatorial power of technology fuels the first of the ten trends, in which robotics, the Industrial Internet of Things (IIoT), digital twins, and 3-D or 4-D printing (also known as additive manufacturing, or AM) combine to streamline routine tasks, improve operational efficiency, and accelerate time to market.

Next-level process automation and virtualization

Note: Momentum score is the weighted growth rate across 6 categories: investments, news, patents, companies, research publications, and search trends. The first three categories are illustrated above.

Combined momentum score

For an idea of the broad implications of next-level process automation and virtualization, consider that 50 percent of today’s work activities could be automated in the next few decades, spurring powerful changes to the future of work, labor costs, and public policy. This will occur as robots become ever-more intelligent and capable. Production will gain scalability even as production lead times decline. This trend will shift competition toward capital-expenditure investments in automation technology and toward the social, emotional, and technological skills needed as intelligent machines take over more physical, repetitive, and basic cognitive tasks—all while compressing value chains as companies use AM to make products and components closer to home.

Why it matters

Process automation

Tech trend

Disruptions

Implications

Self-learning (and rapidly reconfigurable) robots will drive the automation of physical processes beyond routine activities to include less predictable ones, leading to fewer people working in these activities and a reconfiguration of the workforce. Policy makers will be challenged to address labor displacement, even as organizations will need to help employees reskill toward more complex tasks and reconfigure the future of work and the workplace to better enable humans to work alongside machines.

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As advanced simulations and 3-D or 4-D printing continue to dematerialize processes and make them virtual, dramatically shorter development cycles will become possible, for example, by the integration of design and product development through visualized simulations of optimized prototypes. An already fast world characterized by ever-shorter product and service life cycles will continue to accelerate, further pressuring profit pools and speeding the strategic and operational practices that tightly correlate with successful digital efforts.

Virtualization

Automotive

Applications

An automotive OEM used the IIoT to connect 122 factories and more than 500 warehouses globally, while optimizing manufacturing and logistics processes, consolidating real-time data, and implementing analytics and machine-learning throughput.

Manufacturing

A big manufacturer used collaborative robots (cobots) mounted on automatic guided vehicles to load pallets directly, without human involvement, increasing operational efficiency and safety.

Municipalities

The city of Carson, NV, created a digital twin to simulate future water supply based on usage hours, optimizing water availability and saving operating costs.

Aerospace

A global aerospace supplier prototyped a 25 percent lighter 3-D-printed fuel nozzle it could quickly produce at scale, with no increase in complexity.

The Industrial Internet of Things allows companies to integrate devices, sensors, and machines used for manufacturing processes and to enable a common platform for gathering and analyzing the data these sensors and devices record.

The Industrial Internet of Things

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Autonomous robots, collaborative robots (cobots), software robots (bots), and mobile robots can all be configured through software and AI-driven intelligence to automate routine tasks such as data extraction and cleaning via existing user interfaces.

Robots/cobots/robotic process automation

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A digital twin is a digital replica of a physical-world asset or process that integrates data from both the digital and material worlds, enabling companies to run virtual simulations before committing to physical-world actions.

Digital twins

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This is the process of joining materials (often including plastics, steel, and ceramics) to make objects from 3-D-model data, usually layer upon layer. It’s also known as additive manufacturing.

3-D or 4-D printing

Process automation & virtualization

Future of clean technologies

Previous trend

Future of connectivity

Next trend

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As climate risk rises, the need for clean-energy sources is increasing. Several different technologies address the issues across the energy-delivery chain, ranging from carbon-neutral energy generation to energy storage, distribution, and carbon capture.

Clean technologies

After four years, the utility had increased its operating profit by 19 percent per year, leading to annual savings of $178 million while improving its regulatory performance score by 7 percent a year and reducing leakage by 30 percent. Customer complaints declined by almost a third.

Utilities

This city now has an opportunity to implement a bold strategy to attain its targets, continue to influence the national and global debate on climate change, and reinforce its leadership in the field.

Public sector

A global brewer introduced energy-efficiency measures, process redesigns, and fuel changes targeting substantial cost savings and 50 percent carbon abatement.

Drinks

A global conglomerate identified the solar photovoltaic (PV) industry as a promising area for growth and moved swiftly to access specialist capabilities through M&A.

Conglomerate

Applications

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Advanced simulations and 3D/4D printing continue to dematerialize processes through virtualization leading to dramatically shorter development cycles (e.g., integration of design and product development through visualized, simulations of optimized prototypes)

…and process vizualization

It’s more clear that the energy sector is being disrupted than it is exactly how that disruption will play out. The improving costs and capabilities of technology play a key role, of course, but policy and societal changes also effect supply and demand. Traditional players may find their capabilities have increasingly limited application as new platforms and “smart” technologies emerge. Some sectors are converging, others are emerging, and value is shifting within as well as outside of industries. The power sector is also undergoing disruption as value is shifting to new forms of generation and to consumer-centered energy services (such as the trend for “smart everything”), where traditional sector capabilities may have limited application and new platforms built by technology start-ups can rapidly enter the industry.

Future of clean technologies

Disruptions

Tech trend

As clean technologies come down the cost curve, they become increasingly disruptive to traditional business models, affecting both industry structure and competitive dynamics. Companies must keep pace with emerging business-building opportunities by designing operational-improvement programs relating to technology development, procurement, manufacturing, and cost reduction and by grasping how climate-change mandates affect energy costs and alter the balance sheet of carbon-intense sectors while increasing the performance standards that accelerate the adoption of next-generation clean technologies. Advancing clean technologies also promises an abundant supply of green energy to sustain exponential technology growth, for instance, in high-power computing.

Why it matters

Combined momentum score

The tenth trend reflects new technologies addressing the rapidly growing need for clean-energy generation. These include systems for smart-energy distribution in the grid, energy-storage systems, carbon-neutral energy generation, and fusion energy. These new technologies will have broad application:

Future of clean technologies

Trend 10:

Future of clean technologies

Next-generation materials

Previous trend

Process automation & virtualization

Next trend

Manufacturing

Applications

Healthcare companies can create nano drug-delivery systems using biodegradable nanoparticles as drug carriers, for example, through micellar nanoparticles.

Healthcare and pharma

The disruptive potential of next-generation materials is broad. Companies in the construction, automotive, packaging, and manufacturing sectors, for example, are integrating sustainable materials and renewable-energy sources into their processes, even as advances in materials sciences help create smart materials with programmable properties that respond to stimuli from external factors. These have application in the design of materials and products that use thermo, electro-, and photochromism; piezoelectricity; shape memory; self-healing; and phase-change attributes, among other characteristics.

Next-generation materials

Implications

Disruptions

Tech trend

Materials design and discovery hold a critical place in the 21st-century economy, with broad potential impact spanning the transportation, health, microelectronic, and renewable-energy industries. By changing the economics of a wide range of products and services, next-generation materials with significantly higher efficiency in many as-yet-untouched application areas may well change industry economics and reconfigure companies within them.

Why it matters

Combined momentum score

Innovations in materials sciences are at the heart of the ninth trend as next-generation materials like graphene and 2-D materials, molybdenum disulfide nanoparticles, nanomaterials, and a range of smart, responsive, and lightweight materials enable new functionality and enhanced performance in pharma, energy, transportation, health, semiconductors, and manufacturing. Because they have a lighter environmental impact, next-generation materials will be critical for tomorrow’s sustainable economies. While many of these materials are still in the research stage, others are closing in on their commercial potential. Molybdenum disulfide nanoparticles are already being used in flexible electronics, while graphene has helped propel a resurgence in 2-D semiconductors. More new materials are on the way as computational-materials science combines computing power and associated machine-learning methods and applies them to materials-related problems and opportunities.

Next-generation materials

Trend 9:

Next-generation materials

Bio Revolution

Previous trend

Future of clean technologies

Next trend

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The Bio Revolution is a powerful new wave of innovation that is expected to transform business and society beyond healthcare.

The Bio Revolution

The technology beneath the trend

Agriculture and pharma incumbents are collaborating with Caribou Biosciences, CRISPR Therapeutics, and Pairwise to harness unique biocapabilities.

R&D ecosystems

Trace Genomics is profiling the soil microbiome to interpret health- and disease-risk indicators in farming.

Precision

Ginkgo Bioworks, which produces bacteria with industrial applications, created the Ferment Consortium to give spin-off companies full access to its genome-mining platform for cell programming.

Platforms

Amyris’s new production methods combine digital and biological skills to provide pure, stable skin-care products in high volume, at low cost, and from renewable resources.

Capabilities

Applications

Recent McKinsey Global Institute research found that more than half of the potential direct economic impact from biological technologies—applied to nearly 400 use cases in multiple sectors—is outside of healthcare, notably in agriculture and food, materials and energy, and consumer products and services. The Bio Revolution will transform the competitive landscape in at least four ways: biological capabilities as a source of competitive advantage, platform-based business models accelerating scientific discoveries, the opportunity for more personalization and precision products and services, and the spread of new relationships driven by barbell-shaped ecosystems.

The Bio Revolution

Disruptions

Tech trend

The rapid pace of biological science will soon bring competitive disruption—and not just in healthcare. As biological innovations penetrate industries such as food, consumer health, and materials, they are yielding higher margins in exchange for increased personalization from consumers and patients. New markets may emerge, such as genetics-based recommendations for nutrition, even as rapid innovation in DNA sequencing leads ever further into hyperpersonalized medicine and rapidly accelerated vaccine development. At the same time, the Bio Revolution’s opportunities and risks require navigating not just competitive implications but also ethical and even moral issues.

Why it matters

Combined momentum score

The eighth trend reflects a confluence of advances in biological science combined with the accelerating development of computing, automation, and AI, which together are giving rise to a new wave of innovation called the Bio Revolution. The trend promises a significant impact on economies and our lives and will affect industries from health and agriculture to consumer goods, energy, and materials. The biomolecules’ dimension of the revolution, which includes “omics” and molecular technologies, has evolved as the fastest-growing, most cutting edge of biological science, but biomachines, biocomputing, and biosystems are also important dimensions. Some innovations come with profound risks rooted in the self-sustaining, self-replicating, and interconnected nature of biology that argue for a serious and sustained debate about how this revolution should proceed.

The Bio Revolution

Trend 8:

Bio Revolution

Trust architecture

Previous trend

Next-generation materials

Next trend

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Distributed-ledger technology (DLT) records transactions across a distributed infrastructure on either private or public networks, encrypting them through unique, unchangeable hashes (such as the SHA 256 algorithm). Using “consensus mechanisms,” multiple nodes in a network then verify items based on permissions or economic incentives to reach majority consensus, at which point transactions are added to the ledger.

Distributed-ledger technology and blockchain

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Zero-trust security is an approach to preventing data breaches by eliminating the concept of trust from an organization’s network architecture and instead following the principle of “never trust, always verify.”

Zero-trust security

The technologies beneath the trend

One retailer streamlined its supply-chain management by recording all processes and actions from vendor to customer and coding them into smart contracts on blockchain.

Retail

A retail company wanted to securely segment their networks via zero-trust security to protect point-of-sale devices and cardholder information while enabling rapid connectivity, with cost as the number one priority.

Retail

One company experienced significant global service downtime due to a regional power outage. This company’s few data centers were closely located, with little of the data and network segmentation required to isolate outages.

Advanced industries

One company recognized it faced exfiltration risk for sensitive data. By deploying a zero-trust-security approach, it encrypted data, both at rest and in transit, and implemented the additional controls required to restrict cloud-service providers from accessing that same data.

Advanced industries

Applications

Trust architectures help commercial entities and individuals establish trust and conduct business without the need for intermediaries, even as zero-trust-security measures address growing cyberattacks. Countries and regulatory bodies may likely have to rethink their current regulatory oversight of the flow of wealth. DLTs will reduce cost before they enable transformative business models. Efficiencies arise by speeding transactions in both the private and public sectors, among other ways. Government agencies, for example, may find it faster to process land-use registries, while fintech attackers are already using ledgers to accelerate financial payments and lending.

Trust architecture

Disruptions

Tech trend

Trust architectures both mitigate risk and, for companies in certain industries, increase it. Cyberrisk goes down as companies use zero-trust security measures to reduce the threat of data breaches. For other industries, however, strategic risk rises with the threat of disintermediation by distributed data ledgers. Companies will also need to pay attention to the shifting role of regulatory oversight; DLT applications don’t always fit easily into existing regulatory frameworks and may prompt diverse responses from different countries and regulatory bodies. More broadly, growing cyberrisk means overall cybersecurity spending will increase dramatically. The monetization of trust technologies (such as cryptocurrency and supersecure data rooms) is poised to do the same.

Why it matters

Combined momentum score

The seventh trend describes a set of technologies and approaches designed for a world of increasing cyberattacks, a world in which more than 8.5 billion data records were compromised in 2019 alone. These trust architectures provide structures for verifying the trustworthiness of devices as data flows across networks, APIs, and applications. Trust architectures could include distributed-ledger technologies (DLTs), of which blockchain is one, and “zero-trust security” approaches to preventing data breaches. In addition to lowering the risk of breaches, trust architectures reduce the cost of complying with security regulations, lower the operating and capital expenditures associated with cybersecurity, and enable more cost-efficient transactions, for instance, between buyers and sellers.

Trust architecture

Trend 7:

Trust architecture

Future of programming

Previous trend

Bio Revolution

Next trend

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It is the concept of machine-written programs that target (and often reach) operator- or human-set goals, such as to win a game of chess. Using machine learning, neural networks span a search space within a possible program space to test, iterate, and identify the most efficient program to reach a given goal.

Software 2.0

The technology beneath the trend

One start-up created a model-as-a-service platform to help financial-services companies navigate risk associated with model deployment and to help keep models “healthy” with metrics and dashboards for easier monitoring and debugging. This platform reduces users’ model-design and deployment time from months to minutes without compromising risk and compliance assurance.

Financial services

As a result, median deployment time at Netflix reduced by 16 times, from four months to seven days.

Entertainment

Implementation paradigms for Software 2.0 achieve more with less by moving code written by humans (Software 1.0) to code written by optimized neural networks.

Automotive

Applications

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Software 2.0 creates new ways of writing software and reduces complexity. However, as companies look to scale their software-development capabilities, they will need to master DataOps and MLOps practices and technology to make the most of the future of programming.

The future of programming

Implications

Disruptions

Tech trend

This trend makes possible the rapid scaling and diffusion of new data-rich, AI-driven applications. The relative homogeneity of neural networks can also support new open-source libraries and are increasingly modular, interoperable, and usable across domains. That lowers technical barriers to entry for these classes of applications and gives deeper advantage to those able to source and refine the data needed to train these models through reinforcement learning.

Why it matters

Combined momentum score

The sixth trend relates to the rise of “Software 2.0,” in which programmers are replaced by neural networks that use machine learning to develop software. It promises to unlock higher-order, edge use cases like autonomous vehicles, where the only way to progress is through AI models. At the other end of the spectrum, Software 2.0 will also provide organizations with a far easier, more iterative, and intuitive way to customize existing code and automate mundane programming tasks through low-code and no-code approaches. Software 2.0 will be further accelerated by emerging trends in machine learning that “abstract away” many of the difficulties and complexities that currently hinder the development and application of AI models.

The future of programming

Trend 6:

Future of programming

Applied AI

Previous trend

Trust architecture

Next trend

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Speech technology duplicates and responds to the human voice.

Speech technology

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Natural-language processing helps create seamless interactions between humans and technologies in applications such as data-to-story translation.

Natural-language processing

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Computer vision uses machine-learning algorithms to help machines make sense of images, videos, PDFs, or text and to translate that visual data through specialized software algorithms (and contextual knowledge from humans) into actionable concepts for decisions.

Computer vision

A global aerospace supplier prototyped a 25 percent lighter 3D-printed fuel nozzle it could quickly produce at scale, with no increase in complexity

Aerospace

The city of Carson, NV used a digital twin to simulate future water supply based on usage hours, optimizing water availability and saving operating costs

Municipalities

A retailer used computer vision, NLP, and speech technology to build and personalize a 360° customer view to enable tailored retail experiences at home and in-store, mediated through human-like virtual assistants and product-exploration tools.

Retail

A crop-protection producer fed live satellite images of fields through a computer-vision algorithm to detect plant viruses well before they would have become visible to human workers, enabling farmers to make early intervention with mild pesticide doses.

Chemicals

Applications

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…and process vizualization

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As AI matures and continues to scale, it will enable new applications (such as more rapid development cycles and detailed customer insights), eliminate labor needs for repetitive tasks (such as filing, document preparation, and indexing), and support the global reach of highly specialized services and talent (such as improved telemedicine and the ability of specialized engineers to work on oil rigs from the safety of land).

Applied AI

Implications

Disruptions

Tech trend

An upcoming explosion in AI applications is set to augment nearly every aspect of human–machine interaction and power the next level of automation, both for consumers and businesses. Applied AI will further disrupt research and development through generative models and next-generation simulations. While any company can get good value from AI if it’s applied effectively and in a repeatable way, less than one-quarter of respondents report significant bottom-line impact. That isn’t surprising—achieving impact at scale is still elusive for many companies, not only because of the technical challenges, but also because of the organizational changes required.

Why it matters

Combined momentum score

The fifth trend deploys AI algorithms to train machines to recognize patterns and interpret and act on those patterns— helping computers make sense of real-world data, including videos or images (using computer vision), text (through natural-language programming [NLP]), and audio (using speech technology).

Applied AI

Trend 5:

Applied AI

Next-generation computing

Previous trend

Future of programming

Next trend

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Neuromorphic chips, or application-specific integrated circuits (ASICs), overcome the “von Neumann bottleneck,” a limitation to more traditional computing that occurs when processors get overloaded by too many sequential demands or as chips overheat when too many transistors are loaded on top of them. Because advanced AI applications, such as adaptive robotics and faster mobile-device processing, rely on energy-hungry deep-learning and neural networks, neuromorphic chips provide the specialized hardware needed to overcome the limits of more general-purpose CPUs at lower cost and with less energy—enabling the neuromorphic computing that mimics the human brain and nervous system.

Neuromorphic chips

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Quantum computing uses quantum mechanics to solve computational problems more efficiently. Quantum-computing hardware is in early development, with progress depending on solutions to technical bottlenecks, including scalability of the number of quantum bits (qubits) available for computing, substantial decrease in error rate, and the development of quantum random-access memory (RAM).

Quantum computing

A global aerospace supplier prototyped a 25 percent lighter 3D-printed fuel nozzle it could quickly produce at scale, with no increase in complexity

Aerospace

The growing ability of robots to sense, adjust, and make independent decisions plays a crucial role in the Fourth Industrial Revolution. Yet, the artificial neural networks required can strain traditional CPUs.

Adaptive robotics

Google offers tensor processing units (TPUs) as a cloud solution to bring machine-learning capabilities to the mass market, resulting in machine-learning applications that are 27 times faster at 38 percent lower costs versus graphics processing units (GPUs).

Machine learning

The city of Beijing used a classical quantum optimization solver on a special-purpose quantum computer to optimize traffic flow and reduce traffic jams between the city center and the airport.

Urban logistics

Applications

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High computational capabilities allow new AI use cases, such as molecule-level simulation, that can significantly reduce the empirical expertise and testing needed for a range of applications, leading to the following: disruption across industries such as materials, chemicals, and pharmaceuticals; highly personalized product developments, for instance in medicine; the ability to break the majority of cryptographic security algorithms, disrupting today’s cybersecurity approaches; and the faster diffusion of self-driving vehicles.

Next-generation computing

Implications

Disruptions

Tech trend

Next-generation computing enables further democratization of AI-driven services, radically fast development cycles, and lower barriers of entry across industries. It promises to disrupt parts of the value chain and reshape the skills needed (such as automated trading replacing traders and chemical simulations reducing the need for experiments).

Why it matters

Combined momentum score

The fourth trend reflects the rapid approach of quantum computing and neuromorphic computing, with the latter involving the development of specialized microchips called application-specific integrated circuits (ASICs). Next-generation computing could help find answers to problems that have bedeviled science and society for years, unlocking unprecedented capabilities for businesses. It also promises to cut development time for chemicals and pharmaceuticals with simulations, accelerate autonomous vehicles with quantum AI, and transform cybersecurity—all while reducing hardware costs in IT, quickening machine learning, and enabling more efficient searching of unstructured data sets.

Next-generation computing

Trend 4:

Next-generation computing

Distributed infrastructure

Previous trend

Applied AI

Next trend

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Edge computing enables companies to capture, monitor, and analyze data locally and close to its source while reducing the amount of data sent to a centralized data hub, increasing speed and significantly reducing latency.

Edge computing

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Cloud computing offers distributed computing resources through ready-to-use, vendor-managed services (including computing power, storage, advanced-analytics tools, and database access).

Cloud computing

A global aerospace supplier prototyped a 25 percent lighter 3D-printed fuel nozzle it could quickly produce at scale, with no increase in complexity

Aerospace

One manufacturer combined Internet of Things sensors at the edge with centralized cloud-data centers to monitor and analyze maintenance issues in real time, allowing more timely maintenance of manufacturing machinery, lower maintenance costs, and higher return on assets.

Manufacturing

In 2018, it took the Broad Institute of MIT and Harvard eight minutes to sequence a human genome (at the rate of 16 terabytes per day). Since then, cloud-based analytics has lowered sequencing time by 400 percent.

Education

A European utility migrated about 90 percent of its applications to the public cloud, reducing 15 percent of its IT-run costs and simplifying its portfolio by retiring one-third of its applications.

Utilities

Applications

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…and process vizualization

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Wide availability of IT infrastructure and services through cloud computing will vaporize on-premise IT infrastructure and commoditize IT setup and maintenance, while the democratization of infrastructure will help shift competitive advantage away from IT to software development and talent. The growing computing power of edge devices (defined generally as hardware that controls data flows at the border between two networks) will allow AI to run locally, for instance, as it automates processes and tasks or optimizes warehouses and logistics networks, enabling low latency in these applications while maintaining central control as edge devices connect into cloud-based central nodes.

Distributed infrastructure

Implications

Disruptions

Tech trend

Business implications arising from trend three include the democratization of IT infrastructure, especially computing power, and a corresponding shift in importance away from IT capabilities toward software-development skills and the talent it requires. Driving this shift is the move to more centralized “as a service” (XaaS) models of delivery enabled by cloud technologies as companies abandon more traditional on-premise IT infrastructures. This shift also suggests new, more modular configurations of business organizations built around “platforms” of activities and technologies that target specific business goals, including digital transformation, new talent requirements (more system architects, for example), lower costs, and higher innovation. More broadly, as data flows into the cloud, barriers to entry fall, enabling strategic moves into adjacent markets as traditional sectoral boundaries continue to blur and ecosystems continue growing in importance.

Why it matters

Combined momentum score

The third trend brings together cloud and edge computing to help companies move computing power further toward the edge of their networks—enabling them to reach data-hungry devices, with far-less latency, in a greater number of locations that are even more remote and to accelerate decision making with advanced analytics on demand. This trend will help companies boost their speed and agility, reduce complexity, save costs, and strengthen their cybersecurity defenses.

Distributed infrastructure

Trend 3:

Distributed infrastructure

Future of connectivity

Previous trend

Next-generation computing

Next trend

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The Internet of Things includes sensors, software, and other technologies embedded in everyday objects, enabling them to send and receive data.

The Internet of Things

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This next generation of wireless connectivity supports a 100-fold increase in the number of simultaneous connections while improving speed (100 times faster than LTE/4G), latency, and reliability (an improvement from 20 milliseconds to less than one millisecond with 99.99% reliability).

A Chinese railway station used 5G for automated detection of station incidents and anomalies via IoT (for example, videos, flow detection).

Transportation

A sports stadium’s 5G upgrade targeted real-time updates on players, improved security, crowd-sentiment analytics, and HD 360° augmented-reality replays for fans.

Sports and entertainment

A leading telco began construction on an industry-leading greenfield factory using best-in-class information and operational technology integrated through 5G into a seamless network of IoT devices.

Telecommunications

Applications

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With either high-band or low- to mid-band 5G reaching up to 80 percent of the global population by 2030, enhanced coverage and speed of connections across long and short distances will enable new services (such as autonomous driving), business models (such as connected services), and next-generation customer experiences (such as live virtual reality).

The future of connectivity

Implications

Disruptions

Tech trend

Superfast connectivity (and internet) has broad implications for organizations. It supports the creation of new services and business models linked to sensor-enabled intelligent products, yields new value-chain offerings (for example, predictive services, augmented-intelligence services), and creates the potential for companies to more seamlessly personalize offerings across channels and create heightened customer experiences. In mobility, for example, IoT sensors and near-global coverage can help manufacturers capture vehicle signals, monitor the condition of each system in the car, and notify the owner to schedule repairs before a breakdown occurs, improving the vehicle’s durability and life span.

Why it matters

Combined momentum score

The second trend combines fifth-generation (5G) broadband cellular networks and the Internet of Things (IoT) to enable faster connectivity across longer distances, with exponentially faster downloads and latency (the time it takes to retrieve data) reduced to nearly nothing. Far-greater network availability and capability will drive broad shifts in the business landscape, from the digitization of manufacturing (through wireless control of mobile tools, machines, and robots) to decentralized energy delivery and remote patient monitoring.

The future of connectivity

Trend 2:

Future of connectivity

Process automation & virtualization

Previous trend

Distributed infrastructure

Next trend

By 2024, we estimate that more than 50 percent of user touches will be augmented by AI-driven speech, written word, or computer-vision algorithms, while one billion connected cameras will collect and share visual data by 2021.

This trend promises to improve customer satisfaction through new customer interfaces and interaction methods—such as searching Amazon for products based on photos. More seamless human– machine interactions are also simplifying applications by translating speech, text, and images provided by humans into machine-readable instructions, boosting human productivity and lowering operating expenses.

How fast are these technologies moving? By 2025, more than 50 billion devices will be connected to the IIoT, generating 79.4 zettabytes of data yearly. Annual installations of industrial robots, which have increased two times to about 450,000 since 2015, will grow to about 600,000 by 2022, even as 70 percent of manufacturers will be regularly using digital twins by 2022. Across industries, about 10 percent of today’s manufacturing processes will be replaced by AM by 2030.

We identified hundreds of use cases across 17 commercial domains. Implementing the most promising ones in just four sectors—mobility, healthcare, manufacturing, and retail—could increase global GDP by $1.2 trillion to $2 trillion by 2030.

By 2022, some 70 percent of companies will employ hybrid or multicloud-management technologies, tools, and processes, which are the hallmarks of distributed IT infrastructures. This shift toward distributed IT infrastructure will be reflected by a rise in the software sourced by companies from cloud-service platforms, open repositories, and enterprise software-as-a-service (SaaS) providers—from today’s 23 percent to nearly 50 percent in 2025, if current trends continue, with the potential for this to jump to 80 percent if adoption accelerates.

Preparing for next-generation computing requires identifying whether you’re in a first-wave industry (such as finance, travel, logistics, global energy and materials, and advanced industries) and whether your business depends on trade secrets and other data that must be safeguarded during the shift from current to quantum cryptography. Others companies, for now, should continue to monitor this trend closely.

The continuing progress in next-generation computing, especially neuromorphic (ASICs) chips, brings the needed processing power closer to edge devices to support adaptive robotics with fewer trade-offs. Early applications include self-balancing bicycles and neuromorphic “event” cameras that enable object tracking and high-speed vision control.

While Software 2.0 will let companies address a new class of higher-order, edge use cases with less code, and therefore fewer developers, successful software producers will still need to evolve their talent—as well as their tech stack and development practices (such as MLOps , which combines infrastructure, tools, and workflows to provide faster and more reliable machine-learning pipelines— similar to how DevOps supports and enables better development of more traditional software). Their relationship with data will also change as data—and approaches to refining data—continue to further emerge as a valuable competitive advantage.

Key components of Software 2.0 include MLOps, which facilitates the development and deployment of machine-learning models at scale. MLOps combines infrastructure, tools, and workflows to provide faster and more reliable machine-learning pipelines—similar to how DevOps supports and enables better development of more traditional software.

One automotive-company player has leveraged Software 2.0–driven AI to deliver approximately 800,000 autopilot cars that have collected more than three billion miles of driving data, which is used to continually improve their AI.

In contrast to perimeter-based security models, zero-trust security uses segmentation to control access even within a network by verifying the user, the system, and the context for accessing data.

Blockchain is a type of DLT, a public network with a shared ledger without central authority controlled by economic incentives for nodes to update the ledger.

Organizations need to assess their “bQ” or biological quotient—the extent to which they understand biological science and its implications. They should then sort out the resources they need to allocate to biological technologies and capabilities and whether to integrate those into their existing R&D or partner with science-based start-ups.

This collection of technologies amounts to an expansion of human understanding of biological processes at the intracellular level and an increasing ability to engineer molecules and pathways.

Nanomaterials, for their part, make possible improved automotive safety and aerodynamics, better ways to make chips, and more-efficient energy transmission and storage—all while enabling additive-manufacturing facilities to evolve beyond traditional thermoplastics to materials with greater flexibility, customization, and functionality.

Nearly all of these potential disruptions also reduce waste and further enable a circular economy, for instance, by reducing the weight of manufactured parts (in aerospace, for example) while boosting safety and reliability levels.

Vertically aligned carbon nanotubes have been used to generate batteries with three times the storage and ten times the power as conventional batteries.

Manufacturers can use graphene-based composites to enhance composite properties, for instance, in rust-preventing paint and in sports equipment.

For example, the company NanoCarrier is creating nano drug-delivery systems for cancer pharmaceuticals.

For example, the company Applied Graphene Materials creates graphene dispersions that can be used for paint and coatings, as well as in composite materials.

French company NAWA Technologies is focused on creating ultrastrong, multifunctional lightweight materials that can also store energy, whether in a vehicle, airplane, building, or mobile device.

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Next-generation materials include sustainable materials, nanomaterials, molybdenum disulfide nanoparticles, smart and responsive materials, graphene and 2-D materials, and lightweight materials. Nanomaterials are engineered particles of nanoscale dimensions that provide increased conductivity or surface areas, which applies to a wide range of industries, including pharmaceuticals, where nanomaterials can act as an agent carrier. Graphene, which is composed of pure carbon, provides better strength-to-weight ratios, with a range of commercial-market applications from chemical sensors to supercapacitors, construction paints, and plastic manufacturing. Molybdenum disulfide nanoparticles help lubricate difficult-to-maintain machinery, such as satellites, and improve the performance of solar cells. Smart materials provide programmable properties that respond to stimuli, such as memory alloys used in manufactured parts that return to their original shape after being misshapen or deformed. Lightweight materials, meanwhile, reduce weight and improve fuel performance in industries from aerospace to mobile. Applications include composite aerospace parts and composite automotive wheels.

Next-generation materials

power, such as renewables (solar photovoltaic, solar thermal, and wind), clean coal, carbon capture and sequestration, smart-grid and metering technologies, energy-storage solutions, energy efficiency, and waste-to-energy opportunities

•

transport, such as clean vehicles, electric vehicles (hybrid, plug-in hybrid, and battery), fuel cells, and batteries

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buildings and infrastructure, such as automation, HVAC, windows, insulation, home-energy management, appliances, and LED lighting

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water, such as wastewater treatment and desalination/membranes

•

Nuclear fusion, should it prove viable, holds the potential to supply humanity with abundant, safe energy. However, in the absence of progress on nuclear fusion, there are other disruptive effects of clean-technology and energy-tech trends. These include the need for different types of energy-generation and distribution infrastructure. A greater reliance on batteries, smart grids, and alternative energy sources could have the opposite impact, meaning energy prices rise in the short term as “old” energy sources get replaced by renewables and many industries are required to make capital-expenditure investments.

A zero-trust-security setup diversified data storage across multiple servers, data centers, and regions, enabling greater resilience when faced with local outages.

The retailer achieved 50 percent reduction in security-related capital expenditures and operating expenditures by simplifying compliance with payment card industry (PCI) data-security standards.

This made it easier to trace food provenance (leading to safer consumption), required fewer human actions in the chain, and improved tracking of lost products.

The technology beneath the trend

The technologies beneath the trend

Future of connectivity

Next trend

Future of clean technologies

Previous trend

Process automation & virtualization

Previous trend

Distributed infrastructure

Next trend

Future of connectivity

Previous trend

Next-generation computing

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Distributed infrastructure

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Applied AI

Next trend

Next-generation computing

Previous trend

Future of programming

Next trend

Applied AI

Previous trend

Trust architecture

Next trend

Future of programming

Previous trend

Bio Revolution

Next trend

Trust architecture

Previous trend

Next-generation materials

Next trend

Bio Revolution

Previous trend

Future of clean technologies

Next trend

Next-generation materials

Previous trend

Process automation & virtualization

Next trend

Jacomo Corbo, based in London, is a partner at QuantumBlack, a McKinsey company. Nicolaus Henke is a senior adviser and senior partner emeritus in McKinsey’s London office. Ivan Ostojic is an alumnus of the Zurich office. The authors wish to thank the following members of the McKinsey Technology Council: • Ajay Agrawal, University of Toronto • Azeem Azhar, Exponential View • Benedict Evans, Entrepreneur First • Peyman Faratin, Parameter Ventures • Tom Inskip, Afiniti • Jordan Jacobs, Radical Ventures • Ben Lorica, Gradient Flow • Karen Silverman, The Cantellus Group • Peter Xi Chen, Covariant The authors also wish to thank the following McKinsey colleagues: Aaron Aboagye, Aamer Baig, Sam Bourton, Radhika Chadwick, Michael Chui, Yetunde Dada, Matthias Evers, Klemens Hjartar, Keiko Kusaba, Daniel Pacthod, Jeremy Palmer, Kelsey Robinson, Tamim Saleh, Hamid Samandari, Hugo Sarrazin, Jan Schreier, Tobias Silberzahn, Virginia Simmons, Navjot Singh, Kate Smaje, Sven Smit, Bob Sternfels, Alex Sukharevsky, and Rodney Zemmel.

Acknowledgements

This research examines a range of factors to identify the technology trends that matter most to top executives and the companies they lead. For every trend, we calculated a momentum score based on the growth rate of the technologies underlying the trends, which we derived from an in-depth analysis of six proxy metrics: patent filings, publications, news mentions, online search trends, private-investment amount, and the number of companies making investments. We then rolled the scores of the underlying technologies into a single composite score for the trend itself. Examining composite momentum scores—together with a given trend’s industry applicability and technical maturity—can help executives recognize how much disruption a trend is likely to cause and how soon that disruption will have business implications. The underlying metrics are diverse, the better to account for the varied perspectives each represents. The number of research publications within a field provides a leading indicator of trends as they emerge. Patent filings give a measure of the importance placed on a particular trend by corporations. The quantity of private investment, as well as the number of companies making investments, indicates whether a clear financial interest exists for a specific trend. Finally, search trends and news coverage reveal the level of public interest in a trend. Combining early indicators with measures of public and financial interest creates a holistic view of each trend and provides a good way to rank and compare their potential impact. Using the growth rate as the basis of the momentum score differentiates areas that are merely large from those that are on their way to massive. Finally, we reviewed our analytical results with external experts on McKinsey’s Technology Council, leading to a unique perspective that combines research analysis with qualitative insights from some of the leading thinkers of our time.

About the research

Next-generation computing

Distributed infrastructure

Future of programming

Bio Revolution

Nanomaterials

Future of clean technologies

Next-level process automation and virtualization

Future of connectivity

Trust architecture

Applied AI

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Cloud computing offers distributed computing resources through ready-to-use, vendor-managed services (including computing power, storage, advanced-analytics tools, and database access).

Cloud computing

Edge computing

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Quantum computing

Neuromorphic chips

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The Internet of Things includes sensors, software, and other technologies embedded in everyday objects, enabling them to send and receive data.

The Internet of Things

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Computer vision

Natural-language processing helps create seamless interactions between humans and technologies in applications such as data-to-story translation.

Natural-language processing

Speech technology duplicates and responds to the human voice.

Speech technology

Metaflow by Netflix provides a unified API to the infrastructure stack to design workflow, run it at scale, deploy to production, and provide automatic version and experiment tracking.

Buying a major stake in a leading global PV manufacturer boosted the share price of the responsible division by 40 percent in the months thereafter.

An environmentally aware city tackled the next frontier of carbon abatement through a combination of behavioral and technological measures ranging from congestion pricing to building retrofits.

Frontline staff helped a wastewater plant conduct an onsite diagnostic to identify improvement levers and launch a customized transformation program to support continuous improvement.

You’re invited

Michael Chui, MGI Partner

Lareina Yee, Senior Partner

To a McKinsey Live webinar on ‘The future of business: Tech trends that matter’ on Wednesday, September 15